Office of Transportation EPA420-B-05-019 

and Air Quality October 2005 

COMMUTER v2.0 Model 

Coefficients 
EPA420-B-05-019 

October 2005 

COMMUTER v2.0 Model Coefficients 

Transportation and Regional Programs Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

NOTICE

This Technical Report does not necessarily represent final EPA decisions or positions.

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

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

technical information and to inform the public of technical developments.
COMMUTER v2.0 Model Coefficients

The travel impacts calculated in the COMMUTER Model are based on logit mode-choice 

coefficients. The COMMUTER Model impacts are highly sensitive to the values of these 

coefficients, which are used to predict mode share changes in response to changes in 

travel time and cost associated with transportation control programs. Given this high 

degree of sensitivity, it is important that COMMUTER Model users understand the basis 

for these coefficients and how they are validated and used in transportation modeling. 

Potential users of the model should evaluate the influence of the coefficients, perform 

sensitivity tests to understand their impacts, and verify that there is consistency among 

the coefficient values used in related transportation planning models and the coefficient 

values used in this model for the city or local area being analyzed. The purpose of this 

discussion is to educate users about these issues. This discussion covers the following 

elements relating to the use of mode-choice coefficients in the COMMUTER Model: 

• Modeling Techniques outlines the various modeling processes that use 

coefficients of this type and identifies how the coefficients are used in the 

various models. 

• Review of the Coefficient Values identifies the coefficients used in the 

COMMUTER Model and defines the range of values, averages, and 

categories that are used in the model. 

• Documentation of Sources lists the specific documentation of the 

coefficient sources. 

Overview 

The COMMUTER Model employs a simplified logit modeling process (called pivot 

point) that relies on locally derived coefficients to evaluate the influence of alternative 

measures on travel behavior. The coefficients are derived from observed travel behavior 

using standard survey techniques, statistical analysis, and modeling methods. 

Coefficients that have not been derived from observed travel behavior (such as composite 

measures or transferred coefficients) are not included as they could bias the average 

values. The fact that the coefficients are all derived using similar statistical methods 

explains why the coefficients are reasonably similar across the country. A review of the 

coefficients indicates that while they are relatively consistent across the country, there is 

enough variation in values between cities that it is essential that users understand the use 

of these coefficients. This report provides the user with this background information. 

The mode-choice coefficients employed in the COMMUTER Model have been 

“validated” and are widely used in urban transportation modeling for a number of 

reasons, which include the following: 
• The coefficients are derived from observed travel behavior using standard 

survey techniques, statistical analysis, and modeling methods. 

• These coefficients are not estimated separately but rather as functions of 

the mode split behavior and are related to all variables included in modechoice model equations. 

• The coefficients are reasonably consistent across the country. 

• Many metropolitan regions have used these coefficients to “backcast” 

known mode share conditions, e.g., to test the accuracy of their forecasting 

models. 

• Metropolitan areas also have used these coefficients to verify before and 

after conditions of new transit services. 

Modeling Techniques 

Coefficient values are used in all types of modeling techniques and represent similar 

behavioral aspects of travel in each case. The primary difference in the modeling 

techniques is that standard logit models estimate probabilities that a person would choose 

a certain mode (e.g., driving alone, carpooling, transit, etc.) based on all variables that 

impact travel decisions. Conversely, pivot-point models estimate these probabilities 

based only on the changes in specific variables. Both the TDM Evaluation Model and 

the COMMUTER Model are based on a pivot-point technique. 

The pivot-point logit technique is a simplified version of the logit modeling process 

found in most mode-choice models, which are developed at the metropolitan level by 

Metropolitan Planning Organizations (MPOs). The primary difference between the 

standard logit modeling process and the pivot-point technique is as follows: 

• Standard logit mode-choice models, as applied in regional travel models, 

include many different parameters, such as transportation level of service 

(e.g., travel times, transit fares, parking costs); area characteristics (e.g., 

employment density); and socioeconomic and demographic characteristics 

(e.g., income, household type). In order to apply the models, baseline 

levels and changes in each variable must be known for all variables. 

• Pivot-point models are a simplified form of the logit mode-choice model. 

