United States Policy EPA 231-R-98-006

Environmental Protection (2126) August 1998

Agency Washington, DC 20460

Technical Methods for

Analyzing Pricing Measures to

Reduce Transportation

Emissions

Technical Methods for Analyzing Pricing Measures

to Reduce Transportation Emissions

U. S. Environmental Protection Agency i

Table of Contents

1.0 INTRODUCTION ................................................................................................................................... 1-1

n1.1 GUIDANCE PURPOSE ............................................................................................................................. 1-1

n1.2 DEFINITION OF MARKET-BASED MEASURES .......................................................................................... 1-2

n1.3 GUIDANCE ORGANIZATION .................................................................................................................. 1-3

2.0 OVERVIEW OF MEASURES AND ANALYSIS REQUIREMENTS................................................ 2-1

n2.1 INTRODUCTION ..................................................................................................................................... 2-1

n2.2 OVERVIEW OF MARKET-BASED MEASURES ............................................................................................... 2-2

2.2.1 Description of Market-Based Measures................................................................................................ 2-2

2.2.2 Impacts of Market-Based Measures on Travel and Vehicle Use .......................................................... 2-11

2.2.3 Relationships Between Travel and Emissions..................................................................................... 2-16

2.2.4 Overall Advantages and Disadvantages of Market-Based Measures................................................... 2-21

n2.3 EVALUATION OF MARKET-BASED MEASURES...................................................................................... 2-24

2.3.1 Analytical Process.............................................................................................................................. 2-24

2.3.2 Identifying and Assessing Areas of Impact......................................................................................... 2-30

2.3.3 Analysis Guidance............................................................................................................................. 2-43

n2.4 PACKAGING MEASURES FOR BEST EFFECT ............................................................................................ 2-61

3.0 GUIDELINES FOR ANALYSIS OF MARKET-BASED MEASURES.............................................. 3-1

n3.1 INTRODUCTION ..................................................................................................................................... 3-1

n3.2 EVALUATION GUIDANCE FOR INDIVIDUAL PRICING MEASURES ............................................................. 3-3

3.2.1 Parking Pricing ................................................................................................................................... 3-4

3.2.2 Modal Subsidies................................................................................................................................. 3-12

3.2.3 Pump Charges.................................................................................................................................... 3-19

3.2.4 Emissions Fees................................................................................................................................... 3-25

3.2.5 Roadway Pricing................................................................................................................................ 3-30

n3.3 REVIEW OF METHODS AND PROCEDURES ............................................................................................... 3-37

3.3.1 Land Use/Activity Allocation Methods.............................................................................................. 3-42

3.3.2 Vehicle Ownership............................................................................................................................. 3-44

3.3.3 Vehicle Mix ....................................................................................................................................... 3-45

3.3.4 Trip Generation ................................................................................................................................. 3-47

3.3.5 Trip Distribution ............................................................................................................................... 3-51

3.3.6 Mode Choice ...................................................................................................................................... 3-54

3.3.7 Time of Day....................................................................................................................................... 3-63

3.3.8 Traffic Assignment/Route Choice....................................................................................................... 3-65

3.3.9 Emissions Modeling........................................................................................................................... 3-68
Technical Methods for Analyzing Pricing Measures

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Agency

Table of Contents

(continued)

4.0 IMPLEMENTATION ISSUES ................................................................................................................. 4-1

n4.1 INTRODUCTION....................................................................................................................................... 4-1

n4.2 EPA POSITION ON DOCUMENTATION....................................................................................................... 4-3

4.2.1: Public Policy Context .......................................................................................................................... 4-3

4.2.2 Legal Authority .................................................................................................................................... 4-4

4.2.3 Administrative Capacity....................................................................................................................... 4-4

4.2.4 Technology Availability ....................................................................................................................... 4-5

4.2.5 Revenue Generation and Reinvestment................................................................................................. 4-5

Appendix A: Modeling Pricing Measures in a Traditional Modeling Environment --

A Case Study Approach ...........................................................................................................................A-1

Appendix B: The STEP Analysis Package: Description and Application Examples .................B-1
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List of Figures

2.1 Schematic Overview of Four-Step Planning Process .............................................. 2-25

2.2a Analysis Requirements For: Parking Pricing ......................................................... 2-46

2.2b Analysis Requirements For: Modal Subsidies........................................................ 2-50

2.2c Analysis Requirements For: Pump Charges .......................................................... 2-53

2.2d Analysis Requirements For: Emissions Fees .......................................................... 2-56

2.2e Analysis Requirements For: Roadway Pricing ...................................................... 2-59
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List of Tables

1.1 Guidance Overview................................................................................................... 1-4

2.1 Overview of Market-Based Measures...................................................................... 2-4

2.2 Potential Magnitude of Market-Mechanism Impacts............................................. 2-5

2.3 Characteristics and Comparative Advantages of Pricing Measures ..................... 2-17

2.4 VOC Emissions for Typical 1987 Automobile......................................................... 2-19

2.5 Anticipated Impact of Measures in Analysis Hierarchy ........................................ 2-31

3.1 Impacts of Parking Pricing........................................................................................ 3-4

3.2 Impacts of Modal Subsidies ...................................................................................... 3-12

3.3 Impacts of Pump Charges......................................................................................... 3-19

3.4 Impacts of Emission Fees .......................................................................................... 3-25

3.5 Impacts of Roadway Pricing..................................................................................... 3-30

3.6 Example of VMT Adjustment..................................................................................... 3-69
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1.0 Introduction

n 1.1 Guidance Purpose

State transportation and air quality planners have requested the Environmental Protection

Agency (EPA) and the Department of Transportation (DOT) for assistance in how to

quantify the impacts of transportation pricing measures in their regional transportation

models. They need this assistance to develop regional transportation plans, transportation

improvement programs, and state implementation plans. The modeling enhancements

may also be useful for demonstrating conformity. This report, jointly funded by the EPA

and the DOT, responds to those inquiries and provides technical assistance on best practice

approaches for analyzing various transportation pricing policies.

This document is intended strictly to provide technical recommendations and does not

advocate the use of any specific policy measures. Individual states and regions are always

encouraged to select those policies that best match local priorities in meeting National

Ambient Air Quality Standards.

Market-based approaches to transportation policy can provide many potential benefits

ranging from reduced congestion and improved regional economic performance to

reductions in emissions of criteria pollutants. These policies have often not been analyzed

since many existing transportation planning models are not able to provide adequate

analyses of these issues. This document will enable regional and state transportation

planners to consider a broad selection of innovative market-based policy measures that

may provide significant air quality benefits.

Although market mechanisms can be applied in all areas of environmental policy, this

guidance is rooted in EPAs authority for implementation of the Clean Air Act

Amendments of 1990. It thus discusses only methodologies for assessing air quality

benefits. Specifically, this guidance describes methodologies cities and states can use to

analyze the air quality benefits of market-based transportation policies. The intent of the

guidance is to:

• Assist transportation, planning, and air quality professionals at state and local

levels in analyzing the emissions reduction potential of market-based approaches;

and

• Provide state air agency and U. S. EPA field staff with technical guidance for

reviewing analyses of market-based measures proposed as part of state and local

air quality planning processes.

Neither consumer nor traveler response to new policies is ever perfectly predictable.

Nonetheless, both traditional and more recent travel demand modeling provide powerful

tools for analyzing the likely response to changes in transportation prices. This guidance
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discusses those tools, and highlights analytical steps necessary to assure that policies are

well-formulated and evaluated properly from a technical standpoint. It then briefly

discusses non-technical implementation issues.

Proper use of methodologies described in this document will generally satisfy EPA that

appropriate analysis has been conducted. EPA has no intent to limit areas from using other

methodologies that might also provide an accurate assessment of the consequences of

market-based measures, so long as these methodologies are fully described in an

application for SIP credits. No guidance can anticipate all local conditions and EPA is

eager to help potential implementers of market-based measures solve any challenges they

face. Because this guidance cannot cover all topics related to market-based policies, it also

refers readers to studies that provide more detailed information on particular topics. As

guidance, this document describes expected practices, but establishes no legally binding

requirements.

It should be noted that any analytical method used to estimate emissions benefits for SIP

credits must meet all requirements for SIP purposes. It should be further noted that any

credits granted for implementation of market-based mechanisms will reflect the uncertainty

inherent in the methods used.

n 1.2 Definition of Market-Based Measures

The term market-based measures here refers to pricing mechanisms which reduce

transportation emissions. That is, they harness the marketplace.

This guidance document discusses five major categories of measures:

1. Parking Pricingremoving or reducing subsidized or free parking privileges;

2. Modal Subsidiesreducing the cost of non-single occupancy vehicle (SOV)

modes;

3. At-the-Pump Charges (hereafter abbreviated to Pump Charges) changing

prices at the fuel pump to increase the marginal cost of auto use (including

vehicle miles traveled fees);

4. Emissions Feeslevying fees directly on vehicles or their use in a way that

reflects their emissions production; and

5. Roadway Pricingcharging road users directly for travel on specific

roadways, with potential price differentials based on time of day, congestion

levels, or miles traveled.

Market-based measures typically include actions that either (1) change the cost of specific

trips (e.g., roadway pricing, parking charges, and transportation subsidies) or (2) change

more general or cumulative characteristics (e.g., charges on vehicle emissions or miles

traveled).
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n 1.3 Guidance Organization

Effectively using market forces in setting transportation policy requires careful work in

policy design, community involvement, planning, and implementation. This guidance

addresses prospective analysis and discusses other considerations only as they affect EPAs

ability to approve a SIP containing market-based measures. Specifically, this guidance

describes:

• The scope and nature of market-based measures that affect travel patterns;

and

• Methodological approaches for estimating the impacts of market-based

measures on travel emissions when these measures are included in State

Implementation Plans submittals.

Following this introduction, the guidance is made up of three substantive chapters and two

technical appendices, described below and in Table 1.1.

Chapter 2 is directed to persons in managerial and decision-making positions with regard to

the planning or implementation of air quality improvement measures. This audience

includes state and regional transportation officials, planning directors, members of air

quality or transportation steering/review committees, and EPA regional staff.

Chapter 2 also provides a summary of key points for more technically-oriented readers too

busy to review the full guidance document. Chapter 2 defines and describes market-based

measures, reviews the analytic challenges involved in estimating travel and air quality

impacts of market-based measures, describes characteristics of current planning and

analysis techniques, and summarizes standards of accepted basic practice, including

suggestions for more advanced practices that would yield greater accuracy and confidence.

Chapter 3 is directed at the practitioner and is much more specific in its technical guidance

on evaluation techniques for market-based approaches. Interested managers and

decisionmakers also are encouraged to read this chapter. Analogous to Chapter 2, Chapter

3 describes five groups of market-based measures, and discusses how these measures are

presumed to affect travel behavior and emissions. The subsequent discussion presents

detailed, step-by-step methodological guidelines and procedures that can be used to

evaluate the transportation and emissions impacts of each measure. With these analytical

recommendations, Chapter 3 describes the expected impact and treatment of the measure

at each stage of the travel behavior modeling hierarchy.

Chapter 4 discusses implementation issues. Topics include: putting market-based measures

into the public policy context; addressing issues of legal authority, administrative capacity,

and technology availability; and consideration of revenue generation and reinvestment

opportunities.

Appendices A and B provide technical examples of (A) a traditional modeling approach,

and (B) an advanced modeling approach. Appendix B also discusses some policy issues as

part of its discussion of how to approach modeling questions.
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Table 1.1 Guidance Overview

Audience Key Chapters to Read Goals

Managers/

Decisionmakers

Chapter 2 Provide overview on measures and summarize analytical

issues

Practitioners/

Modelers

Chapter 2, Chapter 3,

Appendix A, Appendix B

Compare measures and give step-by-step guidelines on

analysis

All Chapter 4 Address implementation issues: legal, administrative,

and technological
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2.0 Overview of Measures and

Analysis Requirements

n 2.1 Introduction

This chapter serves several purposes. It first provides an introduction to market-based

measures: what they are, specific examples and applications, and how they are effective

in reducing emissions. Second, this chapter discusses the importance of accurate

evaluation of the impact of these measures, with specific attention to the challenges that

may be posed by current analysis tools and data. Third, the chapter presents guidelines

for effective evaluations, both to support local assessment and acceptance and to allow

EPA to grant emissions credit for these measures should they appear in a State

Implementation Plan submittal.1

This chapter is specifically directed to persons in managerial and decisionmaking positions

in planning or implementing air quality improvement measures, including state and

regional transportation officials, planning directors, members of air quality or

transportation steering/review committees, and EPA regional staff. This chapter

discusses the importance of considering market measures, articulates EPAs

understanding of the methodologies used to evaluate their impacts, and offers insights

and suggestions as to methods and procedures that should lead to enhanced

evaluations. This chapter does not directly address implementation issues; these are

covered in Chapter 4.

 

1

 This guidance manual does not deal directly with emissions from freight transportation. This is an

important emissions source, particularly of NOx and particulate matter. While many measures in

this manual would affect freight, the manual does not discuss the models and data for evaluating

freight impacts. See Federal Railroad Administration, Federal Highway Administration, and US

Environmental Protection Agency, Air Quality Issues in Intercity Freight: A Guidebook for Estimating the

Travel and Emissions Impact of Intercity Truck and Rail Intermodal Freight Activity and Effects of

Improvement or Control Strategies, draft final report, prepared by Cambridge Systematics, Inc., et al.,

December, 1996.
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2.2.1 Description of Market-Based Measures

In general, a market-based measure adjusts the price of a given commodity in the

marketplace to accurately reflect the actual cost of producing and using that

commodity. In the realm of public policy and emissions programs specifically, this

means incorporating air quality costs in prices paid by consumers.

The prices currently paid for transportation deviate from full cost pricing in two ways.

First, prices are distorted by financial subsidies, such as employer subsidies for employee

parking, transit subsidies from non-transit revenue sources, and taxes collected from

non-highway drivers and used to underwrite highway infrastructure. Prices also are

distorted from failure to include external costs, such as air quality and health costs.

Mechanisms offered for consideration in this report are once-removed from the ideal,

since it is difficult to alter all policies that affect the current marketplace all in one step.

These strategies are more properly regarded as pricing mechanisms, which begin showing

users the true cost of travel and emissions.

A number of strategies apply pricing to influence travel and other emissions-related

decisions. This guidance document discusses five major categories of measures.

Summarized in Table 2-1 and discussed in greater detail in the text below, they include:

• Parking pricing;

• Modal subsidies;

• Pump charges;

• Emissions fees; and

• Roadway pricing.

The magnitude of the impacts of these measures could be considerable. Estimated

impacts of a sample of market mechanisms are presented in Table 2-2.2

Not all of these measures are purely market based. For example, subsidies reduce

prices to below economic costs. Such subsidies may, however, alter comparative prices

to better reflect relative social benefits and/or differences in social and economic costs.

 

2

 See Greig Harvey et. al., Transportation Pricing for California: An Assessment of the Air Quality,

Congestion, Energy, and Equity Impacts, Vol. 1: Summary Report, Draft, California Air Resources

Board, 1995. This article can provide further details on the potential magnitudes of pricing strategy

impacts. The numbers presented in Table 2-2 are based on tables prepared in this document.
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This section describes various categories of pricing strategies and presents their major

features; it is followed by a discussion (Section 2.2.2) of how these measures are

expected to affect travel behavior and emissions.
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation Emissions2-4 UProtection AgencyTable 2.1 Overview of Market-Based MeasuresMeasure Category Overall Intent Major Variations Emissions-RParking Pricing Remove/reduce subsidized parking

privileges.

Employee parking feesRates to favor short-term parkingover long-term

Shift work trips arrangements compressed wperiods, leadinvehicle trips an

General parking fees Shift travel to a

destinations, letrips and VMT

Modal Subsidies Improve attractiveness of non-SOV

modes by reducing their cost.

Parking cash outHOV subsidiesNon-motorized mode subsidies

Increase use omodes, leadinand VMT, part

Pump Charges (including theirrelative, VMT fees)

Raise cost of fuel to reflect actualcosts to construct or maintaininfrastructure.

Per gallon fuel prices reflectingdifferent cost allocations

Raise overall ctravelers, causreduced trip leof trips, leading and VMT.

Emissions Fees Impose charges on vehicle owners

proportional to vehicle emissionsproduction.

Age-based registration fees(periodic fee indexed to vehicleage and VMT)

Reduce ownerhigher-emitting 

Emissions fee (VMT x measuredemissions rate)

Reduce owneremitting vehiclvehicle mainte

Roadway Pricing Impose direct charges for use of

transportation facilities.

Full-time facility tolls/fees Shift travel to a

or destinationsvehicle trips an

Congestion pricing: variableprices linked to congestion levelsor time of day

Shift travel to adestinations, areduced vehicimproved flow/
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Table 2.2

Potential Magnitude of Impacts

Market Mechanism VMT Trips Time Fuel/CO2 ROG (VOC) CO 1) Regionwide Congestion Pricing 0.6 - 2.6 0.5 - 2.5 1.8 - 7.6 1.8 - 7.7 1.5 - 6.2 2) Regionwide Employee Parking Charges

2a) $1.00 per day 0.8 - 1.1 1.0 - 1.2 1.0 - 1.1 1.1 - 1.2 0.9 - 1.2 02b) $3.00 per day 2.3 - 2.9 2.6 - 3.1 2.5 - 3.0 2.6 - 3.0 2.5 - 3.1 2

3) Gasoline Tax Increase

3a) $0.50 per gallon 2.3 - 2.8 2.1 - 2.7 2.4 - 2.8 5.8 - 7.4 2.2 - 2.7 23b) $2.00 per gallon 8.1 - 9.6 7.6 - 9.2 8.4 - 9.7 24.3 - 27.3 7.8 - 9.5 7

4) Mileage and Emissions-based

Registration Fees 24a) Fee Range from $40-$400 annually 3 0.2 - 0.3 0.1 - 0.2 0.2 - 0.3 3.4 - 4.4 7.4 - 9.5 74b) Fee Range from $10-$1,000 annually 4 2.9 - 3.6 2.7 - 3.3 2.7 - 3.5 6.3 - 7.9 15.8 - 19.3 15

5) VMT fee of $0.02 per mile 4.6 - 5.6 4.4 - 5.4 4.8 - 5.7 4.8 - 5.7 4.5 - 5.5 4Example of combined effects: moderate impact

Mechanisms 1, 2a, 3a, and 4aCurrent levels of transit service 4.6 - 6.3 4.4 - 6.2 5.7 - 11.1 13.8 - 18.5 13.9 - 17.6 14

Example of combined effects: high impact

Mechanisms 1, 2b, 3b, and 4bExtensive investments in transit service 16.2 - 19.1 15.5 - 18.4 19.0 - 24.0 39.2 - 44.1 31.1 - 36.3 31

(1) Effects forecast for San Francisco, Los Angeles, Sacramento, and San Diego by DHS, "Transportation Pricing for California," prepared for CARB, October 1994.(2) Fees would be paid annually at the time of registration, based on an estimate of annual emissions.(3) Effects based on annual mileage and average model year emissions as reflected in EMFAC7F.(4) Effects based on -actual odometer readings and in-use tailpipe measurements.

Estimated Decreases from 1991 Mobile Source Baseline (in pe

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Parking Pricing

Parking Pricing removing or reducing subsidized or free parking

privileges that serve as an incentive to drive.

Free or discounted vehicle parking creates a strong incentive for vehicle ownership and

driving for all types of travel, both work and non-work. National data suggest that nine

out of 10 workers in metropolitan areas park for free or at below-market prices, yet

employer-paid parking is treated as a tax-exempt fringe benefit by federal and state tax

laws, a subsidy value of up to $170 per month.3 Free parking provides a similar

attraction for non-work travel for shopping and other purposes in suburban and

outlying areas, in contrast to parking in downtowns and activity centers which is

priced, but where there also exist alternatives to driving. Internalizing the cost of

parking would reduce the demand for private vehicle travel in favor of alternatives.

There are many ways in which parking pricing can be introduced, the major variation

being whether it is applied exclusively to work travel, or to non-work travel as well.

Pricing commuter parking is perhaps the most common application of parking pricing.

Employees may be seen as more captive to their location than non-work travelers, and

because of their repetitive travel to the same site during peak hours, they usually have

access to the best transportation alternatives. In many instances, employees already pay

a parking charge if they work in a downtown or activity center; a small percentage may

experience some parking cost as a result of the employer's involvement in a trip

reduction program. Employee parking pricing as a regional pricing mechanism can take

many forms, including:

• Imposing a parking tax on all or a portion of the parking supply;

• Shifting public policy to eliminate or reduce tax exemption of parking

subsidies;

• Eliminating early bird/all-day discounts, or replace these with peak-period

surcharges;

• Introducing a minimum parking charge for all employees (i.e., where all

parking is raised to some threshold level, say $3 per day);

• Requiring parking charges at sites that meet certain criteria (e.g., with 100 or

more employees, where vehicle trip rates exceed a standard, etc.);

• Replacing free parking with a transportation allowance applicable to various

modes, coupled with introduction of parking rates;

• Offering the value of the subsidized parking space back as a cash credit to

persons who would agree not to drive (popularly termed Cash Out);

• Offering discounted parking for High Occupancy Vehicles (HOVs); and

 

3

 This amount is adjusted periodically by the IRS in $5 increments to adjust for inflation.
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• Levying area entry fees on Single Occupancy Vehicles (SOVs), particularly in

peak periods (these are generally considered more of a congestion pricing

action).

Attaching parking prices to non-work travel is more problematic. Parking charges for

non-work travel include:

• Imposing [higher] charges at major traffic generators, such as malls and

activity centers, shopping centers, hospitals, educational institutions, and

entertainment sites;

• Levying a parking tax on public or private parking operators;

• Restricting off-site parking in residential areas and/or installing meters or

increasing metered rates; and

• Restricting parking supply or raising parking costs at residential locations.

Modal Subsidies

Modal Subsidies: Underwriting part of the cost of non-SOV modes,

thus reducing their price relative to SOV modes to increase their

attractiveness.

By lowering the cost of alternative modes such as transit or ridesharing relative to the

automobile or by providing direct (or indirect) incentives for non-motorized modes

(bicycle and walking), the attractiveness of these modes increases and their usage would

be expected to increase. As alternative mode use increases, a reduction in vehicle trips

and vehicle miles traveled (VMT) would be expected to reduce emissions. If subsidies

lead to proportionately higher use of alternative modes for work trips in peak periods,

then congestion relief also might occur, potentially with additional emissions benefits.

There are a number of ways in which modal subsidies can be introduced or enhanced

through public policy initiatives:

• Lowering the usage cost of non-SOV alternatives, for example, reducing

transit fares, or exempting HOV users from various tolls and fees;

• Allowing employers to grant (or employees to receive) tax-exempt subsidies

for carpool or non-motorized mode use, or to grant larger subsidies to

transit or vanpool users;

• Offering tax credits to employers to cover their cost of providing subsidies;

and

• Directing implicit subsidies to rideshare users in the form of gasoline

allowances, parking discounts, vanpool capital or insurance assistance or

maintenance, etc.

Reliance on modal subsidies as an emissions reduction strategy has two major problems.

First, they require a net outlay of resources to support, which if not financed by a specific

revenue source, will fall as a burden on employers or state/local governments. Second,
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they may further distort current imperfections of the marketplace, by adjusting for

existing subsidies through the addition of new subsidies, and potentially inducing

demand on highways with freed-up capacity. While this may, in principle, begin to

equal out the subsidy treatment across modes, it does not serve the purpose of making

current prices a better reflection of costs, and hence, may just further skew the operation

of the marketplace. While these limitations are important, subsidies are advantageous in

that travelers view them as a benefit, rather than a cost.

At-the-Pump Charges (and their relative, VMT fees)

At-the-Pump Charges—Using the pump price of fuel to increase the

price of driving and thus discourage private vehicle (especially SOV)

use.

By raising the price of fuel, this measure reduces emissions by increasing the overall cost

of travel for all private vehicle trips, thus affecting the frequency and length of such

trips. A pump-based charge can be based on numerous factors. For example, pumpbased charges can be:

• Structured to cover a more complete proportion of the cost of road

construction and maintenance;

• Used as a medium to collect insurance payments under a pay-as-you-go

insurance coverage policy, as is being considered in some states; and/or

• Structured to account for social costs, such as emissions (often through

links to VMT).

Experience has demonstrated that fuel price increases can be controversial at both the

national and state levels. Some states may, however, see some benefits in designing

systems that more accurately reflect the marginal social costs of vehicle usage. As with

all the policies discussed in this document, state and regional decision makers are

encouraged to develop the best strategies for meeting emissions reduction targets.

Emissions Fees

Emissions Fees—Levying fees directly on individual vehicles or their

use in a manner that reflects their emissions production.

This category of pricing measure consists of charges based on vehicle characteristics,

including emissions production, rather than on travel behavior. It attaches a higher cost

to owning high-emitting vehicles, discouraging ownership and usage, as well as

encouraging proper maintenance. Exactly which is affected moreownership or

usedepends on the structure of the particular mechanism.

Emissions fees may be based on:

• Vehicle age;
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• Vehicle type; and/or

• Vehicle emissions, using:

− Estimated emissions; or

− Measured emissions, as determined at an annual emissions test.

In most cases, emission fees also would be linked to VMT, based on odometer

readings.

Generally, emissions fees are imposed as part of a vehicle registration fee, and tied to

required emissions tests. While annual fees may be most common, fees also may be

assessed on a more frequent basis or in the form of a VMT-based emissions fee at the fuel

pump. This version of the measure potentially could have a greater impact on travel.

Roadway Pricing

Roadway Pricing—Imposing direct user charges for travel on specific

roadways, with potential price differentials depending on time of day or

congestion levels.

Fuel taxes and vehicle registration fees are the conventional means for traveler financial

support of the highway system. These measures, however, neither cover the entire cost

of supplying that transportation capacity, nor serve as any indicator of the level of service

which is being demanded from the transportation system at any given time.

Road pricing establishes a more direct and immediate link between the cost of roadway

use and payment for that use through:

• On-the-spot collection of fees or tolls;

• Pre-paid permits; or

• Electronic monitoring and periodic billing.

Manually collected tolls may be somewhat counterproductive as the resulting delays at

toll booths can increase emissions levels. However, electronic toll collection systems

solve this problem.

Roadway pricing can be narrowly focused, such as installation of tolls on individual

bridges or tunnels. Alternatively, roadway pricing can be more broadly implemented

by, for example, applying them to a system of facilities that define a corridor, to a

particular functional class of facilities such as all expressways, or—in its most extensive

form—to all travel into, within, and through an area.

In the specific case of congestion pricing, the usage fee is assessed at the marginal cost for

each additional vehicle. This places a premium on the privilege of travel during peak

demand periods. Travelers may face fee schedules ranging from peak-only fees to fees

that are variable by time of day, facility, or level of use. Congestion pricing provides

incentives for travelers to take congestion costs into account when making trip decisions,
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thus leading to more efficient use of facilities and avoiding construction of expensive

new capacity. By reducing congestion, such pricing also provides higher levels of service

to those willing to pay for it as well as auxiliary benefits, such as providing better access

for public safety vehicles.4

Seeing an increase in travel cost based on roadway prices, the traveler may either pay

the increase in cost or opt for alternatives. For work travelers, where the destination is

largely fixed in the short run, the choices include different modes, different routes, or, in

the case of congestion pricing, different times of day. Non-work travelers face the same

options, as well as the ability to choose destinations that can be reached without

payment of tolls, or that provide more services for the given outlay.

2.2.2 Impacts of Market-Based Measures on Travel and Vehicle Use

In response to a properly designed and implemented program of market-based

measures, travelers can be expected to do any or all of the following:

• Change travel mode to walk, bike, transit, carpool;

• Change destination;

• Change travel route;

• Change time of day of travel;

• Change the pattern of trip making (e.g., consolidate trips or group them into

chains);

• Substitute telecommunications for travel;

• Adopt alternative work schedules or other arrangements to reduce the number

of trips;

• Shift patterns of vehicle use in multi-vehicle households and change

maintenance practices; and

• Make changes in residence and employment location (and thus change pattern

or rate of growth of community).

Each of the described market-based measures, with the exception of emissions fees,

affects emissions by first affecting vehicle travel and usage patterns. Emissions fees, if

 

4

 Congestion pricing creates greater economic efficiency in the sense that it increases the net benefits to

society. This is because congestion results in significant costs to society, and these costs can be

greatly reduced if fees are used to reduce congestion. The funds collected through fees then can be

returned to the public in another form. The fee is thus not a true cost to society, but simply a transfer,

while the cost of congestion has been reduced. Those road users who value their trips the most are

willing to pay congestion fees in return for reduced congestion and will benefit from the fees on

balance. Those road users less willing to pay for peak-period travel will prefer switching modes,

routes, or time of travel, and the disbenefits they experience under congestion pricing will be smaller

than the net benefits to the first group of users. Regardless of the equity implications of this

approach, it generates net benefits to society.
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imposed through an annual or periodic fee, would be expected to have only an indirect

impact on travel patterns, primarily by affecting vehicle ownership. Although the other

four measures influence travel patterns, they nonetheless vary in the specifics of their

impacts on travel and emissions. In brief:

• Parking pricing reduces or redirects vehicle trips by increasing costs at

destinations, often through the levy of visible out-of-pocket charges at the

site. Parking pricing is most effective when it is applied regionally.

• Modal subsidies increase the use of less-polluting modes through a

reduction in their relative price.

• Pump charges reduce total vehicle travel by raising the price of fuel.

• Roadway pricing reduces vehicle trips along given roadways by raising the

en-route cost of travel, either on a per-mile, per-facility, or per-entry basis

(Roadway pricing also can affect total travel if it is implemented on all

roadways in a given area).

• Congestion pricing is a variation of roadway pricing that reduces vehicle

travel on congested facilities at congested times of day.

There is ample evidence to substantiate that travelers do respond to cost changes. Much

depends on the level of the charge and the availability of substitutes. The higher the

income of travelers, the less likely travelers are to respond to modest changes in the price

of fuel, parking, or vehicle ownership.

The relative impact of pricing mechanisms depends on the pricing instrument itself and

how direct a signal it provides; charges which are periodic and disassociated with travel

behavior are unlikely to be linked in a conscious decision-making process. The

availability of realistic options also is an important consideration, although more options

exist (e.g., carpooling, deliveries, telecommuting) than are commonly acknowledged and

options would increase substantially if driving prices are raised. Revenues derived from

pricing also can enhance the number and quality of these options. Under the current

system of subsidized and indirect pricing, however, these options are not likely to

develop.

The balance of this section summarizes the key assumptions underlying market-based

measures and describes expected impacts of each measure in greater detail.

Impacts of Parking Pricing

Parking pricing provides direct signals to the traveler on the cost of driving. Because a

parking charge is often collected at the point of use, it has an explicit linkage to the

particular trip. Many studies have concluded that such an out-of-pocket cost has a

greater impact on decisionmaking than costs that are averaged over a longer time period

or multiple uses.

If parking pricing is implemented at a site that must be accessed (e.g. a work site) it is

difficult to avoid or ignore, particularly in the short run. Faced with new or increased

parking charges, work travelers would be expected to consider shifting to non-SOV

modes, telecommuting, and/or compressed work week arrangements. The effect on
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emissions would be through reduced vehicle trips and VMT, primarily during congested

peak travel periods.

Non-work travelers faced with parking charges would be expected to shift modes or

travel less, such as by grouping trip functions to accomplish more on the same trip tour,

(the effect is likely to be stronger if there are no free parking areas). However, non-work

parking charges also may be avoided simply by traveling to another destination, which

may entail more travel as a result of being further away.

It can be physically difficult to institute parking charges at a high proportion of

destinations, particularly for small employers and for non-work situations. Ensuring

that the charges are actually implemented, and not diminished by employer or merchant

subsidies, requires enforcement. One other characteristic of parking pricing is that it has

less impact on longer trips, since parking charges represent a smaller proportion of

overall trip costs; this may be advantageous in addressing certain pollutants since

pricing would have a greater impact in reducing short trips and associated cold starts.

The effectiveness of any parking pricing arrangements depends on the level of the price

change and the uniformity of the application. Major differences in rates across a region

(e.g., suburb versus core) or exemption of many sites from pricing are expected to

encourage shifts in choice of destination to avoid or minimize the charge, even if such a

change involved more travel. This is particularly a concern with non-work travel. Such a

result could have an undesirable effect on total emissions.

Impact of Modal Subsidies

Modal subsidies have the advantage that they are comparatively easy to implement.

The traveling public is not likely to resist being offered a discount, in contrast to paying a

charge.

The disadvantage of modal subsidies is that they must be paid for from some source,

thus raising the question of who bears this cost (employers, governments, taxpayers,

etc.), although revenues raised through other pricing measures could serve as the

funding base for these subsidies (potentially a mechanism for addressing equity

concerns). A troubling aspect of subsidizing alternative travel modes is that it may

exacerbate current market pricing distortions, by trying to overcome one subsidy with

another. Evidence also suggests that travel behavior is less influenced by a cost incentive

than a disincentive. In light of these characteristics, modal subsidies are best seen as a

strategy supportive of some other active pricing mechanism.

Impact of Pump Charges (including VMT fees)

Pump charges are one of the most broad-reaching pricing strategies available.

Implemented directly through the cost of fuel, a pump charge closely tracks vehicle

usage rates, bears upon the travel decisions of all travelers and trip purposes, and can be

implemented with relative technological and administrative ease. In general, fuel price

surcharges cause travelers to:

• Switch modes;
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• Restructure travel to emphasize shorter trips or grouping of trips;

• Seek a wider variety of options for satisfying activity needs, including nontravel alternatives and locational preferences for more compact and

integrated developments; and/or

• Purchase more fuel-efficient vehicles.

The last of these will tend to eventually offset the VMT benefits of higher fuel prices,

although decreased energy consumption is still beneficial.

For work travel, the primary response of a pump-based charge is a shift from driving to

other modes or to alternative work arrangements (e.g., telecommuting or shortened

work weeks). For non-work travel, destination shifts or errand/trip chaining might be

more common.

Because fuel price surcharges increase cost burdens in direct proportion to the number

of miles traveled, they would be expected to affect longer trips more than shorter ones.

As such, they can be complementary to fixed charges like parking pricing, which

diminish in influence as trip length increases as parking prices become a smaller

proportion of total trip costs. Higher pump charges also could encourage less rapid

acceleration, better maintenance and other driving-style improvements to reduce fuel

consumption.

Impact of Emissions Fees

Emissions fees vary in their impacts, depending on how they are designed. Most

simply, a surcharge is added to the standard vehicle registration fee that reflects

vehicular emissions. In simple applications, requiring almost no additional

administration, the fee is indexed to the type and year of vehicle as a proxy for

contribution to air-borne pollutants. Depending on the level of the surcharge, this

approach would be expected to discourage ownership of older and presumably higheremitting vehicles.

More targeted applications link charges to each vehicles annual VMT, or base charges

on emissions rates as actually measured at the time of inspection. These approaches

have a more direct effect on ownership and usage rates of high-emitting vehicles. Any

effect on daily travel behavior, however, would depend on how closely and with what

impact the owner links the fee to the level of travel; fees that are administered

infrequently have a more tenuous tie to daily travel decisions. The primary advantage of

emissions fees is that they target emissions production directly, and cut across all

ownership and use situations. The primary disadvantage is that the simpler

applications may not produce the desired results, while the more complex (e.g., real-time

emissions rate and VMT) are more challenging to implement.

Addressing particulate emissions through emissions fees poses a unique challenge,

because reentrained dust emissions depend on the location and road type (e.g. unpaved

versus paved) more than the vehicle type. Emissions fees tailored to these particulates

would be difficult to design. Fees based on VMT could impact particulates, but could

not be readily targeted to high-emitting activities or vehicles in the same way NOx or

VOC-based fees could be, for example.
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Depending on the specific fee structure to be implemented, the following consumer

responses to emissions fees might be anticipated:

• Consumers will replace high-emitting vehicles with cleaner ones (also

creating greater pressure on manufacturers to produce such vehicles);

• If the ownership fee is very high, households will reduce the number of

vehicles owned;

• Multi-vehicle households will make greater use of their cleaner vehicles;

and

• Where fees are based on actual measured emissions rates, consumers will

maintain vehicles to the highest possible standards.

Reductions also may occur in overall rates of travel, depending on how much of the fee

is based on VMT usage, and how frequently the fee is levied and/or people are

reminded of the fee (infrequent/periodic fees may not be strong disincentives to daily

use).

Impact of Roadway Pricing

Roadway pricing is an attractive approach because it is a classic application of direct

user charges. Travelers are charged for the number of trips they make and, in the case

of congestion pricing, for the congestion cost that they impose on the transportation

system. Road pricing serves as a fairly immediate signal to travelers to consider

alternatives to driving, including travel by a different mode, time of day, or route.

Congestion pricing offers one of the few direct mechanisms to reduce congestion and to

address the emissions impacts specifically caused by congestion.5

Road pricing schemes are most effective when they have full coverage of the road

system (including collectors and local roads). However, it is probably more feasible to

price controlled-access facilities. Pricing systems need to consider possibilities for route

diversion onto un-priced arterials and local streets. Traffic-calming strategies and

changes in signalization could reduce the efficiency of alternatives and make the priced

alternative clearly superior and worth the price.

To avoid road pricing, travelers may shift to any of the following:

• Unpriced routes that are ill-equipped to accept such traffic or entail greater

VMT to reach intended destinations; or

• Other destinations that are further away and/or not as serviceable by nonSOV options.

 

5

 It should be noted that there might be cases where congestion relief allows speeds to increase to

levels where emissions rates are actually higher, at least for some pollutants. In other cases, speeds

may increase such that emissions rates are reduced.
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Comparison of Characteristics Among Measures

Table 2-3 summarizes the major characteristics of the market-based measures described

above. Each measure has distinct strengths and weaknesses. No single measure adopted

by itself is likely to achieve all program objectives. In fact, the most effective program

will be one that combines complementary measures, thus improving overall traveler

acceptance and maximizing emissions impact.

Moreover, impacts can vary among the different specifications of an individual measure

and differences in implementation. This calls for careful definition and evaluation of

individual measures, identification of potential benefit-cost synergy among measures,

and analysis of how individual measures can be integrated into an overall system plan.

2.2.3 Relationships Between Travel and Emissions

Market-based measures are implemented to achieve two types of social objectives:

increased travel mobility, measured in terms of transportation access, level of service and

travel trip time; and reduced vehicular emissions. The ultimate impact of market-based

measures on emissions is closely tied to program setting and pollutants to be addressed.

Areas attempting to achieve compliance with or maintain National Ambient Air Quality

Standards (NAAQS) need plans to reduce ozone, carbon monoxide (CO), or particulate

matter (PM). Areas with ozone violations must reduce the component pollutants of

volatile organic compounds (VOCs), oxides of nitrogen (NOx), and CO. These pollutants

have different characteristics, stem from different sources and conditions, and are

typically addressed by slightly different strategies. Program design is complicated by

pollutants different relationships to factors such as speed range, flow conditions,

number of cold starts, VMT, and vehicle ownership or use characteristics.

Only careful analysis can identify and evaluate the overall air quality impacts of pricing

measures. However, knowing the particular circumstances that affect the various

pollutants is important in defining the group of strategies likely to be most effective in

meeting a particular areas objectives. This section describes the emissions that are

commonly the focus of market-based measures and identifies transportation variables

associated with each, thus pointing out the travel behavior that must be influenced to

best reduce emissions of each type of pollutant.
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U. S. Environmental Protection Agency 2-17Table 2.3 Characteristics and Comparative Advantages of Pricing Measures

Measure Method of Achieving

Impact

Advantages Disadvantages

Parking Pricing Cost pressure at destination

reduces vehicle use

• Relatively direct signal

• Relatively strong signal (out-of-pocket cost)

• Affects shorter trips more than longer (limiting cold

starts)

• Results in better trip planning (less inefficient

chaining use)

• Difficult to implement in many lo

• Creates demand for lower-pric

more distant (causing higher V

• Relatively easy to evade

• Difficult to enforce

ModalSubsidies

Pull to alternate modes throughlower comparative prices

• Easy to implement (through benefit rather than cost)

• Shift to modes that pollute less per person trip

• Requires societal outlay

• Further distorts market pricing 

cheaper than cost

• Can cause sub-optimal shift in 

carpool)

• Incentive may be less effective 

Pump Charges Raise cost of vehicle travel on

per-mile basis to affect VMT

• Technologically easy to implement, minimal

administrative costs

• Affects all travel (non-work, as well as work)

• Encourages shorter, grouped, or non-motorized

trips

• Increases fleet efficiency

• Must be substantial to have tra

• VMT impact can be diminished 

replace current fleet.

Emissions Fees Fees on high-emitting vehicles;

reduce use and/or improvemaintenanceHigh registration fees todiscourage vehicle ownership

• Direct impact on source of emissions

• Reaches all vehicle owners/users

• Technical implementation not difficult for simple

versions

• Encourages better maintenance, thus improves

emissions rates

• Infrequently levied fees weak 

• More effective fees are difficult 

RoadwayPricing

Per-mile fee reduces VMT • Relatively direct signal of usage cost

• Peak-pricing affects peak-period travel and

congestion directly

• Improves mobility

• Complex/expensive to impleme

• If manual payment, may produ

emissions

• In incomplete pricing network, 

alternate routes, destinations, o
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Volatile Organic Compounds (VOCs)

VOCs are unburned hydrocarbons (HC), typically generated by gasoline combustion

engines. VOC emissions per vehicle have been substantially abated through automobile

technology and fuels programs over the last decade, though it remains a problem in

many areas and threatens to be a long-term issue as both VMT and the number of

vehicular trips continue to rise. VOC emissions are influenced by speed range, flow

conditions, number of vehicle trips and, VMT.

Engines produce VOCs in these elements of operation:

• Cold Start EmissionsCold start emissions occur when a vehicle is first

started and the catalytic converter is cold. For a given vehicle and weather

conditions, cold start VOC emissions are the same for all trip lengths, thus

they are higher as a percentage of total VOC emissions for short trips.

As noted in the table below, cold start emissions represent about 65 percent

of total VOC emissions for a 5-mile trip, 53 percent of total VOC emissions

for a 10-mile trip, and 39 percent for a 20-mile trip.

• Running Emissions Running emissions occur once the catalytic converter

has warmed up. VOC running emissions vary with the number of miles

driven and the speed of travel. Assuming the same speed conditions, a 10-

mile trip would produce twice the VOC emissions of a five-mile trip. VOC

emissions generally decline rapidly with speeds up to about 25 mph, fall

more gradually up to 55 mph, and then rise moderately over 55 mph. A

vehicle traveling in free-flowing traffic emits only one-third as much VOC

emissions as one in congested traffic. As VOC running emissions are

proportionately related to trip length, they increase as a proportion of total

emissions as trip length increases. The running emissions for a 5-mile trip

are roughly 21 percent of total trip emissions, 35 percent for a 10-mile trip,

and 52 percent for a 20-mile trip.

• Hot SoakHot soak emissions occur after the vehicle is turned off when

residual heat in the engine causes fuel in the gas line and carburetor to

evaporate. These emissions also are independent of trip length; they

therefore represent a greater share of short-trip than long-trip emissions

(approximately 14 percent of a 5-mile trip, 12 percent of a 10-mile trip, and

9 percent of a 20-mile trip).

While longer vehicle trips produce less emission per mile, they still, of course, produce

more total emissions. A 20-mile trip, for example, produces 35 percent more emissions

than a 10-mile trip and 64 percent more than a 5-mile trip.
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Table 2.4 VOC Emissions for Typical 1987 Automobile,

Normal Operating Conditions (grams of HC)

 5-Mile Trip 10-Mile Trip 20-Mile Trip

Cold Start 9 9 9

Running Exhaust 3 6 12

Hot Soak 2 2 2

Total 14 17 23

Grams/mile 2.8 1.7 1.2

Source: California Assembly Office of Research, Exhausting Clean Air: Major Issues in

Managing Air Quality. Sacramento, CA (1989) p.4

Carbon Monoxide

CO is formed during incomplete combustion, and occurs when carbon in fuel is

partially oxidized rather than fully oxidized to form carbon dioxide (CO2). CO is an

odorless and colorless gas. High concentrations can be found in confined spaces like

parking garages, poorly ventilated tunnels, or along roadsides during periods of heavy

traffic ("CO hotspots").

CO interferes with the absorption of oxygen by hemoglobin in the blood. Lack of oxygen

impairs the cardiovascular and nervous system, with symptoms including chest pain,

headaches, dizziness, nausea, fatigue, and slower reflexes. In addition, CO impairs

visual perception, work capacity, manual dexterity, learning ability and performance of

complex tasks. CO can affect fetal growth and tissue development, and results in death

at extremely high concentrations.

In 1992, the transportation sector produced about 77 percent of all CO emissions

nationally, or about 75 million short tons. Highway vehicles contributed about

80 percent of transportation sector CO emissions. Carbon monoxide is produced at a

significant rate by heavy-duty diesel (HDD) trucks and at an even higher rate by

gasoline-powered vehicles. As of 1993, heavy-duty diesel vehicles contributed about 1.5

percent of all CO emissions nationally, and highway vehicles overall contributed about

62 percent of all CO emissions nationally. 6

 

6

 National Research Council, Expanding Metropolitan Highways: Implications for Air Quality and Energy

Use, Special Report 245, National Academy Press, Washington, DC, 1995.
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In general, CO emissions increase as the proportion of fuel in the air-fuel mixture

increases. Cold-starts, heavy engine loads, and high speeds are major causes of

incomplete oxidation of the carbon present in fuel.

Nitrogen Oxides

Nitrogen oxide (NOx) is an important pollutant in the generation of ozone and in some

cases of PM. Although NOx emissions are a product of gasoline-powered engines for

vehicles, diesel engines are the most significant producers of NOx emissions. Diesel

engines power heavy-duty trucks and also a variety of off-road sources, including rail,

air, and marine transportation. Buses are also powered by diesel engines, and contribute

heavily to NOx emissions. HDD trucks contribute as much as 35 percent to 50 percent of

regional mobile source NOx emissions, and 10 percent to 25 percent of total regional

NOx.

7

While NOx emissions from passenger vehicles have been substantially curtailed by

catalytic converters, commercial diesel vehicles are still significant NOx emitters. Given

progress in reducing VOCs for the 1996 15 percent reduction demonstration, NOx has

now become a problem focus in many areas.

NOx emissions obey different travel relationships than VOCs. NOx emissions are high

at speeds under 10 mph, fall slightly to speeds of about 20-25 mph, rise slowly to speeds

of about 45 mph, and then increase rapidly with speeds above 45 mph. Hence,

strategies that relieve congestion and improve operating speeds to over 10 mph are

generally effective in reducing CO, VOCs and NOx emissions; programs that increase

speeds above 25 mph will reduce CO and VOCs but may increase NOx emissions

(depending on pre-program speeds); and speeds over 45 mph result in substantial NOx

emissions, though VOCs and CO emissions continue to decline until speeds of 55 mph

are reached. Current models indicate that PM emissions are not much affected by

speed, except that higher speeds lead to more travel and therefore more PM.

Selection of an emissions-reduction strategy based on travel patterns and speed must

accommodate divergent effects across pollutant types. Moreover, the ratio of NOx to

VOC reductions is important. As the ratio between a regions VOCs and NOx increases,

the appropriate strategy for controlling ozone changes. At a VOC/NOx ratio of 10 or

less, for example, VOC controls are more effective, but at VOC/NOx ratios of 20 or

more, NOx control is more effective. In between these extremes, NOx and VOC policies

are more comparable in their effectiveness. These variations also must be taken into

account in program design and evaluation of program impacts.

 

7

 Air Quality Issues in Intercity Freight, see footnote 1.
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Particulate Matter

EPA estimates show that about 26.4% of PM-10 sources are due to transportation. This

excludes miscellaneous sources and is based on direct emissions, but road dust kicked

up by traveling vehicles is another significant source. Tailpipe emissions also create

secondary PM-10 emissions from the oxidation of NOX and SOX to nitrates and sulfates

in the atmosphere; also, some of the VOC compounds are also oxidized to secondary

particulates.

PM-2.5, or fine particulates, result from two major types of sources: 1) the incomplete

combustion of fossil and biomass fuels in motor vehicles, boilers, furnaces and open

burning sources and 2) gaseous emissions such as SO2, NOX, and VOCs from boilers

and other fossil fuel combustion processes that transform in the atmosphere to form

sulfate and nitrate particles. The resuspended soil component of PM-2.5 is generally less

than 5-15%. EPA is continually reviewing and improving estimates of the emission

factors and inventories of PM-2.5.

Revisions to the particulate matter standard were announced on July 18, 1997. The

review of hundreds of peer-reviewed scientific studies, published since the original PM10 standards were established, provided evidence that significant health effects are

associated with exposure to ambient levels of fine particles (PM-2.5) allowed by the

previous standard. These new standards are available in 62FR 38652 or at the

following world wide web address: http://www.epa.gov/ttn/oarpg/rules.html.

Pricing strategies to reduce sources of particulates would probably vary based on the

sources within a region and various climatic factors. In general, reductions in VMT,

rather than specific congestion reduction strategies or emissions fees tailored for other

pollutants, may be more effective.

2.2.4 Overall Advantages and Disadvantages of Market-Based Measures

Consumers who have grown accustomed to subsidized prices (i.e., prices that do not

reflect the full social and economic costs of their decisions) are likely to oppose any

change in the status quo unless it can be demonstrated that they will be made better off

(or not worse off) as a result of the change. Travelers are no different from other

consumers and thus the introduction of market-based measures to date has been

unpopular with the public and their elected representatives.
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Demonstrating the advantages of market-based pricing measures is critical to gaining

their ultimate acceptance. Some important advantages of market-based measures,

which may be used to increase support for their introduction, include the following:

• Applying market-based measures to achieve program objectives (in

contrast to regulatory actions that compel governments or individuals to

assume particular courses of action), provide considerable latitude for

individual freedom of choice.

• By reducing driving subsidies and shifting costs to those who are

responsible for them, market-based measures give individuals more

information about their transportation costs and choices.

• Consumers can make more rational trade-offs between how much they want

to pay and how and when they want to travel under a pricing system

based on true costs; the current system of indirect taxes and subsidies

obscures the information necessary for such choices.

• Rather than locking in current institutional and service concepts, marketbased measures support creativity to develop new and more efficient solutions

to travel and access needs currently met mainly through private vehicle

travel.

• Market-based measures can be applied to all types of travel and trips, not

just employment-related travel. This is in contrast to TCMs, which have

been criticized as having a limited (and perhaps inequitable) impact

because they affect only a portion of travel activity.

• Market-based measures are typically self-financing, and in fact may provide

revenue to support their implementation, as well as other transportation

improvements.

• Market-based measures can substantially reduce the cost of transportation

(both direct and indirect). Reduced vehicle dependency can result in

improved travel time and reliability in movements of people and goods,

reduced construction and maintenance costs, lower taxes, and fewer

accidents in addition to lower health costs, less environmental damage and

clean up across a range of media: water, climate, noise, solid and

hazardous wastes, and habitat.

• Market-based measures can help states and local areas delay or offset the

need for expensive new transportation capacity, help finance that capacity

when it is needed, and result in more efficient usage. They also can help

cut costs for maintenance and government services.

• Longer-term, structural changes induced by market-based measures are

critical to the sustainability of transportation and air quality plans, and can

help areas avoid recurring updates of their SIPs in search of new and

stronger controls to offset VMT growth.
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Market-based measures are not, of course, without their shortcomings. Perceived

disadvantages include:

• Political resistance, as noted above, to any form of direct transportation

pricing, elimination of subsidies, or imposition of new taxes.

• Technological and administrative challenges and uncertainties in

implementation.

• Concerns about fairness and equity, including, for example, whether

travelers have reasonable alternatives and whether they impose increased

burdens on the poor or other disadvantaged groups.

These concerns are important to consider in program design, and should not be

dismissed by local decision-makers. Political consensus on complementing policy

changes at the local level will be required. This document will allow decision-makers to

evaluate the costs and benefits of alternative market-based policy options. (Issues of

implementation and practical acceptance are discussed in Chapter 4).8

 

8

 Issues related to public involvement, public acceptance, and equity are also discussed in: USEPA,

Opportunities to Improve Air Quality through Transportation Pricing Programs (EPA 420-R-97-004),

September 1997. This document is also available at the following world wide web address:

http://www.epa.gov/OMSWWW/gopher/Market/pricing.pdf
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n 2.3 Evaluation of Market-Based Measures

Real-life examples that demonstrate how pricing can influence transportation choices

exist: transit ridership clearly fluctuates with fare changes; bridge and tunnel usage

demonstrate sensitivity to toll changes; HOV usage responds to exclusion from tolls; and

automobile owners respond to fuel price levels by changing their driving behavior.

Currently, planners and decision-makers do not have system-wide implementations of

pricing initiatives at their disposal (such as those discussed in this guidance document)

to furnish detailed empirical evidence of their effectiveness and related impacts. DOT's

Value Pricing Pilot Program (formerly the Congestion Pricing Pilot Program) is

generating valuable experience and research into the potential effectiveness of pricing

policies. For the immediate future, however, estimation of the impacts of these

measures will likely come from simulation tools and other analytical approaches

described in this section. There also are examples from other countries upon which

evaluations can be based.

2.3.1 Analytical Process

Most major metropolitan areas perform transportation planning analysis through some

variation of the four step process, which derives its name from the four basic analysis

modules through which it assesses travel demand: Trip Generation, Trip Distribution,

Modal Split, and Traffic Assignment. This process, which is pictured in Figure 2.1,

methodically translates spatial activity patterns into trip flows, which are then

represented on a transportation network to reflect specific travel volumes on facilities,

with respective speeds and levels of congestion.

Supporting these major elements are important upstream functions, such as land

use/activity allocations, detailing of the transportation network, and vehicle ownership

forecasts, which provide essential starting inputs to the process. Although these subfunctions may not be thought of as demand estimation procedures, they have an

important role in profiling travel demand. Allocation of trips to peak and off-peak time

of day is also an important function.

Emissions are calculated as an end product of this process. Analysts extract

information on vehicle trips, miles of travel and speeds from the transportation model

and link it with information on the regional vehicle fleet mix and emissions

characteristics. This typically is accomplished through application of EPAs MOBILE

model (version 5a), or in California, the EMFAC model developed by the California Air

Resources Board (CARB). PM10 emissions are calculated using EPAs PART 5 Model.

This section describes the steps in the analysis hierarchy and issues associated with

these analyses. This serves as a prelude to the subsequent discussion on analysis of

specific market-based measures.
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Regional Growth

Forecasts

Land Use

 Allocations

Vehicle Ownership

Trip Generation

Trip Distribution

Mode Split

Time of Day/

Peaking

Traffic Assignment

Emissions

Figure 2.1 Schematic Overview of Four-Step Planning Process

Precursor Activities Four-Step
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2.3.1.1 Steps in the Analysis Hierarchy

Nine steps comprise the analysis hierarchy, including three precursor activities (regional

growth forecasts, activity allocations, vehicle ownership), the components of the four

step process (Trip Generation, Trip Distribution, Modal Split, and Traffic Assignment),

as well as its implied time of day/peaking component, and, last, the emissions

evaluation.

Regional Growth Forecasts

The regional planning process begins with forecasts of population and economic

growth. Usually, economic growth, stated in terms of basic employment, is estimated

first, followed by estimation of the population growth that would be stimulated by those

jobs. Then, population-serving employment and attendant population increases are

estimated. Frequently, these estimates are keyed to federal and state control totals.

Activity Allocations

Once jobs and population information is compiled, they are located spatially across the

region, based on development trends, master plans, incomes/economic criteria, or a

variety of other factors. Specific allocations of jobs and households are made to

geographic units, generally traffic analysis zones that loosely conform to groupings of

census tracts. This process may involve analytic techniques (so-called land use

models which reflect transportation access and cost issues), but most commonly are

the result of an iterative negotiating process.

Vehicle Ownership

Travel activity forecasts are driven by the economic resources available to households.

Most travel models represent this through the number of vehicles owned by a

household, which are then used throughout the process. Some model systems rely

directly on income figures, and do not estimate vehicle ownership. Vehicle ownership

estimates are typically done through simplistic cross-classification models, reflecting

differences by income, location and other socioeconomic characteristics. The estimates

usually ignore land use and alternative mode availability as explicit factors.

Trip Generation

From the information on population and employment activity, actual trip making is

estimated through trip generation analysis techniques. Factors are applied to the

population/economic activity levels to estimate productions and attractions, where

households are assumed to produce trips and employment and commercial activity are

assumed to attract trips. These estimates of household trip making are generally cast in

terms of person trips, though in most models these are motorized vehicle person trips

only and exclude walk/bike trips. Some model systems are even more limited and

forecast only private vehicle trips. Trip estimates also are developed for each major trip

purpose (work, shopping, personal business and social/recreational). Trip production

rates are based on such factors as household size, income/vehicle ownership, location,
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etc. Trip attractions are based on the type of employment activity and other factors.

Again, land use, density, and transportation facilities and price/source levels are

frequently not entered into the forecasting process.

Trip Distribution

The next step is to match the trip productions and attractions to create connected trips for

each zone-to-zone (origin-destination) pair. This usually is done using a gravity model,

where productions and attractions for each trip purpose are matched based on the

size of the relative production/attraction and inversely to the separation or travel

difficulty between the two zones. This step tends to represent destination choice.

Mode Choice

The next step in the process projects a travelers choice of desired travel mode for the

given trip. Generally, this entails choices among automobile, automobile passenger, and

transit, since trips which would be candidates for walking or bicycle have been

separated out at the trip generation stage. A small number of models do include nonmotorized modes among the choice options. Typically, this assessment is made for each

trip purpose and each zone pair using a statistical model that considers the relative

attractiveness of each alternative, expressed in terms of its travel time and cost, and

weighted by the characteristics (income, vehicle ownership, etc.) of the traveler, and

estimates the probability with which each mode is likely to be chosen. Some models

relate mode choice and destination choice, but this is not common at present.

Time of Day

Most travel models attempt to account for the time of day that travel occurs, which is

obviously critical to portraying peak versus off-peak travel conditions. However, rather

than treat this as an active travel choice, the models handle this phenomenon through

peaking factors. These factors simply proportion a certain percentage of trips into the

peak and off-peak periods, based on survey observations. A separate travel network

and assignment is necessary to reflect peak and off-peak conditions. It should be noted

that a fair number of areas do not have a peak (i.e., a constrained flow) network, and

work only with 24-hour conditions.

Traffic Assignment

Once blocked into time-of-day groups, trips are then placed into the actual

transportation network using a statistical procedure that assigns them to routes that

represent the most desirable path to their chosen destination (generally the least time

path). This simulates the choice of travel route by the traveler, although like time of

day, it is a decision that the process makes for them through a procedure that shifts

trips around until the resulting volumes on the links best represent observed traffic

levels. Traffic assignment also is important as the part of the process where level of

service and congestion is determined, from which travel speeds are calculated for use

elsewhere in the process.
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The realism of the forecasts from this multi-step procedure depends on the extent to

which the various steps are operating with the same information. Thus, a fair amount

of cycling of information among the steps is required. It is necessary to pass information

from later steps (e.g., travel times as calculated during traffic assignment) to earlier steps

(e.g., trip distribution or modal split) as inputs to the calculations (i.e., traveler

decisionmaking) at those stages. Thus, preceding steps contribute inputs to succeeding

steps, with the process continuing until a defined state of equilibrium is reached. Model

systems vary greatly in the degree and sophistication with which they feedback

information among steps and reach a satisfactory equilibrium.9

Emissions Estimation

This step is not part of the conventional transportation modeling approach, but is

necessary when estimates of vehicle emissions are required for air quality analysis.

Typically, this procedure is performed through a special model, such as MOBILE or

EMFAC, which takes information on vehicle trips, vehicle miles of travel, and speeds, by

facility type, combines it with information on the composition of the local vehicle fleet

and its emissions characteristics, and generates estimates of emissions by type of

pollutant. MOBILE is used to estimate VOC, CO and NOx emissions, while a similar

model, PART5, is used to estimate PM10. EMFAC also includes PM10 for tail pipe, brake

wear, and tire wear emissions, while PART5 is used for re-entrained dust emissions.

Future EMFAC versions may incorporate re-entrained dust emissions (It should be

noted that MOBILE produces emission factor estimates rather than estimates of

emissions amounts).

2.3.1.2 Issues

Two sets of issues are raised concerning the suitability of these analytic tools in

performing evaluations of market-based mechanisms. The first issue is that the fourstep process has various limitations as a tool for examining the impacts of market-based

approaches in general, and pricing measures in particular. The second issue is that the

analytic capabilities of candidate regions and agencies vary widely.

It is generally acknowledged that the four-step process was not developed as a tool for

performing sensitive policy testing. It was originally developed and has been primarily

used for generating vehicle traffic volumes for highway planning and sizing purposes.

Over time, enhancements have been made that increase its capabilities and

sophistication, but a number of shortcomings still exist that raise concerns when

examining pricing actions:

 

9

 Work is underway in several regions under the U.S. DOT/FHWAs Travel Model Improvement

Program to develop a new generation of travel models that more effectively integrate these steps,

considering individual activity scheduling, the changing of trips into tours, and the potential for

trip sequencing and time of day of travel, as well as recognizing the effects of transportation on

growth patterns. Many of these improvements are already available to local agencies through the

Texas Transportation Institute.
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• Pricing is often not rigorously included in the model's structure. Typically, the

only choice dimension that is sensitive to cost is mode choice.

• The accuracy of the pricing relationship in the models is challenged by the

heavy emphasis on travel time in model development, and the relative lack of

good variation in pricing conditions in most U.S. urbanized areas when

compiling input data.

• It is expected that travelers will respond differently to pricing actions

depending on their income status, although this is not explicitly accounted for

in most models.

• Based on a structure that approximates behavior through zonal averages, and

the specification of the models themselves, it often is necessary to restrict

consideration of a measure to the most basic definition.

• Commercial and through (external-external) travel is generally not

addressed in the application of policy actions, especially pricing.

• Whereas pricing actions would be expected to have a pervasive effect on travel

behavior, ranging from changes in destination, route, time of day, or even

location choice, most models consider price only in relation to mode choice.

Also, these decisions would be expected to have some interdependency,

whereas the models usually represent behavior as a sequential-step process.

Added to these inherent limitations, there is the further complication that overall

capability varies widely by location. The model systems of areas like Portland, Oregon

or San Francisco are fairly progressive and have many of the features that would raise

confidence in their use for pricing measure analysis. However, there are many other

major metropolitan areas whose models are fairly rudimentary or which have

acknowledged flaws or limitations. Added to this are a number of areas that might

choose to look at pricing measures, but which have little or no modeling capability at

all. These might be counties or urban areas that are too small to have sophisticated

transportation planning capabilities. The planning process for many of these areas, for

example, may be only a three-step process (excluding mode choice) or less.

This guidance document has been developed with appreciation for the demand that

pricing measures place on the analytic process and the wide range of analytic

capabilities that exist. It is EPA's desire that areas that need additional help in reaching

their attainment goals consider market-based measures. In this regard, the candidate

area should endeavor to perform the most accurate and realistic assessment possible,

within its capabilities, when submitting such measures for emissions credit. EPAs

decision as to how much credit to grant will be based on: (1) whether the analysis

supports the requested credit; and (2) whether the steps leading to implementation are

realistic. Obviously, the greater the significance and contribution of the measure to the

area's attainment goals, the more carefully the analysis procedures must be reviewed

when deciding to grant credit. However, EPA will work cooperatively with the

performing agency to support its efforts and help achieve an accurate determination of

credit.
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2.3.2 Identifying and Assessing Areas of Impact

This section presents how each measure is expected to affect travel and emissions.

