Office of Transportation EPA420-B-05-018 

and Air Quality October 2005 

Procedures Manual for the 

COMMUTER Model v2.0 
EPA420-B-05-018 

October 2005 

Procedures Manual for the

COMMUTER Model v2.0

Transportation and Regional Programs Division 

Office of Transportation and Air Quality 

U.S. Environmental Protection Agency 

Prepared for EPA by 

J. Richard Kuzmyak 

Thomas R. Carlson

Robert G. Dulla

Sierra Research, Inc.

Stephen D. Decker

Christopher D. Porter

Erin E. Vaca

Cambridge Systematics, Inc.

NOTICE 

This technical report does not necessarily represent final EPA decisions or positions.

It is intended to present technical analysis of issues using data thatC are currently available.

The purpose in the release of such reports is to facilitate the exchange of

technical information and to inform the public of technical developments which

may form the basis for a final EPA decision, position, or regulatory action.
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 ......................................... 4 
 

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 ............... 12 
 

Emissions Calculations ............................................... 20 
 

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

3. Assembling Required Background Information ............................. 22 
 

Overview .......................................................... 22 
 

Required Model Inputs ............................................... 24 
 

4. Estimating Travel Impacts of Employer Support Programs .................... 34 
 

Overview of Employer Support Strategies ................................ 34 
 

Analytic Approach for Estimating Travel Impacts .......................... 36 
 

5. Estimating Travel Impacts of 
 

Alternative Work Schedules ........................................... 45 
 

Overview of Alternative Work Schedule Programs ......................... 45 
 

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

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

Multiple Options .................................................... 54 
 

6. Estimating Travel Impacts of Travel Time and Cost Changes .................. 56 
 

Overview of Travel Time and Travel Cost Strategies ........................ 56 
 

Types of Time/Cost Strategies Covered by COMMUTER Model .............. 59 
 

Analytic Approach for Estimating Travel Impacts .......................... 61 
 

Coefficients Used in Pivot-Point Logit Model Approach ..................... 66 
 

............................................................... 69 
 

Example Calculation of Change in Time or Cost through Logit Pivot-Point Model
 

7. Converting Travel Impacts to Emissions Reductions ......................... 73 
 

Overview of Procedure ............................................... 73 
 

Changes in Total Trips ................................................ 74 
 

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Changes in Total VMT ............................................... 76 
 

Allocation Between Peak Period and Off-Peak ............................. 79 
 

Emission Impacts Methodology ........................................ 83 
 

Appendix A 

Definition of Modeling Terms .......................................... 88 
 

Appendix B
 

Fuel Parameters for Default Emissions Factors ........................ A-4 
 

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List of Tables 

page 

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

2. Overview of the COMMUTER Model
 

Analysis Procedure ................................................... 4 
 

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

Conventional Analysis Methods ......................................... 4 
 

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 ............... 12 
 

Emissions Calculations ............................................... 20 
 

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

3. Assembling Required Background Information ............................. 22 
 

Overview .......................................................... 22 
 

Required Model Inputs ............................................... 24 
 

4. Estimating Travel Impacts of Employer Support Programs .................... 34 
 

Overview of Employer Support Strategies ................................ 34 
 

Analytic Approach for Estimating Travel Impacts .......................... 36 
 

5. Estimating Travel Impacts of 
 

Alternative Work Schedules ........................................... 45 
 

Overview of Alternative Work Schedule Programs ......................... 45 
 

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

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

Multiple Options .................................................... 54 
 

6. Estimating Travel Impacts of Travel Time and Cost Changes .................. 56 
 

Overview of Travel Time and Travel Cost Strategies ........................ 56 
 

Types of Time/Cost Strategies Covered by COMMUTER Model .............. 59 
 

Analytic Approach for Estimating Travel Impacts .......................... 61 
 

Coefficients Used in Pivot-Point Logit Model Approach ..................... 66 
 

............................................................... 69 
 

Example Calculation of Change in Time or Cost through Logit Pivot-Point Model
 

7. Converting Travel Impacts to Emissions Reductions ......................... 73 
 

Overview of Procedure ............................................... 73 
 

Changes in Total Trips ................................................ 74 
 

Changes in Total VMT ............................................... 76 
 

Allocation Between Peak Period and Off-Peak ............................. 79 
 

Emission Impacts Methodology ........................................ 83 
 

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Appendix A 

Definition of Modeling Terms .......................................... 88 
 

Appendix B
 

Fuel Parameters for Default Emissions Factors ........................ A-4 
 

Table 3-1 Default Values Provided in the COMMUTER Model ................. 25 
 

Table 3-2 Definition of Office and Non-Office Employment .................... 28 
 

Table 3-3 Default Shares for a Standard Regional Analysis ..................... 30 
 

Table 4-1 Popular Employer Support Strategies to Encourage Specific Modes
 

of Travel ..................................................... 35 
 

Table 4-2 Increase in Percent Using Model by Support Program Level ............ 37 
 

Table 4-3 Composition of Modal Support Strategy Programs .................... 38 
 

Table 5-1 Percent of Trips Shifted by Length of Peak Period .................... 48 
 