Pivot-point models “pivot” off the baseline mode share, based on the 

change in value for certain variables of interest (e.g., transportation LOS). 

It is not necessary to know the baseline levels of any other variables, since 

these baseline levels are reflected in the starting mode share. It is also not 

necessary to know levels of other variables, such as demographic 

characteristics, that are assumed not to change. 

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Standard logit mode-choice models, as applied in regional travel models, can be used to 

test a broader variety of impacts than pivot-point models. However, since they are 

integrated with the full regional travel model, they can only be used in conjunction with 

the entire set of data and modeling processes incorporated in the model. Regional travel 

models are widely used by MPOs to test the impacts of changes in automobile and transit 

levels of service, population, employment, demographics, and other variables such as the 

pedestrian environment. Pivot-point models are based on the same behavioral 

information (coefficients) and modeling methodology used in regional travel models, yet 

they apply this methodology in a simplified approach that can be used in stand-alone 

analysis. 

The TDM Evaluation Model, developed by Comsis Corporation in 1993 and sponsored 

by Federal Highway Administration, uses the same pivot-point methodology as described 

in this memorandum and the COMMUTER Model User Guide, with the following 

exception. The TDM Evaluation Model applies the coefficients to zone-to-zone trip 

activity data (trip tables generated by regional models are input directly into the TDM 

Evaluation Model); in other words, coefficients are applied separately to trip 

characteristics and LOS changes between each pair of origin and destination zones. The 

COMMUTER Model, in contrast, applies the coefficients to a single set of trip activity 

data, whether it is aggregate metropolitan area data or individual employer data. 

The coefficients used in the TDM Evaluation Model and the COMMUTER Model are 

also very similar. Composite coefficients used in the TDM Evaluation Model were 

derived from MPO area travel demand models, and average COMMUTER Model 

coefficients for small, medium, and large size metropolitan areas were also developed 

from MPO area travel demand models. The primary difference in the coefficient values 

is that those in the COMMUTER Model are based on more, recent data. 

Review of Coefficient Values 

Review - The coefficient values used in the COMMUTER Model are defined as follows: 

• In-vehicle travel time (in minutes) for transit modes.* 

• Out-of-vehicle travel time (in minutes) is divided into walk and wait 

parameters. The walk coefficient is used for both auto and transit modes, 

and the wait coefficient is exclusive to transit modes. 

• Cost (in cents) is separated by auto (parking costs) and transit (fare). Auto 

*

 The COMMUTER model was not designed to assess impacts from large changes in the 

transportation system. As a result, it assumes that in-vehicle travel time remains constant for 

auto modes (drive alone, carpool, and vanpool) and only allows in-vehicle travel time changes to 

be applied to transit. Transportation system changes that produce measurable impacts on invehicle travel time for auto modes cannot be assessed with the COMMUTER model and must be 

treated with a full “four step” travel demand model. 

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operating costs were also considered, but are typically the same as transitfare coefficients. 

These parameters are typically established in units of minutes and cents for inclusion into 

travel demand models and are set to match these units in the COMMUTER Model for 

consistency. Since there is no guarantee that these units would match an individual city’s 

travel demand forecasting model coefficient units, this should be checked prior to use of 

the COMMUTER Model. Recognizing that the typical units for cost change inputs and 

outputs in travel demand forecasting models are dollars, the COMMUTER Model 

internally applies a cents-to-dollars conversion factor to the cost-related coefficients that 

are typically reported in cents when combining cost coefficients with cost inputs in 

dollars. If the user supplies their own local coefficients (instead of using the model’s 

city-specific or area size defaults), the time-related coefficients must be entered in units 

of minutes and the cost coefficients must be in cents. 

All coefficients identified above are expected to be negative, to represent the fact that as 

the value of the parameter (time or cost) increases, the probability that a person would 

choose that mode (auto or transit) decreases. The larger the negative value for a 

coefficient, the greater its impact on the affected mode. A review of a range of values 

shows that the coefficient values change from city to city and apparently change over 

time, and these changes in the coefficients can have significant impacts on the results of 

modal choices. 

