EPA420-F-00-042

October 2000

%1//76'4/QFGN%QGHHKEKGPVU

Transportation and Regional ProgramsDivision

Office of Transportation and Air Quality

U.S. Environmental Protection Agency
COMMUTER 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.
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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). 
*

 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 in-

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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.

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.*
vehicle 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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 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

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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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 -0.0467 -0.0931 -0.1165 -0.0210 -0.0210

Maximum -0.0150 -0.0219 -0.0280 -0.0018 -0.0008

Overall Average -0.0258 -0.0484 -0.0548 -0.0083 -0.0061

City Size

less than 750,000 -0.0207 -0.0373 -0.0563 -0.0065 -0.0038

750,000 to 2,000,000 -0.0241 -0.0472 -0.0468 -0.0071 -0.0064

more than 2,000,000 -0.0281 -0.0521 -0.0584 -0.0094 -0.0065

Age of Values

Less than 1980 -0.0300 -0.0491 -0.0629 -0.0110 -0.0110

1980 to 1990 -0.0289 -0.0562 -0.0543 -0.0069 -0.0046

More than 1990 -0.0204 -0.0409 -0.0505 -0.0079 -0.0044

The coefficient values by city size show a clear trend toward larger coefficients for larger

cities, with the exception of transit wait time for small cities. This anomaly could be due

to a small sample size in this category or values that do not change by city size for this

variable. The coefficient values over time also show a general trend toward lower

coefficient values in later models, but this is generally believed to be reflective of the fact

that models with more variables and better explanatory powers would have lower values

for these variables. 

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.
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Willumsen (Wiley Publishers, 1990), page 302.

Table 2

Examples of Sensitivity Testing of Coefficient Values

Example #1: Reduced Transit Travel Time

Travel Time (min.) Cost (cents)

Auto in- Transit Transit Transit 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 Auto Transit

Small 0.000 0.103 0.000 0.000 0.000 0.000 89.0% 11.0%

Medium 0.000 0.120 0.000 0.000 0.000 0.000 88.9% 11.0%

Large 0.000 0.134 0.000 0.000 0.000 0.000 88.7% 11.3%

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

Travel Time (min.) Cost (cents)

Auto in- Transit Transit Transit 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 Auto Transit

Small 0.000 0.103 0.000 0.000 -0.326 0.000 85.4% 14.6%

Medium 0.000 0.120 0.000 0.000 -0.356 0.000 84.8% 15.2%

Large 0.000 0.134 0.000 0.000 -0.468 0.000 83.1% 16.9%

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
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P P e

Pe P e

e

e e

Trans

Trans

U

Auto

U

Trans

U

Trans

Auto Trans

' ( )( )

.

(. ) (. ) .

.

. = ×

× +× = ××+ × =

∆

∆ ∆

010

0 90 010

110%

0 103

0 0 103

 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.

 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:

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
*

 S. Decker and R. Dulla, "Technical Memorandum #1: Identification of Literature for

Task 2, and the Synthesis and Analysis of Criteria for Task 3," memorandum to John Hall,

Environmental Protection Agency, June 30, 1998.

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Documentation of Sources

The sources for the model coefficients used in the COMMUTER Model are identified in

this section. The majority of these sources were also identified and presented under EPA

Work Assignment 98 0-06.*

Document #1: - I-95/I-595 Multimodal Transportation Master Plan % Revised Travel

Forecasting and Analysis Methodology Report, Cambridge Systematics, Inc. and

Reynolds, Smith, and Hills, Inc, prepared for Florida Department of Transportation,

District 4, 1998.