Discussion is organized by area of impact, presented in the same order as the analysis

hierarchy. This section also describes the analytical tools that may be used to assess

those impacts.

Table 2-4 lists each of the five market mechanisms, as well as an assessment of where in

the travel demand hierarchy they would be likely to have impact. Regional growth

forecasts have been excluded as a step, since their value is almost always exogenously

determined. Vehicle ownership appears in two ways: (1) conventional vehicle

ownership, which represents the number of vehicles owned by households as a factor in

travel decisionmaking for many model systems; and (2) vehicle mix (shown as a

component of the emissions generation step), which represents the types of vehicles

owned and their composition in the vehicle fleet mix.

Table 2-4 indicates where a pricing measure would have an impact in this structure,

and the expected level of importance of that impact. Impact importance is rated using

the following scale:

• Primary: The impact is major and basic to the measure; every effort should

be made to evaluate its effects.

• Secondary: The impact is judged to be an important effect which may

even exceed the primary effect and thus must be considered; however,

while its formal analytical evaluation (i.e., a complete regional travel

demand modeling analysis) may not be possible, an area should provide

documentation to show that the effect has been considered in the most

reasonable manner possible.

• Trace: Some impact would be expected, but its implications for emissions

would be minimal and no evaluation would be required.

• None: For practical purposes, no impact would be expected.

A draft version of the impact hierarchy outlined above was originally developed as the

result of a meeting that included EPA and DOT staff and contractors involved in the

production of this report. Subsequently, the draft hierarchy was reviewed by EPA and

FHWA staff and modified. EPA and FHWA review and modification led to the

hierarchy outlined above.

Table 2-4 should be used with several caveats in mind. First of all, level of importance

valuations in the table are not linked to time periods. For example, while the land use

impacts are often, long-term the distinctions among primary, secondary and trace

effects are generally linked to the ultimate level of impact expected, rather than how

long it takes for that impact to fully materialize.
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U. S. Environmental Protection Agency Table 2.5 Anticipated Impact of Measures in Analysis Hierarchy

Measure Category

Area of Impact Parking Pricing Modal Subsidies Pump

Charges (VMTfees)

EmissionsFees

Roadway 

Land Use/ActivityAllocations

Secondary Trace Secondary Secondary Secondar

Vehicle Ownership Secondary Trace Trace Primary SecondarTrip Generation Primary for work,

trace overall*

Trace* Secondary* Trace* Primary if 

otherwise

Trip Distribution Secondary Secondary Secondary Trace PrimaryMode Choice Primary Primary Primary Trace PrimaryTime of Day Secondary Secondary None None Primary

 (through pricing)

Traffic Assignment Trace Trace Trace None PrimaryEmissions/Vehicle Mix None None Primary

(change vehiclemix)

Primary(changevehicle mix oremissionsfactors)

None

* An issue if Trip Generation does not include non-motorized modes.
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Second, level of importance valuations do not account for major differences in price levels.

For any of the measures to have an effect on behavior, the level of the price applied

would have to be sufficient to be felt. At low price levels, all of the instruments would

be expected to function primarily as revenue generators, and have only marginal effects

on travel behavior. However, the relative level of impact implied by the importance

valuations would be expected to stand across a wide range of price levels.

The analytical tools or approaches described in this section are categorized as basic

analysis or advanced analysis, according to how they are rated by EPA submission

requirements:

• Basic Analysis: Procedures that EPA suggests be used when formal

analysis is required if the measure is to be considered for emissions credit

(that is, when the measure is expected to have a primary and direct impact

or a significant indirect effect).

• Advanced Analysis: Suggested procedures beyond the basic analytical

procedures that EPA believes would result in better estimates of the travel

and emissions impact of the measure if applied. Use of Advanced Analysis

Techniques will enhance the acceptability of an application for SIP credits.

In many cases, the same analytical technique is considered basic in one circumstance

(e.g., when the measure will have a primary impact), but advanced in another analytical

context (e.g., when the measure will have a secondary or trace impact); these techniques

are thus categorized as basic or advanced analysis. This consolidated discussion of

analytical techniques helps avoid undue repetition in Section 2.3.3, below, on analytical

requirements for individual market-based measures.

2.3.2.1 Land Use/Activity Allocations

Some pricing measures can affect long-term locational decisions and land use patterns

and both short and long-term individual decisions. These measures might either induce

more concentrated growth patterns that might be favorable for longer term

transportation and emissions management, or could produce divergent trends toward

dispersed land use patterns and greater automobile dependence.

Effects of Market-Based Measures on Land Use/Activity Allocations

Much depends on the level and type of cost impact of the particular measure, but most

of the measures could conceivably have an impact on this underlying aspect of travel

behavior. Despite the potential impact of a change in land use/locational choice, these

impacts are listed as secondary in Table 2-4 because of the uncertainty associated with

projecting these changes with current modeling tools.

Parking pricing would be expected to induce a locational effect, with greater

preferences for locations that provide more concentrated amenities and services and

better transportation alternatives. If applied differentially, businesses and households

might be expected to show preferences for locations with lower parking rates. For

example, should the policy be to raise downtown rates relative to suburban rates, this

would be expected to increase the attractiveness of suburban development. If parking
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were implemented uniformly, and resulted in some areas having better design/amenity

or offering better access through non-SOV modes, the measure also might induce

locational shifts to these areas.

Modal subsidies would be expected to have only a trace effect on land use decisions,

unless they were of significant size and the public perceived them to be permanent (in

availability and in relation to the cost of other modes). At high levels, dedicated

subsidies might shift locational preferences to areas where these price advantages could

be exploited, that is, where transit or other subsidized modes are more available and

useful in reaching desired destinations.

Pump charges would be expected to have a concentrating (secondary) effect on land use,

since they function to visibly raise the cost of driving for all travel purposes. Thus,

households might be expected to locate in areas where less dependence on private

vehicle travel was required.

Emissions fees, if scaled primarily to emissions output and not VMT, would have little

more than a trace effect on locational decisions. However, an emissions fee that is

substantial, or based heavily on VMT, could encourage households to own fewer

vehicles and thus prefer areas with mixed land uses and which otherwise provide better

access to desirable locations without private vehicles.

Roadway pricing might have an important (secondary) effect. Widespread pricing

would tend to encourage locating where there is less reliance on private vehicles.

Assuming only selected facilities would be priced, households and businesses would be

expected to locate so as to minimize long-term reliance on these facilities. The impact of

this on air quality is uncertain. Area-wide implementations of road pricing would tend to

reduce VMT and encourage a greater mixing of uses. Congestion pricing would be

limited in influencing location choice, particularly if fees were peak-only. To the extent

that congestion pricing leads to travel time decreases, it could lead to more dispersed

new development, could allow better access to currently congested centralized

developments, or both. Area cordon fees may lead to more transit-oriented development,

provided the priced area remains attractive relative to other locations.

Analytical Techniques for Land Use/Activity Allocations

Advanced Analysis

Two approaches are generally applicable for evaluating the impacts on land use/activity

allocation of market-based measures:

• Estimate impacts using a land use model that is sensitive to

transportation costs (through incorporation of generalized cost, for

example);10 or

 

10 The DRAM/EMPAL model, for example, developed by University of Pennsylvanias Dr. Stephen

Putman, is commonly used in the United States to model land use changes.
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• Develop one or more growth scenarios to depict the alternate ways in

which regional growth might respond to changes in transportation costs of

the type proposed. This might be done in conjunction with application of

land use models, and involve development and integration of alternative

land use/urban design configurations supportive of alternatives (e.g.,

subsidized) modes.11

Because land use/activity allocation effects are identified as secondary impacts, no

formal analysis is called for, although a statement of anticipated impacts should be

included in most cases.12

2.3.2.2 Vehicle Ownership

Effects of Market-Based Measures on Vehicle Ownership and Vehicle Mix

Pump charges would encourage drivers to shift to owning and/or using more fuelefficient vehicles. This would have an important primary effect on vehicle types, which

should be captured in the emissions modeling step, in terms of the fleet mix used to

calculate regional emissions factors. These more efficient vehicles might or might not be

lower-emitting.

Pump charges that serve as a medium for pay-as-you-go insurance might have an effect

on vehicle ownership, although the direction of the effect would be hard to predict.

Such charges would significantly boost the cost of vehicle ownership and use for groups

that do not carry insurance (a legal violation in most cases, acting to discourage

ownership). Pay-as-you-go charges, however, may be more economical for owners

currently insured and could encourage ownership.

Emissions fees, if significant enough, could affect the number of vehicles owned (hence

a primary impact). Emissions fees would likely cause households to shift ownership to

(and possibly use) newer and/or lower-emitting vehicles. This would be an important

primary effect that would need to be reflected in the emissions modeling step through

adjustments to the fleet mix.

 

11 An outstanding example of this approach is Portland, Oregons Region 2040 planning process,

and a closely related analysis process by 1000 Friends of Oregon, the Land Use, Transportation,

and Air Quality Connection (LUTRAQ). Region 2040 information is available from Portland Metro,

and LUTRAQ information from 1000 Friends of Oregon See, for example, 1000 Friends of Oregon,

Making the Connections: A Summary of the LUTRAQ Project, February 1997.

12 In a relevant recent case, a US District court ruled that as part of planning for a major new highway,

Illinois DOT would need to develop a separate set of socioeconomic/land use forecasts based on

what would likely occur with vs. without construction of the road. A single set of

socioeconomic/land use assumptions for both build and no-build scenarios for evaluation of ozone

precursors was deemed to fail the legal requirements of the National Environmental Policy Act. US

District Court (Northern District of Illinois, Eastern Division), Case No. 96 C 4768. Sierra Club,

Illinois Chapter; South Corridor Against the Tollway, Inc.; Environmental Law and Policy Center of

the Midwest; and Business and Professional People for the Public Interest vs. US DOT/FHWA,

Illinois FHWA, Illinois DOT, and Illinois State Toll Highway Authority.
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No other market-based measures would be expected to have a major direct effect on the

number of vehicles owned by households. The primary effect on vehicle ownership levels

for these other measures would be a result of induced shifts in location (land

use/activity allocations) to areas where fewer vehicles were required.

Analytical Techniques for Vehicle Ownership and Vehicle Mix

Basic or Advanced Analysis

Analytical tools used for evaluating ownership patterns apply primarily to vehicle mix

because that is the factor expected to be most affected by market-based measures.

Approaches include:

• Analytic procedures such as hedonic models to forecast shifts in ownership

patterns;

• Research studies on shifts in vehicle ownership in response to price, and

application of factor methods to estimate shifts; and

• Special analyses, such as stated preference surveys, to investigate likely

responses to ownership that might result from alternative fee structures.

2.3.2.3 Trip Generation

Market-based measures can have a range of impacts on the generation of trips from

households. Probably the key factor in terms of the impact on trip generation is the

overall scope of the measure and its comprehensiveness in increasing the overall cost of

travel by single-occupant vehicles. Ideally, trip generation should account for all persontrips. Non-motorized trips are generally not accounted for in current modeling systems,

which could constitute an important limitation when evaluating pricing systems.

Effects of Market-Based Measures on Trip Generation

Many analysts assume that the number of trips generated by a household is not

particularly sensitive to transportation factors, including cost. They suggest that a

household's propensity to travel is determined by its need to satisfy specific activities,

which are dictated by its size and socioeconomic composition. Rather than forego

activities and make fewer trips in response to higher prices for vehicle travel, households

would be expected to make less severe changes in their pattern of trip making  for

example, more trips to closer/different destinations or, shift to other modes. These are

important effects, but are generally dealt with elsewhere in the travel analysis hierarchy.

There are growing challenges to this view, however, particularly in light of new

information and telecommunications systems that are reshaping our economy and

communities. For example, an increasing number of workers are opting to work at

home under telecommuting arrangements; many others have shifted to a modified work

schedule. In these cases, the number of trips made to a physical work site is reduced

and/or the time of travel changed, suggesting that the household might make fewer

total trips as a result.
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Similarly, shopping and entertainment needs are being increasingly satisfied through

cable TV, the Internet, video movie rentals, deliveries, etc. There also are other ways in

which households could shift their activity patterns to group trips together into trip

chains, possibly accomplishing multiple functions in a single trip. Such adjustments

to activity patterns could show up in overall trip rates in response to higher travel costs,

although their impact is presently not well understood.

Parking pricing could influence trip generation in many different ways and depends

heavily on the details of the pricing changes. For example, requiring employers to charge

employees for parking (or instituting a cash-out program) could have primary impacts

on work trips but, probably, virtually no impacts on non-work trips. If non-work

parking were priced, one could expect far larger impacts, though their character might

differ. Impacts of non-work parking pricing might emphasize long-term land use

changes by retail locations. Since land use effects are captured elsewhere in the

modeling system, trip generation would probably only have a trace impact for non-work

trips.

Modal subsidies would only have a trace impact on trip generation. This would

probably tend to affect work travel more than non-work travel. Minor increases in

carpooling and transit use would be likely.

Pump charges could have a secondary impact on trip generation. In the short run, this

would be reflected by better scheduling of activities and vehicle usage within households

(often a household with more than one vehicle will increase use of the more fuel efficient

vehicle).

Roadway pricing could have a primary impact on trip generation if applied area-wide.

An increase in the overall cost of travel would be expected to result in household

activities being more efficiently scheduled and coordinated. Limited roadway pricing,

such as tolls on individual facilities or limited congestion pricing would only be expected

to have a trace impact.

Emissions fees are not expected to have more than a trace impact on trip generation.

Note that if these fees are mileage-based, and if the mileage-based component of fees is

significant, they could have a greater effect.

Analytical Techniques for Trip Generation

Basic Analysis

Aside from uncertainty regarding the actual impacts, practical concerns have been

raised regarding the modeling conventions used to assess impacts. Frequently, trip

generation models estimate only those household person trips that may be served by

vehicle. They do not include trips that would be made by walking or bicycle. Therefore,

in situations where a household may respond to a change in travel conditions by shifting

some trips to pedestrian or bike travel, this shift would not be captured accurately in the

trip generation analytical step.

Some regions employ models that are even more restrictive; they consider only trips

made in vehicles, not person trips. Typically, these are smaller areas where the private

vehicle is the overwhelmingly dominant mode and the planning process is less complex
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in that they are not concerned with transit or non-motorized alternatives. Thus, if a

region's trip generation model estimates all person trips, the effect of pricing on trip

generation would be expected to be at a trace level, since allowance would be made for

vehicle trips to shift to non-motorized modes.

However, if trip generation analysis is based only on vehicle or vehicle-eligible trips, then

pricing would be expected to show at least a secondary effect, at least in the case of pump

charges, but possibly also for parking pricing and modal subsidies, because vehicle trips

would shift to non-motorized modes would disappear from the inventory.

Advanced Analysis

In general, if the trip generation analysis deals only with motorized-vehicle trips, then

an adjustment should be made to the vehicle trip generation estimates to reflect shifts to

non-motorized or non-automobile-driver options. Wherever possible, trip generation

analysis should be performed separately for each purpose and income strata, to support

subsequent analysis of trip distribution and mode choice.

Moreover, if land use analysis suggests shifts in activity or land use patterns, then the

vehicle ownership and trip generation analytical steps should be repeated to reflect

relocation of activity and its association with potentially different rates of trip making

and vehicle ownership.

2.3.2.4 Trip Distribution

Effects of Market-Based Measures on Trip Distribution

All market-based measures, except emissions fees, would be expected to influence choice

of destination, that is, trip distribution. As noted earlier, if emissions fees are mileagebased, and if the mileage-based component of these fees is significant, they could have a

greater effect. The same caveat applies to all references to the effects of emissions fees

throughout this section. Parking pricing would be expected to influence destination

choice at a secondary level. Increased parking rates would increase the attraction to

destinations that provide better access to desirable locations without private vehicles. If

parking rates were uneven in their application, travelers would be attracted to

comparable destinations with zero or lower parking rates, unless the priced area also

offered better access, more services or a higher level of amenities.

Modal subsidies would be expected to have a secondary effect. Lowered costs of

selected non-SOV modes (through subsidies) would be expected to increase the

attractiveness of those destinations accessed by subsidized modes.

Pump charges would have a secondary effect on destination choice. By raising the

comparative costs of private vehicle travel, this measure would be expected to increase

the attractiveness of closer destinations and/or destinations easily reached by alternative

modes.
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Emissions fees could have a trace effect on choice of destination. If the mileage-based

component of the charge were significant, then it could encourage travel patterns that

would reduce VMT, although the imposition of this fee on a periodic/annual basis

would be expected to dampen its effect as a signal to daily travel.

Roadway pricing would be expected to have the largest effect of all pricing measures on

destination choice, constituting a primary impact. Prices would tend to induce

consumers to reduce vehicle trips, to reduce the length of trips, and also to consolidate

destinations. If prices were imposed on select facilities, travelers would be expected to reexamine their options (particularly for non-work travel) and choose to:

• Travel to alternative (possibly more distant) destinations not subject to

pricing;

• Travel to alternative destinations served by priced facilities that offer good

alternatives, special amenities, or are closer than their original choices; or

• Travel to the same or alternative destinations, where they obtain quicker

access at an acceptable price using priced facilities (more likely among

higher income travelers).

Area-wide road pricing would result in shifts to closer destinations. Isolated roadpricing systems could, in some cases, result in increased travel.

Analytical Techniques for Trip Distribution

Basic or Advanced Analysis

Areas that have trip distribution models that incorporate price sensitivity directly

through a generalized cost procedure can account for the effects of market-based

measures explicitly. Many trip distribution models are sensitive to travel time but not

cost. Some agencies address this in their models by incorporating a generalized cost

formula. This characterizes the full cost of travel by private vehicle from each origin to

each destination in terms of both the travel time cost and the monetary cost (use of

travel time equivalents is discussed in Section 3.3.5.3.). Some models also consider the

time and money cost of transit, where it is available, and average it into the overall

measure of generalized cost.

If an agency has a model system with a generalized cost procedure that can be made to

reflect the cost of market-based measures, this should be used to perform a revised trip

distribution analysis that incorporates the proposed cost measures. Moreover, trip

distribution analysis should be performed separately by trip purpose and income strata.
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2.3.2.5 Mode Choice

Effects of Market-Based Measures on Mode Choice

All of the pricing measures, with the exception of emissions fees, would be expected to

have a primary effect on the choice of travel mode.

Parking pricing would clearly serve to discourage driving, and, in particular, driving

alone, in favor of other alternatives, and will shift some shorter trips to walking or biking

if suitable alternatives and access exist.

Modal subsidies would create a direct attraction to the mode or modes being

subsidized.

Pump charges would encourage use of alternative modes that would allow the traveler

to avoid or reduce the higher cost of private vehicle travel.

Emissions fees could have a trace effect on mode choice through the mileage-based

component of the charge. Again, however, the effect would be greatest if fees are

collected frequently and/or there are frequent education and reminder efforts.

Roadway pricing would encourage use of alternative modes to avoid or reduce the cost

of priced facilities, although this effect would be in combination with consideration of

alternative destinations, routes and times of day at a much higher level than with the

other measures.

Analytical Techniques for Mode Choice

Basic or Advanced Analysis

With the exception of emissions fees, all market-based measures would be expected to

have a primary effect on mode choice. The performing agency can conduct the basic

analysis with any of the three following options:

• Accepted mode choice model;

• Accepted quick response methods; or

• Elasticity methods.

An agency with access to a mode choice model is expected to use that model to conduct

any required analyses of the impact of market-based measures. It may be permissible to

adjust the factors included in these models or their coefficient values, provided adequate

documentation is supplied on the nature of and need for the adjustment.

Areas without a mode choice model but with access to person trip tables may consider

acquiring one of several quick response methods, which are widely available. These

include: the TDM Evaluation Model from the Federal Highway Administration, the

QRS planning system, and various methodologies developed to support CMAQ project

analyses. Information on these tools is given in Section 3.3.6.9.
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If an agency does not have reasonable access to either a mode choice model or one of the

above surrogate procedures, analysis may be based on application of elasticities from

research studies or from the mode choice model of similar urban areas. Section 3.3.6.9

provides references on sources for such elasticities and guidance toward their proper

application.

Advanced Analysis

Advanced analysis of impacts on mode choice would use current generation, more

sophisticated, mode choice models such as nested multinomial logit models, which

permit testing of a wide array of alternative modes. The range of transportation service

and socioeconomic variables that they incorporate offers greater flexibility in relating

strategies and more realistic estimates of travel response. Some models have separate

coefficients for categorically different cost items, such as transit fares, parking subsidies

or parking costs, and HOV costs, and should result in more accurate estimates of

response to corresponding policies. Models that reflect the interaction of mode choices

and destination choices are desirable and EPA encourages their development.

More realistic analysis will result from application of separate models for each primary

trip purpose, including home-based work, home-based other, and non-home based trips.

Many agencies, however, do not have mode choice models for non-work travel.

Accuracy would be further enhanced by accounting for effects by income strata, as well

as trip purpose, in order to reflect differences in cost sensitivity by income level. This

would also help analyze the equity-related issue of differential impact by income group.

Finally, model accuracy can increase if models can capture the attractiveness of an area

to other travel options, including non-auto travel. Just as the design of the road system

increases or decreases the impedance a driver experiences, so the walking and cycling

infrastructure helps determine whether, how often, and how far people walk or bicycle.

And just as impedance for drivers can be quantified, new work has significantly

improved the quantification of determinants of walking and bicycling. Recent work on

Pedestrian Environment Factors and Urban Density Factors has helped improve

modeling in Portland, for example.13

2.3.2.6 Time of Day

Effects of Market-Based Measures on Time of Day

Only three of the described market-based measures would be expected to have any

effect at all on travel time of day; neither pump charges nor emissions fees would

influence time patterns since assessment is not linked to specific trips.

Parking pricing could have a secondary effect on time of day if the differential between

peak rates and off-peak rates were sufficient to shift users with flexibility to off-peak

travel. In many cases, however, impacts would be at a trace level.

 

13 See, for example, 1000 Friends of Oregon, Making the Land Use, Transportation, Air Quality Connection:

Volume 4A, the Pedestrian Environment. 1993. Portland: 1000 Friends of Oregon.
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Modal subsidies that favor alternatives (mode and/or specific service within that

mode) used primarily in peak periods could shift users from unsubsidized

modes/services used off-peak to the subsidized service during peak periods.

Nonetheless, only trace effects would be expected for the level of subsidies generally

proposed unless subsidies vary by time of day.

Roadway pricing in the specific case of congestion pricing would be expected to have a

primary effect on time of day choice. Higher peak-period prices would be expected to

shift lower-valued trips to off-peak travel and would draw some trips into the peak if a

higher level of service becomes available as a result of the pricing program.

Analytical Techniques for Time of Day

Basic or Advanced Analysis

Time-of-day analysis procedures are still uncommon in transportation planning practice.

Some agencies have time-of-day models. For those that do not, analysis could be based

on procedures borrowed from another location or based on a special analysis at the

affected site to identify relationships and estimate behavioral factors. More specifically,

analysis of the impacts of market-based measures expected to have a measurable impact

on time of day could be based on any of three approaches:

• Use of a time of day choice model acquired from another location (see

Section 3.3.7);

• Consultation of references on time of day sensitivity, which could include

analyses of other area programs, transit or parking peak pricing results, or

other special analyses; or

• Performance of a special stated preference survey analysis to develop a

model for the area under investigation.

2.3.2.7 Traffic Assignment/Route Choice

Effects of Market-Based Measures on Traffic Assignment/Route Choice

While all of the pricing measures affect traffic assignment in terms of revised traffic

patterns, only roadway pricing directly targets route choice.

Roadway pricing forces travelers to make conscious decisions about spatial patterns

and, as such, has a primary impact on traffic assignment. Travelers may have a choice

between routes that are tolled but that offer a higher level of service/travel time and

another set of routes that are not tolled but that have a lowerlevel of service/travel time.

As with destinations, the choices ultimately made depend on the specific characteristics

of the pricing scheme and the available travel alternatives.
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Analytical Techniques for Traffic Assignment/Route Choice

Basic or Advanced Analysis

Roadway pricing explicitly forces travelers to evaluate and respond to the trade-off

between tolled roadways that may have a higher level of service and toll-free routes that

may be more congested. They also may choose shorter trips or forego some trips that

would previously have been taken. The anticipated close links between roadway pricing

and traffic assignment (route choice) mandate analysis of these effects. Yet, traveler

route choice is not directly handled by most traffic assignment models. There are

various options for addressing anticipated impacts, including:

• Use of procedures that represent toll and free path opportunities to

travelers in an assignment routine. These are relatively advanced

techniques, however (see Section 3.3.8.5); or

• Representation of the effect of tolls for a given facility as an increase in

travel time through use of value of time relationships to estimate the

appropriate value. This raises the cost of a tolled facility to a traveler

considering its use (also see Section 3.3.8.5).

2.3.2.8 Emissions /Vehicle Mix

Effects of Market-Based Measures on Emissions /Vehicle Mix

All market-based measures should ultimately affect the level of emissions generated.

Two market-based measurespump charges and emissions feescould directly affect

vehicle mix and, through that, emissions rates.

Pump charges could induce shifts in favor of vehicles with greater fuel economy. The

net effect on emissions is the result of two factors: lower fuel consumption per mile

traveled (and potentially overall) and possibly different emissions characteristics (rate of

emissions per mile). The overall relationship between pump charges and vehicle

mix/emissions could be considered primary, though the indirect link of pump charges

working through increased efficiency and lower total fuel consumption could be

considered secondary. Pump charges would induce travelers to shift to more efficient

vehicles, partially offsetting the benefits of fuel price increases.

Emissions fees would act similarly to the pump charge, except that by inducing a shift

in the vehicle fleet toward cleaner vehicles, it would constitute a primary effect.

Influence of a VMT component of emissions fees would also show up in lower aggregate

mileage rates within the fleet.
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Analytical Techniques for Emissions /Vehicle Mix

Basic Analysis

Agencies should use an approved emission factor model to calculate emissions, with

factors computed to reflect any program-induced adjustments in the regional vehicle

mix. Areas without formal emissions factor models would be expected to explain how

the average emissions rates would change with the induced shifts in vehicle fleet

composition and/or use.

Basic or Advanced Analysis

Analysis of anticipated shifts in vehicle fleet composition or usage can utilize the

following approaches:

• Use of hedonic models (models of consumer satisfaction with particular

vehicle attributes) to project changes in vehicle ownership in relation to cost

differences;

• Consultation of research studies on shifts in vehicle ownership in response

to price, and application of factor methods to estimate shifts; or

• Performance of special analyses (using revealed or stated preference survey

methods) to forecast possible shifts in vehicle ownership and use.

These methods are further detailed in Chapter 3, Section 3.3.3.

2.3.3 Analysis Guidance

This section presents EPAs recommendations for evaluation of market-based

mechanisms and describes the types of analyses that EPA expects to see accompany

submissions of the respective measures for emissions credit. The guidance descriptions

in this chapter are at a general level, designed to communicate to managers and decisionmakers EPAs general thinking; specific step-by-step guidance suitable to direct technical

staff in the actual evaluation practices is provided in Chapter 3, further supported by

examples in Appendices A and B.

The table on the following page summarizes rules of thumb for submissions to EPA;

impacts are assessed for individual market-based measures at each stage of the analysis

hierarchy (refer to Table 2.3 for description of anticipated impacts). Clearly, the more

careful the analysis, the more credit EPA can be expected to approve.
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Rules of Thumb for Submissions to EPA on Market-Based Measures

Expected Impact Acceptable Analysis

Primary effect Formal analysis expected for impact on that step of analysis hierarchy.

Secondary effect Must include some treatment of impact on that stage of analysis hierarchy, though

requirements are less formal.

Trace effect Agency expected to take note of possible side effects, which could occur under special

applications of the measure.

In laying out its recommendations for effective analysis, EPA has taken into

consideration the wide range of capabilities that are available to those considering

market-based measures. Because of the strong likelihood that there will be sites with

minimal analysis capabilities, the guidance recommendations have been made flexible

enough to allow these states to respond.

This flexibility does not take away from the position, however, that an accurate,

comprehensive analysis is extremely important. Accuracy and completeness are

important to EPA's review and decision to grant emissions credit. Therefore, agencies

proposing pricing measures should consult with a wide range of parties to get as much

assistance as possible in developing good analytical approaches. Furthermore, an

accurate analysis also is essential to the performing agency's ability to assess the impacts

and tradeoffs that are critical to local acceptance and implementation. EPA's position is

to support the best possible analysis, to make allowances for states with limited

capabilities, but to expect that states will take every reasonable step to perform the best

analysis within their ability.

The remainder of this section presents analysis recommendations for each of the five

measure groups: parking pricing, modal subsidies, pump charges, emissions fees, and

road pricing. To avoid repetitive discussion of analytical approaches, EPA submission

components are often noted only briefly with reference to the more extended discussion

of basic or advanced analysis organized by impact category in Section 2.3.2.

Recommendations for analysis are separated into basic analysis and advanced analysis

and, within each of these, according to expected level of impact from primary to less

important effects.

Advanced analysis is optional from the standpoint of EPA guidance. This reflects an

acknowledgment of current limitations, but also willingness on EPAs part to work with

all areas in exploring advanced strategies to deal with long-term air quality concerns.

Nonetheless, EPA expects that those areas with advanced planning tools will use those

tools to their best ability. Areas that do not have such tools or capabilities are

encouraged to strive to acquire those advanced capabilities, both to serve their own

current needs and to support other air quality and planning requirements that are likely

to be part of future planning and funding tests.

In addition to attention to analysis tools and procedures, EPA places importance on the

quality of data used to conduct the analyses, particularly with regard to data age,

coverage, and accuracy.
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The level of scrutiny applied by EPA to analyses that accompany submissions of marketbased measures will be tailored to each circumstance. In particular, the level of credit

claimed for the measure and its importance in achieving a given areas attainment or

maintenance goals will be a factor considered by EPA in its review. More rigorous

scrutiny can be offset through a more accurate and defensible analysis.

2.3.3.1 Parking Pricing

Suggestions for basic analysis and advanced analysis pertaining to parking pricing are

illustrated in Figure 2.2a.

Basic Analysis for Parking Pricing Measures

Mode Choice: The primary effect of parking pricing on travel and emissions would be

through its impact on mode choice. A submission that seeks emissions credit for parking

pricing must pay particular attention to the shift of travelers from private vehicles,

especially single-occupant vehicles, to transit, ridesharing or non-motorized alternatives.

Given the importance of parking pricing on mode choice, submitting agencies should use

the most sophisticated procedures available to them, following the order of preference

described in Section 2.3.2.5 on analytical techniques for mode choicebasic or advanced

analysis (i.e., mode choice models, quick response techniques, elasticity analysis).

In performing appropriate analysis, the performing agency must address, either

explicitly in its analysis or through a listing of assumptions, how it has accounted for the

following types of questions:

• What percentage of parking situations and consumers are affected by the

proposed policy, and how is this reflected in the tested rate?

• What travel groups are affected by the proposed price (e.g., commuters,

shoppers, etc.) and how is this reflected in the trip populations to which the

analysis is applied?
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Figure 2.2a Elements of Analysis for: Parking Pricing

ELEMENTS of Basic Analysis ANALYSIS STEP

(Type of Impact)

RECOMMENDATIONS for Advanced Analysis

Formal analysis not required;

Statement of possible effects if rates are

applied with geographic differences.

LAND USE

ALLOCATION

(Secondary)

Develop alternative growth scenarios or apply land use

models capable of considering changes in transportation

access and cost;

Integrate alternative land use/urban design supportive of

alternative modes into land use models, or adjust subjectively

to evaluate. 

None VEHICLE

OWNERSHIP

 (None)

Re-run if shifts in land use (above).

 

None

TRIP GENERATION

(Trace)

Re-run if shifts in land use or vehicle ownership;

Try to incorporate change in cost;

Run separately by purpose and income;

If using motorized vehicle trip tables only, adjust trip rates to

reflect shifts to non-motorized and non-automobile driver

modes using market share analysis or other model. 

Formal analysis not required;

Statement of possible effects if differential

rates are applied.

TRIP

DISTRIBUTION

 (Secondary)

Run separately by time of day if time-variant rates;

Run separately by purpose and income;

Explicitly model to reflect impact of change in parking cost.

 

Analyze through mode choice model, if

available;

Elasticity factor or quick response

analysis if no model;

Document sources and assumptions.

MODE CHOICE

(Primary)

Use separate models for each purpose;

Run separately by purpose and income;

Separate coefficient for parking cost;

Account for pass-through effect and HOV exemption/

discount;

Estimate shift to non-motorized/ non-travel modes. 

None TIME OF DAY/

PEAKING

 (Secondary)

Try to model if parking prices vary by time of day or short/

long term;

Model by trip purpose and income.

 

Standard assignment analysis, if capability

exists.

TRAFFIC

ASSIGNMENT

 (Trace)

Standard analysis

 

None EMISSIONS RATE/

VEHICLE MIX

 (None)

None

 

Standard analysis, if emissions model

available;

If no model, apply emissions factors to the

change in vehicle trips and VMT.

EMISSIONS

ANALYSIS

Standard analysis.
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• What proportion of the tested fee is assumed to be passed through to the

traveler (affects tested rate)?

• Does the policy provide for exclusion of or parking discounts to HOVs?

• Is the dollar value of the policy being tested consistent with the year in

which the elasticity was developed?

• What other factors are likely to be introduced or changed during the

evaluation period that could affect the implementation or traveler response

to the proposed application?

Traffic Assignment and Emissions Analysis/Vehicle Mix: Subsequent to the required

mode choice analysis, areas with full model systems are expected to perform traffic

assignment analyses and apply an emissions factor model to determine the resulting

change in emissions. If these procedures are not available, emissions impacts may be

estimated by applying emissions factoring methods to estimated net changes in vehicle

trips and VMT, taking cold start effects into account if they are considered significant.

Land Use and Trip Distribution: Parking pricing that is applied evenly in an area could

have important secondary impacts on land use/activity allocations and trip

distribution. Mode shifts would tend to shift people to locations with more available

services. Parking pricing that is applied unevenly in an area also could have important

secondary impacts on land use/activity allocations and trip distribution. If the proposed

parking pricing strategy will not be uniform across different geographic and travel

purpose markets, then the performing agency should address this concern in its

submission.

While a formal analysis of these effects is not required, the submitting agency should

provide at least a statement of the potential impact from the proposed strategy on nearterm choice of destination and longer-term land use trends, and the anticipated

implications of these shifts for air quality. This statement should include

acknowledgment by decision-makers of the anticipated impact.

Suggestions for Advanced Analysis of Parking Pricing Measures

Use of the following advanced analysis techniques could produce a more

comprehensive and accurate analysis of the impact of parking pricing; these are listed in

approximate order of presumed importance of effects (most important first).

Mode Choice: More advanced evaluation of mode choice impacts would utilize more

sophisticated models (described in Section 2.3.2.5 as advanced analysis for mode choice)

such as nested multinomial logit models, incorporating parking cost coefficients

(where possible), and differentiating among income strata and trip purpose.

Trip Distribution: Enhanced analysis would be based on use of a trip distribution

model sensitive to trip costs such as parking pricing, or on a model that uses timeequivalencies to account for such costs (see Section 2.3.2.4 on advanced analysis for trip

distribution).
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Land Use/Activity Allocations: Programs that introduce major changes in the level or

pattern of parking prices could affect longer term land use and activity patterns.

Advanced analysis may be warranted, using procedures such as a land use model

sensitive to cost and/or growth scenarios (see Section 2.3.2.1 above on advanced

techniques and, for a more technical discussion, Section 3.3.1 of the following chapter).

Vehicle Ownership and Trip Generation: If the trip generation procedure deals only

with motorized-vehicle trips, then an adjustment should be made to the vehicle trip

generation estimates to reflect shifts to non-motorized or non-automobile-driver options;

the analytical approaches described in Section 2.3.2.3 are appropriate for advanced

analyses of parking pricing effects.

Time of Day: Unless the parking policy results in major changes in cost structure

between peak and off-peak periods, it is unlikely that a sufficient time-of-day travel shift

would occur that would be discernible by analysis. Advanced time of day analysis, if

warranted, would follow those approaches described in Section 2.3.2.6, above (see also

the more technical discussion in Section 3.3.7); these include choice models, other

references, or special surveys.

2.3.3.2 Modal Subsidies

Suggestions for both the basic analysis and the advanced analysis pertaining to Modal

Subsidies are illustrated in Figure 2.2b and described below.

Basic Analysis for Modal Subsidy Measures

Mode Choice: Because the only primary effect of modal subsidies on travel and

emissions is through mode choice, to be acceptable, an analysis should demonstrate a

convincing evaluation of the modal split effect of the proposed subsidy. (Some effects

on trip distribution may be likely with mode shifts).

The options for completing the basic analysis include, in order of preference, use of:

accepted mode choice model; accepted quick response methods; or elasticity methods

(see basic or advanced analysis, Section 2.3.2.5).

The performing agency also is expected to relate how it has accounted for the following

types of questions:

What percentage of travelers will be affected by the proposed policy, in terms of

receiving a subsidy, and how is this reflected in the tested rate?

• What travel groups are affected by the subsidy (e.g., commuters, shoppers,

etc.) and how is this reflected in the travel base which has been used for

analysis?

• What is the actual subsidy that is expected to be passed through to the

traveler?
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• Does the policy reward different groups at different levels (e.g., ridesharers

versus transit users) and how does the analysis represent the competition

among modes?

• Is the dollar value of the policy being tested consistent with the year in

which the elasticity was developed?
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Figure 2.2b Elements of Analysis for: Modal Subsidies

ELEMENTS of Basic Analysis ANALYSIS STEP

(Type of Impact) RECOMMENDATIONS for Advanced Analysis

Formal analysis not required;

Statement of possible effects if subsidy

levels are significant.

LAND USE

ALLOCATION

(Trace)

If significant impact on inter-mode cost relationships, develop

alternative growth scenarios or run land use models with

consideration of cost;

Integrate alternate land use/ urban design supportive of

alternative modes.

 

None VEHICLE

OWNERSHIP

(Trace)

Re-run if shifts in land use. (If significant mode shift,

ownership should drop).

 

None TRIP GENERATION

(Trace)

Re-run if shifts in land use or vehicle ownership;

Try to incorporate change in subsidies;

Run separately by purpose and income;

If vehicle-based trip generation, account for non-motorized/

non-automobile driver shifts.

 

Formal analysis not required;

Statement of possible effects if subsidies are

at significant level.

TRIP

DISTRIBUTION

 (Secondary)

Run separately by purpose and income strata;

Try to reflect impact of change in cost among alternatives and

mode shift.

 

Analyze through mode choice model, if

available;

Elasticity factor or quick response

analysis if no model;

Document sources and assumptions.

MODE CHOICE

(Primary)

Use separate models for each purpose;

Run separately by purpose and income;

Make certain models appropriately sensitive to the different

subsidized modes/ instruments;

Perform supplemental analysis to assess effects on use of

subsidized or non-motorized modes.

 

Formal analysis not required;

Statement of possible effects if subsidies are

significant.

TIME OF DAY/

PEAKING

 (Secondary)

Perform supplemental analysis to account for peak/ off-peak

effect of subsidies.

 

Standard analysis if have assignment

procedure.

TRAFFIC

ASSIGNMENT

(Trace)

Standard analysis

 

None EMISSIONS RATE/

VEHICLE MIX

 (None)

None

 

Standard analysis if have emissions model;

If no model, apply emissions factors to the

change in vehicle trips and VMT.

EMISSIONS

ANALYSIS

Standard analysis
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• What other factors are likely to be introduced or changed during the

evaluation period that could affect the implementation or traveler response

to the proposed application?

Traffic Assignment and Emissions Analysis/Vehicle Mix: Subsequent to the required

mode choice analysis, areas with full model systems are expected to perform traffic

assignment analysis and apply an emissions factor model to determine the resulting

change in emissions. If these procedures are not available, emissions impacts may be

estimated by applying emissions factoring methods to estimated net changes in vehicle

trips and VMT, taking cold start effects into account if they are considered significant.

Land Use, Trip Distribution and Time of Day: Modal subsidies also may have secondary

impacts on land use/activity allocations, trip distribution, and time of day. Impacts on

land use trends and trip distribution would be anticipated only from significant levels of

subsidy. Time of day impacts might occur if subsidized rates drew travelers into or out

of peak periods. In such cases, although a formal analysis would not be required, EPA

expects a statement of anticipated impacts.

Suggestions for Advanced Analysis of Modal Subsidy Measures

Expanding the evaluation to include the following components would generate a more

comprehensive and accurate analysis of the impact of modal subsidies; these

suggestions are listed in descending order of importance.

Mode Choice: More extended analysis would incorporate those approaches described

as advanced analysis in Section 2.3.2.5 above (i.e., mode choice models that specifically

break out and allow for interactions among each of the candidate travel modes), may

include separate coefficients for parking costs, transit fares, etc., and are preferably run

for each primary trip purpose, including home-based work, home-based other, and

non-home based trips. Mode choice should be performed separately for each purpose

and income strata.

Trip Distribution: Extended evaluation would incorporate those procedures described

under advanced analysis in Section 2.3.2.4 on trip distribution (i.e. models that are

sensitive to cost considerations and that distinguish among trip purposes and income

strata).

Time of Day: Only if the subsidy policy results in major changes in cost structure

between peak and off-peak should a formal analysis be attempted of time of day travel

shifts. Such analyses are described in Section 2.3.2.6 (and Section 3.3.7) and include

models borrowed from other locations, previous research, or conducting special surveys.

Land Use/Activity Allocations: If significant levels of modal subsidy suggest a different

cost structure for regional travel, estimating this effect may be important. Because

modal subsidies would result in across-the-board changes in the cost of travel, significant

subsidies could lead to longer-term shifts in residential and employment locations to

areas that require less automobile dependence. Approaches for extended evaluation

include those described in Section 2.3.2.1 on advanced analysis for land use/activity

allocations, including land use models sensitive to such costs and growth scenarios.
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Vehicle Ownership and Trip Generation: On their own, modal subsidies would not be

expected to cause households to own fewer vehicles or make a different number of trips.

If alternative land use patterns are determined in the previous step, then the vehicle

ownership and trip generation steps should be re-run to reflect the relocation of activity

and its association with the respective vehicle ownership and trip rates in the new

location.

If the trip generation procedure deals only with motorized-vehicle trips, then an

adjustment should be made to the vehicle trip generation estimates to reflect shift to

non-motorized or non-automobile-driver options.

Wherever possible, trip generation analysis should be performed separately for each

purpose and income strata, to support the subsequent analysis of trip distribution and

mode choice.

2.3.3.3 Pump Charges and VMT Fees

Suggestions for both the basic analysis and the advanced analysis pertaining to pump

charges are illustrated in Figure 2.2c. The primary effects triggered by pump charges

would be on mode choice and vehicle mix. Higher fuel prices would be expected to

reduce the demand for private vehicle travel and increase ownership and use of highermpg vehicles or alternative-fuels/electric cars.

Basic Analysis for Pump Charges

Mode Choice: Basic analysis includes those techniques described as basic or advanced

analysis in Section 2.3.2.5 on mode choice impacts, that is (in order of preference): mode

choice models, quick response methods, or elasticity methods.

The performing agency also is expected to indicate how it has accounted for the

following factors or concerns:

• What percentage of regional travelers will be affected by the increased

pump price, versus those who would be expected to evade the price

increase by purchasing fuel outside the area; how is this reflected in the

assumptions regarding the tested price?

• What average cost per mile is being assumed, and upon what average mpg

is it based?

• Is the dollar value of the policy being tested consistent with the year in

which the elasticity was developed?
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Figure 2.2c Elements of Analysis for: Pump Charges

ELEMENTS of Basic Analysis ANALYSIS STEP

(Type of Impact) RECOMMENDATIONS for Advanced Analysis

 

Formal analysis not required;

Statement of possible impacts. LAND USE

ALLOCATION

(Secondary)

Develop alternative growth scenarios; or run land use

models with change in transportation access and cost

considered;

Integrate alternative land use/urban design supportive of

alternative modes into land use models.

 

None VEHICLE

OWNERSHIP

(Trace)

Re-run if shifts in land use.

 

If trip generation for vehicle trips only,

account for non-motorized and nonautomobile driver modes.

TRIP GENERATION

(Trace)

Re-run if shifts in land use or vehicle ownership;

Try to incorporate change in cost;

Account for non-motorized or non-auto driver trips;

Run separately by purpose and income.

 

Formal analysis not required;

Statement of possible effects.

TRIP

DISTRIBUTION

(Secondary)

Run separately by purpose and income;

Explicitly model to reflect impact of change in costs.

 

Analyze through mode choice model, if

available;

Elasticity factor or quick response

analysis if no model;

Document sources and assumptions.

MODE CHOICE

(Primary)

Use separate models for each purpose;

Run separately by purpose and income;

Supplemental analysis to assess any effects of shift toward

higher mpg vehicles;

Supplemental analysis to assess demand for non-motorized

modes.

 

None TIME OF DAY/

PEAKING

 (None)

None

 

Standard assignment analysis if have

assignment procedure.

TRAFFIC

ASSIGNMENT

 (Trace)

Standard analysis

 

Indicate assumptions regarding change in

vehicle mix to reflect shift in mpg anticipated.

EMISSIONS RATE/

VEHICLE MIX

(Primary)

Adjust vehicle fleet mix to reflect changes in ownership and

use.

 

Standard analysis if have emissions model;

If no model, apply emissions factors to

change in vehicle trips and VMT.

EMISSIONS

ANALYSIS

Adjust vehicle fleet mix to reflect changes in ownership and

use.
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Trip Generation: If the trip generation process forecasts only vehicle trips, an attempt

should be made to estimate the shift to non-motorized or non-automobile-driver modes

as a result of higher fuel prices, following the recommendation for advanced analysis in

Section 2.3.2.3 on analytical techniques for trip generation.

Traffic Assignment: Following mode choice analysis, areas with appropriate capability

are expected to perform a revised traffic assignment for input to the emissions analysis.

Emissions/Vehicle Mix: The performing agency should relate its assumptions on

changes in average mpg due to shifts in vehicle mix, the impact this would have on the

effective cost per mile used in each of the analysis steps, and whether this would be

expected to result in important differences in impacts for different market groups,

particularly by income level. Analysis requirements can be met with use of an emission

factor model that incorporates vehicle mix impacts or, if that is not available, a narrative

description that details program-induced changes in emissions rates in response to

increased fuel prices.

Suggestions for Advanced Analysis of Pump Charges

A more comprehensive and accurate analysis of the impact of pump charges could be

achieved with more extended analysis, using the following techniques (listed in

descending order of importance).

Mode Choice: Appropriate evaluation is described under advanced analysis in Section

2.3.2.5 on analytical techniques for mode choice (i.e., mode choice models such as

nested logit models, with attention to differentiation among trip purposes and income

levels).

Emissions/Vehicle Mix: Extended evaluation of emissions/vehicle mix effects is

described as basic or advanced analysis in Section 2.3.2.8, and includes hedonic models,

research studies, and/or special surveys.

Trip Distribution: Extended evaluation of trip distribution effects would incorporate

the advanced analysis approach described in Section 2.3.2.4; that is, a trip generation

model sensitive to cost, used to perform a revised trip distribution analysis (performed

separately by trip purpose and income strata if possible), which also reflects changes in

attractiveness of destinations (and purposes) due to fuel-price based overall increases in

travel costs.

Land Use/Activity Allocations and Trip Distribution: Because fuel price increases

would result in across-the-board changes in the cost of private vehicle travel, significant

increases in price could lead to near-term shifts to closer trip destinations and longerterm shifts in residential and employment locations to areas that require less automobile

dependence. Because these are secondary impacts, no formal analysis is required, but the

performing agency would be advised to perform such an analysis if it has the capability,

or at a minimum, state its assumptions as to the anticipated impacts this price change

would have on trip distribution and land use/activity allocations.

If significant levels of fuel prices suggest a different cost structure for regional travel, it

may be important to estimate this effect on long-term land use locational preferences.
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Two options are described under advanced analysis in Section 2.3.2.1 on land

use/activity allocations, that is: land use models sensitive to cost, and growth scenarios

to depict the alternate ways in which the growth patterns might adjust to an overall

change in pricing. Scenarios might be developed in conjunction with application of the

land use model, and involve development and integration of alternative land use/urban

design configurations supportive of subsidized modes.

Vehicle Ownership and Trip Generation: If shifts in land use patterns are determined in

the step above, then the vehicle ownership and trip generation steps should be re-run to

reflect the relocation of activity and its association with potentially different rates of trip

making and vehicle ownership. Wherever possible, trip generation should be performed

separately for each purpose and income strata, to support the subsequent analysis of

trip distribution and mode choice.

In general, an increase in fuel price would not be expected to have a major impact on

the overall number of trips made by households. However, if the local planning process

considers vehicle-serviceable trips rather than person trips in trip generation, then the

evaluating agency should investigate any impact of higher fuel prices on shifts to nonmotorized or non-automobile-driver modes, and to closer destinations.

2.3.3.4 Emissions Fees

Suggestions for both the basic analysis and the advanced analysis pertaining to

emissions fees are illustrated in Figure 2.2d and discussed below. (If there is a

significant VMT component, then address also the Pump Charges guidance).

Basic Analysis for Emissions Fees

The primary effects of emissions fees would be on vehicle mix and emissions. If the

proposed fees imposed a significant cost on vehicle ownership, then impacts on land use

and vehicle ownership also could be important. Because emissions fees (as defined

here) would be assessed on a periodic basis, they would not be expected to affect daily

travel choices.

Vehicle Mix/Emissions Analysis: Fees levied on vehicles in relation to their age and/or

emissions production (emissions rate, perhaps coupled with VMT) would have the

primary effect of encouraging replacement of higher emitting/older vehicles and/or

encouraging higher use of lower-emitting vehicles in multi-vehicle households.

Evaluation required in submissions to EPA includes the basic or advanced analyses

techniques described in Section 2.3.2.8, above; that is, hedonic models, research studies,

and/or special surveys (these methods are further detailed in Chapter 3, Section 3.3.3).

The agency also should make an effort to indicate emissions fee impacts on relative

usage patterns of particular vehicles, accompanied by a statement on assumptions

relating impacts to market segment (income). For fees based on actual (measured)

emissions rates, evaluation should include an estimate of the effects on improved

maintenance.
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Figure 2.2d Elements of Analysis for: Emissions Fees

ELEMENTS of Basic Analysis ANALYSIS STEP

(Type of Impact) RECOMMENDATIONS for Advanced Analysis

If measure affects vehicle ownership/ use,

submit statement that assesses effects on

locational patterns.

LAND USE

ALLOCATION

 (Secondary)

If price significantly affects vehicle ownership or VMT, then

consider development of alternate growth scenarios or

application of land use models to reflect change in costs on

number of vehicles.

 

Assess if measure affects number of

vehicles owned, following procedures for

vehicle mix analysis.

VEHICLE

OWNERSHIP

(Primary)

Re-run if shifts in land use;

Possible supplemental analysis to estimate change in vehicle

mix and possibly usage patterns favoring cleaner vehicles.

 

Investigate impacts if land use or vehicle

ownership affected. TRIP GENERATION

(Trace)

Re-run if shifts in land use or vehicle ownership;

If trip generation does not include non-motorized, nonautomobile driver modes, adjust to assess impact;

Run separately by purpose and income. 

Investigate impacts if land use or vehicle

ownership affected, or if VMT fee significant;

Otherwise, no analysis.

TRIP

DISTRIBUTION

(Trace)

Run separately by purpose and income;

If significant VMT cost effect, incorporate cost modeling.

 

Investigate impacts if land use or vehicle

ownership affected, or if VMT fee significant;

Otherwise, no analysis.

MODE CHOICE

(Trace)

Use separate models for each purpose;

Run separately by purpose and income;

Supplemental analysis to assess any effects of shift toward

higher mpg vehicles;

Assess demand for non-motorized modes, or impact of

periodically-assessed charge on daily travel.

 

None TIME OF DAY/

PEAKING

 (None)

None

 

Investigate impacts if land use, vehicle

ownership, distribution or mode choice

changes;

Otherwise, no analysis.

TRAFFIC

ASSIGNMENT

 (None)

Standard analysis

 

Indicate assumptions to account for shift in

vehicle mix to cleaner vehicles.

EMISSIONS RATE/

VEHICLE MIX

(Primary)

Adjust vehicle fleet mix to reflect change in ownership and

use;

Adjust emissions rates for better vehicle maintenance, if a

feature of measure.

 

Standard analysis if have emissions model;

If no model, apply emissions factors to

change in vehicle trips and VMT.

EMISSIONS

ANALYSIS

Adjust vehicle fleet mix to reflect change in ownership and

use;

Adjust emissions rates for better vehicle maintenance, if a

feature of measure.
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Areas with emissions factor models are expected to use these to assess the result of the

proposed strategy on emissions and to incorporate the effect of emission fees on vehicle

mix and/or improvements in maintenance.

In the absence of an emissions factor model, agencies may apply emissions factor

relationships to changes in vehicle trips and VMT, with appropriate supporting

documentation of their analysis and assumptions for emissions factors that reflect

anticipated shifts in fleet mix and maintenance levels.

Vehicle Ownership: If proposed emissions fees lead to significant cost increases in

automobile ownership, household ownership rates may be reduced, particularly

ownership of high-emitting vehicles. Evaluation would follow that described under

vehicle mix/emissions analysis above.

Land Use/Activity Allocations: If vehicle ownership is affected by a sizable emissionsbased increase in registration fees, or if the VMT component of emissions fees is

significant, households could shift to locations where fewer vehicles and/or less

dependence is required. This effect should be investigated.

Suggestions for Advanced Analysis of Emissions Fees

Listed in declining order of importance, the following analyses could help generate a

more comprehensive and accurate evaluation of emissions fee impacts.

Vehicle Mix and Emissions Analysis: Because the primary impact of emissions fees is

expected to be on vehicle ownership patterns, enhanced evaluation of emissions/vehicle

mix effects could be achieved through more sophisticated application of the same

techniques described above under basic analysis (i.e., hedonic models and/or secondary

analyses, such as stated preference surveys to explore vehicle ownership and usage). In

addition, the agency could use the above techniques to investigate whether the VMT

aspect of the fee produces any change in daily travel behavior, how large a fee would

have to be applied, and how frequently it would have to be assessed.

The most accurate emissions analysis will probably use an accepted emissions factor

model adjusted to account for these changes, rather than application of emissions

factors to net trip and VMT changes.

Vehicle Ownership and Land Use: Vehicle ownership would be affected by significantly

higher emissions-based registration fees. Agencies proposing such fees should estimate

vehicle ownership patterns, by income group if possible. In turn, revised ownership

rates should then be incorporated in a revised land use analysis that reflects the impact

of fewer vehicles and higher costs, using models, if available, or best professional

judgment.

Mode Choice, Trip Distribution, Trip Generation: Generally, emissions fees collected

through infrequent periodic assessments would not be expected to lead to major

adjustments in daily travel behavior. If the VMT component of the fee is either

significant or controlling, however, investigate its effects on mode choice, trip

distribution, and trip generation by consulting research studies or performing a

supplemental analysis of relative impact on daily behavior of annual (or periodic) fees.
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Any changes in vehicle ownership and household location also would have an indirect

effect on trip generation, distribution, and mode choice, and should be assessed. Trip

generation analysis should reflect total person trips, and allow for shifts to non

automobile-driven and non-motorized modes.

2.3.3.5 Roadway Pricing

Suggestions for both the required and advanced analysis pertaining to roadway pricing

are illustrated in Figure 2.2e and discussed below.

Basic Analysis for Roadway Pricing

Because it is a fairly complex measure in terms of travel behavior impacts, there are

more primary effects associated with roadway pricing than any of the other measures.

Roadway pricing would be expected to have a primary impact in relation to mode

choice, trip distribution, and traffic assignment, and, if peak period or congestion

pricing is instituted, time of day choices as well.

Trip Distribution: Travelers would be expected to respond to the pricing of specific

transportation facilities by favoring comparable destinations which can be reached

without payment of a toll (where such a substitution can occur). There are several

options for approaching this analysis. The more rigorous approaches are described in

Section 2.3.2.4 as basic or advanced analysis techniques for trip generation, which

include cost-sensitive trip generation models and generalized cost procedures.

Sites that do not have formal trip distribution modeling capabilities should formulate a

statement that reflects a careful assessment of the anticipated impacts of the road

pricing scheme on shifts in travel patterns.

Traffic Assignment (Route Choice): Traffic assignment becomes particularly important

in the case of road pricing since travelers can choose between tolled routes that may

have a higher level of service, and toll-free routes that may be more congested.

This choice is not directly handled by most traffic assignment models. The options for

addressing anticipated impacts, described in Section 2.3.2.7 on basic or advanced

analysis techniques for traffic assignment, include assignment routines for toll and free

paths, or use of travel time equivalency estimates (see also Section 3.3.8.5).

If an area cannot perform traffic assignment analysis, or if the tolled facility can be

easily avoided, then it may not be possible to consider the emissions impacts of roadway

pricing because the impacts would be too uncertain.

Mode Choice: Mode choice is still an important factor in considering road pricing.

Analytical options include those detailed above in Section 2.3.2.5 as basic or advanced

analysis techniques for mode choice (i.e., mode choice models, quick response

methods, or elasticity methods).
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Figure 2.2e Elements of Analysis for: Roadway Pricing

ELEMENTS of Basic Analysis ANALYSIS STEP

(Type of Impact)

RECOMMENDATIONS for Advanced Analysis

Formal analysis not required;

Statement of possible effects due to pricing

of select facilities.

LAND USE

ALLOCATION

(Secondary)

Develop alternative growth scenarios or run land use models

to reflect cost related to priced network;

Integrate alternative land use/urban design supportive of

alternative modes.

 

None VEHICLE

OWNERSHIP

(Trace)

Re-run if shifts in land use.

 

None TRIP

GENERATION

(Trace)

Re-run if shifts in land use;

Run separately by purpose and income;

Consider shifts to non-motorized alternatives, if area-wide

pricing and vehicle-only trip generation. 

Analyze if have trip distribution model;

If no model, provide estimates of expected

impacts;

Account for non-motorized trips if vehicle

trips only.

TRIP

DISTRIBUTION

(Primary)

Run separately by purpose and income;

Explicitly model to reflect change in cost and travel time

changes.

 

Analyze through mode choice model, if

available;

Elasticity factor or quick response

analysis if no model;

Document sources and assumptions.

MODE CHOICE

(Primary)

Use separate models for each purpose;

Run separately by purpose and income;

Directly represent price for trip (rather than travel time

equivalent);

Possible supplemental analysis to ascertain response to

electronic/ periodic charge assessment.

 

Must analyze if apply time of day pricing;

Document sources and assumptions. TIME OF DAY/

PEAKING

(Primary)

Acquire/ develop model to simulate response to time of day

pricing;

Possible supplemental analysis to gauge time of day shifts.

 

Must analyze if pricing applies to selected

facilities;

Can use elasticity factor methods;

Document sources and assumptions.

TRAFFIC

ASSIGNMENT

(Primary)

Develop and use toll diversion model to reflect toll/ free path

options;

Possible side analysis to gauge route choice shifts.

 

None EMISSIONS

RATE/ VEHICLE

MIX (None)

None

 

Standard analysis if have emissions model;

If no model, apply emissions factors to the

change in vehicle trips and VMT.

EMISSIONS

ANALYSIS

Standard analysis;

Possibly adjust emissions rates for major speed changes on

facilities.
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Most likely, an area considering road pricing would have a reasonably sophisticated

modeling system, complete with a mode choice model. Application of elasticity

techniques would only be expected to be valid where simple pricing schemes are being

considered (i.e., a bridge or tunnel with few/no parallel routes).

Important issues that should be addressed in the analysis include:

• Will the toll be paid to toll booth operators, to exact change machines, or

electronically through advanced vehicle identification (AVI) transponder

technology? What are the queuing and delay impacts of the chosen

method?

• Are HOV users exempt from the toll, or charged discounted rates?

• Is the dollar value of the policy being tested consistent with the year in

which the elasticity was developed?

• What other factors are likely to be introduced or changed during the

evaluation period that could affect the implementation or traveler response

to the proposed application?

Time of Day: Peak-period congestion pricing versions of road pricing would be

expected to affect time of day choices as well as route choice and, if such strategies are

being considered, the agency should attempt to estimate the impact of the fee on peak

versus off-peak travel demand by trip purpose and income group.

Options for conducting this analysis are described above in Section 2.3.2.6, basic or

advanced analysis techniques for time of day, and include: time of day choice models,

references to other research, and survey analysis. Under extenuating circumstances, the

area may be allowed to use assumptions to suggest the degree of change, but it is

expected to provide documentation as to the assumptions and their degree of impact on

the result.

Land Use/Activity Allocations: Implementation of toll pricing on selective

transportation facilities would be expected to cause a shift in locational trends, which

could reduce use of these facilities. Because these are secondary impacts, no formal

analysis is required. Nonetheless, the performing agency is expected to state its

assumptions (and show the concurrence of decision-makers) regarding the anticipated

impacts that the system of road pricing would have on land use/activity allocations.

This is particularly important in the context of partial-network pricing.
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Suggestions for Advanced Analysis of Roadway Pricing

In descending order of importance, the following analyses would contribute to a more

comprehensive and accurate evaluation of roadway pricing impacts.

Traffic Assignment (Route Choice): The best analysis would use a procedure including

both toll and free paths as options to travelers, taking into consideration the fact that

traveler choice will be sensitive to type of trip and traveler income level (see Section

2.3.2.7 for a more extended discussion, and Section 3.3.8 for a more technical

presentation).

Time of Day: Time of day choice flexibility is particularly important in assessing the

impacts of congestion pricing schemes. Shifts in time of day would be expected to vary

significantly by trip purpose and income level. Time of day transportation modeling is a

relatively uncommon practice; options described above in Section 2.3.2.6 include time of

day choice models, references to other research, and surveys (Section 3.3.7 describes

appropriate models).

Trip Distribution: Shifts to alternative destinations induced by road pricing could be

important, particularly with partial-network pricing and numerous travel or route

alternatives; impacts are likely to vary by trip type and traveler income levels. Include

distribution effects of reduced congestion.

Application of a trip distribution model which is sensitive to cost through a generalized

cost relationship to individual trip purposes and income strata would be essential to an

effective analysis.

Mode Choice: Mode choice analysis would be most accurate if it were accompanied by

the described advanced analyses of trip distribution, route choice, and time of day

alternatives. It also would be most accurate if performed with separate models of each

trip purpose and evaluated separately for each income strata.

Land Use/Activity Allocations: Road prices would be expected to make some areas

more attractive than others in terms of future locational preferences. Options for

approximating and accounting for these trends include use of land use models that

incorporate sensitivity to cost, and/or the development of alternative growth scenarios.

It would be particularly meaningful to develop land use and urban design scenarios

which would complement travel alternatives to those sites with priced facilities/access.

Vehicle Ownership and Trip Generation: If alternative land use patterns are

anticipated, the vehicle ownership and trip generation steps should be re-run to reflect

the relocation of activity and its association with potentially different rates of trip

making and vehicle ownership. When possible, trip generation analysis should be

performed separately for each purpose and income strata, to support the subsequent

analysis of trip distribution and mode choice.
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n 2.4 Packaging Measures for Best Effect

The preceding discussion of market-based measures and guidelines for their evaluation

necessarily provide a somewhat one-dimensional view of the nature and impact of these

strategies. In reality, such measures would seldom be deployed in isolation, as described

here. Rather, they would be deployed as part of a carefully considered package of

different market measures and other actions to complement and support the marketbased measure(s). Market-based measures serve as signals and incentives to change;

other actions provide the appropriate setting and options necessary to complete the

process.

