Procedures Manual for

Estimating Emission

Reductions from Voluntary

Measure and Commuter

Choice Incentive Programs

United States

Environmental Protection

Agency

Air and Radiation EPA420-R-00-015

October 2000
Procedures Manual for 

Estimating Emission Reductions from

Voluntary Measure and Commuter Choice Incentive Programs

prepared for:

U. S. Environmental Protection Agency

prepared by:

J. Richard Kuzmyak Thomas R. Carlson Stephen D. Decker

Robert G. Dulla Christopher D. Porter

Erin E. Vaca

Sierra Research, Inc. Cambridge Systematics, Inc.

9509 Woodstock Ct. 1801 J Street 1300 Clay Street, Suite 1010

Silver Springs, MD 20910 Sacramento, CA 95814 Oakland, CA 94612

(301) 495-8814 (916) 444-6666 (510) 873-8700
-iProcedures Manual for

Estimating Emission Reductions from

Voluntary Measure and Commuter Choice Incentive Programs

Table of Contents

page

1. Introduction .........................................................1

2. Overview of the COMMUTER Model Analysis Procedure . . . . . . . . . . . . . . . . . . . 4

What is the COMMUTER Model? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Conventional Analysis Methods ........................................ 5

How the COMMUTER Model Approach is Different . . . . . . . . . . . . . . . . . . . . . . . 7

Baseline Data Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Selection of Strategies and Development of Scenarios . . . . . . . . . . . . . . . . . . . . . . 11

Computational Methods for Estimating Transportation Impacts . . . . . . . . . . . . . . . . 11

Emissions Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Model Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3. Assembling Required Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Required Model Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4. Estimating Travel Impacts of Commuter Choice Support Programs . . . . . . . . . . . . 33

Overview of Employer Support Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Analytic Approach for Estimating Travel Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . 35

5. Estimating Travel Impacts of Alternative Work Schedules . . . . . . . . . . . . . . . . . . . 44

Overview of Alternative Work Schedule Programs . . . . . . . . . . . . . . . . . . . . . . . . . 44

Nature of Emissions Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Analytic Approach for Estimating Travel Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Multiple Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

6. Estimating Travel Impacts of Travel Time and Cost Changes . . . . . . . . . . . . . . . . . 54

Overview of Travel Time and Travel Cost Strategies . . . . . . . . . . . . . . . . . . . . . . . 54

Types of Time/Cost Strategies Covered by COMMUTER Model . . . . . . . . . . . . . . 57

Analytic Approach for Estimating Travel Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Coefficients Used in Pivot-Point Logit Model Approach . . . . . . . . . . . . . . . . . . . . . 64

Example Calculation of Change in Time or Cost through 
-iiLogit Pivot-Point Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

7. Converting Travel Impacts of Emissions Reductions . . . . . . . . . . . . . . . . . . . . . . . . 70

Overview of Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Changes in Total Trips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Changes in Total VMT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Allocation Between Peak Period and Off-Peak . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Emission Impacts Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Appendix A - Definition of Modeling Terms
-1-

1. Introduction

This manual describes a set of procedures that can be used to evaluate voluntary mobile

source emission reduction and commuter choice incentive programs for State

Implementation Plan (SIP) and employer-based commuter choice programs. To simplify

their use, they have been implemented in a spreadsheet model, entitled COMMUTER,

whose operation is explained in a companion report entitled “COMMUTER Model User

Manual for Analysis of Voluntary Mobile Source Emission Reduction and Commuter

Choice Incentive Programs.”

The procedures are intended to be simple to apply, with data requirements the minimum

necessary to evaluate alternative programs. At the same time, the analytical techniques

employed in these procedures are consistent with existing state-of-the-practice travel

modeling techniques widely employed by transportation professionals. The calculation

methodologies are largely based on those used in the Federal Highway Administration

Travel Demand Management Evaluation Model (FHWA TDM Evaluation Model),

developed by COMSIS Corporation in 1993. This model has been widely applied in the

evaluation of employer-based TDM programs for the purposes of calculating travel

impacts. 

The analytical process described in this report is illustrated by the flowchart in Figure 1-1. 

The basic sequence of steps in this process can be used to assess travel impacts without

use of the COMMUTER model. The sequence of procedural steps is outlined below.

& First, adjust the baseline (existing) mode shares to reflect the impacts of

support and incentive programs such as rideshare matching and provision of

bicycle facilities. To make the adjustments, apply mode shift factors from

lookup tables that have been compiled from the literature and national

experience.

& Once the impact of support and incentive programs have been accounted for,

use travel modeling techniques to estimate the impacts of strategies related to

travel time or cost savings on mode choice. Support and incentive programs

include site-specific employer programs such as on-site transit pass sales,

rideshare matching, and guaranteed ride home programs. Those strategies that

can be measured by savings in travel times and costs include transit use

frequency, high occupancy lanes, and transit fare reductions. 
-2-

Baseline Mode Shares

Adjust mode shares for 

“soft” programs

Adjust mode shares for 

time and cost incentives

Compute changes in 

trips

Compute changes in 

VMT

Compute changes in 

total emissions

Lookup tables

Coefficients

Background 

transportation data

Program 

Information

Figure 1

Overview of Calculation Procedure

& Convert the changes in mode shares to changes in motor vehicle trip ends. Use

the average trip length by mode to convert these changes in trip ends to changes

in vehicle miles traveled (VMT).

& Use predefined sets of emissions factors to estimate the emissions impacts.
-3-

The following sections discuss each step in the process in greater detail for both modeland manually derived applications. The discussion covers definitions of terms,

assumptions and analytical techniques, and coefficients and parameters that enter the

calculations.

It is highly recommended that analysts use locally derived values for the parameters

whenever possible to be consistent with local travel behavior characteristics in the

metropolitan area of analysis. In the absence of local data, the analyst may use default

values for program effectiveness that have been provided in this manual and built into the

spreadsheet model.

The following sections provide guidance in setting parameter values. Typically,

employers will want to use parameter values or values provided by their local

transportation planning agency, if available, before relying on the defaults listed in this

guidance.

###
-4-

2. Overview of the COMMUTER Model

Analysis Procedure

What is the COMMUTER Model?

COMMUTER is a spreadsheet-based computer model that has been developed by EPA to

help areas obtain air quality credit from VMEP-type programs. It is specifically intended

to calculate the travel and emissions effects that might result from implementation of

voluntary employer transportation management programs. Areas may claim up to 3% of

their mobile source emission reductions from regional implementation of such programs,

and the COMMUTER model is designed to help areas justify their credit claims by

providing quantitative justification of the associated reductions. The tool may be used by

regional planning or air agencies who are interested in pursuing this VMEP credit on a

region-wide basis, or by individual employers who may be seeking tax credit for

implementing a voluntary commuter choice incentive program. 

Conventional transportation analysis tools normally used for regional planning and SIP

development are not well suited to the VMEP analysis task for several reasons, the most

important of which are the following:

& Traditional transportation analysis tools are unable to address many of the

voluntary commuter-choice measures, such as rideshare matching support or

alternate work schedules, in their structure; and

& Use of the traditional analysis tools is a complex and labor-intensive process. 

This would limit analysis to only trained specialists, thereby excluding

employers and other interested groups from engaging in the process, and

dramatically cut down on the trial-and-error nature of determining what

particular combination of measures is the most effective and acceptable for a

given VMEP program.

COMMUTER offers a substantial savings in time and effort over the traditional approach

by employing some key shortcuts in the amount of data used and the number of microlevel calculations that are performed. The result is a conscious but judicious tradeoff of

some accuracy for a significant increase in ease and flexibility for the user.
-5-

Figure 2-1

Traditional Transportation and Emissions Analysis Process

Conventional Analysis Methods

To understand the nature and operation of the COMMUTER model approach, it is helpful

to contrast it with the traditional approach. Figure 2-1 shows how a mobile source

emissions analysis would be done in normal practice. The process generally consists of

first estimating the transportation effects of a particular policy, program, or improvement

in the suite of models collectively known as the “four-step” process. This results in a

revised summary of vehicle trips, VMT, and operating speeds, which then become the

inputs to the emissions modeling procedure. Vehicle travel activity is mated with

emission production rates, typically through the latest version of EPA’s MOBILE

emissions model (EMFAC in California), which applies emissions factors that have been

specially adjusted to reflect local vehicle, regulatory, and climatalogical conditions.
-6-

The four-step transportation analysis process is so named because of its separate

consideration of the elements of Trip Generation, Trip Distribution, Mode Choice and

Traffic Assignment. 

& The Trip Generation step converts population, employment, income, vehicle

ownership, land use and other factors into an estimate of the number of trips

that will be made by households (productions) or attracted by economic and

personal activities (attractions). 

& Trip Distribution then determines “where” households will travel to meet

their various needs, or conversely, “from where” travelers will come to serve

the various economic or personal attractions. The result is a set of tables (or

matrices) that depict trip flows between origins and destinations. The origins

and destinations in these “trip tables” correspond to specific geographic

“zones” defined by the planning agency. In a typical metropolitan area, there is

likely to be more than 1,000 such zones, meaning that their combined function

as both origins and destinations can result in several thousand origindestination pairs for which trip flows have been calculated, and upon which

analysis may be necessary. Also, trip tables are generally developed for

separate trip purposes, the most common of which are Home-Based Work,

Home-Based Non-Work, and trips that are not based from home, so-called

Non-Home-Based trips. 

& Trip tables are then processed through a Mode Choice step, which predicts the

travel modes that will be used to make the various trips for each origindestination pair and for each trip purpose type (work, non-work, etc.). The rate

of use of particular modes (usually auto driver, auto passenger, and transit) is

based on which of these alternatives is available, the comparative service

offered by each alternative, and characteristics of the traveler. This

determination is usually made through a “mode split” model, and is calculated

separately for each origin-destination pair, resulting in a new (additional) set of

trip tables for private vehicle trips and transit person trips.

& Once vehicle trip volumes by origin-destination are known, the final step is to

distribute these trips onto the regional highway system in a process known as

Traffic Assignment. The computer is asked to overlay the regional trip tables

onto the system of freeways, arterials, collectors, and local streets and roads,

and to determine which combination of routes will be taken to complete the

trips most efficiently. To do this, the computer must have information on the

carrying capacity of each facility (on a segment-by-segment basis). Trips are

incrementally loaded onto the highway network to see what volumes and travel

speeds will result, and then reassigned to new paths in progressive iterations

until all travelers are realizing the most efficient (minimum travel time) path

for their particular trip given the overall volume of travelers. Major urban

areas will generally perform such a traffic assignment for an average 24-hour

day, and also for a case that represents peak period or peak hour conditions. 

The peak period traffic assignment is crucial in identifying congestion
* The TDM Evaluation Model was developed by COMSIS Corporation in 1993 in conjunction with a

comprehensive program of research and development of reference and guidance tools by the Federal

Highway Administration and Federal Transit Administration. Additional information of potential value to

VMEP Tool users may be found in the related reports from that research effort: Implementing Effective

Travel Demand Management Measures: Inventory of Measures and Synthesis of Experience, DOT-T-94-

02 (September 1993), Guidance Manual for Implementing Effective Employer-Based TDM Programs,

DOT-T-94-05 (November 1993), and Guidance Manual for Areawide Travel Demand Management

Programs, (October 1992).

-7-

conditions and in studying both work travel and competitive mode choice

situations.

The outputs of the transportation modeling process are then converted for use in the

MOBILE emissions model. MOBILE requires the user to supply information on the

local context in order to tailor the emissions rates appropriately to the individual area. 

The information required includes composition of the regional vehicle fleet (by age and

size group) from vehicle registration and usage data; various climatological data,

including temperature and season; and existing emissions control programs in place, such

as reformulated fuels or inspection and maintenance programs. An important element in

adapting the MOBILE model and emissions factors to the region is also applying speed

correction factors to the respective emissions rates to reflect different rates of emissions

that occur at different operating speeds. The transportation inputs are used to make this

adjustment, which may be as simple as a single across-the-board adjustment for all travel,

or specific adjustments for VMT occurring on freeways vs. other facilities and under peak

or off-peak conditions.

How the COMMUTER Model Approach is Different

From the above, one can begin to see the value of having a simpler and more versatile

procedure for analyzing VMEP and commuter choice program strategies. While all of the

steps outlined above do not have to be repeated for every analysis using the conventional

models, it is still necessary to change the input data for each origin-destination pair in the

regional trip tables that will be affected by the policy or program measure, re-estimate the

modal split, and then redo the traffic assignment step in order to get the new speed and

VMT impacts required by MOBILE. 

The COMMUTER model approach simplifies the quantification requirements for VMEP

programs by making selective simplifications, as well as enhancements, to the

conventional four-step model oriented process. The procedure is heavily based on the

Federal Highway Administration’s Travel Demand Management Evaluation Model

(FWHA TDM Model), developed in 1993 for a similar purpose, to facilitate analysis of

TDM programs and strategies for congestion management and air quality programs.*

Prior to its acceptance by the FHWA for national distribution as a planning tool, the TDM

model underwent significant sensitivity testing, and has been applied widely across the

country by planning agencies, transportation agencies, and employers or employer
-8-

organizations. It was therefore viewed as a good base from which to build the

COMMUTER model.

The decision to build a new procedure to support VMEP program analysis needs and

requirements, rather than simply modifying and adopting the FHWA TDM model, was

based on the following factors:

& The FHWA TDM model was not designed to calculate emissions.

& The FHWA TDM model, while much simpler than the four-step models, was

still designed primarily to work with trip tables, and was also seen as more

complex than some members of the target audience would be able to work

with.

& The FHWA TDM model lacked a modern “Windows”-type user interface,

meaning the user would not be able to use a mouse or other convenience

procedures to ease application.

& It was seen as desirable to recheck and, as necessary, update or enhance the

calculation procedures or impact values coded into the TDM model.

The resultant COMMUTER model has the structure and general features pictured in

Figure 2-2. It is essentially a three-step procedure:

1. The user establishes a baseline by supplying essential information on local

conditions.

2. An analysis scenario is selected from among available options.

3. Impacts on vehicle trip making, VMT, and its distribution between peak and

off-peak travel periods are calculated and used to estimate the change in

emissions of VOCs, CO, and NOx. 

Each of these steps is described briefly below, simply to provide an introduction to how

the overall technique operates. Subsequently, each remaining numbered section of this

Procedures Manual describes in some detail each of the individual steps and procedures. 

The objective is to provide the analyst with a detailed explanation of how the model is

operating, the assumptions employed, and the data and methods that are used to calculate

the particular impact. Numeric examples are provided in the individual topic sections to

illustrate each module and its calculation procedure, supplemented in most cases by

sample applications across a range of starting conditions or values of the particular

measure to provide insight as to the measure’s and the model’s sensitivity. Uncertainties,

where important, are identified and described.
-9-

Figure 2-2

COMMUTER Estimating Procedure

This Procedures Manual does not provide the step-by-step guidance on the actual use of

the COMMUTER model. This help may be found in the COMMUTER User’s Manual,

which provides a practical introduction to each of the model features, accompanied by

illustrations of the respective model screens or functions, with direct instructions on what

data are needed and where and how they should be entered.
-10-

Baseline Data Requirements

The COMMUTER user is required to provide some basic information to the model in

order to establish a starting point, or baseline, from which to measure changes, as well as

to communicate some important local conditions that are used in performing the analysis. 

This information consists of the items shown in box number Å in Figure 2-2. The

required items are summarized briefly below.

& Area Size and Type: Qualifies the area as Large (over 2 million), Medium

(750,000 to 2 million), or Small (under 750,000), and whether the analysis is

being directed at the regional downtown core, a medium-density area or

activity center, or a low-density suburban area.

& Analysis Scope: Whether the analysis is being applied at the scale of an entire

region, for a specific employment site, or some variation in between.

& Employment Base: Since the analysis is being directed at work travel, it is

important to know the size and composition of the regional (or other

application scale) employment base. In particular, it is important to know the

number of employees working in “Office” as opposed to “Non-Office”

occupations, and also the proportion of this base that is expected to be affected

by the VMEP strategies. A discussion of the important distinction between

total and affected employment is presented in Section 3.

& Starting Modal Split: The existing modal shares for work trips in the study

area, specifically percent of persons making work trips by auto drive alone,

auto carpool, vanpool, transit, walk, bicycle, and other .

& Vehicle Occupancy: Average number of persons traveling in a carpool unit or

a vanpool.

& Average Trip Length: Average trip length (distance), for all work trips

(including transit), and separately for vanpool, bicycle and walk.

& Peak Period Duration: Length of weekday peak travel period in hours.

& Peak/Off-Peak Work Trips: Percent of daily work trips that are made during

peak travel periods.

& Peak/Off-Peak VMT: Total daily VMT from work trips that occurs during

peak and off-peak travel periods.

& Peak/Off-Peak Travel Speeds: Average speeds on freeways and non-freeway

roads, during peak and off-peak travel periods.
-11-

The need for this information is explained in greater detail in Section 3, and specific

instructions on where to obtain the information and how to use it in the model are spelled

out in the Users’ Guide. Default values are provided in the model for many of the items,

although users are strongly urged to enter their own unique information. Some special

instructions apply to users of the COMMUTER model who are regional planning

agencies vs. employers and other less experienced in transportation planning methods and

data.

Selection of Strategies and Development of Scenarios

The COMMUTER model allows the user to select from and test a variety of strategies.

As shown in Figure 2-2, these include (as numbered in the diagram):

Æ Employer TDM Support Strategies: Non-monetary inducements to

encourage employees to use alternative modes rather than drive alone. These

include rideshare matching services, vanpool formation assistance, on-site

transit information and/or pass sales, transportation coordinators, guaranteed

ride home. 

Ç Alternative Work Schedules: Arrangements such as flexible or staggered

work hours, compressed work weeks, and telecommuting.

È Travel Time Improvements: On-site or adjacent area modifications to

improve access to work sites from transit, or by walking or biking. Also

includes preferential (close-in/reserved) parking for carpools or vanpools, and

improvements to transit service.

É Travel Cost Changes: Measures such as imposition of parking fees,

differential rates or discounts for carpools or vanpools, transit fare subsidies, or

inspecific modal incentives or disincentives to any or all modes.

Specific strategy options are discussed in Section 4 for Employer TDM Support, in

Section 5 for Alternative Work Schedules, and Section 6 for Travel Time Improvements

and Travel Cost Changes. With few limitations, the user may mix and match any number

or combination of these strategies within a given “scenario”, and obviously can create and

test as many scenarios as desired. By so doing, the user will gain insight into which

strategies are the most effective for their particular situation, and will discover the

flexibility they may have to achieve their desired transportation management goal in more

than one way.

Computational Methods for Estimating Transportation Impacts
-12-

The COMMUTER model escapes the technical and resource intensity of the traditional

four-step modeling approach by taking some calculated shortcuts. These shortcuts retain

the essence of the full-blown approach, but simplify the effort for a typical VMEP

analysis by an order of magnitude. Of course, because these shortcuts cut back on the

many minute calculations that are normally done in a trip-table-based analysis, a certain

degree of uncertainty is introduced by replacing a large number of individual

computations with a single “aggregate” calculation. However, given the modest

increment of change typically expected from VMEP programs (and for which EPA is

prepared to grant credit), the COMMUTER model is seen as a reasonable compromise

between ease of use and accuracy.