Table 1 presents the ranges of values for all cities and shows the average coefficient 

values by city size and over time. The average values in this table demonstrate some 

trends that transportation planners rely on, such as the following: 

• Walk time is twice as onerous as in-vehicle travel time. 

• Wait time is more onerous than walk time. 

• Approximately three cents of parking cost is equal to one minute of invehicle travel time, which translates to an average rate of only $1.80 per 

hour. 

• Transit fares are less onerous than parking cost. 

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The city-specific and overall average coefficient values supplied with the COMMUTER 

Model were updated for COMMUTER release version 2.0 (2005). This update included 

a review of current modeling practice in various metropolitan areas to identify the most 

Table 1 Range of Coefficient Values 

Range of Values 

In-Vehicle 

(minutes) 

Walk Time 

(minutes) 

Transit 

Wait Time 

(minutes) 

Auto 

Parking 

Cost (cents) 

Transit 

Fare (cents) 

Minimum -0.0450 -0.0931 -0.0978 -0.0173 -0.0135 

Maximum -0.0113 -0.0186 -0.0155 -0.0004 -0.0004 

Overall Average -0.0253 -0.0473 -0.0466 -0.0056 -0.0040 

recent model coefficients, and removal of all coefficients that were more than 20 years 

old (i.e., developed prior to 1986). Following this review, coefficients were compared 

among metropolitan area size class. Unlike in the review of coefficients for 

COMMUTER version 1.0, however, no statistically significant differences by size class 

were identified. Also, trends in coefficients over time were evaluated; again, however, 

no statistically significant basis was identified for establishing a clear time-trend for the 

coefficients. 

The ranges shown in Table 1 exclude a handful of outliers that lay substantially out of the 

range expected for these types of coefficients. 

Examples 

The values of the coefficients can best be described with examples, as shown in Table 2. 

The first example shows the impacts of improved transit service by itself. The second 

example shows the impacts of improved transit service combined with an additional 

charge for parking. These examples present the type of sensitivity analysis that some 

regional agencies conduct to compare the effects of variables both individually and in 

combination. The size of the coefficient affects the impact of the variable on the mode 

share, but only in context with the pivot-point logit model equation. One description of 

this approach can be found in "Modeling Transport" by J. de D. Ortuzar and L.G. 

Willumsen (Wiley Publishers, 1990), page 302. 

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Table 2 Examples of Sensitivity Testing of Coefficient Values 

Example #1: Reduced Transit Travel Time 

Travel Time (min.) Cost (cents) 

Auto 

invehicle 

Transit 

invehicle Transit 

Walk 

Transit 

Wait Auto Transit 

Original 20 40 10 10 100 150 

Improved 20 35 10 10 100 150 

Difference 0 -5 0 0 0 0 

New Mode Shares: 

Utility Change Auto Transit 

City 1 0.082 89.2% 10.8% 

City 2 0.126 88.8% 11.2% 

City 3 0.171 88.4% 11.6% 

Example #2: Reduced Transit Travel Time + Increased Parking Cost 

Travel Time (min.) Cost (cents) 

Auto 

invehicle 

Transit 

invehicle Transit 

Walk 

Transit 

Wait Auto Transit 

Original 20 40 10 10 100 150 

Improved 20 35 10 10 150 150 

Difference 0 -5 0 0 50 0 

New Mode Shares: 

Utility Change Auto Transit 

City 1 0.082 -0.029 89.0% 11.0% 

City 2 0.126 -0.281 85.7% 14.3% 

City 3 0.171 -0.533 81.7% 18.3% 

The calculations presented in Table 2 include default coefficients by population size 

input into the COMMUTER Model, and assume current mode shares of 90% auto and 

10% transit. In the first example: 

• Level of service (LOS) changes include improved transit service (five 

minutes faster in-vehicle travel time); and 

• Computations indicate an expected increase in transit use from 10% to 

11.0%, 11.1%, and 11.3% for small, medium, and large size metropolitan 

areas, respectively. 

In the second example: 

• LOS changes include improved transit service (five minutes faster invehicle travel time) combined with a parking cost increase from $1.00 to 

$1.50. 

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• The computations show an expected increase in transit use from 10% to 

14.6%, 15.2%, and 16.9% for small, medium, and large size metropolitan 

areas, respectively. The greater shift to transit in Example #2 illustrates 

the combined effect of the service changes. 

