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Applications of New Travel Demand Forecasting Techniques to Transportation Planning





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                              NOTICE

          This document is disseminated under the sponsorship of
          the Department of Transportation in the interest of
          information exchange.  The United States Government
          assumes no liability for its contents or use thereof.





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                 APPLICATIONS OF NEW TRAVEL DEMAND
                      FORECASTING TECHNIQUES
                    TO TRANSPORTATION PLANNING

                A Study of Individual Choice Models

                            Prepared by

                          Bruce D. Spear

                            March 1977

                 U.S. Department of Transportation
                  Federal Highway Administration
                    Office of Highway Planning
                      Urban Planning Division


          For sale by the Superintendent of Documents, U.S.
          Government Printing Office, Washington, D.C. 20402





                         TABLE OF CONTENTS

Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . iv.

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . .v.

List of Tables. . . . . . . . . . . . . . . . . . . . . . . . . vi.


  CHAPTER

     I.   INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . 1

     II.  AN OVERVIEW OF INDIVIDUAL CHOICE MODELS . . . . . . . . 5

          Background. . . . . . . . . . . . . . . . . . . . . . . 5
          The General Structure. of individual Choice Models. . . 6
          Properties of Individual Choice Models and Their
          Implications for Transportation Planning. . . . . . . .10
          Summary . . . . . . . . . . . . . . . . . . . . . . . .17
          References - Early Research in individual Choice
          Models. . . . . . . . . . . . . . . . . . . . . . . . .18


     III. INDIVIDUAL CHOICE MODELS AS ELEMENTS IN THE TRADITIONAL
          TRAVEL DEMAND FORECASTING PROCESS . . . . . . . . . . .19

          Background. . . . . . . . . . . . . . . . . . . . . . .19
          Suitability of Individual Choice Models for Mode
          Split Analysis. . . . . . . . . . . . . . . . . . . . .21
          A Summary of Recent Applications. . . . . . . . . . . .24
          References. . . . . . . . . . . . . . . . . . . . . . .33

          CASE STUDY NO. 1 - AN INDIVIDUAL CHOICE MODEL FOR SAN
          DIEGO . . . . . . . . . . . . . . . . . . . . . . . . .34

          Background. . . . . . . . . . . . . . . . . . . . . . .34
          Description of the Model. . . . . . . . . . . . . . . .34
          Data Preparation. . . . . . . . . . . . . . . . . . . .36
          Model Development and Calibration . . . . . . . . . . .37
          Model Validation and Sensitivity Tests. . . . . . . . .40
          Application of the Model as a Planning Tool . . . . . .41

                                 i





          CASE STUDY NO. 2 - THE DEVELOPMENT OF MODE CHOICE MODELS
          FOR THE TWIN CITIES AREA. . . . . . . . . . . . . . . .47

          Background. . . . . . . . . . . . . . . . . . . . . . .47
          Description of the Models . . . . . . . . . . . . . . .48
          Data Preparation. . . . . . . . . . . . . . . . . . . .49
          Model Calibration . . . . . . . . . . . . . . . . . . .51
          Model Validation. . . . . . . . . . . . . . . . . . . .59
          Sensitivity Analysis. . . . . . . . . . . . . . . . . .61
          Application of the Models in the Planning Process . . .65

     IV.  EVALUATING TRANSPORTATION SYSTEM MANAGEMENT POLICIES WITH
          INDIVIDUAL CHOICE MODELS. . . . . . . . . . . . . . . .67

          Background. . . . . . . . . . . . . . . . . . . . . . .67
          The Suitability of Individual Choice Models for
          TSM Planning. . . . . . . . . . . . . . . . . . . . . .68
          A Summary of Recent Applications. . . . . . . . . . . .72
          References. . . . . . . . . . . . . . . . . . . . . . .81

          CASE STUDY NO. 3 - EVALUATING THE IMPACT OF POLLUTION
          CONTROL STRATEGIES ON REGIONAL TRAVEL DEMAND. . . . . .82

          Background. . . . . . . . . . . . . . . . . . . . . . .82
          Description of the Models . . . . . . . . . . . . . . .82
          Application of the Models to Los Angeles Data . . . . .87
          Results of Policy Analysis. . . . . . . . . . . . . . .90

          CASE STUDY No. 4 - EVALUATING THE EFFECTIVENESS OF
          CARPOOLING INCENTIVES AT REDUCING FUEL CONSUMPTION. . .94

          Background. . . . . . . . . . . . . . . . . . . . . . .94
          Description of the Models . . . . . . . . . . . . . . .94
          Forecasting Procedure for the Models. . . . . . . . . 103
          Results of Policy Analysis. . . . . . . . . . . . . . 105


          V.   FORECASTING THE DEMAND FOR NEW TRANSPORTATION
               SYSTEMS AND MAJOR SERVICE IMPROVEMENTS . . . . . 111

          Background. . . . . . . . . . . . . . . . . . . . . . 111
          The Suitability of Individual Choice Models for
          New Mode Demand Forecasting . . . . . . . . . . . . . 112
          A Summary of Recent Applications. . . . . . . . . . . 114
          References. . . . . . . . . . . . . . . . . . . . . . 120

                                ii





          CASE STUDY NO. 5 - STUDYING THE FEASIBILITY OF FEEDER BUS
          SERVICE TO A SUBURBAN RAILROAD STATION. . . . . . . . 121

          Background. . . . . . . . . . . . . . . . . . . . . . 121
          Description of the Model. . . . . . . . . . . . . . . 121
          Data Preparation and Forecasting Procedure. . . . . . 122
          Demand and Revenue 'Projections . . . . . . . . . . . 125
          Cost Estimates and Economic Analysis. . . . . . . . . 127


          CASE STUDY NO. 6 - DESIGNING A PUBLIC TRANSPORTATION
          SYSTEM FOR SUBURBAN COMMUNITIES . . . . . . . . . . . 131

          Background. . . . . . . . . . . . . . . . . . . . . . 131
          Description of the Models . . . . . . . . . . . . . . 132
          Data Preparation and Demand Forecasting Procedures. . 134
          Analysis of Alternative Systems . . . . . . . . . . . 139


 APPENDICES

     A.   ISSUES WHICH HAVE EMERGED FROM INDIVIDUAL CHOICE
          MODEL RESEARCH. . . . . . . . . . . . . . . . . . . . 143

     B.    WHERE TO OBTAIN REFERENCES LISTED IN THIS REPORT . . 153

                                iii





                         ACKNOWLEDGEMENTS


     I wish to thank the many individuals who provided me with
     valuable information and comments during the preparation of
     this report.  I am especially grateful to:

Mr.  John C. Bennett     -    Peat, Marwick,, Mitchell & Co.,
                              Washington, DC.
Mr.  Daniel Brand        -    Executive Office of Transportation
                              and-Construction, Boston, MA.
Mr.  Raymond H. Ellis    -    Peat, Marwick, Mitchell & Co.,
                              Washington, DC.
Mr.  David S. Gendell    -    Federal Highway Administration,
                              Washington, DC.
Ms.  Nancy Hammond       -    Metropolitan Transportation
                              Commission, Berkeley, CA.
Dr.  David T. Hartgen    -    New York State Department of
                              Transportation, Albany, NY.
Mr.  Kevin E. Heanue     -    Federal Highway Administration,
                              Washington, DC.
Mr.  Thomas J. Hillegass -    Urban Mass Transportation
                              Administration, Washington, DC.
Mr. John F. Hoffmeister, III
                         -    Metropolitan Council, St. Paul, MN.
Mr.  William A. Jessiman -    Cambridge Systematics, Inc.,
                              Cambridge, MA.
Dr.  Frank S. Koppelman  -    Northwestern University, Evanston,
                              IL.
Mr.  Gerald Kraft        -    Charles River Associates, Inc.,
                              Cambridge, MA.
Dr.  Steven R. Lerman    -    Cambridge Systematics, Inc.,
                              Cambridge, MA.
Dr.  Peter S. Liou       -    Maryland Department of
                              Transportation, Silver Spring, MD.
Dr.  Thomas E. Lisco     -    Chicago, IL.
Dr.  Jordan Louviere     -    University of Wyoming, Laramie, WY.
Mr.  David B. Miller     -    Jack E. Leisch & Assoc., Evanston,
                              IL.
Mr.  Joel Miller         -    Alan M. Voorhees & Assoc., McLean,
                              VA.
Mr.  Fred Reid           -    Urban Travel Demand Forecasting
                              Project, Berkeley, CA.
Mr.  James M. Ryan       -    Federal Highway Administration,
                              Washington, DC.
Mr.  Gordon W. Schultz   -    R. H. Pratt Assoc., Inc., Kensington,
                              MD.
Ms.  Louise E. Skinner   -    Federal Highway Administration,
                              Washington, DC.
Dr.  Peter R. Stopher    -    Northwestern University, Evanston,
                              IL.
Mr.  Joseph R. Stowers   -    System Design Concepts, Inc.,
                              Washington, DC.
Mr.  Edward Weiner       -    U.S. Department of Transportation,
                              Washington, DC.


     I have tried to incorporate most of the suggestions I.
     received into this report.  The final manuscript was typed by
     Ms. Barbara Bryant, and I am particularly grateful for her
     expertise and patience during the many revisions which were
     made.

                                iv.