Table 6-2 Logit Mode-Choice Coefficients for Individual Urban Areas ............ 67 
 

Table 6-3 Default Coefficient Values ...................................... 68 
 

Table 7-1 Percent of Trips Shifted by Length of Peak Period .................... 80 
 

List of Figures 

page 

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

2. Overview of the COMMUTER Model
 

Analysis Procedure ................................................... 4 
 

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

Conventional Analysis Methods ......................................... 4 
 

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 ............... 12 
 

Emissions Calculations ............................................... 20 
 

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

3. Assembling Required Background Information ............................. 22 
 

Overview .......................................................... 22 
 

Required Model Inputs ............................................... 24 
 

4. Estimating Travel Impacts of Employer Support Programs .................... 34 
 

Overview of Employer Support Strategies ................................ 34 
 

Analytic Approach for Estimating Travel Impacts .......................... 36 
 

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5. Estimating Travel Impacts of 
 

Alternative Work Schedules ........................................... 45 
 

Overview of Alternative Work Schedule Programs ......................... 45 
 

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

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

Multiple Options .................................................... 54 
 

6. Estimating Travel Impacts of Travel Time and Cost Changes .................. 56 
 

Overview of Travel Time and Travel Cost Strategies ........................ 56 
 

Types of Time/Cost Strategies Covered by COMMUTER Model .............. 59 
 

Analytic Approach for Estimating Travel Impacts .......................... 61 
 

Coefficients Used in Pivot-Point Logit Model Approach ..................... 66 
 

Example Calculation of Change in Time or Cost through Logit Pivot-Point Model
 

............................................................... 69 
 

7. Converting Travel Impacts to Emissions Reductions ......................... 73 
 

Overview of Procedure ............................................... 73 
 

Changes in Total Trips ................................................ 74 
 

Changes in Total VMT ............................................... 76 
 

Allocation Between Peak Period and Off-Peak ............................. 79 
 

Emission Impacts Methodology ........................................ 83 
 

Appendix A 

Definition of Modeling Terms ........................................ -88­

Appendix B 

Fuel Parameters for Default Emissions Factors ........................ A-4 
 

Figure 1 Overview of Calculation Procedure ................................. 2 
 

Figure 2-1 Traditional Transportation and Emissions Analysis Process .............. 5 
 

Figure 2-2 COMMUTER Estimating Procedure ................................ 9 
 

Figure 2-3 Illustration of Share Adjustment Process ........................... .16 
 

Figure 2-4 Logit Relationship ............................................. 18 
 

Figure 4-1 Illustration of Share Adjustment Process ............................ 44 
 

Figure 6-1 Disutility of Mode m ........................................... 63 
 

Figure 6-2 Modal Share .................................................. 65 
 

Figure 7-1 Translation of Travel Demand Changes into Emissions Reductions ...... 73 
 

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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. 

C	 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. 

C	 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. 

* Conformity regulations (40 CFR 93.110) require the use of the latest planning assumptions in conformity 

analyses. Whenever the COMMUTER model is use for SIP or conformity purposes, the regulations require 

that the latest planning assumptions be used for conformity determinations. Default data should not be 

used. 

-1­
Figure 1
 

Overview of Calculation Procedure
 

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 

C 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). 

C Use predefined sets of emissions factors to estimate the emissions impacts. 

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

-2­
 

Background Program Baseline Mode Shares transportation data Information 
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. 

### 

-3­
 
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 Best Workplaces for Commuters (BWC) and 

other commuter choice-type programs. It is specifically intended to calculate the travel 

and emissions effects that might result from implementation of voluntary employer 

transportation management programs. The tool may be used by regional planning or air 

agencies who are interested in pursuing this 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 this type of analysis task for several reasons, the most 

important of which are the following: 

C	 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 

C	 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 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. 

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 

-4­
 
emissions model (EMFAC in California), which applies emissions factors that have been 

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

Figure 2-1
 

Traditional Transportation and Emissions Analysis Process
 

-5­
 
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. 

C 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). 

C	 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. 

C	 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. 

C	 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 

-6­
 
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 BWC and other 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 origindestination 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 

workplace commuting 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 organizations. It was therefore viewed as a good base from 

which to build the COMMUTER model. 

* 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 

COMMUTER 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
The decision to build a new modeling tool, rather than simply modifying and adopting 

the FHWA TDM model, was based on the following factors: 

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

C 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. 

C	 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. 

C	 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 volatile organic compounds (VOC), carbon monoxide (CO), 

oxides of nitrogen (NOx), carbon dioxide (CO2), particulate matter (PM2.5), 

and six air toxics. 

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. 

-8­
 
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 

-9­
 
illustrations of the respective model screens or functions, with direct instructions on what 

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

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 â in Figure 2-2. The 

required items are summarized briefly below. 

C	 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. 

C	 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. 

C	 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 strategies under consideration. A discussion of the important distinction 

between total and affected employment is presented in Section 3. 

C	 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 . 

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

a vanpool. 

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

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

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

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

peak travel periods. 

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

peak and off-peak travel periods. 

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

roads, during peak and off-peak travel periods. 

-10­
 
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): 

•	 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. 