Each coefficient, when multiplied by the corresponding change in LOS, indicates a 

change in “utility” for the mode. Utility is a relative measure of attractiveness; 

essentially, the coefficients are converting changes in different units (minutes, cents, etc.) 

into similar terms so they can be directly compared. Larger coefficient values on 

variables with the same units indicate a higher value on that variable. For example, as 

shown in the Table 1 coefficients, out-of-vehicle travel time is valued more highly than 

in-vehicle travel time (people dislike to wait). The utility changes from each LOS 

component are then combined to determine an overall change in mode share using the 

logit model equation. 

The underlying computations are shown below for Example #1 with a small metropolitan 

area. 

Utility change: )U = Coefficient × Change in LOS 

Transit utility change: )UTrans = (- 0.207) * (- 5) = 0.103 

New transit mode share: 

PTrans × e

ΔUTrans 010 0 103 . × e . 

P' = = = 110% Trans PAuto × e

ΔU ) + (PTrans × eΔU ) (. × e0 ) + ( . × e0. 103 . ( Auto Trans 0 90 010 ) 

where 

P’Trans = new transit mode share 

PTrans = base (existing) transit mode share 

PAuto = base auto mode share 

)UTrans = transit utility change 

)UAuto = auto utility change 

Area-Specific Coefficients 

Table 3 shows the area-specific coefficients provided with the COMMUTER Model. In 

most cases, these coefficients were obtained from travel model documentation or 

personal communication with travel demand forecasters in each area. For COMMUTER 

version 2.0, forecasting staff in the 10 largest metropolitan areas of the country were 

contacted in December 2004 to identify the most recently available coefficients. In 

addition, updated coefficients were obtained through available documentation from other 

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metropolitan areas. Special effort was made to update older coefficient values; values 

based on data from before 1986 were discarded in COMMUTER version 2.0. 

For some areas, model coefficients were provided for specific categories (e.g., by mode 

or by income level). In these cases, coefficients had to be combined for consistency with 

the coefficient categories used in COMMUTER. Coefficients were combined in the 

following ways: 

In-Vehicle Travel Time – For areas with different auto and transit in-vehicle travel time 

coefficients, a combined IVTT coefficient was developed by weighting the mode-specific 

coefficients by commute mode share from the 2000 U.S. Census. This was done for the 

following cities: 

• Dallas 

• Detroit 

• Philadelphia 

• Seattle 

Transit Wait Time – For areas where transit time coefficients are split between less than 

7 minutes and greater than 7 minutes, the transit time coefficient for greater than 7 

minutes was used. This is because the transit time coefficient for less than 7 minutes is a 

2 to 3 times the transit time coefficient for greater than 7 minutes. The reason for this is 

that it is anticipated that for headways of over 15 minutes, a traveler will attempt to 

“schedule” his or her arrival at the transit stop and therefore the wait is less onerous. 

This was done for the following cities : 

• Atlanta 

• Denver 

• San Diego 

• Tucson 

Transit Fare – For areas where the transit fare was calculated based on income, a 

combined transit fare coefficient was developed by weighting the income specific 

coefficients by income shares reported in the household survey. This was done for the 

following city: 

• Denver 

Also, a handful of coefficients either were unavailable for specific models, or were 

removed because their values lay outside the expected range for such coefficients. In 

cases in which coefficients were unavailable or removed, substitute values were inserted 

based on other coefficients in the area’s model, using the ratio of two different categories 

based on an average ratio of coefficients across other cities. For example, in Los 

Angeles, the substitute walk time coefficient shown in Table 3 (-0.1073) is equal to the 

Los Angeles in-vehicle travel time coefficient times the average ratio of in-vehicle travel 

time to walk time coefficients calculated across other cities in the dataset (2.38). Cost 

coefficients were substitute based on an average value of time computed at $5.79 per 

hour. The following substitute coefficients are included in Table 3: 

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• Los Angeles – Walk Time 

• Sacramento, Tucson – Auto Parking and Transit Fare 

• San Diego – Transit Fare 

Substitute coefficients were not included in the calculation of average coefficient values. 