                          LIST OF FIGURES

                                                               page

     2.1  Graphs of the Logit and Probit Functions. . . . . . . . 8
     2.2  Example of Ecological Correlation . . . . . . . . . . .12

     3.1  The Traditional Travel Demand Forecasting Process . . .20
     3.2a San Diego Mode Choice Models. . . . . . . . . . . . . .38
     3.2b Definition of Variables Used in the Models. . . . . . .39
     3.3  Graph of Transit Mode Split Versus Excess Time. . . . .44
     3.4  Twin Cities Home Based Work Mode Choice Models. . . . .53
     3.5  Twin Cities Home Based Other Mode Choice Models . . . .54
     3.6  Twin Cities Non Home Based Mode Choice Models . . . . .55
     3.7  Twin Cities Abstract Mode Choice Models . . . . . . . .56
     3.8  Definitions of Variables Used in the Models . . . . . .57

     4.1a Travel Choice Models Used in the EPA Study. . . . . . .84
     4.1b Definitions of Variables Used in the Models . . . . . .85
     4.2  FEA Auto Ownership Models . . . . . . . . . . . . . . .97
     4.3  FEA Work and Non Work Travel Choice Models. . . . . . .98
     4.4  Definitions of Variables Used in the Models . . . . . .99
     4.5  Model Linkages for FEA Carpooling Study . . . . . . . 102
     4.6  The Effects of Gasoline Price Increases by Income Group
          and Location. . . . . . . . . . . . . . . . . . . . . 108

     5.1  Homewood Feeder Bus Demand Models . . . . . . . . . . 123
     5.2  Map of Homewood Showing the Zones and Zonal
          Trip Origins. . . . . . . . . . . . . . . . . . . . . 124
     5.3  Map of Homewood Showing the Routes of,the Feeder Bus
          System. . . . . . . . . . . . . . . . . . . . . . . . 126
     5.4a Total Expected Ridership on the Feeder Bus System at
          Various Fare Levels . . . . . . . . . . . . . . . . . 128
     5.4b Total Expected Revenue Versus Fare Relationship for the
          Feeder Bus System . . . . . . . . . . . . . . . . . . 128
     5.5a Expected Ridership for Three Years. . . . . . . . . . 129
     5.5b Total Expected Revenue for Three Years - Fares of 15
          cents, 25 cents, and 35 cents . . . . . . . . . . . . 129
     5.6  Expected Operational Funding Requirements
          Versus Fare . . . . . . . . . . . . . . . . . . . . . 130
     5.7  Schaumburg/Hoffman Estates Transit Demand Models. . . 133
     5.8  Shopping Center Questionnaire . . . . . . . . . . . . 135

                                v.





                          LIST OF TABLES

                                                               page

     3.1  Demand Elasticities for the San Diego CBD Model . . . .42
     3.2  Sensitivity Analysis with the San Diego CBD Model . . .43
     3.3  Comparison of the Twin Cities Models with O/D Survey
          Results . . . . . . . . . . . . . . . . . . . . . . . .58
     3.4  Comparison of Average Trip Distances. . . . . . . . . .60
     3.5  Direct Elasticities for Five Mode Work Trip Model . . .62
     3.6  Sensitivity Test Results for the Twin Cities Models . .64

     4.1  Estimation Results for the Los Angeles Models . . . . .88
     4.2  Policy Impacts on Los Angeles Mode Split Estimates. . .92
     4.3  Impacts of Pollution Control Strategies on Estimated
          Regional VMT. . . . . . . . . . . . . . . . . . . . . .93
     4.4  Predicted Impacts of Carpooling Policies. . . . . . . 104

     5.1  Service Characteristics of Alternative
          Transit Systems . . . . . . . . . . . . . . . . . . . 136
     5.2  Daily Ridership Estimates by Market Segment . . . . . 138
     5.3  Transit Fare Analysis . . . . . . . . . . . . . . . . 140

                                vi.





                             CHAPTER I

                     INTRODUCTION AND PURPOSE

One of the primary responsibilities of today's transportation
planner is to forecast future demand for transportation and to
predict how that demand will change in response to alternative
transportation policies.  The accuracy of these forecasts depends
to a large extent on the models and methodologies used to analyze
travel demand behavior.

A substantial amount of research has been devoted in recent years
to the study of travel demand behavior, and many promising new
models and methodologies have been developed in conjunction with
this research.  Relatively few of these developments have found
their way into conventional planning practice, however.  A major
barrier to their more widespread implementation seems to be a lack
of communication between the travel demand researchers and those
actually involved in transportation planning.

The purpose of this report is to provide a communication link
between the travel demand researcher and the transportation planner
by documenting applications of a class of new travel demand models
which have been used to address current issues in transportation-
planning.  The report is intended to serve both groups.  To the
transportation planner, it illustrates how the models have been
applied in planning practice and their

                                 1





advantages over more conventional forecasting techniques.  To the
travel demand researcher, it points out research issues which still
need to be resolved in order to make the models more responsive to
planning needs.

The class of models which are discussed in this report are known as
disaggregate behavioral models to those engaged in travel demand
research.  The terminology is somewhat misleading, however, and has
become a source of confusion to many transportation planners. 
Throughout this report, these models will be referred to as
individual choice models.

In Chapter II, a general overview of individual choice models is
given, including brief discussions on what the models look like,
how they differ from more conventional planning models, how they
are calibrated, and some properties which make them particularly
suitable as forecasting tools.  The chapter is designed to give the
reader enough background information to be able to understand how
and why models were used in specific planning applications. (For
those readers who wish to know more about individual choice models,
a discussion of three major issues and the current state-of-the-art
research being done to resolve them is presented in Appendix A at
the end of this report.)

The remaining chapters present three areas of transportation
planning where individual choice models have been applied.  Chapter
III discusses the use of individual choice models as elements in
the conventional travel demand forecasting process.  Chapter IV
describes how individual choice models have been used to evaluate
the impacts of alternative short range transportation policies such
as Transportation System Management options.

                                 2





Finally, Chapter V shows how these models have been applied to
predict the demand for new transportation service.

Each applications chapter gives a general overview of the planning
issue, including a description of the problems and the suitability
of the models in helping to resolve these problems.  Recent
applications of the models are summarized, and additional research
needed to improve their overall performance is discussed.  Specific
case studies are cited to better illustrate various techniques used
in overcoming some of the problems associated with model
applications.

It is not the intention of this report to advocate the use of
individual choice models in all planning applications.  It is
hoped, however, that the information contained in this report will
enable planners to objectively evaluate the suitability of
individual choice models in light of their own needs and
limitations.  By doing this, a potentially powerful tool can be
added to those already available for travel demand forecasting.

                                 3





                                 4





                            CHAPTER II

              AN OVERVIEW OF INDIVIDUAL CHOICE MODELS

BACKGROUND

Travel demand models based on the observed choices of individual
tripmakers have been in existence since the early 1960's.  They
first appeared as the result of academic research in the field of
transportation economics.1

These "disaggregate behavioral demand models" as they came to be
known, were used to evaluate the relative importance of certain
transportation variables in tripmaking decisions, or to derive
values of time for cost-benefit analyses.  Mode choice was the most
frequently modelled travel decision, although at least one study
modelled route choice to derive values of time.2

It was not until about 1970 that the transportation planning field
became fully aware of these models and their potential use in
travel demand forecasting.3  Since that time, a substantial amount
of research has been devoted to making individual choice models
responsive to the needs of transportation planners.  Specifically,
research has focused on 1. developing a
___________________________

1   A list of early studies involving individual choice models is
     given in the reference section at the end of this chapter.

2   T. C. Thomas, The Value of Time for Passenger Cars: An
     Experimental Study of Commuter's Values, Stanford Research
     Institute, Menlo Park, California, May 1967.

3   P. R. Stopher and T. E. Lisco, Modelling Travel Demand: A
     Disaggregate Behavioral Approach - Issues and Applications,"
     Transportation Research Forum Proceedings.  Vol.  XI, No. 1. -
     1970.

                                 5





theory of individual choice behavior; 2. simplifying the
computational requirements of model building; 3. identifying new
and more powerful explanatory variables: 4. resolving some of the
issues which limit the application of individual choice models to
other travel demand decisions; and 5. demonstrating the
capabilities of these models in solving practical planning
problems.

In this chapter, we present a brief description of individual
choice models.  First, the general structure of this class of
models is introduced.  Model characteristics and their implications
for various planning applications are then discussed.  This chapter
is intended to provide the reader with enough background
information to understand subsequent discussions of individual
choice models in specific planning applications.


THE GENERAL STRUCTURE OF INDIVIDUAL CHOICE MODELS

Individual choice models are all based on the following
relationship:

     The probability that an individual will choose a particular
     alternative is a function of the characteristics of the indi-
     vidual and of the overall desirability of the chosen
     alternative relative to all other alternatives.

The desirability of an alternative is usually re-presented through
a linear combination of variable known as a linear utility
expression.  An example of a linear utility expression is given in
equation 2.1:

                                 6





(2.1)     UAUTO =  0.25 + 1.00 (in vehicle time)
                    + 2.50 (out of vehicle time)
                    + 0.33 (out of pocket cost)

Each variable represents some characteristic of the alternative
which helps to distinguish it from other possible alternatives. 
The relative influence of each variable in determining the overall
desirability of the alternative is given by its weight coefficient. 
In equation 2.1, a unit change in the variable "out of vehicle
time" will have 2.5 times the impact on the overall desirability of
the auto mode than a unit change in the variable "in vehicle
time."4

Very often, a constant will be added to the linear utility
expression.  This constant is known as a bias coefficient, and has
the effect of giving a value to the linear utility expression which
is independent of the included variables.  The bias coefficient can
be interpreted as representing the net influence of all those
characteristics not explicitly included as variables.