Like the FHWA TDM model on which it is based, COMMUTER’s simplicity derives

from an “incremental” type of analysis procedure, often referred to by transportation

planners as “pivot-point” analysis. Rather than rerun the entire suite of transportation

models in the four-step process (or at least the mode choice and the assignment steps),

which entails making changes in the service variables and computing impacts for each

affected origin-destination pair in the regional trip tables, a pivot-point approach simply

extrapolates from the existing condition, or baseline. The degree of incremental

adjustment in this extrapolation depends on the current modal share balance, the types of

strategies that are being tested, to what modes and with what intensity they are being

applied, and what is known about the relationship, or sensitivity, between the particular

strategy (or service characteristics it affects) and demand for the mode. For modest

changes in mode shares, such as would be expected with Travel Demand Management or

VMEP type strategies, this incremental extrapolation is a convenient and acceptably

accurate alternative to the more rigorous analysis methods.

There are various ways this incremental adjustment/extrapolation can be done, some

much more analytically sophisticated than others. 

& Elasticities: Applying a relationship derived from observing degree of change

in behavior in response to a change in an underlying variable.

& Share Adjustment Relational Factors: When formal elasticity relationships

have not or cannot be developed because of insufficient data or research on a

relationship, or because individual relationships cannot be isolated with

statistical confidence, factors may be developed that suggest the type of change

that would be expected in mode share based on a particular type of action

taking place, which may vary with the type of mode, setting, packaging

assumptions with other strategies, presence of a regulation, or type or size of

employer. 

& Multimodal Travel Demand Models: Coefficients may be taken from

multimodal travel demand models, and applied either to an individual mode or

strategies or to multiple modes and strategies simultaneously.
-13-

The preferred approach among these options is the use of coefficient-based travel demand

methods. Elasticities can be very helpful as planning aids, since they provide some

statistical basis for inferring a relationship between travel demand and a transportation

service or cost change. Where elasticities are limited is in being able to take into account

the interactive effects that occur when multiple actions are applied or multiple modes are

involved. For example, if a setting had two modes—auto and transit—and two strategies

were proposed—an auto parking charge along with a 10-minute reduction in transit travel

time, an elasticity method would not be able to project the combined effect of these

strategies on the subsequent demand for auto and transit. In contrast, a coefficient-based

multimodal approach has the ability to look at the combined effects of multiple strategies

and deal realistically with the cross-modal effects. The relational factors approach is a

fallback procedure when formal statistical relationships have not yet been developed.

As with the TDM model, the COMMUTER model uses two of the above procedures for

calculating travel response to VMEP strategies:

& Logit Pivot-Point Model: A multimodal pivot-point model using coefficients

and computational procedures from accepted logit-based mode choice models;

and

& Look-Up Tables: Relational factors from empirical research, arrayed in lookup tables where increments of change are associated with particular types of

programs, reflecting different application assumptions, levels of intensity, and

setting.

Figure 2-2 classifies the various strategies incorporated in the COMMUTER model into

four groups. The classification system mainly relates to how the strategies are analyzed

in the model. The first two—Employer TDM Support Programs (2), and Alternative

Work Schedules (3)—are analyzed using relational factors in look-up tables, with a

normalization procedure applied to the adjusted shares to ensure that changes are

proportionate across the available alternatives and do not allow final choices to exceed

100%. The strategies that involve changes to either travel time or cost, however,—

Travel Time Improvements (4) and Travel Cost Changes (5)—are analyzed through the

more rigorous logit pivot-point procedure. Each of these is described briefly below, and

then in more detail in the individual report section that deals with that strategy type. 

Share Adjustments for Employer Support TDM Actions Using Look-Up Tables - While

virtually any attribute that plays a role in travel choice can be included in the structure of

a logit model, usually the models are limited to only factors that are related to travel time

or cost. Partly this is because there has not been great interest in trying to include such

diverse and specialized strategies as carpool matching programs and employer

transportation coordinators in regional travel models, and partly it is difficult to

incorporate these relationships within standard mode choice models for reasons of

available or appropriate data, or being able to demonstrate a functional relationship with

acceptable statistical confidence. Transportation analysts and economists have commonly

concluded that the biggest changes in travel behavior are those induced by changes in the
* See, for example Part III: Synthesis of Findings in the report: COMSIS Corporation, Implementing

Effective TDM Measures, for the Federal Highway Administration, DOT-T-94-02. Section 3 (September

1993). Also, in work done for the California Air Resources Board and the South Coast Air Quality

Management District by COMSIS in 1993 for the purpose of developing new plan development and plan

review software for Employer Trip Reduction Programs under Regulation XV, extensive analysis of Reg.

XV employer plan data from SCAQMD files coupled with new survey data from 43 California employers

failed to find significant impacts on travel arising from a variety of popular non-monetary support type

programs offered by employers.

-14-

underlying economics among the alternatives, such as policies that try to lessen the travel

time disadvantage of using transit or carpool over driving alone, or seeking parity in the

cost of travel by raising the cost of driving alone or discounting the cost of traveling by

alternative modes.*

As with the TDM Model, the COMMUTER model categorizes the various Employer

TDM Support Measures in group 2 into four discrete categories of effort, presented as

“levels.” Each level constitutes a program of actions undertaken by the employer that is

intended to enhance the attractiveness of alternative modes and thereby increase the

likelihood that employees will use that mode over driving alone. Level 1 programs

reflect minimum effort by the employer, and hence produce the smallest change in

behavior, whereas Level 4 programs reflect the maximum effort and maximum travel

behavior impact. Specific named strategies describe what is assumed to be provided in

each level, with higher-level programs generally including everything that was offered in

the lower-level program, as well as some new measures or special enhancements.

There are individual support programs for each alternative mode except pedestrian: 

Carpool, Vanpool, Transit, Bicycling. The support programs are also specific and

relevant to the individual mode, such as on-site pass sales being part of the Transit

support program. Invoking any of the Support Programs triggers a procedure that adds an

increment of mode share to the mode that is receiving the support, with the adjustment

increments being greater for higher levels of each program. The adjustment values are

stored in look-up tables for easy access and use by COMMUTER. A simple illustration

of how this analysis would be done in the COMMUTER model is provided in Figure 2-3.

When a scenario is designed that includes support measures for more than one mode at a

time (a common occurrence), the COMMUTER model must distribute the impact across

the modes. The model does this by adding the shares to the modes being targeted, and

then readjusting the shares of all modes—those targeted and those not—such that the

total for all modes once again equals 100%. This requires a proportionate adjustment

across all modes, which means that modes with the largest starting shares (such as drive

alone) when asked to forfeit shares will lose the greatest absolute number of users, while

small share modes (like vanpool) will lose the least. As part of the normalizing process,

even the targeted mode will have to forfeit some of its new share to ensure proportionate

redistribution.

In Figure 2-3, a starting balance of modal shares is shown in the box at the upper left,

with a pending program of TDM Employer Support measures described on the right. The
-15-

Figure 2-3

Illustration of Share Adjustment Process

example suggests that the employer (or group of employers) will implement Level 2

Carpool, Vanpool, and Transit support programs, and a Level 4 Bicycle support program.

The lookup tables show these options being selected for the test scenario: the Level 2

Carpool program contributes a 2% increase to the current 13% carpool mode share,

Level 2 Vanpool contributes a 1% increase to the existing 1% vanpool, Level 2 Transit

contributes 2% to the current 5% share, and Level 4 Bicycle contributes 1% to the current

1% bike share. As shown in the Share Adjustment Process box in the lower half of the

figure, the Support Program share increments are added to the base shares, and a new

total computed. Obviously, the new total with the program increments added will exceed

the total for the baseline distribution; the baseline would normally total 100%, except
-16-

that a special treatment is provided for the Walk mode. Walk is the only mode for which

TDM support enhancements have not been developed, raising the concern that

walkers—like solo drivers—can only “lose” ground when support measures are applied

to the other alternatives, since those adjustments would otherwise come out of the Walk

share of 4%. To prevent this from happening until such time as a set of support factors

can be developed for Walk, the Walk mode is kept out of the mode share adjustment

process. Its 4% is shown in parentheses in the table, indicating this treatment.

With Walk out of the equation, the baseline mode shares total 96%, and the new mode

shares total 102%. An adjustment must be made to re-apportion the gains (or losses for

SOV) to account for the reality that (1) the total cannot exceed 100% (96% in this

example), and (2) that the modes will “compete” for the new shares. In other words, if an

improvement is made to Carpool as an option, it would be expected that it would attract

new users not only from solo drivers, but also in some measure from Transit and the other

modes if indeed Carpool had received a comparative advantage. And if both Transit and

Carpool received improvements, both would be likely to “steal” share from SOV, but also

from each other. The procedure to accomplish this redistribution in the COMMUTER

model for support programs is to “normalize” the gross adjusted shares. In the example,

the previous total of 96% is divided by the equivalent new total, or 102%, producing an

adjustment factor of 0.941. Each share—except for Walk—is subsequently multiplied by

the adjustment factor to obtain the final revised share expected from this scenario. As

may be seen, the biggest adjustment is taken from Drive Alone, which falls from 75% to

70.6%, while Carpool rises from 13% to 14.1%, Transit rises from 5% to 6.6%, Vanpool

and Bicycle go from 1% to 1.9%, and Other falls from 1% to 0.9%.

Section 4 provides greater detail on this procedure as it applies to Employer TDM

Support Programs. Specifically, the actual look-up reference tables used in the

calculations are provided, along with key related assumptions regarding characteristics of

the employment base, and special considerations that apply to using the model for an

individual employer vs. applying it at a regional or multi-employer level.

Trip Adjustments for Alternative Work Hours Strategies - The procedure is virtually the

same as applied to Alternative Work Schedules, except that the changes that are induced

are solely to shift the given trip from the peak period to the off-peak period. No mode

shifts are calculated in conjunction with these strategies. The benefit in terms of

emissions, therefore, is not in the elimination of a trip but in its movement to a lesscongested travel period. With less congestion, vehicle speeds will be higher, which will

affect the emissions rates in the model. In most cases, as with VOCs, the speed change

results in fewer emissions, while in others, say with NOx, a higher speed may increase

emissions.

As with the Employer TDM strategies, the COMMUTER contains a separate look-up

table for each strategy: Flexible Hours, Staggered Hours, 4/40 and 9/80 Compressed

Work Weeks, and Telecommuting. However, unlike the Employer TDM procedure

where the user selects a particular “level” of program package and intensity, with the

Alternative Work Hours strategies the user indicates only what percentage of employees

will be “eligible” for each program type. The procedure then uses this percentage to
-17-

determine how many trips are eligible for the given program, e.g., 4/40 work weeks, and

applies a factor that calculates how many of these trips will be shifted outside the peak. 

The trips that are shifted from peak period to off-peak period are shifted at the current

mode split. In other words, if 75% of all trips in the peak period were Drive Alone, then

the model assumes that 75% of those shifted will be Drive Alone, with the remainder

comprised of Carpool, Transit, Bike, Walk, etc.

The procedure guards against an illogical number of employees participating in Work

Hours programs, to prevent double counting. However, users are able to test the

assumption that workers may be eligible for more than one Work Hours program, leading

to the result that the total eligible for Work Hours programs can exceed 100%. To

prevent against the number of people actually participating in these programs exceeding

this logical limit, the final calculated participation is determined by normalizing the

eligible percent to an assumed maximum participation of 100%. An example of this

procedure and illustration is provided in Section 5, along with presentation of the look-up

table values and underlying assumptions.

Analysis of Travel Time and Cost Strategies Using Logit Pivot-Point Procedure - A

certain class of travel demand models, known as “logit” models, provides relationships

that can be tapped for this complex analysis. These models are rooted in economic utility

theory, and assume that the attributes that comprise each travel option carry a certain

amount of utility, or value, to the customer. If the attributes for each alternative were

rolled up into a single measure of utility for each alternative, the theory holds that the

consumer will choose the alternative that provides the greatest overall utility. In the case

of travel mode choice, where the attributes of importance to the customer are the mode’s

time and cost, the traveler would be expected to select the mode that minimizes his/her

time and cost. Higher values of time and cost are undesirable, and may be viewed as a

“disutility”; hence, the choice process becomes one of minimizing disutility, rather than

maximizing utility. This distinction is mainly a matter of semantics, in order to be

consistent with the terminology that will be used to explain the model.

Logit models do a remarkably good job of predicting what mode an individual traveler

will choose from a set of alternatives or, when applied in a regional context, what choices

an entire population will make on a proportional basis from an array of alternatives. 

When applied for a population, the model estimates not a single preferred mode, but the

percentage of travelers who will choose each of the offered modes, resulting in an

estimate of modal split. 

The mathematics behind the logit model may seem complicated, but the principal by

which it works is not too hard to grasp. The model computes the probability (a number

between 0% and 100%) that Mode A will be chosen when it is compared to its competing

alternatives, Modes B, C, and D. It does this by first calculating the utility (disutility,

actually) of each alternative, or U, for a given trip. A typical utility expression might look

like this:

U (Mode A) = α0 + α1 (In-Vehicle Travel Time) + α2 (Walk/Wait Time) + α3 (Cost)
-18-

Figure 2-4

Logit Relationship

P Mode

e

eeee

UtilityM ode A

UtilityM ode A UtilityM ode B UtilityM ode C UtilityM ode D ( ) Α = +++

The utility of Mode A is a weighted sum of the travel time and cost incurred in using

Mode A for the given trip. The "weights" are provided by the model coefficients, which

are the α0, α1, α2,and α3 terms in the utility equation above. The coefficients relate the

sensitivity of the traveler to the particular characteristic, i.e., signifying how important it

will be in the choice process. The values of the coefficients are determined through a

statistical estimating procedure (known as maximum likelihood estimation) applied to

data from local travel surveys. Once the coefficients have been determined, the

calculation of the probability of Mode A (or any of the other modes) being chosen is

computed as:

To use the logit model to analyze the effect of any policy or program, it is necessary to

change the utility expression for each affected mode by relating the time or cost change

from the strategy policy to each individual travel unit in the analysis. If the travel

universe is an individual employer, the unit of analysis is the individual. If the universe

is a region or subarea, the unit of analysis would be each of the individual origindestination pairs that is influenced by the policy.

Because of the exponential form of the logit equation, the mode choice predictions from a

logit model produce an S-shaped curve, as illustrated in Figure 2-4.

This relationship is clearly non-linear, and as the figure implies, identical changes in the

travel characteristics of a given mode will not produce the same degree of change in
-19-

mode share in all cases, but will depend greatly on where the traveler is “starting” on the

S-shaped curve. In cases where the starting share is limited, say 1% to 3%, an

improvement of X minutes of travel time or Y dollars of travel cost would not produce as

large an increase in share as would occur if the mode were starting with a moderate share,

say 5% to 10%; a starting point in the dominant range, say 30% to 60%, would produce

the most substantial changes. Above a certain mode share the mode is so dominant (so

free of competition) that additional improvements in service or cost will start resulting in

modest or decreasing returns. The implications of this relationship are important: if a

VMEP program is to be sited in an area where existing transit or carpool use is high, then

VMEP strategies that provide additional advantages to those modes should result in

healthy increases. However, if a VMEP program is to be cited in an area where solo

driving is dominant and there is little or no transit or carpool use, significant

enhancements and incentives would be required to obtain the same shifts of travelers to

transit or carpool.

While logit mode choice models are powerful and accurate, traditional application at an

individual origin-destination pair level would require significant data development effort

for every scenario tested. However, the tool can also be applied with reasonable accuracy

to the existing mode shares, by setting up the model to predict instead the change in

utility that would result from the change in the individual attributes:

 ∆ U (Mode A) = ∆ α1 ( In-Vehicle Travel Time) + ∆ α2 (Walk/Wait Time) + ∆ α3 (Cost)

Instead of having to develop revised utility expressions for every mode and every analysis

unit, the analysis can merely “pivot” from the existing share by knowing the value of the

coefficients and the degree of change that will be introduced by the individual strategy or

program. Moreover, in this equation, more than one service variable can be changed at a

time (e.g., walk time and cost), and more than one mode can be affected at a time, and the

choice probabilities for all modes may be calculated simply by making the appropriate

modifications to each mode’s utility expression. This feature promises a realistic

accounting for the competition among modes, and reflects the gain/loss of different

strategies applied in different measures to several modes.

In the COMMUTER model, the logit pivot-point procedure is used only to calculate the

impacts from the Travel Time Improvements or Travel Cost Changes strategies. The

application of logit pivot point to these strategies in the model is discussed in detail in

Section 6 of the Manual.

Sequencing Order of Calculations in the COMMUTER Model - The order in which the

COMMUTER model performs its calculations of travel changes is as follows:

1. It first calculates the changes due to Alternative Work Hours. This serves to

readjust the travel population baseline to determine how many trips will be

shifted to the off-peak, and how many will remain in the peak period and be

subjected to application and analysis of the mode-choice oriented VMEP

strategies.
-20-

2. Next, mode shares of the remaining peak trips are readjusted to reflect the

effects of the Employer TDM Support strategies. 

3. All time and cost related strategies are tallied up and brought into the logit

pivot-point procedure, which is then applied to the revised mode share starting

point from step 2.

Emissions Calculations

Separate calculations are performed for each of the two activity parameters modeled:

VMT and trips. Emission reductions from each of these travel impacts are then added

together to produce combined emission reduction impacts from changes in travel due to

the TCMs evaluated. Each of these calculations is performed for both the peak and offpeak periods.

First, reductions in VMT and vehicle trips due to the TCM strategies are computed by

subtracting existing activity levels from the “final” (after-TCM) activity levels.

Second, the regional speed inputs were used to linearly interpolate the stabilized emission

factors by speed increment to represent these specific speeds.

Third, the number of existing and final vehicle trips were multiplied by the cold start

percentages under existing and final conditions to estimate the number of cold trips for

which to apply the cold start offset.

Finally, the VMT- and trip-based emission reductions were summed together. Daily

reductions were also summed from peak and off-peak reductions.

Model Outputs

Key outputs include the following:

& Baseline and final mode shares for each mode, including percent of trips

eliminated;

& Percent of trips shifted from the peak to off-peak period;

& Baseline and final peak, off-peak and daily VMT;

& Baseline and final peak, off-peak and daily vehicle trips; and

& Changes in total daily emissions for each criteria pollutant (HC, CO, and NOx).
-21-

###
-22-

3. Assembling Required Background Information

Overview

Types of Data Required - An analysis of commuter choice strategies using the

COMMUTER model requires certain basic information in order to establish the travel

baseline and to properly initialize some key parameters that are used in the analysis. This

section of the report lists each of the required items and explains why it is needed and

how it is important to the analysis. Items included in this list and discussed in this section

include the following:

& Metropolitan Area Size

& Application Setting Characteristics 

& Affected Employment 

& Mode Choice Model Coefficients

& Starting Mode Shares

& Average Trip Lengths

& Vehicle Occupancy

& Peak and Off-Peak Travel Characteristics

The discussion of input data requirements in this Procedures Manual focuses mainly on

confirming what information is required and optional ways to supply that information, in

relation to the methods being applied by the COMMUTER model. The accompanying

Users’ Manual offers the procedural step-by-step guidance to the user on where to find

this information, how to format it properly, and how to enter it into the COMMUTER

Model.