There are two major benefits from packaging market-based measures into balanced

programs:

• Balanced programs offer synergy that will generally produce a greater

impact than a market-based measure acting alone; and

• Balanced programs that offer tangible and attractive choices are more likely

to foster public acceptance of the proposed pricing measure component of

the program.

This guidance manual cannot offer hard and fast rules detailing the most effective

packaging strategies to accompany pricing initiatives, given the wide variation in

settings, needs, and circumstances. In general, however, pricing measures are expected

to work best if the following general principles are applied:

1. Match actions to consumer responses Anticipate the types of responses

consumers are likely to make in response to a particular pricing measure.

Analysts can use the guidance presented in the preceding sections, coupled

with the performing agencys knowledge and experience. Analysts also

should evaluate reciprocal actions that could be mated with these

responses.

2. Identify realistic alternatives Where a measure is likely to raise interest

in an alternative mode of travel, identify alternatives that will be attractive

and appropriate in that setting. For example, consider transit service

improvements if a high density corridor or downtown area is involved. If

the impact area is more suburban, transit may still have a role, but actions

to stimulate carpooling or vanpooling may be more suitable.

3. Consider non-motorized modes Broaden the analysis to include nonmotorized alternatives such as walking and biking, which are often

overlooked when options are considered. A fair portion of shorter trips

may be amenable to walking or biking under the appropriate

circumstances; shifting these trips from private vehicles to non-motorized

modes could eliminate substantial emissions associated with vehicle coldstarts. More specifically, consider enhancing circumstances associated

with bike and pedestrian modes such as: improving sidewalks; and
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pedestrian access to buildings; providing frequent, safe pedestrian crossings;

alleviating vehicle/pedestrian design conflicts; separating vehicles from

pedestrians and reducing speeds; and providing bicycle lanes, paths,

suitable lockup/storage facilities, and lockers or change facilities at

destinations. Also consider approaches that encourage mixed use land

development, such as reduced taxes for development that reduces

transportation costs.

4. Consider positive incentives for desirable alternatives Identify and

evaluate incentives for users of the more environmentally efficient modes.

Incentives such as fare subsidies to transit or vanpool users, parking

subsidies for HOVs, and equipment rebates can provide a powerful

economic complement to market-based measures that primarily increase the

cost of travel for driving or use of high-emitting vehicles.

5. Consider barriers to counter-productive traveler choicesArticulate

strategies to limit or direct the choices of travelers who might select

counter-productive alternatives in response to the market-based measure.

Depending on who they are and the purpose of their travel, many travelers

have flexibility in their choice of route, destination, and time of day. When

faced with market-based incentives, these travelers would be expected to

consider and use that flexibility in their own best interest. Analysts should

anticipate these responses and, unless these secondary responses support

emissions reduction objectives, should propose complementary actions that

reinforce desired behavior patterns. Mechanisms include: limitations on

off-street parking coupled with site parking charges; and limitations on use

of secondary/neighborhood roads as alternatives to a priced facility.

6. Explore outreach and indirect support Consider complementary actions in

addition to financial incentives and physical alternatives that support

travelers faced with changes in their travel context. For example,

information outreach, education, and marketing can ease barriers to use of

alternative travel modes. They can also explain and demonstrate the

problems caused by driving and the virtues of walking, bicycling, and

telecommunications. Effective matching programs can put people into

acceptable ridesharing arrangements. Employers can play a very valuable

role in supporting employee travel choices, including: guaranteed rides

home in event of late work nights or family emergencies; assistance with

dependent care; preferential or covered parking for ridesharing employees;

and assistance with vanpool formation and operation. Employers also can

offer opportunities to work at home through telecommuting arrangements,

or introduce modified work schedules to reduce the number of physical

trips to work sites.

7. Promote land use planning that supports alternative travelRecognize the

importance of an environment that supports the use of alternative travel

patterns. If persons are expected to travel to targeted destinations without
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their personal vehicles, they must be confident that they can achieve their

objectives at that location without a car at their disposal. This calls for

innovation in urban design that allows people to circulate and accomplish

activities pleasantly through walking, biking, transit or through shuttle

services. This also means designing those centers so that the mix of uses is

varied, complementary and attractive. Street space should be managed in

these areas to provide pedestrians with safety from motor vehicles, freedom

of movement, attractive visual surroundings, and comfortable settings for

conversation, rest and shopping.

8. Use program revenues to support transportation and emissions goals

Clearly and explicitly link the revenues that are collected through

application of a pricing action with the use of those revenues. If the public

perceives that the revenues extracted from them are used for the direct

enrichment of their environments, rather than diverted to other uses, they

should show stronger support for those programs since they are getting

something back from their contribution. Revenues from pricing programs

are often a convenient and adequate source to finance a variety of

supporting strategies.

For more information on techniques for effective packaging, the reader is referred to

other information sources, such as Implementing Effective Travel Demand Management

Actions, which is recommended as a comprehensive source for packaging and impact

information and contains considerable information on a wide variety of strategies and

packaging tips, as well as a significant number of references to other sources on specific

related topics.14

 

14 COMSIS Corporation et al., Implementing Effective Travel Demand Management Measures: Inventory of

Measures and Synthesis of Experience, prepared for the Federal Highway Administration and the

Federal Transit Administration, U. S. Department of Transportation, Washington, D.C., September

1993 (DOT-T-94-02).
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3.0 Guidelines for Analysis of

Market-Based Measures

n 3.1 Introduction

This chapter provides guidelines and suggestions for those responsible for performing

the actual evaluation of market-based measures for emissions credit. Whereas the

preceding chapter was designed for managers and those without a technical background,

this chapter was developed for those versed in the basic principles of transportation

modeling and analysis. Expanding on topics discussed in Chapter 2, this chapter

employs terminology and concepts that assume greater familiarity with transportation

and emissions modeling tools and procedures. The intent is to provide fairly specific

language as to EPA’s expectations when reviewing credit submissions.

The chapter is divided into two main sections. The first section, Section 3.2, presents

guidance on each of the five basic groups of market-based measures (parking pricing,

modal subsidies, pump charges, emissions fees, and road pricing). The second section of

the chapter, Section 3.3, is a reference compendium on methods and procedures suitable

for the types of analyses suggested in Section 3.2. The reference compendium is

organized according to steps in the analysis process. The methodological guidelines in

Section 3.2 refer extensively to the methodological discussions of Section 3.3, many of

which also list external references for further details.

This document has been prepared more as guidance than traditional requirements. EPA

recognizes that that no single approach can work for all sites and that areas vary widely

with respect to analytic tools, data, and staff capabilities. It is EPA's intention to suggest

analytic approaches for best practicable evaluation of the proposed market-based measures.

EPA will use this guidance when evaluating an area's submission of a measure for credit.

However, EPA also desires to work cooperatively with any area that is seriously

considering market-based measures and will support reasonable best efforts to introduce

such measures. EPA expects those areas with more sophisticated analytical tools to use

them to the best possible advantage in performing an accurate assessment. At the same

time, areas with limited analytic capability should not be discouraged from investigating

market-based measures, and are encouraged to use this interest as a basis for upgrading

their analytical tools for future investigations.

This chapter was designed to give direction to technical staff and, as such, goes beyond

basic description of analytical approaches. Nonetheless, some practitioners may want

additional information, such as clear-cut examples or instructions on how to make
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particular changes to their models or how to apply their models in specific circumstances.

Appendices A and B address these needs by providing detailed technical procedures on

model applications.

Appendix A describes a “Case Study Model,” which involves practical modification of a

conventional four-step model system to improve its sensitivity to pricing. In addition to a

description of the modification itself, the appendix demonstrates model applications to

test market-based measures. Appendix B describes a somewhat different approach that

uses household data and sample enumeration methods to obtain increased flexibility and

potentially greater accuracy in evaluating pricing measures.

Performing sites are encouraged to contact their respective EPA regional offices for more

information. In the course of undertaking an evaluation of market-based measures,

agencies also are advised to:

• Establish and maintain a dialogue with EPA and other organizations with

approval authority;

• Confer with appropriate agencies to coordinate assumptions and data;

• Consult with state and local air agencies and EPA on planned analytic

approaches to obtain agreement on the proposed approach; and

• Consult with transportation agencies, environmental and community groups,

and possibly consultants or academics to make sure that analyses and

assumptions are reasonable and accepted.
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n 3.2 Evaluation Guidance for Individual Pricing Measures

This section provides step-by-step guidance for evaluation of each of the following five

types of market-based measures:

• Parking Pricing (Section 3.2.1);

• Modal Subsidies (Section 3.2.2);

• Pump Charges (Section 3.2.3);

• Emissions Fees (Section 3.2.4); and

• Road Pricing (Section 3.2.5).

The discussion of each follows a common four-part format. Each of the five segments

begins with an introductory table that summarizes the relationship between the given

measure and each of the nine steps in the analysis hierarchy with regard to the following:

• Form of impact expected at that step—that is, how the market-based measure

will work to affect transportation factors analyzed in that step of analysis;

• Type of impact expected—primary, secondary, trace, none and derivative (i.e.,

it must be addressed as a result of impacts in other steps); and

• Impact indicator—that is, measures of performance that should be employed

in the evaluation of each step.

Discussion begins with a statement of Anticipated Impacts, or those effects that are

expected to be important in evaluating the specified measure. This is followed by a short

summary statement of the Basic Analysis. The third component is Guidelines for Analysis,

which presents step-by-step procedures or considerations that EPA would expect to see

applied in evaluating the given measure. Analysis recommendations progress from

“basic analysis” to “advanced analysis,” which is more likely to result in the granting of

maximum emissions credit.
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3.2.1 Parking Pricing

3.2.1.1 Anticipated Impacts

The primary impact of parking pricing would be on Mode Choice. Parking prices raise

the cost of vehicle trips and thus increase the attractiveness of alternative modes,

including transit, ridesharing and non-motorized options. They may also affect trip

distribution, shifting destination to those more accessible without private vehicles. The

degree to which other options

become more attractive depends

very much on attendant factors,

such as:

• Importance of reaching the

particular destination;

• Amenity/design characteristics

of that location; and

• Service/quality of the options

that serve that location.

Parking pricing collected as a daily

fee may have a greater effect than

other pricing measures, since it is

directly associated with the cost of

the particular trip. Parking pricing

is expected to have a greater effect

on shorter trips, for which it

represents a higher proportion of

overall trip costs. This has

important implications for

affecting cold starts and microlevel traffic problems.

Parking pricing policies also raise

practical concerns about

distributional impacts. It often is difficult in practice to:

• Apply parking pricing uniformly with regard to geography (e.g., suburb versus city),

type of site (site size and accessibility, on-street versus lots or spillover options, etc.),

and

• Eliminate employer or merchant discounting.

Travelers with flexibility in choice of destination may shift away from locations with increased

parking costs. This could apply to many non-work trips, and also to work and other fixedlocation destinations in the longer run. In turn, this could have an effect on land use as the

comparative advantages/disadvantages of various locations shift in response to pricing levels

and uniformity, quality of alternatives, amenities, etc.

Table 3.1 Impacts of Parking Pricing

Analysis

Step

Impact Form Impact

Type

Impact Indicator

Land Use/

Activity

Allocation

Greater

attractiveness of

areas with lower

prices (uneven

application) or with

better alternatives &

supportive land

use/density

Secondary Population and/or

employment, by

zone

Vehicle

Ownership

None None Vehicles per

household per zone

Vehicle Mix None None Percent of vehicles

by VMT

Trip

Generation

Possible effect on

attractions

Primary on

work-trips,

trace overall

Number of person

trips to/from each

zone

Trip

Distribution

Diversion to

destinations with

lower prices or better

alternatives

Secondary O/D patterns

(average trip length)

Mode Choice Decreases private

vehicle use

Primary Person trips by

mode; % transit;

% walk/bike;

# vehicle trips;

average vehicle

occupancy

Time of Day Shift time of day Secondary

(if time of

day differs)

Trips by time period

Traffic

Assignment

Shift in trips and VMT

as result of mode

choice & distribution

Derivative

effect

Link volumes; VMT

Emissions Reduced vehicle trips

and VMT

Derivative

effect

Tons or kg. of VOC,

NOx, CO, PM
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If parking pricing is applied at different rates to different travel markets (e.g., commuters

versus non-commuters, or peak versus off-peak), then travelers with time flexibility can be

expected to shift their chosen time of travel.

3.2.1.2 Basic Analysis for Parking Pricing

Aside from the standard analysis of emissions, parking pricing analysis should include a

careful assessment of the impacts on mode choice (person trips by mode) and the

resultant effects of mode choice changes on overall vehicle trips, VMT, and emissions. If

parking rates are to be implemented non-uniformly across the region, the effect on shifts

in trip distribution (particularly for non-work travel), land use, and time of day (if

applicable) should be assessed, either through formal analysis/assumptions or through a

discussion that describes the anticipated impacts on travel and emissions.

3.2.1.3 Guidelines for Analysis

1. Parking Pricing and Land Use/Activity Allocations

Parking pricing can affect long-run locational patterns by decreasing vehicle trips,

particularly to locations affected by the policy. EPA does not require a formal analysis of

parking pricing’s effects on land use, although it would be more important to consider

land use impacts if pricing is not uniform. If parking prices are not applied uniformly

across the region, businesses and employers might shift to areas with lower charges or

areas with better travel alternatives (e.g., transit) and supporting density. Households

are expected to make long-term shifts in residence to areas that support lower cost travel

patternsthat is, access to work/non-work destinations that have parking with low or no

fees or that offer effective travel alternatives.

If an area's planning process includes a land use allocation model that incorporates

transportation cost through a measure such as composite impedance,

1

 the area may wish to

consider using this model to investigate parking price impacts on long-term development

trends. However, these results should be interpreted with caution given the limited

accuracy of such models and the existence of other factors that can affect location trends.

Section 3.3.1.2 provides more information on the nature and use of land use models.

Agencies may wish to consider formulating alternative growth scenarios based on various

anticipated effects that development shifts might have on regional travel and emissions.

This approach is particularly relevant for agencies that do not use models for land use

analysis. This option, detailed in Section 3.3.1.1., involves defining alternative land

use/urban design schemes that support the alternative modes and/or travel patterns

encouraged by the proposed parking pricing policy.

 

1

 Composite impedance is a measure that combines the time and cost of highway and transit (and

perhaps other) modes into one statistic; see Section 3.3.5.1.
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If the proposed parking policy entails major disparities in regional coverage or level, and

the agency cannot perform a formal analysis of its impacts, the agency should provide a

statement that reflects local decisionmakers’ appraisal of (1) the long-term consequences

such a policy would be expected to have on regional development, travel trends, and

emissions; and (2) what impact, if any, this will have on other land use and transportation

investment analyses.

2. Parking Pricing and Vehicle Ownership

Because parking pricing is expected to have little or no direct impact on vehicle

ownership, no formal analysis is required. Indirect impacts on vehicle ownership could

occur as a result of household location shifts described in (1) above; in this case, advanced

analysis would include re-running the vehicle ownership analysis.

3. Parking Pricing and Vehicle Mix

Parking pricing is not expected to have a direct impact on ownership vehicle mix, thus no

formal analysis is required. Given that parking pricing is likely to affect short trips more

than long trips, however, parking pricing could affect vehicle usage patterns; this should

be taken into consideration in determining vehicle distribution used for the emissions

estimation step.

4. Parking Pricing and Trip Generation

It is unlikely that parking pricing will have a measurable impact on the overall number of

person trips made by households as forecast in trip generation analyses, thus formal

analysis is not required.

Household vehicle trip rates could be affected by parking pricing in the following ways:

• Higher numbers of workers involved in telecommuting or compressed work

weeks;

• Increased errand chaining to reduce the number of stops at priced

destinations; and/or

• More use of telecommunications for non-work activities.

While these responses may affect the pattern of household tripmaking, it is unclear

whether they affect the overall number of trips or simply trip characteristics, such as

destination, trip length, mode, or time of day. Because there are no formal existing

methods for dealing with these responses, however, no formal analysis is required (see

Section 3.3.4.5).

There is some concern regarding what types of trips are depicted by the trip generation

process used for analysistotal person trips or only those trips that are vehicle serviceable.

Because the latter approach excludes walking and bicycle trips, important shifts in
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household trips from vehicular to non-motorized modes are likely to be underestimated;

Section 3.3.4.4 discusses this aspect in greater detail.

Areas that do not currently forecast total person trips are encouraged to upgrade to such

a framework as a future goal; for the present, these areas should try to consider these

effects in their analysis as best they can. Section 3.3.6.6 suggests ways in which areas

currently unable to project non-motorized modes can account for these effects later in the

mode choice step.

Smaller urban areas without mode choice models may have an even more restricted

definition of trip generation, considering only vehicle trips (i.e., only those person trips

that are made in private vehicles, excluding transit). Trip tables limited to private vehicles

inhibit analysis of pricing strategies, although options for overcoming this constraint are

discussed in the mode choice segment below.

Parking pricing may have an effect on trip attraction rates, although there are no known

models that are directly sensitive to parking pricing in this way. For example, suburban

shopping sites tend to have higher vehicle trip attraction rates than downtown areas,

although this may simply derive from a failure to account for transit and non-motorized

trips. Areas are advised to be sensitive to these differences in their trip generation

analyses.

Parking pricing is not expected to have a major (or predictable) impact on

freight/commercial traffic, except in the event that parking supply itself also becomes

more restricted, for example, due to limitations on curb parking. It also is unlikely that

parking pricing would have a tangible effect on through (or “external-external”)

trafficthat is, trips originating from and destined for locations outside the area of the

pricing scheme.

5. Parking Pricing and Trip Distribution

Parking pricing is expected to have only a secondary effect on trip distribution; thus, no

formal analysis is required. If parking pricing is applied differentially, however, a

statement of possible effects should be included.

Trip distribution analysis can be a particularly important element in assessing the travel/

emissions response to parking pricing in the following ways:

• Trip purpose: Travelers who have the flexibility to shift to alternative

destinations for some trip purposes, such as shopping and social/recreation,

would be expected to consider and perhaps shift to destinations which serve

more needs and/or are accessible without a private vehicle and if parking

charges increase in only some locations to those with free or lower parking

charges (unless there are offsetting amenity or utility circumstances).

Travelers are less likely to shift destinations on trips with more rigidly

determined destinations, such as work, school or personal business, at least

in the short run.
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• Trip length: Parking charges will have a higher proportionate effect on

shorter trips, which also are strong candidates for transit or non-motorized

alternatives (see mode choice discussion below).

Advanced analysis for areas with appropriate modeling capability includes evaluation of

the effect of parking pricing on trip distribution, both for non-uniform and uniform

pricing applications. This is best done with a gravity model that incorporates cost in its

measure of separation through a feature such as composite impedance (see Section 3.3.5.1).

If parking price is added to overall cost of travel to the respective destination, a different

pattern of destination choices in the trip table can be expected to result. If the separation

measure in the gravity model does not directly incorporate cost, then it may be

reasonable to transform the parking cost into a travel time equivalent (see Section 3.3.5.3).

This analysis should be done independently for each trip purpose and income strata, or

done with an equivalent income-sensitivity procedure (see Sections 3.3.6.3 and 3.3.6.4). If

parking pricing varies by time of day, trip distribution analysis should be performed

separately for each period.

Users are cautioned that in typical gravity models, which are doubly constrained, the

model tries to allocate (through iterations) the same number of trip attractions to each

zone that the trip generation model has estimated would be attracted to that zone. If the

relative attractiveness of a zone decreases because of an increased parking price, the

gravity model will still try to force a certain number of attractions to go to that zone. This

could cause unintended distortions in trip patterns. That problem could be minimized by

including some type of price-based variable (like composite impedance) in the attraction

model. Another option is to switch to a logit destination choice model to allocate

attractions, as is done in Portland, Oregon or San Francisco, for example (see Section

3.3.5.4).

Areas without trip distribution modeling procedures will have difficulty accounting for

the impacts that would result from differential application of parking pricing. In this

case, there are two options:

1. Assume only applications of parking pricing that would affect all areas at

the same level, and avoid applications to non-work travel; or

2. If the above option is not realistic, then clearly state assumptions on how the

proposed implementation is expected to affect the pattern of regional trip

origins and destinations and how this is reflected in the estimated emissions

reductions.

6. Parking Pricing and Mode Choice

Changes in choice of mode is the principal impact expected from parking pricing. All

areas considering parking pricing are expected to furnish estimates of:

• Probable impact on number of person trips by mode; and

• Change in number of vehicle trips and average vehicle occupancy.
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Areas with Mode Choice Models

Areas with mode choice models in their planning systems are expected to (1) use them

for this analysis, or (2) explain to EPA their reasons for not doing so. In the absence of

appropriate models, agencies should use “elasticity factor” or “quick response” analysis.

Advanced analysis includes disaggregating analysis by purpose and income, applying

separate coefficients for parking costs, and estimating shifts to non-motorized modes and

non-travel.

EPA offers the following guidelines for the best possible mode choice analysis results:

• Type of Model: Multinomial logit (or similar probability-based) models are

expected to provide the most realistic estimates of impact; models with a

“nested” choice structure are among the most advanced of these (see Section

3.3.6.5 for more information). An ability to represent different automobile

occupancy levels as “modes” increases accuracy in estimating the response

to parking charges, particularly if special exceptions in parking fees are made

for HOVs. An ability to account for non-motorized modes and multi-modal

(e.g. park and ride) options will increase realism of modeling, subject to the

capabilities and information furnished by the overall model system.

• Parking Cost Coefficient: Some models carry a separate coefficient value for

parking cost, which suggests that parking cost carries more weight than

routine operating cost (typically by a factor of 1.5 to 2.0). Agencies may wish

to incorporate such a coefficient in their models (see Section 3.3.6.1).

• Non-Work Mode Choice Models: There should be a mode choice model for

each separate trip purpose. Some areas model only work trip mode choices

and estimate non-work mode choice through a “factoring” process; this is

not likely to yield a credible result for non-work trips (see Section 3.3.6.4).

• Income Stratification: Mode choice analysis should be performed separately

for different income levels (strata), both to allow for different cost sensitivity

and to later account for distributional impacts. An acceptable alternative

approach would be to base the analysis on mode cost prices divided by

traveler income (see Section 3.3.6.3).

• Program versus Effective Rates: Agencies should not take it for granted that

travelers will bear the full weight of program-based increases in parking

prices. Effective rates may be altered by employer subsidies or other

discounts. Unless the policy explicitly assumes full rate pass-through, some

assumption should be made regarding effective rates paid per zone. It also is

desirable to develop models that consider the distribution of parking costs by

site, rather than using zonal averages. The STEP approach discussed in

Appendix B offers such a capability.

• Cost Basis: Mode choice modeling is typically based on the comparison of

costs for a one-way trip. Thus, it is generally appropriate to apply half of the

daily parking to each one-way work trip. For non-work trips, where rates

may be hourly, or for trip chains with multiple destinations, the relevant

price per trip may be less clear and analysis will require explicit

assumptions.
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• Adjustment for Inflation: The dollar value implied in the suggested parking

price must be consistent with the base year for which the model coefficient

was developed. Generally, this requires deflating the tested parking price

with a time-inflation factor that makes parking costs compatible with the

coefficient in real terms (see Section 3.3.6.2).

• HOV Special Rates: Few, if any, mode choice models permit testing of

different parking prices for different automobile occupancy levels. Such

distinctions are important if the proposed pricing strategy provides

incentives to carpools or vanpools. The TDM Evaluation Model (discussed

in Section 3.3.6.9) may be useful to areas that wish to perform such an

analysis.

Areas without Mode Choice Models

Areas that do not have mode choice models will have a more difficult time assessing

parking pricing impacts, but may still do so. Suggestions for evaluations by these areas

include:

• Areas with Person Trip Tables: Areas with person-trip tables (by individual

trip purpose), can obtain an estimate of modal shifts by applying one of the

following methods:

- Quick Response Methods: Mode choice analysis can be achieved

with a number of techniques that use sketch planning or pivot-point

methods. These include the FHWA's TDM Evaluation Model and the

QRS software system (see Section 3.3.6.9 for more information).

- Elasticities: Mode choice analysis can be approached through

application of pricing elasticity estimates from empirical studies or

from mode choice models of similar areas (see Section 3.3.6.9 for more

information and references). It must be stressed that elasticities will

vary widely from region to region, depending on base costs, travel

times, urban-design factors, and mode shares as well as socioeconomic factors.

• Areas with Vehicle Trip Tables (not Person Trip Tables): Some areas

perform analysis using only vehicle trip tables. It is still possible to evaluate

mode choice responses to parking pricing if the agency first generates an

estimate of person trips and then uses the derived estimates with either of

the methods identified above. Person trips can be derived by (1) dividing

vehicle trips by average vehicle occupancy, or (2) using a technique such as

the TDM Model to produce a starting person trip table (note that the TDM

model does not include non-motorized trips, however).

• Areas with No Trip Tables: Areas without trip tables have fewer analysis

options, but can develop at least a rough estimate of mode split response to

parking pricing if they can estimate total person trips, total vehicle trips (or

average vehicle occupancy), and VMT through HPMS or another source.

Elasticity methods can then be used to factor the base trip rates to reflect the
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change in parking. Segmenting the base population by average trip length, if

possible, may increase accuracy.

Generally, if an area's planning tools and data are limited to trip tables or gross trip and

VMT measures, the range of policy options that can be evaluated is also limited. Only the

most simplistic definitions can be considered, such as a uniform charge on all parking,

which also raises questions as to realism of the assumptions and assessment of the impact

(both locally and to EPA).

7. Parking Pricing and Time of Day

Parking pricing effects on time of day of travel are likely to be minimal in most cases;

thus, no formal analysis is required. Shifts in travel time of day are anticipated only if

parking rates varied substantially by time of day, in which case the analysis should be

treated more like congestion pricing (Section 3.2.5-7). Presumably, most parking schemes

directed at work travel would be in effect all-day, and thus traveling off-peak would offer

no advantage. Restructuring daily rates vis-à-vis hourly rates away from all-day

discounts for commuters and toward more favorable short-term rates, however, could

induce non-work travelers to shift time patterns. Analysis of such effects is quite

complicated and requires either fairly specialized tools or off-line methods (see Section

3.3.7).

8. Parking Pricing and Traffic Assignment/Route Choice

Parking pricing is likely to have limited or no direct effect on traffic assignment. Parking

pricing applied in a special geographic pattern (e.g., downtown peripheral parking

zones) may, in some cases, generate micro-level traffic concerns. If these threaten to be

significant, then traffic microsimulation procedures should be considered (see Section

3.3.8.3 for more information).

Areas with traffic assignment as part of their transportation analysis process are expected

to perform a new assignment to reflect the impacts of mode choice and trip distribution

analyses on vehicle trip patterns and their corresponding VMT levels in the travel

network. These inputs are necessary for application of the emissions model in the next

step. Areas lacking traffic assignment capability must rely on net changes in vehicle trips

and VMT to perform emissions analysis.

9. Parking Pricing and Emissions

All analyses of parking pricing should conclude with an estimate of the net changes in

VOC, NOx, and CO. Areas with PM10 attainment problems also should evaluate changes

in anticipated PM10 emissions. Parking pricing is not expected to alter emissions rates or

emissions analysis factors related to baseline vehicle mix. However, because shorter trips

are likely to be most affected by parking pricing, emissions analysis cold start factors

should be adjusted or evaluated separately to account for changes.

Areas with traffic assignment capability are advised to use results from the traffic

assignment process in an emissions factor model, such as MOBILE or EMFAC (PART5
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where appropriate), to generate estimates of emissions (see Section 3.3.9). Areas without

traffic assignment capability are limited to application of emissions factors to net changes

in vehicle trips and VMT (also see Section 3.3.9), again accounting for possibly

disproportionate changes in cold starts.

3.2.2 Modal Subsidies

3.2.2.1 Anticipated Impacts

Modal subsidies are expected to have

their primary impact on Mode Choice.

The reduced cost of the subsidized

mode increases its attractiveness

relative to all other modes,

particularly driving alone. Subsidies

could include reductions in cost for

transit users, rideshare travelers,

walkers, and cyclists. Modal

subsidies also might include

incentives for alternative work

arrangements (e.g., telecommuting,

compressed work weeks).

Impacts depend on the subsidy level

(and its net value to the recipient),

mode or modes subsidized (modal

competition), and quality of the

alternatives (service/coverage).

Subsidies can increase the

attractiveness of destinations where

the alternatives are best deployed,

which could affect trip distribution

and long-term land use decisions.

Subsidies that affect peak/off-peak

differences could trigger time of day

shifts. Significant subsidies could

possibly influence vehicle ownership

decisions, but probably more as a

secondary result of household

relocation.

Table 3.2 Impacts of Modal Subsidies

Analysis

Step

Impact Form Impact

Type

Impact Indicator

Land Use/

Activity

Allocation

Areas where subsidy

applied can be more

attractive

Trace

(Secondary

level if

significant)

Population and/or

employment by zone

Vehicle

Ownership

May eliminate need

for multiple vehicles

Trace Vehicles per

household per zone

Vehicle Mix None None Percent of vehicles by

VMT

Trip

Generation

Could induce nonmotorized trips or

alternate work

schedules

Trace Number of person

trips to/from each

zone

Trip

Distribution

Destinations

reachable by

subsidized mode

more attractive

Secondary O/D patterns (average

trip length)

Mode

Choice

Encourages use of

subsidized modes

Primary Person trips by

mode; % transit;

no vehicle trips;

Average vehicle

occupancy

Time of Day Shift trips to periods

with most subsidy

benefit

Secondary

(if time of

day differs)

Trips by time period

Traffic

Assignment

Shift in trips and

VMT as result of

mode choice &

distribution

Derivative

effect

Link volumes; VMT

Emissions Reduce vehicle trips

and VMT

Derivative

effect

Tons or kg. of VOC,

NOx, CO, PM
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3.2.2.2 Basic Analysis for Modal Subsidies

In addition to the standard analysis of emissions, agencies should carefully assess the

impacts on mode choice (person trips by mode) and, in turn, mode choice effects on

overall vehicle trips, VMT, and emissions. If subsidies are mode-oriented, submitting

agencies should investigate the effects on other modes. If subsidies favor particular

locations, in terms of modal access or other amenities, agencies also should address the

indirect impacts on trip distribution and land use.

3.2.2.3 Guidelines for Analysis

1. Modal Subsidies and Land Use/Activity Allocations

Modal subsidies are unlikely to affect long-run locational patterns unless they are

substantial, considered permanent, and linked to concurrent investment in alternative

mode and/or land use/urban design strategies that complement the subsidy; these latter

also would add to the sense of “permanence.” EPA does not require a formal analysis of

subsidy impacts on land use unless the nature of the subsidy suggests an obvious impact

on long-term trends; in this latter case, EPA requires a statement of possible effects.

2. Modal Subsidies and Vehicle Ownership

Modal subsidies are expected to have little or no direct impact on vehicle ownership and,

thus, no formal analysis is required. If an agency feels its subsidy program will directly

affect vehicle ownership levels, the agency should present appropriate assumptions or

analysis to support this claim.

There may be an indirect effect on vehicle ownership, however, if modal subsidies are

expected to affect land use through household shifts to areas with lower rates. Advanced

analysis could then include a re-run of vehicle ownership evaluation to reflect anticipated

land use impacts.

3. Modal Subsidies and Vehicle Mix

Modal subsidies are expected to have little or no impact on vehicle mix and, thus, no

formal analysis is required. Since modal subsidies can have differential effects on

different trip types and lengths, however, shifts in usage patterns may be important in

determining the regional fleet mix used in the emissions estimation step.

4. Modal Subsidies and Trip Generation

It is unlikely that modal subsides will have a measurable impact on the overall number of

person trips generated by households. However, an analytical problem arises if trip

generation is only performed for vehicular trips since the overall vehicle trip reduction
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effects will be understated if a subsidy draws travelers from private vehicle to transit,

non-motorized or telecommuting modes.

Modal subsidies might influence household trip generation, and estimates of trip

generation, through:

• Shifts to or from non-motorized modes, if those modes are not reflected in

trip generation estimates; and/or

• Attraction of non-work trips to subsidized transit that may have not been

made previously, or were made by non-motorized mode (and not included

in the trip generation rate).

While a formal analysis is not required, the performing agency may wish to estimate

modal subsidy impact on trip generation if the proposed subsidy:

• Stimulates alternative work arrangements;

• Involves transit or non-motorized modes; and

• Is set at a significant level (e.g., free off-peak transit).

While there are no known formal methods (i.e., methods typically included in a complete

regional travel demand modeling analysis) for dealing with these impacts, the user may

consider off-line or sketch planning techniques such as described in Section 3.3.6.6

(Special Modes) and 3.3.6.9 (Off-line Methods). If the agency does not perform an

analysis, it should offer an assessment of the anticipated impact of the subsidy on vehicle

trip generation.

5. Modal Subsidies and Trip Distribution

Subsidies that favor travel to particular destinations can affect destination choices. The

effects on destination choice for work-related trips would only be realized in the longer

term; the effects for non-work trips, however, could well be realized in the shorter term.

Trip lengths also may be affected, depending on the nature of the subsidy (whether it is

fixed or distance-based).

As these effects are likely to be secondary, a formal analysis is not required. If subsidies

are significant, however, agencies should provide a statement of possible effects on trip

distribution. Agencies with trip distribution models can perform an analysis of modal

subsidy effects on destination choices.

Models that incorporate a composite impedance measure (see Section 3.3.5.1) can estimate

the effect of most subsidies directly, by altering the respective generalized travel costs for

each affected origin-destination pair. This approach is generally effective for motorized
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modes. Current techniques, however, do not deal well with subsidies that greatly

influence the choice between non-motorized and motorized mode destination choices.2

If subsidies vary by destination, a different pattern of destination choices in the trip table

would be expected to result. Analysis should be disaggregated by trip purpose and

should account for differences in income though income stratification or other techniques

(see Section 3.3.5.2).

Areas without trip distribution models of the type described here will not be able to

perform this analysis. These areas are advised to exercise caution when interpreting the

modal choice effects of subsidy policies and should state how failure to account for

changes in trip distribution might affect their final analysis.

6. Modal Subsidies and Mode Choice

Because modal subsidies are expected to have their greatest impact on mode choice,

formal analysis should be used. All areas considering modal subsidies should furnish an

estimate of the change in the number of person trips by mode, the change in number of

vehicle trips and average vehicle occupancy, and the change in the percent of travelers

using transit as a result of the particular policy.

Modal subsidies alter the relative cost and attractiveness between subsidized and

unsubsidized modes, thus altering competition among the modes for person trips.

Modal subsidies are expected to have limited or no impacts on commercial travel or

through travel. Because the relationships are complex, areas with mode choice models in

their planning systems are encouraged to use them for this analysis.

EPA offers the following guidelines to help agencies develop the best possible mode

choice analysis:

Areas with Mode Choice Models

• Type of Model: Multinomial logit (or similar probability-based) choice

models will provide the best estimates of impact, provided the relevant

modes are included in the model structure. Nested models are expected to

provide the most accurate estimates (see Section 3.3.6.5).

• Suitable Modes/Coefficients: Since most mode choice models include cost

relationships for transit fare and automobile operating cost, they can

accommodate many subsidy policies. Coefficients’ suitability is addressed in

Section 3.3.6.1. If subsidies for HOV travel involve parking discounts, then

 

2

 It would be necessary to incorporate measures of bike/pedestrian generalized cost measures into

the models. Initially, such models would need to be estimated where there is a large difference

in non-motorized mode utility and use, with a transfer of coefficients to regions lacking such

diversity, using choice model coefficient scaling (see section 3.3.6.8).
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use of a parking coefficient may be appropriate (see Sections 3.3.6.9 and

3.2.1.6).

• Accommodation of Modes: In order to test the full range of modal pricing

options, the mode choice model should incorporate different costs for each

sub-mode (e.g., walk to transit, drive to transit, drive alone, 2-person carpool,

3-person carpool, 4-person carpool, and perhaps even vanpool, walk and

bike). Not all models accommodate such refinements. Alternatives include

use of the TDM Evaluation Model (Section 3.3.6.9), which allows subsidies to

be targeted individually to various carpool levels, vanpools, and transit, and

distinguishes between direct out-of-pocket and less direct per-mile costs.

Most model systems (including the TDM Model) do not accommodate nonmotorized modes. Areas considering subsidies to these modes will need to

consider off-line methods for their evaluation (see Sections 3.3.6.6 and

3.3.6.9).

• Non-Work Mode Choice Models: Mode choice analysis of modal subsidy

impacts should be performed separately for each separate trip purpose; use

of factoring methods is discouraged (see Section 3.3.6.4).

• Income Stratification: The performing agency should perform mode choice

analysis separately for each income stratum, or use an alternative approach

to address income group differences in sensitivity and impact (see Section

3.3.6.3).

• Cost Basis: Costs should be specified on the basis of a (daily) one-way trip.

• Adjustment for Inflation: The dollar value implied by the proposed subsidy

should be deflated for consistency to model coefficient's year of calibration

(see Section 3.3.6.2).

• Special Policy Definitions: Certain subsidies may be difficult to test with

existing models because they may require specialized information; for

example, parking cash out requires knowing whether the commuter

currently has free parking, whether the employer leased the parking, and

what the value would be to the employee back as a subsidy. Policies of this

nature can sometimes be addressed through sample enumeration methods (see

Section 3.3.6.7) or use of stated preference methods (see Section 3.3.6.8).

Areas without Mode Choice Models

Areas that do not have mode choice models will have a more difficult time assessing

subsidy policies, but may still do so. Suggestions for these areas are as follows:

• Areas with Person Trip Tables: If agencies have access to trip tables based

on person trips (by individual trip purpose), they can estimate the impact of

modal subsidies on modal shifts through application of one of the following

methods:

- Quick Response Methods: Mode choice analysis can be achieved

with a number of techniques that use sketch planning or pivot-point
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methods. These include the FHWA's TDM Evaluation Model and the

QRS software system (see Section 3.3.6.9 for more information).

- Elasticities: Mode choice analysis can be approached through

application of pricing elasticity estimates from empirical studies or

from mode choice models of similar areas (see Section 3.3.6.9 for more

information and references). It must be stressed that elasticities will

vary widely from region to region, depending on base costs, travel

times, and mode shares as well as socio-economic factors.

• Areas with Vehicle Trip Tables (not Person Trip Tables): Some areas

perform analysis using vehicle trip tables only. It is still possible to evaluate

mode choice responses to parking pricing if the agency first generates an

estimate of person trips and then uses the derived estimates with either of

the methods identified above. Person trips can be derived by (1) dividing

vehicle trips by average vehicle occupancy, or (2) using a technique such as

the TDM Model to produce a starting person trip table (note that the TDM

model does not include non-motorized trips, however).

• Areas with No Trip Tables: Areas without trip tables have fewer analysis

options, but can develop at least a rough estimate of mode split response to

parking if they can estimate total person trips, total vehicle trips (or average

vehicle occupancy), and VMT through HPMS or another source. Elasticity

methods can then be used to factor the base trip rates to reflect the change in

parking. Segmenting the base population by average trip length, if possible,

may increase accuracy.

Generally, areas that are limited to trip tables or gross trip and VMT measures can

evaluate only a limited range of policy options and consider only the most simplistic

policy definitions.

In all cases, more advanced analysis includes the use of separate models for each

purpose, disaggregated by income levels, and an accompanying supplemental analysis to

assess the effects on the use of subsidized or non-motorized modes.

7. Modal Subsidies and Time of Day

Modal subsidies can have secondary effects on time of day of travel, but, in most cases,

the effects are expected to be minimal and formal analysis is not required.

Subsidies that entail major peak/off-peak differentials may trigger migration into or out

of peak periods, particularly for non-work trips. Since ridesharing is almost exclusively a

commuter mode, ridesharing subsidies are not expected to produce much of a shift in

time of day unless time of day differentials include off-peak periods close to conventional

work day beginning and end.

Subsidy-induced differentials in transit pricing by time of day, however, could produce

travel time shiftslower off-peak fares would be expected to attract non-work riders and

a small number of workers, while relatively lower peak fares might draw some non-work

trips into the peak, as well as some work-based transit trips.
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If time of day impacts are considered possible, methods such as those described in

Section 3.3.7 may be of use. In the particular case of transit, time of day demand

information may be available through studies (possibly local) of transit peak/off-peak

fare adjustments.

8. Modal Subsidies and Traffic Assignment

Modal subsidies are not expected to exert any special influence on traffic assignment.

Areas that have traffic assignment as part of their transportation analysis process,

however, should perform a new assignment analysis to reflect the impacts of mode shifts

on vehicle trip and VMT levels, and to accommodate these changes in the travel network

pattern. These inputs are necessary for application of the emissions model in the next

step.

Areas which do not have traffic assignment capability must rely on net changes in vehicle

trips and VMT to perform emissions analysis.

If HOV subsidies are high enough, demand may justify new or expanded HOV facilities.

Regarding analytical procedures, this would require some additional effort in network

coding and assignment procedures; however, this would be a categorically different

policy from a straight HOV subsidy and would argue for a formal HOV analysis.

9. Modal Subsidies and Emissions

All analyses of modal subsidies should conclude with an estimate of the net changes in

the major pollutants for which they are claiming credit. Modal subsidies are not expected

to affect emissions through emissions rates or through changes in regional vehicle fleet

distribution; thus, no special attention need be paid to these impacts in the emissions

analysis process.

Areas with traffic assignment capabilities are strongly encouraged to use an emissions

factor model, such as MOBILE or EMFAC or PART5, to furnish estimates of emissions,

using the results from the traffic assignment step (see Section 3.3.9).

Areas without traffic assignment capability are limited to application of emissions factors

to net changes in vehicle trips and VMT (see Section 3.3.9), accounting for the possibly

disproportionate changes in cold starts.
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3.2.3 Pump Charges

3.2.3.1 Anticipated Impacts

Pump charges (or similar VMT-based

fees) increase the per-mile cost of

driving. This is expected to reduce the

demand for private vehicle travel. The

expected primary impact is on mode

choice, though significant price

increases also could affect trip

distribution, long-term locational

decisions (land use), and possibly trip

generation. Households also are

expected to react to higher fuel prices

by shifting to ownership and/or use of

higher-mpg vehicles, thus affecting

vehicle mix.

Table 3.3 Impacts of Pump Charges

Analysis

Step

Impact Form Impact

Type

Impact Indicator

Land Use/

Activity

Allocation

Causes location in

less auto-dependent

areas

Secondary Population and/or

employment by zone

Vehicle

Ownership

If shifts in location

encouraged

Trace Vehicles per

household per zone

Vehicle Mix Shift to higher mpg

vehicles

Primary Percent of vehicles

by VMT

Trip

Generation

Overall higher cost

of travel

Secondary Number of person

trips to/from each

zone

Trip

Distribution

Causes shorter

trips/closer

destinations

Secondary O/D patterns (average

trip length)

Mode

Choice

Decreases private

vehicle use

Primary Person trips by

mode; % transit;

no vehicle trips;

Average vehicle

occupancy

Time of Day None None Trips by time period

Traffic

Assignment

Change in trips and

VMT; may affect

speeds

Derivative

effect

Link volumes; VMT

Emission

Analysis

Reduces vehicle

trips and VMT; may

affect speeds

Derivative

effect

(possible

change in

vehicle mix)

Tons or kg. of VOC,

NOx, CO, PM

3.2.3.2 Basic Analysis for Pump Charges

Aside from the standard analysis of emissions, analysis of pump charges should include

mode choice (person trips by mode) and the effect of mode choice shifts on overall vehicle

trips, VMT, and emissions. Agencies also should address the implications of a potential

shift to higher-mpg vehicles on effective operating cost and vehicle mix (which, in turn,

affects factors used in emissions analysis).

3.2.3.3 Guidelines for Analysis

1. Pump Charges and Land Use/Activity Allocations

Pump charges (or VMT fees) influence long-run locational patterns by favoring locations

that support reduced dependence on private vehicles (e.g., better walk access, availability

of transit, etc.). Because this is designated as a secondary impact, a formal analysis is not

required; however, EPA expects some acknowledgment of the long-range impacts.
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If pump charges (or VMT fees) are significant, they may influence land use/locational

effects. Although a formal analysis is not required, any agency anticipating major

changes in pump prices should submit at least a statement of likely long-term impacts on

regional development trends, travel, and emissions. The agency may or may not support

this statement with a more rigorous analysis of anticipated impacts.

Areas with land use allocation models (see Section 3.3.1.2) that incorporate transportation

cost through some measure like composite impedance (see Section 3.3.5.1) may wish to use

these models to investigate potential land use impacts as part of their advanced analysis.

Another approach would be to consider one or more alternative growth scenarios that: (1)

are based on a system of higher travel costs, (2) incorporate alternative transportation

investments, and (3) include various land use/urban design elements (Section 3.3.1.1).

2. Pump Charges and Vehicle Ownership

Pump charges (or VMT fees) are unlikely to directly affect overall levels of vehicle

ownership and, thus, no formal analysis is required. If shifts in land use/locational

patterns are projected in Step 1, however, then these changes should be carried forward

into a revised assessment of vehicle ownership.

3. Pump Charges and Vehicle Mix

Given the primary impacts anticipated, submitting agencies should indicate their

assumptions regarding the effects of pump charges on vehicle mix.

Assuming that program-based pump charges are proportional to gallons of fuel

purchased (rather than set as fixed fees per transaction or based on recorded VMT),

households can be expected to respond by gradually shifting ownership and/or use to

higher-mpg vehicles.3 Shifts to higher-mpg vehicles could have two effects:

1. Offset some of the gains from pump-based charges by reducing cost-per-mile

of surcharges; and

2. Shift the mix of vehicles reflected in the emissions model, thus the effective

emissions rate.

If the increase in fuel price will be significant, the performing agency should assess the

shift in ownership and usage to higher-mpg vehicles. This assessment could employ:

• Forecasting tools, such as hedonic or other vehicle share models (see Section

3.3.3.1);

• Scrappage models and studies (see Section 3.3.3.2);

 

3

 If ongoing emissions charges are pure VMT-based charges (using smart-card or other mileage

tracking technology) where fuel consumption is not used as a proxy or intermediate variable,

there is no incentive to shift to higher-mpg vehicles.
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• Empirical/research studies or trend data (see Section 3.3.3.3);

• Stated preference analyses (see Section 3.3.3.4); and

• Elasticity methods (see Appendix A discussion related to Vehicle

Registration Fees).

Clearly, this assessment has applicability beyond those agencies that employ vehicle

ownership as a formal part of their planning system, since this assessment gauges the

changes in average mpg, cost per mile of driving, and vehicle distribution variables used

for emissions analysis.

4. Pump Charges and Trip Generation

Because pump charges increase the overall cost of vehicle usage for all trips regardless of

purpose, they could have an impact on the number of activities that households support

with trips, i.e., trip generation. More likely, however, households would adjust trip

characteristics without altering the number of trips, for example, by favoring other modes,

shorter trips, and increased trip chaining. Thus, formal analysis of the impact of pump

charges (or VMT fees) on trip generation is not needed.

In general, EPA encourages agencies to do the following:

• Re-assess trip generation if land use or vehicle ownership shifts are

projected in earlier steps and the agencies have trip generation models based

on these inputs; and/or

• Perform an assessment of the impact of increased pump charges (or VMT

fees) on trip generation if the agencies have trip generation models able to

reflect cost through a measure such as composite impedance (see Section

3.3.5.1). 4

Agencies basing trip generation on vehicle serviceable or vehicle only person trips rather

than all person trips may face concerns regarding accuracy in this step since higher fuel

prices can shift trips to non-motorized modes. These agencies may use off-line methods

to approximate mode-choice shifts (see discussion below in sub-section 6). EPA

encourages these agencies to upgrade their models to deal specifically with nonmotorized trips in the future.

 

4

 A difficulty in modeling the influence of an increase in the cost per mile of automobile travel is

that at the trip generation stage of a four-step model, there is no specific trip length from which

to gauge the actual increase in the user cost of travel. Also, for transit users and bicyclists, there

would be no increased user cost. One way around this is to use the mode choice model to

calculate a composite accessibility variable which reflects changes in the generalized price of

reaching all destinations via all modes. There are few current trip generation models that are

sensitive to user cost (see Section 3.3.4.1.).
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5. Pump Charges and Trip Distribution

Vehicle per mile charges will have a secondary effect on the distribution of trips. By

increasing the effective separation between zones, longer-distance trips will have a higher

price and thus be discouraged. The impact on work trips is expected to be long term,

while the impact on non-work trips could be realized in the near term.

Although formal analysis is not needed, submitting agencies should submit a statement

of possible effects. Agencies that anticipate significant increases in pump prices as a

result of the proposed policy should perform a trip distribution analysis in relation to the

higher fuel prices if they have trip distribution models that:

• Accommodate costs through measures such as composite impedance (Section

3.3.5.1); or

• Incorporate travel time approximation techniques (Section 3.2.5.3.).

Where possible, this analysis should be performed separately by trip purpose and income

level. In addition, if the price per gallon is likely to trigger a shift to higher-mpg vehicles,

agencies should consider the effect of this shift on cost per mile.

Areas that do not have trip distribution model capability, or that anticipate only insignificant

changes in prices, should document their assumptions and effects on final results.

6. Pump Charges and Mode Choice

Because pump charges (and VMT fees) are expected to have a primary effect on mode

choice, a formal assessment of anticipated effects should be conducted.

Pump charges (or VMT fees) increase the per-mile cost of private vehicle travel both

absolutely and relative to other modes; this should increase the demand for alternative

modes. Transit and non-motorized modes would be exempt from price increases, thus

improving their comparative cost position. Although rideshare modes would be assessed

as private vehicles, this mode would still enjoy a cost advantage over solo driving since

the increased price would be distributed among several travelers. Although the

comparative cost positions of bike and walk modes also are improved, these modes may

be less affected because of the short trip length (small total cost impact for competing

automobile trips). However, significantly higher fuel prices could generate more nonmotorized trips to closer destinations. Commercial vehicle trips could be affected

depending on the nature of the business activity.

Areas with Mode Choice Models

Areas with mode choice models should use them to evaluate mode choice effects of

pump prices (see Section 3.3.6 for model characteristics). In using these models, agencies

should increase operating costs for all private vehicle modes by the increase in cost per

mile times the origin-destination trip length as determined from the highway distance

matrix. The following guidelines should be applied to this analysis:
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• Calculation of Cost Impact: For a VMT fee, the increase in cost per mile is

direct. For pump charges, the incremental cost per mile must be determined

by first dividing the change in price per gallon by the average mpg of the

regional vehicle fleet. In the event of significant price increases yielding a shift in

the vehicle mix toward higher mpg vehicles, the revised average mpg should be used

to estimate the tested per-mile cost impact.

• Control for Inflation: Agencies should ensure that the cost increase input to

the model has been inflation-adjusted to match the year of calibration of the

model coefficient (see Section 3.3.6.2).

• Income Stratification: The mode choice analysis should be performed

separately by income strata, or with an acceptable alternative approach (see

Section 3.3.6.3).

• Trip Purpose Distinctions: Separate mode choice models should be applied

for each trip purpose (see Section 3.3.6.4).

Areas without Mode Choice Models

Areas without mode choice models may estimate mode choice impacts from pump

charge or VMT fee policies using less formal methods. Suggestions for these areas

include:

• Quick Response Methods, which include computer software methods like

the FHWA TDM Model and the QRS system (particularly useful for areas

with trip tables); and/or

• Elasticity and Sketch Planning Methods, which include various pivot-point

methods and elasticity estimates that may be applied to either a trip table

base or more aggregated travel estimates.

These techniques and their application are detailed in Section 3.3.6.9. Areas that do not

formally account for non-motorized modes in mode choice may wish to do so by

considering one of the off-line methods listed in Section 3.3.6.9.

Areas without formal mode choice models or trip tables are cautioned that their analyses

may have questionable accuracy with respect to differences among travelers regarding

trip purpose, trip length, modal alternatives, and income level.

7. Pump Charges and Time of Day

There could be a second-order effect on peak/off-peak travel distribution to the extent

that total vehicle trips, VMT, and congestion are reduced by pump charges (or VMT fees).

This effect, however, is not expected to be measurable; and no analysis is needed.
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8. Pump Charges and Traffic Assignment

Most of the impact from pump charges (or VMT fees) on traffic assignment is likely to

come from reductions in length and number of vehicle trips. However, increases in the

per-mile cost of driving also may affect route selection for given origin-destination pairs.

If the additional cost is high enough, it will encourage drivers to select paths that

minimize cost as well as time (e.g., selecting shorter but less speedy routes). In general,

these effects are expected to be at the trace level, and standard assignment analysis is

expected only of those agencies with assignment procedures.

Agencies with appropriate capabilities can estimate traffic assignment impacts of pump

charges by building highway paths based on a combination of travel time and user cost,

using the per-mile user cost of vehicle operation and an assumed average value of travel

time to motorists. All major software packages are believed to provide this capability.5

Areas without traffic assignment capability must rely on net changes in vehicle trips and

VMT to perform emissions analysis.

9. Pump Charges and Emissions

All analyses of pump charges (or VMT fees) should conclude with an estimate of net

changes in VOC, NOx, and CO (and PM10 for areas with PM10 problems). The impacts on

emissions are expected to derive from projected changes in vehicle trips and VMT

(without shifts in emissions rates), and shifts in mpg and possibly speeds from the traffic

assignment step (with concomitant changes in emissions rates).

Areas with traffic assignment capability and with emissions factor models, such as

MOBILE or EMFAC, are expected to use these procedures to estimate the emissions

changes. PART 5 should be used if particulates are a problem. If analysis in Step 3

indicates a change in the regional vehicle fleet mix, then the agency should use the new

vehicle distribution results to alter the distribution in the MOBILE or EMFAC model and

resultant emissions factors (see Section 3.3.9).

Areas without emissions model capability, that is, areas that rely strictly on emissions

factor estimates, should detail their assumptions and the procedures they used if they

have modified their base emissions rates to reflect the shift in vehicle fleet mix.

 

5

 It may be possible to make this analysis sensitive to income groups by first building (skim and

assign) separate paths for each income group, so that low-income drivers are assumed to use

routes that minimize vehicle operating cost and high-income drivers to use routes that minimize

travel time.
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3.2.4 Emissions Fees

3.2.4.1 Anticipated Impacts

Emissions fees, as defined for this guidance, consist of increases in vehicle registration

fees based on some combination of vehicle emissions characteristics (age and/or

emissions rate) and usage (odometer reading). The emissions element rather than VMT is

presumed to be dominant; that is, emissions would account for the greatest variation in

the level of the fee. (VMT-based fees are addressed as a variation of pump charges in

Section 2.2.3.)

The primary impact anticipated is

increasing ownership and greater

usage of newer, low-emitting

vehicles. If emissions fees are

significant, they also could affect the

number of vehicles owned. In turn,

this could influence land

use/locational patterns, which then

affects trip generation.

If emissions fees are assessed

infrequently, it is unlikely that they

would have direct effects on travel

unless education campaigns make

people aware of the charges.

.

3.2.4.2 Basic Analysis

Agencies proposing to introduce emissions fees should analyze the effects of this measure

on regional vehicle mix and the subsequent effects on emissions attributable to a change

in vehicle mix. Additional assessments should be used if significant impacts are

anticipated for vehicle ownership and, from that, for land use and other secondary

effects.

Table 3.4 Impacts of Emission Fees

Analysis

Step

Impact Form Impact

Type

Impact Indicator

Land Use/

Activity

Allocation

Lowers demand for

auto-dependent

environments

Secondary

(if vehicle

ownership

is affected.)

Population and/or

employment by zone

Vehicle

Ownership

Reduces demand

for vehicle

ownership

Primary Vehicles per household

per zone

Vehicle Mix Increases

ownership & use

of newer/ lowemitting vehicles

Primary Percent of vehicles by

VMT

Trip

Generation

If vehicle ownership

affected

Trace Number of person trips

to/from each zone

Trip

Distribution

If vehicle ownership

affected

Trace O/D patterns (average

trip length)

Mode

Choice

If vehicle ownership

affected

Trace Person trips by mode;

% transit; no vehicle

trips; average vehicle

occupancy

Time of Day None None Trips by time period

Traffic

Assignment

None None Link volumes; VMT

Emissions Changes vehicle

mix, maintenance

Primary Tons or kg. of VOC,

NOx, CO, PM
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3.2.4.3 Guidelines for Analysis

1. Emissions Fees and Land Use/Activity Allocations

Two instances in which emissions fees could trigger land use/locational impacts, are:

1. Major VMT component in fee: If the emissions fee has a major VMT

component, the cost per mile for vehicle use could deter vehicle use. In this

case, analysis should follow the guidelines for pump charges as well (Section

3.2.3).

2. Significant increase in automobile ownership costs: If the emissions fee

significantly increases the cost of vehicle ownership, it may deter vehicle

ownership. A reduction in household vehicle ownership could increase the

demand for location-efficient areas.

If the nature or magnitude of emissions fees are considered sufficient to affect vehicle

ownership rates, EPA suggests that the performing agency address the potential impact

on land use/locational patterns with a land use allocation model, if one is available and

suitable, or through scenario testing. Agencies should prepare a statement that reflects

the agency’s assumptions of the potential impacts on long term location patterns and the

overall impact on emissions if the agency (1) does not have appropriate analysis tools, or

(2) elects to declare this impact to be insignificant.

2. Emissions Fees and Vehicle Ownership

Emissions fees can trigger a reduction in vehicle ownership if the fees cause a significant

increase in costs of vehicle ownership. The performing agency should account for shifts in

vehicle ownership if the agency (1) thinks that its proposed emission fee may have an

impact on vehicle ownership levels and (2) uses vehicle ownership in its modeling

process. The agency may use analytic methods or the best possible assumptions.

Techniques suitable for this analysis include:

• Forecasting tools, such as hedonic or other vehicle share models (see Section

3.3.3.1);

• Scrappage models and studies (see Section 3.3.3.2);

• Empirical/research studies or trend data (see Section 3.3.3.3);

• Stated preference analyses (see Section 3.3.3.4); and

• Elasticity methods (see Appendix A discussion related to vehicle registration

fees).

3. Emissions Fees and Vehicle Mix

Emissions fees are expected to have their primary effect on vehicle mix. Households

would be expected to either replace high-emitting vehicles with cleaner ones, and/or shift

use within multi-vehicle households in favor of low-emitting vehicles.
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In some circumstances, such as in a revenue-neutral feebate approach, an emissions fee

program could actually increase the number of motor vehicles by encouraging

diversification in favor of low-emissions vehicles. This would be in contrast to recent

trends toward vehicles with high weight/power/performance characteristics. If the

agency’s proposed strategy is likely to encourage net expansion in vehicle fleet size, the

agency should address the effect of its proposed emissions fee program on overall vehicle

ownership levels as well as on distribution.

Emissions fees also may affect the composition or use of commercial vehicle fleets

registered in the affected jurisdiction. Emissions fees would not, however, affect through

traffic or external travel vehicles.

Section 3.3.3 provides options that may be used to estimate shifts in vehicle mix.

4. Emissions Fees and Trip Generation

In general, emissions fees are expected to have only trace effects on trip generation.

(Recall that infrequent assessment of emissions fees would mean limited direct impact on

daily travel decisions.). Nonetheless, emission fee effects on the costs of vehicle

ownership could lower the number of vehicles owned by households. In turn, this could

affect trip generation analysis in jurisdictions that use models based on vehicle

ownership. These agencies may wish to estimate the impacts on trip generation of

changes in vehicle ownership.

Higher annual costs of ownership and operation of high-emitting vehicles also could

discourage their use in favor of low-emitting vehicles in multi-vehicle households,

thereby altering household interactions and travel decisionmaking. This could generate

more errand chaining, and/or use of non-motorized modes, thus affecting trip generation

for motorized trips and trip characteristics. It also could induce various other effects that

influence mode choice, destination, or trip timing. Formal procedures to evaluate these

impacts are not in common use; off-line techniques are the only realistic approach for

addressing them (see recommendation under emissions fees and vehicle ownership

above).

5. Emissions Fees and Trip Distribution

Emission fees are expected to have only trace effects on trip distribution. Although

formal analysis is therefore not needed, some investigation of effects should be

undertaken by agencies anticipating impacts on land use and vehicle ownership and by

agencies contemplating fees with a significant VMT component.

Reduced vehicle ownership could have an effect on trip distribution results in models

that employ vehicle ownership to estimate trip generation. If earlier steps in the analysis

indicate changes in automobile ownership, areas with such models are advised to repeat

the trip distribution step to account for ownership changes.

There are no known techniques, however, that permit direct assessment of trip

distribution effects in response to changes in vehicle “availability” (i.e., vehicle
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ownership). One possible technique is to use a composite impedance approach (as

defined in Section 3.3.5.1), stratified by vehicle availability. If the coefficients are arranged

so that households with fewer vehicles are more sensitive to trip cost (as a surrogate for

distance), then such households' trips would be more likely to be distributed to closer

destinations, yielding a revised pattern of trips by origin-destination.

Trip distribution also might be affected by the VMT component of an emissions fee if that

component is significant enough. It is difficult, however, to analyze the VMT-based

impact because the relationship between a periodictypically annualfee and daily travel

is uncertain.

In the absence of documented relationships, areas that believe the VMT component of

their emissions fee to be significant are advised to use the same methods as they would

for pump charges (Section 3.2.3.5), which are VMT-sensitive. Alternatively, such areas

might consider one of the off-line techniques described in Section 3.3.6.

6. Emissions Fees and Mode Choice

Emissions fees are not expected to produce a major impact on mode choice. If the

proposing agency believes the VMT component of a proposed fee will be a substantial

element in mode choice, however, the agency should undertake a mode choice analysis

similar to that specified for pump charges, incorporating the cost from the emissions fee

as a change in operating cost.

Caution should be taken in treating the emissions fee as a cost element if the fee is administered

infrequently (i.e., annually), since there is little information on travelers’ translation of

periodic fees into daily/per mile costs. If the performing agency chooses to evaluate a

mode choice effect in this instance, its assumptions regarding periodic fee assessment and

its effect on behavior should be clearly documented.

Emissions fees can affect mode choice indirectly through changes in vehicle ownership. If

prior analysis indicates an impact on vehicle ownership, agencies with a mode choice

model that includes vehicle ownership in its specification may revise their estimates of

mode distribution of person trips.

7. Emissions Fees and Time of Day

Emissions fees are not expected to have any measurable effect on the time of day of

travel. Therefore, no analysis is needed.

8. Emissions Fees and Traffic Assignment

In general, emissions fee measures are not expected to exert any special effect on traffic

assignment and, thus, no formal analysis is needed.

If travel changes are calculated in any of the preceding steps, for example, land use,

vehicle ownership, trip distribution, or mode choice, then agencies with assignment

procedures should use them to determine changes in vehicle trips and VMT. Areas
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without traffic assignment capability must rely on net changes in vehicle trips and VMT

to perform the subsequent emissions analysis.

9. Emissions Fees and Emissions Calculations

Given the anticipated impact of emissions fees on emissions, EPA requires proposing

agencies to (1) indicate how the vehicle fleet mix and VMT distribution will be changed

by the particular fee structure, and then (2) revise the appropriate parameters in the

emissions model (MOBILE or EMFAC; PART5 for PM areas) to reflect the impact on

average emissions factors (see Section 3.3.9 for information on procedures to effect this

change).