Values for the weight and bias coefficients are estimated as part
of the model calibration procedure.  These coefficients can then be
used to compute a value for the linear utility expression when new
variable values are input.

In order to predict whether or not a particular alternative will be
chosen, the value of its linear utility expression must be
transformed into a probability value, ranging between zero and one. 
There are a number of mathe-
___________________________

4   It should be emphasized that the weight coefficients represent
     relative influence per unit of change in the variable.  Thus,
     if "out of vehicle time" was expressed in units of seconds and
     "in vehicle time" in minutes, the relative impact of "out of
     vehicle time" would be 2.5 x 60 = 150 times that of "in
     vehicle time."

                                 7





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                                 8





matical functions which can be used to make this transformation. 
They are usually characterized by S-shaped curves as shown in
figure 2.1. The two functions most commonly used in individual
choice modelling are the cumulative normal or probit function, and
the logit function.  The mathematical expressions for these two
functions are given by equations 2.2 and 2.3.


Click HERE for graphic.


Individual choice models cannot be calibrated using simple curve
fitting techniques like linear regression analysis.  This is
because the dependent variable of an individual choice model is a
probability, which cannot be observed.  What can be observed are
the actual choices made by individuals when they are faced with two
or more alternatives.  A technique known as maximum likelihood
estimation is therefore used.  This procedure searches for
coefficients which, when multiplied by appropriate values of
alternative characteristics, generate probabilities which are most
likely to produce the

                                 9





observed distribution of choices for the sample.5  Although
generalized maximum likelihood estimation is extremely complex and
difficult to perform, a number of computer programs have been
developed which do maximum likelihood estimation specifically for
logit and probit models.  The required input data for these
programs typically include variables describing the individual and
each available alternative, and a dependent variable identifying
which alternative was actually chosen.  The output includes the
computed values for each weight and bias coefficient, and
statistical measures of how well the calibrated model fit the
observed data.  These statistics are not as well known nor as well
defined as goodness-of-fit measures for a technique like least
squares regression, but they do provide a standard by which
alternative model formulations can be compared.


PROPERTIES OF INDIVIDUAL CHOICE MODELS AND THEIR IMPLICATIONS FOR
TRANSPORTATION PLANNING

Individual choice models have certain properties which directly
affect their use in transportation planning.  In this section, four
basic properties and their implications will be introduced.  In
later chapters, these properties will be discussed relative to
specific transportation planning applications.
Property 1: Individual choice models are calibrated using
observations of individual choice behavior as input data.
___________________________

5   A reasonably detailed discussion of the application of maximum
     likelihood estimation to logit and probit models is presented
     in P. R. Stopher, Transportation Analysis Methods,
     Northwestern University, Evanston, Illinois, 1970,-Chapters
     16-19.

                                10





Conventional transportation planning models are generally
calibrated using data which has been aggregated in some manner
(such as the mean income for a zone, or the mean trip rate for a
specific income group).  This difference in calibration data has
major implications with respect to data collection and model
statistics:

a.   Individual choice models are more data efficient than
     conventional transportation planning models.

     Individual choice models can use each trip record from a home
     interview survey as a separate observation.  Conventional
     models, on the other hand, must combine anywhere from ten to
     one hundred individual trip records to get a stable value for
     a zonal or group mean.  Consequently, the amount of data
     needed to calibrate individual choice models may be
     considerably less than that for aggregate models.

b.   Individual choice models make use of the total variation in
     the calibration dataset

     It has been shown that a significant amount of the variation
     in transportation supply and socioeconomic variables is lost
     when individual trip records are aggregated into zonal
     means.6,7 Consequently, zonal models can, at best, account
     for only a small part
___________________________

6   C. R. Fleet and S. R. Robertson ("Trip Generation in the
     Transportation Planning Process," Highway Research Record,
     240, Washington, D.C., 1968) found that 80% of the variance in
     socioeconomic variables was intrazonal.

7   D. McFadden and F. Reid ("Aggregate Travel Demand Forecasting
     from Disaggregated Behavioral Models." Transportation Research
     Record, 534, Washington, D.C., 1975) showed that from 13.6 to
     65.2% of the variance in transportation supply variables was
     intrazonal.

                                11





Click HERE for graphic.

                                12





     of the total variation in the calibration sample.  Individual
     choice models, on the other hand, incorporate all of the
     variance found in the trip records, and are, therefore, likely
     to account for more of the variability present in the data.

c.   Individual choice models are less likely to be biased by
     correlations among aggregate units.

     One danger with using zonal means to calibrate models is that
     individual behavior may be masked by unidentified
     characteristics associated with the zone.  This phenomenon is
     known as ecological correlation.  Figure 2.2 illustrates how
     it can occur.  In this example, a model relating trip
     frequency to income was developed using zonal means,
     disregarding the effects. of zonal land use patterns. 
     Although zone B has a higher mean income than zone A, its mean
     trip 'frequency is lower because the land use in zone B is
     more conducive to walk trips.  A model based on zonal means
     would, nonetheless, predict trip frequency to decrease as
     income increases.  A model based on observations of
     households, on the other hand, would show trip frequency to
     increase with income because the zonal,means would no longer
     be used to develop the relationship.

d.   Individual choice models can be applied at any level of
     aggregation.
      A model which has been calibrated using individual trip
     records can be used to forecast tripmaking behavior for any
     aggregation of trips or individuals, be it geographical units
     like traffic zones, or socioeconomic units like market
     segments.  Models calibrated

                                13





     using zonal means, however, can only be applied at the zone
     level or at some geographical aggregation of those zones.8


Property 2:    Individual choice models are probabilistic.
That is, they estimate the probabilities of choosing each
alternative rather than stating explicitly which alternative will
be chosen.  Thus, individual choice models can make use of various
probability concepts.  The two most frequently applied concepts are
mathematical expectation and joint probabilities.

a.   The total number of people expected to use a particular travel
     alternative is equal to the sum of their individual choice
     probabilities.

     This can be expressed mathematically by the following
     equation:

(2.4)          Ei  =   Pin
                      n

where     Ei  the expected number of people who will choose
               alternative i;

          Pin the probability that an individual (or market
               segment) n will choose alternative i, as computed by
               the individual choice model.

b.   A set of interdependent choice decisions can be modelled
     separately as conditional choices.  The resulting
     probabilities can then be multiplied together to produce a
     joint probability.9
___________________________

8   F. S. Koppelman, "Prediction with Disaggregate Models: The
     Aggregation Issue," Transportation Research Record, 527,
     Washington,,D.C., 1974: pp. 73-80.

9   M. Ben-Akiva and F.S. Koppelman, "Multidimensional Choice
     Models: Alternative Structures of Travel Demand Models,
     Special Report 149, Transportation Research Board, Washington,
     D.C., 1974: pp. 129-142.

                                14





For example, separate choice models could be developed for trip
frequency, destination choice, mode choice, and route choice.  Then
to find the probability of making a trip to a particular
destination via a particular mode and route, the probabilities
could be combined as follows:

(2.5)     P(f,d,m,r) = P(f).P(df).P(mf,d).P(rf,d,m)

where     P(f,d,m,r) =   the probability of going to destination d, 
                         via mode m, along route r,

          P(f)      =     the probability of making a trip;

          P(df)     =   the probability of going to destination d,
                         given that a trip is made;

          P(mf,d)  =    the probability of using mode m, given
                         that a trip is made to destination d;

          P(rf,d,m) =   the probability of travelling route r,
                         given that a trip is made to destination
                         d, via mode m.

These probability concepts have proven to be extremely useful in
transportation planning applications, and will be discussed again
in Chapter IV.

Property 3: Explanatory variables are included in individual choice
models by means of the linear utility expression.
This structure permits almost any number of variables to be
combined into a single composite value which represents the
relative desirability of an alternative.  This property has three
important implications for transportation planning applications:
a.   The linear utility expression facilitates the inclusion of
     policy variables.

     Most transportation policies can be represented as changes in
     the attributes of a transportation alternative.  With
     individual choice models, as

                                15





     long as the attributes can be quantified-in some way, they may
     be included in the linear utility expression.  Conventional
     models, on the other hand, are often too rigidly structured to
     allow more than one policy variable to be included at one
     time.

b.   The weight coefficients can be used directly to determine
     attributes important to the choice decision.
     If the coefficients of a linear utility expression are
     converted to a common metric (either by standardization or by
     dividing a common coefficient into every other coefficient)
     then the resulting magnitudes represent the relative
     importance of each attribute to the choice decision.

c.   The linear utility expression can be used to compute demand
     elasticities with respect to transportation attributes
     included in the model.
     The elasticity of demand is defined as the percent change in
     demand for a particular alternative, given a one percent
     change in the value of one of its attributes (this is known as
     a direct elasticity), or a one percent change in the value of
     an attribute of a competing alternative (this is known as a
     cross elasticity).10

     These concepts are extremely useful in investigating the
     sensitivity of demand to small changes in policy such as fare
     increases.

Property 4:  Individual choice models are based on theories of
individual choice behavior.
___________________________

10  See T. A. Domencich and D. McFadden, Urban Travel Demand: A
     Behavioral Analysis, American Elsevier Publishing Co., New
     York, 1975: pp. 84-85 for a discussion of travel demand
     elasticities and how to compute them.