•	 Alternative Work Schedules: Arrangements such as flexible or staggered 

work hours, compressed work weeks, and telecommuting. 

•	 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. 

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

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

in specific 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 

-11­
 
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 analysis effort 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 most workplace commuter 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 many TDM-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. 

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

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

C	 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. 

C 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. 

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 

-12­
 
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 workplace commuting strategies: 

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

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

and 

C	 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 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 

-13­
 
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. 

* 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
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 

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 

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. 

-15­
 
Figure 2-3
 

Illustration of Share Adjustment Process
 

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. 

-16­
 
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 

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 

-17­
 
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 disutility expression might 

look like this: 

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

The disutility 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 disutility 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: 

eUtilityModeA 

( 

e

P Mode Α ) = UtilityModeA + eUtilityModeB + eUtilityModeC + eUtilityMode D 

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

change the disutility 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 

Figure 2-4
 

Logit Relationship
 

-18­
 
travel characteristics of a given mode will not produce the same degree of change in 

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 program is to be sited in an area where existing transit or carpool use is 

high, then strategies that provide additional advantages to those modes should result in 

healthy increases. However, if a 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 disutility that would result from the change in the individual attributes:

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

Instead of having to develop revised disutility 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 disutility 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 strategies. 

-19­
 
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, VMT-based emissions changes are calculated for peak and off-peak periods, 

based on changes in VMT and on average regional speeds by period and facility type. 

Emission factors (expressed in grams/mile) are provided with the model in 5-mph 

increments and interpolated to represent emission factors at the actual speeds provided. 

Third, start-based emissions changes are calculated for peak and off-peak periods, using 

start emission factors (expressed in grams/start) and the change in number of starts. 

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

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

Model Outputs 

Key outputs include the following: 

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

eliminated; 

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

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

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

C Changes in total daily emissions for each pollutant.
 

###
 

-20­
 
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: 

C Metropolitan Area Size 

C Application Setting Characteristics 

C Affected Employment 

C Mode Choice Model Coefficients 

C Starting Mode Shares 

C Average Trip Lengths 

C Vehicle Occupancy 

C 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. 

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 information 

that is used to prepare the SIP or the conformity analysis. Since regional COMMUTER 

users would likely be the designated MPO, access to the appropriate information should 

be fairly straightforward. For others who might conduct a COMMUTER analysis at 

-21­
 
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 a COMMUTER 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: 

C Total employees working at the given site; 

C Total traveling to the site on a given workday; 

C Mode choice (percent by mode); 

C Number traveling to the site during peak period; 

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

work schedules; and 

C 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. 

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. 

-22­
 
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 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: 

C Large: Population of 2 million or more 

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

C Small: Population under 750,000. 

-23­
 
Table 3-1 

Default Values Provided in the COMMUTER Model 

Data Item Suggested Default Source 

Work Trip Mode 

Shares 

Auto – Drive Alone 78.2% 

Auto – Carpool 12.1% 

Vanpool 0.5% 

Transit 4.9% 

Bicycle 0.4% 

Walk 3.0% 

Other 0.8% 

Total 100.0% 

2000 U.S. Census 

Average Commute 

Trip Length (in 

miles) 

Average Person-trip Length: 12.7 

Average Trip Length - Carpool: 12.0 

Average Trip Length - Vanpool: 20.4 

Average Trip Length - Transit: 11.7 

Average Trip Length - Bicycle: 2.9 

Average Trip Length - Walk: 0.9 

2001 Nationwide 

Household 

Transportation 

Survey (NHTS) 

Average Vehicle 

Occupancy (persons 

per vehicle) 

Average Carpool Occupancy: 2.25 

Average Vanpool Occupancy: 7.19 

2000 Census (2-4 

person carpool) 

2001 NHTS (5+ 

person carpool) 

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 Nationwide 

Personal 

Transportation 

Survey (NPTS) 

Databook, Table 6­

32 

Average Travel 

Speeds (mph) 

Area Freeway Freeway Arterial Arterial 

Size Peak Off-Peak Peak Off-Peak

 Large 40.2 49.022.6 25.1

 Medium 46.8 54.723.2 25.5

 Small 50.5 56.424.2 26.0 

2002 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 

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 

-24­
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: 

C Central business district (CBD) or radial corridor; 

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

or 

C Suburban, low-density area. 

This information is important because 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 

similar sites in a national sample.*

 This is discussed in more detail under “starting mode 

shares” below. 

*

 2000 Census journey-to-work statistics. 

-25­
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 strategies, it is necessary to describe the employment base that will be subject 

to the program. The COMMUTER model requires the following information: 

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

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

C 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 more than 

70% of all establishments have fewer than 10 employees, however, these employ only 12 

percent of the workforce .* Therefore, some users may wish to restrict assumptions on 

program coverage to only employers of 10 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, defined Office and Non-Office through 

SIC (Standard Industry Classification) codes, as shown in Table 3-2. Of Course, SIC has 

since been replaced by the North American Industry Classification System (NAICS), 

however, the descriptions and breakdown in table 2-3 are still useable in setting 

parameters for modeling. 