Also, the Baltimore coefficients were not included because they are taken from the travel 

demand model for the Washington, D.C. area which is already included in the dataset. 

Table 3 Area-Specific Coefficients 

In-Vehicle 

Travel Time Out-of-Vehicle Travel Out-of-Pocket Travel 

(min) Time (min) Cost (cents) 

Walk Transit -- Auto - Transit - 

Location Year All Modes Time Wait Parking 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 

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 

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Documentation of Sources 

Model documentation – Coefficients for the following cities were obtained from model 

documentation (date after city indicates year of source data): 

• Atlanta (2002) - Atlanta Regional Commission, Mobility 2030 : Model 

Documentation, December 2004. 

• Baltimore (1993) - Metropolitan Washington Council of Governments, COG/TPB 

Travel Forecasting Model Version 2.1 D #50 Calibration Report, November 17, 

2004. 

• Columbus (1999) - PB Consult / Parsons Brinckerhoff for the Mid-Ohio Regional 

Planning Commission, The MORPC Travel Demand Model: Validation and Final 

Report, December 17, 2004. 

• Dallas (1996) - Cambridge Systematics for the North Central Texas Council of 

Governments, NCTCOG Mode Choice Model, August 2002. 

• Los Angeles (1996) - Cambridge Systematics, Inc., SCAG Regional Mode Choice 

Model Development Project, Final Report, October 28, 1996. 

• Milwaukee (1991) - Southeastern Wisconsin Regional Planning Commission, 

Travel Simulation Models for the Milwaukee East-West Corridor Transit Study, 

May 1993. 

• New York (1996) – Parsons Brinckerhoff Quade & Douglas with Cambridge 

Systematics and others for New York Metropolitan Transportation Council, 

Transportation Models and Data Initiative : Technical Memorandum Task 14.15 

&16 / Milestone C Best Practice Model Development – Travel Details: Pre-Mode, 

Mode Choice, and Stops Model and Travel Patterns: Journey Frequency and 

Destination Choice, August 2001. 

• Portland (1994) - Metro, The Phase III Travel Demand Forecasting Model: A 

Summary of Inputs, Algorithms, and Coefficients, June 1, 1994. 

• Sacramento (2001) - DKS Associates for Sacramento Area Council of 

Governments, Model Update Report, Sacramento Regional Travel Demand 

Model, Version 2001 (SACMET 01), March 8, 2002. 

• San Francisco (1990) - Metropolitan Transportation Commission, Travel Demand 

Models for the San Francisco Bay Area (BAYCAST-90) Technical Summary, 

June 1997. 

• Seattle (1990) - Cambridge Systematics, Inc. with Urban Analytics for Puget 

Sound Regional Council, Land Use and Travel Demand Forecasting Models : 

New Model Documentation, June 30, 2001. 

• Tucson (2000) - Cambridge Systematics, Inc. for Pima Association of 

Governments, PAG Model Evaluation and Improvement Plan : Travel Demand 

Forecasting Model, August 2003. 

• Washington D.C. (1994) - Metropolitan Washington Council of Governments, 

COG/TPB Travel Forecasting Model Version 2.1 D #50 Calibration Report, 

November 17, 2004. 

Personal Communication - Many of the coefficients were obtained directly from the 

consultant travel demand modelers responsible for regional model estimation. These 

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metropolitan areas included the most recent model updates conducted in the following: 

• Cleveland (1994); 

• Denver (1997); 

• Detroit (1996); 

• Phoenix (1991); 

• Reno (1991). 

• San Diego (1995); 

• Santa Cruz, California (1990). 

Previous Model – The following coefficients were taken from COMMUTER Model 

Version 1.0: 

• Albuquerque (1992); 

• Boston (1991); 

• Chicago (1990); 

• Phoenix (1991); and 

• San Juan (1990). 

Summary 

The COMMUTER Model will be a more powerful tool to the user if the impacts of the 

coefficients are understood. The best means to achieve this understanding is to use 

sensitivity testing similar to that presented in this discussion. This will serve to indicate 

the general impacts to changes in cost or time variables as well as to identify that the 

model and data are being applied correctly. 

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