Emissions fees based on direct measurement of vehicle emissions output by inspection

may result in improved maintenance and reduced emissions rates. EPA’s Office of

Mobile Sources has developed a methodology for estimating emission reductions from

inspection/maintenance (I/M) programs. The methodology is based upon the observed

effectiveness of different I/M short tests for identifying high-emitting vehicles and the

observed emission reduction associated with commercial repairs resulting from I/M

programs.

Areas without emissions model capability, which rely strictly on emissions factor

estimates, must detail their assumptions and the procedures they use to modify existing

emissions rates to reflect the shift in vehicle fleet mix.

If the proposed emissions fee policy is expected to generate changes in travel, resulting in

a new travel assignment in Step 8 (or revised vehicle trips and VMT for areas without

models), the submitting agency should calculate the revised levels of VOC, NOx, CO, and

PM10 (where it is an issue) due to these changes in travel and in fleet mix/emissions

factors.
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3.2.5 Roadway Pricing

3.2.5.1 Anticipated Impacts

The impact of roadway pricing on

travel and emissions is complex.

Traveler responses to roadway pricing

are conditioned by roadway coverage

(i.e., the extent and number of

facilities that are priced), availability

of alternatives, and trip purpose.

Traveler responses include changes in

destination, route, mode, and, under

congestion pricing, time of day of

travel. Activity planning may be

effected to schedule trips more

efficiently to minimize costs.

The impact on emissions depends on

reduction in VMT and changes in flow

conditions (reduced congestion).

Location and land use can be affected

in the long term by the pricing

scheme.

Table 3.5 Impacts of Roadway Pricing

Analysis

Step

Impact Form Impact

Type

Impact Indicator

Land Use/

Activity

Allocation

Changes relative

attractiveness based

on pricing and

alternatives to driving

Secondary Population and/or

employment by zone

Vehicle

Ownership

Depends on type of

location shift

Trace Vehicles per household

per zone

Vehicle Mix None None Percent of vehicles by

VMT

Trip

Generation

May reduce vehicle

trips if location shift

or “area” fees

Primary if

area-wide—

otherwise,

trace

Number of person trips

to/from each zone

Trip

Distribution

Pricing affects

access (cost/

time) of particular

destinations

Primary O/D patterns (average

trip length)

Mode

Choice

Use alternatives to

avoid road price

Primary Person trips by

mode; % transit;

%walk/bike; no

vehicle trips; average

vehicle occupancy

Time of Day Congestion pricing

causes time shifts

Primary

(if

congestion

pricing)

Trips by time period

Traffic

Assignment

Compound:

reduces trips &

VMT; causes route

shifts

Primary Link volumes; VMT

Emissions Reduces vehicle

trips and VMT; also

congestion & speeds

Derivative

effect

Tons or kg. of VOC,

NOx, CO, PM

3.2.5.2 Basic Analysis

Submitting agencies should analyze the impacts of roadway pricing on mode choice, trip

distribution and route choice through traffic assignment. Agencies considering

congestion pricing forms of roadway pricing also should evaluate the anticipated impact

of the measure on time of day of travel. Emissions analysis should reflect changes in

vehicle trips, VMT, and speeds.

Roadway pricing is expected to have a secondary, rather than primary, impact on land

use; thus, formal analysis is not needed. However, submitting agencies should include a

statement that describes their assumptions and the measure’s anticipated impacts on

long-term land use.
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3.2.5.3 Guidelines for Analysis

1. Roadway Pricing and Land Use/Activity Allocations

In the long term, roadway pricing is likely to affect locational patterns, particularly if

roadway pricing is applied only to selected facilities. However, if a large number of

facilities are priced then it becomes very difficult to make longer trips without paying. In

that case there is a strong incentive to avoid vehicle trips, particularly longer ones. In

turn, businesses and households have an incentive to shift to locations that either (1) are

not dependent on priced roadways or (2) will benefit from higher levels of service on the

priced roads. “Area-wide” pricing reduces or even eliminates incentives to re-locate. The

degree and pattern of relocation depend on roadway pricing rates and scope of the

coverage. Substantial improvement of alternatives and supportive land use planning

together with roadway pricing, would positively reinforce these trends.

Although a formal analysis of roadway pricing effects on land use is not needed,

submitting agencies should include either (1) an assessment of the implications of the

proposed policy on travel and emissions or (2) a statement that describes the area's

assumptions and interpretation of potential impacts. This statement should reflect the

appraisal of area decisionmakers as to the impact on long-term land use and

transportation plans and programs.

Performing agencies with access to land use allocation models (Section 3.3.1.2.) that

incorporate transportation cost through a measure like composite impedance (see Section

3.3.5.1) may use such models to investigate roadway pricing impacts on long-term

development trends. Results should be treated with caution, however, in view of the

analytical limitations of these procedures.

Submitting agencies (particularly those that do not use models for land use analysis)

might consider formulating alternative growth scenarios (Section 3.3.1.1) to account for the

effects on regional travel and emissions from roadway-pricing induced development

shifts. These scenarios also should reflect any complementary actions with regard to land

use alternatives or urban design.

2. Roadway Pricing and Vehicle Ownership

No direct impact is anticipated on vehicle ownership as a result of roadway pricing and,

thus, no formal analysis is needed. If land use and household location shifts are projected

in Step 1, however, these shifts may translate to revised vehicle ownership levels;

agencies should account for these effects.

3. Roadway Pricing and Vehicle Mix

Roadway pricing is expected to have only limited impacts on the types of vehicles used

by households and, therefore, regional vehicle fleet distribution; no formal analysis is

suggested.
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4. Roadway Pricing and Trip Generation

Roadway pricing can have a measurable direct impact on household person trip

generation. This is particularly true when an area-wide pricing policy is pursued. EPA

suggests, when an area-wide pricing policy is pursued, that analysis of trip generation

should be conducted. Areas with trip-generation models that incorporate a measure of

generalized transportation cost (i.e., composite impedance) are encouraged to use those

methods to assess the impact on trip generation (see Section 3.3.5.1.). Use of these

techniques is likely to show significant impacts on total trip generation, and it would be

beneficial for areas to develop this capability if they are considering area-wide pricing.

Shifts in land use and vehicle ownership should be carried through to the trip generation

stage when area-pricing is being modeled.

Roadway pricing that focuses on a single corridor or facility is less likely to have major

impacts on trip generation. In these cases, most of the impact will be from mode and

route shifting, changes in destinations, and time-of-day (if congestion pricing is used). At

a minimum, areas modeling single corridor pricing effects should document assumptions

used for any anticipated changes in trip generation.

5. Roadway Pricing and Trip Distribution

Roadway pricing is likely to have a primary impact on trip distribution; thus, a formal

analysis of anticipated effects is suggested. In the short term, roadway pricing is likely to

affect only non-work trips through shifts in destinations selected in response to pricing

patterns. In the long term, work as well as non-work trips could be affected as workers

change their work and/or home locations to avoid the additional road price (These latter

effects may be more appropriately modeled through the land use allocation step.).

Areas with trip distribution models that can account for the effect of cost through a

measure such as composite impedance (Section 3.3.5.1) are encouraged to use those models

to assess the impact of roadway pricing on the origin-destination patterns of non-work

trips (both short-run and long-run) and work trips (long-run). Agencies should perform

independent analyses for each trip purpose by income strata or use some other approach

to account for income differences (Section 3.3.5.2).

Areas with trip distribution models that do not incorporate cost directly in their measure

of separation may consider using a travel time approximation for the increased link cost

(see Section 3.3.5.3).

Areas without trip distribution models will have difficulty accounting for roadway

pricing impacts; these agencies may not be able to perform a realistic analysis given the

complexity of roadway pricing programs. Pricing applications considered by these

agencies would likely be limited to situations where chances for toll-avoidance are

minimal (e.g., pricing of a strategic bridge or tunnel).

In contrast to some of the other market-based measures considered, road pricing can have

a fairly important effect on commercial and through traffic. For commercial users, the
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existence of a road user charge may actually prove to be an advantage, given anticipated

impacts on road congestion on priced roadways and the importance of travel time to

commercial users. The ultimate net benefits depend on the nature of the goods or

services being transported.

When travel time is of higher value than the anticipated increase in cost, local commercial

traffic (as well as personal traffic) will be drawn to the priced facility if pricing results in

higher levels of service. If pricing-induced increases in travel cost are considered

important, however, trips will shift to unpriced facilities; this, in turn, may generate local

traffic congestion and emissions problems.

Like local commercial traffic, through traffic will also weigh the toll costs against the

value of time saved on priced facilities. Price sensitive through-travelers will shift to nonpriced alternative routes or be inclined to pick an alternative route around the given

region unless they simply know of no alternatives. Traffic response to trade-offs between

time and money are difficult to assess with currently available methods, although new

methods are under development.6 Agencies wishing to assess these impacts are

encouraged to do so within the limits of their analytic tools and data. All assumptions

should be clearly documented.

6. Roadway Pricing and Mode Choice

Roadway pricing is likely to have a primary impact on mode choice. Agencies should

submit a formal assessment of the impact of roadway pricing on person trips by mode,

change in vehicle trips, and average vehicle occupancy and change in transit trips.

Areas with mode choice models in their planning systems are encouraged to use them for

this analysis. However, it should be noted that the analysis of roadway pricing presents

several important modeling issues and special requirements:

• Network Representation of Tolls: Modeling the pricing of specific

roadways requires a concerted effort to represent the price in the highway

network. This is equivalent to properly coding a toll value on all affected

individual links. When coding is completed, the path-building and

skimming software must be alerted to the presence of the toll and must be

capable of creating a matrix that reflects the total toll cost of traveling on all

 

6

 Three recent studies have directly addressed air quality and travel impacts of freight

transportation.

 Cambridge Systematics, Sierra Research, and Jack Faucett Associates, “Air Quality Issues in

Intercity Freight,” prepared for the FRA, the FHWA, and U.S. EPA, draft final report, December

1996

 Cambridge Systematics et al., “A Guidebook for Forecasting Freight Transportation

Characteristics Demand,” NCHRP Report 388, National Academy Press, Washington, DC, 1997.

Cambridge Systematics, COMSIS Corporation, and Alan Horowitz, “Freight Planning Manual,”

developed for the Federal Highway Administration.
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origin-destination pairs. It is somewhat simpler to model area-wide pricing

since the matrix manipulation program in most modeling software packages

can build a cost matrix representing the additional cost of traveling into the

affected area from any origin zone.

• Mode Cost Sensitivity to Tolls: Few mode choice models are set up to

accept toll costs explicitly; most deal only with automobile operating cost and

possibly parking cost. This leaves open the issue of what coefficient value to

assign to tolls. If the toll is paid physically at the point of charge, it may be

reasonable to treat it as an out-of-pocket cost comparable to parking and

apply a parking cost coefficient (see Section 3.3.6.1). If the toll cost is

determined electronically, with billing issued periodically, then it may be

more appropriate to treat it as an operating cost. The agency should state the

assumptions underlying its choice of coefficient. The agency also may wish

to consider stated preference methods to ascertain the relevant sensitivity (see

Section 3.3.6.8).

• Queuing and Delay: If tolls are collected through automatic identification

methods (i.e., no toll booths), delays related to toll payment would be

minimal. When tolls are collected manually, however, toll booth operations

may well produce queue delays that affect (1) travel time on the facility and

(2) micro-level traffic effects that contribute to emissions. Agencies may

consider using traffic microsimulation methods to assess these effects (see

Section 3.3.8.3).

• Type of Model: Multinomial logit, especially nested logit models, provide

the most realistic estimates of mode choice impacts, particularly when toll

treatment differs across modes (see Section 3.3.6.5).

• Non-Work Mode Choice Models: There should be a mode choice model for

each separate trip purpose (see Section 3.3.6.4).

• Income Stratification: The mode choice analysis should be performed

separately by income strata, or through an equivalent method that accounts

for income differences (see Section 3.3.6.3).

• Adjustment for Inflation: It is important that the dollar value of the toll

price be adjusted for compatibility with the base year for which the model

coefficient was developed. This generally means deflating the tested price by

a time-inflation factor that makes the costs compatible with the coefficient in

real terms (see Section 3.3.6.2).

• HOV Special Rates: If the policy incorporates toll exemptions or discounts

for HOVs, few mode choice models will provide sufficient analytical support.

Off-line methods, such as the TDM Evaluation Model, are suggested for

assessing this effect (see Section 3.3.6.9).

Areas without mode choice models will have a difficult time adequately assessing a measure

as complex as roadway pricing. Off-line methods, which include quick-response

techniques and sketch-planning/elasticity approaches (see Section 3.3.6.9), may be used
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with caution; agencies using such methods must document their assumptions regarding

potential limitations of the analysis.

7. Roadway Pricing and Time of Day

If roadway pricing is applied evenly throughout the day, then only limited impacts on

travel time of day would be expected. However, if the implementation involves

peak/off-peak congestion pricing differentials, time of day shifts are anticipated; and

these should be analyzed.

Current time of day analysis methods that employ “peak factors” are probably not

sufficient to capture time of day effects. Modified procedures, such as those described in

Section 3.3.7, may be considered. Analysts should estimate the specific sensitivity of

travelers' start times to price differentials at different times during the day. Non-work

trips are likely to exhibit greater sensitivity than work trips because of their greater

flexibility (and perhaps cost sensitivity), although some workers may be able to shift

hours outside the peak. The sensitivity of travelers' trip times to price differentials is

uncertain in current practice, but it may be possible to make an assessment that can be

refined subsequently using observed data. Such initial estimates may be based on

empirical sources or on stated preference surveys (see Section 3.3.7.4).

All applications of roadway pricing that involve time of day price differentials should

make an estimate of the time of day shift in travel, using approximation methods such as

those described above for broad applications of the policy. However, agencies introducing

more complex applications are expected to use more sophisticated analysis methods.

8. Roadway Pricing and Traffic Assignment

Roadway pricing has a special effect on traffic assignment because pricing of specific links

in the highway network is likely to induce some shifting to alternative routes, particularly

by cost sensitive travelers with some flexibility (lower income and non-work travelers).

This would probably constitute the policy’s primary effect and should be evaluated.

Evaluating agencies should be careful to ensure that all alternative paths to the tolled

network are represented in order to address the diversion issue.

Areas with traffic assignment models may perform an analysis of roadway pricing

impacts on revised vehicle trips, VMT, and speeds using one of the following methods:

• Toll Diversion Model: Some areas have developed models that specifically

offer travelers a choice of a toll or free path during assignment. This

approach is described in Section 3.3.8.5.

• Travel Time Equivalent: A less accurate, but acceptable, approach is to

translate the impact of the toll into a travel time equivalent (also described in

Section 3.3.8.5). This approach, however, disregards the likelihood that

travelers with a higher value of time will opt to use a tolled facility even at

higher cost simply to take advantage of the improved travel time. That is,

this method assumes away traveler willingness to pay for time saved.
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Areas without traffic assignment capabilities will probably not be able to evaluate

roadway pricing options unless alternative route choice options are greatly limited (e.g.,

isolated bridge or tunnel).

9. Roadway Pricing and Emissions

All analyses of roadway pricing should conclude with an estimate of the net changes in

the major pollutants for which credit is being claimed.

If roadway pricing has an effect on emissions rates, it would be through changes in

congestion levels, rather than through impacts on vehicle mix. That is, roadway pricing

implemented as congestion pricing can affect emissions rates if it significantly affects level

of service and travel speeds.

Areas able to perform traffic assignment are advised to use an emissions factor model,

such as MOBILE or EMFAC (PART5 for particulate emissions), to furnish emissions

estimates, using the results from the traffic assignment process (note that MOBILE

produces emission factor estimates rather than estimates of emissions amounts).

Areas without emissions models may apply emissions factor estimating methods to the

net changes in travel projected from traffic assignment.

Areas without traffic assignment capability are not expected to be able to perform this

assessment, except in limited circumstances as earlier described.
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n 3.3 Review of Methods and Procedures

Clearly, the most reliable basis for projecting the impacts of changes in pricing

levelsespecially major changeswould be to statistically track and measure consumer

response to such actions under (and in relation to) actual conditions. Before-and-after

studies, possibly involving consumer panels, can provide great insight into the expected

and unexpected nature of response. Unfortunately, there are relatively few studies (in

the United States) in which such measurements have been made. It will be important to

institute such monitoring systems in coming years and as new policies are tested to

obtain a fuller understanding of consumer response.

In the interim, we must rely on existing planning and emissions analytic procedures and

develop new ones. As indicated in Chapter 2, current capabilities vary widely across the

U.S. Virtually all major urbanized areas and many smaller areas use some variation of the

“four-step” planning process for all transportation-related analyses. This process portrays

transportation decision-making as a series of sequential steps. The four core steps are trip

generation, trip distribution, modal choice, and traffic assignment. These systems work from

trip tables that describe travel movements between origins and destinations defined by traffic

analysis zones. These trips are “assigned” to a coded transportation network to simulate

actual traffic flows and conditions on actual facilities. Emissions analysis is then performed as

a separate step, utilizing basic travel outputs (vehicle trips, VMT) from the four-step process.

In practice, there are many variations within each analytical stratum with respect to:

• Level of detail used to characterize travel by purpose, time of day, income

and other socioeconomic differences;

• Detail in the travel networks; and

• Sophistication of individual models.

Smaller urbanized areas, where transit service is minimal or non-existent, may not have a

formal mode choice capability (these systems are characterized as “three-step” models).

In some areas, capabilities are even more limited. With this wide range of capabilities

among areas that might consider market-based measures, it is difficult to prescribe use of

particular methods or “standard” techniques. Rather, the objective of this guidance is to

encourage areas to perform the best possible analysis using their existing tools and data,

and to recommend special procedures that may help them to improve their analysis.

Those with more sophisticated analytical capabilities will be rewarded with greater

opportunity to receive emission credits.

It is commonly acknowledged that current-generation travel forecasting tools are not

sensitive to pricing; rather, price sensitivity has had to be back-fit into their structure.

These forecasting tools were developed primarily to guide the location and design of

highway facilities; cost was not included in their structure because of an underlying
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assumption that the price of transportation would change at the same rate as inflation,

thus would not be an important future criterion.7 The gasoline crises of the 1970s and

1980s, together with federal requirements related to transit system funding, caused many

areas to initiate upgrades to their models to formally include pricing.

Pricing sensitivity, however, is limited to mode choice in most models. It is reasonable to

believe that the effects of many pricing measures will extend beyond mode choice to

include destination choice, route choice, and even time of day; some of these may have

even more significant impacts than mode choice. It also is reasonable to expect that

longer-term effects on household or business locational decisions can affect land use,

vehicle ownership, and trip generation.

Current models may be unable to deal with these broader impacts. In many areas, model

capabilities are in a state of flux as they (1) undergo change and enhancement in response

to the requirements of the CAAA and ISTEA and (2) respond to new data available from

the 1990 Census and numerous urban area regional travel surveys.

Until agencies have greater experience with actual implementation of major pricing

applications, there will be limited empirical evidence upon which to base or validate

projected impacts from these measures. Hence, it is necessary to extract the best possible

analysis from existing travel forecasting tools. There are numerous ways that these tools

can be adapted to improve their sensitivity and accuracy in the evaluation of pricing

measures. Adaptive procedures include:

• Accounting for cost throughout the travel demand choice hierarchy through

a generalized cost measure such as composite impedance or accessibility;

• Sharpening estimates of price coefficients through special surveys or

transferring coefficients or models from other sites;

• Improving sensitivity to differences in response by income level through

income stratification or similar techniques;

• Gaining initial insight into relationships between special pricing instruments

and travel behavior response through stated preference surveys;

• Using sample enumeration methods to achieve greater specificity for particular

measures as they are experienced by households and possibly greater

accuracy in estimating response; and

• Use of sketch planning or off-line methods to support formal methods where

tools or capabilities do not currently exist.

This section provides a brief review of the steps in travel forecasting that may be involved

in assessing pricing actions and evaluating their impacts on travel and emissions. The

intent is not to provide a full review of the transportation planning process, but rather to

 

7 Transportation cost also was a difficult element to include in model structures because of the

models’ heavy reliance on travel time and the close correlation between [per-mile] cost and travel

time for most trips in most U.S. urban areas.
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draw attention to those aspects of the process that are important in relation to pricing.

This section describes modeling concepts that offer improved accuracy for analyzing

pricing and presents references for additional information on their development and use.

This section is designed to address the range of needs and capabilities that may exist

among areas desiring to perform pricing analysis. Larger urbanized areas may include

many of these concepts in their modeling systems, while other areas seek to develop such

capabilities as part of a longer-term model enhancement process. Other areas with more

limited capabilities may need help identifying more basic techniques, as well as help

prioritizing future model development activities.

The reader is directed to two primary resources that serve as important guides for

agencies involved in performing transportation air quality analysis:

1. Manual of Regional Transportation Modeling Practice for Air Quality

Analysis8 (hereafter referred to as the “NARC Manual”), developed in 1993

for the National Association of Regional Councils, is a major resource on

current model capabilities and deficiencies in relation to air quality

applications. It provides a thorough review of the requirements of the Clean

Air Act and their impact on travel forecasting tools, the nature and capability

of current tools, and suggestions toward most effective methods. Numerous

references to example sites and literature sources are provided.

2. Travel Model Improvement Program (TMIP)9, which is being jointly

sponsored by the U.S. Department of Transportation, the U.S. Department of

Energy, and the U.S. Environmental Protection Agency to foster both near

and long-term improvements in travel forecasting tools. An October 1994

report on Short Term Model Improvements10 provides a valuable update on

model enhancements that are underway or in place for addressing air quality

and other closely related modeling issues.

Both of these sources were used extensively in preparing the synthesis of analytic

procedures in this section.

In addition to these two primary references, the reader should be aware of several key

research projects now underway or recently completed which are charged with

developing major enhancements and new approaches to the analysis of transportationrelated emissions. These include:

 

8

 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, 1993.

9 Cambridge Systematics, Inc., “Travel Model Improvement Program: Short Term Model

Improvements,” prepared for the Federal Highway Administration, U.S. Department of

Transportation, 1994.

10 B. Spear, “New Approaches to Travel Forecasting Models: A Synthesis of Four Research

Proposals,” Travel Model Improvement Program, Volpe National Research Center for the U.S.

Department of Transportation and the U.S. Environmental Protection Agency, January 1994.
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• National Cooperative Highway Research Program (NCHRP) Project No. 8-

33: Quantifying Air Quality and Other Benefits and Costs of

Transportation Control Measures: This study is undertaking a

comprehensive review of the analytic techniques used for evaluating TCMs.

It is also charged with designing a new framework that addresses the overall

linkages between transportation, emissions, and atmospheric models.

Computational limitations in each of the steps are to be addressed

individually, as are those features that affect accuracy in the step-to-step

linkages.

Phase 1 of NCHRP Project Number 8-33 is complete, and copies of Phase 1

reports are available on loan from the NCHRP. Research papers based on

Phase 1 work are expected to be available for review in Fall 1997. Working

titles for these papers are “Relationships between Implemented

Transportation Control Measures and Measured Pollutant Levels,” and

“Development of an Improved Framework for the Analysis of Air Quality

and Other Benefits and Costs of Transportation Control Measures.”

• NCHRP Project No. 25-11: Modal Emissions Factor Development:

Undertaken by researchers at the University of California at Riverside, this

study is charged with developing a new emissions-factor model that

addresses the deficiencies of the current-generation MOBILE and EMFAC

models with regard to sensitivity to vehicle and transportation inputs.

Specifically, current models are insensitive to variations that occur in vehicle

emissions under normal driving conditions where acceleration, idling, steady

state, etc., produce very different emissions rates. Current models smooth

over these variations by using emissions factors based on an average speed.

This project is obtaining substantial new vehicle activity and emissions data.

This data will be used to develop a modal emissions model that (1) is

compatible with the types of traffic simulation models used to design and

evaluate transportation operational improvements, and (2) accurately reflects

the impacts of speed, engine load and start conditions on emission.

Research has been completed for Phase 1 of NCHRP Project Number 25-11.

This phase called for 1) review of the existing literature on factors in the

vehicle operating environment; 2) definition of the domain and distribution

of the modal parameters of each vehicle operating mode; 3) evaluation of

extent to which existing models meet analytical needs; 4) design of testing

protocol to measure vehicle modal emissions; 5) preliminary testing of a

sample of vehicles currently in use; and 6) development of a working model

for analysis.

• NCHRP Project No. 25-7: Improving Transportation Data for Mobile

Source Emissions Estimate: This study by the University of Tennessee and

SAIC, et al., is specifically directed at appraising the data requirements

needed for transportation/air quality modeling. Key elements of the research

include: 1) identifying transportation variables that are available or needed

for preparing air quality forecasts; 2) identifying techniques for quantifying

these variables; and 3) describing the inter-relationships between

transportation and emissions rates.
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Project number 25-7 has been completed, and the final report will be

available on a loan basis pending a publication decision by the NCHRP.

• Air Quality Issues in Intercity Freight: Sponsored by the U. S. DOT and

EPA, this project has developed methods for evaluating the emissions

impacts of intercity freight operations, especially truck and intermodal rail.

Extensive work has been done to develop practical planning procedures to

(1) realistically assess intercity freight travel characteristics and reactions to

policy actions (TCMs), and (2) refine emissions factor estimates for these

modes.

Finally, it also should be noted that this guidance document is followed by two technical

appendices that may be of great relevance to reviewers:

• Appendix A: A Case Study Adaptation of a Regional Model to Increased

Price Sensitivity. Appendix A provides an illustrative, step-by-step example

of how a typical four-step modeling process was strategically upgraded and

enhanced to improve its ability to analyze pricing measures, and then

applied to test a range of market-based measures. The base model that was

transformed is the Greater Metropolitan Washington Council of

Governments (MWCOG) model. The improvements are hypothetical;

because the adaptation was done as a “demonstration,” typical steps to

thoroughly validate the revised model were not performed, and the revised

model is not in use by MWCOG. However, the “case study” model is useful

in demonstrating how enhancements such as those suggested in this chapter

could be made.

• Appendix B: An Example of the Application of the STEP Process to the

Analysis of Pricing Strategies. Appendix B illustrates how sample

enumeration techniques can be used to link travel survey data and

conventional modeling methods, which would provide additional flexibility

and even accuracy to analysis of pricing measures’ impacts on air quality.

These methods have been used to analyze pricing TCMs in Seattle,

Washington and several California cities.

Neither of the approaches set forth in these appendices represent an analytic procedure

prescribed by EPA. They are provided for illustration only, to show analysts how specific

adjustments and innovative applications are being made to address the complex range of

impacts that may derive from market-based measures. The procedures described also are

useful in demonstrating two very different ways of approaching analysis of market-based

measures — through concerted direct modifications to the existing four-step process, and

through a “parallel” approach that moves outside the conventional process for key

analytic assessments. Both approaches have distinct advantages and disadvantages,

which must be evaluated by the performing agency.
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3.3.1 Land Use/Activity Allocation Methods

Most regional planning processes start with a procedure that allocates household and

employment activity to specific geographic locations across the region. The pattern and

level of these allocations then becomes the basis for all subsequent estimates of trip levels

and flow patterns.

A key question for analysis of pricing measures is whether these locational decisions

might be or should be sensitive to levels and variations of transportation pricing effects.

This question has two levels: (1) “real world” sensitivity of household and business

locational decisions to pricing measures and (2) analytical sensitivity of the transportation

planning process to pricing measures.

This is perhaps one of the least precise steps in the current transportation analysis

process, though it represents what can be a very significant factor in long-term travel

trends and effectiveness of air quality and other management strategies. Currently, most

land use allocation decisions are made through a process of negotiation, with

considerable political involvement and concern. Market realism may or may not be

prominent in this process.

There are two basic ways in which areas attempt to account more explicitly for the effects

of market forces or transportation policy on growth allocations: scenario approaches and

land use allocation models.

3.3.1.1 Scenario Approaches

Generally, population and employment allocations based upon hard data (including

population and employment trends, land availability and price, land use occupancy and

rent, zoning and land use decisions, economic development plans and market

assessments, measures of current and anticipated transportation accessibility, etc.) are

more realistic and sensitive to market forces than those based purely on “fair share”

allocations or political aspirations. Some areas address their uncertainty in both level and

distribution of growth by formulating one or more scenarios that are then carried forward

into the analysis. These scenarios can be used to reflect major differences in expectations

linked to pricing, urban design and zoning/growth management changes, or other

policies. Analysts should take care that eventual emissions results that stem from these

scenarios reflect realistic and implementable land use assumptions.11

 

11 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” 1993, Sec. 3.3.2, pp. 3-13 to 3-18.
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3.3.1.2 Land Use Allocation Models

Formal mathematical models for land use allocation exist and are in use in a small

number of regions. Options include DRAM/EMPAL12 (most commonly used land use

model in U.S.), POLIS13 (currently used in the San Francisco Bay Area), the HerbertStevens Model14 (used in the Penn-Jersey Study), MEPLAN15, METROSIM16, TRANUS17

,

and TOPAZ18. A recent draft report prepared for EPA’s Office of Mobile Sources provides

a comprehensive review of the various land use modeling tools currently available.19

The models mentioned above generally use zone-level time series data on population,

employment, land availability and accessibility to allocate population and employment

forecasts to subareas. Use of these models has been limited, however, both by the effort

required to run them and by questions about their predictive accuracy.

Factors that appear to limit the accuracy of current generation land use models include: a

tendency to extrapolate past trends, rather than incorporate behavioral decision-making;

a tendency to base household location decisions substantially on the work trip; limited

accounting for market responses (real estate, speculative investments, ethnicity) and timelags; and weak representation of transportation accessibility and cost.

 

12 S.H. Putman, “DRAM/EMPAL - ITLUP: Integrated Transportation Land-Use Activity

Allocation Models: General Description,” S.H. Putman Associates, Philadelphia, Pennsylvania,

1991.

13 P. Prastacos, “Urban Development Models for the San Francisco Region: From PLUM to

POLIS,” Transportation Research Record 1046, 1985.

14 J.P. Herbert and B.H. Stevens, “A Model for the Distribution of Residential Activity in Urban

Areas,” Journal of Regional Science 2(2), 1960, pp. 21-36.

15 MEPLAN was developed by staff at the Center for Land Use and Built Form Studies at

Cambridge University, at the firm of Applied Research of Cambridge, and at the firm of Marcial

Echenique and Partners. MEPLAN allocates exogenous forecasts of basic employment to analysis

zones and then allocates non-basic employment and population to zones based on costs of travel,

floor space, and the availability of other goods and services. Demand for accessibility generates

rent levels, and land uses are segregated from one another based on capacity to pay rent.

16 METROSIM models regional impacts on land use patterns and transportation systems that may

result from project development, and was developed by Alex Anas of the University of Buffalo.

The model can analyze impacts for up to 3,500 analysis zones. For each zone, METROSIM models

employment, real estate demand, household demographic characteristics, and travel patterns.

17 TRANUS shares many characteristics with MEPLAN. Development of the TRANUS model is

outlined in T. de la Berra, “Integrated Land Use and Transport Modeling,” Cambridge

University Press, 1989.

18 J.F. Brotchie et al., “Alternative Approaches to Land Use Modeling,” Commonwealth Scientific

and Industrial Research, United Kingdom, 1981.

19 Systems Applications International, Inc., “Evaluation of Modeling Tools for Assessing Land Use

Policies and Strategies,” draft report prepared for Office of Mobile Sources, April 1997.
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These models can aid land use impact evaluations, provided they are applied with careful

judgment. Similar to typical trip distribution models, land use models generally employ

travel time in their allocation process. One way to enhance their utility for pricing

analysis is to introduce pricing directly as a variable through a measure such as composite

impedance (see Section 3.3.5.1). The Bay Area model system utilizes such a formulation,

and would be worth further study by areas considering such enhancements and/or

where the rate or pattern of growth is a major issue.

Readers are encouraged to consult the NARC Manual (Section 3.3.2) and the TMIP's

review of Short-term Travel Model Improvements (Section 3.0) for more information on

land use models and potential enhancements.20

3.3.2 Vehicle Ownership

Vehicle ownership is often used as an input for U.S. urban area model systems. Agencies

in some areas use vehicle ownership rates (or a measure of vehicle availability) as a

measure of households’ propensity to travel in trip generation and other modeling steps.

Very few areas explicitly estimate a policy-sensitive model of vehicle ownership rates as

part of their overall transportation modeling system. Most planning professionals believe

that explicit representation of vehicle ownership results in more accurate and realistic

transportation analysis. Evidence suggests that vehicle ownership influences person trip

generation in ways that extend beyond the effect of income. Vehicle availability may

have a key effect on discretionary trips and on the level of errand chaining. Households

with fewer vehicles per licensed driver are more pressed to work out vehicle use

priorities, which affects vehicle sharing decisions and use of non-motorized modes.

Where residential densities and levels of transit service are high enough to affect vehicle

ownership levels, explicit prediction of vehicle ownership would clearly be expected to

increase forecasting accuracy.

Model systems that rely exclusively on household income and not vehicle ownership can

still offer good accuracy, depending on how well income is specified as an explanatory

variable. Generally, this means that other modeling steps must exhibit strong

relationships between travel and income.

 

20 Throughout the remainder of this section, the Greig Harvey et al., publication entitled “A

Manual of Regional Transportation Modeling Practice for Air Quality Analysis,” National

Association of Regional Councils, December 1993, will be referred to as “the NARC Manual.”

 The report by Cambridge Systematics, Inc., entitled “Travel Model Improvement Program: Short

Term Model Improvements,” (prepared for the Federal Highway Administration, U.S. Department of

Transportation, 1994), will be referred to throughout the remainder of this report as “the Short-term

Model Improvements Report.”
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There are several types of vehicle ownership predictive models. These include:

• Cross-Classification Models: tables of factors derived from Census or travel

survey data (generally have no explicit linkage to transportation conditions).

• Nomographs or Curves: empirical curves that reflect the fraction of

households owning a particular number of vehicles, based on household size

and income level.

• Mathematical Models: actual equations that calculate the number of

vehicles owned, based on the value of specific equation variables.

Generally, the form of model is less important than the sensitivity with which it reflects

and responds to key socio-demographic and policy variables, especially pricing.

Aggregation of key variables may affect accuracy levels. For example, some models

produce an estimate of average vehicles per household rather than preserve the

distribution of number of households by number of vehicles. The reader is encouraged to

consult the NARC Manual21 for a more detailed discussion of the attributes of vehicle

ownership models.

In order to analyze the effect of pricing measures such as registration fees, these models

must be somewhat sensitive to the price of purchasing a vehicle and the annual cost of

owning one. In the context of travel modeling, increases in ownership cost should be

reflected in decreased numbers of multiple-vehicle households and increased numbers of

one-vehicle households (possibly even an increase in zero-vehicle households). The

inclusion of ownership cost is not a common feature in current vehicle ownership models,

presenting an area for research, along with composition and use of vehicles (discussed

below under vehicle mix). At least one vehicle ownership model has been documented

that is sensitive to user cost.22

3.3.3 Vehicle Mix

Vehicle mix, which is a special area of concern in modeling pricing impacts on vehicle

ownership, is not accounted for in conventional vehicle ownership analyses. Vehicle

ownership models account exclusively for the number of vehicles owned by a household,

and do not deal in any way with the characteristics or use patterns of those vehicles. Yet,

vehicle mix is an important factor for policies such as emissions-based registration fees or

even fuel prices, where the respective economic forces may be more likely to influence the

type of vehicle owned or its relative use, rather than the number of vehicles owned.

 

21 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993

22 K. Train, Qualitative Choice Analysis, Theory, Econometrics, and an Application to Automobile

Demand, The MIT Press, Cambridge, Massachusetts, 1986.
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The accuracy of emissions analyses based on an emissions factor model depends on the

distribution of the regional vehicle fleet and its average emissions rates, which derive

from vehicle distribution. Shifts in the age or type of vehicles owned would obviously

affect this relationship, as would different patterns of use, since the distribution is

weighted by VMT for analysis.

Typically, vehicle distribution is not directly considered by conventional travel analysis

tools. Hedonic models, scrappage studies, empirical studies, special studies, and sample

enumeration offer approaches to considering fleet vehicle mixes. It should be noted that

there may be overlap between these categories of approaches. For example, empirical

studies may include some examples of special studies. The bullets below provide brief

descriptions of these analytical tools and some illustrative examples

• Hedonic Models—These models analyze the relationship between consumer

demand for characteristics of goods rather than for goods themselves. These

models can be used, for example, to project price-induced shifts in

ownership of vehicles with different characteristics. Hedonic models are

complex and may only be an option where staff have a strong economics

background. Works by Train and by Golub and Kitamura offer two

examples of hedonic vehicle choice models.23, 24

• Sample Enumeration—Sample enumeration involves, in the vehicle mix

context, taking a sample of households within a region and extrapolating to

the regional population base from the sample’s vehicle ownership and/or

usage responses to pricing measures. Survey samples have the advantage

that they can be re-surveyed in follow-up analyses to study actual responses

to changes in price signals (see Section 3.3.6.7 below for more information).

• Scrappage Studies—Scrappage studies have been performed in conjunction

with programs to buy back or otherwise accelerate the retirement of older,

high-emitting vehicles from the regional fleet. These studies offer

estimating methods that may be of value to many areas.2526

• Empirical Studies—Periods of major change in fuel prices relative to income

during the 1970s and early 1980s, and major changes in the price of vehicles

and their characteristics relative to real incomes, have produced trends in

ownership that are reflected in various research studies. These studies could

 

23 K. Train, Qualitative Choice Analysis, Theory, Econometrics, and an Application to Automobile

Demand, The MIT Press, Cambridge, Massachusetts, 1986.

24 T. Golob and R. Kitamura, for the California Energy Commission. It should be noted that the

analysis conducted by Golob and Kitamura is not publicly available as of this writing.

25 U.S. Environmental Protection Agency, “Accelerated Retirement of Vehicles,” in Transportation

Control Measure Information Documents, March 1992.

26 Beth Deysher and Don Pickrell, Emissions Reductions from Vehicle Retirement Programs, U.S.

Department of Transportation, John A. Volpe National Transportation Systems Center, 1996.
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be used to develop estimates or assumptions regarding shifts in ownership

or use patterns that might occur in response to price signals.27, 28

• Special Studies—Locally-performed studies involving stated preference

techniques could produce estimates of consumer response to particular

types of vehicle-based pricing policies (see Section 3.3.6.8 below for more

information). Recent work sponsored by the California Energy Commission

has used such methods to ascertain the demand for electric vehicles. The

study used travel data from panels of households to establish current travel

and vehicle use patterns, and investigated how these patterns might change

under different pricing and vehicle attribute conditions.29

3.3.4 Trip Generation

This step of the planning process estimates the number of daily trips that are produced by

households [productions] or that originate in a zone [attractions]. These trips usually are

estimated separately by trip purpose. The simplest classification is home-based work,

home-based other, and non-home based trips; more complex systems split home-based

non-work trips into shop, school, and “other” and disaggregate non-home-based trips

into work-related and other.30

Trip productions and attractions are estimated through separate procedures. These may

be either cross-classification relationships, matching a particular trip “rate” with a

particular combination of determining characteristics, or regression models, which use a

functional relationship expressed as an equation. The cross-classification models are the

most common approach, and can be reasonably accurate depending on the variables used

for stratification, the degree of stratification, and the reliability of the trip rates

themselves. Regression models are generally regarded as less accurate (unless they are of

a fairly sophisticated non-linear form).

Significant references exist on trip generation practices. The reader is advised to consult

Section 3.3.5 of the NARC Manual or the TMIP Short-term Travel Model Improvement

report for more information and references on the general subject.31

 

27 David L. Greene, “Vehicle Use and Fuel Economy: How Big is the Rebound Effect?,” The Energy

Journal 13, No. 1, 1992, pp. 117-143.

28 Don Pickrell, “Automobile and Gasoline Demand Revisited,” Volpe National Transportation

Systems Center, U. S. Department of Transportation, 1992.

29 C. Kavalec, “CALCARS: The California Conventional and Alternative Fuel Response

Simulator,” California Energy Commission, April 1996.

30 Some models, such as those of the Maryland National Capital Parks & Planning Commission,

have evaluated trips generated by time of day and purpose, (Montgomery County, Maryland)

and applied distribution, mode choice and parking factors.

31 Cambridge Systematics, Inc., “Travel Model Improvement Program: Short Term Model

Improvements,” prepared for the Federal Highway Administration, U.S. Department of

Transportation, 1994.

Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993
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There are a number of issues related to trip generation methods that are important when

considering the suitability of models to evaluate pricing actions; these include:

• Person trips versus vehicle trips;

• Inclusion of transportation and cost variables;

• Disaggregating household by vehicles (income) and size and workers; and

• Trip chaining and other trip adjustments.

Procedures exist within current practice or are being looked at in research efforts that

offer to enhance trip generation capabilities in dealing with pricing.

3.3.4.1 Person Trips versus Vehicle Trips

Most trip generation models furnish estimates of daily person trips; however, these trips

are commonly limited to vehicle trips and exclude non-vehicle trips such as walking and

bicycle trips.32 Smaller planning agencies may even restrict trip production estimates to

person trips made in private vehicles (eliminating transit entirely).

Exclusion of non-motorized or non-automobile trips from trip generation analysis

generally stems from capabilities elsewhere in the model system, especially mode choice

and trip distribution analyses. Including non-automobile trips in vehicle trip-only systems

implies a separate need for a mode choice model, which greatly increases the system’s

complexity. Yet, agencies that use vehicle trip-only approaches seldom deal with nonautomobile travel. Consideration of non-motorized modes requires an approach that:

• Looks at a finer-grained pattern and mix of land uses, street space allocation

and design; and

• Considers proximity, as well as mobility.

The availability and consideration of these modes as travel options in response to pricing

(or land use) actions is clearly important; their inclusion in the analytic process may

represent a future standard for progressive model systems and planning agencies. Areas

that have developed true person trip generation capabilities include Portland, Oregon and

Montgomery County, Maryland. The EPA and the FHWA are currently developing a

manual for MPOs to use to upgrade their micro-scale design features modeling practice.

This manual should be available in 1998 through the Travel Model Improvement

Program.

As a near-term goal, agencies that limit trip generation to vehicle trips should endeavor to

expand their systems to full person trips.

 

32 A vehicle-trip is the one-way movement of a vehicle between two points. A passenger- or

person-trip is defined as one passenger making a one-way trip from origin to destination.

Person-trips are measured as the sum of the number of passengers added across all vehicle-trips.

Thus, person-trips are equal to vehicle-trips only if the average vehicle occupancy is one

passenger per vehicle.
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3.3.4.2 Inclusion of Transportation and Cost Variables

Few trip generation models relate the effect of transportation level of service or cost to

household trip productions. More research is needed to determine whether a major

change in transportation cost or level of service would affect the overall number of person

trips made by a household, particularly in light of non-motorized travel options and

growing opportunities for telecommunications or delivery as substitutes for travel.

Transportation characteristics and costs might be incorporated through the development

of a measure of composite impedance (see Sec. 3.3.5.1 below). Composite impedance is a

measure which combines the time and cost of highway and transit (and perhaps other)

modes into one statistic. In the trip generation context, composite impedance is the unit

of measure used to determine how far travelers are willing to go from a defined origin.

More specifically, the question is phrased as “How many jobs (or other activity variable)

in each zone can be reached within X units of composite impedance from that zone?” The

value of X, the tolerance limit for composite impedance, would be determined in the

calibration phase.

Analytically, an increased road price (or reduced level of service) would increase the

composite impedance associated with trips along that road, thus reducing the

destinations that could be reached within a fixed impedance limit from zones in the area.

As composite impedance increases and accessibility decreases, trip frequency should also

decrease because in general it becomes more difficult to go anywhere, particularly by

vehicle.

The Bay Area model system includes composite impedance (termed accessibility) in its trip

generation model for home-based shopping, and is one example of an attempt to include

such a relationship in trip generation (see the NARC Manual, Sec. 3.3.5, pp. 3-36-37 for

more information).

Trip generation also can respond indirectly to pricing. If pricing applications are

evaluated with models or procedures that affect an earlier-step change in landuse/activity location, or in vehicle ownership levels through some type of composite

impedance relationship, then trip generation estimates would be subsequently affected.

The Portland, Oregon vehicle ownership model, for example, is sensitive to transit service

accessibility (and pedestrian environmental quality) and, in turn, trip generation is

sensitive to vehicle ownership.

3.3.4.3 Disaggregating Households by Vehicles (Income) and Size

Most trip generation and distribution models are handicapped because they must use

zonal-average data. One of the more important recent modeling improvements is

disaggregation of certain zonal data by characteristics that influence travel. A common

practice is to split households jointly by:

• Household size and vehicles available, used by Washington, DC, Detroit,

Philadelphia, San Francisco, and Portland, Oregon; or
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• Household size and household income, used by New Orleans, Atlanta,

Northern New Jersey, Dallas, Denver, Phoenix and Minneapolis-St. Paul.

Such disaggregation of household data produces cells that are more homogeneous in

nature, reducing the variance in trip rates. Using income or vehicles as one of the

dimensions also allows the explicit identification of trips by some measure of wealth,

which can be useful for modeling the differential effect of pricing on various groups.

Typically in these submodels, analysts use Census data to establish the relationship

between zonal averages and the distribution of households by number of persons (1, 2, 3,

4 and 5+) and number of vehicles (0, 1, 2+). From these individual distributions, the

region's joint distribution of households by size and vehicles is used to estimate the

equivalent joint distribution for each zone. The reader is encouraged to consult Appendix

A for a discussion of this procedure, demonstrated by the case study model.

3.3.4.4 Trip Chaining and Other Trip Adjustments

Trip chaining is one likely response to an increase in the cost of travel (or reductions in

the number of vehicles available to a household); that is, individuals group trips to make

more efficient use of vehicles or reduce the cost associated with a given trip. This could

have an effect on total VMT and potentially an impact on cold starts.

No conventional models deal explicitly with trip chaining33, although research is ongoing

under the Travel Model Improvement Program, and improved operational models that

deal with this activity should be available in the next few years.34, 35, 36, 37 Data from the

1974 and 1980 gasoline shortages might be investigated to gauge the impact of chaining

responses to travel constraints.

Current efforts to develop activity-based models represents one important approach to

addressing methodological difficulties posed by trip chaining and other trip adjustments.

In this approach, the number of trips generated by a household is not considered to be a

function of household characteristics directly, but of activities that generate travel. This

approach is expected to be useful because respondents typically can provide better

 

33 Montgomery County, Maryland: TRAVEL2 Model deals with chaining in a form that is

sensitive to land use (Maryland National Capital Parks and Planning Commission, 1991).

34 Ernio Cascetta (University of Naples - Italy), Agostino Nuzzolo (University of Tor Vergata -

Rome - Italy), Vito Velardi (ELASIS - Salerno - Italy), “A System of Mathematical Models for the

Evaluation of Integrated Traffic Planning and Control Policies.” Unpublished research paper,

Laboratorio Richerche Gestione e Controllo Traffico, Salerno, Italy, 1993.

35 Moshe Ben-Akiva, John L. Bowman, and Dinesh Gopinath, “Travel Demand Model System for

the Information Era” Transportation, vol. 23, pp. 241-266, 1996.

36 Yoram Shiftan, “A Practical Approach to Incorporate Trip Chaining in Urban Travel Models,”

Fifth National Conference on Transportation Planning Methods Application, a Compendium of

Papers, 1995.

37 Konstadinos Goulias, Ram Pendyala, and Ryuichi Kitamura, “Practical Method for the

Estimation of Trip Generation and Trip Chaining,” University of California Transportation

Center, University of California, Berkeley, 1991.
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information about the number of travel-generating activities they are involved in, and can

provide better information about the number of trips associated with each activity.

Households can affect overall trip rates through other trip generation adjustments, such

as telecommuting and other telecommunications applications and alternative schedules

(compressed work weeks), which reduce the physical number of home-based work trips.

The TDM Evaluation Model allows for some estimation of the change in person trips due

to these strategies (see Section 3.3.6.9. below). However, there is some question as to

whether households that engage in telecommuting or alternate schedules actually reduce

overall household trip rates or simply substitute other travel. Research is still necessary

on this issue before these pattern shifts can be assumed to lower household trip rates.38

3.3.5 Trip Distribution

The trip distribution analytic step links together the “trip ends” (i.e., productions and

attractions) that are estimated in trip generation, into specific origin-destination trip

movements, or “trip tables.”

In most instances, the distribution of trips from an origin zone to a potential destination

zone is performed using a gravity model. This model distributes trips as a function of the

number of trip attractions relative to the degree of separation among the zones. Almost

all areas measure separation by highway travel time.

Researchers have long known, however, that factors other than highway time play a role

in the allocation of trips among destinations. For example, there is considerable evidence

that the presence of good transit service between two zones will increase the number of

person trips between those zones. A logical extension of this concept is that other

dimensions of separation, such as prices that users pay for transportation, also influence

destination choice. The significance of these factors, which is supported by intuitive logic

and empirical observation, should be taken into account.

This section discusses several topics that affect analytic accuracy of the trip distribution

step: composite impedance, income stratification, travel time equivalency, over-allocation

of attractions, trip distribution as “destination choice,” and importance of short trips.

3.3.5.1 Composite Impedance

Gravity modeling that uses composite impedance as its measure of separation is one

approach that is being tested and used as a way of accounting for pricing effects in trip

distribution. Composite impedance, a measure that combines the time and cost of travel

modes into one statistic, is generally computed as the natural logarithm of the sum of the

exponentiated disutilities for all available modes (including non-motorized), i.e., the

natural log of the denominator of a logit mode choice model, also called the “log sum.”

 

38 P. Mokhatarian et al., “Effectiveness of Telecommuting as a Transportation Control Measure,”

University of California, Davis, 1994.
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This approach has been used in the model systems of New Orleans, San Francisco,

Boston, Atlanta and Denver.39 Most areas that use this formulation use it only for work

trips, but theoretically it is applicable to all trip purposes. The Case Study Model in

Appendix A also illustrates such an application.

3.3.5.2 Income Stratification

The New Orleans model adds another dimension to trip distribution that merits

consideration: income stratification. The composite impedance calculation accounts for

income level of the traveler, as well as time and cost of all modes. This is practical

because all elements of the New Orleans model (from trip generation to mode choice) are

income-stratified.

The benefit of income stratification in this context is that the effect of price on destination

choice is differentiated according to traveler [household] income level. Since pricing

changes will generally influence low income travelers more strongly than high income,

stratifying by income allows the trip destination choice to vary realistically in

discontinuous fashion relative to income. Other models provide similar sensitivity by

dividing price variables by average household income for travelers expressed as cents per

minute.

3.3.5.3 Travel Time Equivalency

Many agencies include travel time only in their measure of separation for trip distribution

and mode choice analyses. These agencies may approximate the effect of an increase in

cost for certain measures by translating cost into an “equivalent” value of travel time.

Such agencies incorporate a cost increase from market-based measures as an increase in

travel time impedance for a given origin-destination pair. The increment in time is

computed by using traveler value of time to convert cost to minutes of equal value, which

can be interpreted from the coefficients in the mode choice model.40

Time equivalents can vary by situation and type of market measure to reflect variations in

traveler price sensitivity. For example, a pricing action that affects automobile operating

cost would probably translate as a lower value of time than an out-of-pocket cost such as

a parking charge. Also, non-work travel would be expected to exhibit a “lower value of

time/higher value of cost” ratio than work travel.

 

39 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993, Sec. 3.3.6, pp. 3-47,48.

40 In the Washington, D.C. Council of Governments model, for example, the ratio of the homebased work auto running time coefficient to the auto operating cost coefficient = 0.0173/0.0035,

or about $0.05 per minute.
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3.3.5.4 Over-Allocation of Attractions

With a doubly-constrained trip distribution procedure, such as the standard gravity

model, it may prove necessary for trip attraction estimates to be sensitive to user costs.

Otherwise, trip pattern distortions could occur under certain pricing scenarios as the

gravity model tries to reallocate attractions in response to cost changes, but the attraction

model would maintain the same attractions in a zone. This could result in over-allocation

of attractions.

This problem can be minimized by including some type of price sensitive variable, such

as composite impedance, in the attraction model. Another option is to switch to a logit

destination choice model to allocate attractions, as discussed below.

3.3.5.5 Trip Distribution as “Destination Choice”

Trip distribution analysis attempts to represent the process of travelers choosing

destinations. Ultimately, it approximates behavior by matching origin and destination

“potentials,” offset by the degree of difficulty in traveling between zone pairs.

Some areas have implemented a more advanced procedure that explicitly models

destination choice as a behavioral process, much like mode choice. These models

estimate the probability that a traveler will choose a particular destination based on the

“utility” or attractiveness of that place relative to other places; attractiveness is defined by

characteristics of the destination, the traveler, and travel conditions, including cost.

The behavioral approach offers a more realistic evaluation of destination choice behavior,

particularly with regard to cost factors, than conventional gravity models, which require

considerable manipulation with adjustment factors to match “behavior” with observed

origin-destination trip volumes. Portland, Oregon uses such a model, with separate

formulations for home-based work, school, college, and other; non-home based workrelated; and non-work.41

Some areas also have found it advantageous to model destination choice and mode choice

simultaneously, particularly for non-work travel where these decisions are so

interrelated. The Bay Area exemplifies this approach with its logit probability model for

shopping trips.42

 

41 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993, Sec. 3.3.6, pp. 3-45,47.

42 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993, Sec. 3.6, pp. 3-47,48.
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3.3.5.6 Importance of Short Trips

Pricing measures place a great premium on being able to effectively model short-distance

trips, since short vehicle trips contribute substantial emissions from cold starts. These are

the trips that are perhaps most amenable to diversion to transit or non-motorized modes.

The importance of short trips in emissions policies raises concerns about the existing level

of detail in analytical representation of transportation highway and zonal networks.

In fact, the importance of short trips could justify an increased level of network detail,

down to the level of minor collectors and perhaps local streets in applicable areas,

accompanied by smaller traffic analysis zones to overcome problems with nonrepresentation of “internal” trips. Moreover, this same concern might argue for better

data on walk and bicycle trips and infrastructure, transit accessibility, and information on

the mix of land uses.

3.3.6 Mode Choice

The mode choice step estimates the share of trips made using each mode of travel.

Within the four-step process, mode choice models generally stand out as being the most

rigorously developed and directly price-sensitive analytic procedure. Many larger urban

areas have developed sophisticated discrete-choice logit models that estimate the share of

person trips by mode, based on the socioeconomic level of the traveler, and the time and

user cost attributes of the various modes. Models typically address the major vehicular

modesautos and transitwith shared-ride often treated as a separate mode. More

advanced models also include non-motorized modes (walk and bike), which is an

important consideration for pricing.

There are a number of characteristics or application procedures that can maximize the

sensitivity and accuracy of mode choice models with regard to pricing. Topics in this

section include:

• Satisfactory model coefficients;

• Effects of inflation;

• Income stratification;

• Differentiation by purpose;

• Integrating mode and destination choices;

• Nested versus multinomial models;

• Special modes;

• Sample enumeration methods;

• Stated preference surveys; and

• Off-line methods.
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3.3.6.1 Satisfactory Model Coefficients

The ultimate product from a mode choice analysis depends most directly on the value

and validity of the coefficients that are used to indicate traveler sensitivity to pricing. An

area can take one of several approaches if it is not comfortable with its coefficients, wishes

to confirm its coefficients, or does not have particular coefficients:

• Develop new coefficients/models: Development of a new mode choice model

is a data- and time-intensive task. Yet, in conjunction with the 1990 Census,

many areas commissioned new regional travel surveys that, taken together

with Census data, serve as an excellent opportunity for model update or

upgrade. A number of references can provide guidance to areas interested in

developing new coefficients and/or models (see NARC Manual Section 3.3.7

for information on models, or Section 3.6.2 for information on data sources and

requirements43). Areas interested in developing new models or greatly

modifying current models should consider incorporating such features as:

nesting, income stratification, non-motorized modes, and special pricing

variables; these topics are discussed elsewhere in this section.

• Conduct surveys: Coefficients can be estimated from formal (cross-sectional or

longitudinal) travel surveys of revealed preference. Assuming these surveys

correctly capture the appropriate travel conditions, they may be fairly accurate

in describing travel behavior response.44 However, revealed preference surveys

must be conducted in response to actual pricing changes or signals.

Stated preference surveys, which derive estimates of response sensitivity from

reactions to carefully staged “hypothetical” pricing situations (or from a

combination of real and stated preference data), may be an acceptable

alternative or complementary approach. Stated preference surveys are

discussed in more detail in section 3.3.6.8.

• Transfer coefficients: An area that is not comfortable with its cost coefficients

for pricing analyses may consider transferring model coefficients from another

urban area. The disaggregate nature of logit models generally supports

transferability. Analysis of coefficients from different areas indicates that the

variation among these coefficients is not as great as might be expected,

suggesting that careful transference is possible.

The most critical issue is ensuring that independent variables are consistent

within the same model, and that the “donor” model itself is the result of a welldocumented development and application process. Inflation adjustment of

price coefficients (to reflect the year in which the original model was developed)

is another major consideration in transferring pricing coefficients (inflation

 

43 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993.

44 Cambridge Systematics, Inc., “Travel Model Improvement Program: Short Term Model

Improvements,” prepared for the Federal Highway Administration, U.S. Department of

Transportation, 1994.
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adjustment is discussed below). For example, if the original model was

calibrated using 1980 prices, the new model must either: (1) have its prices

converted to 1980 dollars; or (2) have its original cost coefficients adjusted to

reflect the effect of inflation since 1980.

• Synthesize coefficients: If it is not possible to derive a particular cost

sensitivity from direct sources, it may be acceptable to synthesize a coefficient

value and then perform a series of sensitivity tests on it. The model would be

run for a series of parameter values above and below the hypothesized value;

the results of these simulations (mode choice, trip length, VMT, etc.) would be

charted for each run.

Comparing the variation in results with theoretical values, coupled with

judgment from experienced practitioners, can often help identify an acceptable

value for a parameter. Although such an analysis cannot conclusively identify

the proper value for an unknown parameter (even though carefully reasoned

and conducted), it may provide a suitable interim result until better information

can be found.

• Develop specialized coefficients: Some models have different coefficients for

parking cost than for regular automobile operating cost; these may be higher by

a factor of 1.5 to 2.0. This difference may reflect a higher sensitivity to parking

as an out-of-pocket cost, or it may be a function of higher density/more

restricted parking supply in areas that are priced. Parking cost coefficients may

be synthesized from other models. Separate coefficients for roadway tolls are

not in widespread use. Both parking and toll coefficients might be

approximated initially by such methods as stated preference surveys.

3.3.6.2 Effects of Inflation

Traveler sensitivity to a given dollar price, reflected in model coefficients, will change

over time with the general rate of inflation. As a general rule, the monetary value of a

pricing policy should be deflated to reflect the change in prices between the forecast year

and the year in which the model is calibrated. Extreme care should be taken in forecasting

what the value of a price will be in a future year. It is commonly assumed that all travel

prices will increase in the future at the same rate as inflation, and, thus, that there is no

change in the relative price among travel choices. This may be an acceptable assumption,

though it should be noted that automobile operating costs have generally been dropping

in real terms since WW II.45

 

45 It should be noted, however, that the environment for non-auto modes like walking, biking and

transit, has steadily eroded, further decreasing the utility of these modes relative to the auto

which has been increasing in terms of vehicular and environmental design features. This

suggests that the generalized cost for auto may continue to decrease in its rate over time, leading

to greater demand than would occur under the assumption of constant pricing.
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3.3.6.3 Income Stratification

One of the biggest issues raised in evaluating pricing strategies is how impacts vary with

the income level of the traveler. Two concerns are raised: assuring an accurate estimate of

traveler response in light of real income differences and accounting for the distribution of

winners and losers. Since mode choice is such a significant step in travel demand analysis

of pricing policies (in terms of impact and cost specificity), it is important to account

directly for income differences when applying mode choice models.

Generally, income differences can be accommodated by applying purpose-specific models

independently to each income stratum (usually quartiles or quintiles). It is essential that

the trip tables brought into mode choice from trip distribution already have this

segmentation. Readers are encouraged to consult both appendices for examples of how

this procedure is performed. Another way income differences can be accommodated is

by developing mode choice models with coefficients that are directly sensitive to traveler

income.46

3.3.6.4 Differentiation by Purpose

Traveler response is likely to vary by trip purpose and mode choice. Coefficients valid

for one purpose may be invalid for another type of trip. Some model systems include a

true mode choice model only for home-based work (HBW) trips. Non-work trips, usually

home-based other (HBO) and non-home based (NHB) are calculated as a distance-based

factor multiplied by the HBW shares. This implies that the non-work mode shares move

up or down in the same proportion as the HBW mode shares, which is unlikely.

Evidence suggests that mode choice in non-work trips is more sensitive to price and less

sensitive to time than for work trips. Therefore, it is preferable to develop separate mode

choice coefficients for work and non-work trips. This is particularly important if a

composite impedance measure is developed from the mode choice model to be used in

trip distribution and possibly elsewhere.

While the Census of Transportation (CTPP) has been a robust source of calibration data

for work trips, more extensive home interview surveys (perhaps as large as 1.0 to 1.5%)

may be needed to furnish enough observations of non-work trips to permit calibration of

mode choice coefficients. The NPTS micro-sample provides one source for non-work

model estimation. Montgomery County, MD has such a model system, developed from

about a 0.5% to 1.0% sample.47

 

46 See M. Ben-Akiva and S. Lerman, Discrete Choice Analysis: Theory and Application to Travel

Demand, MIT Press, Cambridge, MA, 1985.

47 “Montgomery County, Maryland: Travel2 model deals with chaining in a form that is sensitive

to land use,” Maryland National Capital Parks and Planning Commission, 1991.
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3.3.6.5 Nested versus Multinomial Models

Most first-generation mode choice models have been multinomial models, where all

modes in the alternative set are assumed to represent independent options in the model.

However, when alternatives are closely related, straight multinomial logit models can

pose problems because they assume that the alternatives represent realistic independent

choices (e.g., a red bus versus a blue bus on the same service schedule).

Probit model formulations are one way of addressing this issue, but they are highly

complex and have difficulty incorporating more than two modes. The preferred

approach has become the nested logit, which is easier and more practical to develop and

apply. The nested model splits the choice process into tiers, where the choices at any

given level are relatively independent. This ensures that two similar modes, for example,

do not capture an independent share of the total, but rather split the subtotal after the

“generic” share for that type of mode is determined.

There are many resources on logit and probit models for the interested reader (consult the

NARC Manual, Section 3.3.7, or Ben-Akiva and Lerman for references48); examples of

nested logit mode choice models are used in Portland, Oregon and San Francisco. The

reader also is advised to consult the illustration in the Case Study Model in Appendix A,

in which a nested logit model was adapted and applied for mode choice.

3.3.6.6 Special Modes

Some modes are not well accommodated by conventional mode choice procedures; this is

a function of the models as well as the data available. This is a second-order, but

important, issue for pricing because these modes are often key options. Examples

include special transit services or improvements that depart from the coded network,

including auto-access/park-and-ride, and non-motorized modes. Bike and walk modes

are considered in only a small number of metropolitan area models, with Portland being

an example.

Modeling bike and walk is made even more challenging because many of the trips are

intra-zonal (and hence not well represented), may not be well represented in the base

data on trip rates (field and subsequent trip generation), and require special descriptors in

the models. Portland, Oregon includes non-motorized modes in their mode choice

model,49 as do Montgomery County, Maryland and Sacramento, California.

 

48 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993.