Areawide vs. Individual User - The COMMUTER model has been designed for

application at either a regional (multi-employer) level or for an individual employment

site. While the types of strategies that might be evaluated for these two applications are

roughly the same, the input data requirements are somewhat different. Since an

application at a regional or areawide level requires an integration of results over a large

number of employers, it is necessary to have information that describes the size,

composition, and travel characteristics of that population. For an individual employer,

the regional composition characteristics are not relevant, and it becomes more important

to be able to describe in some detail how that particular set of employees is traveling.
-23-

Therefore, regional (or multi-employer) users of the model would be expected to provide

information that describes the corresponding employment base, and certain assumptions

about applicability of particular strategies to these employers, or the extent to which these

strategies are already in place. Much of this information will be derived from the region’s

or local area’s transportation planning process, which is the same process that is used to

prepare the SIP or to test transportation plans for conformity with the SIP. Since regional

COMMUTER users would likely be the designated MPO, access to the appropriate

information should be fairly straightforward. For others who might conduct VMEP

analysis at either a regional or areawide level, such as departments of transportation,

municipalities, counties, employer organizations, or air agencies, it is expected that these

groups would need to work with the regional MPO to obtain this information and ensure

its proper interpretation.

Those users performing VMEP analysis at an individual employment site will not require

most of the compositional or travel information needed for the areawide analysis, but will

require some fairly specific information on their individual case. Primarily, the

individual site users will need information on the travel characteristics of the respective

employees. This includes the following:

& Total employees working at the given site;

& Total traveling to the site on a given workday;

& Mode choice (percent by mode);

& Number traveling to the site during peak period;

& Number engaged in telework, compressed work weeks, or other alternative

work schedules; and

& Existence of staggered work hours or shifts, and percent of employees

accounted for in each.

This information is generally obtained through employee travel surveys, so the individual

site user will not need to be concerned with regional transportation data, models and

planning procedures. Guidelines are provided in the COMMUTER model and the Users’

Guide to help users determine what data they need, how to obtain it, and how to use it. 

Default values are provided for relationships or data inputs that might not be readily

available. However, even though the COMMUTER model gives the user a considerable

degree of independence and flexibility, an individual site user is still strongly urged to

consult with the relevant local planning agency, for the purpose of answering questions

about assembly of input data, assumptions about background conditions, advice on

selection of program strategies to be tested, or interpretation of results. In particular,

employers should consult their planning agencies to obtain or confirm the model

coefficients that will be used in the analysis (see Section 6 for more discussion on these

coefficients).

Where differences occur in the data requirements or responsibilities between regional vs.

individual users, the Procedures Manual and the Users’ Manual will point out these 

differences and explain how each user should proceed.
-24-

Default Values - While most of the input data items that are requested by the

COMMUTER model should be relatively easy to obtain, the COMMUTER model also

provides default values for many of the items. These default values have been provided

mainly as a convenience to the user, to encourage use of the model by a broad audience

whose familiarity with transportation issues or access to certain data may be limited. The

defaults may also be of service in performing reasonableness checks of any user-supplied

data.

While these default values are provided to help the user conduct an analysis without

undue difficulty or expense in obtaining local data, users are nevertheless urged to seek

out and utilize current local information wherever possible. This will increase the

validity of the resulting travel and emissions impacts, and help EPA reach a favorable

determination when reviewing the applicant’s request for COMMUTER emissions

credits. Those measures for which default values are provided are listed in Table 3-1. 

This listing shows the default values currently assigned to each measure, and the source

of the information used to develop the default value.

Users may wish to experiment with the COMMUTER using the default values in order to

develop familiarity with its operation and the types of impacts it projects for various types

of VMEP programs. However, programs submitted for air quality credit should give

evidence of due effort to utilize local data wherever possible. 

Sources for Background Data - For regional or areawide application of the COMMUTER

model, most of the necessary background data should be available to or through the

responsible transportation planning agency in the area. This will normally be the regional

MPO, although county or municipal planning agencies or departments of transportation

may also be a resource depending on the extent of their planning activity. These

organizations should be capable of supplying information on regional employment, modal

split, VMT and travel speeds, such as are required by the model. For individual site

users, most of the background information will be obtained through an employee travel

survey.

Required Model Inputs

The remainder of this section is devoted to identifying each of the required data items and

providing information on typical sources. The availability of defaults for each item is

noted. In addition, a glossary of transportation planning terms is provided in Appendix A

to clarify the meaning of any terminology that the user may not be familiar with.

Metropolitan Area Population Size Category - The COMMUTER model distinguishes

among three size categories of metropolitan area when evaluating certain impacts:

& Large: Population of 2 million or more

& Medium: Population under 2 million but > 750,000.

& Small: Population under 750,000.
-25-

Table 3-1

Default Values Provided in the COMMUTER Model

Data Item Suggested Default Source

Work Trip Mode

Shares

Auto – Drive Alone 75.4%

Auto – Carpool 13.2%

Vanpool 0.5%

Transit 5.3%

Bicycle 0.4%

Walk 4.0%

Other 1.1%

Total 100.0%

1995 Nationwide

Personal

Transportation

Survey (NPTS)

Average Commute

Trip Length (in miles)

Average Person Trip-Length: 11.4

Average Trip Length – Vanpool: 17.7

Average Trip Length – Bicycle: 1.8

Average Trip Length – Walk: 1.0

1995 Nationwide

Personal

Transportation

Survey (NPTS)

Average Vehicle

Occupancy (persons

per vehicle)

Average Carpool Occupancy: 2.2

Average Vanpool Occupancy: 6.0

1990 Census

Transportation

Planning Package

Journey-to-Work

Duration of Peak

Period

3.0 hours

Percent of Work

Trips Occurring in

Peak Period

61.4% 

33.8% in 6 – 9 A.M peak

27.6% in 4 – 7 P.M. peak

1990 NPTS

Databook, Table

6-32

Average Travel

Speeds (mph)

Area Size Peak Off-Peak

 Large 31.8 37.1

 Medium 34.6 39.7

 Small 34.8 39.7

1995 Highway

Performance

Monitoring

System (HPMS)

Affected Employment Distribution % only, user must supply

total

Office occupations: 79.7%

Non-Office occupations: 20.3%

1997 Statistical

Abstract of U.S.,

Table No. 660

The primary use of this information is to select appropriate default coefficients for use in

the logit mode choice pivot point model, which is used for evaluating time or cost-based
-26-

strategies. However, since users will generally be expected to select and use the unique

coefficients that have been specifically developed for their own region, and used in the

officially sanctioned regional planning process, this information item is mainly for default

situations only. This guidance on coefficient use is covered explicitly in Section 6.

Analysis Scope - This information item indicates to the COMMUTER model whether the

analysis is to be performed at a regional or site-specific level. This selection by the user

signals the modeling procedure to expect input data and specification of strategies to

correspond to a single employment site or a regional population of employers. This

affects what strategies are eligible, the data required of the user, and the calculation of the

results. 

Analysis Area Type - The COMMUTER model has a “placeholder” for this input, but

does not currently use it in the travel impact calculations. If this input were active, it

would work as follows. For certain types of applications, the model would need to know

the “type” of area in which the program would be sited, with the options being:

& Central business district (CBD) or radial corridor;

& High density activity center or suburban CBD (other than the regional CBD); or

& Suburban, low-density area.

This information is important because VMEP programs sited in these three different types

of areas are likely to have very different impacts in response to the same strategies, and as

a result, certain strategies may be more effective in one environment than another. In

regional CBDs or travel corridors into those areas, conditions generally favor transit and

carpool use, and so existing mode shares for transit and carpool are probably the highest

in the region. In contrast, suburban low-density areas typically show great reliance on

private vehicle travel, while suburban downtowns or major activity centers fall

somewhere in between. This distinction is important since the COMMUTER model’s

pivot-point procedures calculate changes in behavior based on what was there before. 

This was discussed briefly in Section 2 in the modeling discussion.

In most cases, supplying this information to the model will not be necessary for it to

correctly compute its results. This is because the travel input data will generally inform

the model of what the starting mode shares are. If the user is an individual site, the

employee travel survey data will contain the correct starting mode shares; if the analysis

is for the overall region, the work trip mode split from the regional transportation

planning process will provide the appropriate starting point. Where this item may

become important is if an analysis were being done for a subarea or municipality

involving multiple employers, where the total employment was known but the modal

shares were not. In this instance, the use of this parameter would instruct the model to

provide appropriate default mode shares for this type of environment as represented by
*

 1990 Census Transportation Planning Package journey-to-work statistics.

** U.S. Bureau of Labor Statistics, Monthly Labor Review, November 1996.

*** 1977 Statistical Abstract of the United States, Table 662.

-27-

similar sites in a national sample.* This is discussed in more detail under “starting mode

shares” below.

When representative default mode shares by area type can be developed in a future

version of the COMMUTER model, this input will be activated as described above.

Affected Employment Base - For regional (or any other areawide or multi-employer)

analyses of VMEP strategies, it is necessary to describe the employment base that will be

subject to the VMEP program. The COMMUTER model requires the following

information:

& The total number of employees in the employment base to which the program

actions will be applied, referred to as affected employment; and 

& The proportion in occupations defined as “Office” or “Non-Office” functions.

The total number of employees establishes the size of the traveling population under

study, and is used to calculate baseline vehicle trips, VMT, and emissions. This number

may constitute the entire regional employment population, or it may be only a portion of

that population based on locally established rules of eligibility. For example, it might be

assumed that employers below a certain size would not be expected (or encouraged) to

participate in voluntary commuter choice programs. For example, nationally 38% of all

employees work for employers with fewer than 50 total employees, and a total of 49%

work for employers with fewer than 100 employees.** Therefore, some users may wish to

restrict assumptions on program coverage to only employers of 50 or more or 100 or

more. Others may wish to restrict applicability to only those workers who travel to a

work site during peak travel hours (6 to 9 a.m., and 4 to 7 p.m.); nationally, only 61.4% of

workers travel during these hours, with 38.6% traveling at some other time.***

The calculation of impacts also takes into account differences between workers employed

in “Office” vs. “Non-Office” occupations. Generally, research on TDM program

effectiveness has shown that many strategies are more relevant and have greater impact in

“Office” occupations, consisting of clerical, professional and managerial fields, than in

“Non-Office,” consisting of sales and manufacturing occupations. The FHWA TDM

model, from which this procedure was adopted, defines Office and Non-Office through 

SIC (standard industry classification) codes, as shown in Table 3-2. 

Based on this information, the model provides default percentage of 79.7% and 20.3% to

help the user partition total local employment into office and non-office categories.
-28-

Table 3-2

Definition of Office and Non-Office Employment

Type SIC Code Industry 1996 Employment Percent

Office

40-49 Transportation & Public

Utilities

6,316,000 5.3%

50-59 Wholesale Trade 6,587,000 5.5%

50-59 Retail Trade 21,597,000 18.1%

60-69 Finance, Insurance,

Real Estate

6,977,000 5.8%

70-89 Services 34,359,000 28.9%

90-99 Government 19,461,000 16.3%

Total Office 95,296,000 79.7%

Non-Office

10-14 Mining 570,000 0.5%

15-17 Construction 5,407,000 4.5%

24-39 Manufacturing 18,282,000 15.3%

Total Non-Office 24,259,000 20.3%

Source: 1997 Statistical Abstract of the United States, Table 660.

An extremely important issue with regarding this input is that it does not represent total

regional employment. The box below addresses this distinction.

“Affected Employment”

In addition to asking the regional user for information on overall employment size and

distribution, the COMMUTER model also takes into account what portion of this

population would be affected by a given strategy. It may be assumed that the entire

regional employment base is affected by the strategy, or only some portion, based on

minimum size criteria, location relative to the strategy, assumptions that the strategy is

already being offered, or other factors. This input is not part of the baseline data

requirement for the user, but rather is supplied on a strategy-by-strategy basis once the

user begins to design scenarios for testing. Hence, it is covered in more detail in

subsequent sections that deal with the respective strategy groups. However, it is

mentioned here because it represents another way in which the user can delimit the

employment base to which the VMEP strategies are applied, as an alternative to

making these restrictions in the baseline itself.
-29-

Existing Work-Trip Mode Shares - The pivot-point analysis techniques used by the

COMMUTER model to estimate travel changes rely on a starting set of mode shares for

the employment population being analyzed. If the application is for an individual site (or

small number of sites), this mode share information comes directly out of the employee

travel survey data that the user must supply. If the application is for a region or similar

areawide/multi-employer setting, then the starting mode shares must be for the respective

population, and will probably be obtained from existing regional transportation planning

information. The regional MPO is the most likely source for this information, although

subarea analyses may find source data from county or municipal planning agencies,

and/or existing employer travel surveys.

The VMEP model calls for the percentage of daily work trips that are made by the

following modes:

& Auto – Drive Alone

& Auto – Carpool

& Vanpool

& Transit 

& Bicycle

& Walk

& Other

Most major metropolitan areas will have information that can be used to calculate mode

shares for home-based work travel (HBW) for at least auto driver, auto passenger, and

transit. Vanpool is typically not broken out as a separate mode, nor is bicycle or walk,

and so assumptions will have to be made as to baseline shares of these modes. Auto

passenger may be used along with auto driver to determine carpool mode share (where

carpool is not reported separately), but typically this calculation cannot reveal the share of

multi-passenger trips that are vanpool. Where local information on mode shares for

particular modes is not available, defaults in the model have been provided, and the user

can opt to use the default shares for the absent modes and adjust the share values for the

local “known” modes proportionately. 

The COMMUTER model also makes an adjustment internally to account for workers

who regularly work at home. This proportion averaged about 2.96% in the 1990 Census

Journey to Work. It nets out this percentage from the total employment base when it

calculates baseline work trips and VMT, and thereafter applies VMEP strategies only to

those 97.04% of workers who travel.

The default shares provided in the model have been derived from the 1990 Census

Journey-to-Work files, and adjusted to reflect the work-at-home factor described above. 

The default shares for a standard “regional” analysis developed in this manner are shown

in Table 3-3.
-30-

Table 3-3

Default Shares for a Standard Regional Analysis

Mode

Reported 1990 Census

Commute Choice

Excluding Work 

at Home

Auto-Drive Alone 73.19% 75.42%

Auto-Carpool 12.84% 13.23%

Vanpool 0.52% 0.54%

Transit 5.12% 5.28%

Bicycle 0.41% 0.42%

Walk 3.90% 4.02%

Other 1.08% 1.09%

Work at Home 2.96%

Total 100.00 100.00%

Total Excl. Work at Home 97.04%

For individual site applications, shares for all modes of interest should be known directly

from the data obtained through employer surveys. However, the defaults can be used to

fill gaps as described above for the regional user.

Logit Model Coefficients - The COMMUTER model calculates changes in travel from

time- or cost-based strategies through a logit mode choice model procedure. The critical

elements in this procedure are the coefficients, which supply the “weights” that signify

how important each choice variable is to the traveler. The model employs coefficients for

In-Vehicle Travel Time, Out-of-Vehicle Travel Time (including Walk Time and Transit

Wait Time), Auto Parking Cost, and Transit Fare. These coefficients are used in a

metropolitan area’s transportation planning model, and are developed uniquely for each

area from analysis of local survey data.

The COMMUTER model provides coefficient values for the user through a look-up

menu. The coefficient values for most U.S. metropolitan areas where the COMMUTER

model might be used are included in the menu. When setting up to use the model, the

user should designate those coefficients for their respective metropolitan area from the

menu, and the model will automatically bring them into play.

For users who do not find their area listed in the menu, there are two options. The

preferred option is to contact the responsible regional planning agency and obtain the
-31-

correct coefficients from the local model. The backup option is to make use of the

default coefficients that have been programmed into the model. A separate set of

coefficients is provided for Large, Medium and Small metropolitan areas. For users who

are able to obtain coefficients from the local planning agency, but do not have values for

all of the required coefficients, it is recommended that the default coefficients be used

instead of the partial set of local coefficients. Substituting default coefficients for missing

values, thus creating a hybrid, is discouraged since the two sources of coefficients may

have been developed from very different assumptions.

Section 6 of this report presents further discussion on use of the logit coefficients, and a

listing of those metropolitan areas currently included in the COMMUTER model.

Average Trip Lengths - The model employs information on average trip lengths for all

modes when computing change in VMT. Since each travel mode has a different average

trip length, the shifting of a commute trip from drive alone (or any other mode) to another

mode will constitute a different VMT reduction depending on what modes are involved in

the shift. The model has internally coded default values as follows:

& Auto Drive Alone: 11.8 miles (source: 1995 NPTS)

& Auto-Carpool: 12.4 miles (source: 1995 NPTS)

& Vanpool: 17.7 miles (source: estimated from 1990 Census travel

times)

& Transit: 11.6 miles (source: 1995 NPTS)

& Bicycle: 1.8 miles (source: 1995 NPTS)

& Walk: 1.0 miles (estimate)

& Other: 11.6 miles (source: 1995 NPTS)

Average Person Trip Length: 11.42 miles

(calculated from mode shares and modal trip lengths)

Average Vehicle Trip Length: 11.85 miles

(calculated from drive alone, carpool, and vanpool mode shares, trip lengths and

AVO)

The user is given the opportunity and is encouraged to supply local values for average

vanpool, bicycle and walk trip length, as well as for overall person trip length. These data

may be obtained through the regional planning agency or, for individual site applications,

from respective employee travel surveys.

Average Vehicle Occupancy - Average vehicle occupancy is also used in calculating

changes in VMT as well as vehicle trips. The model provides the following default

values for Carpool and Vanpool occupancy:

Average Carpool Occupancy: 2.23 (source: 1990 Census)
*

 1995 Nationwide Personal Transportation Survey.

-32-

Average Vanpool Occupancy: 6.00 (source: 1990 Census)

The user is given the opportunity and is encouraged to supply local values for average

carpool and vanpool occupancy. These data may be obtained through the regional

planning agency or, for individual site applications, from respective employee travel

surveys.

Peak Period Duration - The model calculates emissions reductions based on changes in

travel during both the peak and off-peak periods. Typically travel conditions are less

congested in the off-peak period, resulting in higher average travel speeds and lower rates

of emissions.

While the actual timing of the peak travel period may differ from area to area, the typical

length of the peak period (either a.m. or p.m.) as recorded in the 1990 NPTS survey is

three hours. This number is coded as a default into the model, but the user has the option

to use local data and is encouraged to do so. Again, these data are obtained from the local

planning agency.