* U.S. Bureau of Labor Statistics, Occupational Employment and Wages, May 2003; Bulletin 2567; 

September 2004 

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

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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 strategies are applied, as an alternative to making these 

restrictions in the baseline itself. 

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 

-27­
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 COMMUTER model calls for the percentage of daily work trips that are made by the 

following modes: 

C Auto – Drive Alone
 

C Auto – Carpool
 

C Vanpool
 

C Transit 
 

C Bicycle
 

C Walk
 

C 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 3.26% in the 2000 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 the strategies only to 

those 96.74% of workers who travel. 

The default shares provided in the model have been derived from the 2000 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. 

-28­
 
Table 3-3 

Default Shares for a Standard Regional Analysis 

Mode 

Reported 2000 Census 

Commute Choice 

Excluding Work 

at Home 

Auto-Drive Alone 75.70% 78.25% 

Auto-Carpool 11.71% 12.11% 

Vanpool 0.48% 0.49% 

Transit 4.73% 4.89% 

Bicycle 0.38% 0.39% 

Walk 2.93% 3.03% 

Other 0.81% 0.84% 

Work at Home 3.26% 

Total 100.00% 100.00% 

Total Excl. Work at Home 96.74% 

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 

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 

-29­
 
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, which are based 

on 2001 NHTS data: 

C Auto Drive Alone: 12.7 miles 

C Auto-Carpool: 12.0 miles (2-4 person carpool) 

C Vanpool: 20.4 miles (5+ person carpool) 

C Transit: 11.7 miles 

C Bicycle: 2.9 miles 

C Walk: 0.9 miles 

C Other: 12.1 miles 

Average Person Trip Length: 12.2 miles 

(calculated from mode shares and modal trip lengths) 

Average Vehicle Trip Length: 12.7 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: 2.25 (source: 2000 Census) 

Average Vanpool Occupancy: 7.19 (source: 2001 NHTS) 

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 

-30­
 
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: 

C	 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). 

C	 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). 

C 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). 

*

 1995 Nationwide Personal Transportation Survey. 

-31­
C	 Peak and off-peak travel speeds (Section 7). 

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

(Section 7). 

C	 Information on current emissions requirements, standards, and programs 

(Section 7). 

-32­
 

### 
4. Estimating Travel Impacts of Employer 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 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. 

*

 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. 

-33­
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: 

C	 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. 

C	 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. 

C 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. 

C 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. 

-34­
 
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: 

C	 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. 

C	 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. 

* 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). 

-35­
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% 2.00% 4.00% 

Non-Office 0.20% 0.40% 1.40% 2.00% 

Vanpool 

Office 0.40% 1.00% 2.00% 4.00% 

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% 

Sources: FHWA TDM Model, 1993 (carpool, vanpool, and transit); Cambridge Systematics, Inc. 

for COMMUTER version 1.0, 1999 (bicycle). Carpool and vanpool office levels 3 and 4 updated 

2005 by Eric Schreffler. The Level 3 and Level 4 carpool and vanpool support impacts have 

been reduced to reflect current professional opinion that support measures are less effective than 

direct financial incentives and disincentives to commuters, all else being equal. 

C	 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. 

-36­
 
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 gettogethers 

Non-monetary vanpool development assistance 

Policy of flexible work schedules to accommodate vanpool schedule 

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 

Bicycle 

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: Workplace information and promotional activities 

-37­
 
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 

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. 

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 

-38­
 
illustrate this point, the participation information communicated to the COMMUTER 

model might instead look like this:* 

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 2.0% 

50% @ Level 4 = 0.50 x 4.0% 

Net increase in carpool share = 0.0% + 0.24% + 2.0% = 2.24% 

So with this example, the model would add 2.24% 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: 

C The employment base is comprised entirely of Office employment; and 

C 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. 

*

 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 

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. 

-39­
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 

if the baseline employment were comprised of, say 80% Office and 20% Non-Office, the 

calculation above would be revised as follows, yielding 2.03% as the Carpool share 

adjustment instead of 2.24%: 

Carpool Mode Share Increase: 

38% @ Level “0” = 0.38 x 0.0%
 

12% @ Level 3 = 0.12 x [(2.0% x 80%) + (1.4% x 20%)]
 

50% @ Level 4 = 0.50 x [(4.0% x 80%) + (2.0% x 20%)]
 

Net increase in carpool share = 0.0% + 0.23% + 1.8% = 2.03%
 

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%
 

-40­
 
The net increase that may then be attributed to the Carpool support program of the 

scenario, after crediting for existing efforts, is 2.03% - 0.36%, or 1.67%. 

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 % 

Bicycle 0.4 % % 0.4 % 

-41­
 
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 

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%. 

-42­
 
However, in this process Drive Alone drops from 75% to 70.6%, losing the greatest share 

because its share was initially the largest. 

Figure 4-1
 

Illustration of Share Adjustment Process
 

###
 

-43­
 
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: 

C	 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. 

C	 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. 

C	 Compressed Work Weeks: Rather than working a standard 5-day/8-hourper-day 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. 

C	 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, 

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 

-44­
 
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. 

C 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.* 

C	 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. 

C	 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. 

C	 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. 