                                16





If one accepts that individuals behave in a rational manner, then
it can be argued that they will tend to make the same choices when
given similar alternatives, regardless of where they ate.  This
implies that a fully specified11 individual choice model should be
able to predict the choice behavior of individuals in locations
other than that for which the model was calibrated.  Unfortunately,
no model can ever be fully specified, and some recalibration is
usually required.  However, the data requirements for
recalibrating an individual choice model are minimal and can
usually be met with a small sample from the new study area.12

SUMMARY
The successful application of any model depends upon how well its
favorable properties can be utilized.  Clearly, the greatest assets
of individual choice models are their relatively small data
requirements, their consistency with theories of individual choice
behavior, and the ease with which policy variables can be included
in the linear utility expression.  In the remaining chapters we
shall examine specific applications of individual choice models in
transportation planning to show how these properties were used to
reduce the time spent in gathering data and to achieve more
realistic predictions of travel demand behavior.
___________________________

11  A fully specified model is one in which every factor' which
     influenced the choice decision is explicitly accounted for.

12  A procedure for recalibrating individual choice models using
     existing model parameters and a small update dataset is given
     by S. R. Lerman, C. F. Manski, and T. J. Atherton, Non-Random
     Sampling in the Calibration of Disaggregate Choice Models,
     final report to the Federal Highway Administration, February
     1976.

                                17





EARLY RESEARCH IN INDIVIDUAL CHOICE MODELS

M. E. Beesley, "The Value of Time Spent in Travelling: Some New
Evidence," Economica 1965.

C. A. Lave, "A Behavioral Approach to Modal Split Forecasting,"
Transportation Research 3,, No. 4, 1969.

T.   E. Lisco, The Value of Commuters' Travel Time: A Study in
Urban Transportation Planning, unpublished Ph.D. dissertation,
University of Chicago, Illinois, 1967.

R. G. McGillivray, Binary Choice of Transport Modes in the San
Francisco Bay Area, unpublished Ph.D. thesis, University of
California, Berkeley, 1967.

D. A. Quarmby, "Choice of Travel Mode for the Journey to Work: Some
Findings," Journal of Transport Economics and Policy, 1, No. 3,
1967.

P. R. Rassam, R. H. Ellis, and J. C. Bennett, "The n-Dimensional
Logit Model: Development and Application," Highway Research Record,
369, 1971.

P. R. Stopher, "A Probability Model of Travel Mode Choice for the
Work Journey," Highway Research Record, 283, 1969.

T. C. Thomas, The Value of Time for Passenger Cars: An Experimental
Study of Commuters' Values, Stanford Research Institute, Menlo
Park, California, May 1967.

S. L. Warner, Stockastic Choice of Mode in Urban Travel: A Study in
Binary Choice, Northwestern University Press, Evanston, Illinois,
1962.

                                18





                            CHAPTER III
            INDIVIDUAL CHOICE MODELS AS ELEMENTS IN THE
           TRADITIONAL TRAVEL DEMAND FORECASTING PROCESS

BACKGROUND

The first applications of individual choice models in urban
transportation planning were in mode split analysis.  This happened
for a number of reasons.  First, as pointed out in Chapter II, most
of the early research in individual choice models dealt with the
mode choice decision.  Secondly, unlike trip distribution or
traffic assignment which had fairly standardized computational
procedures, mode split analysis was often conducted in an ad hoc
manner.  Thus, new models could be introduced with relatively
little opposition.  Finally, at the time individual choice models
first appeared, mode split analysis was of major concern only in
larger urban areas.  Since these areas also had larger
transportation planning staffs which could support research
personnel, they would be more likely to try innovative planning
techniques.  It is no surprise, therefore, that individual mode
choice models first appeared in such cities as Chicago, San Diego,
and Washington, D. C.

In the following section, properties of individual choice models
which make them particularly suitable for mode split analysis are
reviewed.  Some recent applications of individual mode choice
models in urban transportation planning studies are then
summarized, and efforts made to expand the models to other travel
demand decisions are discussed.  Finally, two models are examined
in detail as illustrative case studies.

                                19





Click HERE for graphic.


                                20





THE SUITABILITY OF INDIVIDUAL CHOICE MODELS FOR MODE SPLIT ANALYSIS

Individual choice models are particularly, and in some respects
uniquely suitable for analyzing and forecasting mode split.  This
potential was noted relatively early in their development.1  To
better understand the reasons for the models' attractiveness,
however, it is first useful to see where mode split analysis fits
into the traditional travel demand forecasting process.

Figure 3.1 illustrates the travel demand forecasting process as it
is currently applied in most urban transportation planning studies. 
The models are run sequentially with little or no feedback between
them.  This means that the output of a model near the beginning of
the process (e.g. trip generation) is used as input for the next
model (trip distribution).  Rarely are the outputs of later models
used to check the accuracy of earlier modelling phases.

Mode split takes the output from the trip distribution model, which
consists of a zone to zone person trip matrix, and allocates these
trips to various travel modes based on the relative service levels
provided by the modes.  The output of this modelling phase consists
of separate trip matrices for each travel mode which are then used
to compute highway traffic volumes and transit loadings.  Sometimes
a separate model is used to compute an average auto occupancy level
before auto trips are input to traffic assignment.
___________________________

1   S. Reichman and P. R. Stopher, "Disaggregate Stochastic Models
     of Travel Mode Choice," Highway Research Record, 369,
     Washington, D.C., 1971. pp 91-103.

                                21





While mode split operates on the person trip matrix output from
trip distribution, it does not need this matrix for calibration. 
Mode split models are calibrated by observing the number of trips
diverted from one travel mode to another as the relative levels-of-
service between the modes change.  Thus, the availability of
appropriate data is critical to the success of the mode split
model.  Individual choice models have two properties which can ease
this data problem:

1.   Individual choice models can be calibrated with a small
     database.
     By using trips as the units of observation rather than zonal
     means, individual mode choice models require significantly
     fewer sample points for calibration than aggregate models. 
     This advantage can be critical when low transit usage makes it
     difficult to obtain a sufficient number of transit trips to
     compute statistically reliable zonal mode splits.

2.   Calibrated individual choice models may be transferable to
     other urban areas.

     Because individual choice models are calibrated using
     observations of individual tripmakers, it has been argued that
     they are less susceptible to the locational biases associated
     with zonal aggregate data.  This suggests that a planning
     study having, little or no calibration data could "borrow" a
     calibrated mode choice model from another area and apply it
     with some confidence that the relationships would remain
     stable across geographical bounds.  In fact, individual choice
     models have been transferred geographically with some success. 
     Examples are presented in the following section.

Mode split analysis gives the transportation planner the greatest
opportunity to evaluate the impacts of policies over which he has
some control.

                                22





This is because most transportation policies are directed at
changing the level-of-service characteristics of alternative travel
modes.  Such changes would tend to have the most direct impact on
mode choice and, because of the sequential, non-feedback structure
of the traditional travel demand forecasting process, would show
little or no impact on trip generation or distribution.  Thus, mode
split models should facilitate the inclusion of transportation
policy variables:

3.   Individual choice models are policy sensitive.
     The linear utility expressions found in individual mode choice
     models consist mainly of variables which describe the level-
     of-service provided by each alternative mode.  By modifying
     the values of these variables, a planner can represent a
     variety of transportation policies.  The impact of these
     changes can then be evaluated by examining the resultant
     changes in mode choice probabilities.

The separation of auto occupancy from mode split in the traditional
travel demand forecasting process has resulted more from the
inability of early mode split models to handle more than two modes
at one time than from any attempt to reflect human behavior.  In
contemporary planning studies there is a strong emphasis on
policies to promote carpooling.  It would therefore be desirable to
combine auto occupancy and mode split to look at the impacts of
carpooling policies on other travel modes.  With individual choice
models, this is possible:

4.   Individual choice models can compare several alternatives in a
     single model.
     Almost any number of alternative choices may be included in a
     multinomial logit model.  By defining one or more levels of
     automobile

                                23





     occupancy as distinct travel alternatives, a combination mode
     split/ auto occupancy model can be developed.  This not only
     makes the modelling process more efficient, but may be more
     realistic in terms of human choice behavior.

In mode split analysis, there is generally a limited number of well
defined alternatives.  This is extremely helpful in the application
of individual choice models since the attributes of each available
alternative must be defined explicitly in the linear utility
expression.  In other phases of the travel demand forecasting
process, the alternatives may be too numerous or not well enough
defined to be easily represented in such a manner.  This seems to
have been one of the major obstacles to the application of
individual choice models in trip generation and distribution.

A SUMMARY OF RECENT APPLICATIONS
In this section some examples of individual choice models used in
transportation planning studies are summarized to illustrate the
types of variables used and the versatility of the models. 
References for these and some additional applications are given at
the end of this chapter.

One of the first multinomial logit mode choice models was developed
for San Diego County by the transportation consulting firm of Peat,
Marwick, Mitchell, and Co (PMM & Co.).2  (This model is discussed
in greater detail in Case Study No. 1 following this summary
section.) The model was used for the home to work trip only.  Mode
splits for other trip purposes were
___________________________

2   Peat, Marwick, Mitchell, & Co. Implementation of the n-
     Dimensional Logit Model, final report to the Comprehensive
     Planning Organization, San Diego County, California, May 1972.

                                24





computed using a simplified direct estimation procedure.3  Three
travel modes were considered for the work trip: Auto driver, auto
passenger, and transit passenger.  Due to the difference in
observed transit usage between trips destined to the CBD and
elsewhere, separate CBD and non-CBD models were calibrated.  The
models used differences in line-haul time, out-of-vehicle time, and
travel cost to distinguish between the auto and transit passenger
modes, and a transformed income variable to distinguish auto
drivers from auto passengers.  The calibrated models accurately
reproduced the total number of auto passengers, auto drivers, and
transit passengers for the base year.  Moreover, the models did
reasonably well in reproducing the observed mode split in two other
cities, Boston and San Francisco, which had significantly different
transit service characteristics.