 M. Ben-Akiva and S. Lerman, Discrete Choice Analysis: Theory and Application to Travel Demand,

MIT Press, Cambridge, MA, 1985

49 Cambridge Systematics, Inc., Calthorpe Associates, and Parsons Brinckerhoff Quade & Douglas,

Inc., Making the Land Use Transportation Air Quality Connection: The Pedestrian Environment,

Volume 4A, for 1,000 Friends of Oregon, December 1993.
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Most areas will have to analyze these modes “off-model,” usually relying on evidence

from other implementations and adjusting regional trip tables accordingly. 50 Another

possible way of representing these modes might be through sample enumeration methods,

discussed below.

Another example of a “special mode” is any mode used to access transit. Access modes

can include walking, bicycling, auto travel, and transit itself. Many of the difficulties

associated with modeling transit-access modes are described above, with the additional

complication that transit access points may need to be treated as trip attractors

themselves. Mode of access to transit has been modeled in some cases as part of the

nested logit mode choice model. In the mode choice model, walk and drive access are

modeled at the same level or nest. The Washington Council of Governments (WashCOG)

mode choice model is an example of transit access modeled in this way.51

3.3.6.7 Sample Enumeration Methods

Some analysts have suggested that they can realize a higher level of flexibility and

realism, and possibly accuracy, if they evaluate pricing actions through a sample of

households, rather than work with the zonal averages of the conventional 4-step model.

In this approach, called sample enumeration, a sample of households (generally derived

from a regional home interview survey, although Census data may be a source) is used to

represent the regional travel base, and becomes the level at which the models are applied.

While this method is being introduced in connection with mode choice, in fact it may be

applied to various other steps in the travel demand hierarchy. The household sample is

drawn to represent the range of geographic, socioeconomic and travel situations that

would occur in the regional population. Generally, the sample is related (by zonal

location) to the 4-step modeling process through the regional trip tables and the

corresponding travel times and costs of the transportation alternatives.

Policies are then “applied” directly to the households; evaluation takes advantage of

important information on household composition, vehicle ownership, income, and

proximity to activities and alternatives to generate fairly robust estimates of response to

the given policy. These results are then “weighted” back to reflect the behavior of the

population.

The process may or may not involve actual modification of regional trip tables and

performance of traffic assignment in estimating emissions. In the view of practitioners,

this approach implies a tradeoff between the accuracy and flexibility that can be derived

from a household-level analysis and the intricacies of a network-based traffic assignment,

though traffic assignment can be performed.

 

50 Delaware Valley Regional Planning Commission, “An Analysis of Potential Transportation

Control Measures for Implementation in the DVRPC Region,” May 1994.

51 FY 94 Development Program for MWCOG Travel Forecasting Models; Volume A: Current Applications,

June 1994
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This technique has been applied in a number of locations, including California and

Washington state, and has shown good potential.52, 53, 54 It offers the additional advantage

of flexibility in analyzing certain alternatives or behaviors that can be traced at a

household level, but not at a zonal aggregation level. For example, analysis at the

household level is particularly useful for tracing short bike/walk trips, access to special

alternatives, automobile ownership/fleet composition/usage issues, and greater

specificity of particular pricing issues (such as parking cash out, etc.). The reader is

encouraged to consult Appendix B, which reports on an application of this approach.

3.3.6.8 Stated Preference Surveys

Stated preference surveys represent an interesting option for gaining insight into mode

choice response to policies, alternatives, and situations when solid empirical information

does not exist.55 Stated preference surveys might be used, for example, because no data

has yet been generated on a new type of travel mode or a special type of pricing

instrument with unique characteristics.

In a stated preference approach, it is possible to derive statistical estimates of “tradeoff”

rates between various alternatives or their attributes by making respondents choose from

among them in measured ways that indicate the relative importance of key attributes.

These rates can then be evaluated in relation to the type of traveler and his/her

circumstance.

The validity of the derived statistical relationships relies on how well the alternatives are

portrayed to (and understood by) the respondent, and their comparison to known

“standards.” While stated preference surveys rely on hypothetical situations, comparison

of “elasticity” relationships derived from stated preference with more conventional

revealed preference surveys or models have shown surprising corroboration. The results

from these surveys should be used with caution, but they offer an important interim tool

for agencies to estimate relationships between pricing instruments and travel behavior

response, not just in mode choice but in relation to destination, time of day, route choice,

etc.

Stated preference methods were developed by the private market research industry, and

have been used successfully for many years to aid companies in identifying the critical

attributes of their product, and maximizing those attributes to gain market share over

competitors. Use of the techniques in transportation is a fairly recent development, but

 

52 Greig Harvey, Curbing Gridlock: Peak Period Fees to Relieve Congestion, Transportation Research

Board Special Report 242, Vol. 2, National Academy Press, Washington, D.C., 1994.

53 Greig Harvey, “Transportation Pricing and Travel Behavior,” presented at 69th Annual Western

Economic Association International Conference, June 1994.

54 Greig Harvey et al., “Transportation Pricing for California: An Assessment of the Air Quality,

Congestion, Energy, and Equity Impacts,” Vol. 1: Summary Report, Draft, California Air

Resources Board, June 1995.

55 Cambridge Systematics, Inc. and Barton-Aschman Associates, Travel Model Improvement

Program: Short Term Travel Model Improvements, U.S. Department of Transportation, U.S.

Environmental Protection Agency, U.S. Department of Energy, October 1994, pp. 1-7,1-8.
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there are numerous examples of its successful application.56, 57, 58, 59, 60 Portland and

Denver have both utilized stated preference methods in relation to pricing. Portland has

used stated preference methods to explore time of day choice, conducting its survey in

conjunction with its recent home interview survey, while Denver used such a survey to

assist in development of a route choice model (described under traffic assignment,

below).

3.3.6.9 Off-Line Methods

A number of planning agencies may have limited ability to perform mode choice

analysis, either because they do not have mode choice models or the models they have

are insufficient for evaluation of the particular measure. The NARC Manual cites a

number of “off-line” methods that may be valuable as substitute or complementary

approaches. Examples include:

• TDM Evaluation Model: The Federal Highway Administration is distributing

a special software product that can substitute for or enhance a conventional

mode choice model. The TDM Model is a spreadsheet approach that

incorporates a “pivot-point” procedure to estimate mode choice changes. The

model forecasts the change from an initial mode share based on information

regarding changes in decision variables associated with the policy action. A

wide variety of pricing and other strategies can be considered, individually or

grouped into programs, with great flexibility to vary the levels of the strategies

and target their application.

The starting base may be trip tables from an existing planning process, special

trip tables formed from survey data, or simply aggregate estimates of person,

vehicle and/or transit trips. The coefficients in this model have been

“synthesized” from national experience and can be altered by the user to use

local or other coefficient estimates. Results are both aggregate and in trip table

format. The trip table results can be returned to the four-step process (if

applicable) for traffic assignment and emissions.61

 

56 M. Ben-Akiva and S. Lerman, Discrete Choice Analysis: Theory and Application to Travel Demand,

MIT Press, Cambridge, Massachusetts, 1985.

57 M. Ben-Akiva et al., “Combining Revealed and Stated-Preference Data,” prepared for

publication in Marketing Better.

58 Cambridge Systematics, Inc. and Hague Consulting Group, VFT Feasibility Study: Market

Analysis, Final Report, July 1988.

59 David A. Hensher, P. Truong, and P.O. Barnard, “The Role of Stated Preference Methods in

Studies of Travel Choice,” Journal of Transport Economics and Policy, January 1988, pp. 45-57.

60 J. Bates, “Econometric Issues in Stated Preference Analysis,” Journal of Transport Economics and

Policy, January 1988.

61 COMSIS Corporation, Users Guide: Travel Demand Evaluation Model, for the Federal Highway

Administration, 1993 (For information contact McTRANS, distributing agent for FHWA at 904-

392-0378).
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The TDM Model has special capabilities for trip table manipulation or creation.

The model interfaces directly with the common planning software packages

(TRANPLAN, MinUTP and EMME/2), enabling easy interactions with the core

model system. The TDM Model allows analysts to alter starting trip tables, or

create a trip table from scratch (e.g., for transit or vanpool trips). Trip tables

may be created entirely from scratch using survey data, if desired, or more

simplistic estimates of total tripmaking and starting mode shares may be used

to fuel the model. For areas with vehicle-only trip tables, it may be feasible to use the

model “in reverse,” knowing average occupancy rate, to create a person trip table

suitable for performing mode choice analysis. (Unfortunately, the TDM model does

not incorporate non-motorized modes, and this should be adjusted for in its

application.)

• Quick Response System: The quick response system procedure (QRS) consists

of a collection of short-cut, parametric methods that may be used for

transportation analysis in the absence of a local model system. Procedures exist

to cover the steps of trip generation through traffic assignment, and include

mode choice. It exists in either microcomputer or manual form, and allows

users with minimum data and computer capability to form reasonable estimates

of travel response.62, 63

• Elasticity Factoring Methods: Agencies without access to computerized

planning methods or to either of the above techniques may consider using

factoring methods to derive estimates of mode choice responses to pricing

strategies. Such an analysis requires the following travel estimates:

- Person trips

- Vehicle trips (or average occupancy rate)

- Transit trips (if applicable)

- Vehicle miles of travel

Ideally, these estimates would be in the form of trip tables, separated by trip

purpose (work, non-work). Given information on trip length for each origindestination trip combination and starting modal shares, analysts should be able

to estimate the change in mode shares triggered by the respective pricing action.

The report “Transportation Air Quality Analysis Sketch Planning Methods”64

provides a convenient spreadsheet method for performing this analysis. The

 

62 COMSIS Corporation, “NCHRP Report 187: Quick Response Urban Travel Estimation

Techniques and Transferable Parameters,” 1978.

63 COMSIS Corporation, QRS User's Manual, for the Federal Highway Administration, January

1984.

64 Cambridge Systematics, Transportation Air Quality Analysis Sketch Planning Methods, Volume I:

Analysis Methods, prepared for the U.S. Environmental Protection Agency, 1979.
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reader may also consult various sources for estimates of elasticities, but should

be careful to choose a value or values appropriate to local conditions.65, 66, 67

In the event that the agency does not have trip tables, it may be acceptable to

estimate policy responses from a more aggregate estimate of regional trip

making. Baseline estimates can come from:

- Local travel survey data;

- Census CTPP estimates;

- Population or activity estimates factored by measured or ITE vehicle

trip rates; and

- HPMS estimates.

Obviously, the more coarse the background estimate, the less accurate the

estimated response and the more simplistic must be the strategy definition. The

above references are recommended as sources for elasticities and application

methods.

3.3.7 Time of Day

The ability of a model system to simulate differences in travel volumes and conditions at

different times of the day is critical to realistic estimates of speeds, congestion levels, and

emissions, particularly in larger urban areas with pronounced travel peaks. Yet, time of

day allocation of travel is done in a very approximate way, using peaking factors derived

from local surveys to apportion a certain percentage of daily trips into one or more peak

hours or periods.

Time of day is an important dimension for many TCMs, since changes in the level of

congestion and time distribution of travel affect important calculations regarding speeds

and travel volumes, which fuel the emissions model. Time of day takes on special

importance in relation to pricing measures that vary by time of day, for example,

roadway congestion pricing. With major price differentials between peak and off-peak, it

is likely that travelers who are sensitive to cost and/or who have flexibility in trip timing

will consider shifting their departure to another time period; alternatively, persons

without flexibility and/or with a high value of time might pay a higher price to travel in

the peak (and possibly even enjoy a higher level of service).

This is a fairly complex choice that is not well handled by conventional peaking factor

approaches. Research is underway in this area; sites interested in time of day forecasting

 

65 COMSIS Corporation, Users Guide: Travel Demand Evaluation Model, for the Federal Highway

Administration, 1993 (For information contact McTRANS, distributing agent for FHWA at 904-

392-0378).

66 R.H. Pratt Associates, Traveler Response to Transportation System Changes, prepared for the

Federal Highway Administration, Second Edition, 1981.

67 Apogee Research, Inc., Costs and Effectiveness of Transportation Control Measures: A Review and

Analysis of the Literature, prepared for the National Association of Regional Councils, January

1994.
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(because of their interest in congestion pricing types of measures) might investigate the

following activities or options:

• Approximating methods;

• Time of day choice models;

• Time of day factors; and

• Stated preference surveys

3.3.7.1 Approximating Methods

Current travel models do not attempt to accommodate potential traveler response to

pricing measures that vary by time of day. Such analysis requires procedures that are

sensitive to travel by time period, which in turn would require input data on price by

period. This type of information would be cumbersome to create, but would not be

outside the reach of current software.

For example, consider a model to estimate the percent of daily trips that begin in the

morning peak three hours, which could be applied in two phases. The first phase would

calculate one percentage split for all O/D pairs, separately by trip purpose. The second

phase would consider the cost of making each trip during the morning peak and during

the off-peak, and then adjust the initial percentage split based on the peak/off-peak cost

differential for that O/D pair.

Non-work trips would be expected to exhibit greater sensitivity to time of day price

differentials than work trips because of greater traveler flexibility. The exact sensitivity of

trip start times to price differentials is speculative in light of current knowledge, but it

should be possible to make a reasoned estimate using stated preference methods (see

discussion below), which can be refined later using observed data.

3.3.7.2 Time of Day Choice Models

Domestic modeling practices have not featured formal modeling of time of day as a travel

choice. Several researchers have devoted particular attention to this element and

performed work that may serve as a basis for agencies that wish to advance their

capability.68, 69

3.3.7.3 Time of Day Factors

If trip generation models can be made to include a measure of accessibility (composite

impedance) that incorporates cost and travel conditions at different times of day, it may

 

68 Kenneth A. Small, “Fundamentals of Pure and Applied Economics,” Urban Transportation

Economics, Vol. 51, Harwood Academic Publishers, 1993.

69 P.R. Stopher and A.H. Meyburg, Urban Transportation Modeling and Planning, Lexington Books,

1975.
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be possible to reflect time of day preferences through the trip rates themselves, prior to

trip distribution and mode choice.70 If overly simple measures of accessibility are used,

however, the value of such an approach would be minimal. It is also possible to develop

separate trip generation models by trip purpose by time of day, followed by separate trip

distribution and mode choice analysis; this has been done by Montgomery County,

Maryland.

3.3.7.4 Stated Preference Surveys

Traveler response to time of day pricing/service differences may be approximated

through stated preference surveys. Sites also might consider synthesizing time of day

relationships from other travel contexts that employed differential time of day pricing,

such as bridge tolls or peak/off-peak transit fare differentials.

3.3.8 Traffic Assignment/Route Choice

The traffic assignment step estimates the traffic volumes for each link in the

transportation network, for each time period that is considered. Traffic assignment is

critical to the transportation analysis and emissions process; it produces the estimates of

link volumes, speeds/travel times, and vehicle miles of travel (VMT) that are key inputs

to earlier planning steps, particularly trip distribution and mode choice, and are the

primary determinants of emissions when input to the emissions factor model.

Traffic assignment consists of assigning trips from a trip table to a transportation network

according to specified criteria, almost exclusively the minimum travel time path. Travel

cost is generally not included as a criterion, although some models account for tolls.71 It

is feasible to include cost in assignment, and therefore extend its effect more broadly

through the other steps of the modeling process (see discussion below).

The details of the traffic assignment process are not of central concern in this manual. It

is clear, however, that the accuracy of travel speeds generated by traffic assignment are of

key importance. First, travel speeds are critical to effective analysis of all transportation

alternatives, including those affected by pricing mechanisms. Second, travel speeds are a

direct input to emissions analysis.

Additionally, there is concern about how well conventional assignment models deal with

the issue of route choice when a roadway pricing system creates competing opportunities.

 

70 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993, p. 3-58.

71 Cambridge Systematics, Inc. and Barton-Aschman Associates, “Travel Model Improvement

Program: Short Term Travel Model Improvements,” prepared for U.S. Department of

Transportation, U.S. Environmental Protection Agency, U.S. Department of Energy, October

1994, Sec. 4.0, p 4.1.
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Techniques in use or under development that may be of interest in addressing these

issues include:

• Incremental loading and equilibrium assignment methods;

• Dynamic assignment;

• Traffic microsimulation;

• Speed feedback and recursion; and

• Path choice.

3.3.8.1 Incremental Loading and Equilibrium Assignment Methods72

Incremental loading and equilibrium assignment methods are largely replacing the all-ornothing methods and provide much more realism in allocation of trips to paths. Among

other things, these new methods greatly increase the realism with which level of service

variations are represented and, in principle, provide a means for incorporating the cost of

highway travel as well as travel time, which can then be used in the earlier steps of the

modeling process.

3.3.8.2 Dynamic Assignment73

Dynamic assignment, which is an improvement on conventional “static” assignment

procedures, addresses problems with demand variations within an analysis time period

and begins to account for queuing impacts on speed.

3.3.8.3 Traffic Microsimulation74, 75, 76

The outputs of a typical traffic assignment analysis reflect average link speeds, and do

not document variations in those speeds that would result from queuing at intersections,

entrance ramps, or under congested stop/go conditions. Such variations in speed can

significantly affect vehicular emissions; valid estimation can be critical to proper

 

72 Greig Harvey et al., “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993, Section 3.3.9, p. 3-66.

73 Cambridge Systematics, Inc. and Barton-Aschman Associates, “Travel Model Improvement

Program: Short Term Travel Model Improvements,” prepared for the U.S. Department of

Transportation, U.S. Environmental Protection Agency, U.S. Department of Energy, October

1994, Section 4.0, pp. 4-1 to 4-7.

74 Cambridge Systematics, Inc. and Barton-Aschman Associates, “Travel Model Improvement

Program: Short Term Travel Model Improvements,” prepared for the U.S. Department of

Transportation, U.S. Environmental Protection Agency, U.S. Department of Energy, October

1994, Sec. 5.3, pp. 5-4 to 5-7.

75 Patrick DeCorla-Souza et al., “A Trip-Based Approach to Estimate Emissions with EPA's

MOBILE Model,” Paper No. 940376, Transportation Research Board, 1994.

76 E. Ruiter, “Highway Vehicle Speed Estimation Procedures,” prepared for the U.S.

Environmental Protection Agency, 1991.
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computation of emissions. Research is ongoing in this area to improve the linkage

between the assignment models and emissions analysis through use of traffic

microsimulation techniques. The current NCHRP Project No. 25-7: “Improving

Transportation Data for Mobile Source Emissions Estimates,” is developing emission

profiles that can be linked to a microsimulation to account for these effects.

3.3.8.4 Speed Feedback and Recursion77

Modeling accuracy would benefit from a formal recursive procedure that cycles

information from assignment back to the earlier steps, thus ensuring compatible values

among the steps when equilibrium is reached. If this is not done the inconsistency

between the assumptions that are inputs to different modeling steps raises serious

questions about the reliability of the final outputs. If pricing is introduced as a factor in

more of the modeling steps through a measure like composite impedance, then it becomes

even more important to ensure passage of this information through the relevant steps.

Methods are currently under development by the Federal Highway Administration in

conjunction with the Travel Model Improvement Program, and are in use in various

places.78

3.3.8.5 Path Choice

Few conventional models are able to adequately model the effect of the cost of driving on

route choice, which becomes a particular issue in evaluating roadway pricing. A

common procedure is to convert the toll value to an equivalent number of extra minutes

by dividing it by some value of travel time. Thus, when a route is considered as a

potential path, the effect of the toll would be reflected in an increased travel time. This

may be acceptable as a default analysis, but it greatly clouds the issue of choice

discrimination among classes of travelers, some of whom would recognize the toll as a

signal to avoid the facility and some of whom might be expected to pay the toll in

exchange for a higher level of service (shorter travel time).

Toll diversion models are a more realistic way to account for route choice responses to

pricing measures. Toll diversion models split vehicle trips for each O/D pair into “toll”

and “free” trips. This split is based on a comparison of toll and free path

characteristicsmainly travel time and cost (at least one model also includes “number of

toll stops” as a variable). The toll path is the one that would be taken if the trip were

forced to use the toll facility and the free path is the one that would be taken if the trip

were prohibited from using the toll facility. Popular planning software packages such as

TRANPLAN, MinUTP, and EMME2 are able to create and skim these paths.

 

77 Greig Harvey et al. “A Manual of Regional Transportation Modeling Practice for Air Quality

Analysis,” National Association of Regional Councils, December 1993, Sec. 3.3.9, pp. 3-70, 71.

78 Montgomery County, Maryland has documented the effects of incorporating full feedback in a

composite equilibrium trip distribution/mode choice/network assignment model (Levinson &

Kuman, 1992)

Incorporating Feedback in Travel Forecasting: Methods, Pitfalls, and Common Concerns, Travel Model

Improvement Program, March 1996.
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Simplified versions of these models have been used for years by toll road planners, most

often by applying manual adjustments (based on rough estimates of travel time savings)

to estimated toll road traffic volumes. The process has been implemented within a

computerized traffic forecasting model for use in planning a toll road in the Denver

region.79

This same procedure can be adapted to analysis of priced roadways. Unfortunately, this

method is subject to the same limitation as methods to deal with the mode choice and

time of day effects of congestion pricing: lack of observed data with which to calibrate a

model. The Denver model is based on a small-sample stated preference survey of Denver

area residents, most of whom had never used a toll road. The model has undergone

extensive reasonableness checks and its results have been accepted as plausible.

The reader is referred to the case study model application detailed in Appendix A for an

example of this procedure.

3.3.9 Emissions Modeling

The final step in the analysis process is the translation of the transportation effects

estimated in the other analytic steps into quantities of the respective mobile source

pollutants: VOC, NOx and CO, and PM. Areas with comprehensive regional model

systems generally perform this step by applying EPA's MOBILE5a model or, in

California, the EMFAC7F model, and PART5 for reentrained dust emissions. These

models accept input from the traffic assignment process on vehicle trip ends, VMT and

speeds, sorted by facility type, geography and time of day. These inputs are then

translated into emissions through the application of emissions factors, which are based on

the regional vehicle fleet mix, average annual distance traveled by class, average travel

speeds, ambient temperatures, inspection and maintenance programs, fuel policies and

other factors.

This manual does not detail the development and specific operating features of the

MOBILE or EMFAC models or the PART 5 model. Readers who desire more information

can consult existing reference documents on these models.80,81,82 What is of particular

concern here is the effect that particular pricing actions may have on the vehicle mix that

is included in the models. Pricing measures such as fuel taxes and emissions fees would

 

79 J. Heisler et al., “Estimating Toll Diversion Using Existing Transportation Planning Software,”

presented at the Second Conference on the Application of Transportation Planning Methods,

April 1989.

80 Sierra Research, Inc. and Jack Faucett Associates, “Evaluation of MOBILE Vehicle Emission

Model,” prepared for Volpe National Transportation Systems Center, U.S. Department of

Transportation, December 1994.

81 California Air Resources Board, “Methodology for Estimating Emissions from On-Road Motor

Vehicles, Volume I: EMFAC7F,” Draft Report, June 1993.

82 U.S. Environmental Protection Agency, Office of Mobile Sources, “Draft User’s Guide to

PART5: A Program for Calculating Particle Emissions from Motor Vehicles,” February 1995

(EPA-AA-AQAB-94-2).
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have their principal effect on changes in the composition of the regional fleet. It is this

change in vehicle mix, separate from (or in addition to) any change in travel level, that

would alter emissions levels. Section 3.2.2.3 addressed issues associated with changes in

vehicle mix and suggested methodologies and sources for estimating these changes.

Areas that analyze fuel price or registration fee measures should use their analysis of the

probable changes in regional vehicle fleet mix to alter the base distribution in MOBILE (or

EMFAC). The Case Study Model application in Appendix A presents an example of how

this can be done.

Users should recognize that changes in vehicle mix may be the compounded result of two

shifts:

• Vehicles may shift in proportion by model year; and

• Vehicles may also shift by relative use (VMT).

Historical trends, such as reflected in the Nationwide Personal Transportation Survey and

the Motor Vehicle Manufacturers Association statistics, indicate a tendency to use new

cars more than older ones; this is reflected in the annual VMT assigned to each vehicle

age-category in the analysis. Thus, an analytic process that re-apportions fleet mix by age

(to favor newer vehicles), yet maintains mileage rates (VMT) in each age-stratum at their

prior levels implicitly assumes that a shift to newer vehicles means more VMT each year

— an unlikely result. Analytically, if not corrected, this would result in newer, cleaner

models in the MOBILE distribution being given an unrealistically high VMT weighting.

Unless some type of analysis or survey research is performed to support adjustments by

year and mileage, users should normalize the distribution of mileages to account for this

effect. In the example below, the ratio of pre-shift [VMT*share] to post-shift [unadjusted

VMT* share] is 0.97 (i.e., 6.69/6.89). Adjusted VMT is obtained by applying this ratio to

the original VMT figures for each age stratum.

Sum Original Veh. Dist. x Ann VMT = 6.69k;

Sum Revised Veh. Dist. x Ann VMT = 6.89k

Adjustment = 6.69/6.89 = 0.97

Table 3.6 Example of VMT Adjustment

Age Pct. of Annual Revised Effect. Adjusted

(yrs) Vehicles VMT

000

Veh. Pct. VMT

000

 VMT (*)

000

1 15 15 20 15 14.6

2 12 14 13 14 13.6

3 10 13 9 13 12.6

4 8 12 5 12 11.6

5 5 10 3 10 9.7

50% 50%
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4.0 Implementation Issues

n 4.1 Introduction

The ability of market-based measures to achieve their intended objectives depends on:

(1) measures that are technically effective; and (2) an implementation program that is

administratively and politically practicable. Applying sound analysis to the estimation

of travel and emission impacts of market-based measures is the necessary first step in

receiving emissions reduction credit for a proposed program. Submitting agencies

should also provide complementary documentation to assure reviewing agencies that

reasonable progress can be expected in program implementation.

In short, agencies proposing to incorporate and receive credit for market-based

measures should address the implementation issues outlined in this section to receive

EPA approval. The significance of specific implementation issues will vary from one

area to another, and from one measure to another; this chapter presents generic

guidelines that may not apply in some instances. For example, this section notes that

any needed technology must be shown to be available, yet some measures require no

technology deployment at all.

Implementation covers a broad array of concerns. It includes substantive issues such

as legal authority and administrative capacity. It also includes more politicallyoriented issues; clearly, the method and form in which market-based measures are

packaged and introduced influences public acceptance and ultimate program success.

A sampling of implementation questions might include:

• What organization will bear responsibility for implementation, administration

and enforcement?

• What other measures will be combined with the subject measure? Would

these other measures offer attractive and meaningful choices?

• How significant are technical and financial uncertainties? How would public

acceptance and program goals be affected if a partial program were

implemented?

• How are the resulting revenues allocated  back to complementary

transportation services, or used for other societal purposes? Should fees be set

so revenues equal the cost of these complementary uses/services, that is, a

revenue-neutral policy?

• How will equity issues be handled?
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The way in which issues such as these are addressed will determine: (1) how the

measure itself is defined and its impact quantified; and (2) the likelihood that the

measure will actually be implemented as proposed. In turn, these factors will influence

the emissions credit determination.

The Clean Air Act requires EPA to recognize and approve SIP credit for any

quantifiable, permanent, and enforceable emissions reduction; it does not give EPA

authority to advise on implementation issues that do not (directly) affect air quality.

Thus, although all implementation issues contribute to ultimate program success, this

section discusses only those implementation issues that affect an implementing bodys

ability to produce predicted air quality benefits. Several research institutions are

available to advise cities and states on the non-air quality aspects of implementing

market-based measures. EPA and the Department of Transportation also are able to

provide research and related assistance.1

Experience with implementation of market-based measures is growing but is still

limited. EPA is committed to working with states and their agencies to resolve any

implementation uncertainty and to respond to reasonable assumptions where

experience has not yet provided a sure guide. This chapter of the Guidance summarizes

EPAs position on supporting documentation to address the following types of

implementation issues:

• Public policy context;

• Legal authority;

• Administrative capacity;

• Technology availability; and

• Revenue generation and reinvestment.

 1 Much of the information in this chapter has been summarized from Special Report 242, Volume 2,

Curbing Gridlock: Peak-Period Fees to Relieve Traffic Congestion, Transportation Research Board,

Washington, D.C., 1994.
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n 4.2 EPA Position on Documentation

States and their subsidiary agencies and implementing authorities requesting emissions

credit for market-based measures should provide a narrative summary of critical

implementation issues along with their technical analysis of travel and emissions

impacts.

While there are no standard analytical approaches for examining implementation

issues, EPA needs enough information to (1) be assured that the intended market-based

measures can be implemented over the timetable proposed and (2) understand

uncertainties that may affect the implementation timetable and total emissions

reductions.

The discussion of implementation issues identified below should, at a minimum,

describe:

• Current circumstances likely to affect implementation in each issue area;

• Actions currently underway to advance implementation of proposed

measures;

• Planned or proposed future actions, with projected timetables; and

• Implementation issues as yet unresolved, remaining uncertainties, and

their potential implications for the implementation of proposed measures.

In each case, the submitting agency should include relevant empirical or quantitative

information or reference the appropriate sources. In short, if pricing measures are to be

credible and creditable, sponsors should provide reasonable evidence that the actions

underway will lead to implementation within the specified timetable.

4.2.1 Public Policy Context

Like any transportation or emissions policy, successful implementation of proposed

market-based measures will depend, in part, on how well they can be introduced and

integrated into the citys or states general priorities. It is important to acknowledge that

public relations and public participation may be as influential as any technical analysis

in the implementation of pricing mechanisms. For instance, the current consumer costs

of travel may be widely perceived as the actual market costs. An increase in consumer

costs would consequently be regarded in a negative light, rather than as a movement

toward a more equitable cost structure. Therefore, educating and involving the public is

a very important element of any pricing strategy.

Sponsors should briefly describe the public policy setting in their region, priorities

among competing objectives, and the extent to which implementation of market-based

measures has been integrated with major public policy objectives. Sponsors also should
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address issues related to public involvement and education regarding planned

implementation of market-based mechanisms.2

4.2.2 Legal Authority

Because this class of measures is new to many areas, and may involve multiple agencies,

questions of legal authority may be raised that were not raised for other kinds of

emissions-control strategies. Local units of government sponsoring, endorsing, or

implementing market-based measures should document that they have the full legal

power to do so, based on statutory or delegated authority. The credibility of a measure

depends on the availability of such authority. Sponsors should review and confirm legal

authority to ensure that they are able to enact and enforce the proposed market-based

measures.

Sponsoring agencies should answer the following questions:3

• What is the nature and source of the legal authority?

• Are the proposed measures a reasonable exercise of this authority?

• Are the revenue-raising provisions and pricing structures consistent with

existing taxing authority?

If any measures affect multiple jurisdictions, sponsors should detail the mechanisms to

be used and the basis for crafting agreements among multiple units of government.

If implementation requires legislative action, sponsors must detail the process, prospects

and timetable for any required legislative action to establish or clarify legal authority.

4.2.3 Administrative Capacity

In addition to legal authority, sponsoring agencies should demonstrate that capacity

exists to effectively administer all aspects of the proposed measure.

Sponsors of market-based measures should describe the administration that will support

each measure, noting areas in which increased competence or capability may be needed

and outlining the approach and timetable for making necessary improvements.

Depending on the measure, implementation will require adequacy in some or all of the

following areas:

 2 For additional discussion of issues related to public involvement, public acceptance, and equity,

see: USEPA, Opportunities to Improve Air Quality through Transportation Pricing Programs(EPA 420-R97-004), September 1997. This document is also available at the following world wide web address:

http://www.epa.gov/OMSWWW/gopher/Market/pricing.pdf

3 See articles by Olson and by Pietrzyk in Transportation Research Board Special Report 242, Vol. 2,

1994.
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• Staffing levels and skills;

• Computer resources;

• Budget support;

• Audit capability;

• Accounting capability;

• Ability to process revenues;

• Contracting capability; and

• Leasing and procurement capability.4

4.2.4 Technology Availability

Some measures, such as congestion pricing, require technology deployment. In these

cases, sponsors should do the following in their submission:

• Document access to the required technology, either available currently offthe-shelf or available in the future in time for scheduled implementation;

• Evaluate the technologies to be deployed to ensure that they (1) are capable

of achieving the objectives established for each measure, and (2) operate

effectively and efficiently;

• Summarize the results of technical analyses and assessments that have

been carried out for the proposed measures; and

• Note constraints, areas of uncertainty, and areas needing further

development in the deployment of new technologies.

Assessments for congestion pricing, for example, would cover the following technology

areas: in-vehicle units, vehicle to roadside communication systems, vehicle detection

and classification systems, and payment and accounting systems.

4.2.5 Revenue Generation and Reinvestment

In a number of cases, pricing measures generate new revenue. This revenue could be

used in two basic ways: (1) to reduce other fees or taxes; or (2) to invest in public

programs, particularly in transportation. In the case of fee or tax offset, sponsors do not

need to detail revenue use.

In general, sponsors should describe:

• Anticipated programs for which new revenues will be used;

 4 See Olson in Transportation Research Board Special Report 242 Vol. 2, 1994.
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• Anticipated impacts of those investments on travel behavior and emissions;

• Analysis used to estimate travel and emissions impacts, including

consideration of synergism and overlaps;

• Actions and timetables anticipated in implementing the reinvestment

strategy and programs; and

• Anticipated difficulties or uncertainties in executing the reinvestment

strategy and how they will be overcome.

If revenues are to be reinvested in any way that affects transportation demand or

emissions, sponsors should detail demand and the final emissions impacts.

Reinvestments with such impacts include:

• Investment in transit facilities: generates shifts in mode share and

reductions in vehicle trips and VMT;

• Investment in traffic flow improvements or preferential facilities for highoccupancy vehicles: generate increases in travel speed and shifts in mode

share;

• Revenue used to support transit fares: produce shifts in mode share,

reductions in vehicle trips and VMT;

• Investment in vehicle buy-back programs: induce changes in fleet mix and

emission characteristics;

• Financial support of local government and business trip reduction programs:

support reductions in vehicle trips and VMT; and

• Enhancement of pedestrian and bicycle environment, or non-motorized access

to public transportation: support reductions in vehicle trips and VMT.

Many market-based measures gain effectiveness when supported by non-market

measures, for example, expanding the diversity of transportation choices through

increased transit options or enhanced pedestrian environment. Land use strategies may

likewise be supportive. These are mentioned here because some may be potential

recipients of program-generated revenue.

Emissions effects can be established by iterating through the modeling process described

in earlier sections, adjusting the processes and values for effects of the reinvestment,

where they can be determined or estimated. Alternatively, effects can be estimated

independently.

If planned reinvestment  whether in fixed capital, or operations and maintenance 

is expected to generate these types of impacts at a significant level, additional iterations

in the modeling analysis may be required to capture the full effects and claim full

emissions credit.
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Appendix A

Modeling Pricing Measures in a Traditional Modeling Environment

-- A Case Study Approach

A.1 Introduction

Almost all existing travel models were developed not for policy analysis, but for planning

major transportation facility improvements. One of the unintended consequences of this is

that most models are not well suited for analyzing the full range of travel impacts of

different transport pricing strategies. Although these models are generally not structured for

assessing the impacts of such public and private policy actions, they still represent the best

available data base for travel forecasting within most urban areas. Thus, there is a strong

need either to modify existing models or develop new models that can meet a wider range of

analysis requirements.

It was not the goal of this research effort to develop and calibrate a new model set, because

that would have required time and data resources that were not available. Instead, this

project took the approach that many good models exist in different urban areas and that it

should be possible to create a model set that could serve the studys purposes by

borrowing appropriate parts from various models and assembling them into one model set

that would exhibit reasonable sensitivities towards pricing measures. A side benefit of this

approach is that the resulting model would not be biased towards any one urban area, but

would represent a composite picture of a variety of areas.

The model would, however, be applied and tested using actual data from one metropolitan

area, in order to produce more realistic results than an analysis using a hypothetical urban

area. The resulting model set is referred to as the Case Study Model, because it was used to

analyze the different market-based case studies for this project. This model is described

further in the next section.

It should be noted that the Case Study Model is not intended to be one that can be picked up

and applied as is to any particular area for use in analyzing market-based TCMs. It was

only intended for use in this national-level overview analysis of the potential emission

reductions of market mechanisms. However, the model documentation is presented here as

part of the technical guidance for urban transportation planning staffs who may seek to

adapt their local models to better analyze pricing strategies. As such, the following is

intended to assist such staffs to develop and use similar modeling advances so as to conduct

more credible forecasts of the effects of market mechanisms in their areas.
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A.1.1 Case Study Model Specification

As noted above, the Case Study Model set is adapted from various four-step travel

forecasting models from urban areas across the U.S. Despite some of the problems

associated with the four-step process, it is still the most widely used and readily understood

modeling approach. It was an explicit premise of this project that most of the problems with

the four-step process are related to the way in which the steps are applied and other

deficiencies in the input data, rather than flaws that are inherent in the process itself. The

projects researchers hypothesized that an enhanced model set could be developed that

would satisfy the need for specific sensitivity to pricing measures within the context of the

four-step process.

Figure A-1 is a flowchart depicting the overall structure of the Case Study Model set. The

following sections describe how each component of the model was crafted, with emphasis

on the nature of the sensitivity to pricing. It bears emphasis that although each component

of the Case Study Model set might not be considered the most advanced state of the art

technique, the way they are combined, as shown in Figure A-1, is unique and is the most

important feature of the model set.

The Case Study Model is applied to data representing 1996 conditions in the Washington,

D.C. metropolitan area. This city and scenario were selected because the data were readily

available, courtesy of the Metropolitan Washington Council of Governments (MWCOG).

However, neither the Case Study Model nor any results of this analysis are endorsed or

supported by MWCOG. It also bears emphasis that this model has not been fully calibrated

to any set of data and cannot be said to represent the specific sensitivity of Washington area

travelers (or any other travelers) to pricing. Its purpose is to demonstrate the structure of a

pricing-sensitive model and to produce results that are reasonably illustrative of the range of

emission reductions that might be associated with the assumed levels of transport pricing

changes. Neither EPA nor the authors warrant that the results estimated by this model

would be achieved in actual practice.

A.1.2 Submodels

The Case Study Model set includes two important submodels: households by vehicles and

size, and parking cost.

Households by Vehicles and Size Submodel: Most existing trip generation and distribution

models are handicapped in many cases by working with zonal average data. One of the

most substantial modeling improvements in recent years has been the increasingly popular

practice of subdividing certain items of zonal data by those characteristics which influence

travel. A common practice is to split households jointly by size and vehicles available (or

household income). Washington, D.C., Detroit, Philadelphia, San Francisco, and Portland

(Ore.) are examples of urban areas which use vehicles, while New Orleans, Atlanta,

Northern New Jersey, Dallas, Denver, Phoenix, and Minneapolis-St. Paul use income. This

produces cells of households which are more homogeneous in nature, reducing the
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variance in trip rates. Using income or vehicles as one of the dimensions also allows the

explicit identification of trips by some measure of wealth, which can be useful for modeling

the differential effect of pricing on the different groups.

The submodel operates by using Census data to establish the relationship between zonal

averages and the distribution of households by integer values. The size submodel splits

households by number of persons: 1, 2, 3, 4, and 5+, and the vehicle submodel splits

households by number of vehicles: 0, 1, 2+. For the Case Study Model, data on households

by vehicles available were provided by MWCOG, and so only a size submodel was needed.

Figure A-2 presents the size submodel.

Once the individual distributions of households by size and by vehicles are known for a

zone, the regions joint distribution of households by size and vehicles is used to estimate the

joint distribution for each zone. The size and vehicle submodels and the joint distribution

submodel used in the Case Study Model were developed from a 1987-88 home interview

survey in the Washington, D.C. metropolitan region. The regional joint distribution is shown

in Table A-1. This distribution is used as a seed for each zone and then modified to match

the desired distributions of households by size and by vehicles for that zone.

Parking Cost Submodel: The Case Study Models parking costs were estimated using the

MWCOG parking cost model. This model estimates average daily parking cost in a zone as a

function of the number of home-based work person trip attractions per square mile in that

zone. A threshold value is included, so that no parking cost is estimated for most zones. The

parking cost submodel is implemented as a look-up table, shown graphically in Figure A-3.

The values in this chart represent the average cost of parking between those who pay

something and those who pay nothing and are expressed in cents per day, in 1980 year

dollars. (Throughout the Case Study Model set, all costs are expressed in 1980 dollars.) The

average parking cost value estimated by this submodel can be overridden by the user for any

zone.
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Build peak and off-peak highway toll 

and free path skims 

Apply toll path choice model to calculate 

weighted peak highway skims 

Figure A-1 

Case Study Travel Forecasting Model Flowchart 

vehicle 

ownership data

Calculate peak and 

off-peak composite 

impedance 

Trip generation 

by purpose Trip distribution by purpose

Mode choice by purpose

Toll path choice model by purpose 

Combine, balance, and assign vehicle trips

Calculate final VMT, speeds, 

and emissions 

Report of daily mobile source emissions 

Misc. PPAQ input files: 

Traffic patterns 

District equivalencies 

etc.

Spreadsheets for 

estimating fee impacts 

on vehicle age mix

transit times 

and fares, 

parking cost

highway network 

households and 

employment

vehicle 

ownership data

transit times 

and fares, 

parking cost

toll/free 

highway skims 
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Figure A-2

Household Size Submodel

Proportion of Households by Size vs. Average Persons/Household

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

1.05 1.55 2.05 2.55 3.05 3.55 4.05 4.55 5.05

Average Persons per Household

Proportion of Households

1 person

2 persons

3 persons

4 persons

5+ persons

Table A-1

Regional Joint Percentage Distribution of Households by Size and Vehicles

Vehicles

Size 0 1 2+ Total

1 4.2% 18.6% 2.1% 24.9%

2 1.1% 9.7% 24.7% 35.5%

3 0.3% 2.6% 14.9% 17.8%

4 0.1% 1.5% 13.4% 15.0%

5+ 0.0% 0.6% 6.2% 6.8%

Total 5.7% 33.0% 61.3% 100.0%

Source: 1987/88 MWCOG Home Interview Survey.
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A-6 U. S. Environmental Protection Agency

A.1.3 Trip Generation Specifications

This component is the most traditional part of the model set. Daily home-based person trips

produced by households are estimated as a function of the joint number of households by

size and vehicles ownership. Daily person trips attracted to a zone are estimated as a linear

function of households and employment by type. Four trip purposes are used: home-based

work (HBW), home-based other (HBO), non-home-based (NHB), and truck. NHB and Truck

vehicle trips are calculated as trip ends only; i.e., origins = destinations = trip ends. The trip

rates include all trips and the split of trips that begin or end outside the region (external) are

estimated as a percentage of total trip ends. External trips are then split off as a separate trip

purpose. Internal/external productions are distributed to external stations in proportion to

the 1990 traffic count at the external station.

Figure A-3

Parking Cost Submodel

Parking Cost Submodel

0

50

100

150

200

250

300

350

0 50 100 150 200 250 300 350 400 450 500

Home-Based Work Trip Attractions per Square Mile (Thousands)

Cents per Day (1980 $)

Source: MWCOG.

Work trip ends are balanced to the attraction total and trip ends for the other purposes are

balanced to production totals. The trip rates and attraction equations were derived from the

models of Washington, D.C., Dallas, and Minneapolis-St. Paul. The trip generation model is

presented in Tables A-2 to A-4. This is a very simplistic model, not so much calibrated as

synthesized, whose main focus was on matching the Washington, D.C. regions total number

of trips by purpose. This trip generation model is not sensitive to pricing.
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Table A-2

Trip Production Model

HBW trips/HH Vehicles

Size 0 1 2+

1 0.372 1.147 1.583

2 1.105 1.540 2.581

3 1.912 2.347 3.367

4 1.912 2.347 3.367

5+ 3.038 3.187 3.951

HBO trips/HH

1 3.074 3.190 3.335

2 4.437 5.220 5.452

3 4.930 8.120 8.497

4 6.293 12.180 12.644

5+ 8.207 15.950 19.372

Table A-3

Trip Attraction Model

HBW Attractions = 1.484 * total employment

HBO Attractions = 0.45 * office employment + 13.50 * retail employment + 3.00 * other

employment + 0.75 * households

NHB Trip Ends = 0.555 * office employment + 2.59 * retail employment + 1.11 * other

employment + 0.37 * households

Truck Trip Ends = 0.011 * office employment + 0.212 * retail employment + 0.212 * other

employment + 0.042 * households

Table A-4

External Trip Percentages

HBW 5.9%

HBO 1.5%

NHB 2.0%

Truck 5.9%
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A.1.4 Trip Distribution Specifications

In most areas, the distribution of trips from an origin zone to potential destination zones is

performed using a gravity model. This model distributes trips as a function of the number of

trip attractions and a measure of the separation of the zones. Almost all areas use highway

time as this measure of separation. However, researchers have long known that factors

other than highway time play a role in the allocation of trips to destination zones. For

example, there is considerable evidence that the presence of good transit service between two

zones will increase the number of person trips between those zones. A logical extension of

this concept is that other components of this separation, such as prices that users pay for

transportation, should also influence destination choice and thus be accounted for. This

effect appears to have an intuitive and empirical basis that cannot be ignored.

So far, very few urban areas have developed distribution models that are sensitive to the

prices travelers pay and service levels of all travel modes (also referred to as composite

impedance). Some examples include San Francisco, Boston, New Orleans, Atlanta, and

Denver. Most areas that use this formulation use it only for work trips, but in theory it

should be applicable to all trip purposes. This has become a well documented process and

was adopted for the Case Study Model.

The Case Study Model distributes trips for all purposes with a gravity model that uses

composite impedance (CI) as its measure of zonal separation. This impedance is defined as

the log sum from the mode choice model, i.e., the natural logarithm of the inverse of the sum

of the exponentiated disutilities of all available travel modes (the denominator of the mode

choice equation). This method was adopted from the New Orleans regional model. This

version of composite impedance includes the following travel costs: auto operating cost, tolls,

transit fares, and parking fees.

The use of this function makes the allocation of trips to destination zones sensitive to

differences in those user costs. For example, if transit fares were to decrease in a certain

corridor, not only would the transit share increase for those trips (from the mode choice

model), but the number of person trips in that corridor would increase slightly, because the

decrease in fare causes a decrease in the separation between zones in that corridor. Further,

using information from the mode choice model addresses the need for model
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connectivity, even though the models are still applied sequentially. Equation A-1 presents the

log sum equation as used in this model.

CI

ee e

ij U tr U da U cp = +

+ +





 



 



 

 −− −  25 2 1 0 * ln .

() ( ) ( ) (A-1)

where:

CIij = composite impedance from zone i to zone j

U(tr) = disutility of Transit mode

U(da) = disutility of Drive Alone mode

U(cp) = disutility of Carpool mode

The disutility equations are presented in the discussion of the mode choice model, below.

Peak (congested) travel times and user costs were used for HBW trips; off-peak times and

user costs were used for the other purposes. A separate CI value was calculated for each of

three vehicle ownership groups and three transit access areas (walk to transit, drive to

transit, no transit access). A weighted average of these nine values was then calculated. The

value of 2 in equation A-1 is to ensure that the resulting value is greater than zero and the

value of 25 is to spread the CI values over the range of 1 to 254.

In the New Orleans model, composite impedance is used to distribute work trips, but not

non-work trips. In the calibration of that model, it was found that travel prices and transit

service were uncertain influences on non-work destination choice. However, it seems

intuitive that travel cost to users should affect non-work trip patterns in some way and so

the Case Study Model uses composite impedance to distribute home-based other, non-homebased, truck, and external trips as well.

Special friction factors (F-factors) were developed for use with the composite impedance

values. These were estimated such that the resulting trip tables would have the same

average trip length and average CI as the known trip tables from the Washington, D.C.

area. The F-factor curves are shown in Figure A-4. In order to adhere to MINUTP

requirements, the F-factors are scaled to be integers between the values of 1 and 999,999.

Various trip distribution calibration statistics are shown in Table A-5. The observed data

are based on 1996 trip tables previously estimated by MWCOG. These results suggest that

the CI-based gravity models reproduce known trip patterns fairly well.
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Figure A-4

F-Factor Curves

F-Factors

1

10

100

1,000

10,000

100,000

1,000,000

0 50 100 150 200 250

Composite Impedance

F

HBW

HBO

NHB

Truck

Ext

Table A-5

Trip Distribution Statistics

Avg. Composite Imped. Intrazonal Trips

Purpose Observed Estimated Observed Estimated

HBW 68.10 67.90 116,300 133,300

HBO 45.58 47.52 538,500 581,500

NHB 45.81 47.04 314,100 269,800

Truck 61.88 61.84 10,000 11,200

I/X75.24 75.29 --- ---

The resulting trip tables were found to sharply overestimate travel across the Potomac River

and so K factors were developed to force a better correspondence with the traffic counts on

the bridge crossings. HBW trips across the river were factored by 0.20, while all other

purposes were factored by 0.25. The need for such factors is supported by the existing

MWCOG model, which uses extra time penalties on the Potomac River bridges to accomplish

essentially the same function.

A.1.5 Mode Choice Specifications

Within the four-step process, current mode choice models stand out as being the most

rigorously developed and properly price-sensitive component. Many larger urban areas

have developed sophisticated logit models which estimate the share of person trips by mode,

based on the socioeconomic level of the traveler and the time and user cost attributes of the
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U. S. Environmental Protection Agency A-11

various modes. The Case Study Model mode choice model is based on the approach used in

the Washington, D.C. area, which is considered fairly representative of good practice.

The HBW mode choice model incorporates a Carpool model which estimates the split among

2-person carpools, 3-person carpools, and 4+-person carpools. Those percentages are used

to estimate the average attributes of the Carpool mode, allowing the model to then split

total person trips into Drive Alone, Carpool, and Transit modes. Separate walk-access and

drive-access markets are used to calculate the Transit split. This split is sensitive to various

auto mode attributes, including terminal time, driving time, auto operating cost, tolls, and

parking cost, as well as transit attributes, including walk time, initial wait time, transfer time,

in-vehicle time, and transit fare. A special high-occupancy vehicle (HOV) feature allows the

modeler to define HOVs as having either 2, 3, or 4+ persons per vehicle and uses special

travel times and costs for such trips. The Washington models sensitivity coefficients were

replaced with those representing an average of experience from logit models in numerous

urban areas around the country (which turned out to be quite similar to the Washington

areas coefficients).

Equation A-2 presents the logit mode choice model structure.

P e

ee e ijma

U m

U tr U da U cp

a

aaa

= + +

−

−− −

( )

() ( ) ( ) (A-2)

where:

Pijma = probability of choosing mode m for a trip from zone i to zone j by 

travelers with auto availability a

U(tra) = disutility of Transit mode for travelers with auto availability a

U(daa) = disutility of Drive Alone mode for travelers with auto availability a

U(cpa) = disutility of Carpool mode for travelers with auto availability a

U(ma) = disutility of mode m (Transit, Drive Alone, or Carpool) for travelers with 

auto availability a

The disutility of the Carpool mode is a weighted average of the disutility of each integer

Carpool occupancy mode:

U(cp) = P2 * U(2) + P3 * U(3) + P4 * U(4) (A-3)

where:

U(2), U(3), U(4) = disutility of 2-person, 3-person, and 4+-person carpools

P2, P3, P4 = probability of the trip being made in a 2-person, 3-person, or 4+- 

person carpool, given that the traveler is in a carpool,

calculated as:

P e

eee ijoa

U o

UUU

a

aa a

= + +

−

−−−

( )

() () () 23 4 (A-4)
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A-12 U. S. Environmental Protection Agency

where:

Pijoa = probability of the trip being made in a vehicle with occupancy o for a trip from

zone i to zone j by carpoolers with auto availability a

U(oa) = disutility of carpool occupancy o for travelers with auto availability a

This is considered a quasi-nested logit structure in that the carpool model (equation A-4) is

not used to directly calculate the carpool share, but rather to calculate the average carpool

disutility (equation A-3), which is then used in the primary model (equation A-2). Table A-6

presents the disutility equations for the HBW primary model and the carpool model.

For home-based other and non-home-based trips, the MWCOG model does not use the logit

equation to calculate a mode share. Instead, it multiplies the estimated HBW mode shares by

a factor that varies with distance (and, for HBO trips, by vehicle ownership). A similar

approach was used in the Case Study Model, except that the non-work mode shares are

based on off-peak highway and transit times. The factors are shown in Table A-7.

A very important point to note about the cost coefficients is that they were originally

calibrated using prices expressed in 1980 year dollars. Thus, when this model is applied, all

prices must be adjusted to represent the equivalent price in 1980 dollars. For example, prices

in 1993 dollars must be deflated to 1980. This is done by multiplying them by a deflation

factor of 0.57, which is the ratio of the U.S. Urban Consumer Price Indices of 1993 to 1980 (=

82.4/144.4). Neglecting to account for this effect will result in the model being improperly

sensitive to price.

This model was not calibrated, per se, but the bias coefficients were adjusted so as to achieve

approximately the same transit trips and auto occupancy that had been forecasted for the

Washington, D.C. region for 1996. Those statistics are shown in Table A-8.

Prices were carefully specified in this model. All prices were expressed in cents per one-way

trip, in 1980 year dollars, because that is the year for which the original coefficients were

developed. Auto operating cost was calculated as 11.0 cents/mile, multiplied by the

minimum path distance for each O/D pair. This is the incremental cost of using a vehicle for

each trip and does not include any of the fixed costs of vehicle ownership. Parking cost to

users was calculated using the density-based model shown above in Figure A-3 and divided

by two to represent half the daily price. For non-work trips, the average parking cost to

users is assumed to be 20% of the daily parking price. The toll (roadway price) input

represents the weighted average toll for each O/D.
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Table A-6

Home-Based Work Mode Choice Disutility Equations

Prime Mode Choice

U(tr) = 0.05 * (WLK + WT1 + WT2) + 0.02 * RUN + 0.004 * FARE + 0.02 * AAC + ACBIAS

U(da) = 0.1001 * TERM + 0.02 * RUN + 0.004 * OPCST + 0.0095 * (PKCST + TOLL) +

DABIAS

U(cp) = 0.0519 * TERM + 0.02 * (RUN + 1.1*(CPOCC-1)) + 0.004 * OPCST/CPOCC +

0.0095 * (PKCST + TOLL)/CPOCC + CPBIAS

CPOCC 

1. 0

P(2)

2.0

P( 3)

3.0

P(4)

4.394

=

+ +

Carpool Share

U(2) = 0.1823 * TERM + 0.07 * (RUN + 1.1) + 0.014 * OPCST/2 + 0.0333 * (PKCST + TOLL)/2

U(3) = 0.1823 * TERM + 0.07 * (RUN + 2.2) + 0.014 * OPCST/3 + 0.0333 * (PKCST + TOLL)/3 +

3PBIAS

U(4) = 0.1823 * TERM + 0.07 * (RUN + 3.733) + 0.014 * OPCST/4.394 + 0.0333 * (PKCST +

TOLL)/4.394 + 4PBIAS

Bias Constants by Vehicles Owned

Vehicles 0 1 2+

ACBIAS 2.1333 0.9360 0.9501 (if auto used to access transit system)

DABIAS 3.3077-0.0439 -1.0552

CPBIAS 2.9882 1.0989 0.4200

3PBIAS 1.2951 1.7186 1.6952

4PBIAS 2.1106 2.5532 2.1909

Abbreviations:

WLK = transit walk time TERM = highway terminal time

WT1 = transit initial wait time OPCST = auto operating cost to the user

WT2 = transit transfer time PKCST = daily parking cost to the user (divided by

2)

RUN = transit in-vehicle time TOLL = average toll paid

FARE = transit fare CPOCC = average occupancy of Carpool mode

AAC = auto access time to transit

All times are in minutes and costs are in cents, expressed in 1980 year dollars.
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A-14 U. S. Environmental Protection Agency

Table A-7

Non-Work Mode Share Factors

HBO HBO NHB NHB

Vehicles Transit Auto Pass. Transit Auto Pass.

Distance (mi.) Owned Factor Factor Factor Factor

0-1 0 0.5261 2.1790 0.1334 2.0081

1 0.2374 1.5515 0.1334 2.0081

2+ 0.2374 2.7676 0.1334 2.0081

1-2 0 0.6428 2.1790 0.1334 2.0081

1 0.1807 1.5515 0.1334 2.0081

2+ 0.1807 2.7676 0.1334 2.0081

2-3 0 0.6428 3.7090 0.1334 2.0081

1 0.1807 2.0419 0.1334 2.0081

2+ 0.1807 3.3290 0.1334 2.0081

3-4 0 0.6428 2.9104 0.1334 2.0081

1 0.1807 2.0419 0.1334 2.0081

2+ 0.1807 3.3290 0.1334 2.0081

4-5 0 0.6428 2.1036 0.1334 2.0081

1 0.1807 2.0419 0.1334 2.0081

2+ 0.1807 3.3290 0.1334 2.0081

5-6 0 0.6236 2.0978 0.1334 2.0081

1 0.1607 1.9051 0.1334 2.0081

2+ 0.1607 3.3290 0.1334 2.0081

6-7 0 0.6236 1.8674 0.1334 2.0081

1 0.1607 1.9051 0.1334 2.0081

2+ 0.1607 3.3290 0.1334 2.0081

7-10 0 0.6236 1.7769 0.1334 2.0081

1 0.1607 1.9051 0.1334 2.0081

2+ 0.1607 3.3290 0.1334 2.0081

10-30 0 0.5156 1.5292 0.1274 2.0081

1 0.1514 1.8150 0.1274 2.0081

2+ 0.1514 3.3290 0.1274 2.0081

30-50 0 0.0 1.5057 0.0 2.0081

1 0.0 1.7791 0.0 2.0081

2+ 0.0 3.3290 0.0 2.0081

Table A-8 Summary of Mode Choice Results

HBW HBO NHB Total

Transit Trips* 675,900 460,300 110,300 1,246,500

Percent Transit 15.4% 4.5% 2.8% 6.7%

Vehicle Trips 3,309,700 7,513,700 3,052,600 13,876,000
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U. S. Environmental Protection Agency A-15

Auto Occupancy 1.125 1.314 1.239 1.252

* Linked trips.

pair, which is the skimmed toll value multiplied by the percent of drivers which are

estimated to use the toll path for that trip.

The mode choice model outputs vehicle trips by LOV and HOV occupancy categories. The

HOV occupancy criterion can be 2, 3, or 4 persons/vehicle; in this project it was set to 3.

The Washington area does have HOV highway facilities and their impact was included in

the analysis.

A.1.6 Path Choice and Traffic Assignment Specifications

Studies of toll roads focus on drivers trade-off between paying a toll and saving time.

Traditionally, relatively less attention has been paid to drivers path choices, as planners have

relied mainly on traffic assignment software to handle that task. However, recent toll road

studies have discovered that such software is inadequate for modeling complex toll vs. time

trade-offs and have developed more sophisticated models of path choice. These models

determine the best free path for each zone-to-zone pair, i.e., the best path that does not use

the toll facility. They then determine the best toll path, which is the best path that includes

the toll facility. Those paths are analyzed to determine their time and toll difference, which

is then used in a logit model to estimate the split of trips between the two paths. This is done

for every zone-zone pair in the network. Separate toll and time sensitivities are used for

work and non-work trips. Recent advances in assignment software permit the two resulting

trip tables to be assigned simultaneously, but each to its own set of paths. Within the

multiple iterations of assignment, trips are allowed to migrate between paths to a limited

degree in response to congestion. The result is a more realistic assignment of trips to toll

facilities, in a manner that is sensitive to the level of toll as well as to the capacity of the

alternative non-toll routes.

This kind of process has been recently used in toll road studies in Denver and New Jersey,

and was adapted for use in the Case Study Model, under the assumption that it is suitable

for analyzing roadway pricing measures. The resulting toll values affect not only the path

choice, but also the choice of mode, which uses toll as an input, as noted above. Because toll

is part of the composite impedance calculation, toll values affect the distribution of trips as

well.

The toll diversion model is a logit formulation that splits all vehicle trips for each O/D pair

into toll and free trips. The equation is as follows:

P toll

e ij U toll free ( ) .

( ) = −

1 0

∆ (A-5)
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A-16 U. S. Environmental Protection Agency

where:

P(toll)ij = probability of choosing the toll path for a trip from zone i to zone j

∆U(toll-free) = difference in disutility between toll and free path

= 0.301 * (toll time - free time) + 0.0127 * toll path toll + 1.5 (HBW trips)

= 0.191 * (toll time - free time) + 0.0272 * toll path toll + 0.3 (all other trips)

toll is in cents and time is in minutes

The toll time is computed based on paths that are built by temporarily reducing the time on

all tolled links by 10%. This encourages any trip which is near the toll link to use it. The free

path time is computed by prohibiting the use of all tolled links. Throughout the Case Study

Model, highway paths for skimming and for assignment are built on the basis of minimizing

impedance, where impedance is calculated in equivalent cents as:

impedance (in cents) = 10.0 * time (in minutes) + 11.0 * distance (in miles)

The 10.0 factor on time is derived from an assumed effective value of travel time of $6.00 per

hour (roughly 22% of the Washington, D.C. areas mean 1990 household income). The 11.0

factor on distance represents the user cost of operating an automobile, as noted above. This

formulation ensures that user cost influences highway paths.

The traffic assignment procedure uses four iterations of incremental, capacity-restrained

assignment, with 25% of the trips assigned on each iteration, following current MWCOG

practice. Thus, the assignment of trips is sensitive to roadway capacity in an incremental

fashion: some trips see an open roadway, while others see a congested one. The input

daily vehicle trips are split by four categories: low-occupancy vehicle (LOV) free path, LOV

toll path, HOV free path, and HOV toll path, with each category of trips being assigned to its

specific set of paths, respecting the presence of priced roadways and HOV roadways. The

output of this process is a loaded network with daily traffic volumes on each link.

A.1.7 Emissions Calculation Specifications

The estimation of mobile source emissions requires two basic data items from the traffic

assignment: VMT and speed. U.S. EPAs MOBILE5a emission factor program is applied to

calculate emission rates in grams/mile for the criteria pollutants (the EMFAC7F program is

used in California). These rates are a function of the mix of vehicles by eight types, the

average distance they travel per year, average travel speeds, ambient temperatures,

inspection and maintenance (I/M) programs, fuel policies, and other factors. The PPAQ

(Post-Processor for Air Quality) program developed by Garmen Associates is used to read the

loaded network, revise the speed calculations by facility type and time period, summarize

VMT by facility type and time period, and apply the MOBILE5a emission factors. PPAQ

requires a series of input tables that reflect the mix of vehicle types by roadway type, the

percentage of traffic by hour, and other parameters that describe traffic patterns in more

detail. These parameters have been adopted from work recently performed in the

Philadelphia region. The result of a PPAQ run is an estimate of total daily tons of HC, CO,

and NOx from on-road mobile sources.
Technical Methods for Analyzing Pricing Measures

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U. S. Environmental Protection Agency A-17

Changes in most of the pricing measures under study, such as roadway pricing, transit fares,

and parking costs, will be reflected in the assigned link volumes. The exceptions are the

measures involving registration fees that are based on age or emission level. It is assumed

that such strategies have no measurable impact on the amount of VMT, but will affect the

mix of vehicles by age. Strategies that make it more expensive to own an older vehicle

should result in fewer older vehicles on the road. Since older vehicles were generally built to

less stringent emission standards and are usually less well maintained, a reduction in such

vehicles can be expected to reduce the emission rates calculated by MOBILE5a.

Spreadsheets have been developed in this study to analyze age-based fees and emissionbased fees. These spreadsheets estimate the impact of different fee structures on the default

MOBILE5a vehicle mix by year. The spreadsheets use a very simplistic relationship between

the annual user cost of owning a vehicle and the percentage of vehicles in each age category.