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 

*

 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. 

-45­
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. 

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 

*

 This percentage was derived from data compiled in a 1980 study “Behavioral Impacts of Flexible Work 

Schedules.” 

-46­
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 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: 

-47­
 
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 

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. 

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. 

-48­
 
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: 

C	 The percent of employees who will be eligible for the new program, or who 

will be expected to participate; 

C	 The percent who are already eligible for (or participating in) a similar 

compressed work hours program; and 

C	 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. 

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. 

-49­
 
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 

significance between using the Percent Eligible vs. the Percent Participating option in the 

model. 

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.” 

-50­
 
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: 

C The percent of employees who will be eligible for the new program, or who 

will be expected to participate; 

C	 The percent who are already eligible for (or participating in) a similar 

Telecommuting program; and 

C	 The average number of days per week that employees will telecommute, i.e., 

work at home, if different from the 1.5 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 10% 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. 

The percent of eligible office commuters who would be assumed to telecommute, if given 

the option, has been reduced from 32 percent in COMMUTER version 1.0 to 10 percent 

in COMMUTER version 2.0. This change reflects more recent survey findings and 

analyses that suggest about one in ten workers who are in a job that is suited to working 

from home, have an employer who would allow telecommuting, and who want to begin 

telecommuting will do so.*

 This percent shift of eligible workers can be modified within 

the COMMUTER Model if the user has local empirical data that suggests employees will 

shift at a greater level. This might be due a more aggressive program that encourages a 

greater proportion of the workforce to adopt telecommuting, or if the user is modeling a 

single worksite where the proportion of targeted telecommuters is known and is higher 

than 10 percent. 

*

 See: Joanne H. Pratt and Associates, Telework America 1999 Survey and Key Findings, prepared for 

International Telework Association and Council, 1999; LDA Consulting, 2003 State of the Commute 

Report, prepared for the Metropolitan Washington Council of Governments, June 2004; Mokhtarian, P. and 

Salomon, I., "Modeling the Choice of Telecommuting 2: A Case of the Preferred Impossible Alternative," 

Environment and Planning A 28, 1996, pp. 1859-1876. 

-51­
For each employee who participates in a Telecommute program, the following trip 

reduction will be estimated: 

1.5 days per week work at home / 5 days per week = 0.3 trips per day eliminated. 

(user has option of changing the 1.5 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
 

10% Participate in Program
 

X
 

Daily Person Trips Eliminated
 

(0.3 based on 1.5 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 (10%) X 

(0.3 trips)1.5 days/week X (.85 vehicle trips per employee) = 383 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: 

-52­
 
(100,000 employees) X (75% Office) X (30% – 10% participation.) X (32%) X 

(0.3 trips)1.5 days/week X (.85 vehicle trips per employee) = 3,825 daily vehicle trips 

removed 

This is a very different outcome, 3,825 vs. 383 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. 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.” 

Research suggests that some of the travel benefits of telecommuting will be offset by 

additional trips made from home for other purposes. 

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. 

-53­
 
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. 

### 

-54­
 
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: 

C	 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. 

C	 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. 

C 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. 

* 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). 

-55­
C 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. 

C 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. 

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 

Access In-Vehicle Access
 

Time Time Time
 

Home Work 

Wait 

Time 

Transfer 

Time 

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. 

-56­
 
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. 

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 

-57­
 
observed.*

 There are many variations on this theme of using parking cost as a 

transportation demand management incentive, including the following: 

C Combining parking fees for SOVs with free or subsidized parking for carpools 

or vanpools. 

C 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. 

C	 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 

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: 

* See again: FHWA Implementing Effective Travel Demand Management Measures (1993) and TCRP B4: Cost-Effectiveness of TDM Strategies (1995). 

-58­
C Increased frequency of service (i.e., shorter headways between vehicles), 

which allows reduction in wait time or transfer time. 

C More direct service, which could result in both reduced in-vehicle travel time 

as well as elimination of transfer time. 

C 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 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 

-59­
 
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. 

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%. 

-60­
 
The probability of selecting a given travel mode m is calculated through the following 

equation: 

eum 

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) 

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 

* 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 

(continued...) 

-61­
Mode 

m Share feature of this curve is that its slope is steepest in the middle, and this is where small 

changes in utility (or disutility) 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 disutility 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 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 

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: 

Figure 6-1 

100%
 

90%
 

80%
 

70%
 

60%
 

50%
 

40%
 

30%
 

20%
 

10%
 

0%
 

Disutility of Mode m

(...continued)
 

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.”
 

-62­
( ) p m( )× e−ΔU m

'( ( ) ( p m) = 

[(e−ΔU m − 1)× p m )] + 1 (1) 

where: 

p’(m) = original share of mode m 

p(m) = the new share of mode m 

)U(m) = the change in disdisutility 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 disutility 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. 

-63­
 
Mode 

m Share 100% 

90% 

80% 

70% 

60% 

50% 

40% 

30% 

20%
 

10%
 

0%
 

Figure 6-2 

Modal Share

Disutility of Mode m

“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 

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 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. 