This document provided travel model coefficients for various cities including the

following:

 Composite Model (made up of coefficients from 1960 to 1984 - Schultz,

Gordon. Memorandum to Seattle Metro files, March 5, 1991);

 Atlanta Model (from 1989 % two sources: (1) Cambridge Systematics and

Barton-Aschman Associates, Short Term Travel Model Improvements,

prepared for the USDOT Travel Model Improvement Program, October

1994; and (2) Metropolitan Atlanta Rapid Transit Authority,. North Line

Corridor Alternatives Analysis/DEIS Environmental Impact Statement,

Atlanta, Georgia, Deliverable 10: Methodology for Analysis of Service

and Patronage Impacts, prepared for the Urban Mass Transportation

Administration, April 1989);

 Detroit Model (from 1984 - Cambridge Systematics, Inc., SEMCOG

Travel Demand Model Refinement Project, Final Report, July 1992);

 Los Angeles Model (from 1996 - Cambridge Systematics, Inc., SCAG

Regional Mode Choice Model Development Project, Final Report,

October 28, 1996);

 Milwaukee Model (from 1991 - Southeastern Wisconsin Regional

Planning Commission, Travel Simulation Models for the Milwaukee EastWest Corridor Transit Study, May 1993);

 Portland Model (from 1994 - Metro, The Phase III Travel Demand

Forecasting Model: A Summary of Inputs, Algorithms, and Coefficients,

June 1, 1994);

 Philadelphia Model (from 1996&preliminary - Cambridge Systematics,
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Inc., Task 3 Report: Nested Mode Choice Model, prepared for the

Delaware Valley Regional Planning Commission, March 1998);

 Sacramento Model (from 1994 - DKS Associates, Model Development

and User Reference Report, prepared for the Sacramento Area Council of

Governments, October 1994); and

 Tucson Model (from 1994 - JHK and Associates and Cambridge

Systematics, Inc., PAG Travel Demand Study, submitted to Pima

Association of Governments, March 1994).

Miami model coefficients were also identified in this document for in-vehicle travel

times. 

Document #2 - Short-Term Travel Model Improvements, Cambridge Systematics, Inc.,

with Barton Aschman Associates, prepared for U.S. Department of Transportation,

Federal Highway Administration, Federal Transit Administration, Office of the Secretary,

and U.S. Environmental Protection Agency, October 1994.

This document provided travel model coefficients for the following cities:

 New Orleans (from 1960);

 Minneapolis (from 1970);

 Cincinnati (from 1978);

 Los Angeles (from 1975);

 San Francisco (from 1980);

 San Juan, Puerto Rico (from 1984);

 Seattle (From 1977);

 St. Louis (from 1984); and

 Washington, DC (from 1980 and 1984).

Document #3 - Travel Demand Forecasting Processes Used by Ten Large Metropolitan

Planning Organizations, Institute of Transportation Engineers, prepared by ITE

Technical Council Committee 6Y-53, February 1994.

This document was used by Cambridge Systematics to identify additional travel model

coefficients for use in the COMMUTER Model. Metropolitan area travel model

coefficients identified from this source included the following:

 Detroit (from 1965);

 St. Louis (from 1965);

 Washington, DC (from 1980);

 Pittsburgh (from 1978);

 Dallas (from 1984);

 Denver (from 1985); and
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 Atlanta (no year defined).

Document #4 - TDM Evaluation Model User’s Guide, Comsis Corporation, prepared for

Federal Highway Administration & Federal Transit Administration, 1993.

This document provided travel model coefficients for various cities including the

following sources (by city):

 Honolulu (from 1982);

 Cobb County, Georgia (from 1990);

 I-80 New Jersey (from 1990);

 Houston (from 1994);

 Somerset, New Jersey (from 1989);

 Los Angeles (from 1982);

 Miami (from 1982);

 Minneapolis (from 1991 and 1975);

 New Orleans (from 1980);

 Phoenix (from 1990);

 San Juan, Puerto Rico (from 1982);

 Seattle (from 1991 and 1978);

 St. Louis (from 1982); and 

 Washington, DC (from 1985).

Other Documents/Sources - Many of the coefficients were obtained directly from the

consultant travel demand modelers responsible for regional model estimation. These

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

 Santa Cruz, California (from 1990);

 Cleveland (from 1994);

 Phoenix (from 1997); 

 San Diego (year undefined); and

 Reno (from 1991).

Others were obtained directly from the travel demand model documentation reports

prepared for each metropolitan area including the following:

 San Francisco (from 1990);

 San Juan (from 1990);

 Albuquerque (from 1992); and

 Phoenix (from 1997).
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Other sites were obtained from NCHRP 187 including the following:

 Chicago (from 1990);

 Salt Lake City (year undefined); and

 Portland (from 1992).

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.