For each age category, a value is computed representing the annual fee divided by the

estimated market value of the vehicle. This value is the change in fee as a percent of the

vehicles value. An elasticity of -1.0, derived from literature on the price elasticity of new car

sales, is applied to that change in fee. The resulting value is the estimated percentage change

in fleet share for that age category. This is done separately for each of the eight MOBILE5a

vehicle types and MOBILE5a defaults are used for the initial age mix. It is assumed that

none of the registration fee policies would affect the total number of vehicles; only the age

mix. Thus, all of the vehicles removed from older age categories are shifted to newer age

categories, in their existing proportions.

The output of these spreadsheets is a revised set of vehicle age mixes by vehicle type that can

be input directly into MOBILE5a. Table A-9 shows an example spreadsheet, analyzing a fee

schedule for light-duty gasoline vehicles (passenger cars) that varies from $100 for 4 year old

vehicles to $900 for vehicles of 20 years or more.

The revised age mix is entered into PPAQ, along with the loaded highway network from the

assignment step. PPAQ splits the link volumes by time period, adjusts the speeds on each

link, summarizes VMT and average speed by time period, facility type, and jurisdiction,

applies MOBILE5a to each scenario, multiplies the resulting emission rates by the VMT,

and produces a concise summary of the regional mobile source emissions of interest. Table

A-10 is a sample of the summary page of PPAQ output.
Technical Methods for Analyzing Pricing Measures

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A-18 U. S. Environmental Protection Agency

Table A-9

Sample Vehicle Age Mix Adjustment Spreadsheet (Age Fee)

LDGV elasticity: -1.00

estimated fee change MOBILE5a registration mix by year (starting 1 July 90) new/base

year value change in fee as % of value base % by yr new % by yr % left cum % left ratio

25 $400 $900 225.0% 1.10% 0.00% 1.10% 1.10% 0.0%

24 $500 $900 180.0% 0.30% 0.00% 0.30% 1.40% 0.0%

23 $600 $900 150.0% 0.40% 0.00% 0.40% 1.80% 0.0%

22 $700 $900 128.6% 0.50% 0.00% 0.50% 2.30% 0.0%

21 $900 $900 100.0% 0.60% 0.01% 0.59% 2.89% 2.4%

20 $1,000 $900 90.0% 0.80% 0.10% 0.70% 3.58% 13.0%

19 $1,200 $800 66.7% 1.10% 0.41% 0.69% 4.27% 37.1%

18 $1,500 $800 53.3% 1.50% 0.77% 0.73% 5.01% 51.2%

17 $1,800 $700 38.9% 1.40% 0.93% 0.47% 5.48% 66.5%

16 $2,100 $700 33.3% 1.90% 1.38% 0.52% 6.00% 72.6%

15 $2,500 $600 24.0% 2.40% 1.98% 0.42% 6.41% 82.6%

14 $3,000 $600 20.0% 3.70% 3.23% 0.47% 6.88% 87.3%

13 $3,500 $500 14.3% 4.70% 4.41% 0.29% 7.17% 93.9%

12 $4,100 $500 12.2% 5.40% 5.23% 0.17% 7.34% 96.8%

11 $4,900 $400 8.2% 5.00% 5.09% -0.09% 7.26% 101.7%

10 $5,700 $400 7.0% 5.10% 5.28% -0.18% 7.08% 103.5%

9 $6,700 $300 4.5% 5.00% 5.33% -0.33% 6.75% 106.6%

8 $7,900 $300 3.8% 5.60% 6.03% -0.43% 6.32% 107.6%

7 $9,100 $200 2.2% 7.70% 8.44% -0.74% 5.58% 109.6%

6 $10,500 $200 1.9% 8.10% 8.93% -0.83% 4.75% 110.3%

5 $12,000 $100 0.8% 8.40% 9.39% -0.99% 3.76% 111.8%

4 $13,500 $100 0.7% 8.20% 9.19% -0.99% 2.77% 112.1%

3 $14,700 $0 0.0% 8.30% 9.39% -1.09% 1.68% 113.1%

2 $14,800 $0 0.0% 7.90% 8.94% -1.04% 0.64% 113.1%

1 $17,922 $0 0.0% 4.90% 5.54% -0.64% 0.00% 113.1%

100.0% 100.0% 0.0%

avg age: 8.2 7.1 avg fee: $235

Note: LDGV = light-duty gasoline vehicle
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Table A-10

Sample PPAQ Output

EPA Market-Based Measures Study: 1996 Base Page 36

_____________________________________________________________________________________________

____

 Total Emissions by Scenario:

 ----------------------------

 VMT VHT Avg Spd VOC HC CO NOx

 Scenario (veh-mi) (veh-hr) (mph) (kg) (kg) (kg)

 ------------- ---------- ---------- ---------- ---------- ---------- ----------

 A 67F 1T 1 6959230 141448 49.2 7289 49933 13690

 A 67F 1T 2 9368194 324159 28.9 16466 107271 16541

 A 67F 1T 3 17085802 411706 41.5 22108 142505 31823

 A 67F 1T 4 11197222 189783 59.0 12909 131713 32602

 A 67F 2T 1 819904 20345 40.3 942 6434 1404

 A 67F 2T 2 1285529 76520 16.8 3435 22543 2085

 A 67F 2T 3 2004210 49856 40.2 2563 16282 3452

 A 67F 2T 4 951494 17818 53.4 926 6571 2052

 A 67F 3T 1 6361328 340178 18.7 12872 99395 10507

 A 67F 3T 2 9973934 1049888 9.5 42272 273395 17313

 A 67F 3T 3 15549907 822746 18.9 35269 249739 25847

 A 67F 3T 4 7382279 232147 31.8 9719 70717 12602

 A 67F 4T 1 4687831 393935 11.9 13371 102474 8178

 A 67F 4T 2 7350054 1080890 6.8 40505 267663 13550

 A 67F 4T 3 11459147 962954 11.9 38330 260217 19982

 A 67F 4T 4 5440198 256613 21.2 9549 74506 8818

 A 67F 5T 1 1564851 115915 13.5 4071 31003 2687

 A 67F 5T 2 2745873 450143 6.1 17039 110003 5153

 A 67F 5T 3 3572584 244698 14.6 10154 69201 6048

 A 67F 5T 4 1958525 99925 19.6 3616 28669 3129

 A 67F 6T 1 2094284 66910 31.3 2903 20651 3575

 A 67F 6T 2 3674875 117784 31.2 5964 37679 6094

 A 67F 6T 3 4781289 152757 31.3 7395 48753 8159

 A 67F 6T 4 2621150 83743 31.3 3457 25087 4355

 ---------- ---------- ---------- ---------- ---------- ----------

 TOTAL - kg 140889696 7702861 18.3 323124 2252406 259645

 - tons 356.179 2482.827 286.207

 HC Emissions Breakdown:

 Exhaust 186606 kg 205.696 tons 57.8%

 Evaporative 33070 kg 36.453 tons 10.2%

 Refueling 0 kg 0.000 tons 0.0%

 Running 91180 kg 100.507 tons 28.2%

 Resting 12268 kg 13.523 tons 3.8%

______________________________________________________________________________

PPAQ/MOBILE5

04-21-1994 / 00:57:18

Note: A 67 refers to the summary jurisdiction, of which there is only one.

F x refers to the facility type.

T y refers to the time period: 1=AM peak, 2=PM peak, 3=midday, 4=night.
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Post-processor programs such as PPAQ are commonly used in air quality modeling. This is

mainly because historically, the constrained speeds that result from most traffic assignments

are not sufficiently accurate. These speeds represent just one particular value and they tend

to include other elements of impedance, as needed to obtain accurate assigned volumes.

Many planning agencies have developed their own speed processor programs to adjust these

constrained speeds to be more realistic and to summarize VMT by various stratifications, in

preparation for running MOBILE5a.

A.1.8 Model Application

The case study model set is applied in a series of 15 program steps. The MINUTP planning

software system is used for most of these. Custom FORTRAN programs were written to

prepare the land use data and to apply the mode choice model. PPAQ and MOBILE5a are

stand alone programs and the age mix spreadsheets are in Microsoft Excel. The full model

set requires about 9 hours to apply using an 80486-based computer running at 66 MHz.

The case study model set was applied using basic land use and network data from the

Washington, D.C. area (1,478 zones), representing approximate 1996 forecast conditions.

However, since the model incorporates components from various cities, the results do not

reflect actual (or forecasted) conditions in Washington and cannot be compared to the results

from the Washington areas own model set.

The Washington area is projected to comprise about 1.8 million households, 4.0 million

persons, and 3.0 million jobs. The area has an extensive Interstate system, including a

Beltway around the District of Columbia and the close-in Maryland and Virginia suburbs (I95/I-495) . In addition to an extensive bus network, the area is served by the Metrorail

system and four commuter rail lines. Major HOV facilities exist in Virginia on the Shirley

Highway (I-395 and I-95 to the south) and I-66 to the west. There is one existing toll road,

connecting the Beltway to Dulles International Airport to the west.

As Figure A-1 shows, the model set is applied backwards:

• The path choice model is applied first, to derive weighted average highway

time, distance, and toll values. Peak period values are used for work trips and

off-peak values are used for all other purposes.

• The mode choice program is then applied to calculate the composite impedance

value by zone-zone pair.

• Trip generation and distribution are applied to estimate person trips by purpose.

• The mode choice program is applied again, this time to split person trips by

mode.

• The path choice model is applied again to split vehicle trips by toll vs. free path.

• The vehicle trips are assigned to the highway network.

• The age mix spreadsheets are applied to determine changes in the age mix.
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• PPAQ and MOBILE5a are applied to compute emissions from the loaded

network.

The case study model has not been calibrated in the true sense, since it was developed

from data representing several urban areas. However, the results of the various components

were checked for internal consistency and to ensure an approximate level of correspondence

with 1996 total VMT estimates for the Washington, D.C. area.

A.1.9 Summary of Advanced Features of the Case Study Model

As noted above, the Case Study Model set is not based on new research and does not

represent any breakthrough in the state of the practice in travel demand modeling. Its

advancement is that it was created from the best pricing-related features from several other

well-documented model sets that have been extensively tested throughout the years. It is

very likely the first time that these various components have been assembled in this exact

manner and demonstrates one way in which the four-step process can be enhanced to be

sensitive to policy issues such as pricing.

The most noteworthy features of this model set are:

Use of composite impedance for trip distribution for all trip purposes

Integration of a toll/free path choice model within a four-step process

Mode choice model, including a nested carpool occupancy model

Parking price sub-model within the mode choice model that permits separate

specification of parking prices for LOVs and HOVs

Assignment procedure that simultaneously handles LOV/HOV and toll/free

path trips

Ability to easily calculate effects of vehicle age mix changes

Integration of mobile source emission calculations within a four-step travel

model

A.2 Enhancements to the Case Study Model

The resource constraints of this project limited the development of the Case Study Model. As

this work progressed, several shortcomings became apparent. These are described here as

guidance for enhancing this particular model set, and other models in general.

Income Stratification: It would be preferable to stratify the entire model set by income level.

This would permit the identification of differential price sensitivities by income level and

would facilitate the examination of the differential effect of pricing policies by income level.

Unfortunately, time did not permit the development of such a model, nor was the 1990

Census Transportation Planning Package (CTPP) available at the time this work was

conducted. Now that CTPP files have become more widely available, income stratification
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should be pursued more vigorously. This is important because there is considerable evidence

which suggests that people of different income levels respond to pricing measures differently,

and public policy considerations suggest the need to analyze how the implementation of

such measures affects each income group. More investigation into the combined effects of

income and vehicle ownership on traveler behavior should also be conducted. Traveler

surveys need to collect information on household income (or income category, such as

quartile). Progress is being made in the difficult area of estimating the stratification of trip

attractions by income.

Vehicle Ownership: There is growing evidence that the effect of vehicle ownership on

person trip generation cannot be discounted, even after considering the effect of income.

Vehicle availability may well have a measurable effect on discretionary trips and on the level

of trip chaining. Considerable attention is now being paid to developing vehicle ownership

models that are sensitive to income, locational, density, accessibility, and/or transit service

variables.

In order to analyze the effect of registration fees or one-time purchase surcharges/rebates,

these models must also be at least marginally sensitive to the price of buying a vehicle and

the annual user cost of owning one. In the context of travel modeling, increases in

ownership cost should be reflected in decreased numbers of multiple-vehicle households and

increased numbers of one-vehicle households. There might even be an increase in zerovehicle households, but that is much less clear. At least one vehicle ownership model

sensitive to user cost has been well documented [5].

Speed Feedback: The Case Study Model now begins with peak and off-peak speed values,

the peak speeds having been derived from previous MWCOG model runs. It would be

preferable to run the entire model set at least one more time in each application, using the

speeds from the first run (modified as necessary to more closely match observed data) as

peak speeds in the second run. This is particularly important for estimating congestion

pricing scenarios. However, in this model, two iterations would require a total of 18 hours

(this is one of the disadvantages of an interconnected model set -- changing one thing

changes everything and so the entire model set must be iterated to ensure equilibrium).

Doing this would have severely limited the ability to meet this projects schedule and so was

not done.

In addition, numerous studies have documented the sensitivity of emission rates to average

speeds. Thus, it is vital to have reasonably consistent and accurate speeds for emission

modeling. In this study, the PPAQ program was used to adjust assignment speeds by facility

type and time period.

Auto-Access to Transit Trips: Very few models include such trips in their vehicle trip table.

To do so requires access to transit network data (such as park-and-ride [PnR] lot locations)

that was unavailable for this project, as well as additional processing steps and time. Still,

this phenomenon should not be overlooked, because some improvements to transit service

can increase emissions by enticing some who carpool or who walk to transit to switch to
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driving to transit. Although most drive-access trips are short, they almost always involve a

cold engine, and therefore cold start emissions. Increasing the number of such trips might

not compensate for the passenger car VMT that is removed from the network.

Parking Price Measurement: The cost of parking to the user is one of the most important

variables in determining mode choice. Thus, it would be preferable to measure it more

carefully, such as specifically modeling the proportion of travelers who have free parking

(instead of accounting for that effect in the average value, as the Case Study Model does).

This would require some additional data, which is difficult to obtain, but it would improve

the models ability to respond directly to policies which change the percent of travelers who

park for free. It would also be preferable to include variables other than density in the

parking cost submodel, to increase its accuracy.

In addition, if strategies to change non-work parking pricing are to be properly modeled, an

improved method of estimating non-work parking prices is needed. This submodel would

probably be based on employment density and locational variables. The cost of all-day

parking to commuters is relatively stable and closely related to work trip end density, as

suggested above in Figure A-3. Of course, non-work travelers have a shorter parking

duration time and are even more likely to park for free than commuters, but otherwise little

is known about their average parking costs.

Effect of Price on Trip Generation: In this model set, the number of person trips per

household is completely insensitive to the incremental user cost of travel by any or all modes.

As was noted above, that might be remedied by including some kind of composite

accessibility measure at the zonal or household level, which would require additional

research. This would make travel estimates sensitive to area-wide pricing measures such as

a gasoline tax increase. It would also address the phenomenon of induced travel that is a

concern to some planners by making trip rates sensitive to the general level of development

in a region and the general quality of highway and transit service.

With a doubly-constrained trip distribution procedure such as the standard gravity model, it

would probably prove necessary for the trip attraction estimate to be sensitive to user cost (if

the gravity model uses composite impedance). Otherwise, trip pattern distortions could

occur under certain pricing scenarios as the gravity model tries to reallocate attractions in

response to cost changes, but the attraction model would maintain the same attractions in a

zone.

Time of Day: This model set estimates total daily travel only. Although separate peak and

off-peak impedances are used to represent those periods, the model does not account for the

possible migration of trips from one period to the other. Such migration might occur due

to congestion, pricing, or employer policy. A basic model that splits daily travel into three or

four periods is not conceptually difficult; such models are currently used in several cities.

Further refinements to make that model sensitive to peak/off-peak time savings should be

readily feasible. However, to analyze peak-only pricing or congestion pricing measures, the

model also needs to be sensitive to the peak/off-peak travel cost differential and it is
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uncertain whether the data to define such sensitivity exists. However, it may be necessary to

synthesize such a sensitivity (or perhaps estimate it from stated preference surveys or the

experience of transit systems with peak/off-peak fares) and re-examine it in the context of

an actual congestion pricing experiment. This is important, because a likely major response

to peak-only pricing measures is to shift the time of travel.

Trip Chaining: As in most other travel forecasting models, this model does not directly

estimate any effects on trip chaining. Many researchers believe that a logical and likely

response to increases in the price of travel (or reductions in the number of vehicles available

to a household) is for people to use their vehicles more efficiently. This could have some

effect on VMT and potentially a greater impact on cold starts. It would be helpful if the

model set could reflect this phenomenon.

Nested Logit Model: Although the multinomial logit model is the most widely used formula

for mode choice modeling, it is starting to be replaced by the nested logit model. This is

because the true nested model is more adept at handling sub-mode splits (e.g., bus vs. rail,

within the transit mode). There is some evidence that the nested logit structure more closely

represents travelers trade-offs of attributes when selecting a travel mode. The nested logit

also eliminates the independence of irrelevant alternatives property, which can cause

mode share estimation errors for travel modes with similar characteristics.

Separate Mode Choice Models by Purpose: The Case Study Model includes a true mode

choice model only for HBW trips. HBO and NHB mode shares are calculated as a distancebased factor multiplied by the HBW shares. This implies that the non-work mode shares

move up or down in the same proportion as the HBW mode shares, which is unlikely. There

is evidence from around the country that the mode share of non-work trips is more sensitive

to price and less sensitive to time than for work trips, which is logical. It would be preferable

to develop separate mode choice coefficients for work and non-work trips. This is

particularly important, given that the mode choice coefficients influence trip distribution in

this model as well.

One of the reasons why there is so much attention to work trips is because the CTPP

provides a potentially robust source of calibration data for commuting. More extensive home

interview surveys, perhaps as large as 1.0 - 1.5%, may be needed to capture enough

observations of HBO and NHB trips (especially using transit) to permit the calibration of

usable coefficients.

Improved Vehicle Age Mix Submodel: The Case Study Models vehicle age mix submodel is

overly simplistic and based on rough approximations. For example, its assumption that the

age mix is sensitive to the ratio of the annual fee divided by the value of the vehicle is open to

question. The submodel that estimates the average value of the vehicle by age and type was

not rigorously calibrated due to time constraints but should be redeveloped based on known

data. Further, for the emission-based fee, some provision in the submodel should be made to

allow motorists the option of repairing the vehicle to improve its emissions, thus avoiding

some or all of the fee. Additional research is necessary to determine if an annual or quarterly

fee would truly affect vehicle ownership decisions or day-to-day travel choices (or both).
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Finally, some research should be conducted into the effect of leasing vs. buying on vehicle

replacement decisions.

Long-Term Land Use Allocation Effects: The Case Study Model, as with most travel

demand models, simulates travel choices that are made in the short-term; i.e., hours, days,

weeks. However, those pricing strategies which travelers perceive to be permanent can also

have long-term influences; i.e., months or years. It would be very desirable for the travel

model to be supplemented by a land use model that could adjust the allocation of households

and jobs in response to such pricing changes. For example, a significant change in the toll on

a bridge might be expected ultimately to persuade some existing travelers, over time, to

change their location of residence, work, shopping, etc. Such tolls might also influence the

locational decisions of residents and/or employers who are new to the region. Modifying

future land use in response to pricing changes is one way to respond to the issue of induced

demand. The few land use allocation models now in use employ travel time in their

allocation process, just as typical trip distribution models do. Introducing pricing variables,

such as composite impedance, might be one way for land use allocation models to be

sensitive to the travel cost of users.

It should also be noted that the sensitivity of long-term locational decisions to pricing is

probably quite different than short-term decisions on where to travel or by what mode. This

long-term sensitivity is probably lower, but additional research is needed to quantify this

difference.

Emissions Estimate Sensitive to Trips: The Post-Processor for Air Quality (PPAQ) program

used in this project greatly facilitates the calculation of the emissions burden from each

scenarios loaded network. However, PPAQ uses only VMT in its calculations. Although the

additional emissions from cold starts, and hot soak and diurnal VOCs are included in the

MOBILE5a emission rates, these should more properly be modeled as a function of the

number of trips rather than VMT. This would more accurately differentiate the emissions

impact of those strategies that reduce vehicle trips from those that reduce VMT.

This can also be partially addressed through an alternative technique for estimating cold

start emissions [8]. That method uses purpose-based factors to split the vehicle trips into

cold- and hot-start trips. The two trip tables are then assigned and the assignment software

keeps track of where the cold start VMT occurs on the network. This provides an estimate of

cold start VMT on each link, which improves the estimation of cold start emissions without

having to calculate the change in vehicle trips.

A.3 Application of the Case Study Model

In this project, market-based measures to reduce mobile source emissions were classified into

several groups. Within each group, specific variations of the measures were identified for

detailed analysis. The following sections discuss how the Case Study Model was used to

evaluate each measure. This information is presented primarily to provide guidance to travel
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demand forecasters in the development and use of similar techniques for evaluating marketbased measures in their own areas.

A.3.1 Parking Pricing Measures

Analysis of parking pricing measures involves adjusting parking costs for a specific part of

the region (e.g., the CBD), and/or a specific time period (peak vs. off-peak), and/or a

specific segment of the travel market (e.g., work trips). Parking pricing measures are

represented by a fixed incremental change or a percentage adjustment to existing parking

costs. Parking costs are represented in the model by specifying the average daily cost for

parking in each traffic analysis zone. A separate cost can be supplied for each zone for both

LOV parking and HOV parking. This allows the specification of strategies with differential

pricing based on vehicle occupancy.

Parking costs are used to compute combined impedances and to estimate modal shares. This

affects the estimation of traveler choice of destination and mode of travel (with the potential

to affect travel by time of day). Five parking scenarios were examined in two broad

categories:

• Increase in average cost of parking (all spaces)

(a) $4 per day increase in the core area and $2 increase elsewhere

(b) $10 per day increase in the core area and $5 increase elsewhere

(c) $5 per day increase in the core area and no change elsewhere

• Increase in average cost of parking (employee parking only)

(a) same as (a) above but only applied to home based work trips

(b) same as (b) above but only applied to home based work trips

Each of the scenarios are represented by adjusting the zonal data file used in the combined

impedance step and the mode choice step. All incremental cost adjustments are converted to

1980 dollars by using the 0.57 factor prior to adjusting the parking costs in the zonal data

file. Table A-11 summarizes the impacts of this scenario.
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Table A-11

Parking Cost Impacts

Model Step Impact 

Trip Distribution The composite impedance changes for paths destined to zones that

have modified parking costs. This does not change the total number of

trips that are attracted to these zones since that value is fixed from the

trip generation step. It does change the distribution of trips to zones

with modified parking costs.

Mode Choice Changes in parking cost affect both the split between auto and transit

and the split between LOV auto trips and HOV trips.

Assignment VMT decreases due to fewer vehicle trips.

Emissions HC, CO and NOx emissions decrease due to the decrease in VMT.

A.3.2 Roadway Pricing Measures

Roadway pricing involves charging a fee (or toll) for vehicles to use certain roadways or to

cross a border into a designated area. The toll can vary by time of day, by vehicle occupancy,

and/or by the level of congestion on the roadway. Most such proposals would have the toll

collected through automatic vehicle identification so that no delay would be imposed in

collecting the toll.

It was assumed that this toll would influence travelers choice of 1) destination zone, 2)

travel mode, and 3) highway path. The Case Study Model does not include a time of day

submodel, so influences on the time of travel could not be estimated. In effect, the toll is

assumed to be assessed to all vehicle trips (LOV and HOV) on the selected roadways at all

times of the day. In this scenario, the selected roadways were those specific freeway and

expressway links in the Washington, D.C. region that had been shown, through previous

trip assignments, to have an estimated peak hour volume/capacity (V/C) ratio of 0.9 or

higher. These were almost all of the Interstate and major through Primary roads in the

region (727 one-way links out of a total of 18,400 links). Although these priced roadways

represent 4% of the total coded network, they carry about 31% of the total daily VMT.

One level of toll was analyzed: $0.10/mile. This is a typical high-side value of the toll being

charged in newly proposed toll roads in various areas. The first step was to convert this into

1980 year dollars by multiplying by 0.57, as noted above. The equivalent 1980 toll level is

then $0.057/mile. The increased cost of using certain roads was first reflected in the path

skimming step, in which path-building impedance is defined as the sum of auto operating

cost (cost/mile multiplied by distance), and the money value of travel time. Priced roadway
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paths and non-priced (free) roadway paths were then built and input to the toll diversion

model, which estimated the priced route/free route share for all O/D pairs. The average toll

paid is then the toll on the shortest path, multiplied by the proportion of drivers who are

estimated to use that path. A weighted average of the priced and free travel time and the

priced and free distance were also computed.

The average time, distance, and toll for each O/D pair was then input to the mode choice

program in order to calculate composite impedance. That impedance value was used in the

gravity model to distribute trips from origins to destinations. The average time, distance, and

toll were again input to the mode choice program, this time to compute the split among

travel modes. The toll diversion model was applied a second time to split the vehicle trips by

priced/free path. The assignment program assigned priced trips to the priced paths and free

trips to the free paths. Within the four assignment iterations, priced trips were allowed to

migrate to the free paths (and back) in response to changing relative congestion levels

between priced and free roadways; however, free trips were required to stay on a free path.

Table A-12 summarizes the impacts of this scenario on the travel choices.

Table A-12

Roadway Pricing Impacts

Model Step Impact 

Highway Skims Highway paths are modified to select paths that minimize the total

cost. Average time, distance, and toll values are estimated that reflect

the impact of the priced roadways.

Trip Generation No impact.

Trip Distribution Composite impedance increases for paths using the priced roads due to

the toll. It also increases slightly for paths using free roads because

they are longer (drivers are accepting increased distance, and

operating cost, in order to avoid the toll).

Mode Choice Cost of driving increases; driving alone decreases, and carpooling and

transit increase slightly.

Toll Diversion Presence of tolls causes a share of the remaining vehicle trips in

affected O/D pairs to switch to the free paths.

Assignment VMT decreases due to fewer vehicle trips. However, congestion

increases on the free roads, resulting in much slower overall average

speed.

Emissions HC and CO emissions increase, and NOx emissions decrease due to the

lower average speed.

These results fit with the initial expectations and the results of other research, up through the

toll diversion model. The assignment results, however, bear further explanation. Although

some switching to carpools and transit occurs, this does not occur in significant numbers.
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Some travelers switch destinations to one which can be reached via a free route. The

majority of travelers are those who do not change their destination or mode and the primary

method for them to escape tolls in this model is by switching to a free route. In this

scenario, the free routes are generally the arterials, which already had slower average speeds

to begin with, and those speeds are made even slower with the additional traffic from the

priced freeways. Reducing average network speeds generally has a negative effect on HC

and CO (although this depends on the existing average speed values, as noted above).

Part of the problem may lie in the definition of congestion. In this scenario, the priced

roads were those freeways and expressways with a V/C of 0.9 or more. Although V/C is a

good indicator of a roadways congested speed compared to its free-flow speed, that is a

false indicator of emissions. That is, MOBILE5as emission rates are not based on congestion,

per se, but absolute average speed. Consider the case of a freeway section with a free-flow

speed of 60 mph. With congestion, the speed might drop to 40 mph, a difference of 33%.

But according to MOBILE5a, vehicles on that roadway will still have lower emission rates

than vehicles on a nearby arterial, whose congested speed might typically be 35 mph. This is

true even through the arterials free-flow speed might be 40 mph, in which case the arterial

is experiencing less congestion than the freeway (i.e., a speed drop of 13%).

Still, this example highlights some of the complexity of roadway pricing analysis. Even with

the enhancements made to the Case Study Model, the following shortcomings are still seen:

• No price-sensitive time of day model.

• Toll diversion model not specifically calibrated to Washington, D.C. area.

• No speed feedback in the model set.

The above problems notwithstanding, a key observation of this analysis is that a networkbased procedure is essential to the complete analysis of roadway pricing measures. The

interactions among traveler choices of destination, mode, and path are too complex to model

without performing a specific assignment of vehicle trips to a roadway system.

A.3.3 Vehicle-Based Measures

A.3.3.1 VMT Fee

A fee would be assessed for each vehicle-mile driven. It was assumed that this fee would be

collected in such a manner that travelers would be aware of it on a daily basis and therefore

the fee would influence their short-term travel decisions. Thus, the VMT fee was modeled in

terms of an increment to the automobile operating cost parameter. No impact on vehicle

ownership levels, land use, or long-term locational choices was assumed.

Three levels of VMT fee were analyzed: $0.04/mile, $0.10/mile, and $0.40/mile. The first

step was to convert these into 1980 year dollars by multiplying by 0.57, as noted above. The
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equivalent 1980 fee levels are then $0.023/mile, $0.057/mile, and $0.228/mile. These values

were then added to the base automobile operating cost of $0.11/mile to yield new

automobile operating costs of $0.133/mile, $0.167/mile, and $0.338/mile. The mode

choice/composite impedance application program includes a specific user-coded parameter

for auto operating cost per mile and this parameter was adjusted for each run.

In addition, the increased cost of driving was reflected in the highway network path

building and skimming steps, performed using the MINUTP PTHBLD program. Since paths

are built based on an impedance that includes time and operating cost, changes in the

operating cost will affect paths. In PTHBLD, this change is achieved by coding DCOST as

133, 167, or 338, for the three pricing levels. Table A-13 presents a summary of the travel

choices affected by this change, as reflected in the Case Study Model.

It should be noted that the Table A-13 shows no impacts of VMT fees on trip generation. This

is because the Case Study Models trip generation module is not sensitive to pricing changes.

Table A-13

VMT Fee Impacts

Model Step Impact 

Highway Skims Highway paths are modified to select paths that are shorter in

distance, to minimize the cost of travel.

Trip Generation No impact.

Trip Distribution Composite impedance increases with distance (operating cost) and

goes up relatively faster for longer-distance trips; thus, travelers tend

to choose destinations that are slightly closer. Average trip distances

decrease slightly.

Mode Choice Cost of driving increases; driving alone decreases, carpooling

decreases slightly, and transit use increases.

Toll Diversion No impact.

Assignment VMT decreases due to fewer vehicle trips and shorter trip lengths;

reduced VMT means less congestion, resulting in faster average

speed.

Emissions Emissions of all pollutants decrease due to less VMT and higher

average speed.

A.3.3.2 Fuel Tax

The tax on all motor fuels would increase. It was assumed that this increase would be

perceived as simply more expensive gasoline and that this would influence the short-term
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decisions of travelers. Thus, the fuel tax was modeled in terms of an increment to the

automobile operating cost parameter.

Three levels of fuel tax were analyzed: $0.20/gallon, $1.00/gallon, and $1.50/gallon. These

were converted into 1980 year dollars: $0.114/gallon, $0.570/gallon, and $0.855/gallon.

These were then converted into cents/mile by dividing by an average value for on-road fuel

economy. A value of 19 mpg was calculated from the 1990 Nationwide Personal

Transportation Study (passenger cars only), but this was increased to 20 mpg for all

scenarios by assuming that a short- to medium-term (3-12 months) impact of higher fuel

prices would be to encourage the purchase of more fuel-efficient vehicles. The resulting

additional cost per mile is then: $0.0057/mile, $0.0285/mile, and $0.0428/mile, making the

new automobile operating cost parameter: $0.1157/mile, $0.1385/mile, and $0.1528/mile,

respectively.

This new cost of driving was then input to the model in exactly the same manner as for the

VMT fee, as described above. The impacts were the same as noted in Table A-13, although

at a lower level due to the lower level of operating cost increase.

A.3.3.3 Age-Based Registration Surcharge

A surcharge would be placed on the annual registration cost of all vehicles and would vary

with the age of the vehicle. It was assumed that this fee would be collected annually and

that travelers would not be aware of it on a daily basis and therefore the surcharge would

not influence their short-term travel decisions. This surcharge was assumed to affect only

the age mix of the vehicle fleet by encouraging drivers to trade-in their older vehicles for

newer ones. Such older vehicles were presumed to be removed from the fleet (i.e., scrapped)

and replaced with newer vehicles, in roughly the same proportion as newer vehicles

currently exist in the fleet. The MOBILE5a default age mix by vehicle type was used as the

initial age mix.

The analysis methodology presented estimates only the impacts due to changes in vehicle

mix by age and assumes that no changes in VMT occur due to the shift from older vehicles to

newer vehicles. Since the fee is levied solely based on vehicle age, no changes in travel

characteristics are assumed except for changes in vehicle mix by model year. The fee can be

structured in various combinations of fee variation by each separate model years, or can be

charged by certain ranges of vehicle ages, or it can be varied among different vehicle types.

The changes in registration distribution by model year are estimated through a process in

which certain assumptions about the value of the vehicles in each age group and changes in

car ownership behavior are made to quantify the change in number of vehicles under each

model year. The basic principle in the analysis methodology is that the vehicles are

dispensed or scrapped at a rate which is proportional to the ratio of the registration fee to

the market value for that particular model year of vehicle.

General Assumptions:
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It is assumed that market value for a particular model year of vehicle is the

same for all the vehicles under that year.

No drop in the latest model year vehicle registration is assumed because the

market value for these vehicles is much higher than the estimated registration

fee.

LDGT2, HDGV, LDDT and HDDV are assumed to have lower price elasticity

than other lighter duty vehicles because they are mostly used for business

purposes and are less likely to be scrapped or replaced (see Table A-14).

Motorcycles are assumed to have lower price elasticity because of their unique

nature.

Model Specific Assumptions:

(these are assumptions that could be replaced by more accurate data if available to the user)

The same estimated market value is assumed for both LDGVs and LDGT1s.

The estimated market value for a late model year HDDV and Motorcycle are

assumed to be $60,000 and $7,500 respectively.

Table A-14

Price Elasticities by Vehicle Type

Type LDGV LDGT1 LDGT2 HDGV LDDV LDDT HDDV MC

Elasticity -1.0 -1.0 -0.9 -0.5 -1.0 -0.9 -0.5 -0.75

The two major inputs to the analysis are the existing vehicle mix by model year and the

average market value for each model year of vehicle. In the Case Study Model, the

MOBILE5a default vehicle mix is assumed for the base conditions, but these can be replaced

by more accurate local data if available or from any other updated versions of MOBILE.

The other key input to the analysis is the average market value for each model year of

vehicle, which could also be replaced with more accurate local data. For the Case Study

Model, the average market value for the latest model year (1993) light duty gasoline vehicle

is assumed to be $17,922 (source: Automotive News, 26 May 1993, p. 61 ) and the value for

older models is estimated from this number assuming a price deterioration rate which is

proportional to the age of the vehicle. The following summarizes the specific steps of the

analysis:

Step 1 Percent Registration Fee Cost of the Total Market Value:
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The percentage of the registration fee cost of the total market value for that model

year of car is calculated. This percentage of registration fee cost of the total value is

assumed to be equal to the percentage of vehicles scrapped or replaced.

Percent Registration Fee = Registration Fee my* 100 / Total Market Value my

Step 2 Estimation of Vehicle Scrappage or Replacement:

It is assumed that the vehicle scrappage or replacement percentage due to the

registration fee is same as the percentage of registration fee of the total market

value. If the registration fee exceeds the market value of the vehicle (as in the case

of older cars) then all the vehicles under that model year are replaced by newer

model years. For example, if the registration fee is 1% of the market value, then 1%

of those model year vehicles are assumed to be replaced by newer cars. This

approach is applied to all the vehicle model years except for the latest model year

which is assumed to be independent and no scrappage or replacement for this year

is calculated.

To account for the variation among different vehicle types, especially the

differences between lighter and heavy duty vehicles, different price elasticities as

given in Table A-14. are assumed for different vehicle types. Lower elasticities are

assumed for heavy duty vehicles since most of these vehicle types are generally

used for business purposes. The estimated scrappage percentages as calculated

above are corrected for these price elasticities for each vehicle type.

Example: Assume a market mechanism with a $900 registration fee for cars

between ages 15 to 25, $600 for ages 5 to 15, and $300 for ages 0 to 5 years, the

estimated market value of a 24 year old car is $400, the registration fee for this

model year vehicle from the above description is $900, the percentage of

registration fee is more than 100% of the total market value, hence all of these

model year cars are assumed to be replaced by newer cars. Similarly, for a 7 year

old model year car with a market value of $9,100, the registration fee for this

vehicle is 600, which is about 6.6% of the market value. Hence 6.6% of these model

year cars are assumed to be replaced with newer cars.

Step 3 Estimation of Replacement Vehicles

In the analysis all the scrapped vehicles are replaced with newer model year

vehicles in the same proportion as their existing vehicle distribution and weighted

by appropriate values to make the total of the distributed percentages to 100.

Step 4 Results from the Analysis
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All the above described steps have been included in a spreadsheet which performs

the calculations and estimates the resulting vehicle registration distribution. The

resulting revised age mix was used in the PPAQ/MOBILE5a run instead of the

default age mix.

Three levels of maximum surcharge were assumed: $100/year, $500/year, and

$900/year. The maximum surcharge was assessed on vehicles 20 years old or

older, no surcharge was assessed on vehicles 3 years old or newer, and an

intermediate surcharge was assessed on vehicles between 3 and 20 years old,

varying with age (see Table A-9 for an example, showing the $900/year surcharge).

Table A-15 summarizes the impacts.

Table A-15

Age-Based Surcharge Impacts

Model Step Impact 

Emissions Emissions decrease due to shift in age mix of fleet from older to newer 

vehicles.

A.3.3.4 Emission-Based Registration Surcharge

A surcharge would be based on the estimated total annual emissions of all vehicles. An

estimate would be made of the annual emissions of each vehicle (by the 8 MOBILE5a vehicle

types), using the default MOBILE5a emission rates by age and the default MOBILE5a annual

mileage by age. 1993 was assumed as the base year, so a 25-year-old vehicle is of model year

1969, for example. It was assumed that this fee would be collected annually and that

travelers would not be aware of it on a daily basis and therefore the surcharge would not

influence their short-term travel decisions. This surcharge was assumed to affect only the

age mix of the vehicle fleet by encouraging drivers to trade in their older vehicles for newer

ones. Such older vehicles were presumed to be removed from the fleet (i.e., scrapped) and

replaced with newer vehicles, in roughly the same proportion as newer vehicles currently

exist in the fleet. In the definition of this measure, it was assumed that the surcharge would

be based on the estimated emissions for all vehicles of a certain age, not measured emissions.

Vehicle owners would not have the option of repairing and retesting the vehicle. The

MOBILE5a default age mix by vehicle type was used as the initial age mix.

The emissions fee is similar to the age-based registration surcharge, the only difference being

that the fee is levied on the total emissions emitted by the vehicle. The emissions fee has

certain advantages over the registration surcharge in terms of equity, since this measure

changes the price based on total emissions rather than on age. Empirical evidence shows

that newer cars travel much more than the older cars, although this is more than

compensated for by the net emissions per mile (emission factors) being much lower for newer

cars. This type of measure takes these characteristics into consideration and distributes the
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price uniformly over all vehicle age groups based on their total emissions. Equity is also

maintained across different vehicle types, since the heavy-duty vehicles which have higher

emission rates and VMT accumulation are assumed to pay a higher fee compared to the

lighter duty vehicles.

The changes in registration distribution are based on essentially the same technique as

described in the previous section on age-based registration surcharges. The differences in

assumptions are as follows:

General Assumptions:

An average emission factor and VMT accumulation is used for all the vehicles

under a particular model year.
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Model Specific Assumptions:

(these are assumptions that could be replaced by more accurate data if available to the user)

MOBILE5a estimated average emission levels by each model year and default

registration distribution by age are used in the analysis.

The three major inputs to the analysis are the vehicle emission levels by model year, the

annual mileage accumulation by model year, and the average market value for each model

year of vehicle. In the Case Study Model, MOBILE5a default annual mileage accumulation

rates and emission rates were used for the base conditions, but these can be replaced by more

accurate local data if available or from any other updated versions of MOBILE.

The following two steps are used to estimate the change in annual user cost for this measure.

From that point, the analysis proceeds exactly as in Steps 1 - 4 of the age-based registration

surcharge, described above.

Step 1 Estimation of Pollutant Tonnage:

The initial step in the analysis is the estimation of pollutant levels for each model

year of vehicle. The emission rates (g/mile) for each model, accounting for the

appropriate control measures such as inspection and maintenance, reformulated

gasoline, etc., as well as local conditions such as temperature and altitude, are

estimated using MOBILE5a. A single index of pollutant tonnage is calculated by

combining the estimated levels of HC, CO and NOx, using equivalency factors for

each pollutant based on previous EPA studies. The factors used in this project are

shown in Table A-16. The level of each pollutant multiplied by its equivalency

factor is summed to get to the combined emission rate for each model year.

Combined Emission Rate my(gms/mi)

 = EHC* HC level + ECO* CO level + ENOx* NOx level

Table A-16

Pollutant Equivalency Table

Type EHC ECO ENOx

Equivalency 1.0 0.1 0.9
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The combined emission rate by model year multiplied by the annual mileage accumulation

for that model year vehicle gives the annual total tonnage of pollutants:

Combined Annual Tonnage of Pollutantsmy = Combined Emission Ratemy * Annual Mileage Accumulation my

Step 2 Estimation of Emissions Fee by Model Year:

The combined annual tonnage of pollutant as calculated from the previous step for

each model year is multiplied by the emissions fee set for the market mechanism.

This gives the additional price paid by each model years vehicles due to the

emissions fee.

Price due to Emissions Feemy

 = Combined Tonnage of Pollutantmy(tons) * Emissions fee ($/ton)

(From this point, continue with Step 1, from Section A.3.3.3, described above.)

Application of the Methodology:

The calculations involved in the estimation of the changes in vehicle registration

distribution due to emissions fee are performed in a spreadsheet, an example of which is

shown in Table A-17. These revised distributions go into the MOBILE input section of the

PPAQ set up file.

This methodology is a sketch planning technique involving numerous assumptions and

factors in the process which are based solely on engineering judgment and are not

substantiated by empirical results. The methodology itself is in a rudimentary stage and

requires further enhancement with respect to the factors and values assumed in the

process. The key assumption regarding changes in vehicle ownership in response to

external costs such as emissions fees and registration surcharges should be verified. The

assumed independence between changes in vehicle ownership and changes in the annual

vehicle mileage also bears further examination.
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Table A-17

Sample Vehicle Age Mix Adjustment Spreadsheet (Emission Fee)

LDGV combining factor: 1.0 0.1 0.9 elasticity: -1.00 fee/ton: $10,000 MOBILE5a registration mix by year

fee chg (starting 1 July 1990)

model MOBILE MOBILE MOBILE5 MOBILE5 MOBILE5 combined combined estimated change as % of base % new % cum. % new/base

year year ann. miles cum. miles exh HC lvl CO level NOx level level tons/yr value in fee value by year by year % left left ratio

25 1969 4,305 178,369 8.89 101.82 4.35 22.99 0.11 $400 $1,089 272.1% 1.10% 0.00% 1.10% 1.10% 0.0%

24 1970 4,305 174,064 9.44 96.65 4.35 23.02 0.11 $500 $1,090 218.0% 0.30% 0.00% 0.30% 1.40% 0.0%

23 1971 4,305 169,759 9.28 95.30 4.35 22.73 0.11 $600 $1,076 179.4% 0.40% 0.00% 0.40% 1.80% 0.0%

22 1972 4,305 165,454 6.17 81.15 4.35 18.20 0.09 $700 $862 123.1% 0.50% 0.00% 0.50% 2.30% 0.0%

21 1973 4,305 161,149 6.10 80.10 3.51 17.27 0.08 $900 $818 90.9% 0.60% 0.07% 0.53% 2.83% 11.5%

20 1974 4,305 156,844 6.03 79.05 2.91 16.55 0.08 $1,000 $784 78.4% 0.80% 0.20% 0.60% 3.43% 24.5%

19 1975 4,559 152,539 5.19 57.74 2.90 13.57 0.07 $1,200 $681 56.7% 1.10% 0.52% 0.58% 4.02% 46.9%

18 1976 4,829 147,980 5.07 56.56 2.88 13.32 0.07 $1,500 $707 47.2% 1.50% 0.86% 0.64% 4.66% 57.1%

17 1977 5,114 143,151 4.94 55.31 2.99 13.16 0.07 $1,800 $740 41.1% 1.40% 0.89% 0.51% 5.17% 63.9%

16 1978 5,416 138,037 4.80 53.98 2.94 12.84 0.08 $2,100 $765 36.4% 1.90% 1.31% 0.59% 5.76% 69.2%

15 1979 5,737 132,621 4.65 52.58 2.89 12.51 0.08 $2,500 $790 31.6% 2.40% 1.79% 0.61% 6.36% 74.8%

14 1980 6,076 126,884 1.63 15.35 2.39 5.31 0.04 $3,000 $355 11.8% 3.70% 3.53% 0.17% 6.53% 95.4%

13 1981 6,435 120,808 1.70 25.08 1.56 5.61 0.04 $3,500 $397 11.3% 4.70% 4.53% 0.17% 6.70% 96.4%

12 1982 6,815 114,373 1.59 22.94 1.54 5.28 0.04 $4,100 $396 9.6% 5.40% 5.33% 0.07% 6.77% 98.8%

11 1983 7,218 107,558 1.50 21.53 1.42 4.93 0.04 $4,900 $392 8.0% 5.00% 5.06% -0.06% 6.71% 101.1%

10 1984 7,645 100,340 1.50 21.76 1.43 4.97 0.04 $5,700 $418 7.3% 5.10% 5.22% -0.12% 6.59% 102.4%

9 1985 8,096 92,695 1.31 19.21 1.36 4.46 0.04 $6,700 $397 5.9% 5.00% 5.22% -0.22% 6.37% 104.4%

8 1986 8,575 84,599 1.22 17.62 1.29 4.14 0.04 $7,900 $390 4.9% 5.60% 5.93% -0.33% 6.05% 105.8%

7 1987 9,082 76,024 1.11 15.33 1.29 3.81 0.04 $9,100 $380 4.2% 7.70% 8.25% -0.55% 5.50% 107.1%

6 1988 9,619 66,942 1.00 13.63 1.20 3.45 0.04 $10,500 $365 3.5% 8.10% 8.79% -0.69% 4.81% 108.5%

5 1989 10,187 57,323 0.89 11.83 1.10 3.06 0.03 $12,000 $343 2.9% 8.40% 9.23% -0.83% 3.98% 109.9%

4 1990 10,789 47,136 0.72 9.72 1.00 2.59 0.03 $13,500 $308 2.3% 8.20% 9.13% -0.93% 3.05% 111.3%

3 1991 11,427 36,347 0.60 7.76 0.89 2.18 0.03 $14,700 $274 1.9% 8.30% 9.34% -1.04% 2.00% 112.6%

2 1992 12,102 24,920 0.47 5.68 0.78 1.74 0.02 $14,800 $232 1.6% 7.90% 9.01% -1.11% 0.89% 114.1%

1 1993 12,818 12,818 0.34 3.47 0.66 1.28 0.02 $17,922 $181 1.0% 4.90% 5.79% -0.89% 0.00% 118.2%

100.0% 100.0% 0.0%

avg age: 8.2 7.1 avg fee: $360

Note: LDGV = light-duty gasoline vehicle

A.3.4 Modal Subsidies

Analysis of modal subsidy measures involves reducing cost to travelers who use a mode that

is promoted by subsidy. Typical subsidies involve incentives for transit (fare reduction, onsite transit stops), cost incentives for ridesharing (cost reimbursement, reduced parking costs,

preferential parking locations, toll reductions) and transportation allowances (parking cash

out). Two levels of transit fare reduction were studied:

• 50% reduction in transit fares

• 100% reduction in transit fares (i.e. free transit service)

A reduction in transit fares influences a travelers choice of destination zone, travel mode

and (indirectly) highway path. Destination zone choice is affected by changing the

computation of combined impedance which is an input to the trip distribution model. Travel

mode choice is affected in the mode choice model by making transit more attractive and

thereby increasing transit share. Highway paths are affected by assigning fewer vehicle trips

to the highway network which reduces congestion effects and causes path changes to occur

in the traffic assignment model.
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There are two matrix files that reflect zone to zone transit fares in 1980 dollars. These files

(walk to transit fare matrix and drive to transit fare matrix) are modified with the MINUTP

program MATRIX. For the 50% reduction, all cells are factored by 0.50. For the 100%

reduction, all cells are replaced with zero. These revised fare matrix files are specified in the

model setup and used during both the combined impedance calculation and the mode choice

estimation steps. Table A-18 summarizes the impacts of this scenario on the travel choices:

Table A-18

Modal Subsidy Impacts

Model Step Impact 

Trip Distribution The composite impedance decreases for those paths that show a

reduction in transit fare under this scenario. This modifies the

distribution of trips in favor of transit accessible zone pairs.

Mode Choice Transit costs decrease causing transit shares to increase and vehicle

trips to decrease.

Assignment VMT decreases due to fewer vehicle trips. This can also result in some

increases in speed for certain highway facilities.

Emissions HC, CO and NOx emissions all decrease due to the decrease in VMT.

A.4 Other Model Enhancement Issues

A.4.1 Addressing the Unknown Sensitivity to Pricing

The sensitivity of urban travel demand to pricing has not received much attention in the

past. The best known analysis has been the Curtin Rule, which states that a 10% increase

in transit fare leads to a 3% drop in transit ridership. In almost all other applications, the

price of transportation to the traveler has often been assumed to change at the same rate as

inflation and thus be of no more or less importance in the future than today.

The rapid rise in gasoline prices in the mid-1970s and early 1980s, combined with a flurry

of activity in rail transit planning created a need for mode choice models which were

sensitive to auto operating cost to the user. There is now over a decade of experience with

such models, and transportation planners have some confidence that adjusting this

parameter to account for increases in the price of driving, as described in sections A.3.3.1

and A.3.3.2, produces reasonable results that are not too great an extrapolation beyond

present experience. A similar comment can be made for the price of parking, which has

been identified in the past ten years as a significant influence on mode choice.
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However, when it comes to more innovative pricing strategies, such as congestion pricing,

planners in the U.S. have little or no experience on which to draw. Moreover, there is an

intuitive and logical belief that in some way, strategies such as congestion pricing will have a

travel impact far beyond the choice of travel mode. But the problem remains: without actual

experience from which to observe and compare, how can demand modelers estimate the

impacts of such measures without falling back on theory?

A powerful argument can be made that before-and-after studies offer the best evidence of

travelers response to pricing measures. The problem, of course, is that this assumes that a

strategy has actually been implemented, in a relatively controlled manner, and without other

confounding factors that could also influence the outcome. Careful analysis of the last

gasoline crisis might yield some insight into motorists response to steep and rapid increases

in the user cost of driving, assuming one could filter out the impact of the apparent shortage

of supply and the psychological (and real) effects of long lines at the pump. Analyses of the

ridership effects of existing differential peak/off-peak transit fares, before and after

implementation, might produce some insight into the cost sensitivity of trip start time.

Another criticism of the pricing parameters currently used in models is that they are based

on cross-sectional surveys. Such parameters are not based on what a fixed group of people

did over time, but on what many people did at one time. Current model calibration theory

says that if you can take a snapshot survey of enough people with different enough travel

conditions and choices, you can generalize enough to draw conclusions as to how one

traveler would respond to changes over time. Although this is how almost all mode choice

models are presently calibrated, there is some sentiment among planners that it is an

insufficient basis for identifying true traveler sensitivity to pricing.

One alternative which is gaining popularity is the panel survey, which follows a fixed group

of households and individuals over time and attempts to measure changes in their travel

behavior in response to actual changes in their travel conditions. In theory, this should

produce more accurate cost sensitivities, but in practice such surveys are very expensive and

difficult to conduct. Worse, they generally require 18-24 months or more, to allow for three

or more survey waves. Still, such surveys have been conducted in Seattle, Washington

and Montgomery County, Maryland and hold considerable promise for the future.

Another technique that may be useful is the stated preference survey, in which people are

asked how they would respond to a series of hypothetical situations. Such surveys are not as

reliable as surveys of observed behavior, because of the large gap between what people say

they will do (especially under hypothesized conditions which may be far outside their

normal experience) and what they actually do. Still, if performed under carefully controlled

conditions, stated preference surveys can produce estimates of pricing sensitivity for travel

decisions, when no other option is available.

Some mode choice models have been developed by transferring model coefficients from

another urban area. The disaggregate nature (i.e., modeling individual choices) of logit

mode choice models generally supports this. Analyses of coefficients from different areas
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indicates that the variation in these coefficients is not terribly great, suggesting that careful

transference is possible. Perhaps the most critical issue here is to be aware of how the

independent variables were developed for the original model and to maintain consistency in

the new model.

Another major trap to avoid: in transferring pricing coefficients, one must be aware of the

year for which the original model was developed and adjust such coefficients for inflation as

needed. For example, if the original model was calibrated using 1980 prices, then the new

model must either also convert its prices to 1980 year dollars, or adjust the original cost

coefficients to reflect the effect of inflation over the years. For example, the Case Study

Model uses prices expressed in 1980 dollars. To convert its operating cost coefficient (0.004)

to 1993 dollars, it must be multiplied by 0.57, which is the ratio of Urban Consumer Price

Index values for 1980 and 1993, resulting in a 1993 equivalent value of 0.0023.

In some cases it might not be possible to derive a particular cost sensitivity from any of the

above sources. In the last resort, it will generally be acceptable to synthesize a coefficient

value and then perform a series of sensitivity tests on it. The value of the parameter would

be varied above and below the hypothesized value, the model would be re-applied, and the

results (mode choice, trip length, VMT, etc.) would be charted for each variation.

Comparing this variation with theoretical values and a great deal of common sense from a

variety of external reviewers can often help identify an acceptable value for a parameter.

Although such an analysis, even though carefully reasoned and conducted, cannot

conclusively identify the proper value for an unknown parameter, it may provide a suitable

interim result until better information can be found.

A.4.2 Model Development/Application

In revising travel models to be more sensitive to pricing, several development and application

issues must be considered, including the following:

Connectivity and Feedback: As the Case Study Model demonstrates, in order for pricing to

affect each simulated travel choice, the model set must be connected; i.e., all travel prices

must influence each model step. In a four-step model, one implication of this is that the

model must be applied backwards and forwards, as in the Case Study Model. This

increases model application time and resource requirements (e.g., hard disk space) but is

probably unavoidable within the current sequential model structures. Advanced

programming, such as combining the mode choice and distribution steps, can minimize this,

but only at the cost of increased overall complexity.

A related issue is the question of achieving model equilibrium, usually defined as the

consistency of travel speeds throughout the entire model. It is unclear if or how these

improvements to handle pricing would affect the potential and the need to iterate the model

set until speed equilibrium is achieved. It may be that price-sensitive models need to use a

different definition of equilibrium, possibly one that involves achieving consistency of
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equivalent prices throughout the model (equivalent price being defined as actual price plus

the travelers cost of time).

Effects of Inflation: As noted above, the sensitivity of travelers to price, as reflected in the

models coefficients, is expected to change over time with the general rate of inflation. Price

coefficients that are borrowed from other areas (or from different years) must be adjusted to

account for the effects of inflation. This means that cost coefficients can be expected to

decrease numerically over time, as the purchasing power of money erodes, more or less

steadily, over time.

Extreme care must be taken in forecasting price information. It is commonly assumed that

all travel prices will rise in the future at the same rate as inflation, meaning that there is no

change in the relative price between travel choices. This is not always true. For example,

transit fares in many cities have not kept pace with inflation. If a forecast assumes that that

trend will continue into the future, then that must be reflected in reduced fare values for a

future model run. Similarly, as Figure A-5 shows, the trend in automobile operating cost has

been dropping since after World War II, when expressed in 1994 dollars, and the trend is for

continued declines in real dollar terms.

Figure A-5

Trends in Passenger Car Operating Cost

Vehicle Operating Cost Trend and Forecast

Year

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

1950 1960 1970 1980 1990 2000 2010 2020

Historical Forecast: DOE gasoline price Forecast: gasoline price trend
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Different Strategy Specifications: Some market-based strategies have many different

variations which affect how the strategy is implemented and may affect how it is perceived

by the public. However, it is unlikely that any travel model will be sophisticated enough to

be sensitive to such nuances. It is enough of a challenge to create models which are properly

sensitive to a change in actual travel price, much less be influenced by the context in which

the change is implemented.

For example, two ways of increasing the user cost of driving are: 1) increase the gasoline tax,

and 2) convert the cost of motor vehicle insurance from a semi-annual payment to a pay-atthe-pump fee, as has been proposed in California. Although these two measures are quite

different from an implementation perspective, it is questionable if motorists would perceive

them as different (as long as the increased price per gallon was the same). It would seem

reasonable to estimate their quantitative effects on travel in the same manner.

Model Refinement/Development: Some of the enhancements described above can be made

fairly easily, while others require considerable effort. An example of the former would be the

Case Study Model feature which permits the separate consideration of LOV and HOV

parking price, thus allowing the testing of differential parking price by zone by vehicle

occupancy. It would be very easy to incorporate this into a mode choice model which

already uses parking price and splits trips by occupancy level, which quite a few mode

choice models already do. This would require only a few hours of programming to revise the

mode choice application procedure; no new calibration would be required.

At the other end of the spectrum, modifying the distribution model to use composite

impedance is a much more significant task, requiring the development of a new program (or

modification of an existing one) to calculate composite impedance and the complete recalibration of the distribution models to use the new impedance value. This could take from

a few weeks to a few months.

It is important to recognize that the improvement of the pricing sensitivity of most existing

travel models is a difficult undertaking which requires time, data, computing resources, and

staff expertise which are not universally available in local planning agencies. Such a task is

made all the more difficult since agencies are usually expected to pursue this work in

addition to their usual on-going planning and forecasting duties. One way to organize the

workload is to establish formal parallel tracks, one for on-going work and another for new

model development, to ensure that neither effort is neglected.

Of course, agencies which are in the process of updating their models or developing new

models anyway should take all reasonable steps to improve the sensitivity of their models to

pricing. The features of the Case Study Model and the enhancements described in Section

A.5 should be strongly considered. Once a commitment is made to develop new models, the

incremental effort to make such models reasonably price-sensitive is not very large.

A.5 Summary
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A.5.1 Summary of Desirable Model Features

The analyses of this appendix lead to the following major conclusions:

• Improve the Four-Step Process: Although many current four-step models are

now largely inadequate for modeling most pricing measures, this process can be

modified to improve its sensitivity to pricing strategies, as illustrated in this

appendix. Such modifications will address most of the criticisms of this process

and enable it to perform adequately until the next decade, at which time a new

model structure may be sufficiently developed and tested to serve as a

replacement.

• Improvements are Not Easy: A few of the model enhancements described above

are fairly simple, quick, and inexpensive but most of them are not. Most of

these changes will require a recalibration of a regions entire travel model chain,

which is usually a multi-year effort with a budget in the high six figures. If a

region is planning to pursue such an effort anyway, then incorporating these

improvements does not entail a significant incremental investment of resources.

• Improvements are Necessary Anyway: Most of the model enhancements

discussed here would help improve the sensitivity and credibility of travel

forecasts in contexts other than pricing strategies. For example, analyses of

TCMs such as changes in transit supply, land use, ridesharing policy, and HOV

lanes would benefit from these enhancements. Thus, there are several reasons

to enhance existing models.

• Recognize the Unknown Sensitivities: The fact that the sensitivity of certain

travel choices to some pricing measures is still largely unknown cannot be used

as an excuse to eschew their analysis. Additional research, transferable

parameters, and sensitivity analyses can be used to make a reasoned estimate,

leaving room for future revisions to model results when better information is

forthcoming.
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References

1. W. Allen, Users Guide for the MWCOG Mode Choice Application Program,

Microcomputer Version, for the Metropolitan Washington Council of Governments,

June 1993.

2. J. Heisler et al., Estimating Toll Diversion Using Existing Transportation Planning

Software, presented at the Second Conference on Application of Transportation

Planning Methods, April 1989.

3. The Phase III Travel Demand Forecasting Model: A Summary of Inputs, Algorithms, and

Coefficients, METRO, Portland, Oregon, January 1994.

4. W. Allen, Model Calibration for Employee Commute Options Planning , presented at the

Fifth Conference on Application of Transportation Planning Methods, April 1995.

5. K. Train, Qualitative Choice Analysis, Theory, Econometrics, and an Application to

Automobile Demand, The MIT Press, Cambridge, Massachusetts, 1986.

6. Greig Harvey and E. Deakin, A Manual of Transportation-Air Quality Modeling for

Metropolitan Planning Organizations, for the National Association of Regional

Councils, Revised Draft, November 1992.

7. B. Spear, New Approaches to Travel Forecasting Models: A Synthesis of Four Research

Proposals, Travel Model Improvement Program, John A. Volpe National

Transportation Systems Center for the U.S. Department of Transportation. and U.S.

Environmental Protection Agency, January 1994.

8. W. Allen and G. Davies, A New Method for Estimating Cold Start VMT , presented at

the Fourth Conference on Application of Transportation Planning Methods, May

1993.
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Appendix B

The STEP Analysis Package: Description and Application Examples

B.1 Overview

This appendix discusses STEP, a travel demand modeling package designed for planning

applications and policy analysis, and shows how the model was used to analyze a wide

range of transportation pricing measures for four California metropolitan areas: the San

Francisco Bay Area, Los Angeles, San Diego, and Sacramento. We begin with a

description of STEP, then discuss how we used the STEP models to examine the potential

impacts of pricing strategies on travel behavior, traffic volumes, energy consumption,

and emissions for two analysis years, 1991 and 2010. We also briefly describe other

methods used to supplement the STEP analyses, including estimates of the elasticity of

vehicle fuel economy with respect to fuel price, and detailed regional highway network

models. A series of tables present the analysis results.

B.2 Introduction to the STEP Package

STEP is a travel demand analysis package composed of an integrated set of travel

demand and activity analysis models, supplemented by a variety of impact analysis

capabilities and a simple model of transportation supply. STEP is based on

microsimulation - a modeling technique which uses the individual or household as the

basic unit of analysis rather than dealing with population averages. (cf. Orcutt, 1976). 

STEP results are aggregated only after the individual or household analyses are

completed, allowing the user great flexibility in specifying output categories. This is more

commonly referred to as sample enumeration in the literature, or as discrete choice

analysis.

STEP has been applied in a number of Bay Area studies over the years, and has been

adapted for use in studies in Los Angeles, Sacramento, Chicago, and the Puget Sound

region (Seattle). Applications can proceed with model re-estimation specifically for the

region - essentially, by creating a completely new set of models for STEP - but to date

nearly all applications outside the Bay Area have relied on extensive re-calibration of the

default (Bay Area) models plus a limited amount of re-estimation as needed to match

local conditions.
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Several features of STEP supported its choice as the basic modeling tool for the case

studies presented here. STEP's regional, subarea, and corridor-level analysis capabilities

fit well with the scope and scale of the policies under consideration. Its model

formulations can represent a comprehensive set of possible price effects, and its models

display linkages consistent with travel behavior and pricing theory. Its use of

microsimulation makes it possible to address many of the questions about equity and the

distribution of impacts that frequently arise in debates about pricing. Finally, it is far

faster to calibrate STEP for a region than to upgrade the regional models to include

pricing variables, and far faster and less expensive to run STEP than to apply regional

models.

STEP's data analysis capability is another important asset in pricing studies. STEP's

microsimulation formulation permits the package to be used as a survey tabulation

technique employing sophisticated data transforms and linkages. For example, many

travel surveys contain detailed information about the vehicles each household owns and

indicate which vehicle was used for each trip made on the survey day(s). Using STEP,

these vehicle data can be tabulated so that exact usage patterns by model year or vehicle

type can be determined. They also can be related to personal and household

characteristics to yield useful information about low-income households' dependence on

old vehicles and their contributions to vehicular emissions.