-64­
 
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. Many 

metropolitan areas conduct new urban travel surveys approximately once a decade for the 

purpose of updating and upgrading previously-developed models. 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 

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 

-65­
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 Year All Modes Walk 

Time 

Transit 

Wait 

Auto 

Parking 

Transit 

Fare 

Albuquerque 1992 -0.0209 -0.0219 -0.0978 -0.0031 -0.0031 

Atlanta 2002 -0.0256 -0.0639 -0.0256 -0.0031 -0.0013 

Baltimore 1993 -0.0300 -0.0750 -0.0750 -0.0043 -0.0043 

Boston 1991 -0.0314 -0.0330 -0.0550 -0.0173 -0.0083 

Chicago 1990 -0.0282 -0.0440 -0.0960 -0.0021 -0.0008 

Cleveland 1994 -0.0178 -0.0444 -0.0378 -0.0034 -0.0024 

Columbus 1999 -0.0213 -0.0640 -0.0465 -0.0016 -0.0016 

Dallas 1996 -0.0544 -0.0640 -0.0640 -0.0056 -0.0055 

Denver 1997 -0.0180 -0.0540 -0.0180 -0.0014 -0.0012 

Detroit 1996 -0.0512 -0.0186 -0.0186 -0.0041 -0.0041 

Houston 1985 -0.0220 -0.0568 -0.0568 -0.0154 -0.0061 

Los Angeles 1996 -0.0450 -0.1073 -0.0423 -0.0025 -0.0025 

Milwaukee 1991 -0.0157 -0.0412 -0.0412 -0.0045 -0.0045 

New York 1996 -0.0113 -0.0380 -0.0554 -0.0004 -0.0004 

Philadelphia 1986 -0.0391 -0.0316 -0.0511 -0.0026 -0.0012 

Phoenix 1991 -0.0167 -0.0206 -0.0304 -0.0053 -0.0053 

Portland 1994 -0.0394 -0.0646 -0.0397 -0.0135 -0.0135 

Reno 1991 -0.0275 -0.0550 -0.0550 -0.0167 -0.0067 

Sacramento 2001 -0.0250 -0.0380 -0.0380 -0.0025 -0.0025 

-66­
 
In-Vehicle 

Travel Time 

(min) 

Out-of-Vehicle 

Travel Time (min) 

Out-of-Pocket Travel 

Cost (cents) 

Location Year All Modes Walk 

Time 

Transit 

Wait 

Auto 

Parking 

Transit 

Fare 

San Diego 1995 -0.0250 -0.0500 -0.0250 -0.0069 -0.0025 

San Francisco 1990 -0.0333 -0.0931 -0.0523 -0.0021 -0.0021 

San Juan 1990 -0.0366 -0.0717 -0.0752 -0.0066 -0.0066 

Santa Cruz 1990 -0.0163 -0.0325 -0.0325 -0.0045 -0.0036 

Seattle 1990 -0.0176 -0.0206 -0.0155 -0.0024 -0.0024 

Tucson 2000 -0.0178 -0.0400 -0.0200 -0.0018 -0.0018 

Washington D.C. 1994 -0.0300 -0.0750 -0.0750 -0.0043 -0.0043 

Source:Coefficients obtained from a review of current practice by Cambridge Systematics, Inc. (2005). 

•	 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. 

These coefficients are listed in Table 6-3. 

Table 6-3 

Default Coefficient Values 

In-Vehicle 

Travel Time 

(min) 

Out-of-Vehicle 

Travel Time (min) 

Out-of-Pocket 

Travel Cost (cents) 

All Modes Walk Time 

Transit 

Wait 

AutoParking Transit- Fare 

All Cities -0.0253 -0.0473 -0.0466 -.0.0056 -0.0040 

Source: Cambridge Systematics, Inc., 2005. 

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 

-67­
 
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 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: 

C	 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 hypothetical user-supplied coefficients 

for this area. 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
 

C Current Mode Shares: The following local 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%
 

C 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.
 

Transit Service Improvements: 

-68­
 
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)
 

= - 0.0521 × (-2 min) = 0.1042
 

-69­
 
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%): 

-70­
 
 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% 

### 

-71­
 
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. 

Figure 7-1
 

Translation of Travel Demand Changes into
 

Emissions Reductions
 

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 

-72­
 
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 the following pollutants: 

•	 Volatile organic compounds (VOC); 

•	 Carbon monoxide (CO); 

•	 Oxides of nitrogen (NOx) 

•	 Carbon dioxide (CO2); 

•	 Particulate matter (PM2.5), reported for particles smaller than 2.5 microns 

(PM2.5); and 

•	 Six air toxics -- acetaldehyde, acrolein, benzene, 1, 3-Butadiene, 

formaldehyde, and methyl tertiary butyl ether (MTBE). 

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: 

-73­
 
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 

associated with making of a private motor vehicle trip. Passenger occupancy for drive 

alone is, of course, 1.0. 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: 

-74­
 
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. 

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. 

The example assumes the following user-specified trip lengths: 

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
 

-75­
 
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). 

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. 

-76­
 
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 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 

-77­
 
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 

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. 

*

 1995 Nationwide Personal Transportation Survey, Federal Highway Administration. 

-78­
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. 