Work Trips Occurring in Peak Period - While the peak period is when most workers are

assumed to commute to jobs, nationally only 61.4%* of all work trips occur in the a.m. or

p.m. peak travel periods. The remainder, 38.6%, take place in the off-peak hours, and

occur under less congested off-peak travel conditions. The COMMUTER model uses

this relationship to calculate commute trip emissions in both peak and off-peak periods,

where emissions “rates” are different. The user is encouraged to supply the local value

for this relationship, which may be obtained from the local planning agency.

Peak and Off-Peak Travel Speeds - This information is essential in calculating emissions,

since the emissions factors are established through a relationship with average speed (this

relationship is discussed in more detail in Section 7). 

Other Background Information - In addition to the information items presented above, the

user will probably need to obtain some other information in the course of doing an

analysis. These items are briefly mentioned here, and discussed in more detail in the

respective technical section that follows. The items include the following:

& Pre-existing offerings of the various employer TDM support strategies in the

region, the level of the strategy or program offered, and the extent of the

program’s offering across the employment base (Section 4).

& Pre-existing offerings of and employee participation in alternative work

schedule programs, including compressed work weeks, telecommuting, and

flexible or staggered work hours (Section 5).
-33-

& The number (or percentage) and characteristics of employers who will be

affected by or who will be expected to participate in a given program scenario

(Sections 4, 5, and 6).

& Peak and off-peak travel speeds (Section 7).

& Percentage distribution of the regional vehicle class by EPA size class

(Section 7).

& Information on current emissions requirements, standards, and programs

(Section 7).

### 
*

 See: Implementing Effective Travel Demand Management Measures, Federal Highway Administration,

September 1993; or Characteristics of Effective TDM Programs, TCRP Project B-4 Final Report, COMSIS

Corporation, May 1996.

-34-

4. Estimating Travel Impacts of Commuter Choice

Support Programs

Overview of Employer Support Strategies

One way in which employers can try to persuade employees to consider traveling by

alternative modes, rather than driving alone, is to provide various types of support that

make it easier and more attractive to use those modes. These support programs—often

referred to as Commuter Choice or Travel Demand Management (TDM) programs—

typically consist of measures that heighten awareness of the availability of other modes,

provide information on their service or use, or generally make it easier and more

attractive to the employee to consider their use.

These employer support programs generally do not include measurable time or cost

incentives or disincentives. Rather, they serve to provide an improved set of conditions

for the employee to use an alternative, and provide incentives that are tangible and

important, but not necessarily quantifiable by the employee. They may best be seen as

“catalysts” in supporting a change in behavior away from driving alone and providing an

accommodating environment in which those changes can take place, but by themselves

may not be capable of engineering major changes in employee travel.*

 Nevertheless,

employers find these types of strategies attractive because they present little economic

risk, and generally earn employee gratitude.

Many of the strategies in this category of programs are specific to the needs of the

particular mode. For example, Table 4-1 lists typical support strategies that might be

offered to encourage use of the respective mode.
-35-

Table 4-1

Popular Employer Support Strategies 

to Encourage Specific Modes of Travel

Mode Strategies

Carpool

Ride matching (individual employer or tie in to 

 regional matching programs)

Preferential (reserved and/or close-in) parking

Vanpool

Ride matching (individual employer or tie in to 

 regional matching programs)

Preferential (reserved and/or close-in) parking

Use of company fueling or maintenance facilities

Employer capital cost or insurance underwriting

Startup subsidies

Transit

On-site transit information booths

On-site transit pass sales

Sidewalks or shelter

Bike Bike lockers or racks

Shower and changing facilities

Walk Sidewalks or other barrier removal

Showers and changing facilities

In addition to the mode-specific types of strategies listed in Table 4-1, there are actions

employers can take that are almost universal in their applicability across all of the

alternative modes. Examples of these strategies include the following:

& Employee Transportation Coordinators: Persons who are trained to provide

information or advice to employees regarding use of any alternative mode, in

terms of where to go for information, company policy and benefits, etc. 

& Guaranteed Ride Home: Provision to get an employee home by alternative

means if it necessary to work late or in event of a personal emergency, and the

employee did not drive on that day.

& Flexible Work Hours: A formal or informal policy that allows employees

some flexibility over the official office hours in order to meet the schedule of

the chosen alternative mode.

& Promotions: Marketing and other methods to increase awareness of a given

mode or employer incentive, or to provide prizes or awards for meeting some

usage challenge.
* A Survey and Analysis of Employee Response to Employer-Sponsored Trip Reduction Incentive

Programs, Final Report and Technical Appendix A, COMSIS Corp. for California Air Resources Board

(contract #A932-187) and the South Coast Air Quality Management District (August 1993). This study

attempted to develop statistical relationships for a wide range of employer support incentive strategies,

drawing upon both extensive Reg. XV employer plan data from SCAQMD files coupled with new survey

data from 43 California employers. Logit model estimation was used to determine whether statistically

valid relationships existed between these measures and travel behavior. Results were a mixed success,

with the time and cost-related strategies demonstrating the strongest relationships (and reasonable

consistency with conventional models), while most of the non-monetary support strategies exhibited weak

or implausible statistical relationships with demand. Those support strategies showing the strongest

relationships were guaranteed ride home (all modes), transportation coordinators with rideshare matching

(carpool and vanpool), use of company-provided vehicles (carpool and vanpool), transit information center

and on-site pass sales (transit), and bike racks with lockers/showers (bike and walk). 

-36-

Analytic Approach for Estimating Travel Impacts

Because most support-type strategies do not translate into changes in the time or cost of

travel, it is difficult to calculate their impacts on travel in the same way as with time and

cost-based strategies (Section 6). The latter are readily translated into terms that can be

fed into a standard mode choice model, whose coefficients almost universally apply only

to time- or cost-based criteria. While a number of attempts have been made to

incorporate these support measures in traditional model structures, or to develop

representative coefficient or elasticity relationships on their link with travel behavior,

these efforts have not been particularly successful in developing valid mathematical

relationships.*

For this reason, travel impacts related to support programs are estimated by the

COMMUTER model in the same way they were calculated in the FHWA TDM model:

through relational look-up tables. This approach associates the offering of a particular

employer support measure with an incremental change in the mode share of the mode to

which the program is applied. This is done separately for each of the designated

alternative modes: Carpool, Vanpool, Transit, and Bicycle (the model currently does not

incorporate measures for Walk). Table 4-2 lists the changes in mode share that are

credited by the COMMUTER model for each mode and its respective program level.

The mode share adjustment values for support programs shown in Table 4-2 are taken

from the FHWA TDM model. The authors of the TDM model obtained these adjustment

values through a synthesis of empirical results from a large number of case studies. The

TDM model researchers determined that three factors were particularly important in

predicting the level of impact from a program of support actions:

& Level of Program: The significance of the program being offered in terms of

the support measures themselves, the number and type of such measures

combined into the program, and the level of investment reflected. 
-37-

& Type of Employer: A difference in effect was attributed to whether the

measures were being applied in an “Office” or a “Non-Office” employment

environment.

Table 4-2

Increase in Percent Using Model by Support Program Level

Program Level

Program Type of Workplace 1 2 3 4

Carpool

Office 0.40% 1.00% 4.00% 6.00%

Non-Office 0.20% 0.40% 1.40% 2.00%

Vanpool

Office 0.40% 1.00% 4.00% 6.00%

Non-Office 0.20% 0.40% 1.40% 2.00%

Transit

Office 0.20% 0.50% 1.50% 2.00%

Non-Office 0.20% 0.50% 1.50% 2.00%

Bicycle

Office 0.20% 0.50% 1.50% 2.00%

Non-Office 0.10% 0.25% 0.75% 1.00%

Source: FHWA TDM Evaluation Model, 1993.

& Level of Employer Participation: On an areawide basis, the portion of the

employment population that can be expected to take part in the program, and/or

to offer a program of a given level

Program Level - For simplicity, employer support programs have been categorized into

four different levels, ranging from Level 1, reflecting the minimum level of effort, to

Level 4, representing the maximum. Each level is associated with a particular program or

“package” of support actions. The identification of what measures have been assumed to

make up each level is shown in Table 4-3. If an employer (or group of employers) is

presumed to adopt one of these programs, i.e., a given level for a particular mode, it is

assumed that the shown measures will be implemented by the employer. 

Of course, not all employers would be expected to implement the same modal programs

at the same levels uniformly across an entire region. As a result, the model allows for the

user to assume and test different levels of participation across the employment

population. Also, it would be unrealistic to assume that all employers would be “starting

from scratch” in terms of existing programs. Many employers will already have been
-38-

doing one or more of these actions, and the analysis allows for identifying these preexisting efforts and netting out their effects in order to avoid double counting of benefits. 
-39-

Table 4-3

Composition of Modal Support Strategy Programs

Mode Level Strategies Included in Program

Carpool

1 Carpool information activities (tied in with areawide matching)

Quarter-time transportation coordinator

2

All the above, PLUS:

In-house carpool matching service and/or personalized

 carpool candidate get-togethers

3

All the above, PLUS:

Preferential parking (reserved, indoor, and/or close-in)

Flexible work schedule policy to accommodate carpool schedules.

Half-time transportation coordinator

4 All the above, PLUS:

Full-time transportation coordinator

Vanpool

1

Vanpool information activities (tied in with areawide

 vanpool matching and/or third party vanpool programs)

Quarter-time transportation coordinator

2

All the above, PLUS:

In-house vanpool matching services and/or personalized

 vanpool candidate get-togethers

Non-monetary vanpool development assistance

Policy of flexible work schedules to accommodate vanpoolschedule

3

All the above, PLUS:

Vanpool development and operating assistance, including financial

 assistance such as vanpool purchase loan guarantees, 

 consolidate purchase of insurance, and a startup subsidy.

Supporting services such as van washing and fueling

Half-time transportation coordinator

4

All the above, PLUS:

Major financial assistance for development and operations,

 such as employer purchase of vans with favorable leaseback, 

 continuing subsidy, free maintenance, free insurance.

Full-time transportation coordinator

Transit

1 Transit information center

Quarter-time transportation coordinator

2 All the above, PLUS:

Policy of work hours flexibility to accommodate transit schedules/delays

3

All the above, PLUS:

On –site transit pass sales

Half-time transportation coordinator

4

All the above, PLUS:

Guaranteed ride home

Full-time transportation coordinator

1 Provision of on-site bicycle parking (racks or lockers)

2 All the above, PLUS:

Shower and change facilities

3

All the above, PLUS:

Provision of secure bicycle parking (storage lockers or indoor storage)

Development of local bike-friendly infrastructure 

4 All the above, PLUS:
*

 Some employer support programs may apply combinations of strategies that are different from those

described in Table 4-2. In these cases, the analyst may either choose the program level that most closely

compares to the actual program being implemented, or split the impact among programs of two or more

different levels. For example, if a carpool program includes all the elements of a Level 3 program except

-40-

Workplace information and promotional activities

Type of Employment - The research that developed the FHWA TDM model also

concluded that the impact of support programs varied according to the type of workplace,

in which it distinguished between Office and Non-Office employment. This shows up in

travel responses for Office employment that are somewhat greater than the same strategy

applied to Non-Office employment. The explanation for this is that Office employees are

more likely to travel on a routine daily schedule to given work sites, while Non-Office

workers (those in construction, mining, manufacturing, and utility occupations) are more

likely to be traveling at non-peak times, or working shifts, or traveling to different

locations depending on where the current job is.

Employer Participation - If the user is conducting an analysis at a regional or multiemployer level, it is necessary to account for the type of participation that is expected

from the sample of employers. The user will be asked to specify the percentage of

employers who will be expected to participate in each modal support program by level of

the program, as well as the percentage that would not participate at all.

So, for example, if a scenario were created that envisioned employer support programs at

level 2 for vanpool and transit, level 4 for carpool and level 3 for bicycle, the

communication of this information to the COMMUTER model would resemble the

following:

Percent of Employers Participating by Level of Program

Mode None Level 1 Level 2 Level 3 Level 4 Total

Carpool 100% 100%

Vanpool 100% 100%

Transit 100% 100%

Bicycle 100% 100%

More typically, what might happen when a scenario is drawn up is that there would be

much more variation in the types of participation expected, since programs would be

expected to emphasize different modes based on, for example, employer location, or not

include certain groups, for example, employers with fewer than 50 employees. To

illustrate this point, the participation information communicated to the COMMUTER

model might instead look like this:*
for flexible work schedules, the user may want to approximate this program by assigning half of the

participation to Level 2 and half to Level 3.

-41-

Percent of Employers Participating by Level of Program

Mode None Level 1 Level 2 Level 3 Level 4 Total

Carpool 38% 12% 50% 100%

Vanpool 38% 42% 20% 100%

Transit 38% 12% 50% 100%

Bicycle 38% 62% 100%

This distribution suggests that 38% of all employers in the base would not be expected to

implement any of the programs, and that of the remaining set, full participation at the

design level (nominal levels shown in previous table) is probably unlikely, with some

estimated percentage implementing programs that are probably at a level lower than the

level intended. When this distribution is related to the model, the computation of the

change in carpool mode share associated with this participation pattern would be as

follows:

Carpool Mode Share Increase:

38% @ Level “0” = 0.38 x 0.0%

12% @ Level 3 = 0.12 x 4.0%

50% @ Level 4 = 0.50 x 6.0%

Net increase in carpool share = 0.0% + 0.48% + 3.0% = 3.48%

So with this example, the model would add 3.48% to the existing carpool share for this

scenario, and similar calculations would be done for the other modes. This simple

illustration assumes two conditions, however, that are not likely to occur in practice:

& The employment base is comprised entirely of Office employment; and

& Each of these programs is being implemented on a zero base, i.e., there are no

pre-existing program efforts of this type in place.

With regard to employment type, the calculation would be expanded to account for the

different level of impact projected in non-Office situations, as shown in Table 4-2. Thus
-42-

if the baseline employment were comprised of, say 80% Office and 20% Non-Office, the

calculation above would be revised as follows, yielding 3.02% as the Carpool share

adjustment instead of 3.2%:

Carpool Mode Share Increase:

38% @ Level “0” = 0.38 x 0.0%

12% @ Level 3 = 0.12 x [(4.0% x 80%) + (1.4% x 20%)]

50% @ Level 4 = 0.50 x [(6.0% x 80%) + (2.0% x 20%)]

Net increase in carpool share = 0.0% + 0.42% + 2.6% = 3.02%

With regard to accounting for existing program efforts, this is done easily in a table

equivalent to the one above, but instead specifying existing participation rates by level. 

An example is shown below.

Percent of Employers by Existing Program

Mode None Level 1 Level 2 Level 3 Level 4 Total

Carpool 50% 50% 100%

Vanpool 95% 5% 100%

Transit 50% 40% 10% 100%

Bicycle 95% 5% 100%

Given this information, the COMMUTER model will compute the net impact from the

new scenario of actions by subtracting out the portion of the effect that may be attributed

to existing efforts. Using the Carpool example again, the share that may be attributed to

existing efforts is calculated as follows:

Carpool Mode Share Increment Due to Existing Efforts:

50% @ Level “0” = 0.50 x 0.0%

50% @ Level 1 = 0.50 x [(0.4% x 80%) + (0.2% x 20%)]

Credit for Existing Program = 0.0% + 0.36% = 0.36%
-43-

The net increase that may then be attributed to the Carpool support program of the

scenario, after crediting for existing efforts, is 3.02% - 0.36%, or 2.66%.

Site vs. Areawide - Users of the COMMUTER model who are evaluating employer

support programs at an individual site level will find slight differences in how the model

treats a single employment site vs. the more challenging simulation of these programs

applied across a diverse employer population, as discussed above. The basic analytics are

the same, however, employing the same mode share adjustment procedures and affording

the same level of impact. The only difference is that the user does not have to specify the

distribution of the programs across the employment base. 

Model users are asked at the early stage of model application whether the analysis will be

for an individual site or areawide. The model then automatically directs them to the

individual site screen, where they are asked to enter the following information:

Program Focus Existing Level New Level

Carpool

Vanpool

Transit

Bicycle

As with the multi-employer case, the individual site user first enters the “Existing Level”

that best corresponds to the type of program that is being offered now (from 1 to 4, with

the default being Level 0), and then selects the New Level that best represents the

program that will be offered. This is done individually for each mode. The model then

determines the change for each mode under the New Level, and nets out the credit for the

Existing Level. 

The COMMUTER model also offers an alternative procedure that give the user even

more flexibility to experiment with the potential impact of support programs. The

following alternative table is presented in the model:

Program Focus

Existing

Share Increase

New

Share

Carpool 13.2 % % 13.2 %

Vanpool 0.5 % % 0.5 %

Transit 5.3 % % 5.3 %
-44-

Bicycle 0.4 % % 0.4 %

To use this procedure, the user enters the percentage increase that is expected for each

listed mode through the given set of program actions, and the model automatically makes

the adjustment from the Existing Share to the New Share. This type of procedure is

useful only if the user has previously compiled company survey data on how employees’

mode shares would change if a support program were implemented. (Note that the

existing shares in this input table come from the baseline mode share inputs.) 

Absence of Walk Mode - While employers can make an effort to encourage those

employees who have the opportunity to walk to work, the specific incentive measures are

perhaps less tangible for pedestrian travel. Neither the individual site nor the areawide

applications for support programs in the COMMUTER model provide for increasing

walk mode share; however, the model does preserve the share that is already there, by

freezing the starting share of walk trips and not including it in the normalizing process

(described below) that balances the share adjustments across all the other modes. Walk

mode share can be increased through improvements in walk access time to the site, as

described in Section 6.

Normalizing Across Modes - Once the mode shares of each commute mode have been

adjusted upward by the increment associated with the respective support program, the

sum of all modal shares will almost always exceed 100%. In the logit mode split model

procedure (Section 6), the total shares are constrained to 100% by virtue of the way the

equation works, i.e., by dividing up the “pie” in proportion to the respective “utilities” of

each mode. In this more simplistic procedure, however, where increments are added to

each mode without accounting for “where” (what other mode) those new riders come

from, some readjustment must be made to bring the sum of all shares back to 100%. This

is done through a process called normalizing, where each mode’s share is divided by the

new total, proportionately reducing each share so that the total once again is 100%. This

is equivalent to assuming that the increase in each mode share affected by a program will

come from the other modes in proportion to their starting mode shares. 

The important exception to the procedure of proportioning the adjustments across modes

is that Walk is not included in the adjustment process. The normalizing process simply

freezes the Walk share at its existing level, and it neither gains nor loses as a result of the

support program impacts. 

A simple example shown as Figure 4-1 illustrates how the normalizing process works

(this example was also used earlier in Section 2). Initial shares of carpool, vanpool,

transit and bicycle are increased through individual support programs. Carpool is

increased from 13% to 15%, Vanpool from 1% to 2%, Transit from 5% to 7%, and

Bicycle from 1% to 2%. The problem, though, is that these various programs add 6% to

the targeted modes, but no mode has had to give up any users. To correct this, the 6% are

“found” from among all of the other modes, including the ones that have been targeted

(except Walk, which remains excluded from this process). This amounts to multiplying
-45-

each share by the ratio of the new total to the old total, or 96%/102%, or 0.941. This

means that Carpool’s increase from 13% to 15% is dropped back to 14.1%, Vanpool’s

and Bicycle’s 2% dropped back to 1.9%, and Transit’s 7% dropped back to 6.6%. 