Models similar in structure to those used in San Diego were
developed for the Tallahassee Urban Area Transportation Study by
Alan M. Voorhees and Associates (AMV).4  Like the San Diego
models, three travel modes were identified: auto driver, auto
passenger, and transit passenger.  Individual trip records from the
1971 Tallahassee home interview survey, together with zone to zone
travel impedance matrices, were used to calibrate the models. 
Separate models were developed for home based work trips and all
other nonwork trips.  No distinction was made between CBD and non-
CBD destinations.  Three system variables, running time, excess
time, and
___________________________

3   The estimation procedure for non-work trips will not be
     discussed here.  The interested reader is referred to Chapter
     VI of the report Implementation of the n-Dimensional Logit
     Model, PMM & Co., Washington, D.C. 1972.

4   Alan M. Voorhees and Associates, TALUATS Mode Share Model
     Development, unpublished technical memorandum, May 1975.

                                25





travel cost divided by income, were used in the models.  In
addition, unique bias constants were calibrated for the auto driver
and auto passenger modes for four income classifications.  Although
the models were calibrated using absolute values of the system
variables, they were applied using differences between the variable
values of the auto driver mode and those of the auto passenger or
transit passenger modes.  The model was ultimately used to forecast
mode split changes resulting from major improvements to the
Tallahassee transportation system.

Multinomial logit models were also developed for the Denver
Regional Transportation District.5  In this case, the models
examined the choice between drive alone, shared ride, and transit
passenger.  Only two variables were included in the utility
expression: travel cost and a composite travel time consisting of
in vehicle time plus 2.5 times out-of-vehicle time.  Separate
models were developed for four trip types: home-based work, home-
based shop, home-based other, and non-home-based.  For each home-
based trip purpose, separate models were developed for four levels
of income.  The final model set, therefore, contained 13 distinct
models.  These models were calibrated using data from a 1971
origin-destination survey conducted in the Denver region.  The
models were used in RTD's evaluation of alternative transit modes
for Denver.

     A somewhat different model formulation was developed by AMV,
for Atlanta.6  Instead of identifying only three modes, the
transit passenger was
___________________________

5   T. J. Stone and R. L. Thorstad, "The Denver Demand Modelling
     Process," paper prepared for the 56th Annual Transportation
     Research Board Meeting, January, 1977.

6   Alan M. Voorhees and Associates, Results of a Multimode Choice
     Model, prepared for the Metropolitan Atlanta Regional
     Commission, March 1974.

                                26





substratified on whether transit was accessed by walking or by
auto.  To distinguish between these access modes, excess time was
broken into two other variables, access time and waiting time.  All
other system variables used in the Tallahassee model were included. 
Income was also included as an independent variable.  Separate
models were calibrated for three trip types: home-based-work, home-
based-other, and non-home-based.  Unlike the Tallahassee models,
the Atlanta models have not yet been used in a forecasting
capacity.

An even more sophisticated set of individual mode choice models has
been developed for the Twin Cities Metropolitan Council by R. H.
Pratt and Associates (RHP).7  (A more detailed discussion of the
model is given in Case Study No.2.) Separate models were developed
for four income stratifications in each of three trip purposes:
home-based work, home-based other, and non-home based.  In addition
to a three mode model which distinguished between drive alone,
shared ride, and transit passenger, five and six mode models were
developed in which shared ride was stratified into two, three,
four, and five or more person carpools.  In order to identify
sufficient level-of-service differences in the auto occupancy
levels, pick-up time penalties were added to the running times and
travel costs were divided by the occupancy levels.  Other variables
used in the models included auto parking time, income, transit
access, wait, and transfer times, transit fare, and
accessibility.8  The models were cali-
___________________________

7   R. H. Pratt Associates, Inc., and DTM, Inc., Development and
     Calibration of Mode Choice Models for the Twin Cities Area,
     prepared for the Twin Cities Metropolitan Council, August
     1976.

8   Transit accessibility was defined as the percent of jobs in
     the metropolitan region which could be reached within 30
     minutes via transit.

                                27





brated with data obtained from a 1970 home interview travel survey
conducted for the Twin Cities.  All of the computer programs
necessary to calibrate and apply the models were obtained from the
Urban Transportation Planning System (UTPS) software package.9 
Upon completion of the model validation phase, various policy
scenarios being developed by the Metropolitan Council will be
examined.

Despite the popularity of the multinomial logit model, a few
planning studies have developed mode choice models based on the
binary probit formulation.  One such model was prepared by RHP for
the Metropolitan Washington Council of Governments.10  The model
set was sequential in that mode split between transit and
automobile was determined first and a separate auto occupancy model
was applied to the resultant automobile trip matrix.  The basic
mode split model used differences in running time, and travel cost
divided by income in the linear utility expression.11

This expression was transformed into a "free choice" probability
using a table look-up based on the probit function.  The free
choice probability was then adjusted to reflect auto and transit
captivity rates.  The cap-
___________________________

9   The Urban Transportation Planning System (UTPS) is an
     integrated package of computer programs and accompanying user
     materials designed for the analysis of multimodal urban
     transportation systems.  This package was developed jointly by
     the Federal Highway Administration and the Urban Mass
     Transportation Administration.  For information on how to
     obtain UTPS see Appendix B.

10  R. H. Pratt and Associates, Inc., Development and Calibration
     of the Washington Mode Choice Models, Technical Report No. 8,
     prepared for the Washington Metropolitan Council of
     Governments, June 1973.

11  Initial values of the weight coefficients were postulated from
     earlier models, and these values were adjusted through trial-
     and-error until a "best fit" between observed and estimated
     mode split percentages was obtained.

                                28





tivity rates were based on the income level of the tripmaker, the
purpose of the trip, and transit accessibility at both ends of the
trip.  Separate models were constructed for three trip types: Home-
based-work, home-based-nonwork, and non-home-based.  The home-based
trip types were substratified by three income classifications.  The
auto occupancy models used cross classification tables for ten
levels of trip interchange intensity, five levels of parking cost,
and three levels of income.  Separate work and nonwork auto
occupancy models were developed.  These models were used by the
Washington Metropolitan Council of Governments to forecast the
impacts of such transportation system changes as the METRO rail
system.

The work by the Chicago Area Transportation Study (CATS) represents
one of the few attempts at individual mode choice modelling
conducted in house by the staff of an urban transportation planning
study.12  The models were binary choice and analyzed the trade-
offs between automobile and each of three public transit modes;
bus, rail, and a weighted combination of the two.  The models were
developed only for trips whose destination was downtown Chicago. 
Separate models were developed for work and nonwork trip purposes. 
The models were calibrated using data from the 1956 CATS home
interview survey.  The explanatory variables initially considered
in the analyses were differences in travel time and travel cost,
average income for the zone of origin, and distance.  Both logit
and probit formulations were used.  These were compared on the
bases of similarities of coeffi-
___________________________

12  M. F. Wigner, "Disaggregated Modal-Choice Models of Downtown
     Trips in the Chicago Region," Highway Research Record, 446,
     Washington, D.C., 1973.

                                29





cient values and goodness-of-fit to base year data.  No significant
differences were found between the estimates derived using the
logit models and those derived using the probit models.  Only time
and cost differences were found to be statistically significant in
the choice between automobile and rail transit.  All four variables
were statistically significant in the automobile-bus and
automobile-combined choice models.  An application of these models
as policy planning tools is described in Chapter IV.

The Planning and Research Bureau of the New York State Department
of Transportation has been instrumental in expanding the
application of individual choice models beyond that of mode split. 
While much of the work done by this organization may be viewed as
methodological research, almost invariably each study was made in
response to an actual urban or statewide transportation planning
problem.  Thus, New York State Department of Transportation has not
only helped to promote the application of individual choice models;
they have been pioneers in further development of this methodology.
Their studies have included: 1. the application of individual mode
choice models to areawide travel demand forecasting;13  2. a
statewide mode choice model which examined the feasibility of using
attitudinal responses as variables;14  and 3. an automobile
ownership model which also compared the data efficiency of
individual choice models with zonal level models.15
___________________________

13  P. S. Liou, G. S. Cohen, and D. T. Hartgen, "Application of
     Disaggregate Modal-Choice Models to Travel Demand Forecasting
     for Urban Transit Systems," Transportation Research Record
     534, Washington, D.C., 1975.

14  S. M. Howe and G. S. Cohen, "Statewide Disaggregate
     Attitudinal Models for Principal Mode Choice," Preliminary
     Research Report 84, New York State Department of
     Transportation, Albany, N.Y., August 1975.

15  S. M. Howe and P. S. Liou, "Predictive Accuracy of Aggregate
     and Disaggregate Auto Ownership Models," Preliminary Research
     Report 95, New York State Department of Transportation,
     Albany, N.Y., october 1975.