STEP itself was originally developed for sketch planning analyses in the San Francisco

Bay Area (Harvey, 1978). Since that time, all of the models in STEP have been

completely re-estimated and additional models addressing location choice, time-of-day

of travel choice, and congestion effects have been added. The most recent formulations

are nested logit. A number of versions of STEP are currently available, including options

that permit the analysis of activity data as well as travel data, and versions that use

either MOBILE or California EMFAC emissions data.

STEP's models are applied using actual or forecast data on household socioeconomic

characteristics, the spatial distribution of population and employment (land use), and

transportation system characteristics for the selected analysis year(s). The

socioeconomic characteristics of a sample of households and its members are usually

taken from a regional travel survey or from the U.S. Census Public Use Microdata

Sample (PUMS). Population, number of households, and employment by category

(type) are taken from the regional land use data base. Transportation level-of-service

data (times and costs) are derived from the region's travel model system. The land use

data are provided to STEP for subareas (which could be zones, districts, or corridors)

and for the region as a whole; the level-of-service data are provided in the form of large

matrices of interzonal times and costs. STEP then reads through the household sample,

attaching level-of-service and land use data to each household record as necessary. For

each household, STEP uses its models to predict a daily travel and activity pattern for

each individual in the household. Finally, household travel is summed up and

household totals are expanded to represent the population as a whole.
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STEP can analyze any change in the population or in the transportation system that 1)

can be represented in terms of the variables in its models and 2) can be associated with a

specific geographic area or grouping of households. Testing the effect of a change in

conditions or policies is a simple matter of re-analyzing the household sample using the

new data values, and comparing the results with previous outputs. For example, a new

highway or new transit service can be represented by changed travel times and costs for

the areas served; a parking price increase can be represented by an increase in out-ofpocket costs; an increase in income in a particular area or for a particular population

subgroup can be represented by editing the household file to incorporate the revised

incomes. Along similar lines, future years can be represented through proportional

factoring and re-weighting of survey observations to reflect expected regional trends, or

can be based upon a more sophisticated microsimulation of household changes based on

cohort survival and other methods of demographic forecasting.

The sampling framework preserves the richness of the underlying distribution of

population characteristics and permits tabulation by any subgroup with sufficient

observations to be statistically significant. For example, the results can be disaggregated

by income level and age, which would allow an assessment of effects for, say, various

income quintiles among the retired population. This is a significant advantage over an

aggregate model, which uses zonal averages for most socioeconomic-economic data.

STEP maintains its quick response capability while achieving great detail in representing

behavior in part by reducing its detail in representing transportation networks. STEP

does not have an internal transportation network representation and traffic assignment

model, so changes in level of service resulting from changes in demand must be

calculated in another way. Both an approximate method and a more detailed and

conventional network modeling approach have been developed for this purpose.

To approximate the effects of changes in demand on network performance and vice

versa, a simple routine for estimating level-of-service was incorporated into STEP in the

early 1980s (Harvey, 1993). The simplified level of service model uses peak and off-peak

travel times and base case demand estimates to calibrate a supply function for

appropriate spatial groupings of trips (i.e., trips in broadly-defined corridors). The

basic form of this equation is: t=a*(1+[V/C]^b), where t is the travel time in minutes per

mile; V is the volume in vehicles per hour; C is the capacity in vehicles per hour; and a

and b are coefficients fit to each corridor. For each change in demand, the calibrated

function can be used to compute a new equilibrium in the corridor (This is generally

known as the Bureau of Public Roads function for determining travel time on congested

links).

While the simplified level of service model is useful for many analyses, it is intended only

as an approximation of changes in network performance and is likely to be inadequate

in cases where large network perturbations could occur or where specific route choice
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changes are at issue. When network questions are critical, STEP must be used in

conjunction with a more detailed network model.

In the typical application, STEP is interfaced with the region's detailed highway

network. STEP's modal trip outputs are summarized on a district-to-district basis (a

district is defined as an aggregate of the zones for which land use data are reported; for

example, in the Los Angeles region there are 1555 zones and 55 districts defined by the

regional agency). If the policy under analysis results in any significant differences from

the base-case district-to-district trip tables, the differences are used to factor the zone-tozone trip tables in the aggregate model system. The network models are then run using

these new trip tables, and the results are fed back into STEP as a revised set of level of

service inputs. Iterations continue much as is done in a conventional travel model

system until an acceptable level of convergence is achieved. Transit networks also may

need to be run in conjunction with STEP in cases producing significant differences in

highway travel times of a sort likely to affect bus operations.

For certain transportation pricing measures, such as proposals to toll specific links or

facilities in a network, use of the detailed network models together with STEP is of

particular importance. For the case studies presented here, we used the network models

for Los Angeles and the Bay Area to test the route choice effects of congestion pricing,

interfacing in the manner described above with the versions of STEP developed for each

region.

Three major variants of the STEP model system are shown in Figures B-1a, b, and c;

Figure B-1b shows the version used in our pricing studies. The basic data requirements

of the STEP model are summarized in Figure B-2. A typical sequence of activities for a

STEP application is shown in Figure B-3.
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Figure B.1a:

STEP Model Structure with Basic MTC Models

 Household Characteristics Dependent on Travel:

Number of Autos Owned

 Daily Household Trip Choices:

Trip Frequency (HBW, HBS, HBO, NHB)

Trip Destination (BS, HBO, NHB)

Trip Mode Choice (HBW, HBS, HBO, NHB)

Note:

 HBW - Home-Based Work Trips

 HBS - Home-Based Shopping Trips

 HBO - Home-Based Other Trips

 NHB - Non-Home Based Trips
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Figure B.1b:

STEP Model Structure with Enhanced MTC Models

Note:

HBW - Home-Based Work Trips

HBS - Home-Based Shopping Trips

HBO - Home-Based Other Trips

NHB - Non-Home Based Trips

Major Household Location Choices:

Residential Location

Primary Workplace Location for Each Worker 

Household Characteristics Dependent on Travel:

Number of Autos Owned

Daily Household Trip Choices:

Trip Frequency (HBW, HBS, HBO, NHB)

Trip Destination (HBS, HBO, NHB)

Trip Mode Choice (HBW, HBS, HBO, NHB)

Time Characteristics of Household Travel:

Work Arrival Time

Transportation System Performance:

Highway Corridor Delay
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Figure B.1c: STEP Model Structure with Activity-Oriented Models

Major Household Location Choices: Residential Location Primary Workplace Location for Each WorkerHousehold Characteristics Dependent on Travel: Number of Autos Owned

Transportation System Performance: Highway Corridor Delay

Full-time WorkersWork ShiftProbability of WorkingWork Start TimePrimary Work ModeDiversion Home-Work Number, Location, ModeDiversion Work-Work Number, Location, ModeDiversion of Work-Home Number, Location, ModeNon-work Chains Number of Activities Location of Actives Mode Timing

Full-time StudentsProbability of AttendanceSchool Start TimePrimary School ModeDiversion Home-School Number, Location, ModeDiversion School-School Number, Location, ModeDiversion School-Home Number, Location, ModeNon-School Chains Number of Chains Number of Activities Location of Activities Mode  Timing

Part-time WorkersWork ShiftProbability of WorkingWork Start TimePrimary Work ModeDiversion Home-Work Number, Location, ModeDiversion Work-Work Number, Location, ModeDiversion of Work-Home Number, Location, ModeNon-work Chains Number of Activities Location of Actives Mode Timing

Working-Age AdultsNot EmployedNon-Work ChainsNumber of ChainsNumber of ActivitiesLocation of ActivitiesMode Timing

Retired Not EmpNon-WoNumber Number Location Mode Timing

Daily Household Travel Choices:
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Figure B.2:

Primary STEP Data Requirements

Basic Data:

 Regional Household Travel Survey

 1990 US Census Public Use Microdata Sample

For the Survey Year:

 Geography

 land area, population, housing stock

 for tracts, zones, and/or districts

 Network Level-of-Service

 highway, transit

 a.m. peak, p.m. peak, off-peak

 times, costs

For Each Forecast Scenario:

 Geography

 land area, population, housing stock 

 for tracts, zones, and/or districts

 Network Level-of-Service

 highway, transit

 a.m. peak, p.m. peak, off-peak

 times, costs

 Economics

 expected real income growth

 expected real fuel price growth
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Figure B.3:

 Sequence of Activities for a STEP Application

Prepare Survey Data for Initial Analysis:

 Screen Survey for Unusable Observations

 Reweight Survey to Match Key Census Demographic Characteristics 

 Reformat Network and Geographic Data to Match Database Requirements

 Assemble and Test Database

Calibrate the STEP Models:

 Run STEP for Base Conditions

 Compare STEP Calculations with Actual Household Travel Patterns

 Adjust Constants, Beginning with Upper-Level Models, and Rerun STEP

 Iterate the Adjustment Process Until the Overall Fit is Acceptable

Prepare STEP for the Forecast Scenario:

 Adjust Household Data to Reflect Changed Conditions

 Income

 Subarea Population

 Household Type Cohorts

 Reformat Network and Geographic Data to Match Database Requirements

 Assemble and Test Database

 Run STEP to Create a Base Case

Test the Policy Alternative(s) with STEP:

 Alter the Database as Necessary to Represent the Policy Option

 Run STEP to Estimate the Effects of the Policy Option

 Post-Process the STEP Outputs

 Repeat the Analysis Sequence for Variants of the Policy Option
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B.2.1 Transferring STEP Models to Other Regions

Although each application of STEP could utilize models estimated specifically for the

region being studied, a less costly approach is to transfer models estimated in one region to

another. In the case studies presented here, STEP models originally estimated for the Bay

Area were transferred to Los Angeles, Sacramento, and San Diego, with detailed

calibrations and a moderate amount of model re-estimation in each case.

Procedures for transferring models and evaluating their performance are well established -

in fact, many regions routinely use one or more transferred models in their regional model

systems. The procedure for transferring STEP to a new region follows much the same

general sequence of actions and so will be discussed only briefly here.

To transfer STEP to a new region, the required data first must be set up. The region's most

recent household travel survey is obtained and checked (incomplete observations are

excluded), and network data and land use data for the year of the survey are extracted

from the regional modeling data bases. The data are then linked and a trial simulation is

carried out to determine how closely the models to be transferred match the actual travel

patterns in the survey data. Invariably, a sequence of adjustments to model constants (and

sometimes to a small number of coefficients) is necessary to achieve an acceptable

replication of the base travel pattern. These adjustments serve both to capture actual

differences in behavior and to compensate for variation in the way regional planning

agencies define certain variables such as transit wait times, income ranges, and specific

categories of land use.

Once an acceptable simulation of the survey year (the base case) has been obtained in

this fashion, STEP should closely reflect travel conditions and behaviors in the region to

which it is being transferred, and consequently can be used with local data and forecasts

for the full range of modeling applications.

B.3 Applying STEP to Pricing Measures

B.3.1 Overview

The application of travel forecasting models to specific pricing policies is rarely a

straightforward matter. In nearly every case, both the models themselves and the available

data bases impose some limits on the policies that can be tested. For example, the regional

transportation data bases (and models based on the data bases) typically lack information

about the variation of parking price in each zone, and may have only approximate

information about the vehicle used for a specific trip. In cases where such details would

play a large role in determining the impact of a policy being studied, only an approximate

estimate of the policy's effects can be formally estimated through modeling: the analyst

must devise a means of representing the policy as well as possible given the models and
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data, and must be prepared to make off-line calculations and adjustments to improve the

realism of the analysis, or to do further analyses after gathering additional information.

Some discussion of implementation scenarios is necessary simply to determine how a

proposed pricing concept should be analyzed; clearly, however, much more attention to

specifics would be needed in an actual implementation. In our analyses, for example, we

implicitly assume that evasion or outright fraud would be insignificant, hence the measures

would be fully effective as proposed. For most transportation pricing measures,

monitoring, enforcement, and audits would be needed to assure that.

In our case studies we found that it generally was possible to define transportation pricing

strategies in ways that were tractable from an analysis perspective and also yielded

information which is helpful in thinking about policies as they might actually be

implemented. The use of advanced modeling capabilities, along with the availability of

good data, made it possible to explore behaviors that often would be omitted from a more

conventional analysis. Nevertheless, the analyses did require a number of assumptions,

and they have certain limitations that must be acknowledged and taken into consideration

in policy evaluations.

The following sections detail how the pricing concepts analyzed in our case studies were

specified and analyzed. In each case, the underlying rationale for the pricing concept is

stated, a specific pricing measure is defined, modeling assumptions to represent the pricing

measure are outlined, and key implications of the assumptions are noted.

B.3.2 Congestion Pricing

Congestion occurs in the highway system when more vehicles attempt to traverse a

segment of road per unit of time than that segment can accommodate. Such a location is

called a bottleneck. Congestion pricing builds on the simple realization that travelers are

sensitive to the cost of travel; a fee levied at a bottleneck will divert some vehicles from the

traffic stream, reducing congestion. The diversion of a specific vehicle might be to a

different route, time-of-travel, mode, or destination; it could reflect a trip foregone; or, over

the long run, it might follow from a change in residence or workplace location.

Two major design issues arise in thinking about how to use pricing to manage congestion at

a bottleneck:

• Price level - Price can be varied over a wide range to achieve different levels of

traffic improvement. Economic theory tells us that price should be set to reflect

the social cost caused by the marginal user at a bottleneck, less the average

variable cost already paid by users. While this should be the clear goal of any

congestion pricing application, considerations of implementation and

management ease may point toward a simpler price criterion based, for example,

on achieving and maintaining a conventional level of service measure from the
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literature of traffic engineering. We know that the optimal level of congestion

reduction will be unique at each bottleneck, but it is much easier to explain a

generally applicable congestion reduction goal in the policy-making process, and

easier to implement and manage facilities based on observed performance. 

Hence, the actual criterion for setting the congestion price may well be framed in

terms of standard traffic level-of-service metrics (e.g., B, C, D, E). For similar

reasons of simplicity and clarity, specific prices might be chosen to reduce the

amount of change-making required (rounded to the nearest 25 cents or to the

nearest dollar, for instance), although with modern road pricing technologies this

would not be strictly necessary. Periodic adjustments in price are likely to be

needed to maintain effectiveness, and they too would likely be done in simple,

rounded increments of 25 cents or a dollar, unless electronic toll collection were in

place.

• Period of application - Some economists have argued forcefully that congestion

prices should change dynamically in response to traffic conditions, perhaps

varying from minute to minute to achieve the optimal reduction in congestion. 

However, few seriously believe that such a dynamic scheme would be

implemented any time soon, for several reasons: 1) the practical difficulties of

creating, testing, and maintaining the hardware and software required for such a

system; 2) the unresolved theoretical question of whether a truly dynamic system

would produce a stable set of prices; 3) the strong revealed preference of travelers

for predictable conditions, even if the price of predictability is a somewhat higher

average time or cost; and 4) the question of how to treat incident-related delay in

a dynamic pricing environment. An initial congestion pricing scheme more likely

would involve prices that can be explained through relatively simple signage and

do not vary from day-to-day (though weekend-weekday and seasonal variations

might be both desirable and feasible). Hour-to-hour variation might, however, be

used to avoid large price increases and decreases at the peak / off-peak

boundaries, and might be designed as a pyramid of prices centered on each peak

hour in order to be relatively easy for the driver to remember.

In addition to these basic design issues for pricing at a bottleneck, there is a question about

how widely congestion pricing would be applied in the highway network. While pricing

would be easiest to implement on limited access facilities, spillover from priced freeways to

unpriced arterials and collectors could be a problem in some locations.1 Local communities

seem unlikely to tolerate significant traffic diversion to the facilities under their jurisdiction,

 1 The first US congestion pricing project opened in December 1995 on State Route 91 in Orange County,

California; the San Francisco-Oakland Bay Bridge is currently being studied as a second possible

application. Because of the special characteristics of these two applications, spillover to arterials is not

likely to be a major issue. SR 91 pricing will apply only to the new lanes added in each direction, with the

original lanes left unpriced; in the Bay Bridge case there are essentially no realistic alternative highway

routes. The extension of pricing to other facilities such as I-10 in the Los Angeles area or I-80 in the Bay

Area would, however, have to confront the possibility of spillovers to parallel routes.
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and could be expected to oppose freeway pricing schemes if they created or worsened

congestion on local roads. The localities might, however, accept a broader-based pricing

plan which manages traffic on a systemwide basis, especially if part of the revenues were

returned to affected jurisdictions. Widespread implementation of congestion pricing hence

could mean pricing both freeways and parallel routes where significant delay appears.

The congestion pricing measures tested in our case studies were designed to reflect these

observations about the policy environment. We assumed that some form of electronic

payment system would be used rather than toll booths, so that there would be no stopping

to pay tolls. Prices were applied everywhere delay appeared in the highway network (as

represented in each region's model system - freeways and arterials plus some major

collectors). Price levels were set to reduce congestion to meet specific levels of service; we

investigated a range of level-of-service targets and eventually chose LOS D/E for use in all

four metropolitan areas.2 Our analyses allowed prices to vary by corridor, determined peak

definitions by the extent of congestion in each corridor, and permitted different prices to be

charged in each corridor for each hour of the AM and PM peak periods, but we stopped

short of dynamic pricing. Rather, we assumed that travelers would face a fairly simple

schedule of prices by time of day, readily comprehensible to travelers and influencing their

travel behavior and location choices.3

It is important to note that under this pricing approach, users of the facilities in greatest

demand still would perceive traffic as heavy and somewhat constrained, with speeds

below posted limits (at least for the cases considered here, higher speeds would not be as

efficient from an economic point of view). Note also that we assume that prices would be

maintained in constant dollars, meaning that from time to time price adjustments might be

necessary.

The STEP analyses were carried out by focusing on highway performance at the corridor

level, as follows. In the STEP calibration phase each of the metropolitan areas was divided

into major corridors based on topography and highway function. Each district-to-district

trip interchange was assigned to a corridor, and approximate volume-delay relationships

(i.e., expressing travel time per mile as a function of volume and capacity) were developed

for the corridors.4 This was carried out for both the AM and the PM peak in each region.

 2 The choice of LOS D/E was based on analyses of benefit measures from the STEP model which

indicated that stable, near-capacity flows (about 10 percent below actual capacities) were the most

economically efficient traffic regime. Specifically, we used delay reduction per marginal unit of price as

the measure of benefit.

3 We assumed congestion prices would be in effect on non-holiday weekdays only - 250 days a year.

4 The shape of the volume/delay curve is a critical determinant of the outcome of the analysis, because

it indicates how much traffic would have to be removed from the peak in order to achieve a given LOS. To

represent volume/delay relationships for corridor-level changes, STEP uses an equation roughly

analogous to the Bureau of Public Roads (BPR) equation relating link-level volumes and travel times. The

STEP equation was initially developed in a study for the California Energy Commission and later reestimated in studies for the Metropolitan Transportation Commission, the Southern California
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The level of service target was defined in terms of the volume delay function. For the

generic functional form used in this version of STEP, level-of-service D/E corresponds to a

travel time that is about 85 percent longer than the time under free-flow conditions. In

other words, the target level-of-service was represented by a 1.85 ratio of peak to

uncongested travel time in a corridor.

In the Los Angeles region, about 300 aggregate corridors were defined in this manner,

and about 220 of them - 73 percent - were sufficiently congested in the AM peak to justify

congestion pricing. For the San Francisco Bay Area 150 corridors were defined, with 90

(60 percent) meeting the criteria for pricing in the AM peak. San Diego and Sacramento

were both considerably less congested; only 15 percent of the 80 corridors analyzed in San

Diego and 8 percent of the corridors analyzed in Sacramento were candidates for pricing.

To estimate the price needed to achieve the target level of service, STEP was applied to each

sample of households and the average price per mile was adjusted on a corridor-bycorridor basis until all corridors were at or below the 1.85 peak/off-peak travel time ratio,

and no corridor had a higher congestion price than necessary. This took approximately five

iterations (model runs) for each region and each analysis year.

For each region, a specific congestion price was estimated for each corridor and time

period.5 For 1991 conditions, the congestion prices would vary from zero (for the

 

Association of Governments, and the Puget Sound Council of Governments (Seattle region). The equation

expresses the relationship between the ratio of average peak to average off-peak travel times in each

"corridor" - basically a trip exchange - and the aggregate capacity serving that corridor. Separate

estimations were done using data from the detailed highway networks of the three regions; because the

coefficients of all three models were nearly identical, a single equation was implemented in STEP. The

specific functional form is t/to = 1 + (V/C)2.

This corridor function, derived from regional network models, shows travel time climbing rather

gradually as congestion builds. We know from highway operations research that the buildup of

congestion for specific facilities is more abrupt and steeper in the region of capacity flows than this

equation indicates. However, because the corridor function represents an aggregation of facilities of

different types, it reflects the "family" of volume-delay relationships for the freeways, arterials, and major

collectors embedded in the network models and producing their travel time estimates.

The steepness of the buildup of congestion is important in determining what the congestion price would

have to be. If the slope is steeper than our equation indicates, as it would be in a corridor with a single

facility, congestion prices could be lower for a given level-of-service improvement than we report here. 

This is because a steeper slope implies that fewer vehicles would have to be priced off each corridor's

facilities to achieve a given LOS. We tested a number of functional forms in STEP, and the different forms

did indeed produce some variation in optimal prices. For example, letting the slope parameter rise to 4,

the value used in the standard Bureau of Public Roads (BPR) equation, would lower the "optimal"

congestion price by about 40 percent (regional average). Since the BPR curve is for a single freeway

facility, it is much steeper than any corridor curve could be (unless the corridor consisted of a single

freeway). Therefore the BPR value should be viewed as an outer limit.

5 In the four case study regions, PM peak conditions are less sharply congested but last longer than AM
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uncongested exchanges) to as much as $1.00 per mile for a very few corridors, such as the

I-80 corridor and the Bay Bridge corridor in the San Francisco Bay Area and the I-405 and

I-10 corridors in the West Los Angeles - Santa Monica area. In San Diego, the highest

corridor level prices would reach about 40 cents per mile, whereas in Sacramento the

highest corridor prices would be about 20 cents per mile. By the year 2010, congestion is

expected to worsen considerably in all four regions; many more corridors would be

candidates for pricing, and prices would have to be higher to maintain the LOS D/E

target.6

Estimated reductions in travel time, VMT, trips, emissions, and fuel use resulting from the

calculated congestion prices, as well as estimates of the total revenues generated, were

calculated by summing up the analysis results for each corridor. To simplify the

presentation of price levels and provide an indicator of overall price impact, a corridorweighted average price per mile is shown in the tables, and can be thought of as the

average price peak period drivers would face overall. It is not necessarily the price any

individual traveler would experience. For example, the price necessary to obtain LOS D/E

on the San Francisco Bay Bridge in 1991 would have been about $6, or 75 cents a mile for

that corridor, in contrast to the average Bay Area AM peak price of about 9 cents a mile.7

 

peak conditions. Hence evening congestion prices, at least initially, could be somewhat lower but would

be in effect for a somewhat longer period of time than those in the morning peak. However, congestion

pricing would flatten and spread out the AM peak somewhat, diminishing AM-PM differences in prices

and hours of application.

6 One might ask whether the prices arrived at in this manner are the optimal prices. The issue is not

simple to resolve; in the first place it is well understood that user-optimal may not be identical to systemoptimal (Wardrop,1952). User optimality is examined here, although we note in passing that pricing also

could be used to achieve system rather than user optimality. The analysis of user-optimal prices is

particularly complex, because travelers can respond to pricing in a number of ways, shifting trips among

corridors and altering their frequency and times of travel. It is necessary to account for the possibility

that travelers could switch to another route, travel at a different time of day, change modes, choose

different destinations for some trips, increase or reduce the number of trips made, move to a different

residence, or change their place of work. STEP accounts for these phenomena, but because STEP is a

hybrid mix of non-linear demand functions of various types, it is not possible to mathematically prove

the existence of a unique set of congestion prices for a given level-of-service criterion. Simulation offers

an alternative approach for assessing whether model results represent a stable and unique equilibrium,

and we used it to investigate the optimality of our corridor prices. We applied a number of procedures

designed to determine whether STEP would produce different sets of "optimal" congestion prices. These

included adopting different search algorithms in the program code, and starting the searches from

different initial corridor prices. All search strategies that produced stable outcomes were in agreement

with the initial "optimal" prices, which lends some support to the notion of a unique equilibrium.

7 The Bay Bridge congestion pricing studies underway at the time of this writing are discussing

considerably smaller prices, e.g., a $3.00 peak period toll. A $3.00 toll in 1996-1997 dollars would be the

equivalent of a $2.50 toll in 1991 dollars. Such a price increase would be sufficient to cut the queue at the

toll plaza by about a third, but would not achieve LOS D/E.
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Corridor-level results are useful for preliminary planning purposes, but for implementation

planning it is important to translate the results into specific facility charges. Within the

resources of this study, we were not able to test congestion pricing in a full network context

for each of the four case study areas. Instead, we ran STEP for the four areas, then selected

two corridors for more detailed analysis: I-80 from the Carquinez Bridge to the OaklandSan Francisco Bay Bridge, and I-10 from Santa Monica to Downtown Los Angeles - two of

the most congested locations of all those we studied. We ran regional network models for

the Bay Area and Los Angeles to see how prices would need to vary among facilities in the

selected corridors, given the corridor prices and demand levels produced by STEP. The

Tranplan network analysis program was used, with an equilibrium traffic assignment for

the AM peak hour and price incorporated into the route choice criterion.8

Tranplan corridor analyses produce results comparable to STEP if the per-mile price is

applied equally across all facilities in the corridor. With the same price per mile on all

alternate routes, the main effect will be a reduction of overall corridor demand rather than

a rearrangement of traffic among corridor facilities (absent differential prices, traffic in a

congested corridor will distribute itself such that all routes will have about the same travel

times). However, Tranplan analysis made it possible to test link-by-link pricing to more

precisely target bottlenecks in the system. We went through five iterations in which we

manually adjusted link prices in the two test corridors, each time increasing the price on

links that remained congested and decreasing the price on links with better than D/E levelof-service and then running Tranplan to evaluate link-level impacts. Overall corridor delay

reduction tended to improve from iteration to iteration, while overall price levies tended to

fall. After the five iterations, we judged that the effectiveness of congestion pricing, in

terms of reduced delay per dollar, might be 10-15 percent higher in these corridors than the

approximate results of the STEP analyses would suggest. This should be considered when

reviewing the average prices and/or time savings presented in the tables.

What if prices varied by location, but were set at modest prices initially and were increased

only gradually to the levels necessary to avoid stop-and-go driving? This approach would 

give people a chance to adjust their travel and location behavior under prices that

accurately signal the ultimate spatial distribution of impacts. Dynamic models would be

necessary to fully explore the changes that such a pricing approach would produce over

time; STEP does not currently include such dynamic models. However, STEP is able to

evaluate lower-than-optimal congestion prices as would occur in a pricing phase-in (and

perhaps in many cases where prices are set on political as well as technical grounds). We

tested the impacts of lower prices by taking the final corridor congestion prices for the Bay

Area and Los Angeles and applying them in 10 percent increments (i.e., prices at 10

percent of optimal corridor prices, 20 percent of optimal prices, etc.). The STEP results

indicate that the shape of the aggregate demand curve is moderately convex, with slightly

 8 It was possible to use the network provided by the Southern California Association of Governments

for this part of the analysis, but for the Bay Area a new Tranplan network was created as part of the study

(MTC uses UTPS networks and the study team did not have access to this software).
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decreasing effects for each price increment. For each of the two case analyses, the first

price increment of 10 percent produced almost twice the impact of the final increment of

10 percent. This suggests that implementing a constrained price can still be reasonably

effective.

The STEP analyses are for scenarios in which pricing is used to manage congestion

wherever it occurs on the network of highways and arterials; how congestion pricing

would work if implemented on a few facilities is a different question. Even if the ultimate

objective is system-wide implementation, it is likely that initial applications would be spot

pricing - pricing applied to just a few facilities or corridors. As we discussed earlier,

however, closely-parallel routes could receive significant amounts of diverted traffic if a

single congested facility is priced; such traffic diversion could lead to significant congestion

on the parallel routes; and opposition from affected jurisdictions might well be enough to

halt implementation, unless the parallel routes can be priced as well.

Even where diversion to parallel routes is infeasible for most travelers, as is the case for the

San Francisco-Oakland Bay Bridge, or where each facility in a corridor can be differentially

priced, as our analyses of the I-80, I-405, and I-10 corridors considered, a number of

concerns about spot pricing remain. For example, our analyses indicate that

implementation at a single highly-congested location or in a single corridor will alter

regional patterns of trip distribution, residential location, and workplace location, with

specific effects varying with household income level. The result of spot pricing could lead

to a distortion of the spatial structure of the region, because the spot pricing leads to

exaggerated locational impacts. Thus single facility pricing may produce a misleading view

of the eventual areawide effects of congestion pricing.

B.3.3 Employee Parking Charges

In most metropolitan areas, parking is commonly provided to its users free of charge,

although providing such parking can be quite expensive and presumably is recouped in

other ways (e.g., through the prices charged for goods and services, for private parking, or

through public tax subsidies, for public parking). Charging for parking, whether done

through private initiative or in response to government incentives or mandates, would

make the costs of parking more apparent to travelers and would likely reduce auto use

somewhat.

Parking could be priced for all users, and sometimes is (at many commercial garages, e.g.,

or by local governments who install on-street meters). However, proposals for the

implementation of parking pricing often focus on daytime employee parking, since the

associated employee travel typically occurs during the costly peak periods. If employees

had to pay for parking, it is reasoned, they would be more likely to use alternative

commute modes such as transit, carpooling, or walking. In the case studies we present here

we analyze only employee parking charges.
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In comparison with congestion pricing, parking pricing is a relatively simple measure to

analyze using STEP. The average zonal parking price (daily, for work trips, and hourly, for

non-work trips) is a variable in each of the STEP mode choice models, and zone-level

parking price data are available for each of the four metropolitan areas studied here. Thus,

any parking scenario that can be expressed as a change in an average zonal price can be

analyzed using STEP.

Proposed parking price changes do not always target the average zonal parking price,

however. Consider a city in which a substantial amount (varying by zone) of the all-day

parking is provided by a private operator, who charges a daily fee for use. The operator,

perhaps given an incentive by local or state tax policy, decides to raise the fee by $1.00 per

day. To analyze the impact of this increase, it is necessary to have an estimate of the

percent of all-day parking in each zone that is provided by this operator and hence will be

affected by the increase. A number of cities maintain a parking inventory which could

provide this information, although many other cities would have to conduct a special

survey to produce this estimate.

Other parking pricing proposals can be far more complicated to analyze. Consider a

$3.00/day parking surcharge which applies only at employment sites with 100 or more

employees. In order to translate this surcharge into zonal average price estimates, we

would need information about the fraction of workers in each zone who work at sites with

100 or more employees. We would need to account for the possibility that some of those

employees do not provide any parking now, in order to figure out what share of each

zone's employees would be subject to the fee. The possibility that some employees could

avoid a fee at their workplace by parking elsewhere should already be reflected in the

calculation of zonal average parking cost, but we also must consider the possibility that

employers will simply pay the fee themselves rather than passing it on to the employee,

again reducing the number of affected workers (note that certain implementation

strategies, such as treating parking as a taxable benefit or requiring the surcharge to be

collected from the employee as a payroll deduction, would reduce the likelihood and the

impact of this latter concern). Very few cities have an employer and parking data base

organized to support such an analysis, and we have found none that has information on

likely alternative parking sites or on employer responses to such policies. Hence,

calculating the actual increase in zonal average parking charges that our surcharge would

produce could require either a great deal of data collection. Nevertheless, for preliminary

planning purposes it usually will suffice to make some simple assumptions in developing

the data inputs or in interpreting the results. For example, we could analyze the parking

surcharge as if it applied to all employees and then factor the results downward to account

for its more restricted reach: if regional employment data indicate that only 40 percent of

the regions jobs are provided by employers with 100 or more workers, then our impact

estimates should be reduced by about 60 percent.

For our four case studies, we utilized parking cost data files developed by the regional

transportation agencies. These files present only the estimated average employee parking

price (nominal price) by zone. Given the data we had available, we chose to model two
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general policy options: a flat daily charge on all employees who drive alone and do not

currently pay for parking, as well as a daily surcharge on all employee parking, paid or

not. The first option could be thought of as a rough approximation of what prices might be

like if free parking were no longer provided to employees; or it might be thought of as the

result of a policy that imposes an impact fee or tax on free employee parking but waives the

fee on parking that is already priced at or above some threshold level. The second option

would be a flat impact fee (or tax, depending on how it is structured and applied).

Using STEP, a range of daily employee parking charges from $1.00 to $10.00 was examined

for each of the four metropolitan areas. To model the minimum price threshold option,

drive-alone parking fees for all workers in each sample were set to the specified minimum

or to current levels, whichever was higher - fees in zones where existing zonal average

parking fees exceeded the threshold charge were held constant. The second option we

evaluated, a flat fee or surcharge on all employee parking, was even easier to represent

than the minimum price option; the fee was simply added to the employee parking price in

effect in each zone. In both analyses, we assumed that the employees would personally

pay the parking charges (hence we treated the charges as out-of-pocket expenses). We also

assumed that carpool and vanpools would be permitted to park for free at their

destinations, and that no charges would be imposed for park-and-ride parking. These

latter assumptions are generally consistent with the current treatment of HOVs and parkand-ride in the four case study regions.

STEP accounts for the full set of travel effects we would expect parking pricing to have,

including impacts on highway performance, but to verify that STEP's simplified level-ofservice functions provide an adequate representation of the latter, the peak period trip

tables from STEP were assigned using Tranplan to the relevant networks for Los Angeles

and the Bay Area, and the resulting travel times were cycled back through the STEP model.

 No significant changes from STEP aggregate performance measures were identified.

Results for $1.00 and $3.00 parking price increases are reported here. Given the

ubiquitousness of free parking in each of the four regions, the differences between the two

policy options were minimal: the estimated impacts of the parking fees varied by 10 percent

or less (i.e., a reduction of 1 percent in VMT for the minimum price option, a 1.1 percent

VMT reduction for the surcharge).

Our assumptions that prices would apply to all drive-alone vehicles9 and that HOV

parking would be exempt from charges maximize the impact of the employee parking fees.

 In actual implementation, a number of factors could reduce these impacts. For example, as

our earlier discussion pointed out, exemptions of certain employers would reduce the

number of employees in each zone who actually would pay a parking fee, with the impact

varying widely among zones.

 9 To calculate impacts on an annual basis, we assumed employee parking charges would apply 250

days a year.
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In addition, in situations where parking is differentially available to or subsidized for

different income or occupation groups, the impacts of price changes may vary from those

we have shown. Our results assume that a parking fee would be paid by all who drive

alone. But under some conditions the fee might actually be absorbed by the employer; for

example, some blue collar workers have negotiated for free parking as part of their labor

agreements, and a parking surcharge would have to be paid for by the employer or

compensated through offsetting salary increases. In cases such as these, the fee on parking

could vary systematically with income group, and hence be disproportionate to the number

of workers affected.

Finally, the impact of free parking for high-occupancy vehicles deserves special attention. 

Free HOV parking is a common measure in our case study regions and might well be

permitted under a policy to charge for parking; but it is not a necessary feature of the

analysis. If the parking fees apply equally to HOVs, HOV users still experience an

advantage over solo drivers because they can split the cost among all passengers, but the

price differential between drive-alone and HOV decreases - by about 40 percent on

average. Based on STEP runs for all four metropolitan regions, this diminished advantage

would cut the impact of the parking fee by about 15 percent, because fewer current drivers

would switch to HOV and some of those who currently are HOV users would decide to

drive to work.

B.3.4 Fuel Tax Increases

A fuel tax increase would be a direct approach for reducing fuel consumption and also for

reducing greenhouse gas emissions (because CO2 emissions are proportional to fuel

consumed). Its effects on other emissions and travel are muted, though still significant,

because auto purchase decisions and usage patterns can lead to a more efficient vehicle

fleet and reduced per-mile operating costs.

The fuel tax increases analyzed here are expressed as straightforward additions to the atthe-pump price of gasoline and diesel fuel. For our base case, vehicle fleet fuel economy is

about 22 miles per gallon (.0364 gallons per mile). Base-case fuel cost is about $1.20 per

gallon, or 5.45 cents per mile at average fuel economy. With no increase in fleet fuel

economy, a 50 cent per gallon fuel tax increase would add about 2.3 cents and a $2.00 per

gallon tax (or other form of price increase) would add about 9.1 cents to the average permile cost of driving. However, empirical evidence and common sense suggest that the inuse vehicle fleet would become more efficient under a significant fuel price increase. In the

many households with more than one car, household members could quickly arrange to

make more use of their fuel-efficient vehicles and less use of their gas guzzlers, cutting

fuel consumption considerably. Over time, both single-vehicle households and multivehicle households could be expected to increase vehicle fuel efficiency as they replace

some vehicles and retire others.
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How fast and to what degree such vehicle substitutions, replacements, and retirements

might occur in response to fuel price increases has been a matter of considerable dispute. 

The issue is important to our analysis because it could significantly affect the impact of a

fuel tax. Travel and location choices are undoubtedly affected by the costs of vehicle

ownership and operation, i.e., by both the number of vehicles a household chooses to own

and the type and age of its vehicle(s). Faced with higher fuel costs, a household which for

whatever reason does not reduce its per-mile fuel consumption (by changing its vehicle

holdings or changing which vehicles it uses most) will have to devote more of its income to

fuel purchases, or take steps to reduce its vehicular travel (or some combination of the two).

 If on the other hand the household finds it possible to reduce the price effect through

vehicle substitution and replacement, fuel efficiency improvements will have a smaller

effect on travel.10

STEP includes a model of the number of vehicles a household chooses to own, so we were

able to capture the effects of fuel price increases on auto ownership in our analyses. 

However, STEP currently does not address the type or age of the vehicles owned,

information which is needed to estimate the cost per mile under different fuel price

scenarios. We did not have direct access to a model of household vehicle purchase

decisions for this study, so to account for the broader range of impacts, we turned to

outside sources for evidence on the elasticity of fleet fuel economy with respect to fuel price.

The literature from the U.S. and abroad suggests that fleet fuel economy (miles per gallon)

is quite sensitive to the price of fuel. Pickrells recent research (Pickrell, 1993) and his

syntheses for the Presidential Commission on Greenhouse Gas Reduction (a group known

popularly as Car Talk)(Pickrell, 1995) examine the impact of fuel prices and report

findings from a wide range of reputable U.S. and international studies in advanced

economies. He cites numerous estimates of long-run average elasticity of fleet fuel economy

with respect to fuel price in the .5 - .6 range, with estimates as low as .2 to .3 and some

higher than 1.0. An elasticity of 0.5 means that a 25 percent increase in real fuel price (e.g.,

from $1.20 to $1.50) would increase long run average fleet fuel economy from 22 miles per

gallon (mpg) to almost 25 mpg; a 167 percent increase in real fuel price (e.g., from $1.20 to

$3.20) would increase long run average fleet fuel economy from 22 mpg to about 40 mpg

(82 percent). A 40 mpg fleet average sounds high for U.S. conditions, but it cannot be

dismissed out-of-hand, especially for a longer-term scenario (2010 or later) and/or one in

which the price increase was implemented nationwide or in a majority of urban states (so

that manufacturers would have sufficient time and incentive to offer more fuel-efficient

vehicles).

Substantial fuel economy improvements could, in fact, be obtained through shifts in

consumer choices among the vehicles currently available for purchase: for example, by

 1 0 A household's ability to change vehicle holdings is related to its current and expected income, its

current vehicle holdings, ownership and operating costs of the alternatives, etc. The household's

willingness to change its vehicle holdings depends on many additional factors, such as vehicle seating

capacity, comfort, handling, and safety; fuel economy, an element of operating cost, is but one influence.
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purchasing the four cylinder rather than the six cylinder version of a midsize sedan, a

consumer could obtain a 10-15 percent improvement in mpg. This percent increase in fuel

economy is about what a 25-50 cents per gallon price increase would require, at a .5

elasticity. However, for large fuel price increases, an elasticity of .5 would imply that at

least some consumers also would have to change the type of vehicles they own and use,

i.e., greater numbers would have to purchase and use highly efficient vehicles and restrain

their purchase and use of the least efficient ones. Currently over a dozen vehicles are sold

in the U.S. which obtain over 40 mpg, so this seems technically feasible, and may become

more so if gradual improvements in technical efficiency, averaging perhaps 1-2 percent a

year, are forthcoming over the next decade or so, as many analysts expect (Pickrell, 1995). 

Whether buying habits in fact would change in the necessary fashion could be debated.

For further evidence of how fuel prices might affect fleet composition and use, we turned to

models of the vehicle fleet. Since our case study regions were all in California, we were

particularly interested in an analysis tool known as the Personal Vehicle Model (PVM),

which the California Energy Commission has used to estimate the composition of the state's

vehicle fleet by size and age, as a function of the price of fuel and other factors.11 We asked

the CEC to provide some indication of the PVM elasticity of fuel economy with respect to

fuel price, as evidence for California fleet conditions. A run of the PVM made for this study

by the CEC in January 1995 indicated that a $2.00 fuel surcharge would lead to a 2 mpg

increase in fuel consumption (from 22 to 24 mpg), for an average elasticity of .05.

The PVM-estimated elasticity is much lower than the elasticities reported by Pickrell. A

partial reason for the difference is that most national and international long-term elasticity

estimates allow for changes in the products manufacturers offer in response to fuel price

increases. In contrast, the PVM analysis assumed that the price increase would only apply

in California, and that manufacturers would not increase the fuel economies of the cars

they offer in response to a change in only one state, even a state as large as California. The

PVM analysis does allow consumers to purchase more efficient vehicles from those

otherwise available. It does not consider increased relative use of the more fuel efficient

vehicles within each households existing vehicle holdings.

We discussed the fuel economy - fuel price elasticity issue with a number of researchers and

ultimately settled on testing a range of assumptions about the fleet response to fuel price,

expressed in terms of the elasticity of fuel economy (miles per gallon) with respect to price.

Results for three elasticity levels are reported here: 0.5, 0.16, and 0.05. The researchers we

contacted felt (and we agreed) that the .05 PVM elasticity should be used as a lower

boundary, and that a 0.5 elasticity, i.e., the lower end of the .5-.6 estimates from the

national studies, was a reasonable upper boundary for a California-only policy.12

 1 1 The PVM was developed more than a decade ago, and at the time of our study the CEC was engaged

in a multi-million dollar project to replace it with an updated package based on new data and state-ofthe-art modeling concepts. Hence we chose to treat the PVM as one source of evidence rather than to rely

solely on it.

1 2 A California-only gas tax increase seems more plausible for small to moderate tax increases (25 cents
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The fuel economy elasticities can be used to compute average mpg and out-of-pocket

vehicle operating costs per mile resulting from a fuel price increase. For example, consider

a two dollar per gallon increase, i.e., a fuel price of $3.20 per gallon. In comparison to the

current $1.20 per gallon, for which average out-of-pocket expenditure is about 5.5 cents

per mile, the estimated mpg and cents-per-mile costs would be:

Elasticity MPG Cents per Mile

0.00 22 14.6

0.05 24 13.3

0.16 28 11.4

0.50 40 8.0

It is clear from this table why fleet response to fuel price is such an important issue. At a

.05 fuel economy elasticity, the average fuel cost per mile increases by more than 140

percent; this would result in large reductions in travel. By comparison, at an elasticity of

.5, the average fuel cost per mile increases by about 45 percent. In the first case, trip and

VMT reductions account for most of the drop in fuel use, while in the second case,

improved fleet fuel economy accounts for most of the drop in fuel use. Since both the

incidence and the economic implications of the fuel price increase differ markedly between

these two cases, forming a more precise understanding of fleet fuel economy sensitivity to

fuel price is of some importance.

Using our three elasticities, we studied a range of fuel price increases from $0.10 to $3.00 in

10 cent increments. The results for the $2.00 fuel price increase under different elasticity

assumptions are presented here, along with some results for a $0.50 price increase. Results

for these two price levels are sufficient to support generalization about price effects over the

full range.13

 

or less) than for higher ones, especially those of a dollar or more. Of course, it is not necessary to assume

that a fuel tax or other fuel price increase would be implemented in California only: the analyses could

equally well represent the impacts of scenarios involving federal fuel tax increases or state tax increases

implemented in many states. Also, for the analyses presented here, at-the-pump price increases

implemented by sellers would have the same effects as a fuel tax increase. A California-only

interpretation of our analyses does not necessarily require new, highly efficient vehicles to be produced

for the state market (though it might make California an attractive test bed for such vehicles, including

ones currently sold overseas but not now marketed in the U.S.). It does however presume that, of the

vehicles produced for the U.S. market, manufacturers would sell a higher share of the most efficient

vehicles in California. Also, the used car market would be affected; demand for low mpg cars would

decline in the state, and such cars would likely be retired earlier or perhaps shipped to other states or

countries for sale there.

1 3 We calculated impacts on the basis of 250 times the average weekday rate plus 115 weekend and

holiday days at 95 percent of the weekday rate.
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It is worth noting that for some policy objectives, the fuel price (fuel tax) might be adjusted

periodically to maintain the per-mile cost, i.e., to reduce the impact of improved fuel

economy. Such tax adjustments would make sense in terms of paying for road

maintenance, since maintenance costs do not decline proportional to fuel use. Similarly, if

pay-at-the-pump insurance policies were implemented, it would be necessary for the

component of the fuel tax designated for insurance to be de-coupled from fleet efficiency.

 If for either reason the fuel tax were adjusted to compensate for revenue losses due to fleet

efficiency improvements, its effects on VMT, trip rates, delay, and emissions would be

greater than we have estimated here. Essentially, such adjustments would make the fuel

tax very much like the VMT fee discussed below.

B.3.5 VMT Fees

A fee on vehicle-miles of travel (VMT) would directly charge users for the amount of

vehicular travel consumed. A VMT fee therefore could be used to reduce VMT-related

impacts.14 Such a fee also would be a better targeted road user payment mechanism than

the fuel taxes we now use, because drivers could not reduce their exposure to the fee by

purchasing more fuel efficient vehicles.15

Currently, the easiest way of collecting a VMT fee would be through a charge determined

at the time of vehicle registration or vehicle inspection, based on owner-reported or

inspector-recorded odometer readings. However, if one goal of a VMT fee is to reduce

vehicular travel and its negative externalities, the fee should be linked as closely as possible

to day-to-day use of the vehicle. Collecting the VMT fee as part of an annual payment for

vehicle registration would probably be less effective in reducing VMT than more frequent

charges: an annual fee is remote from individual drivers' thinking about their day-to-day

driving behavior, and may be less effective in influencing it. Also, drivers would

discount annual payments compared to more frequent levies.

There is no reason, of course, that a VMT fee tied to registration or I/M programs would

have to be paid annually. One can imagine a variety of alternative arrangements, including

ones in which the registration or I/M fee itself is paid in monthly or quarterly installments.

 One approach might mimic the billing method used by public utilities, in which monthly

 1 4 VMT is roughly related to congestion, though a VMT fee would have a bigger effect on non-work

travel than on work trips, which make up the majority of VMT during the congested peak periods. VMT

is also roughly related to fuel use and to hydrocarbon, NOx, and carbon monoxide emissions. In

contrast, PM1 0 emissions from on-road transportation are closely related to VMT.

1 5 Used as a road user payment mechanism, the VMT fee would have to be adjusted periodically or

indexed to reflect costs of road construction, operations, and maintenance, or if such road costs increase,

the fee's percent cost coverage would decline. Nevertheless, costs to each user would remain

proportional to use. Per-gallon fuel taxes also suffer from declining cost coverage unless adjusted or

indexed, but are far less directly related to use of the roads because of divergent vehicle fuel efficiencies.
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or quarterly bills are based on estimated usage, and a periodic reading (or report) is used to

calculate the additional increment due or credit earned.16

Recent technological developments offer other ways to frequently measure and collect a

VMT fee. It is currently feasible to put in place a VMT monitoring system using automatic

vehicle identification (AVI) technologies and covering all major facilities including freeways

and major arterials. Systems such as these are currently being deployed on tollways in

many parts of the U.S. as well as abroad, and offer timely and accurate fee collection. In

one design motorists purchase debit cards which are displayed on their vehicles; fees are

deducted from the cards electronically as the vehicles pass AVI readers. In another design

the readers record each passing vehicle's identification code and transmit the data to a

computerized system which accumulates the charges and periodically bills the vehicle

owner.

An alternative concept currently in prototype stage would base the VMT fee on an at-thepump reading of an electronic odometer or a special VMT-accumulating smart card; the

corresponding fee would be calculated electronically and could be collected as part of the

payment for fuel, or perhaps recorded and billed separately. In one approach, scanner or

microwave technologies would automatically read the odometer or another on-board

electronic device designed to monitor VMT. In another approach, the motorist would

insert the vehicle's smart card into a special reader, following a sequence of actions much

like those used with the automatic credit card debiting devices now present in many fuel

pumps.

The availability of approaches, high tech or low, for collecting a VMT fee at or close to the

time of road use is important, because such immediate and visible prices are likely to be

treated by travelers essentially as out-of-pocket costs similar to current fuel costs. Here we

treat the VMT fee as a pure increase in the per-mile cost of driving, with no possibility of

avoidance and no discounting by drivers for delayed payment. In essence, the fee

defined in this way would be the equivalent of a fuel tax increase that is indexed to vehicle

fleet efficiency.

VMT fees ranging from 1 to 10 cents per mile were analyzed for each metropolitan area (at

the base case fleet fuel economy, this is equivalent to fuel price increases ranging from $0.22

to $2.20 per gallon). Results for the 2 cents per mile fee are reported here.17 In keeping

with the methodology described earlier, all elements of the STEP model were employed,

from residential location through supply response. For Los Angeles and the Bay Area, we

further checked the results by assigning STEP-based peak trip patterns to the highway

networks. No differences were found that would significantly alter the findings from STEP.

 16 Income and payroll tax collection methods are another possible model: frequent payments are made

based on estimated amounts due and reconciliation of the amounts due is done via an annual report,

subject to audit.

1 7 We calculated impacts on the basis of 250 times the average weekday rate plus 115 weekend and

holiday days at 95 percent of the weekday rate.
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Note that because the results were produced at a regional level, they are for within-region

VMT only. They do not include VMT generated outside each region being analyzed. A

VMT fee designed for revenue generation might, of course, be implemented on a statewide

basis and could be analyzed in that fashion.

A regional VMT fee based on AVI monitoring of road use would be simple enough to

implement. A regional fee based on odometer readings, on the other hand, would charge

the motorist for inter-regional, interstate, and international travel (Mexico, Canada) unless

some mechanism for excluding such travel were devised. One can easily imagine ways to

credit motorists for interstate and international travel; for example, motorists who want a

credit for out-of-state travel could have their odometers read at stations along major entry

and exit routes to the state, or a procedure might be established allowing a tax credit for

documented out-of-state travel, much like the one now used for fuel tax credits for exempt

off-road vehicle use. It would be much more difficult to devise a low-tech way to credit

within state inter-regional travel without creating a major paperwork burden for all

involved. Since Caltrans periodically does statewide travel surveys which include both

within-region and inter-regional travel, one approach might be to use the survey data to

create a system of adjustments for each region to account for the average out-of-region

component of VMT, perhaps by vehicle age.

If the VMT fee were collected infrequently, e.g., once a year based on an odometer reading

or report, its impacts might be somewhat less than we estimate here due to discounting of

future lump-sum payments in comparison to equivalent out-of-pocket payments. Hence

 the results reported here should be viewed as the high end of likely effectiveness.

B.3.6 Emissions Fees

Emissions fees represent a means of reducing tailpipe emissions that could give the

consumer somewhat more flexibility than the current system of mandated performance

backed by vehicle inspection and maintenance. The basic concept is that the total pool of

annual vehicular emissions in a region would be assigned a cost (presumably pollutant-bypollutant), and each vehicle would be charged a fee set to reflect its contribution to the total

emissions burden. Levying such a fee on vehicular emissions arguably would be the most

direct way to instill a sense of personal responsibility for mobile source air pollution.

While the concept may be simple to state, emissions-based vehicle fees are the most difficult

of the pricing policies to define and analyze. Reasons for this are:

• the literature offers widely varying perspectives on the social costs of air

pollution, so an agreement on a monetary basis for the emissions fee is not

easy to reach;
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• estimates of cumulative emissions from individual vehicles are imprecise and

are likely to remain so unless and until vehicles are equipped with accurate,

tamper-proof on-board emissions monitoring devices;

• because knowledge about how consumers would trade off emissions fees,

repair costs, insurance, and other auto-related expenditures is not well

developed, the change in fleet composition resulting from a targeted emissions

fee is difficult to estimate.

We carried out analyses of two prototypical emissions fee strategies, each using a different

type of information about emissions. Following the same line of argument as for the VMT

fee, we assume that emissions fees can be collected on a pay as you go basis, so that they

are perceived by drivers as an out-of-pocket expense. This could be done with a

technologically advanced system such as an on-board monitor, read and billed, e.g., at the

time of fuel purchase; or by combining some other method of fee calculation with a

monthly billing system. If the emissions fee is determined as part of vehicle registration or

inspection/maintenance and is billed annually or biennially as part of those programs, the

fee may well have less influence on day-to-day travel behavior than we show (on the other

hand, a large, infrequent fee might have a big influence on vehicle ownership levels, vehicle

age and type, and vehicle maintenance).

All non-arbitrary emissions fee concepts rely on some assumption about the social costs of

air pollution. Accordingly, we searched through the literature for evidence that would

support a specific emissions fee in each region, and sought the advice of experts in

university research groups and air pollution control agencies. We found that the costs of

air pollution had not been researched consistently for all the case study regions, and that

the sources that do exist show a wide disparity in their damage estimates. Credible cost

estimates for mobile source pollutants range from about .25 cents per vehicle mile to about

8 cents per vehicle mile (using regional damage estimates, reduced by the portion of

emissions not attributable to mobile sources, divided by annual regional VMT). The range

reflects differences in the severity of the pollution problems of the various regions and in

the types of damage considered, as well as disagreements over specific costs in a given

region (controversy is especially acute concerning the interpretation of epidemiological

studies).

Lacking more specific estimates of the social costs of emissions in each of the California

regions, we chose to set our emissions fee to average one cent per vehicle mile. This

represents a plausible, perhaps somewhat conservative estimate of current social costs of

mobile source air pollution in these urban areas. Evidence suggests a much higher

pollution cost in the Los Angeles region and perhaps a lower pollution cost in the Bay

Area. The one cent per vehicle mile average fee would total about $1.15 million per day in

the Bay Area and about $2.9 million per day in the Los Angeles Region, under base year
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(1991) VMT conditions. While the amounts sound high, annual receipts from such a fee

would amount to about 0.3 percent of the gross domestic product of each region.18

Clearly, it would be inaccurate to simply charge each vehicle the regional average per-mile

emissions fee, since vehicles' emissions characteristics vary widely. We therefore analyzed

two possible methods for assigning a per-mile emissions fee to different vehicles. Under the

first method, the per-mile emissions fee would vary by model year and would be based on

data on each model year's average emissions characteristics (i.e., using EMFAC in

California). Under the second method, the per-mile emissions fee would vary with the

actual emissions performance of each vehicle, which might be determined through

emissions testing, remote sensing, or on-board emissions monitoring. The latter approach

would account for the differences in emissions among vehicles of the same model year.

For each household in the four regional travel survey samples,19 we knew the make, model,

and age (year) of the vehicle holdings for the base year, and we knew how each vehicle

actually was used on a representative weekday. Thus, we were able to provide a wellgrounded assessment of how vehicles of different ages and types are used and who would

be impacted by emissions fees.20 However, we did not have access to a model of how

household vehicle holdings or vehicle usage patterns would change as a result of

differential changes in the per-mile cost of vehicle operations, so we had to address these

issues in terms of plausible scenarios rather than modeled estimates.

Fees Based on Average Emissions by Model Year:

For the average emissions by model year approach, we began by determining, for each

region, the average daily within-region VMT and emissions for every vehicle in the regional

travel survey. We extracted from the survey data the vehicle trip sequences and their

characteristics, and inferred whether the trip was a cold start, etc., based on the time

between trips in the trip sequence. We also determined the average trip speed and distance,

deriving these data from the applicable highway networks. We then used EMFAC7F data

specific to each vehicle model year to compute the emissions for each vehicle trip.

From the resulting samples of vehicle trips and their associated emissions, average weekday

emissions and VMT were calculated for each model year on a region-by-region basis.

Annual emissions and VMT for each region were then estimated. The annual VMT

 1 8 We used the same one cent per vehicle mile average fee for the year 2010 analyses, lacking more

specific cost data.

1 9 The most recent regional survey for Los Angeles did not record vehicle make and model data. 

However, the Caltrans statewide survey of the same vintage included these data and had enough

observations in the Los Angeles region to support the analyses described here. For this policy only, then,

we extracted the Los Angeles data from the Caltrans survey and used it for our analyses.

2 0 We calculated impacts on the basis of 250 times the average weekday rate plus 115 weekend and

holiday days at 95 percent of the weekday rate.
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estimates were used to calculate total emissions costs for each region at the postulated one

cent per mile average.

For the year 2010 forecasts, it was necessary to describe the likely vehicle age distribution

and patterns of use for that future year. We made the simple assumption that the 2010

fleet would have the same general characteristics (age distribution, usage profiles) as the

current fleet does. We then applied EMFAC7F 2010 emissions factors to this hypothesized

future fleet's trips to determine the future base case (total VMT and emissions, emissions by

model year, etc.).

For both 1991 and 2010, we used our calculations of emissions by vehicle model year to

apportion the regional emissions cost estimates among model years. The annual VMT

calculations by model year then were used to determine an average emissions cost per mile

for each model year. For example, from the 1991 data for Los Angeles, the average

emissions fee per mile for a 1 year old vehicle would be about 0.4 cents, while the average

emissions fee for a 17 year old vehicle (from the pre-catalyst era) would be about 7.0 cents.

Note that the method we describe here should apply only to miles driven within each

urban area, since emissions costs are calculated and apportioned on a regional basis. If the

collection scheme used odometer readings as the basis for the VMT portion of the fee, some

vehicle owners would be charged for miles driven in other regions or in other states. To

avoid this potential inequity, methods could be developed to estimate in-region and out-ofregion vehicle use and apportion the fee(s) accordingly, and credits could be given for

documented out-of-state travel.

We analyzed the effects of our per-mile emissions fees varying by vehicle age, assuming

that households would not alter their vehicle holdings or pattern of use in response to the

fees. This assumption is not entirely realistic, since households could lower their fees by

replacing their older cars with newer ones, and if AVI measurements or odometer readings

are the basis for the VMT component of the fee, by using their newer cars in place of their

older ones for some trips.21 Nevertheless, the analysis results provide an indication of the

maximum travel impact and the minimum emissions impact that such an emissions fee

could be expected to have; without fleet changes the full impact of the fee would be passed

through as an out-of-pocket cost to the driver, and the emissions reductions would come

from reductions in travel rather than from the use of newer, presumably cleaner, cars.

A more robust analysis would consider how vehicle holdings and usage patterns might

change in response to an emissions fee. The analysis would account for the determinants

of household vehicle ownership and use and would estimate the effects of an emissions fee

on the number of vehicles owned, the vehicle makes and model years, and VMT per

 2 1 Alternatively, VMT could be estimated based on averages by model year taken from survey data. 

This might be simpler to implement than an approach requiring odometer readings, but would remove

much of the incentive for multi-car households to reduce "older car" use by substituting their newer,

presumably cleaner vehicles for certain trips.
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vehicle. Such a comprehensive model was not available to us, but we did have STEP's

internal auto ownership model, which estimates whether a household will have 0, 1, or 2+

vehicles as a function of household characteristics, travel conditions, and vehicle ownership

and operating costs.

We used the STEP auto ownership model to partially account for the effects on the vehicle

fleet as follows. For each region and analysis year, the base case household fleet was used

to estimate the average annual cost of auto ownership for each household. Then, revised

annual ownership costs were computed to reflect the addition of emissions fees for each

vehicle (based on model year and the actual daily VMT revealed in the survey). New auto

ownership probabilities then were calculated using STEP.22

While this method is an improvement over simply representing the emissions fee as an

increase in out-of-pocket costs, we feel that on balance it still is likely to overstate travel

effects and understate emissions effects: For implementation scenarios involving AVI or

odometer readings, households with more than one vehicle could shift use among

household vehicles to reduce their emissions fees without cutting back on travel. Both the

revenues from emissions fees and their impact on households are therefore likely to be

lower than what we have estimated here.

Fees Based on Measured Emissions:

To analyze an emissions fee based on measured emissions, we first needed an estimate of

how emissions vary within each vehicle model year. One possible source of such

information would be the data from vehicle inspection and maintenance tests, but we did

not have access to these data. Therefore we used an alternative source, a database from

Professor Donald Stedman of the University of Denver, containing in-use measurements

obtained passively with his remote sensing device at a location on Rosemead Blvd. in

Southern California.23 Stedman expressed these data as frequency distributions of

emissions by model year.

 2 2 Since STEP does not predict which autos might be disposed of or what model years added when

auto ownership levels change, we imposed a series of assumptions. We assumed that, since the per-mile

emissions fee is higher for older vehicles, households that reduce their auto ownership levels would get

rid of their oldest car(s). We assumed that households maintaining their current auto ownership levels

would also maintain the age distribution of the vehicles they own. Households that added vehicles were

assumed to add car(s) of the average age and fuel efficiency for that ownership level. These assumptions

allowed us to estimate the effects on emissions, fuel use, etc.

2 3 There is some reason for concern that emissions distributions recorded for a single location and

operating environment may not reflect the full spectrum of operating conditions, and thus cannot be

assumed to represent the "high-emitter" distribution for all regimes of urban travel. A similar criticism

would apply, however, to vehicle inspection/maintenance test measurements, which are based on a

single measurement and a specified operations sequence, or to any other data set based on single

measurements and conditions.
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We used the Stedman data to develop a frequency distribution of emissions fees per mile for

each model year in each region. Taking the fleet age distribution and the VMT by model

year estimated from the regional survey data, we used the Stedman emissions distributions

both to estimate the aggregate emissions by model year and to apportion emissions

responsibility within model years. This approach allowed us to assess a higher fee for highemitting vehicles, and a lower fee for relatively clean vehicles, within each model year.24

To estimate the effects of a measurement-based emissions fee, we first made a special STEP

run to create a base case with emissions derived from the high-emitter distributions rather

than from the pure EMFAC data. Since we did not have actual emissions measurements

for the vehicles in our samples, during this run we simulated the presence of high emitters

in the fleet. Each vehicle in the sample was randomly assigned an emissions level from the

distribution for its model year (and tagged with that emissions level for use in the after

analysis). Then, the fee policy was tested using the same method as for the fee based on

model year averages, except that in this case the proposed fees were based on the

emissions level assigned during the before run.

A fee based on measured emissions would probably require new technology of one sort or

another. Tamper-resistant on-board monitoring and recording equipment would be the

preferred approach; fees based on multiple measurements using remote sensing equipment

would be a second option. A third approach would be to use the emissions measurements

from I/M testing, though this would raise a number of issues including whether the fee

should be prospective or retrospective and whether it should be based on before-repair or

after-repair measurements.

With an emissions fee targeting super-emitters, households could be expected to adroitly

manipulate their vehicle holdings and use to minimize the impact of the fee. This would

tend to produce lower travel impacts and higher emissions reductions than shown here.