However, in this process Drive Alone drops from 75% to 70.6%, losing the greatest share

because its share was initially the largest.
-46-

Figure 4-1

Illustration of Share Adjustment Process

###
-47-

5. Estimating Travel Impacts of 

Alternative Work Schedules

Overview of Alternative Work Schedule Programs

Rather than trying to induce a change in mode of travel, this class of commuter choice

strategies is aimed at reducing the frequency of the commute trip (i.e., the number of trips

made by the employee to the site per week), or shifting the timing of the trip outside of

the peak hour or period. These are broadly referred to as Alternative Work Schedules or

Arrangements, and consist of the following popular options:

& Flexible Work Hours: A relaxation in the official daily hours of business

allows employees the flexibility to adjust their personal work schedules to

either come early/leave early, or come late/leave late in order to avoid the most

congested portion of daily commute periods.

& Staggered Work Hours: A more formalized version of the above, where the

employer sets one or more starting/ending times within a small time increment

of each other, so that all employees are not arriving/departing at the same time. 

For example, one half of the workforce may arrive at 8:00 a.m., and the other

half at 8:30 a.m.

& Compressed Work Weeks: Rather than working a standard 5-day/8-hour-perday work week, some employers will allow employees to work a longer work

day, usually either 9 or 10 hours, and build credit in order to be exempt from

traveling to the site on a particular day. The most common versions of

compressed work week arrangements are 4/40, where the employee works four

10-hour days and then takes the fifth day off, or 9/80, where the employee

works nine 9-hour days and takes the tenth day off.

& Telecommuting: Also sometimes referred to as “telework”, this arrangement

allows employees to work off-site usually one or more days per week, being in

communication with the worksite via telephone or computer modem

connection. 

Typically an employer will offer only one of these programs to its employees, since, to a

certain extent, taking part in one nullifies the ability to participate in the other. However,
*

 Flexible work hours as presented here is not to be confused with the flexible work schedule policy

described in Chapter 4 as a way of encouraging alternative mode use by granting leniency to employees

who need to adjust their hours to meet the schedule of a carpool/vanpool or transit.

-48-

there may be occasions when more than one of the programs may be offered, perhaps to a

different segment of the employer’s work force. The COMMUTER model estimates the

impact of each of these Alternative Work Schedule programs, and permits offering more

than one program at a time with some practical constraints imposed to prevent unrealistic

cases of dual eligibility.

Nature of Emissions Impacts

Whereas employer commute choice programs that induce changes in travel mode lead to

reductions in vehicle trips and VMT and thus fewer emissions, Alternative Work

Schedule programs reduce emissions by eliminating trips or pushing them to a less

congested time period. A brief description of how each of the listed programs would be

expected to reduce emissions is provided below.

& Flexible Work Hours: Emissions reductions from flexible work hours are

determined entirely by the number of vehicle trips shifted from the peak period

to the non-peak period. Emissions reductions occur because congestion is less

and speeds are higher in the off-peak. An important assumption with flexible

work hours is that shifting work hours does not take away the incentive to use

transit or ride in a carpool or vanpool, since driving alone may be more

attractive outside the peak period.*

& Staggered Work Hours: Same as flexible work hours, emissions reductions

from staggered hours are dependent entirely on the number of vehicle trips

shifted out of the peak period. The same important assumption as to whether

employees have the same incentive to use transit or rideshare applies as with

flexible work hours.

& Compressed Work Weeks: This strategy reduces emissions by eliminating

vehicle trips to the work site. 4/40 programs eliminate one out of five trips per

week, while 9/80 programs eliminate one trip out of every ten, or 0.5 per week. 

A critical assumption in claiming emissions credits from these trips shifted is

that equal or greater travel does not occur on the individual’s day off. 

& Telecommuting: This strategy also reduces emissions by eliminating vehicle

trips to a work site. As with compressed work weeks, the critical assumption is

that travel on the telecommute day is negligible because the employee is home

working.
*

 This percentage was derived from data compiled in a 1980 study “Behavioral Impacts of Flexible Work

Schedules.”

-49-

Analytic Approach for Estimating Travel Impacts

Three factors enter into the calculation of the travel impacts from Alternative Work

Schedules:

1. Which strategy or strategies are being offered?

2. What type of occupation is the target employment engaged in, i.e., is it suitable

for the particular type of alternative work arrangement? The COMMUTER

model calculates Alternative Work Schedule travel impacts only for Office

employment.

3. What percentage of employees are made eligible for (or are expected to

participate in) the programs by employers?

A fourth factor becomes important in specific relation to emissions impacts, namely, the

length of the peak period as a factor in determining the number of trips that would

actually be shifted outside the peak.

Like the Commuter Choice Support Strategies (Chapter 4), Alternative Work Schedule

programs are also analyzed using factors supplied in lookup tables. The user enters

preferences as to which strategies will be offered, and indicates the percentage of

employees who will be eligible for (or, alternatively, would be expected to participate in)

each. The COMMUTER model then makes a calculation that either removes vehicle

trips from the total or shifts those trips to the off-peak period. Research reviews

identified no elasticities or coefficients that could be used for estimating the response of

travelers to these strategies. As a result, simple factor relationships taken from empirical

research, in combination with some simple mathematical identities, are used to calculate

the travel impacts for these strategies.

The calculation of the travel impact for each of the Alternative Work Hours programs is

explained below.

Flexible Work Hours - Using relationships developed for the FHWA TDM model, the

COMMUTER model assumes that in employment situations where employees are made

eligible for Flexible Work Hours options, 22% will in fact take advantage of that option

and participate in a program that will shift their hours of travel.* Alternatively, users can

specify directly what percent are expected to participate, and in effect replace the 22%

default with their own estimate; there should, however, be some explanation as to what

information justified this assumption. 
-50-

The user is also asked to supply information on the existing percent of employees eligible

for (or already participating in) Flexible Work Hours. As with the Employer Support

Programs (Chapter 4), the net new percentage of eligible (or participating) employees is

used to estimate the impact.

Exactly how many of the employees who elect to participate in Flex Hours actually shift

their time of travel such that the new trip falls outside the peak depends on the length of

the peak period. If the peak period is short (e.g., two hours in duration), then the

likelihood that a trip shifted by flexible work hours will fall outside the peak (and hence

become associated with lower emissions rates) is fairly high, whereas with a long peak

period, say 4 or more hours, only a relatively small percentage would be expected to fall

outside the peak. Table 5-1 shows the shift percentages as related to length of peak

period that are coded into the COMMUTER model.

Table 5-1

Percent of Trips Shifted by Length of Peak Period

Length of Peak Period (hrs) Percent of Trips Shifted

2.0 28.7

2.5 19.2

3.0 13.9

3.5 10.6

4.0 8.5

4.5 7.1

5.0 6.0

Source: Estimating the Effect of Alternative Work Schedules on Travel

Activity and Emissions, FHWA TDM Evaluation Model, 1993.

The user supplies the local information on length of peak period. This item appears on

the screen for Alternative Work Hours strategies as “Percent of Trips Shifted from Peak

Period.” The user may use the default value of 3 hours if local data are not easily

available. If local data are available on the percentage of trips shifted from the peak, they

should be used in place of the defaults given in Table 5-1.

It is important to note that not all of the trips being shifted under Flexible Work Hours are

vehicle trips. Employee trips are shifted at the same modal split as exists in the overall

population; hence, if only 80% of employees drove alone, and 10% rode in 2-person
-51-

carpools, then only 85% (80% + 10%/2) of the shifted trips would be assumed to be

vehicle trips.

The calculation of the travel impact from flexible work hours is then as follows:

Number Daily Vehicle Trips Shifted,

Peak to Off-peak =

Total Affected Employment

X

Percent of Employment that is Office

X

NET Percent of Employees Eligible

X

22% Participate in Program

X

Percent of Trips Shifted 

(Based on Length of Peak Period)

X

Current Private Vehicle Mode Share

The following example illustrates the effect of Flexible Work Hours:

Total Vehicle Trips Shifted from Peak =

(100,000 employees) X (75% Office) X (30% – 10% eligible) X (22%) X (13.9%

shifted)3 hr peak X (.85 vehicle trips per employee) = 390 daily vehicle trips shifted

Alternatively, the user could specify the percent of employees that would be expected to

participate in the program if offered (less the percent already participating). In the above

example, if the “Eligible” percentages were in fact already “Participating,” the calculation

would change as follows:

(100,000 employees) X (75% Office) X (30% – 10% participation) X (22%) X

(13.9% shifted)3 hr peak X (.85 vehicle trips per employee) = 1,772 daily vehicle

trips shifted
-52-

This is a very different outcome, 1,772 vs. 390 trips, than when the participation is

entered directly. For this reason, the user should be aware of the difference in

significance between using the Percent Eligible vs. the Percent Participating option in the

model, and apply caution if using the latter option.

Staggered Work Hours - The travel impacts from Staggered Work Hours are determined

in exactly the same manner as Flexible Work Hours above. The user indicates [either]

the percentage who will be Eligible, or who will be expected to Participate, with the same

caveats as discussed above. The model then calculates the number of trips that will be

shifted, and the number that will be assumed to fall outside the peak and qualify for

favorable emissions treatment.

Compressed Work Weeks - Calculation of travel impacts from Compressed Work Weeks

follows a very similar procedure to the one above for Flexible and Staggered Work

Hours. The principal difference is that employees who opt to participate in Compressed

Work Weeks are assumed to make fewer trips to the worksite, rather than the same

number of trips but shifted out of the peak.

To estimate the effects from Compressed Work Weeks, the user must indicate:

& The percent of employees who will be eligible for the new program, or who

will be expected to participate;

& The percent who are already eligible for (or participating in) a similar

compressed work hours program; and

& Whether the program type will be a 4/40 or 9/80 arrangement (percent by

each).

The COMMUTER model then calculates the number of daily vehicle trips that would be

eliminated from the Compressed Work Week programs, as a function of the total number

of Office (only) employees, the net new percent eligible or participating, the current

modal share, and the effective daily trip reduction attributable to the type of program

(4/40 or 9/80).

As with the Flexible or Staggered work hours programs, if the user specifies the percent

or employees Eligible, the model will assume that 22% of these eligible employees will

participate in the program. If given directly the percent who will participate, it will use

100% of this estimate. 

For each employee who participates in a compressed work week program, the following

trip reduction will be estimated:

4/40 Work Week: 1 trip per week or 0.20 trips per day.
-53-

9/80 Work Week: 1⁄2 trips per week, or 0.10 trips per day.

Again, as with the work hours strategies, the elimination of trips is assumed to be

proportional across all current modes, i.e., they are not all vehicle trips being eliminated.
-54-

The calculation of the travel impact from Compressed Work Weeks is then as follows:

Number Daily Vehicle Trips Eliminated =

Total Affected Employment

X

Percent of Employment that is Office

X

NET Percent of Employees Eligible

X

22% Participate in Program

X

Daily Person Trips Eliminated

(0.20 for 4/40, 0.10 for 9/80)

X

Current Private Vehicle Mode Share

The following example illustrates the effect of Compressed Work Weeks (based on

eligibility):

Total Vehicle Trips Eliminated =

(100,000 employees) X (75% Office) X (30% – 10% eligible) X (22%) X

(0.20 trips)4/40 Week X (.85 vehicle trips per employee) = 561 daily vehicle trips

removed

Alternatively, the user could specify the percent of employees that would be expected to

participate in the program if offered (less the percent already participating). In the above

example, if the “Eligible” percentages were in fact already “Participating,” the calculation

would change as follows:

(100,000 employees) X (75% Office) X (30% – 10% participation) X (22%) X

(0.20 trips)4/40 Week X (.85 vehicle trips per employee) = 2,550 daily vehicle trips

removed

This is a very different outcome, 2,550 vs. 561 trips, compared to when the participation

is entered directly. For this reason, the user should be aware of the difference in
*

 The study “Telecommuting in the United States,” reported in the ITE Journal in December 1992,

presented data from a national survey that indicated that if given a chance to Telecommute, about 32% of

employees would participate and telecommute on a given day. The same study also indicated that of those

employees who did participate in a telecommute program, the average number of days per week that they

would work at home was 1.8 days.

-55-

significance between using the Percent Eligible vs. the Percent Participating option in the

model, and apply caution if using the latter option.

The user should also apply caution in interpreting the trip reduction number, given

uncertainty as to the travel behavior of the employee on the “day off.”

Telecommuting - Calculating the travel impacts from Telecommuting is slightly different

from both prior classes of strategies, but similar to the Compressed Work Week in that

impacts are registered as trips eliminated as opposed to trips shifted from peak to offpeak.

To estimate the effects from Telecommuting, the user must indicate the following:

& The percent of employees who will be eligible for the new program, or who

will be expected to participate;

& The percent who are already eligible for (or participating in) a similar

Telecommuting program; and

& The average number of days per week that employees will telecommute, i.e.,

work at home, if different from the 1.8 day default value coded into the model.

The COMMUTER model then calculates the number of daily vehicle trips that would be

eliminated due to Telecommuting as a function of the total number of Office (only)

employees, the net new percent eligible or participating, the current modal share, and the

average number of days per week that the employee will work at home under the

telecommute arrangement.

As with the Flexible/Staggered work hours or Compressed Work Week programs, the

user may either specify the percent of employees who will be eligible for a Telecommute

program, or the exact percentage that will participate. If the user opts for the “Eligible”

approach, the model will assume that 32% of the eligible employees will participate in

the program.*

 If the percent who will participate is provided directly, the model will use

100% of that estimate.

For each employee who participates in a Telecommute program, the following trip

reduction will be estimated:
-56-

1.8 days per week work at home / 5 days per week = 0.36 trips per day eliminated.

(user has option of changing the 1.8 day default)

Again, as with the other strategies, the eliminated trips are assumed to be proportional

across all current modes, i.e., they are not all vehicle trips being eliminated.

The calculation of the travel impact from Telecommuting is then as follows:

Number Daily Vehicle Trips Eliminated =

Total Affected Employment

X

Percent of Employment that is Office

X

NET Percent of Employees Eligible

X

32% Participate in Program

X

Daily Person Trips Eliminated

(0.36 based on 1.8 days)

X

Current Private Vehicle Mode Share

The following example illustrates the effect of Telecommuting (based on eligibility):

Total Vehicle Trips Eliminated =

(100,000 employees) X (75% Office) X (30% – 10% eligible) X (32%) X

(0.36 trips)1.8 days/week X (.85 vehicle trips per employee) = 1,469 daily vehicle trips

removed

Alternatively, the user could specify the percent of employees that would be expected to

participate in the program if offered (less the percent already participating). In the above

example, if the “Eligible” percentages were in fact already “Participating,” the calculation

would change as follows:
-57-

(100,000 employees) X (75% Office) X (30% – 10% participation.) X (32%) X

(0.36 trips)1.8 days/week X (.85 vehicle trips per employee) = 4,590 daily vehicle trips

removed

This is a very different outcome, 4,590 vs. 1,469 trips, compared to that obtained when

the participation is entered directly. For this reason, the user should be aware of the

significant difference between using the Percent Eligible vs. the Percent Participating

option in the model, and apply caution if using the latter option. The user should also

apply caution in interpreting the trip reduction number, given uncertainty as to the travel

behavior of the employee on the “day off.”

Multiple Options

The COMMUTER model permits the user to examine a program scenario with more than

one of the alternative work hours strategies at one time. If the analysis is being done at a

regional scale over many employers, it is practical to assume that more than one option

would be tested in the same scenario. Generally, the user is advised to limit the total

eligibility—or participation—in all programs to 100% of the affected employment base. 

When the model is asked to estimate the effects of multiple programs in the same

scenario, it simply calculates the program effect for each program, e.g., Flexible Work

Hours, individually, and then simply adds the results to a grand total of “number of trips

shifted to off-peak” and “number of daily trips eliminated.” Unlike the Employer Support

Strategies of Chapter 4, however, because the user is advised to limit total participation to

100%, there is no concern about one program competing for share with the other and

necessitating a tradeoff and normalizing process.

Even an individual employer using the model might opt to make more than one

Alternative Work Schedules strategy available, and so long as the total eligibility or

participation does not exceed 100% of all employees, no reconciliation of shares across

programs is necessary in the model.

However, one could envision the scenario where an employee might be eligible for more

than one program option, or in fact be eligible for any of the options simultaneously. In

this instance, the employee might choose to participate in more than one program, limited

only by the interference that might occur between one program and another. For

example, an employee “eligible” for Compressed Work Weeks, Flexible Work Hours,

and Telecommuting might find it difficult in practice to be able to take advantage of more

than one of these options. If the employee is on a 4/40 work week, for instance, it is

difficult to see how they could also telecommute, or participate in a Flexible Work Hours

program. Nevertheless, to accommodate this real possibility, the model allows the user to

specify more than 100% eligibility or participation across all programs, in essence letting

the employee “decide” which program or combination of programs to take part in. To

deal with this in the model calculations, each program’s share is first computed

individually, as before, assuming that the employee has only one option available. 
-58-

However, once all the individual choices are computed, the sum of the eligibility or

participation rates is computed, and if in excess of 100%, the computed effects of each

program is normalized back to an effective participation of 100%. This gives a very

liberal opportunity to the user to test broad strategies, while still adhering to basic

reasonableness criteria regarding use of more than one alternative at a time.

###
* See for example: Federal Highway Administration. Implementing Effective Travel Demand Management

Measures. (1993), and Transit Cooperative Research Program. Cost Effectiveness of TDM Strategies.

TCRP Project B-4 (1995).

-59-

6. Estimating Travel Impacts of Travel Time and Cost

Changes

Overview of Travel Time and Travel Cost Strategies

Perhaps more than any other type of strategy, those measures that change the time or cost

of making a trip seem to have the most significant impact on travel behavior.*

 As a

simple example, if a traveler had two alternatives for making a trip, A and B, which the

traveler saw as basically equal options, reducing the travel time or the travel cost

associated with alternative B would make B more attractive, and the traveler more likely

to choose that alternative. Exactly how much more attractive it would be depends on how

big the relative changes are, which components of time or cost are changed (since they

are weighted differently by the traveler), and other intangibles such as the purpose of the

trip, the occupation and income of the traveler, and so forth.

Travel time and cost are also the most measurable elements of transportation choice. 

Whereas it may be difficult to precisely measure the value of, for example, a guaranteed

ride home program as an employer incentive to encourage transit or ridesharing,

incentives that involve changes to travel time or cost are quite measurable. As a result,

statistical analysis of travel behavior shows a strong and consistent relationship between

travel choice and the level of time and cost associated with the trip. 