                                30





Perhaps the most ambitious application of individual choice models
in the traditional travel demand forecasting process is currently
being undertaken by the Metropolitan Transportation
Commission,(MTC) for the San Francisco Bay area.  They are in the
process of redesigning their entire travel demand model system
making it more responsive to the types of transportation
issues being faced by the agency, and yet having the ability to
function within the agency's time and budgetary constraints.16  
The contractor has recommended an integrated model framework
similar to the traditional travel demand forecasting process but
using individual choice models to estimate automobile ownership,
work trip distribution, joint work mode choice/automobile
occupancy, joint non-work distribution/mode choice, and joint non-
home based generation/distribution/mode choice.  Only the nonhome
based model will have to be built from scratch.  The other models
have already been developed through the Travel Demand Forecasting
Project being conducted by the University.of California, Berkeley,
or from studies conducted by Cambridge Systematics, Inc., and the
Massachusetts Institute of Technology.  These models will be
recalibrated using data from the 1965 San Francisco Home Interview
Survey.  The model development phase of this project is scheduled
for completion.by the end of August 1976.17

The MTC project is clearly at the forefront of applications of
individual choice models.  Both researchers and planners alike are
watching this
___________________________

16  Metropolitan Transportation Commission, Travel Forecasting
     Model Development Project, Request for Proposal, prepared
     November 1974.

17  Comsis Corporation, Travel Model Development Project Phase 2:
     Work Program, prepared for the Metropolitan Transportation
     Commission, November 1975.

                                31





project closely to see whether the resulting travel demand
forecasts are more reliable or can be obtained more efficiently
than with traditional models.  Its outcome should go a long way
toward determining the ultimate role of individual choice models in
transportation planning.

                                32





                            REFERENCES


M. Ben-Akiva and M. G. Richards, Disaggregate and Simultaneous
Travel Demand Models: A Dutch Case Study, prepared for the Dutch
Ministry of Transport, 1974.

F. X. deDonnea, The Determinants of Transport Mode Choice in Dutch
Cities, Rotterdam University Press, 1971.

S. M. Howe and G. S. Cohen, "Statewide Attitudinal Models for
Principal Mode Choice," Preliminary Research Report 84, New York
State Department of Transportation, Albany, N.Y., August 1975.

S. M. Howe and P. S. Liou, "Predictive Accuracy of Aggregate and
Disaggregate Auto Ownership Models" Preliminary Research Report 95,
New York State Department of Transportation, Albany, N.Y., October
1975.

P. S. Liou, G. S. Cohen, and D. T. Hartgen, "Application of
Disaggregate Modal-Choice Models to Travel Demand.Forecasting for
Urban Transit Systems," Transportation Research Record 534,
Washington, D.C., 1975.

P. S. Liou and A. P. Talvitie, "Disaggregate Access Mode and
Station Selection Models for Rail Trips," Transportation Research
Record 526, Washington, D.C., 1974.

Peat, Marwick, Mitchell and Co., Implementation of the n-
Dimensional Logit Model' , Final Report to the Comprehensive
Planning Organization, San Diego County, California, May 1972.

R. H. Pratt and Associates, Inc., Development and Calibration of
the Washington Mode Choice Models, Technical Report No. 8 prepared
for the Metropolitan Washington Council of Governments, June 1973.

R. H. Pratt and Associates, Inc., and DTM, Inc., Development and
Calibration of Mode Choice Models for the Twin Cities Area,
prepared for the Twin Cities Metropolitan Council, August 1976.

T. J. Stone and R. L. Thorstad, "The Denver Demand Modelling
Process," paper prepared for the 56th Annual Transportation
Research Board Meeting, January 1977.

Alan M. Voorhees, and Associates, Results of a Multimode Choice
Model, prepared for the Metropolitan Atlanta Regional Commission,
March 1974.

Alan M. Voorhees and Associates, TALUATS Mode Share Model
Development, unpublished technical memorandum, May 1975.

M. F. Wigner, "Disaggregated Modal-Choice Models of Downtown Trips
in the Chicago Region," Highway Research Record, 446 . Washington,
D.C., 1973.

                                33





                         CASE STUDY NO. 1

           AN INDIVIDUAL MODE CHOICE MODEL FOR SAN DIEGO

BACKGROUND
The work by Peat, Marwick, Mitchell, and Co., for the San Diego
County Comprehensive Planning Organization represents one of the
first attempts to develop individual mode choice models for use by
a transportation planning agency.  The decision not to use more
conventional models was governed by two criteria.  First, the low
transit usage in San Diego made it infeasible to factor up the
available home interview survey data to obtain statistically
reliable zonal mode splits.  Secondly, there was a desire to
estimate mode split and auto occupancy in one model.  These
factors, along with the consultants' previous experience in
building multinomial mode choice models,18  led to their use in
this study.

DESCRIPTION OF THE MODEL
The model was designed to provide the planning agency with an
operational capability to analyze and forecast splits for three
alternative travel modes: transit passenger, auto driver, and auto
passenger.  Individual mode choice models were developed only for
the home-to-work trip.  Other trip purposes were treated in a less
rigorous manner.  Since preliminary analyses had indicated a
greater propensity to use transit for trips destined to the CBD,
separate models were developed for CBD and non-CBD work trips.
___________________________

18  P. R. Rassam, R. H. Ellis, and J. C. Bennett, "The n-
     Dimensional Logit Model: Development and Application, "
     Highway Research Record, 369, Washington, D.C., 1971, pp 135-
     147.

                                34





The models used the multinomial logit formulation as illustrated
below:

                             exp(UAD)
(3.1)     PAD  =  _____________________________________

                   exp(UAD) + exp(UAP) + exp(UTP)

where     PAD =    the probability of being an auto driver;
          UAD =    the linear utility expression for the auto
                    driver mode; UAP = the linear utility
                    expression for the auto passenger mode;
          UTP =    the linear utility expression for the transit
                    passenger mode.

The variables which made up the linear utility expressions were
based on differences in the modes' level-of-service
characteristics, and socioeconomic attributes of the tripmaker. 
Because of a desire by the agency to study relationships between
the transit program and land use in the San Diego region, the mode
split model had to be coordinated with the land-use model being
used at that time, PLUM.19  This limited the allowable set of
socioeconomic variables to those which could be forecast by the
land-use model.  Only income and auto ownership were considered in
the model development phase.
___________________________

19  For additional information on the Projective Land Use Model
     (PLUM), see An Introduction to Urban Development Models and
     Guidelines for Their Use in Urban Transportation Planning,
     U.S. Department of Transportation, Federal Highway
     Administration, October 1975, pp 67-82.

                                35





DATA PREPARATION
Socioeconomic and mode choice data were obtained from the 1966 Home
Interview Survey conducted by the San Diego Metropolitan Area
Transportation Study.20  The transportation system variables are
presented below, along with their sources.
1.   Auto travel times and distances - from minimum time paths of
     1966 peak zone-to-zone highway networks.
2.   Auto network access time - estimates of zonal averages from
     the California Division of Highways.
3.   Auto parking cost - average daily parking costs for each zone,
     based on data supplied by the Comprehensive Planning
     Organization.
4.   Auto terminal time - calculated as functions of parking space
     availability in each zone.
5.   Transit vehicle time - from minimum time paths of 1966 peak
     zone-to-zone transit networks.
6.   Walk-to-transit time - average walk times for each zone from
     the 1966 transit networks.
7.   Transit wait time - one half the transit headway times up to a
     maximum of 15 minutes.
8.   Transit transfer time - one half the transit headways at the
     transfer point.
9.   Transit fare - from a fare matrix based on the most probable
     zone-to-zone transit route.
___________________________

20  For a description of this survey, see the San Diego
     Metropolitan Area Transportation Study - 1966 Manual, State of
     California, Business and Transportation Agency, Department of
     Public Works, Division of Highways, Urban Planning Division,
     1966.

                                36





Transit service was available to only 368 of the 655 analysis zones
in the San Diego study area.  The calibration file was therefore
limited to interzonal travel between those zones actually served by
transit.  Additional restrictions included:
-    trips were for the home-to-work purpose only.
-    trips had to occur during the morning peak (5 a.m. to 10
     a.m.).
-    the chosen mode had to be either auto driver, auto passenger,
     or transit passenger.
-    there was a valid response to the income question on the
     tripmaker's household record.

The file was stratified into two datasets, based on whether the
destinations were to CBD or non-CBD locations.

MODEL DEVELOPMENT AND CALIBRATION
Model development consisted of finding the best mix of explanatory
variables which would predict mode choice among the three
alternatives.  Selection criteria included consistency of the
variables with behavioral theory and statistical significance of
the calibrated weight coefficients.  Model calibration was
performed using a computerized maximum likelihood estimation
procedure developed by J. G. Cragg and modified by the consultants. 
Preliminary analyses indicated that differences in line-haul time,
excess time, and travel cost were the most appropriate system
variables, and that income was the only available socioeconomic
variable which was statistically significant.  Subsequent work was
undertaken to identify the explicit form these variables were to
take in the linear utility-expressions.  The final models are
presented in figure 3.2

                                37





                             exp (Ui)
          Pi  = ________________________________

                                 3
                                   exp (Ui)
                                j=1


1.   Linear utility expressions for the CBD model

     Uap  =   0.0 *
     Uad  =   -1.4809 + 1.9500 TI35
     Utp  =   1.1636 + 0.0916 DX3 + 0.0563 DL3 +0.0106 DCH

2.   Linear utility expressions for the non-CBD model

     Uap  =   0.0 *
     Uad  =   - 0.5441 + 2.6800 TI35
     Utp  =   1.6600 + 0.1314 DX3 + 0.0192 DL3 + 0.0184 DCH

* Note:   The level-of-service variables (DX3, DL3, DCH) were
          expressed as differences between the automobile and
          public transit.  It was furthermore assumed that the
          choice between auto passenger and auto driver was only a
          function of the household income (TI35).  Therefore,
          since all other modes were compared to the auto passenger
          mode, the linear utility expression for auto passenger
          was set equal to zero, making the exponential expression
          (exp(Uap)) equal to one.