B.4 Impacts of Transportation Pricing Strategies

B.4.1 Basic Analysis Results

This section presents analysis results for the set of transportation pricing measures we

analyzed for our four case study regions. We present a series of 18 tables summarizing the

basic findings of our analyses, both by measure and by region. For each pricing measure,

we present the predicted percentage changes in VMT, trips made, and travel time; fuel

 2 4 An alternative approach would be to use the EMFAC data as the estimate of the average emissions

by model year, and to use the Stedman data (or another source) to represent the underlying distribution of

emissions for that model year. Note that the overall approach does not produce different results if a

higher or lower total emissions burden is assumed.
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consumed and CO2 emitted; ROG, CO and NOx emissions; and annual gross revenues25,

for the years 1991 (the base year) and 2010.

The results for each analysis year represent the long-term effects of pricing measures, i.e.,

the impacts resulting from the pricing measures as if they had been implemented several

years earlier.

The results show that carefully crafted and targeted transportation pricing strategies could

do much to reduce travel times (hence congestion), cut energy use, and reduce emissions, at

the same time increasing gross revenues substantially. At the same time, it is clear that auto

use and its impacts are quite inelastic with respect to most aspects of price. This has two

important implications: first, sizable increases in revenue can be obtained with relatively

little effect on travel; conversely, large price increases are necessary to obtain sizable

reductions in travel and its externalities.

The results also provide an empirical dimension to the notion that the most efficient way to

use price as a mechanism for reducing transportation externalities is to price each

externality in a direct way. Thus, as the tables detail, the most effective pricing strategy for

emissions control (in the sense of emissions reductions per dollar charged) is to target highemitting vehicles as precisely as possible; the most effective strategy for reducing fuel

consumption (and CO2 production) is to raise the price of fuel; the most effective way to

reduce congestion is to impose a toll at congested locations; and so on. Note that we refer

to efficiency and effectiveness here in a purely technical sense. Other factors - ethical,

institutional, political, and social - contribute to a broader assessment that may lead to

different conclusions about policy effectiveness.

Tables B1-B5 present the results organized by pricing measure for the year 1991. Tables B6-

B10 present a subset of the year 1991 results, reorganized by region. Each regional table

includes analyses of the synergistic effects of groups of pricing measures, under two

scenarios:

1. Modest Pricing - A relatively low set of prices from each category (e.g., $1.00

per day parking; $0.50 per gallon fuel tax), coupled with enough investment in

transit only to maintain existing levels of service.

2. Full Pricing - A relatively high set of prices from each category (e.g., $3.00 per

day parking; $2.00 per gallon fuel tax), coupled with investment in transit

corresponding to build-out of each region's long-range transit plan (as expressed

in future network files made available by each MPO). Note that such a transit

expansion would absorb a significant fraction of the pricing revenues.

 2 5 Net revenues depend on the specific implementation strategy selected (public vs. private sector

implementation and administration, technologies used, scope of implementation, timing of

implementation, etc.). In general, implementation designs costing a small fraction (5-15%) of gross

revenues are feasible.
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Tables B11-B18 present the same ensemble of results for the year 2010. The percent

changes presented are changes from a year 2010 base case, created by using STEP as a

forecasting tool. The regions' forecasts of households, household income, and household

size (or population) were used to factor the 1991 household file to create a year 2010

household file for each region. The STEP models then were run to create a year 2010 base

case, using the 2010 household file plus the MPO network data for the year 2010.26

Finally, policy analyses were carried out to predict changes from the future base case.

 2 6 In the Los Angeles region, some adjustments were made to SCAG's highway travel times after

analyses indicated that the SCAG models then in use showed far more trips and VMT than STEP's more

complex models would predict. Otherwise, MPO level-of-service projections were broadly consistent

with STEP internal calculations and were used as provided to form the basis of the 2010 base case.
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Change From 1991 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 

-1.8% -1.7% -5.7% -19.5% -5.8% -0.6% -0.5% -1.8% -6.0% -1.8% -1.0% -0.9% -3.1% -10.5% -2.9% -2.3% -2.2% -6.8% -22.5% -6.7% 

Table B.1

Analysis Results for Congestion Pricing - 1991

Average

Description Region Price

The congestion pricing strategy analyzed here assumes that prices Bay Area $0.09 would be assessed on a per mile basis everywhere that congestionappears in the highway network, including on arterials and collectorstreets as necessary. A technology for electronic toll collection wouldbe required. Roadway message signs or in-vehicle readouts would Sacramento $0.04 provide information about tolls on upcoming segments, likely aspart of a broader highway information system. Prices would not varyminute-by-minute, but would be set to reflect average conditions oneach highway link during each period of the day, perhaps with San Diego $0.06 seasonal adjustments. The results shown here are based on areduction of congestion to level-of-service D/E, defined as a volumeto-capacity ratio of .9. Note that travelers would continue to experiencesome delay under this criterion, but that greater reductions in volume South Coast $0.10 might not be justifiable in economic terms.

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is  of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallo CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of o
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Table B.2

Analysis Results for Employee Parking Pricing - 1991

Minimum

Description Region Price

The parking pricing strategy analyzed here applies only to spaces used Bay Area $1.00 by workers in each region. The intent is to make parking cost explicit $3.00 by requiring each worker to pay at least some threshold cost to park allday at or near the workplace. Only drive-alone vehicles would becharged under this scheme. The analysis was carried out through Sacramento $1.00 adjustments in the average zonal parking price. Basically, if an aver- $3.00 age zonal price in the base data was less than the minimum price tobe tested, it was raised to match the minimum. This is tantamount tosaying that no worker would face an average area price less than the San Diego $1.00 stated minimum. However, basing an analysis on such an average $3.00 implies that some individual workers still might experience priceslower than the minimum.

South Coast $1.00 

$3.00 

Change From 1991 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 RO

-0.8% -1.0% -1.3% -2.3% -1.1% -0.9-2.3% -2.6% -3.7% -7.0% -2.6% -2.5-1.1% -1.2% -1.6% -2.5% -1.2% -1.2-2.9% -3.1% -4.1% -6.0% -3.0% -3.1-1.0% -1.1% -1.5% -2.5% -1.1% -1.0-2.6% -2.9% -3.8% -6.0% -2.7% -2.7-1.0% -1.1% -1.5% -2.5% -1.2% -1.1-2.7% -3.0% -4.2% -7.5% -2.9% -2.8

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is part of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of  CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides 
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation EmissionsB-36 U. S. Envi

Table B.3

Analysis Results for Fuel Tax Increases - 1991

Tax Fuel

Description Region Increment ElasticityThe fuel tax analyzed here is a straightforward addition to the pump Bay Area $0.50 -0.13price of gasoline and diesel fuel. Base fleet fuel efficiency is about $2.00 -0.1322 miles per gallon (.0364 gallons per mile). Base fuel cost is about $2.00 -0.05$1.20 per gallon, or 5.45 cents per mile. With no increase in fleet fuel $2.00 -0.22economy, a $2.00 per gallon fee would add about 9.1 cents to the permile cost of driving. However, both empirical evidence and commonsense suggest that the vehicle fleet would become more efficient Sacramento $0.50 -0.13under a significant price increase, both from substitution of more $2.00 -0.13efficient vehicles within each household and from replacement through $2.00 -0.05vehicle purchase. We tested a range of assumptions about increased $2.00 -0.22fuel efficiency, expressed in terms of the elasticity of fuel consumption(gallons per mile) with respect to fuel price. Three versions are reportedhere: -0.22, which implies an increase in fuel efficiency from 22 tp 35 San Diego $0.50 -0.13mpg for a $2.00 fuel tax; -0.13, which implies an increase from 22 to 28 $2.00 -0.13mpg for a $2.00 fuel tax; and -0.05, which implies an increase from 22 $2.00 -0.05to 24 mpg. The latter figure is consistent with estimates from the $2.00 -0.22California Energy Commission Personal Vehicle Model, which showsa 2 mpg increase from a $2.00 fuel surcharge. Studies of internationalexperience with fuel price changes tend to point toward higher elas- South Coast $0.50 -0.13ticity values. $2.00 -0.13

$2.00 -0.05$2.00 -0.22

Change From 1991 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 RO

-3.9% -3.6% -5.4% -7.5% -9.1% -3.7

-12.6% -12.1% -17.0% -22.0% -31.3% -12.5-16.6% -15.8% -21.8% -26.0% -23.6% -16.3

-7.7% -7.1% -10.1% -12.0% -42.3% -7.5-4.3% -4.0% -5.6% -6.5% -9.5% -4.1

-13.9% -13.3% -17.6% -18.5% -32.4% -13.7-18.4% -17.6% -23.0% -23.0% -25.2% -18.2

-8.5% -7.8% -10.9% -12.0% -42.8% -8.3-4.1% -3.8% -5.5% -7.0% -9.2% -3.9

-13.2% -12.7% -17.3% -20.5% -31.8% -13.0-17.4% -16.5% -22.4% -25.0% -24.3% -17.2

-8.0% -7.4% -10.8% -14.0% -42.5% -7.7-4.1% -4.0% -5.4% -6.5% -9.1% -3.8

-13.3% -12.8% -17.5% -21.0% -31.8% -13.2-17.6% -16.7% -23.4% -29.0% -24.5% -17.4

-8.1% -7.5% -10.6% -12.5% -42.5% -7.9

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate em of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasoline CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitrog
Technical Methods for 

to Redu

U. S. Environmental Protection Agency 

Table B.4

Analysis Results for a VMT Fee - 1991

Description Region

The VMT fee was analyzed as a simple increment of two cents per mile Bay Areain the out-of-pocket cost of driving. No specific assumption was madeabout the method of collection. However, the analysis approach treatsthe price increment as if it were charged in the same manner as a fueltax, i.e., as if the driver were aware of the expenditure from moment to Sacramentomoment. This implies a potentially complex collection scheme involvingreal-time reading of the odometer, perhaps each time a vehicle is fueled.The simpler alternative of billing once a year based on the odometer reading at the time of registration possibly would have less effect on San Diegotravel and emissions, perhaps substantially less, although revenueswould be about the same as shown here.

South Coast

Change From 1991 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 RO

-4.2% -4.0% -5.8% -8.0% -4.2% -4.-4.7% -4.4% -6.1% -7.0% -4.7% -4.-4.4% -4.2% -5.9% -7.5% -4.4% -4.-4.4% -4.2% -6.2% -9.0% -4.5% -4.

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particu of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of ga CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of 
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation EmissionsB-38 U. S. Envi

Table B.5

Analysis Results for Emissions Fees - 1991

Fee

Description Region Basis

Emissions fees are the most difficult of the pricing Bay Area EMFACpolicies to analyze, because knowledge about how consumers would In-Usetrade off emissions fees, repair costs, insurance, and other auto-relatedexpenditures is not well developed. We looked at two broad strategies:1) the emissions fee would be calculated based on EMFAC data for Sacramento EMFACaverage model year emissions per mile and actual VMT (so that the In-Useper mile fee would vary only by model year); and 2) the emissions fee would be based on data about actual performance of each vehicle,obtained perhaps from some type of in-use testing device. Unlike the San Diego EMFACEMFAC-based approach, strategy two would focus high prices on super- In-Useemitting vehicles. In both cases, prices were set so that the fee wouldaverage about one cent per mile over the entire personal vehicle fleet.The analysis was based on assumptions about how the distribution of South Coast EMFACvehicles by age and household income would change in the face of In-Usehigher registration fees (under each strategy). These assumptions thenwere used to adjust auto ownership and out-of-pocket costs and appliedto households in the STEP sample (see text for a full discussion).

Change From 1991 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 RO

-2.0% -1.7% -2.7% -3.5% -4.1% -6.5-1.6% -1.4% -1.9% -1.5% -6.9% -18.2-2.7% -2.3% -3.1% -2.0% -5.2% -8.1-2.2% -1.8% -2.5% -1.5% -7.9% -20.7-2.4% -2.1% -2.9% -2.5% -4.9% -7.5-2.0% -1.7% -2.3% -1.5% -7.6% -20.1-2.2% -1.9% -2.8% -3.0% -4.4% -7.0-1.8% -1.6% -2.1% -1.5% -7.2% -19.4

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is parti of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of g CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides 
Technical Methods for 

to Redu

U. S. Environmental Protection Agency 

Table B.6

Analysis Results for Congestion Pricing - 2010

Average

Description Region Price

The congestion pricing strategy analyzed here assumes that prices Bay Area $0.13 would be assessed on a per mile basis everywhere that congestionappears in the highway network, including on arterials and collectorstreets as necessary. A technology for electronic toll collection wouldbe required. Roadway message signs or in-vehicle readouts would Sacramento $0.08 provide information about tolls on upcoming segments, likely aspart of a broader highway information system. Prices would not varyminute-by-minute, but would be set to reflect average conditions oneach highway link during each period of the day, perhaps with San Diego $0.09 seasonal adjustments. The results shown here are based on areduction of congestion to level-of-service D/E, defined as a volumeto-capacity ratio of .9. Note that travelers would continue to experiencesome delay under this criterion, but that greater reductions in volume South Coast $0.19 might not be justifiable in economic terms.

Change From 2010 Baseline 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG 

-2.8% -2.7% -8.2% -27.0% -8.3% -6.9% -1.5% -1.4% -4.8% -16.5% -4.8% -3.7% -1.7% -1.6% -5.4% -18.5% -5.4% -4.2% -3.3% -3.1% -9.7% -32.0% -9.6% -8.1% 

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate e of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasoline CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitrog
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation EmissionsB-40 U. S. Envi

Table B.7

Analysis Results for Employee Parking Pricing - 2010

Minimum

Description Region Price

The parking pricing strategy analyzed here applies only to spaces used Bay Area $1.00 by workers in each region. The intent is to remove a hidden subsidy by $3.00 requiring each worker to pay at least some threshold cost to park allday at or near the workplace. Only drive-alone vehicles would becharged under this scheme. The analysis was carried out through Sacramento $1.00 adjustments in the average zonal parking price. Basically, if an aver- $3.00 age zonal price in the base data was less than the minimum price tobe tested, it was raised to match the minimum. This is tantamount tosaying that no worker would face an average area price less than the San Diego $1.00 stated minimum. However, basing an analysis on such an average $3.00 implies that some individual workers still might experience priceslower than the minimum.

South Coast $1.00 

$3.00 

Change From 2010 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG 

-0.8% -0.9% -1.3% -2.7% -1.0% -0.8% -2.1% -2.4% -3.5% -7.0% -2.4% -2.3% -1.0% -1.1% -1.5% -2.5% -1.1% -1.1% -2.6% -2.8% -3.9% -6.5% -2.7% -2.8% -0.9% -1.0% -1.4% -2.5% -1.0% -0.9% -2.4% -2.6% -3.8% -7.0% -2.5% -2.5% -0.9% -1.1% -1.5% -2.9% -1.1% -1.0% -2.5% -2.8% -4.2% -8.5% -2.7% -2.6% 

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particula of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gas CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of n
Technical Methods for 

to Redu

U. S. Environmental Protection Agency 

Table B.8

Analysis Results for Fuel Tax Increases - 2010

Tax Fuel

Description Region Increment ElasticityThe fuel tax analyzed here is a straightforward addition to the pump Bay Area $0.50 -0.13price of gasoline and diesel fuel. Base fleet fuel efficiency is about $2.00 -0.1322 miles per gallon (.0364 gallons per mile). Base fuel cost is about $2.00 -0.05$1.20 per gallon, or 5.45 cents per mile. With no increase in fleet fuel $2.00 -0.22economy, a $2.00 per gallon fee would add about 9.1 cents to the permile cost of driving. However, both empirical evidence and commonsense suggest that the vehicle fleet would become more efficient Sacramento $0.50 -0.13under a significant price increase, both from substitution of more $2.00 -0.13efficient vehicles within each household and from replacement through $2.00 -0.05vehicle purchase. We tested a range of assumptions about increased $2.00 -0.22fuel efficiency, expressed in terms of the elasticity of fuel consumption(gallons per mile) with respect to fuel price. Three versions are reportedhere: -0.22, which implies an increase in fuel efficiency from 22 tp 35 San Diego $0.50 -0.13mpg for a $2.00 fuel tax; -0.13, which implies an increase from 22 to 28 $2.00 -0.13mpg for a $2.00 fuel tax; and -0.05, which implies an increase from 22 $2.00 -0.05to 24 mpg. The latter figure is consistent with estimates from the $2.00 -0.22California Energy Commission Personal Vehicle Model, which showsa 2 mpg increase from a $2.00 fuel surcharge. Studies of internationalexperience with fuel price changes tend to point toward higher elas- South Coast $0.50 -0.13ticity values. $2.00 -0.13

$2.00 -0.05$2.00 -0.22

Change From 2010 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG C

-3.6% -3.4% -5.3% -8.5% -8.8% -3.5% -3.5

-11.7% -11.3% -16.8% -25.5% -30.6% -11.6% -11.-15.5% -14.8% -22.8% -36.5% -22.5% -15.3% -15.

-7.1% -6.8% -10.1% -15.0% -42.0% -7.0% -6.9-4.1% -3.9% -5.5% -7.0% -9.3% -4.0% -3.9

-13.2% -12.7% -17.6% -22.0% -31.8% -13.0% -12.-17.4% -16.7% -23.1% -28.5% -24.3% -17.2% -17.

-8.0% -7.7% -11.4% -17.0% -42.5% -8.0% -7.9-3.9% -3.5% -5.5% -8.0% -9.1% -3.8% -3.6

-12.5% -12.0% -17.1% -23.0% -31.3% -12.3% -12.-16.5% -15.7% -22.6% -30.5% -23.5% -16.3% -16.

-7.6% -7.3% -10.6% -15.0% -42.3% -7.5% -7.3-4.2% -3.9% -6.1% -9.5% -9.3% -4.1% -4.0

-13.0% -12.5% -18.7% -28.5% -31.6% -12.8% -12.-17.1% -16.4% -24.8% -38.5% -24.0% -16.9% -16.

-8.2% -7.8% -11.7% -17.5% -42.6% -8.0% -8.0

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate e of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasolin CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitro
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation EmissionsB-42 U. S. Envi

Table B.9

Analysis Results for a VMT Fee - 2010

Description RegionThe VMT fee was analyzed as a simple increment of two cents per mile Bay Areain the out-of-pocket cost of driving. No specific assumption was madeabout the method of collection. However, the analysis approach treatsthe price increment as if it were charged in the same manner as a fueltax, i.e., as if the driver were aware of the expenditure from moment to Sacramentomoment. This implies a potentially complex collection scheme involvingreal-time reading of the odometer, perhaps each time a vehicle is fueled.The simpler alternative of billing once a year based on the odometer reading at the time of registration possibly would have less effect on San Diegotravel and emissions, perhaps substantially less, although revenueswould be about the same as shown here.

South Coas

Change From 2010 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG C

-3.9% -3.7% -5.7% -9.0% -4.1% -3.8% -3.-4.4% -4.1% -5.9% -7.5% -4.4% -4.3% -4.-4.2% -4.0% -5.9% -8.5% -4.2% -4.1% -4.-4.3% -4.1% -6.4% -10.5% -5.2% -4.2% -4.

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particu of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of ga CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of 
Technical Methods for 

to Redu

U. S. Environmental Protection Agency 

Table B.10

Analysis Results for Emissions Fees - 2010

Fee

Description Region Basis

Emissions fees are the most difficult of the pricing Bay Area EMFACpolicies to analyze, because knowledge about how consumers would In-Usetrade off emissions fees, repair costs, insurance, and other auto-relatedexpenditures is not well developed. We looked at two broad strategies:1) the emissions fee would be calculated based on EMFAC data for Sacramento EMFACaverage model year emissions per mile and average model year VMT In-Use(so that the fee would vary only by model year); and 2) the emissions fee would be based on data about actual performance of each vehicle,obtained perhaps from some type of in-use testing device. Unlike the San Diego EMFACEMFAC-based approach, strategy two would focus high prices on super- In-Useemitting vehicles. In both cases, prices were set so that the fee wouldaverage about one cent per mile over the entire personal vehicle fleet.The analysis focused on assumptions about how the distribution of veh- South Coast EMFACicles by age and household income would change in the face of higher In-Useregistration fees (under each strategy). These assumptions then wereused to adjust auto ownership and out-of-pocket costs and applied tohouseholds in the STEP sample (see text for a full discussion).

Change From 2010 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG 

-2.2% -1.9% -2.9% -3.5% -3.9% -5.4% -1.6% -1.4% -2.1% -2.5% -6.6% -17.7% -2.6% -2.3% -3.5% -4.5% -4.0% -5.7% -2.3% -2.1% -3.3% -5.0% -7.4% -20.2% -2.5% -2.2% -3.2% -3.5% -4.1% -5.5% -1.9% -1.7% -2.6% -3.5% -7.1% -19.5% -2.5% -2.3% -3.6% -5.5% -3.9% -5.5% -2.1% -1.9% -3.3% -6.0% -7.2% -18.9% 

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate em of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasoline CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitrog
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation EmissionsB-44 U. S. Envi

Table B.11

Analysis Results for the San Francisco Bay Area - 1991

Strategy Description

1 Regionwide Congestion An Automatic Vehicle Identification (AVI) scheme

Pricing (level-of-service would be used to price the regional freeway andD/E) - Average $0.09 arterial system to maintain level-of-service (LOS)per Mile in Peak D/E

2a Regionwide Employee All workers driving alone in the region would

Parking Charge of $1.00 experience a minimum $1.00 (1991) per day chargePer Day for parking at the workplace.

2b Regionwide Employee All workers driving alone in the region would

Parking Charge of $3.00 experience a minimum $3.00 (1991) per day chargePer Day for parking at the workplace.

3a Fuel Tax Increase Fees would be paid at the pump.

by $0.50 (1991)

3b Fuel Tax Increase

by $2.00 (1991)

4a Mileage- and Emissions- Fees would average 1 cent per mile, and would

Based Fee (Range Approx. be collected frequently, as for the VMT fee.40-400/yr) In alternative 4a, the fee would be based on

4b Mileage- and Emissions- annual mileage and average model year emissions

Based Fee (Range Approx. as reflected in EMFAC7F. In alternative 4b, the10-1000/yr) fee would be based on actual odometer readings

and in-use tailpipe measurements.

5 VMT Fee of $0.02 per mile Fees would be paid often, e.g., in the same manner

as fuel taxes. This implies a potentially complexcollection scheme involving real-time readingof the odometer, perhaps each time a vehicle isfueled. The simpler option of billing once a yearbased on the odometer reading likely would haveless effect on travel and emissions.

Example of Combined 1, 2a, 3a, and 4a with maintenance of current transitEffects: Moderate Impact service.Example of Combined 1, 2b, 3b, and 4b with extensive transit investment.Effects: High Impact (Revenue not reduced to reflect cost of new transit.)

Change From 1991 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG CO 

-1.8% -1.7% -5.7% -19.5% -5.8% -4.5% -4.7-0.8% -1.0% -1.3% -2.3% -1.1% -0.9% -0.9-2.3% -2.6% -3.7% -7.0% -2.6% -2.5% -2.6-3.9% -3.6% -5.4% -7.5% -9.1% -3.7% -3.6

-12.6% -12.1% -17.0% -22.0% -31.3% -12.5% -12.2

-2.0% -1.7% -2.7% -3.5% -4.1% -6.5% -6.5-1.6% -1.4% -1.9% -1.5% -6.9% -18.2% -17.9-4.2% -4.0% -5.8% -8.0% -4.2% -4.1% -4.0-8.1% -7.5% -13.9% -28.9% -18.3% -14.4% -14.6

-19.7% -19.0% -29.1% -46.8% -46.3% -37.4% -37.3

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate emis of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasoline/di CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitrogen
Technical Methods for 

to Redu

U. S. Environmental Protection Agency 

Table B.12

Analysis Results for the Sacramento Region - 1991

Strategy Description

1 Regionwide Congestion An Automatic Vehicle Identification (AVI) scheme

Pricing (level-of-service would be used to price the regional freeway andD/E) - Average $0.04 arterial system to maintain level-of-service (LOS)per Mile in Peak D/E

2a Regionwide Employee All workers driving alone in the region would

Parking Charge of $1.00 experience a minimum $1.00 (1991) per day charge

Per Day for parking at the workplace.

2b Regionwide Employee All workers driving alone in the region would

Parking Charge of $3.00 experience a minimum $3.00 (1991) per day charge

Per Day for parking at the workplace.

3a Fuel Tax Increase Fees would be paid at the pump.

by $0.50 (1991)

3b Fuel Tax Increase

by $2.00 (1991)

4a Mileage- and Emissions- Fees would average 1 cent per mile, and would

Based Fee (Range Approx. be collected frequently, as for the VMT fee.40-400/yr) In alternative 4a, the fee would be based on

4b Mileage- and Emissions- annual mileage and average model year emissions

Based Fee (Range Approx. as reflected in EMFAC7F. In alternative 4b, the10-1000/yr) fee would be based on actual odometer readings

and in-use tailpipe measurements.

5 VMT Fee of $0.02 per mile Fees would be paid often, e.g., in the same manner

as fuel taxes. This implies a potentially complexcollection scheme involving real-time readingof the odometer, perhaps each time a vehicle isfueled. The simpler option of billing once a yearbased on the odometer reading likely would haveless effect on travel and emissions.

Example of Combined 1, 2a, 3a, and 4a with maintenance of current transitEffects: Moderate Impact service.Example of Combined 1, 2b, 3b, and 4b with extensive transit investment.Effects: High Impact (Revenue not reduced to reflect cost of new transit.)

Change From 1991 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG 

-0.6% -0.5% -1.8% -6.0% -1.8% -1.5% -1.1% -1.2% -1.6% -2.5% -1.2% -1.2% -2.9% -3.1% -4.1% -6.0% -3.0% -3.1% -4.3% -4.0% -5.6% -6.5% -9.5% -4.1% 

-13.9% -13.3% -17.6% -18.5% -32.4% -13.7% 

-2.7% -2.3% -3.1% -2.0% -5.2% -8.1% -2.2% -1.8% -2.5% -1.5% -7.9% -20.7% -4.7% -4.4% -6.1% -7.0% -4.7% -4.6% -8.3% -7.6% -11.3% -14.9% -16.3% -13.9% 

-21.1% -20.0% -27.2% -30.2% -45.6% -38.9% 

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate em of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasoline/d CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitroge
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation EmissionsB-46 U. S. Envi

Table B.13

Analysis Results for the San Diego Region - 1991

Strategy Description

1 Regionwide Congestion An Automatic Vehicle Identification (AVI) scheme

Pricing (level-of-service would be used to price the regional freeway andD/E) - Average $0.06 arterial system to maintain level-of-service (LOS)per Mile in Peak D/E

2a Regionwide Employee All workers driving alone in the region would

Parking Charge of $1.00 experience a minimum $1.00 (1991) per day chargePer Day for parking at the workplace.

2b Regionwide Employee All workers driving alone in the region would

Parking Charge of $3.00 experience a minimum $3.00 (1991) per day chargePer Day for parking at the workplace.

3a Fuel Tax Increase Fees would be paid at the pump.

by $0.50 (1991)

3b Fuel Tax Increase

by $2.00 (1991)

4a Mileage- and Emissions- Fees would average 1 cent per mile, and would

Based Fee (Range Approx. be collected frequently, as for the VMT fee.40-400/yr) In alternative 4a, the fee would be based on

4b Mileage- and Emissions- annual mileage and average model year emissions

Based Fee (Range Approx. as reflected in EMFAC7F. In alternative 4b, the10-1000/yr) fee would be based on actual odometer readings

and in-use tailpipe measurements.

5 VMT Fee of $0.02 per mile Fees would be paid often, e.g., in the same manner

as fuel taxes. This implies a potentially complexcollection scheme involving real-time readingof the odometer, perhaps each time a vehicle isfueled. The simpler option of billing once a yearbased on the odometer reading likely would haveless effect on travel and emissions.

Example of Combined 1, 2a, 3a, and 4a with maintenance of current transitEffects: Moderate Impact service.Example of Combined 1, 2b, 3b, and 4b with extensive transit investment.Effects: High Impact (Revenue not reduced to reflect cost of new transit.)

Change From 1991 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG 

-1.0% -0.9% -3.1% -10.5% -2.9% -2.6% -1.0% -1.1% -1.5% -2.5% -1.1% -1.0% -2.6% -2.9% -3.8% -6.0% -2.7% -2.7% -4.1% -3.8% -5.5% -7.0% -9.2% -3.9% 

-13.2% -12.7% -17.3% -20.5% -31.8% -13.0% 

-2.4% -2.1% -2.9% -2.5% -4.9% -7.5% -2.0% -1.7% -2.3% -1.5% -7.6% -20.1% -4.4% -4.2% -5.9% -7.5% -4.4% -4.3% -8.1% -7.5% -12.0% -19.7% -16.6% -13.9% 

-20.3% -19.4% -27.5% -36.4% -45.4% -38.3% 

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particu of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of ga CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of 
Technical Methods for 

to Redu

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Table B.14

Analysis Results for the Los Angeles Metropolitan Region - 

Strategy Description

1 Regionwide Congestion An Automatic Vehicle Identification (AVI) scheme

Pricing (level-of-service would be used to price the regional freeway andD/E) - Average $0.10 arterial system to maintain level-of-service (LOS)per Mile in Peak D/E

2a Regionwide Employee All workers driving alone in the region would

Parking Charge of $1.00 experience a minimum $1.00 (1991) per day chargePer Day for parking at the workplace.

2b Regionwide Employee All workers driving alone in the region would

Parking Charge of $3.00 experience a minimum $3.00 (1991) per day chargePer Day for parking at the workplace.

3a Fuel Tax Increase Fees would be paid at the pump.

by $0.50 (1991)

3b Fuel Tax Increase

by $2.00 (1991)

4a Mileage- and Emissions- Fees would average 1 cent per mile, and would

Based Fee (Range Approx. be collected frequently, as for the VMT fee.

40-400/yr) In alternative 4a, the fee would be based on

4b Mileage- and Emissions- annual mileage and average model year emissions

Based Fee (Range Approx. as reflected in EMFAC7F. In alternative 4b, the10-1000/yr) fee would be based on actual odometer readings

and in-use tailpipe measurements.

5 VMT Fee of $0.02 per mile Fees would be paid often, e.g., in the same manner

as fuel taxes. This implies a potentially complexcollection scheme involving real-time readingof the odometer, perhaps each time a vehicle isfueled. The simpler option of billing once a yearbased on the odometer reading likely would haveless effect on travel and emissions.

Example of Combined 1, 2a, 3a, and 4a with maintenance of current transitEffects: Moderate Impact service.Example of Combined 1, 2b, 3b, and 4b with extensive transit investment.

Effects: High Impact (Revenue not reduced to reflect cost of new transit.)

Change From 1991 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG C

-2.3% -2.2% -6.8% -22.5% -6.7% -5.5% -5-1.0% -1.1% -1.5% -2.5% -1.2% -1.1% -1-2.7% -3.0% -4.2% -7.5% -2.9% -2.8% -2-4.1% -4.0% -5.4% -6.5% -9.1% -3.8% -3

-13.3% -12.8% -17.5% -21.0% -31.8% -13.2% -13

-2.2% -1.9% -2.8% -3.0% -4.4% -7.0% -6-1.8% -1.6% -2.1% -1.5% -7.2% -19.4% -1-4.4% -4.2% -6.2% -9.0% -4.5% -4.3% -4-9.1% -8.6% -15.1% -30.1% -19.4% -16.0% -1

-21.5% -20.7% -31.1% -48.1% -47.8% -40.1% -3

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate em of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasoline/ CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitrog
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation EmissionsB-48 U. S. Envi

Table B.15

Analysis Results for the San Francisco Bay Area - 2010

Strategy Description

1 Regionwide Congestion An Automatic Vehicle Identification (AVI) scheme

Pricing (level-of-service would be used to price the regional freeway andD/E) - Average $0.13 arterial system to maintain level-of-service (LOS)per Mile in Peak D/E

2a Regionwide Employee All workers driving alone in the region would

Parking Charge of $1.00 experience a minimum $1.00 (1991) per day chargePer Day for parking at the workplace.

2b Regionwide Employee All workers driving alone in the region would

Parking Charge of $3.00 experience a minimum $3.00 (1991) per day chargePer Day for parking at the workplace.

3a Fuel Tax Increase Fees would be paid at the pump.

by $0.50 (1991)

3b Fuel Tax Increase

by $2.00 (1991)

4a Mileage- and Emissions- Fees would average 1 cent per mile, and would

Based Fee (Range Approx. be collected frequently, as for the VMT fee.40-400/yr) In alternative 4a, the fee would be based on

4b Mileage- and Emissions- annual mileage and average model year emissions

Based Fee (Range Approx. as reflected in EMFAC7F. In alternative 4b, the10-1000/yr) fee would be based on actual odometer readings

and in-use tailpipe measurements.

5 VMT Fee of $0.02 per mile Fees would be paid often, e.g., in the same manner

as fuel taxes. This implies a potentially complexcollection scheme involving real-time readingof the odometer, perhaps each time a vehicle isfueled. The simpler option of billing once a yearbased on the odometer reading likely would haveless effect on travel and emissions.

Example of Combined 1, 2a, 3a, and 4a with maintenance of current transitEffects: Moderate Impact service.Example of Combined 1, 2b, 3b, and 4b with extensive transit investment.Effects: High Impact (Revenue not reduced to reflect cost of new transit.)

Change From 2010 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG CO 

-2.8% -2.7% -8.2% -27.0% -8.3% -6.9% -6.9% -0.8% -0.9% -1.3% -2.7% -1.0% -0.8% -0.8% -2.1% -2.4% -3.5% -7.0% -2.4% -2.3% -2.4% -3.6% -3.4% -5.3% -8.5% -8.8% -3.5% -3.5% 

-11.7% -11.3% -16.8% -25.5% -30.6% -11.6% -11.5% 

-2.2% -1.9% -2.9% -3.5% -3.9% -5.4% -5.3% -1.6% -1.4% -2.1% -2.5% -6.6% -17.7% -17.5% -3.9% -3.7% -5.7% -9.0% -4.1% -3.8% -3.7% -8.9% -8.4% -16.2% -36.5% -19.9% -15.3% -15.3% 

-19.5% -18.9% -31.1% -57.7% -47.1% -37.9% -37.9% 

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate emissio

of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasoline/diese

 CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitrogen.
Technical Methods for 

to Redu

U. S. Environmental Protection Agency 

Table B.16

Analysis Results for the Sacramento Region - 2010

Strategy Description

1 Regionwide Congestion An Automatic Vehicle Identification (AVI) scheme

Pricing (level-of-service would be used to price the regional freeway andD/E) - Average $0.08 arterial system to maintain level-of-service (LOS)per Mile in Peak D/E

2a Regionwide Employee All workers driving alone in the region would

Parking Charge of $1.00 experience a minimum $1.00 (1991) per day chargePer Day for parking at the workplace.

2b Regionwide Employee All workers driving alone in the region would

Parking Charge of $3.00 experience a minimum $3.00 (1991) per day chargePer Day for parking at the workplace.

3a Fuel Tax Increase Fees would be paid at the pump.

by $0.50 (1991)

3b Fuel Tax Increase

by $2.00 (1991)

4a Mileage- and Emissions- Fees would average 1 cent per mile, and would

Based Fee (Range Approx. be collected frequently, as for the VMT fee.40-400/yr) In alternative 4a, the fee would be based on

4b Mileage- and Emissions- annual mileage and average model year emissions

Based Fee (Range Approx. as reflected in EMFAC7F. In alternative 4b, the10-1000/yr) fee would be based on actual odometer readings

and in-use tailpipe measurements.

5 VMT Fee of $0.02 per mile Fees would be paid often, e.g., in the same manner

as fuel taxes. This implies a potentially complexcollection scheme involving real-time readingof the odometer, perhaps each time a vehicle isfueled. The simpler option of billing once a yearbased on the odometer reading likely would haveless effect on travel and emissions.

Example of Combined 1, 2a, 3a, and 4a with maintenance of current transitEffects: Moderate Impact service.Example of Combined 1, 2b, 3b, and 4b with extensive transit investment.Effects: High Impact (Revenue not reduced to reflect cost of new transit.)

Change From 2010 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG 

-1.5% -1.4% -4.8% -16.5% -4.8% -3.7% -1.0% -1.1% -1.5% -2.5% -1.1% -1.1% -2.6% -2.8% -3.9% -6.5% -2.7% -2.8% -4.1% -3.9% -5.5% -7.0% -9.3% -4.0% 

-13.2% -12.7% -17.6% -22.0% -31.8% -13.0% 

-2.6% -2.3% -3.5% -4.5% -4.0% -5.7% -2.3% -2.1% -3.3% -5.0% -7.4% -20.2% -4.4% -4.1% -5.9% -7.5% -4.4% -4.3% -8.7% -8.2% -14.1% -26.6% -17.6% -13.4% 

-21.0% -20.2% -30.2% -46.1% -46.5% -39.3% 

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particula of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gaso CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of n
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation EmissionsB-50 U. S. Envi

Table B.17

Analysis Results for the San Diego Region - 2010

Strategy Description

1 Regionwide Congestion An Automatic Vehicle Identification (AVI) scheme

Pricing (level-of-service would be used to price the regional freeway andD/E) - Average $0.09 arterial system to maintain level-of-service (LOS)per Mile in Peak D/E

2a Regionwide Employee All workers driving alone in the region would

Parking Charge of $1.00 experience a minimum $1.00 (1991) per day chargePer Day for parking at the workplace.

2b Regionwide Employee All workers driving alone in the region would

Parking Charge of $3.00 experience a minimum $3.00 (1991) per day chargePer Day for parking at the workplace.

3a Fuel Tax Increase Fees would be paid at the pump.

by $0.50 (1991)

3b Fuel Tax Increase

by $2.00 (1991)

4a Mileage- and Emissions- Fees would average 1 cent per mile, and would

Based Fee (Range Approx. be collected frequently, as for the VMT fee.40-400/yr) In alternative 4a, the fee would be based on

4b Mileage- and Emissions- annual mileage and average model year emissions

Based Fee (Range Approx. as reflected in EMFAC7F. In alternative 4b, the10-1000/yr) fee would be based on actual odometer readings

and in-use tailpipe measurements.

5 VMT Fee of $0.02 per mile Fees would be paid often, e.g., in the same manner

as fuel taxes. This implies a potentially complexcollection scheme involving real-time readingof the odometer, perhaps each time a vehicle isfueled. The simpler option of billing once a yearbased on the odometer reading likely would haveless effect on travel and emissions.

Example of Combined 1, 2a, 3a, and 4a with maintenance of current transitEffects: Moderate Impact service.Example of Combined 1, 2b, 3b, and 4b with extensive transit investment.Effects: High Impact (Revenue not reduced to reflect cost of new transit.)

Change From 2010 Base 

VMT/VKT/PM Trips Time Delay Fuel/CO2 ROG CO NO

-1.7% -1.6% -5.4% -18.5% -5.4% -4.2% -4.3% -2.0-0.9% -1.0% -1.4% -2.5% -1.0% -0.9% -0.9% -0.8-2.4% -2.6% -3.8% -7.0% -2.5% -2.5% -2.5% -2.4-3.9% -3.5% -5.5% -8.0% -9.1% -3.8% -3.6% -3.3

-12.5% -12.0% -17.1% -23.0% -31.3% -12.3% -12.2% -11.8

-2.5% -2.2% -3.2% -3.5% -4.1% -5.5% -5.4% -4.6-1.9% -1.7% -2.6% -3.5% -7.1% -19.5% -19.2% -16.2-4.2% -4.0% -5.9% -8.5% -4.2% -4.1% -4.0% -3.8-8.5% -7.8% -14.2% -28.4% -17.9% -13.4% -13.2% -10.1

-19.9% -19.1% -29.6% -48.5% -46.1% -38.2% -38.1% -33.0

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate emissio of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasoline/diese CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitrogen.
Technical Methods for 

to Redu

U. S. Environmental Protection Agency 

Table B.18

Analysis Results for the Los Angeles Metropolitan Region - 2010

Strategy Description VMT/VKT/PM

1 Regionwide Congestion An Automatic Vehicle Identification (AVI) scheme -3.3%

Pricing (level-of-service would be used to price the regional freeway andD/E) - Average $0.15 arterial system to maintain level-of-service (LOS)per Mile in Peak D/E

2a Regionwide Employee All workers driving alone in the region would -0.9%

Parking Charge of $1.00 experience a minimum $1.00 (1991) per day chargePer Day for parking at the workplace.

2b Regionwide Employee All workers driving alone in the region would -2.5%

Parking Charge of $3.00 experience a minimum $3.00 (1991) per day chargePer Day for parking at the workplace.

3a Fuel Tax Increase Fees would be paid at the pump. -4.2%

by $0.50 (1991)

3b Fuel Tax Increase -13.0%

by $2.00 (1991)

4a Mileage- and Emissions- Fees would average 1 cent per mile, and would -2.5%

Based Fee (Range Approx. be collected frequently, as for the VMT fee.40-400/yr) In alternative 4a, the fee would be based on

4b Mileage- and Emissions- annual mileage and average model year emissions -2.1%

Based Fee (Range Approx. as reflected in EMFAC7F. In alternative 4b, the10-1000/yr) fee would be based on actual odometer readings

and in-use tailpipe measurements.

5 VMT Fee of $0.02 per mile Fees would be paid often, e.g., in the same manner -4.3%

as fuel taxes. This implies a potentially complexcollection scheme involving real-time readingof the odometer, perhaps each time a vehicle isfueled. The simpler option of billing once a yearbased on the odometer reading likely would haveless effect on travel and emissions.

Example of Combined 1, 2a, 3a, and 4a with maintenance of current transit -10.3%

Effects: Moderate Impact service.

Example of Combined 1, 2b, 3b, and 4b with extensive transit investment. -22.2%

Effects: High Impact (Revenue not reduced to reflect cost of new transit.)

Change From 2010 Base 

Trips Time Delay Fuel/CO2 ROG CO N

-3.1% -9.7% -32.0% -9.6% -8.1% -7.9% -3-1.1% -1.5% -2.9% -1.1% -1.0% -1.0% -0-2.8% -4.2% -8.5% -2.7% -2.6% -2.7% -2-3.9% -6.1% -9.5% -9.3% -4.1% -4.0% -3

-12.5% -18.7% -28.5% -31.6% -12.8% -12.7% -12

-2.3% -3.6% -5.5% -3.9% -5.5% -5.4% -4-1.9% -3.3% -6.0% -7.2% -18.9% -18.6% -15-4.1% -6.4% -10.5% -5.2% -4.2% -4.2% -3-9.7% -18.9% -43.0% -21.5% -17.1% -16.9% -12

-21.4% -35.7% -67.4% -49.5% -41.1% -40.9% -34

Notes: Revenue expressed in millions of dollars per year; VMT denotes weekday vehicle-miles traveled; VKT is weekday vehicle-kilometers traveled; PM10 is particulate emissions  of 10 microns or less; trips are weekday vehicle-trips; time is weekday vehicle-hours of travel; delay is weekday vehicle-hours of delay; fuel is daily gallons of gasoline/diesel; CO2 is daily tons of carbon dioxide; ROG is daily tons of reactive organic hydrocarbons; CO is daily tons of carbon monoxide; and NOx is daily tons of oxides of nitrogen.

Technical Methods for Analyzing Pricing Measures

to Reduce Transportation Emissions

U. S. Environmental Protection Agency B-53

B.4.2 Equity

One of the biggest contentions raised about strategies that increase the price of

transportation is that, while some people would benefit, others would be unduly hurt. 

Whether from an ethical or a pragmatic political perspective, these equity concerns, which

stem from the possibility of unevenly distributed benefits and costs, are a central

implementation issue for transportation pricing. Price increases are especially a worry for

low income individuals who may not be able to afford the higher costs and hence might be

priced out of certain travel options. Higher transportation prices also are a concern for

moderate income people who have little flexibility about when or where they travel and

hence might have to devote a larger share of their income to transportation.

One would not want to overstate equity issues. First, one might argue that there is nothing

inherently unfair about expecting people to pay for the services they consume, to cover the

costs of damage they do to the environment, and so on, regardless of their socioeconomic

status. In fact, this could be seen as a more equitable result, since it removes undeserved

burdens from others. Second, it is important to note that for many pricing applications,

and especially for congestion pricing, the dollar cost is higher for those who pay it, but time

and other costs decline; many people should be better off despite the higher prices. Finally,

for any of the measures, use of the revenues to improve transportation services could result

in net benefits for most. In short, simply noting that prices are higher does not mean that

the result is necessarily less equitable.

Nevertheless, it is important to have good information on the distribution of costs and

benefits of various transportation pricing strategies, including the status quo, so that the

social and political ramifications can be anticipated and dealt with and so that program

designs can be structured to achieve a satisfactory level of fairness. While full treatment of

the equity issues of transportation pricing would require a separate study, a portion of our

effort was devoted to exploring the impacts of the various pricing strategies on different

groups and interests. Indeed, the analysis procedures described here were designed to

produce as much information about the distributional consequences of pricing as possible.

The distribution of impact and equity can be thought of along many dimensions - income,

class, race. ethnicity, age, sex, and geography are among those commonly considered. For

the illustrative purposes of this section, however, we have chosen to focus our attention

primarily on differences by income level. We split the households of California into five

equal household income groups, and used the resulting quintile boundaries to categorize

our findings throughout the analysis of pricing policies. The five quintiles are:
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation Emissions

B-54 U. S. Environmental Protection Agency

Quintile Income Range (1994$)

1 <= $18,700

2 $18,701-$36,500

3 $36,501-$52,100

4 $52,101-$71,300

5 >=$71,301

Tables B19 and B20 present a distillation of quintile data based on the 1990 U.S. Census

Public Use Microdata Sample for California. It may be helpful to begin a discussion of

equity by first looking at some basic facts about the distribution of income in California, as

shown in these tables.
Technical Methods for Analyzing Pricing Measures

to Reduce Transportation Emissions

U. S. Environmental Protection Agency B-55

Table B.19: 1990 US Census California Statewide Summary

 Public Use Microdata Sample

Share of Households in Each State Income Quintile

 Share by State Income Quintile Total

Region 1 2 3 4 5 Households

Sacramento Region 0.22 0.22 0.21 0.20 0.15 598405

San Diego Region 0.19 0.21 0.21 0.20 0.18 885574

San Francisco Bay Area 0.16 0.17 0.19 0.22 0.26 2242554

South Coast 0.19 0.19 0.20 0.20 0.22 4560620

Balance of State 0.27 0.24 0.20 0.17 0.12 1744923

California Combined Total 0.20 0.20 0.20 0.20 0.20 10032076

Share of Population in Each State Income Quintile

 Share by State Income Quintile Total

Region 1 2 3 4 5 Population

Sacramento Region 0.17 0.21 0.22 0.23 0.17 1560521

San Diego Region 0.15 0.20 0.22 0.23 0.21 2386031

San Francisco Bay Area 0.11 0.15 0.19 0.25 0.31 5852335

South Coast 0.15 0.18 0.20 0.22 0.25 13233643

Balance of State 0.21 0.24 0.22 0.19 0.14 4930037

California Combined Total 0.15 0.19 0.20 0.22 0.23 279962567

Share of Autos in Each State Income Quintile

 Share by State Income Quintile Total

Region 1 2 3 4 5 Autos

Sacramento Region 0.13 0.19 0.22 0.25 0.21 1080383

San Diego Region 0.10 0.17 0.22 0.25 0.26 1577796

San Francisco Bay Area 0.08 0.13 0.18 0.26 0.36 3941140

South Coast 0.10 0.15 0.19 0.24 0.31 8077199

Balance of State 0.17 0.22 0.23 0.22 0.18 3163821

California Combined Total 0.11 0.16 0.20 0.24 0.29 17840339

Share of Resident Workers in Each State Income Quintile

 Share by State Income Quintile Total

Region 1 2 3 4 5 Workers

Sacramento Region 0.09 0.18 0.23 0.27 0.23 715029

San Diego Region 0.08 0.17 0.22 0.26 0.26 1145517

San Francisco Bay Area 0.05 0.12 0.18 0.27 0.37 2993791

South Coast 0.08 0.15 0.20 0.26 0.32 6237629

Balance of State 0.11 0.21 0.24 0.25 0.19 2010851

California Combined Total 0.08 0.16 0.21 0.26 0.30 13102817
Technical Methods for Analyzing Pricing Measures

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B-56 U. S. Environmental Protection Agency

Table B.20: 1990 US Census California Statewide Summary

 Public Use Microdata Sample

Autos per Worker in Each State Income Quintile

 Autos per Worker by Income Quintile Regional

Region 1 2 3 4 5 Average

Sacramento Region 2.16 1.62 1.48 1.38 1.36 1.51

San Diego Region 1.75 1.37 1.33 1.33 1.36 1.38

San Francisco Bay Area 1.89 1.38 1.30 1.25 1.27 1.32

South Coast 1.75 1.28 1.24 1.23 1.28 1.29

Balance of State 2.37 1.66 1.47 1.37 1.42 1.57

California Combined Total 1.93 1.40 1.32 1.27 1.30 1.36

Work-at-Home Share in Each State Income Quintile

 Share by State Income Quintile Regional

Region 1 2 3 4 5 Average

Sacramento Region 0.041 0.029 0.030 0.026 0.037 0.031

San Diego Region 0.042 0.033 0.026 0.032 0.039 0.034

San Francisco Bay Area 0.052 0.034 0.030 0.027 0.034 0.032

South Coast 0.035 0.023 0.022 0.022 0.032 0.027

Balance of State 0.044 0.038 0.032 0.031 0.047 0.037

California Combined Total 0.041 0.030 0.026 0.026 0.035 0.03

Commute Time per Worker in Each State Income Quintile

 Minutes per Worker by Income Quintile Regional

Region 1 2 3 4 5 Average

Sacramento Region 19.17 20.28 21.55 22.89 22.53 21.71

San Diego Region 21.72 21.84 22.28 23.23 23.20 22.65

San Francisco Bay Area 23.24 23.64 25.35 26.20 26.37 25.65

South Coast 25.65 25.30 25.90 26.81 27.28 26.46

Balance of State 18.00 18.70 19.45 20.47 20.26 19.55

California Combined Total 22.84 23.04 24.03 25.20 25.83 24.63

Drive Alone Share for Workers in Each State Income Quintile

 Drive Alone Share by Income Quintile Regional 

Region 1 2 3 4 5 Average

Sacramento Region 0.65 0.71 0.76 0.79 0.77 0.75

San Diego Region 0.62 0.67 0.73 0.77 0.80 0.74

San Francisco Bay Area 0.54 0.61 0.67 0.71 0.73 0.69

South Coast 0.58 0.64 0.70 0.75 0.80 0.72

Balance of State 0.64 0.70 0.75 0.77 0.79 0.74

California Combined Total 0.59 0.65 0.71 0.75 0.78 0.72
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By definition, each income quintile contains one fifth of the total number of households

in the state. But the distribution of household income within the state is uneven; there

are notable differences among regions. For example, the San Francisco Bay Area is

relatively well off, with 48 percent of its households in the top two quintiles and only 33

percent of its households in the bottom two quintiles. In contrast, the small urban and

non-metropolitan areas of the state have just 29 percent of their households in the top

two quintiles and 51 percent in the bottom two quintiles. While housing prices and

other cost-of-living factors may cloud the comparison somewhat, it seems clear that the

ability to pay higher transportation prices is not distributed evenly around the state, but

is higher in its metropolitan areas.

Other important points can be observed be examining the income quintile data. In

particular:

• Population is not distributed evenly among the quintiles. Higher income

households tend to be larger, such that 23 percent of the population is in

the highest quintile and 15 percent in the lowest quintile.

• Auto ownership increases with income. 53 percent of the vehicles for

personal use in California are owned by the top two quintiles, while only

27 percent are owned by the bottom two quintiles. This suggests that

policies which cause a general increase in the cost of auto ownership may

apply disproportionately to upper income groups.

• Households with workers tend to have higher incomes than those which do

not. 56 percent of the workers statewide are in the top two quintiles, while

only 24 percent are in the bottom two quintiles. This suggests that policies

which cause a general increase in the cost of commuting may apply

disproportionately to upper income groups.

• Autos per worker is consistently high in all income groups. Table 20 shows

that quintile 1 - the lowest income group - has the highest auto ownership

per worker. This counter-intuitive result is due to the large group of retirees

falling into that quintile. Removing the retirees from the data base produces

a ratio of autos to workers of 1.25:1 for each of the five quintiles. While this

does not have direct implications for pricing policy, it does suggest that

access to an automobile for the commute is widely distributed in California.

• Drive-alone share for commute travel rises with income. The drive-alone share

statewide is about .59 in the lowest quintile and .78 in the highest quintile, with

similar variation in each region. Putting the mode shares (including the shared

ride data not shown here) together with the proportion of workers in each

quintile, it becomes clear that only about 6 percent of the commute vehicles
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statewide will have drivers in the lowest quintile, while about 35 percent will

have drivers in the highest quintile.

• Commute time per worker rises with income. The average self-reported

commute trip time statewide is about 22.8 minutes for workers in the lowest

quintile and 25.8 minutes for workers in the highest quintile, with similar

variation in each region. Because many of the low income workers' miles are

made by transit (or by foot) at speeds far below auto speeds, even on congested

networks, it is clear that higher income workers' trips must be considerably

longer (in VMT) than those of their lower income counterparts. This illustrates

a crucial point for pricing studies: higher income workers are the largest

contributors to work trip VMT, partly because high income jobs and high-end

housing are relatively sparsely distributed around each region.

• Both low and high income workers are more likely to work at home. About 3.5

percent of workers in the highest quintile and 4.1 percent of workers in the

lowest quintile listed home as the primary place of work in 1990, compared to

3 percent of workers overall. While these phenomena are not well understood,

it is said that participation rates by upper income households have been

increasing in recent years. This may indicate that upper income households

have an important way to blunt the effect of large price increases, namely by

choosing to work at home some of the time.

The PUMS data demonstrate one of the most important facts about equity of the current

transportation system. Truly poor people make relatively little use of the highway

system as it operates today and, consequently, would pay comparatively little under

most transportation pricing scenarios (in absolute terms, not necessarily as a share of

income).

Equity Analyses Using PUMS and STEP

An unstated implication of the PUMS analysis is that the lower middle class - say,

quintiles 2 and 3 - would sustain much of the impact of pricing policies. This hypothesis

was explored in a range of analyses using STEP, examples of which are shown in Tables

B21 and B22. The STEP analysis framework allows us to examine equity issues in detail

because it utilizes specific demographic information, at the individual household level,

that can be associated directly with the effects of each pricing policy.
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Table B.21

Equity Implications of a VMT Fee in the Los Angeles Region - 1991

VMT Fee Absolute Change in Daily VMT by Income Quintile

(cents/mile) Q1 Q2 Q3 Q4 Q5 Total

1 -1.8 -1.9 -1.4 -1.1 -0.5 -6.6

2 -3.4 -3.7 -2.8 -2.2 -0.9 -13.0

3 -4.9 -5.4 -4.1 -3.3 -1.5 -19.2

4 -6.2 -7.0 -5.5 -4.4 -2.0 -25.2

5 -7.4 -8.6 -6.8 -5.6 -2.6 -31.0

6 -8.5 -10.1 -8.1 -6.7 -3.2 -36.6

7 -9.5 -11.5 -9.3 -7.8 -3.8 -42.0

8 -10.5 -12.9 -10.5 -8.9 -4.5 -47.2

9 -11.3 -14.2 -11.7 -10.0 -5.1 -52.3

10 -12.0 -15.4 -12.9 -11.1 -5.8 -57.3

Base VMT (millions) 25.5 45.0 54.8 71.9 92.8 290.0

Per Capita Daily VMT 11.7 17.3 19.1 22.0 25.8 20.0

VMT Fee Percent Change in Daily VMT by Income Quintile

(cents/mile) Q1 Q2 Q3 Q4 Q5 Total

1 -7.0% -4.2% -2.6% -1.5% -0.5% -2.3%

2 -13.3% -8.2% -5.1% -3.1% -1.0% -4.5%

3 -19.1% -12.0% -7.5% -4.6% -1.6% -6.6%

4 -24.3% -15.6% -10.0% -6.2% -2.2% -8.7%

5 -29.1% -19.1% -12.4% -7.7% -2.8% -10.7%

6 -33.5% -22.4% -14.7% -9.3% -3.5% -12.6%

7 -37.4% -25.6% -17.0% -10.8% -4.1% -14.5%

8 -41.0% -28.7% -19.2% -12.4% -4.8% -16.3%

9 -44.2% -31.5% -21.4% -13.9% -5.5% -18.0%

10 -47.2% -34.3% -23.5% -15.4% -6.3% -19.7%

Note: Quintiles defined in terms of 1989 Census household incomes.

 VMT is vehicle-miles traveled in millions per day. Sales tax relief,

 improved transit, and other potential expenditures to mitigate

 impacts on lower income households are not reflected here.
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Table B.22

Equity Implications of Congestion Pricing in the

San Francisco Bay Region - 1991

Average 

Peak Fee Absolute Change in Daily VMT by Income Quintile

(cents/mile) Q1 Q2 Q3 Q4 Q5 Total

1 -0.2 -0.2 -0.1 -0.1 0.0 -0.6

2 -0.3 -0.3 -0.2 -0.1 0.1 -1.1

3 -0.4 -0.5 -0.3 -0.2 0.1 -1.6

4 -0.5 -0.6 -0.4 -0.3 0.1 -2.1

5 -0.6 -0.7 -0.5 -0.4 0.2 -2.5

6 -0.7 -0.8 -0.6 -0.4 0.2 -2.9

7 -0.8 -0.9 -0.7 -0.5 0.3 -3.3

8 -0.8 -1.0 -0.7 -0.6 0.3 -3.6

9 -0.9 -1.1 -0.8 -0.7 0.3 -3.9

10 -0.9 -1.1 -0.9 -0.8 0.3 -4.2

Base VMT (millions) 7.2 14.0 19.6 30.3 44.0 115.0

Per Capita Daily VMT 10.0 15.3 16.8 19.5 22.6 18.3

Average 

Peak Fee Percent Change in Daily VMT by Income Quintile

(cents/mile) Q1 Q2 Q3 Q4 Q5 Total

1 -2.2% -1.2% -0.6% -0.2% 0.1% -0.5%

2 -4.2% -2.3% -1.1% -0.5% 0.2% -1.0%

3 -6.0% -3.3% -1.7% -0.7% 0.3% -1.4%

4 -7.5% -4.2% -2.2% -1.0% 0.4% -1.8%

5 -8.8% -5.0% -2.6% -1.2% 0.5% -2.2%

6 -10.0% -5.7% -3.0% -1.5% 0.5% -2.5%

7 -11.0% -6.4% -3.4% -1.8% 0.6% -2.9%

8 -11.8% -7.0% -3.8% -2.0% 0.6% -3.2%

9 -12.4% -7.5% -4.2% -2.3% 0.6% -3.4%

10 -12.9% -8.0% -4.5% -2.6% 0.6% -3.7%

Note: Quintiles defined in terms of 1989 Census household incomes.

 VMT is vehicle-miles traveled in millions per day. Sales tax relief,

 improved transit, and other potential expenditures to mitigate

 impacts on lower income households are not reflected here.
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Table B21 presents results for VMT fees in the Los Angeles region at levels ranging

between 1 cent and 10 cents per mile. The STEP analysis shows that daily VMT is

skewed heavily toward the upper income quintiles - the highest income quintile

accounts for about one-third of total VMT, while the lowest quintile accounts for less

than 10 percent. Nevertheless, the absolute drop in VMT resulting from a VMT fee is

largest in quintile 2 (the second lowest income level) and smallest in quintile 5 (the

highest income level). The absolute drop in VMT is of the same basic magnitude in each

of the first four quintiles, and the percentage drop is progressively larger the lower the

income level. (Percentages are shown in the second part of the table).

Table B22 presents results for congestion prices ranging from one cent to ten cents per

mile, on average, for the San Francisco Bay Area. Here we find that absolute VMT

decreases are roughly the same among the lowest four quintiles, while VMT for the

highest quintile actually rises (as one would expect for high-value-of-time travelers).

Comparable analyses for parking fees in the San Diego region and fuel taxes in the

Sacramento region (details not shown here) yielded similar results: The largest VMT

decreases per capita are concentrated in the four lowest income categories. While the

VMT drop should not be read as a pure decline in mobility, since some trips are still

made by other modes, shortened, etc., it does show how the mobility impacts would be

distributed in the absence of efforts to improve modal alternatives for impacted

individuals.

Another way to think about equity is in terms of the per capita daily payment by each

quintile. Based on Table B21, the quintile total payments for a 5 cent VMT fee in the Los

Angeles region would be:

Quintile Daily Payment (million $)

 1 0.9

 2 1.8

 3 2.4

 4 3.3

 5 4.5

Out of a daily total of $12.9 million, 35 percent is paid by the top quintile and 61 percent

is paid by the top two quintiles. Similarly, only about six percent of current fuel taxes

are paid by members of the lowest income quintile and 10 percent by the second

quintile. Thus, while the travel/mobility impact falls disproportionately on the lower

income quintiles, the financial burden falls squarely on the upper income quintiles.

It is harder to say how VMT fees and vehicle emissions fees would affect different

income groups; we can estimate impacts on trip making and location choice, and can

forecast auto ownership levels by income group, but we have no direct evidence on how
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the various groups would change the type and age of the vehicles they own in response

to new fees (our analyses on vehicle type and age changes were based on assumptions

provided to the models rather than computed outputs of the models). Nevertheless,

available data do provide some insights into equity impacts. Using data collected by

Caltrans as part of a statewide travel survey, we find that about 55 percent of the

vehicles over eight years old are owned by the top three income quintiles, mostly as

second, third, or even fourth or fifth cars. The remaining 45 percent of the older cars are

owned by the two-fifths of the households with low or moderate incomes. To the extent

that vehicle registration fees fall most heavily on these older vehicles, they also would

fall somewhat more heavily than proportional on low and moderate income households.

Implications

As with any change in tax policy, the distributional consequences of the proposed

change should be carefully examined. The distribution of the burden of the proposed

tax among income groups should be compared with the distributional consequences of

tax alternatives, including the current tax system. If adverse equity consequences are

deemed to be significant, policies to ameliorate those burdens should be examined,

including the use of tax revenues to benefit those who might be disproportionately

affected by the tax change (such as increased funding for public transportation services,

use of revenues to provide tax exemptions (life-line rates) for low-income users, or use of

tax revenues to replace existing taxes that disproportionately burden low-income

taxpayers).
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References

1. Greig Harvey, STEP: Short-Range Transportation Evaluation Program , prepared for

the Metropolitan Transportation Commission, Oakland, California, 1978.

2. Greig Harvey Methodology for Incorporating Transportation System Effects into

Regional Transportation Energy Demand Forecasts, report prepared for the

California Energy Commission under Contract No. 400-82-023, September 1983.

3. D. Pickrell, What Gasoline Taxes Can - and Can't - Do, paper presented at the

Graduate School of Architecture and Urban Planning, UCLA, Los Angeles,

March 16, 1993.

4. D. Pickrell, VMT and Vehicle Fuel Economy Elasticities, Memorandum, Volpe

National Transportation Systems Center, Cambridge, Massachusetts, February

14, 1995.

5. G. Orcutt, Policy Evaluation through Discrete Microsimulation, Second Edition,

Brookings Institute, Washington, D.C., 1976.

6. J.D. Wardrop, Some Theoretical Aspects of Road Traffic Research, proceedings

of the Institute of Civil Engineers 2(1), 1952, pp. 325-378.