The economic framework upon which travel demand analysis and forecasting is based

suggests that travelers view travel as a means of accomplishing a need, and consider

several factors when deciding whether and how to make a trip:

& Trip Purpose: Depending on the type of activity to be satisfied, the need to

make a trip may be less flexible for some purposes than others. Travel to work,

for example, is generally less negotiable than making a social or recreational

trip.
-60-

Home

Access

Time

In-Vehicle

Time

Access

Time

Work

Wait

Time

Transfer

Time

& Trip Frequency: In addition to deciding whether to make the trip, the traveler 

also has the choice, depending on the purpose of the trip, of how often (daily,

less than daily, etc.) the trip will be made.

& Destination: When the traveler is not committed to a single site for satisfying

the activity, as is generally the case with the commute trip, more than one

location may be acceptable.

& Travel Mode: The traveler may have more than one travel mode to choose

from, e.g., driving, walking, taking transit, depending on the coordinates of the

trip and what modes are available.

& Time of Day: Again, depending on the purpose of the trip and the flexibility of

the schedule, the traveler may adjust the hour of the day that the trip is made in

order, for example, to avoid traffic congestion that will make the trip take

longer.

In the case of travel made for the purpose of work, many of the traveler’s options are

limited: generally the trip must be made on a daily basis, to a fixed destination, and at a

given time of day. The Alternative Work Schedule strategies presented in the previous

chapter have introduced some flexibility into this set of choices, allowing some travelers

some discretion over how often they travel from home to the worksite, or at what times of

day. 

Generally, however, the principal choice to the commuter is in what mode of travel to

use. Except for those persons whose households do not own vehicles, the most

commonly used alternative is driving alone. Principal alternatives that represent choices

to driving alone are carpooling, riding transit, or joining a vanpool. Persons whose

residence is reasonably close to the work place may also have the choice of walking or

biking. 

When evaluating these modal choices, the consumer is believed to look at how long it

will take and how much it will cost to travel from door-to-door, as depicted in the figure

below. 
-61-

In other words, the comparison among the options is based on how much time and effort

is involved in getting from the home to the mode of travel (access time), traveling in that

mode to the destination (in-vehicle time), and then getting from the point of debarking to

the final destination (access time). For most travelers, driving a personal vehicle

represents the most convenient solution to this comparison. The auto user walks to the

parked vehicle (usually in a driveway or garage), drives directly to the workplace, and

parks within fairly short walking distance of the workplace. For the great majority of

American workers, the auto user is also likely to find ample free or subsidized parking at

the worksite.

In contrast to the option of driving alone, which is virtually “door-to-door”, each other

mode involves some type of compromise. The carpool or vanpool user must give up

some flexibility of arrival and departure time to accommodate the schedule of the other

riders, and often spend a bit more time traveling just to connect up with the other pool

users (access or in-vehicle time). The transit user must first get to the transit line, either

by walking or driving, then wait for the transit vehicle to arrive (wait time), and then walk

from the transit stop at the end of the trip to the final destination. In some instances, it is

also necessary to use more than one transit service, involving a transfer and potential wait

(transfer time). Persons who would walk or bike to work face not only limitations on

“reasonable distance” (generally 1 mile for walk, about 2 miles for bicycle), and exposure

to weather and traffic, but also physical barriers that make access to the site difficult and

add time.

What makes transit, ridesharing, biking, or walking attractive to some travelers, other

than matters of pure personal preference or constraint, are time or cost advantages of one

mode over the other. If users of carpools, vanpools, or transit can gain a travel time

advantage over the single-occupant vehicle by having a separate facility that avoids

congestion, that can alter the balance. Changes at the destination end that reduce the time

and difficulty of reaching the site are also important. For transit users this might mean a

stop closer to the building or a more direct path of access to the building. For carpool or

vanpool users this might mean close-in preferential parking. For bikers or pedestrians, it

might mean improved access to the site that reduces the time and distance.

Travel cost can also be a factor that tips the balance toward one of the alternative modes. 

The typical SOV user usually only recognizes the cost of operation of the vehicle. Tolls

and parking fees are clearly noted, but are seldom in place, so the SOV traveler sees their

mode as very cost-effective. Transit users, on the other hand, must pay a fare that is no

longer a “nominal” charge in most urban systems. Vanpool users usually pay a monthly

or weekly charge, and carpoolers split their costs to where it is less than driving alone, but

if the trip does not involve parking, tolls or other out-of-pocket fees, the savings may not

be significant. Obviously, bikers and pedestrians have the cheapest alternative, trading

their time and effort for avoidance of monetary cost. 
* See again: FHWA Implementing Effective Travel Demand Management Measures (1993) and TCRP B4: Cost-Effectiveness of TDM Strategies (1995).

-62-

Actions that can make transit, ridesharing, or biking/walking more attractive to potential

users from a cost point of view would entail either increasing the cost of driving alone or

decreasing the cost of using the alternative. Fare subsidies are the most common cost

incentive for transit users. Employers have also been known to subsidize or underwrite

the costs of vanpooling, and there have been instances of employers providing

“equipment” subsidies to encourage biking or walking. 

Perhaps the most significant incentive to the decision to drive or not drive, however, is

the cost of parking. Since most employers offer free or subsidized parking to employees,

this substantial benefit is a major subsidy to employees who drive. Where employers

have imposed charges on parking, major differences in drive alone mode share can be

observed.*

 There are many variations on this theme of using parking cost as a

transportation demand management incentive, including the following:

& Combining parking fees for SOVs with free or subsidized parking for carpools

or vanpools.

& Offering a “Cash-Out” payment in the amount of the employer’s cost to

provide parking to the employee as an incentive to not drive alone.

& Taking the revenues from employee parking fees and distributing them back to

employees as targeted or general subsidies to support use of other modes.

Types of Time/Cost Strategies Covered by COMMUTER Model

The COMMUTER model is fairly flexible in its ability to look at a variety of travel time

or travel cost incentive strategies. These strategies can be targeted at individual modes,

and numerous strategies can be included within the same scenario. The model will deal

with the interactions among the modes under the influence of the selected package of

strategies through a multinomial logit modeling procedure, described in the next section.

Site Access Improvements - The user can test strategies that change the current time

required to access the employment site once it is reached by the respective mode. This

can be done for any or all of the modes at the same time, with different assumptions for

each mode. Testing a negative value of change in access time (e.g., -two minutes)

constitutes an incentive since it reduces travel time, while testing a positive value

represents a disincentive for that particular mode.

Changes in walk access time for carpools, vanpools, or drive alone can be realized

through parking supply management techniques, such as preferential close-in parking for
-63-

pools or on-site vs. off-site parking privileges. Changes in access time for transit, biking,

or walking can be realized through improved site access design, affording a more direct

and safe connection to the transit stop or the local community or sidewalk/street network.

Transit Service Improvements - Most employers are not likely to initiate strategies that

will change the travel time associated with transit service, other than the improvements to

on-site access described above. However, users examining regional applications of the

COMMUTER model may envision and wish to test the consequences of improving the

travel time associated with improved transit service.

Transit service can provide shorter door-to-door travel time as a result of the following

changes:

& Increased frequency of service (i.e., shorter headways between vehicles), which

allows reduction in wait time or transfer time.

& More direct service, which could result in both reduced in-vehicle travel time

as well as elimination of transfer time.

& Faster service, which could result from introduction of express type service,

more direct service, or operation on an exclusive facility/right-of-way that is

free of highway traffic congestion.

COMMUTER model users can incorporate improved transit service in their scenarios by

making changes in either Frequency of Service (which is analyzed as “wait time”) or

Faster Service (which is analyzed as “in vehicle time”). Improvements in ease of

accessing transit—even if they occur off-site—can be evaluated through the Access Time

feature described above.

Changes in Parking Costs - The model allows users to examine the effectiveness of

imposing parking fees. These may either be changes in fees that are currently in place, or

introduction of fees where parking is currently free. Because the user provides the model

with information on how parking cost would change for each relevant mode—Drive

Alone, Carpool, and Vanpool, it is possible to assume a different fee for each mode. 

Through the use of positive (+) and negative (-) entries, it is also possible to treat the

change in parking fee as a surcharge (increase) or as a reduction or subsidy. For example,

if a site currently imposed a $1.00 per day charge for all vehicle parking, it would be

possible to test a $1.00 daily increase (+$1.00) on SOV users while keeping the fee for

carpools at the present $1.00 a day and making parking for vanpools free by entering -

$1.00.

When the model calculates the impact of the parking cost, or any other cost strategy, it

assumes that the cost is divided evenly among the vehicle’s occupants. Thus, for SOVs, a

$1 daily charge increases the cost for the SOV traveler by $1, while an increase in $1 for
-64-

a 2-person carpool would increase the cost for the carpool user by only $.50, and for a 10-

person vanpool by only $0.10.

Fare Cost - The cost element that is most relevant to transit use is the level of fare paid. 

The COMMUTER model allows the user to test the effect of reducing transit fares by

entering the daily change (i.e., per round trip) in cost to the user. Entering a negative (-)

value constitutes a subsidy (or cost reduction), while entering a positive (+) value

constitutes a fare increase. Under certain circumstances a user might want to examine the

effect on employee travel if a general fare increase was proposed by the local transit

agency. 

Other Financial Incentives or Disincentives - In addition to the specific parking and

transit fare measures described above, employers may also utilize pricing strategies in

other ways to either encourage alternative mode use or discourage SOV use. For

example, some employers have imposed a cost for parking on site and then used the

revenues to provide general subsidies to some or all of the alternative modes, including

walk and bicycle. To permit analysis of this type of measure, the COMMUTER model

has a place where the user can enter and test daily round trip cost changes—either

increases or decreases—for any or all of the available modes. These strategies can be

tested simultaneously with changes in parking pricing or transit fares, although the user

should not apply the same strategy in both places in the same scenario.

Analytic Approach for Estimating Travel Impacts

Logit Mode Choice Model - The impact of travel time or cost strategies on travel

behavior are determined in the COMMUTER model through use of an analytical

technique known as a logit mode choice model. Models of this type are considered to be

the state of the practice in transportation planning and policy studies. Most major

metropolitan areas have developed these models from local data and use them routinely

within their regional planning process. They are used by metropolitan planning agencies

for developing regional plans, analyzing the impact of major investment alternatives, or

responding to regulatory requirements (such as conformity analysis). 

Logit mode choice models are particularly appropriate for examining traveler response to

change in travel time or travel cost. Their structure explicitly includes the various

elements of time and cost that were discussed in the previous section. The value of these

elements to the traveler is reflected through coefficients that have been developed through

a statistical estimation process applied to extensive regional travel data. The structure of

the model also expressly incorporates the very practical consideration of determining the

effect of having several strategies operating at the same time—possibly differently on the

individual modes—and having the modes compete with each other based on their relative

cost and time advantages.
-65-

eum

A logit mode choice model relates the utility or “attractiveness” of a travel mode to the

consumer in relation to the utility of the alternatives. The traveler selects the mode that

maximizes his/her utility, i.e., which provides the greatest total value. The model does

this by calculating the statistical probability that each mode will be chosen, and the

individual is presumed to choose the mode with the highest probability. When this

process is performed for a travel population, rather than an individual traveler, the

probabilities become modal shares. For example, if the model computes probabilities of

0.758 for drive alone, 0.133 for transit, and 0.109 for carpool, the individual would be

expected to drive alone, since drive alone commands the highest probability. However, if

these probabilities were obtained as the result of applying the model to a population of

travelers, the probabilities would signify mode shares for that group, i.e., 75.8% would

drive alone, 13.3% would use transit, and 10.9% would ride in carpools.

In the logit model, the share of each available mode depends on the existence and relative

attractiveness of other available travel modes. The probability structure ensures that the

total of the shares of all modes will not exceed 100%.

The probability of selecting a given travel mode m is calculated through the following

equation:

 

P(m) = _________________________

 eu1 + eu2 + .... + eui

where

p(m) is the share of mode m;

e is the exponential function (e to the power x, where e = 2.7183);

um is the utility equation for mode m; and

u1...ui

 represents the utility of other available modes 1 through i.

The utility of each alternative mode is usually a linear function that incorporates the

important choice attributes of the mode, such as travel time and cost, coupled with

weights, or coefficients, that represent the relative importance of the attributes to the

traveler. Logit mode choice model coefficients are typically calculated from household

travel survey data using a statistical curve-fitting technique known as maximum

likelihood estimation. A typical utility function might look like the following:

U(auto) = c+ a(time) + b(cost)
* While the traveler choosing among alternatives attempts to “maximize his utility” when picking his

preferred alternative, in the case of travel mode choice where the key attributes are time and cost, the best

alternative is the one that offers the least time and cost. In this situation, because higher values of time and

cost are seen as disutility, the consumer’s selection of the best alternative is instead a process of

“minimizing his/her disutility.”

-66-

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Mode 

m Share

Disutility of Mode m

Figure 6-1

Where:

U(auto) is the utility of the auto mode;

time and cost represent travel time and cost for the trip using auto; 

c is a constant representing the utility of the mode that is not captured by travel

time or cost; and

a and b are coefficients applied to time and cost, respectively.

This equation produces an S-shaped curve, as shown in Figure 6-1. The graph shows the

percentage use of mode m as a function of the disutility of mode m.

*

 A distinguishing

feature of this curve is that its slope is steepest in the middle, and this is where small

changes in utility have the greatest effect on mode share. At the high and low end of the

curve, the curve flattens out, indicating that even relatively large changes in utility have

little impact on the mode’s share at that point. 

The Pivot-Point Approach - In the COMMUTER model, just as in the FHWA TDM

Model, the logit procedure is applied in what is known as a pivot point approach. In a

traditional application of a logit mode choice model in a regional planning study, it would
-67-

be necessary to relate to the model detailed information on the time, cost and other

relevant factors for each alternative, and for each traveler or origin destination pair that

make up the analysis. This must be done for each alternative or policy analyzed,

requiring a significant amount of time and effort.

The pivot point approach is a less data-intensive method for applying logit mode choice

models. Rather than gathering information on the time and cost components for each

mode for each trip, the analyst has only to describe how much travel time or cost
-68-

p m

pm e

e pm

U m

U m '( ) ( )

[( ) ( )]

( )

= ( )

×

−× +

−

−

∆

∆ 1 1

components will change as a result of the test strategies. In the pivot point approach, the

logit model is not used to estimate a mode share, but to estimate the change in a known

mode share, given a change in transportation system characteristics. In this case, the

procedure starts with a set of mode shares and an assumed change in travel time or travel

cost for each mode. The same logit coefficients described above can then be used to

estimate what the new mode shares would be. The mathematical expression for this,

which can be derived from the basic logit model equation, is as follows:

(1)

where:

p’(m) = original share of mode m

p(m) = the new share of mode m

U(m) = the change in disutility of mode m = a (Travel Time) + b (Travel Cost)

Because this equation does not contain any reference to the original (or background)

values of travel time or Travel Cost, it is not necessary for the user to know those values

or any other conditions about the original trip. All of that information is “contained” in

the utility value for the original mode share. Thus, the user only needs to inform the

model of the starting mode shares, and the procedure will “pivot” off the point described

by the existing shares and adjust those shares in relation to the magnitude of time or cost

changes introduced by the scenario and the mathematical relationships in the logit

equation.

As indicated in the preceding section, mode shares predicted by a logit model follow an

S-shaped curve relationship between disutility for a mode and the probability of its

choice, or, as shown in Figure 6-2, the Modal Share.

As Figure 6-2 illustrates, a given mode m has a starting share of 10%. A strategy or set of

strategies is applied that reduces the disutility of using mode m by a particular amount,

and this change improves the attractiveness of mode m sufficient to increase its share to

30%. The reader can readily see that if the starting share for mode m were different from

10%, then the same change in disutility would produce a very different outcome in

resulting share. If the starting share were higher, the predicted increase would be greater;

if it were smaller than 10%, the predicted change in share would be less than in the

example.

“Seeding” of a Zero Share Mode - One limiting characteristic of the pivot point approach

occurs when one of the modes being targeted for change has an initial share of “zero.”

This is the case if, for example, transit or vanpool were to be introduced under a
-69-

commuter choice program, but there was no current history of use with that mode. 

Because the pivot point approach requires some initial share value to pivot from, any time
-70-

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Mode 

m Share

Disutility of Mode m

Figure 6-2

Modal Share

or cost changes introduced through a set of strategies would have nothing to pivot from,

and the model would predict no change. 

There are two basic ways of dealing with this situation:

1. A nominal starting share can be assumed, such as 0.1%. While the changes

induced in this share through time or cost incentives will be small, they will at

least register the mode as part of the alternative choice set.

2. The user can first employ one of the Employer Support Strategies (Chapter 4),

which serves to arithmetically add an incremental share to what is already

there. If the starting share is 0% and the user specifies a program that increases

share by 2%, then the starting share for the zero mode becomes 2% for

subsequent application of time or cost strategies when the procedure reaches

the logit pivot-point procedure at the end of the computation process.

If the user does not employ option (2) above, the COMMUTER model has been set up to

enter a default share of 0.1% for modes that enter the logit pivot point process with zero

starting share and that have time or cost strategies targeted at them.
-71-

Coefficients Used in Pivot-Point Logit Model Approach

The weights used in the utility expression of the logit model are coefficients, derived

from statistical analysis of relationships embedded in travel survey data. Generally, these

coefficients are developed from an estimation process using local survey data. Following

the 1990 Census, many (if not most) metropolitan areas conducted new urban travel

surveys for the purpose of updating and upgrading models that had been developed from

data last collected in the 1970s or 1960s. Some areas do not conduct travel surveys and

develop new models from scratch because these are very resource intensive and

mathematically challenging exercises. Rather they will “borrow” models and coefficients

from other areas believed to be similar in character, and “calibrate” the models to their

own situation using modest local information. Areas that have their own existing travel

demand models that were developed from very dated information may opt to simply

update their coefficients from modest new information and “calibrate” those coefficients

to where they replicate local behavior when tested.

The logit pivot point procedure in the COMMUTER model incorporates coefficients for

the following time or cost elements:

Coefficient Strategies Applied to:

In-Vehicle Travel Time

(IVTT) Faster Transit Service

Out-of-Vehicle Time

(OVTT), Walk Time

Walk Access Time for:

- Auto drive alone

- Carpool

- Vanpool

- Transit

- Bicycle

- Pedestrian

Out-of-Vehicle Time

(OVTT), Wait Time Transit Wait (or Transfer) Time

Auto Out-of-Pocket Cost

Parking Cost for:

- Drive Alone

- Carpool

- Vanpool

Transit Cost Transit Fare
-72-

COMMUTER model users are asked to indicate what coefficient values they will use in

their analysis. The model provides for two alternatives:

• Site Specific Coefficients: All users are encouraged to obtain and use the

coefficients that have been specifically developed for their respective area by

the regional planning agency, or MPO. Coefficients have been compiled for a

large number of U.S. metropolitan areas, and have been incorporated into the

COMMUTER model. Users may search the menu for their respective areas,

and if it is included in the list, simply click on the choice to load those

coefficients into the model. Table 6-2 lists the sites that are included in the

COMMUTER model at this time, and the respective values of their

coefficients.