                   SAN DIEGO MODE CHOICE MODELS

                            figure 3.2a

                                38





DEFINITION OF VARIABLES USED IN THE MODELS


     Pi   =    probability of a tripmaker taking mode i for his
               work trip

     Ui   =    linear utility expression for mode i

     TI35 =    transformed household income variable
          =    1 - exp(-0.035 INCOME)

     DX3  =    difference in excess time
          =    (auto terminal time at origin) + (auto terminal time
               at destination)
          -    (walk to transit time) - (transit wait time)
          -    (walk from transit time)

     DL3  =    difference in line haul time
          =    (auto travel time) + (auto access time)
          -    (transit in-vehicle time) - (transit transfer time)

     DCH  =    difference in travel cost
          =    (5//mile AUTO DISTANCE) + (AUTO PARKING COST/2)
          -    (transit fare)

                            figure 3.2b

                                39





MODEL VALIDATION AND SENSITIVITY TESTS
Maximum likelihood estimation is based on the criterion that the
calibrated model accurately reproduces the choice distributions of
the sample population.  Thus, a comparison of the mode split
forecast by a model against the observed mode split of the
calibration sample is not a valid test of the model's forecasting
ability.  In recognition of this fact, the San Diego models were
used to forecast mode split percentages for different levels of
income and travel time.  These were compared to the observed mode
splits for the corresponding income and travel time groupings in
the calibration dataset.  The models were able to reproduce the
observed mode split distributions for both income and travel time
with reasonable accuracy.

The San Diego County Comprehensive Planning Organization was
concerned with the ability of the models to make reliable forecasts
under significantly different transit service conditions.  To test
this, the models were run using data from two other transportation
studies - Boston, Massachusetts, and San Francisco, California. 
Both of these areas had significantly better transit service than
San Diego.  The models closely approximated the observed mode
splits in each dataset and displayed no observable bias resulting
from differences in either geographical location or transportation
system characteristics.  These findings increased confidence in the
models' abilities to forecast responses to public transportation
systems providing improved service to the San Diego Metropolitan
area.

                                40





Demand elasticities were computed with respect to each system
variable at the existing mode splits.21  The elasticities for the
CBD model are given in table 3.1. A demand relationship is said to
be "elastic" if it has a value greater than one.  This means that a
one percent change in the value of the explanatory variable will
produce more than a one percent change in the demand for that mode. 
The demand for transit was found to be elastic to changes in excess
time and line haul time, but quite inelastic to changes in fare.

In addition to computing elasticities at the existing mode splits,
sensitivity tests were carried out to determine the expected
changes in mode split resulting from large changes in one or more
of the system variables.  The results of these tests are shown for
the CBD model in table 3.2.

APPLICATION OF THE MODELS AS PLANNING TOOLS
The models were first used to investigate an argument that San
Diego's low transit usage was caused by a pro-automobile bias among
San Diego residents.  In figure 3.3, mode split is plotted against
the difference in excess time between auto and transit for various
differences in line haul time.  It is assumed that travel costs are
equal for the two modes and that household income for this market
segment is $10,000.  If transit service were made equivalent to
that of the automobile in terms of excess time, line-haul time and
cost, the models predict that transit would take 48% of the market
for
___________________________

21  Elasticity is defined as the percent change in mode split
     resulting from a one percent change in the value of one of the
     explanatory variables.  It is a point measure and is almost
     always calculated at the existing mode split.

                                41





                                   Demand Elasticities
                              Auto      Auto      Transit
     System Variable          Driver  Passenger  Passenger

Excess Time Difference *      -0.09     -0.10     +1.08

Line-Haul Time Difference *   -0.06     -0.07     +0.70

Travel Cost Difference *      -0.02     -0.02     +0.27

Income Level                  +0.15     -0.36     -0.33

Transit Excess Time           +0.13     +0.14     -1.48

Transit Line-Haul Time        +0.10     +0.11     -1.22

Transit Fare                  +0.03     +0.03     -0.34

Auto Driving Time             -0.05     -0.05     +0.53

Auto Parking Cost             -0.02     -0.03     +0.29


     * (Auto - Transit)

          DEMAND ELASTICITIES FOR THE SAN DIEGO CBD MODEL
                             Table 3.1

                                42





Click HERE for graphic.


                                43





Click HERE for graphic.


                                44





this segment of the population.  Thus, it could be argued that the
primary cause of low transit usage in San Diego was not a pro-
automobile bias, but poor level-of-service relative to the
automobile.

A specific procedure was recommended for applying the models in
zonal level travel demand forecasting.  Since the system variables
were all derived from zonal skim tree matrices and only the income
variable was computed at the household level, it was suggested that
separate mode,;splits be computed for each income group found in
the zone-to-zone interchanges.  The mode split between any zone
pair could then be computed by multiplying the number of person
trips between the zones for each income group times the mode choice
probabilities for those groups obtained from the models.  While the
need to have trip distributions stratified by income was
recognized, it was suggested that reasonable approximations could
be made by stratifying income on the basis of the origin zone only. 
The need to obtain reasonably accurate estimates of the
transportation system variables, particularly excess time, was also
stressed.

The models were used by the San Diego County Comprehensive Planning
Organization to derive demand and revenue estimates for a number of
alternative transit systems proposed for the San Diego Metropolitan
region.  These estimates eventually formed the basis for
recommendations of a specific transit development program for the
area.22
___________________________

22  Stanford Research Institute, Basis of Benefit/Cost Analysis
     and Fare Revenue Estimates, Technical appendix F to the
     Regional Transportation Plan-Transit Development Program,
     prepared for the Comprehensive Planning Organization of the
     San Diego Region, December 1974.

                                45





In another application, the models were used to study the
feasibility of alternative light rail and express bus systems
proposed for Portland Oregon.23  Even though the models were
transferred from one geographic area to another, they produced
reasonable results when compared with available ridership data for
Portland.  This application adds support to the theory that
individual choice models are geographically transferrable.  It will
be discussed again in Chapter V.
___________________________

13  System Design Concepts, Inc. and Cambridge Systematics, Inc.,
     Demand and Revenue Analysis for Proposed Light Rail and
     Express Bus Systems in Portland, Oregon, Technical Memorandum
     prepared for the Governor's Task Force on Transportation, May
     1974.

                                46





                         CASE STUDY NO. 2
             THE DEVELOPMENT OF MODE CHOICE MODELS FOR
                       THE TWIN CITIES AREA

BACKGROUND
As part of their continuing transportation planning program, the
Twin Cities Metropolitan Council began a major campaign in 1970 to
update their existing travel demand forecasting process.  In 1975,
the transportation consulting team of R. H. Pratt Associates, Inc.,
and DTM, Inc., was commissioned to develop the mode split models
for the new process.  The resultant models had to meet a number of
specific objectives, including:

1.   that they would be sensitive to current transportation policy
     issues;
2.   that they would forecast logical relationships between auto
     driver trips, auto passenger trips, and transit trips;
3.   that the model development and application would not require
     major computer program development;
4.   that the mode choice models would be compatible with other
     modelling phases.

Each of the above objectives indicated that a modelling approach
based on the use of individual choice models would be most
appropriate.  This, together with the fact that individual choice
models had already been applied successfully in several other urban
transportation studies, helped convince the Metropolitan Council's
transportation planning staff to accept the consultant's
recommendation to use individual mode choice models for the Twin
Cities.

                                47





DESCRIPTION OF THE MODELS
The multinomial logit formulation was chosen as the basic model
structure.  This was done for two reasons.  First it made it
possible to analyze more than two modes simultaneously.  Secondly,
there were a number of computer programs available which could be
used to calibrate and apply a multinomial logit model, thereby
eliminating the need for additional software development.

Initially, a three mode model was proposed which distinguished
between drive alone, group ride, and transit.  This represented an
improvement over the model presented in Case Study No. 1, which
used auto driver, auto passenger and transit.  The primary
advantage of the Twin Cities model was that it would be easier to
calculate travel time and cost differences between drive alone and
group ride than between auto driver and auto passenger.

Although the three mode model could distinguish between group rides
and single occupant autos, it would not be able to estimate the
average auto occupancy for the group ride mode.  Therefore, a
separate auto occupancy model was developed.  Like the mode choice
model, it was based on the multinomial logit formulation, and
estimated the probability of forming two person, three person, four
person, or five person carpools.  The auto occupancy model would be
applied to the group ride share which had been estimated from the
mode split model.

In an attempt to eliminate running separate models for mode split
and auto occupancy, a six mode model was also developed in which
the alternative

                                48





modes consisted of drive alone, autos with two occupants, three
occupants, four occupants, five or more occupants, and transit. 
While this expanded model was more desirable in terms of efficiency
of application, there was some doubt as to whether there was enough
data available to calibrate it.  Consequently, both the three-mode
model with a separate auto occupancy model, and the six-mode
combined model were built, with the three-mode model serving as a
back-up.

Separate models were developed for three trip purpose categories:
homebased work, home-based nonwork, and non-home based.  This was
done primarily to make the models compatible with other phases of
the travel demand forecasting process.  However, it also permitted
the models to reflect differences in the circumstances in which the
mode choice decision was made.