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: 

-79­
 
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 

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 

-80­
 
= 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. 

PreScenario Change Post 

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. 

-81­
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 MOBILE6 

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 

MOBILE6-based emission factors to generate daily emission reduction estimates for 

VOCs, NOx, CO, CO2, PM2.5, and air toxics. These reductions are computed as tons per 

day or pounds 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. 

MOBILE6.2 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 

series of MOBILE6.2 emission factors were generated to represent a variety of local 

conditions and loaded into the COMMUTER model. A total of 1,152 separate sets of 

emission factors were generated representing combinations of the following fleet 

parameters: 

•	 Calendar Year of Analysis (6) - 2007, 2009, 2010, 2013, 2019, or 2021; 

•	 Season (2) -summer or winter; 

•	 Climate type (3) - mild, moderate, or severe; 

•	 Inspection and maintenance (I/M) program type (4) -none, basic, enhanced, or 

on-board diagnostics (OBD) only; 

•	 Fuel type (2) - conventional gasoline or reformulated gasoline (RFG); and 

•	 Petroleum Administration for Defense District (PADD) (5) - 1, 2, 3, 4, or 5. 

-82­
 
The "season" and "climate type" parameters are used in combination to determine the 

temperature ranges specified for the MOBILE runs. These ranges are as follows: 

Season Climate Type Min Temp. (deg F) Max Temp. (deg F) 

Summer Mild 55 75 

Summer Moderate 65 85 

Summer Severe 75 95 

Winter Mild 52 72 

Winter Moderate 32 52 

Winter Severe 12 32 

The fuel type and PADD parameters are used to establish a variety of fuel parameters. 

Weighted average values are developed for each PADD based on refinery-level data. 

The fuel parameter values used in the COMMUTER model default runs are shown in the 

table attached as Appendix B. Emission factors were not generated for every possible 

combination of parameters since, for example, RFG is not produced in some PADDs. 

For each combination of the above parameters, separate emission factors were generated 

for the following: 

•	 Vehicle type (10) - Nine individual classes of light-duty vehicles, as well as 

transit buses (diesel); 

•	 Facility type (4) - Freeway, arterial, local road, and freeway ramp; 

•	 Emissions type (2) - Starts (grams/start) and running (grams/mile); and 

•	 Speed categories (13) - 5 mph increments from 5 to 65 mph. 

In MOBILE6 database output, CO and NOx exhaust start emissions are labeled as 

emissions type 2 and exhaust running emissions are labeled as emissions type 1. For 

VOC and air toxics, the eight emissions types, representing both exhaust and evaporative 

emissions, were allocated between starts and VMT (running) emissions in the following 

manner: 

-83­
 
Source Emissions Type Description Assigned To: 

Exhaust 1 Start Start 

2 Running VMT 

Evaporative 3 Hot Soak Start 

4 Diurnal Neither 

5 Resting Neither 

6 Run Loss VMT 

7 Crankcase VMT 

8 Refueling VMT 

Diurnal and resting evaporative emissions are more or less independent of vehicle 

activity, and therefore would not be reduced by reducing either vehicle starts or distance 

traveled. Therefore, they were not assigned to either component and were removed from 

the analysis. 

Particulate matter is reported as an aggregate of the seven solid components of 

particulates computed by MOBILE6.2. These include sulfates (SO4), organic carbon in 

diesel exhaust (OCARBON), elemental carbon in diesel exhaust (ECARBON), total 

carbon in gasoline exhaust (GASPM), lead, brake wear, and tire wear. Sulfur dioxide 

(SO2) and ammonia (NH3), as gaseous components of exhaust emissions, undergo 

secondary atmospheric reactions which make it difficult to directly relate these emissions 

to PM quantities. Therefore, these components are not reported. 

For CO2 and PM, the MOBILE6.2 model does not compute start emissions separately 

and does not vary emissions based on speed. Therefore, only a single running emission 

factor is reported for each vehicle type and facility type combination, and changes in 

these emissions are based on changes in VMT only. For the other pollutants analyzed, 

start-based emissions do not vary by speed so only a single start emission factor is 

reported for each vehicle type and facility type combination. 

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 

-84­
 
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 running emission 

factors by speed increment to represent these specific speeds. Interpolated emission 

factors were developed for each vehicle type and for freeway and arterial facility types, 

for both peak and off-peak speeds. (Emission factors for local roads and freeway ramps 

are calculated at a set speed in MOBILE and do not vary.) 

Third, a weighted average running emission factor was developed for each pollutant for 

the peak and off-peak periods. This factor is the weighting of individual factors by 

vehicle and facility type, based on VMT mix and facility type mix provided by the user 

or specified from COMMUTER default values. 

Fourth, VMT reductions were then applied to the weighted average running emissions 

factors to compute daily emission reductions due to changes in VMT. 

Fifth, the change in vehicle trips (existing vs. final) was multiplied by the start emission 

factors to compute daily emission reductions due to changes in vehicle trips (starts). 

Finally, the emission reductions from changes in VMT and changes in trips were added 

to obtain overall daily reductions in emissions. 