Table 6-2

Logit Mode-Choice Coefficients for Individual Urban Areas

In-Vehicle

Travel

Time (min)

Out-of-Vehicle

Travel Time (min)

Out-of-Pocket Travel

Cost (cents)

Location Area Size Year All Modes

Walk

Time

Transit

Wait

Auto

Parking

Transit

Fare

Albuquerque Small 1992 -0.0209 -0.0219 -0.0978 -0.0031 -0.0031

Boston Large 1991 -0.0165 -0.0330 -0.0550 -0.0173 -0.0083

Chicago Large 1990 -0.0282 -0.0440 -0.0960 -0.0021 -0.0008

Cincinnati Medium 1978 -0.0190 -0.0280 -0.0280 -0.0050 -0.0050

Cleveland Large 1994 -0.0178 -0.0444 -0.0378 -0.0034 -0.0024

Dallas Large 1984 -0.0297 -0.0552 -0.0552 -0.0116 -0.0046

Denver Medium 1985 -0.0180 -0.0540 -0.0282 -0.0095 -0.0044

Detroit Large 1965 -0.0457 -0.0641 -0.1165 -0.0065 -0.0065

Houston Large 1994 -0.0220 -0.0568 -0.0568 -0.0154 -0.0061

Los Angeles Large 1991 -0.0210 -0.0530 -0.0530 -0.0030 -0.0030

Milwaukee Medium 1991 -0.0160 -0.0410 -0.0410 -0.0045 -0.0045

Minneapolis Large 1970 -0.0310 -0.0440 -0.0300 -0.0140 -0.0140

New Orleans Medium 1960 -0.0150 -0.0330 -0.0770 -0.0080 -0.0080

Philadelphia Large 1986 -0.0420 -0.0320 -0.0510 -0.0026 -0.0012

Phoenix Large 1997 -0.0195 -0.0257 -0.0308 -0.0111 -0.0055

Pittsburg Large 1978 -0.0467 -0.0687 -0.0687 -0.0210 -0.0210

Portland Medium 1985 -0.0390 -0.0650 -0.0400 -0.0135 -0.0135

Reno Small 1991 -0.0275 -0.0550 -0.0550 -0.0167 -0.0067

Sacramento Medium 1991 -0.0250 -0.0380 -0.0380 -0.0028 -0.0028

San Francisco Large 1990 -0.0333 -0.0931 -0.0523 -0.0021 -0.0021

San Juan Medium 1990 -0.0366 -0.0717 -0.0752 -0.0066 -0.0066

Santa Cruz Small 1990 -0.0163 -0.0325 -0.0325 -0.0045 -0.0036

St Louis Large 1965 -0.0228 -0.0570 -0.0570 -0.0117 -0.0117

Tucson Small 1993 -0.0180 -0.0400 -0.0400 -0.0018 -0.0018

Wash DC Large 1980 -0.0170 -0.0580 -0.0580 -0.0094 -0.0044
-73-

Source:Coefficients primarily obtained from the sources contained in the Technical Memorandum #1:

Identification of Literature Search for Task 2, and the Synthesis and Analysis of Criteria for Task #3. N/A

is not available. Cambridge Systematics, Inc., 1998.

• Default Coefficients: For users who do not find their city in the menu, a set of

default coefficients has been developed through a synthesis of the cities that are

included. Default “area size” coefficients have been developed for the

following three settings: 

- Large urban areas, with populations greater than 2 million;

- Medium sized urban areas, with populations of 750,000 to 2 million;

and

- Small urban areas, with populations of fewer than 750,000.

These area size default coefficients are listed in Table 6-3.

Table 6-3

Default Coefficient Values

Metropolitan

Area Size

In-Vehicle

Travel Time

(min)

Out-of-Vehicle

Travel Time (min)

Out-of-Pocket

Travel Cost (cents)

All Modes Walk Time

Transit

Wait

AutoParkingTransitFare

Large - over 2,000,000 -0.0281 -0.0521 -0.0584 -0.0094 -0.0065

Med. - 750,000-2,000,000 -0.0241 -0.0472 -0.0468 -0.0071 -0.0064

Small - below 750,000 -0.0207 -0.0373 -0.0563 -0.0065 -0.0038

Source: Cambridge Systematics, Inc., 1998.

Model users reflecting upon the values of the various coefficients will reach the following

conclusion as to which strategies are likely to have the greatest impact. In general, cost

strategies will have a greater impact (per percentage change in existing conditions) than

travel cost. Auto parking costs are known to have major impact on travel choice, and this

is reflected in the coefficient that is higher than any other in the group. Auto parking cost

has almost twice the impact on mode choice as a change in transit fare. Among travel

time elements, strategies that reduce the travel time that occurs “outside” the vehicle, i.e.,

where the user is either walking or waiting to connect with the mode, are generally valued

more than changes that occur to the travel time once the user is “inside” the vehicle. This

is because walking and waiting require more active effort from the traveler, whereas once
-74-

aboard and underway, the traveler is generally seated, comfortable, and able to use the

time for some other purpose. These relationships can be a guide as to where attention

should be placed when designing an effective commuter choice program.

Example Calculation of Change in Time or Cost through Logit Pivot-Point

Model

The following is a simple example to illustrate how the logit pivot-point analysis

procedure would evaluate a typical program that involves strategies that change travel

time and cost. This example is built on the following assumptions:

& Area type and size: This example involves a “large” metropolitan area, and

we are conducting a “regional level” analysis. Because this is a hypothetical

area, the specific model coefficients for the area have not been included in the

model’s menu. As a result, we will use the default coefficients for a large

metropolitan area supplied within the COMMUTER model. The values for

these coefficients are as follows:

IVTT (all modes) = - .0281 min

OVTT (walk, all modes) = - 0.0521 min

OVTT (wait time, transit) = - 0.0584 min

Cost (auto parking) = - 0.0094 cents

Cost (transit fare) = - 0.0065 cents

& Current Mode Shares: The following default modal shares are assumed:

Drive Alone: 75.4%

Carpool: 13.2%

Vanpool: 0.5%

Transit: 5.3%

Bicycle: 0.4%

Walk: 4.0%

Other: 1.1%

& Strategies: The following time and cost related strategies will be included in

the test scenario:

Access Time Changes:

Preferential parking: Drive alone, +2 min.; Carpool, -1 min.; Vanpool, -1 min.

Site Access improvements: Transit: -2 min.; Bike: -2 min.: Walk: -2 min.
-75-

Transit Service Improvements:

More Frequent Service (Less Wait Time): Transit, -2.5 minutes

Faster Service (Less Travel Time): Transit, -5.0 min.

Travel Cost Changes:

Parking Charges: Drive Alone, +$1; Carpool, $0; Vanpool, -$2

Transit Subsidy: Transit, -$1 per day

The first step taken by the model will be to recompute the change in utilities for each

mode [-U(m)], as follows:

Drive Alone:

-UDA = - 0.0521 (OVTTWalkDA) - 0.0094 (Parking CostDA)

= - 0.0521 × (2 min) - 0.0094 × ($1)× 100 cents/$ = -1.0442

Carpool:

-UCP = - 0.0521 (OVTTWalkCP) - 0.0094 (Parking CostCP)

= - 0.0521 × (-1 min) - 0.0094 × ($0) × 100 cents/$ = 0.0521

Vanpool:

-UVP = - 0.0521 (OVTTWalkVP) - 0.0094 (Parking CostVP)

= - 0.0521 × (-1 min) - 0.0094 × ($-2) × 100 cents/$ = 1.9321

Transit:

-UTR = - 0.0281 (IVTTTR/2) - 0.0521 (OVTTWalkTR) - 

 0.0584 (OVTTWaitTR/2) - 0.0065 (Fare CostTR)

= - 0.0281 × (-5 min/2) - 0.0521 × (-2 min) - 

 0.0584 × (-2.5 min/2) - 0.0065 × ($-1) × 100 cents/$ = 0.8975

(Note that the changes in transit service frequency and time are divided by two

to reflect two-way commute trips being applied to daily coefficients.)

Bicycle:

-UBK = - 0.0521 (OVTTWalkBK) 

= - 0.0521 × (-2 min) = 0.1042

Walk:

-UWK = - 0.0521 (OVTTWalkWK)
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= - 0.0521 × (-2 min) = 0.1042

Next, revised mode shares are computed using equation (1):

P’DA = [PDA × exp (-UDA)] / {[(exp (-UDA) - 1) × PDA] + 1}

= [0.754 × e -1.0442] / {[(e -1.0442 - 1) × 0.754] + 1}

= 0.2655 / 0.4460 = 0.5953 = 59.53%

P’CP = [PCP × exp (-UCP)] / {[(exp (-UCP) - 1) × PCP] + 1}

= [0.132 × e +0.0521] / {[(e +0.0521 - 1) × 0.132] + 1}

= 0.1393 / 0.8862 = 0.1572 = 15.72%

P’VP = [PVP × exp (-UVP)] / {[(exp (-UVP) - 1) × PVP] + 1}

= [0.005 × e +1.9321] / {[(e +1.9321 - 1) × 0.005] + 1}

= 0.0345 / 0.9952 = 0.0347 = 3.47%

P’TR = [PTR × exp (-UTR)] / {[(exp (-UTR) - 1) × PTR] + 1}

= [0.053 × e +0.8975] / {[(e +0.8975 - 1) × 0.053] + 1}

= 0.1308 / 0.9537 = 0.1371 = 13.71%

P’BK = [PBK × exp (-UBK)] / {[(exp (-UBK) - 1) × PBK] + 1}

= [0.004 × e +0.1042] / {[(e +0.1042 - 1) × 0.004] + 1}

= 0.0044 / 0.9960 = 0.0045 = 0.45%

P’WK = [PWK × exp (-UWK)] / {[(exp (-UWK) - 1) × PWK] + 1}

= [0.040 × e +0.1042] / {[(e +0.1042 - 1) × 0.040] + 1}

= 0.0446 / 0.9616 = 0.0464 = 4.64%

(Note that the revised share for the other (e.g., telecommute) mode is still 1.1% since

it is unaffected by these specific strategies.)

Final mode shares are then determined by normalizing the revised shares (to total 100%):

 Mode Revised Normalization Final 

Drive Alone 59.67% 0.5953 ÷ 0.9861 = 60.4%

Carpool 15.72% 0.1572 ÷ 0.9861 = 15.9%

Vanpool 3.47% 0.0347 ÷ 0.9861 = 3.5%

Transit 13.71% 0.1371 ÷ 0.9861 = 13.9%

Bicycle 0.45% 0.0045 ÷ 0.9861 = 0.5%

Walk 4.64% 0.0464 ÷ 0.9861 = 4.7%

Other 1.10% 0.0110 ÷ 0.9861 = 1.1%

Total 98.61% 100.0%
-77-

###
-78-

Alternative

Work

Schedules

Shift of Trips

From Peak

to Off-Peak

Calculate

Change in

Off-Peak

Period VMT

Calculate

Change in

Peak Period

VMT

Calculate

Change in

Off-Peak

Period

Emissions

Calculate

Change in

Peak Period

Emissions

Employer

Support

Programs

Travel

Time/Cost

Strategies

Change in

Vehicle

Trips

Figure 7-1

Translation of Travel Demand Changes into

Emissions Reductions

7. Converting Travel Impacts to Emissions Reductions

Overview of Procedure

Once the basic travel impacts for a given set of strategies have been calculated as

described in Chapters 4, 5, and 6, the COMMUTER model then proceeds to estimate the

corresponding change in emissions. This procedure is illustrated in Figure 7-1.
-79-

The changes in travel result from application of Employer Support Programs, Travel

Time or Cost Strategies, and Alternative Work Schedules. As pictured in Figure 7-1, the

first of these two strategy groups produces changes in overall trip making (i.e., number of

vehicle trips), while the Alternative Work Schedules strategies produce changes in the

time that the trips occur, namely in the Peak or Off-Peak.

The change in modal split induced by the Employer Support Programs and the Time/Cost

Strategies is mated with information about average modal trip lengths, and used to

calculate the change in vehicle miles of travel (VMT). In combination with the

information on the proportion of trips shifted from peak to off-peak, the COMMUTER

model then calculates the change in VMT in both peak and off-peak periods.

Using the change in VMT for the two time periods, the COMMUTER model then applies

per-mile vehicle emissions rates (or factors) to these VMT estimates to produce an

estimate of the change in emissions due to the particular analysis scenario. Emissions

changes are calculated for hydrocarbons (HC), oxides of nitrogen (NOx), and carbon

monoxide (CO).

The procedures and assumptions associated with each of these steps are presented in the

sections below.

Changes in Total Trips

Once final mode shares have been calculated based on application of the lookup table or

logit pivot-point model techniques, these mode shares are used to estimate changes in

vehicle-trips and VMT. The model calculates the percentage of persons who will travel

by each mode. For example, assume that the affected travel population consists of 5,000

employees, and the starting mode shares are as follows:

Drive Alone: 75.4%

Carpool: 13.2%

Vanpool: 0.5%

Transit: 5.3%

Bicycle: 0.4%

Walk: 4.0%

Other: 1.1%

100%

Then 5,000 x 0.754 = 3,770 people would drive alone, 660 would travel by carpool, 25

would travel by vanpool, etc. To determine the number of vehicle trips represented by

this population’s modal distribution, it is necessary to divide each vehicular mode by its

passenger occupancy. Only the drive alone, carpool, and vanpool modes are assumed to

generate vehicle trips in this computation; transit, bicycle, walking and other are not
-80-

associated with making of a private motor vehicle trip. Passenger occupancy for drive

alone is, of course, 1.0. Default values of 2.2 persons are assumed for carpools, and 6.0

persons for vanpools. The user has the option of changing these assumptions if better

information is available locally. 

The vehicle trips generated by the example population in relation to the starting mode

shares would be as follows:

Drive Alone: 3,770 ÷ 1.0 persons = 3,770 vehicle trips

Carpool: 660 ÷ 2.2 persons = 300 vehicle trips

Vanpool: 25 ÷ 6.0 persons = 4.2 vehicle trips

for a total of 4,074 vehicle trips being generated by this population of 5,000 employees. 

Whether these are regarded as only one-way trips or daily round trips depends on how the

analyst has defined them in the database. In the COMMUTER model these would be

daily round trips, meaning that it would be necessary to multiply 4,074 vehicle round trips

by 2, to yield 8,148 daily vehicle trips.

The same process is then applied to the new mode shares to arrive at the change in

vehicle trips produced by the test scenario. To illustrate, assume that the test scenario

brought about the following new pattern of mode shares:

Drive Alone: 72%

Carpool: 15%

Vanpool 2%

Transit: 6%

Bicycle: 1.5%

Walk: 7%

Other: 0.5%

100%

The new vehicle trip total generated by this population would be calculated as follows:

5,000 persons [ (0.72)/(1.0)DA + (0.15)/(2.2)CP + (0.02)/(6.0)VP] × 2

or 7,916 vehicle daily trips

So the tested scenario in this case would reduce vehicle trips by 8,148 – 7,916 = 232 trips.
-81-

Changes in Total VMT

To convert changes in trips to changes in VMT, the COMMUTER model applies

information on average trip length for each of the modes to the modal split for both

existing and new conditions. An average trip length is specified in the model for each

mode. While the user is invited to supply local values where they are available, the

following defaults have been coded into the model:

Drive Alone: 11.85 miles

Carpool: 12.21 miles

Vanpool: 17.70 miles

Transit: 11.42 miles

Bicycle: 1.80 miles

Walk: 1.00 miles

Other: 11.42 miles

Average 11.42 miles

The COMMUTER model would calculate baseline VMT for the 5,000 employee example

used above as follows:

Total VMT = [ (3,770 DA trips) x (11.85 miles) + (300 CP trips) x (12.21 miles)

+ (4.2 VP trips) x (17.70 miles) ] x 2 trips per day

= 96,824 daily VMT

When a test scenario has been applied that changes mode split, a slightly different

procedure is used to calculate the new VMT. The reason for this is the assumption that,

when travelers are taken from the drive alone mode and shifted to a new mode, they also

assume the trip length of that new mode. Hence the trips diverted from drive alone are

assumed to reduce the cumulative VMT for drive alone by the amount of the new mode’s

trip length. Assume, for example, that an auto driver shifted to vanpool. The average trip

length for a vanpool trip is 17.7 miles, compared to 11.85 for the drive alone. So the

shifted trip in this case would deduct 11.85 daily drive alone VMT, and add 17.7 miles

divided by 6.0 occupants, or 2.95 miles after switching to vanpool, for a net change of

–8.9 miles.

To effect this debit and credit accounting for VMT based on which modes are exchanging

riders, the following rules guide the analysis:

1. The overall average person-trip length for all individuals making a work-trip

remains the same (i.e., no one changes the location of their workplace or

residence as a result of the implemented programs).
-82-

2. Average person trip-lengths for all modes except drive-alone remain fixed

before and after the program. This means, for example, that those who shift

from driving alone to bicycling have shorter-than-average trip lengths, while

those who shift from driving alone to vanpooling have longer-than-average

trip lengths.

3. Trips eliminated due to telecommuting or compressed work weeks are

assumed to be the same length as the overall average person-trip, so that

elimination of these trips does not change the average person or motor-vehicle

trip length. (This conservative approach assumes that the average trip lengths

will remain the same, even with a reduction in the number of automobiles

using the transportation system as a result of increased telecommuting or

compressed work weeks.) 

4. The average person-trip length for drive-alone commuters is then re-computed

so that the overall average person-trip length is the same as it was before

implementation of the programs. 

An illustration of how this calculation would be performed is shown in the following

table using the previous 5,000 employee example.

Mode

Starting

Share

Average

Trip Length

Daily

VMT

New Mode

Share

New Avg.

Trip Length

New Daily

VMT

D. Alone 75.4% 11.85 89,349 72% 11.66 83,952

Carpool 13.2% 12.21 7,326 15% unchanged 8,325

Vanpool 0.5% 17.70 149 2% unchanged 590

Transit 5.3% 11.42 0 6% unchanged 0

Bike 0.4% 1.80 0 1.5% unchanged 0

Walk 4.0% 1.00 0 7% unchanged 0

Other 1.1% 11.42 0 0.5% unchanged 0

Total 100% 11.42 96,824 100% unchanged 92,867

The baseline mode shares combined with the baseline average trip lengths produce

96,824 total daily VMT. Under the test scenario, the mode shares are changed as shown

in column 5. To calculate the associated new VMT, a new average trip length must be

calculated for drive alone. It is assumed that all other modes retain their original average

trip length and that overall average trip length remains at 11.42 miles, so that any changes

in individual trip length are assumed to occur in the drive alone mode. To calculate the
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adjusted drive alone trip length, the new shares in column 5 are used to weight the

original trip lengths (for all modes except drive alone), and then the new drive alone trip

length that preserves the original 11.42 mile overall trip length is calculated by solving

the following equation for ATLDA:

(0.72 x ATLDA) + (0.15 x 12.21) + (0.02 x 17.7) + (0.06 x 11.42) + (0.015 x 1.8) +

(0.07 x 1.0) + (0.005 x 11.42) = 11.42

Solving for ATLDA yields 11.66 miles for the revised drive alone trip length. Using these

new shares and average trip lengths, the daily VMT resulting from the new scenario is

calculated as 92,867.

As an additional input to the emission calculations, average motor-vehicle trip lengths

(existing and new) must be also be computed. The average motor-vehicle trip length

(ATLMotor Vehicle ) is simply the average of trip lengths for the drive-alone, carpool, and

vanpool modes, weighted by the mode shares for each mode. For the example above,

these values would be calculated as follows:

Baseline ATLMotor Vehicle = (75.4% x 11.85) + (13.2% x 12.21) + (0.5% x 17.70)

 = 10.64 miles

Post-Scenario ATLMotor Vehicle = (72% x 11.66) + (15% x 12.21) + (2.0% x 17.70)

 = 10.58 miles 

Allocation Between Peak Period and Off-Peak

Once the changes in vehicle trips and VMT due to the tested scenario have been

computed, the next step before calculating the emissions impacts is to reallocate the trips

and VMT between the peak and off-peak periods. This adjustment would be necessary

only if the scenario had also included Alternative Work Schedule strategies that affected

the timing of trips. Specifically, this would be the result of applying Flexible Work

Hours or Staggered Work Hours strategies.

The COMMUTER model begins with an initial allocation of trips between the peak and

off-peak periods. This is done by first computing the number of vehicle trips and VMT

for the “affected employment” using the methods above, and then using information

provided by the user on the percentage of daily commute trips that occur in the peak

period. This factor, appearing on the Local Data screen of the model as Percent of Work
*

 1995 Nationwide Personal Transportation Survey, Federal Highway Administration.

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Trips in Peak Period, is supplied with a default computed from national data of 61.7%.*

The user is encouraged to supply local information for this value if available.

When Flexible Work Hours or Staggered Work Hours strategies are applied in a program

scenario, the COMMUTER model estimates the percentage of trips that would shift the

time of travel from the peak to the off-peak periods (process discussed in detail in

Chapter 5). As part of this calculation, the COMMUTER model also takes into account

the length of the peak period. For areas where the peak period lasts the “standard” three

hours, the model predicts that 13.9% of the eligible employee trips will actually be shifted

“outside” the three-hour peak period. If the peak period is shorter, say two hours, a

higher percentage (28.7%) of trips will be shifted, whereas if the peak is unusually long,

say four hours, then only 8.5% will be shifted. The user is asked to supply the model

with the length of the local peak period, if it is different from the 3.0 hour default that is

provided in the model. The information in Table 5-1 is repeated below in Table 7-1 to

illustrate the relationship between length of peak period and percent of trips shifted that is

used in the model.

Table 7-1

Percent of Trips Shifted by Length of Peak Period

Length of Peak Period (hrs) Percent of Trips Shifted

2.0 28.7

2.5 19.2

3.0 13.9

3.5 10.6

4.0 8.5

4.5 7.1

5.0 6.0

Source: Estimating the Effect of Alternative Work Schedules on Travel

Activity and Emissions, FHWA TDM Evaluation Model, 1993

The sample illustration is used below to provide an example of how the COMMUTER

model would perform an allocation of trips between peak and off-peak based on

Alternative Work Schedule programs and local information about the duration of the peak

period and the current share of commute trips occurring in the peak. The calculation

process is described below.
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The model first determines the distribution of baseline trips that occur in the peak and

off-peak periods. The user has selected the default of 61.7% to represent the percentage

of these trips that are occurring in the peak and off-peak periods:

Peak-Period Trips = 5,000 person trips x 0.617 = 3,085

Off-Peak Trips = 5,000 person trips x 0.383 = 1,915

(Note that these are daily round trips; if the individual home-to-work and return

work trips are to be separated, the basis would increase to 10,000 one-way trips. 

Care should be taken when calculating VMT that the basis is consistent with

whether the user is assuming daily or one-way VMT and emissions.)

Assuming the same baseline mode shares as previously, this would produce the following

peak and off-peak vehicle trip and VMT allocation:

Mode

Starting

Share

Daily

Vehicle

Trips

Daily

VMT

Daily Peak

Vehicle

Trips

Daily

Peak

VMT

Daily OffPeak

Vehicle

Trips

Daily OffPeak VMT

D. Alone 75.4% 7,540 89,349 4,652 55,128 2,888 34,220

Carpool 13.2% 600 7,326 370 4,520 230 2,805

Vanpool 0.5% 8.4 149 5.2 92 3.2 57

Transit 5.3% 0 0 0 0 0 0

Bike 0.4% 0 0 0 0 0 0

Walk 4.0% 0 0 0 0 0 0

Other 1.1% 0 0 0 0 0 0

Total 100% 8,148 96,824 5,027 59,740 3,121 37,083

When the hypothetical scenario is then applied that changes the mode shares, the table

above changes to the following:

Mode

Revised

Mode

Shares

Daily

Vehicle

Trips

Daily

VMT

Daily Peak

Vehicle

Trips

Daily

Peak

VMT

Daily OffPeak

Vehicle

Trips

Daily OffPeak VMT

D. Alone 72% 7,200 83,952 4,442 55,128 2,758 34,220

Carpool 15% 681 8,325 420 4,520 261 2,805
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Vanpool 2% 16.7 590 10.3 92 6.4 57

Transit 6% 0 0 0 0 0 0

Bike 1.5% 0 0 0 0 0 0

Walk 7% 0 0 0 0 0 0

Other 0.5% 0 0 0 0 0 0

Total 100% 7,898 92,867 4,872 57,299 3,026 35,568

Now if the test scenario applies a Flexible Work Hours program strategy that assumes

that 30% of all employees will be eligible for the program, the following impacts would

be calculated (and assuming zero existing eligibility):

First, the model calculates the number of employees who would be affected as:

5,000 employees x 61.7% in peak x 80% office x 30% eligible

= 740 employee trips eligible for shifting

Next, the model determines how many trips may be shifted outside the peak

period based on the length of the peak period. In this example, it is assumed that

the peak period has a 3.0 hour duration. Hence the model would assume that

13.9% of the eligible trips would actually shift to the off-peak. Coupled with the

default participation rate of 22%, the example yields:

740 person trips x 13.9% x 22% = 23 person trips shifted from peak to offpeak 

 (23 x 2 = 46 per day)

Those person trips would be presumed to shift from peak to off-peak in the same

proportion as the modal shares for all affected employees as engineered by the test

scenario. In other words, the person trips shifted would be at the rate of 72%

drive alone, 15% carpool, 2% vanpool, 6% transit, 1.5% bicycle, 7% walk, and

0.5% other. Thus, for each person trip shifted, the model in this case would shift:

7,898/10,000 = 0.7898 vehicle trips

and

92,867/10,000 = 9.29 VMT

So if the Flexible Work Hours strategy tested would shift 23 daily person trips, the

model would remove 23 x (2 directions) x 0.7898 = 36 vehicle trips from the peak

period(s) and move them to the off-peak, and similarly shift 23 x 2 x 9.29 = 427

daily VMT from the peak to the off peak.
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PreScenario ChangePost

Scenario

Daily Vehicle Trips, Peak 4,872 - 36 4,836

Daily VMT, Peak 57,299 - 427 56,872

Daily Vehicle Trips, Off-Peak 3,026 +36 3,062

Daily VMT, Off-Peak 35,568 +427 35,995

This would be the new distribution of vehicle trips and VMT by time of day that would

be taken forward into the emissions analysis.

Emission Impacts Methodology

The COMMUTER model uses the estimates of travel changes described above as the

means for computing corresponding emission reductions. 

In a full analysis of travel and emissions impacts such as would occur in the regional

planning process when demonstrating SIP conformity, the detailed computation of travel

changes and their effect on overall regional trips, VMT, and travel speeds would be

processed through the accepted emissions model. In most areas, the MOBILE5b

emissions model would be used; in California, the EMFAC equivalent model would be

used.

As with the traditional transportation analysis procedure that the COMMUTER model

simplifies and replaces for this purpose, so too is the MOBILE emissions model a fairly

time and data intensive analysis process. As a result, a simplified process has also been

developed and incorporated within the COMMUTER model to be consistent in detail and

accuracy with the transportation methods being employed, and maintaining the desired

application ease for the user.

The computed changes in travel activity from baseline levels are coupled with

MOBILE5b-based emission factors to generate daily emission reduction estimates for

hydrocarbons (HC or VOCs), NOx, and CO. These reductions are computed as tons per

day changes in each respective pollutant, and are computed and reported separately for

peak and off-peak travel situations. This section explains the basic emissions modeling

methodology that is used in the COMMUTER model. However, the complexity of the

calculations in this process makes it impractical to provide easy-to-use, manual

procedures for generating emission reduction estimates. Beyond this summary, the user

is referred to the COMMUTER model itself to generate these estimates.

MOBILE5b Emission Factors - To eliminate the need for the user to supply emission

factors representing local fleet conditions in the analysis area of interest, an extensive
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series of MOBILE5b emission factors were generated to represent a variety of local

conditions and loaded into the COMMUTER model. A total of 144 separate sets of

emission factors were generated representing combinations of the following fleet

parameters:

& Calendar year (6) - 1998, 2000, 2002, 2005, 2010, and 2020;

& Season (2) - summer and winter;

& I/M program type (3) - no I/M, basic I/M, and enhanced I/M;

& Fuel volatility class (2) - Classes B and C (corresponding to VOC Control

Regions 1 and 2, respectively); and

& Fuel control program (3) - conventional gasoline, reformulated gasoline, or

wintertime oxygenated gasoline.

The ambient temperature profiles used in these MOBILE5b runs were established as a

function of season and fuel volatility class and were consistent with the profiles used by

EPA in its reformulated fuel rulemaking analysis using the Complex Model as follows:

& Summer, Class B - 69°F to 94°F;

& Summer, Class C - 72°F to 92°F; and

& Winter, either class - 39°F to 57°F.

Fuel volatility (i.e., RVP) inputs in these MOBILE5b runs were also established in a

manner consistent with EPA’s assumptions by season and VOC Control Region. For the

reformulated gasoline (RFG) runs, Phase I RFG was modeled in calendar year 1998,

Phase II was applied in 2000 and later calendar years. 

Emission factors within each set were generated for 13 speeds from 5 to 65 mph at 5 mph

increments. (As explained later, they were used to model the speed impacts of TCMs on

emissions.)

Separate operating mode fractions were also specified for each set of emission factors in a

manner in which stabilized and starting modes could be treated separately and combined

with user inputs of cold start percentages and trip length (trip length is calculated

internally within the COMMUTER model). Stabilized emission factors representing the

operating mode when a vehicle’s engine and catalyst are fully warmed up are represented
* In earlier versions of the MOBILE and EMFAC emission factors models, Bag 2 is often referred to as

that which represents stabilized emissions; however, this is not correct. Although Bag 2 is referred to as

the “stabilized” bag, the stabilized portion of the FTP is actually represented by the combination of bags 2

and 3. This has been corrected in the California Air Resources Board’s latest emission factor model,

MVEI7G, and a similar correction will be implemented in EPA’s upcoming MOBILE6 model. 

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by the combination of Bags 2 and 3 of the Federal Test Procedure (FTP).* In MOBILE5b

parlance, operating mode fractions representing true stabilized emission factors were set

to 0% cold start, 47.9% hot start, and 52.1% “stabilized.” Cold start “offset” emission

factors are represented by Bag 1 minus Bag 3 of the FTP and were used to model a tripbased emissions increment that occurs when the vehicle starts cold. 

Evaporative running loss, hot soak, and crankcase emission factors generated by

MOBILE5b for VOC were also loaded into the program. Although MOBILE5b also

produces diurnal and resting loss evaporative emission factors, they were not used in the

COMMUTER model calculations. These types of emission occur when the vehicle is at

rest. Since it is difficult to accurately estimate changes in vehicle “parked” conditions as

a result of TCMs and since these emission impacts are secondary-level effects, they were

not modeled in the COMMUTER model. 

Emission Reduction Calculation - Separate calculations were performed for each of the

two activity parameters modeled: VMT and trips. Emission reductions from each of

these travel impacts were then added together to produce combined emission reduction

impacts from changes in travel due to the TCMs evaluated. Each of these calculations,

described below, was performed for both the peak and off-peak periods.

First, reductions in VMT and vehicle trips due to the TCM strategies are computed by

subtracting existing activity levels from the “final” (after-TCM) activity levels. Note that

under a regional analysis, the existing activity levels used in the COMMUTER model are

not the same as the baseline activity for the entire region. The existing activity in the

model is based on affected employment, not total population.

Second, the regional speed inputs were used to linearly interpolate the stabilized emission

factors by speed increment to represent these specific speeds. VMT reductions were then

applied to the stabilized emission factors at the appropriate speed to compute VOC, CO,

and NOx emission reductions due to changes in VMT.

Third, the number of existing and final vehicle trips were multiplied by the cold start

percentages under existing and final conditions to estimate the number of cold trips for

which to apply the cold start offset. The cold start offset factors were then multiplied by 

the number of baseline and after-TCM cold trips to produce baseline and after-TCM

starting exhaust emissions of VOC, CO, and NOx. The number of total existing and final

trips (whether they started cold) were multiplied by the evaporative (VOC) hot soak and

crankcase emission factors to generate baseline and after-TCM emissions from these

evaporative modes. Emission reductions from trip changes were then determined by

subtracting these baseline emissions from the after-TCM emissions.
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Finally, the VMT- and trip-based emission reductions were summed together. Daily

reductions were also summed from peak and off-peak reductions.

###
Appendix A

Definition of Modeling Terms
A-1

Definition of Terms

Sources for Definition of Terms:

1. Manual of Regional Transportation Modeling Practice, 1993.

2. TDM Evaluation Model Documentation, 1993.

Affected Employment – Affected employment considers the amount of employees that

will be impacted by a particular commuter choice support program in a given analysis

area for both office and non-office categories. In the analysis presented in this Manual,

the effectiveness of some programs is assumed to differ for these two employment

categories. Total employment affected is also needed to calculate changes in total trips

and vehicle-miles of travel (VMT).

Destination – The geographic area in which any trip terminates.

Elasticity – In a causal relationship, the elasticity of i with respect to j is the percent

change in variable i with respect to the percent change in variable j.

Functional Classification – The classification of urban and rural roadways by function. 

Roadways at the top of the hierarchy (freeways) serve intercity and other long distance

traffic movements, roadways at the bottom (local roads) provide access to land.

Home to Work Trip – A person or vehicle trip that begins either at home or work

(including one trip end at work and the other at home).

HPMS – Highway Performance Monitoring System, a federally-mandated database

consisting of a representative sample of highway roadways by functional classification.

Level of Program Effort - Research has shown that the impact of employer support and

incentive programs and strategies vary according to the level of effort expended on the

program. The level of program effort is a particularly important dimension as it defines

what is actually meant by, for example, a carpool support program. This is an important

distinction in the analysis presented in this Manual. The program level will vary from 0

to 4, 0 representing no program and 4 representing a program of maximum effort. These

program levels are defined separately for carpooling, vanpooling, transit, and bicycle

commuting. Shown below is an example of the program level definitions for a carpool

support program. (Also, see the VMEPTool User Manual for program level examples

for vanpool, transit, and bicycle support programs).

• Level 0 – no program.

• Level 1 – includes carpool information activities (tied in with area-wide

matching) and a quarter time transportation coordinator.
A-2

• Level 2 – includes an in-house carpool matching service and/or personalized

carpool candidate get-togethers (including information activities) and a quarter

time transportation coordinator.

• Level 3 - includes in-house carpool matching and information services plus

preferential (reserved, inside, and/or especially convenient) parking for

carpools, a policy of flexible work schedules to accommodate carpools, and a

half time transportation coordinator.

• Level 4 - includes in-house carpool matching and information services plus

preferential parking for carpools, flexible work schedules, and a full time

transportation coordinator.

Logit – A choice model formulation based on the principle that individuals maximize

utility in choosing among available alternatives. The logit formulation involves

specifying a utility function for each individual, with a deterministic component (that is,

one which depends on characteristics of the individual and of the alternatives) and a

stochastic disturbance (or error term). The logit model, follows the assumption that the

error terms are independent and share the same probability distribution. Most

transportation demand model systems use a logit formulation for mode choice.

Mode Share – Mode shares represent the percentage of person and vehicle travel by type

of transport. Typically, mode shares are measured by drive alone (single occupant

vehicles), high occupancy vehicles (carpooling, vanpooling, and other ridesharing

activity), transit (bus, rail), walking, and bicycling modes of transport.

Off-Peak Period – Occurring during periods of relatively low traffic, not during a peak

period. Trip making in this period typically represents mid-day, non-commute conditions

occurring between 9 a.m. and 3 p.m. 

Origin – The geographic location at which a person or vehicle trip begins.

Peak Hour – The hour during which the maximum traffic occurs within the defined peak

period(s). The peak hour during which traffic is highest varies by roadway functional

classification and place to place (i.e., origin-to-destination).

Peak Period – Whether categorized by purpose or by geographic area, person or vehicle

trips occur at different rates at different times of the day. A graph of trips by time of day

typically reveals one or more peaks. These peaks play a key role in conventional travel

demand analysis, which focuses on maximum infrastructure need in each corridor. The

dominant weekday peaks are in the morning (AM Peak) and the late afternoon (PM

Peak), obviously related to the timing of home to work (commute) trips. A peak can be

characterized by its maximum trip rate (in trips per unit of time) or by duration over

which some threshold trip rate is maintained. The portions of the peak before and after

the peak hour are called shoulders of the peak. 
A-3

Peak Spreading – Lengthening of the peak period, usually accompanied by a flattening of

the peak period(s).

Person Trip – The movement of a person from an origin to a destination, as opposed to

the vehicle trip associated with the same origin-to-destination movement. A carpool

carrying three people from origin-to-destination has made one vehicle trip, its occupants

together have made three person trips.

Soft Programs – “Soft Programs” represent employer support and incentive programs

such as site-specific employer programs. These strategies include on-site transit pass

sales, rideshare matching, and guaranteed ride home programs. 

Trip End – The number of vehicle or person trips ending at a given origin or destination

location.

Trip Frequency – The number of trips per unit of time.

Utility – In transportation modeling, the value (positive or negative) of a particular option,

usually estimated as a function of the travel option’s characteristics as well as traveler or

population characteristics.

Vehicle Miles of Travel (VMT) – The total usage of vehicles in miles traveling on the

transportation system for a given condition – metropolitan area, corridor, facility types

(freeways, arterials), or modes of transportation.

Vehicle Occupancy – For a specific group of travelers, e.g., morning peak workers, this

represents the ratio of person trips to vehicle trips. It is often used as a criterion in

judging the success of trip reduction programs.

Vehicle Trip – An origin-to-destination journey by a single vehicle, as opposed to a

person trip, the origin-to-destination journey of an occupant of the vehicle. A bus

carrying 40 people from an origin to a destination makes one vehicle trip, while its

occupants make a total of 40 person trips.