Transit Bus Emissions - Some TCM strategies may include additional transit service 

(e.g., increased transit service frequency). If the user chooses to analyze transit service 

improvements, emissions increases from this additional service are estimated and 

subtracted from the emissions decreases from reduced personal vehicle travel. Emission 

factors for diesel transit buses were developed and incorporated in the model in the same 

manner as for light-duty vehicles. The user is prompted to enter the additional daily 

VMT from transit vehicles, and may also enter an assumed average speed for these 

vehicles or use the default speed provided (14.8 mph). Speed-based emissions are then 

calculated as described above, although speeds are not assumed to vary for peak vs. 

off-peak periods. Start emissions for buses are not calculated as they are not relevant to 

this analysis. In scenarios where transit service is not changed in the inputs, overall 

transit service, and hence, overall bus emissions, is assumed to be unchanged by any 

additional commuters shifted to transit in the mode share calculations. 

User-Input Emissions Factors 

The user may select the option of importing locally-developed emission factors from 

their own MOBILE6.2 output file. If this option is selected, COMMUTER runs a 

subroutine to process a MOBILE6 database-format output file into a file format suitable 

for import into the COMMUTER model. The procedure for calculating emission 

reductions is identical to the procedure applied to the default emissions scenarios 

included with the COMMUTER model. 

-85­
 
###
 

-86­
 
Appendix A
 

Definition of Modeling Terms
 
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. 

•	 Level 0 – no program. 

•	 Level 1 – includes carpool information activities (tied in with area-wide 

matching) and a quarter time transportation coordinator. 

•	 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. 

A-1
 
•	 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. 

Peak Spreading – Lengthening of the peak period, usually accompanied by a flattening 

of the peak period(s). 

A-2
 
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. 

A-3
 
Appendix B
 

Fuel Parameters for Default Emissions Factors
 
Batch Volume Sulfur Aromatics Olefins Benzene Oxygen Methanol MTBE Ethanol ETBE Tame T-Butanol 

2003 RFG Summer 

PADD 1 WEIGHTED AVERAGE 4,635,357,866 97 21.16 12.81 0.61 2.12 0.01 10.77 0.00 0.02 0.98 0.03 PADD 2 WEIGHTED AVERAGE 1,546,419,377 116 19.88 3.73 0.82 3.54 0.00 0.00 9.42 0.00 0.00 0.00 PADD 3 WEIGHTED AVERAGE 5,648,131,887 131 18.24 10.91 0.55 2.28 0.01 9.56 1.56 0.00 0.77 0.03 

2003 RFG Winter 

PADD 1 WEIGHTED AVERAGE 7,856,210,556 128 20.51 12.57 0.67 1.92 0.01 8.95 0.73 0.00 0.17 0.02 PADD 2 WEIGHTED AVERAGE 819,908,912 210 16.43 7.37 0.82 2.00 0.00 0.00 5.24 0.00 0.00 0.00 PADD 3 WEIGHTED AVERAGE 7,500,187,347 200 18.71 9.50 0.62 2.31 0.01 8.20 2.68 0.00 0.54 0.03 

2003 CG Summer 

PADD 1 WEIGHTED AVERAGE 3,651,741,910 233 26.87 13.23 1.00 0.86 0.00 2.62 1.01 0.10 0.22 0.01 PADD 2 WEIGHTED AVERAGE 11,324,147,175 351 28.70 9.64 1.39 0.33 0.00 0.00 1.09 0.00 0.00 0.00 PADD 3 WEIGHTED AVERAGE 21,336,476,945 306 26.84 13.15 0.95 0.17 0.00 0.83 0.00 0.00 0.11 0.00 PADD 4 WEIGHTED AVERAGE 1,908,376,956 250 27.68 10.71 1.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00 PADD 5 WEIGHTED AVERAGE 3,027,379,030 203 31.18 7.36 1.34 0.32 0.00 1.60 0.00 0.01 0.00 0.00 

2003 CG Winter 

PADD 1 WEIGHTED AVERAGE 4,920,137,802 220 23.94 14.67 0.90 0.40 0.00 0.84 0.81 0.01 0.24 0.00 PADD 2 WEIGHTED AVERAGE 12,848,436,621 267 25.14 9.08 1.27 0.43 0.00 0.00 1.30 0.00 0.00 0.00 PADD 3 WEIGHTED AVERAGE 23,415,419,586 269 24.54 12.23 0.92 0.14 0.00 0.70 0.01 0.01 0.05 0.00 PADD 4 WEIGHTED AVERAGE 1,975,690,694 202 24.40 10.76 1.53 0.02 0.00 0.00 0.05 0.00 0.00 0.00 PADD 5 WEIGHTED AVERAGE 3,653,470,712 140 27.99 8.09 1.51 0.07 0.00 0.06 0.22 0.01 0.00 0.00 

These numbers include refineries, terminals, imports, etc.Some refineries were excluded from the averaging (shown in italics), based on their fuel properties implying that their batches were not substantially 'normal' gasoline.

The volumes shown here reflect the total volume included in the averaging.The averaging algorithm omits from the average for each fuel property any refineries who have a blank value for that fuel property (i.e. blanks are not included as zeros).

B-1