DATA PREPARATION
Most of the data needed to calibrate the models were obtained from
the trip records of a 1970 home interview survey conducted in the
Twin Cities.  Each record contained information on the tripmaker,
the trip itself, the mode of travel used to make the trip, and the
tripmaker's perceptions of travel times and costs associated with
the trip.  Additional data appended to the trip records included
zone to zone travel times and costs from updated highway and
transit networks, and zonal land use data.24

Several special variables were constructed to help make the models
more sensitive to the impacts of transportation policies on
carpooling.  One
___________________________

24  A detailed description of how the calibration file was
     constructed is included in Interim Report I, Calibration File
     Preparation and Development of Preliminary Tabulation of the
     Minneapolis-St. Paul Mode Choice Model Development, prepared
     for the Metropolitan Council by R.H. Pratt Associates, Inc.,
     and DTM, Inc., November 1975.

                                49





factor which was thought to be important was the excess time
involved in picking up carpool passengers.  An investigation was
made of the difference between skim tree travel times and reported
travel times for auto trips having different occupancy levels. 
Time penalties were assigned, based on the average discrepancies
between reported and skim tree travel times for different levels of
auto occupancy for each trip purpose.  Time penalties for the work
trip ranged from 1.1 minutes for a two person carpool to 4.3
minutes for a five person carpools Time penalties for the other
trip purposes were all under one minute.

Another variable was constructed to represent the relative trip
density between zones.  It was believed that as trip density
increases, it would be easier for tripmakers to "pair-up" for
automobile trips, thereby increasing average auto occupancy.  The
variable was derived using the following formula:

(3.2)     TDij     =    LOG10 (Tij/Ai *Aj) * 100.0 + 1000

where     TDij     =    the trip density from zone i to zone j
          Tij =    the trips from zone i to zone j
          Ai  =    the area of the production zone in acres
          Aj  =    the area of the attraction zone in acres

The definition of "area" in above equation depended on the trip
purpose.  For all home-based trips, the area of the production zone
was the net residential land area.  In all other cases, area was
defined as the total usable land in the zone.

                                50





A preliminary analysis indicated that there was a significant
correlation between average auto occupancy for a zone pair and the
trip density variable.  The variable was therefore included in the
model calibration runs.

Another constructed variable was a measure of transit
accessibility.  This was defined as the percent of trip attractions
within a given transit travel time of the production zone.25  An
analysis was made to find the most appropriate value for the
transit travel time.  It was found that transit usage was most
highly correlated with the percent of trip attractions within 30
minutes transit travel time of a zone.

The final calibration dataset contained 31,368 observations, where
each observation included the mode chosen for a particular trip and
48 variables which described the trip itself and the attributes of
each alternative mode.

MODEL CALIBRATION
The models were calibrated using the ULOGIT program available in
the Urban Transportation Planning System (UTPS) computer software
package.  This program has several features which give the planner
or model builder some control over the variable coefficients.  One
feature is the ability to specify that certain variables such as
highway run time and transit run time have equal weight
coefficients.  This was used in the Twin Cities models to equate
level-of-service -attributes for alternative modes.
___________________________

25  A similar variable was used by R.H. Pratt Associates, Inc., in
     constructing a mode split model for Washington, D.C. See
     Development and Calibration of the Washington Mode Choice
     Models.  Technical Report No. 8, prepared for the Washington
     Metropolitan Council of Governments, June 1973.

                                51





The calibrated models are presented in figures 3.4 through 3.8.
There were too few observations of five person carpools for the
work trip to calibrate a six.mode model, so four and five person
carpools were combined into a single alternative.

A third type of mode choice model was developed for the home-based
work and home-based other trip purposes.  These "abstract mode
models" as they were called, only used variables which represented
attributes common to all travel modes, such as travel time and
cost.  No mode specific variable or bias constant were included. 
It was felt that these models could be used to estimate patronage
for new transportation systems where only a general description of
system performance could be predicted.

Income was the only socioeconomic variable included in the final
models, even though two other variables - auto ownership and
whether the tripmaker was a licensed driver - were actually better
predictors of mode choice.  The reason for this was that income was
the only variable which was available in sufficient detail for zone
level applications, and could be forecast with reasonable accuracy. 
It was felt that a slight increase in the goodness-of-fit obtained
by using the other variables would be more than offset by the
difficulty and lack of confidence in forecasting them.

Two rather significant findings emerged from the calibration phase. 
First, it was found that there was a significant difference in the
weights attached to the initial time spent waiting for transit, and
subsequent waits associated with transfers.  This may be partly due
to the fact that both times were computed as half the transit
headway, when in fact people probably have a

                                52





1. Linear utility expressions for five mode model.

     UT =     - 0.044 (WALK + WAIT2) - 0.030 WAIT1 - 0.014 FARE
          - 0.031 (TRN RUN + AUTO ACC) - 0.866 AUTO CONN
          + 0.020 TRN ACC (dest)

     U1 = - 0.206 HWY EXC - 0.031 HWY RUN1 - 0.014 HWY COST1
          + 0.606   INC

     U2 = - 0.349 HWY EXC - 0.031 HWY RUN2 - 0.014 HWY COST2
          + 0.239   INC

     U3 = - 0.605 HWY EXC - 0.031 HWY RUN3 - 0.014 HWY COST3
          - 0.078   INC

     U4 = - 0.605 HWY EXC - 0.031 HWY RUN4 - 0.014 HWY COST4
          - 0.282 INC


2. Linear utility expressions for three mode model.

     UT =     - 0.044 (WALK + WAIT2)   0.032 WAIT1 - 0.020 FARE
          - 0.032 (TRN RUN + AUTO  ACC) - 0.957 AUTO CONN

     U1 = - 0.257 HWY EXC - 0.032 HWY RUN1 - 0.020 HWY COST1
          + 0.567   INC

     UG = - 0.342 HWY EXC - 0.032 HWY RUNG - 0.020 HWY COSTG
          + 0.336 INC - 0.009 HWY DIST


3.   Linear utility expressions for auto occupancy model.

     U2 =     - 0.962 HWY RUN2 - 0.023 HWY COST2

     U3 =     - 0.962 HWY RUN3 - 0.023 HWY COST3 - 0.302 INC

     U4 =     - 0.962 HWY RUN4 - 0.023 HWY COST4 - 0.032 INC


          TWIN CITIES HONE BASED WORK MODE CHOICE MODELS
                            figure 3.4

                                53





1.   Linear utility expressions for six mode model.

     UT =     - 0.020 TRN EXC - 0.008 (TRN RUN + AUTO ACC) - 0.012
FARE
          - 1.537 AUTO CONN - 0.018 TRFRS

     U1 =     - 0.183 HWY EXC - 0.008 HWY RUN1 - 0.012 HWY COST1
          + 0.519 INC

     U2 =     - 0.183 HWY  EXC - 0.008 HWY RUN2 - 0.012 HWY COST2
          + 0.497 INC

     U3 =     - 0.479 HWY  EXC - 0.008 HWY RUN3 - 0.012 HWY COST3
          + 0.588 INC - 0.004 HWY DIST - 0.040 TRN ACC (org)

     U4 = - 0.479 HWY EXC - 0.008 HWY RUN4 - 0.012 HWY COST4
          + 0.456 INC - 0.004 HWY DIST - 0.040 TRN ACC (org)

     U5 = - 0.479 HWY  EXC - 0.008 HWY RUN5 - 0.012  HWY COST5
          + 0.457 INC - 0.004 HWY DIST - 0.040 TRN ACC (org)

2.   Linear utility expressions for three mode model.

     UT =     - 0.018 TRN EXC - 0.007 (TRN RUN + AUTO ACC) - 0.011
FARE
          - 1.507 AUTO CONN - 0.798 TRFRS

     U1 =     - 0.169 HWY EXC - 0.007 HWY RUN1 - 0.011 HWY COST1
          + 0.542 INC

     UG =     - 0.563 HWY EXC - 0.007 HWY RUNG - 0.011 HWY COM
          + 0.439 INC + 0.002 DEN

3.   Linear utility expressions for auto occupancy model.

     U2 =     - 2.194 HWY RUN2 + 0.187 HWY EXC + 0.040 TRN ACC
(org)
          + 0.270 INC

     U3 =     - 2.194 HWY RUN3 + 0.173 HWY EXC - 0.288 INC

     U4 =     - 2.194 HWY RUN4 - 0.135 INC
     U5 =     - 2.194 HWY RUN5

          TWIN CITIES HOME BASED OTHER MODE CHOICE MODELS
                            figure 3.5

                                54





1.   Linear utility expressions for six mode model.

     UT =     - 0.025 TRN EXC - 0.010 (TRN RUN + AUTO ACC) - 0.004
FARE

     U1 = - 0.535 HWY EXC - 0.010 HWY RUN1 - 0.004 HWY COST1
          + 0.004 HWY DIST + 0.005 DEN

     U2 = - 0.588 HWY EXC - 0.010 HWY RUN2 - 0.004 HWY-COST2
          + 0.005 DEN

     U3 = - 0.764 HWY EXC - 0.010 HWY RUN3 - 0.004 HWY COST3
          + 0.005 DEN

     U4 = - 0.873 HWY EXC - 0.010 HWY RUN4 - 0.004 HWY COST4
          + 0.005 DEN

     U5 = - 1.267 HWY EXC - 0.010 HWY RUN5 - 0.004 HWY COST5
          + 0.006 DEN

2.   Linear utility expressions for three mode model.

     UT = - 0.033 TRN EXC - 0.013 (TRN RUN + AUTO ACC) - 0.005
FARE

     U1 = - 0.451 HWY EXC - 0.013 HWY RUN1 - 0.005 HWY COST1
          + 0.005   DEN

     UG = - 0.755 HWY EXC - 0.013 HWY RUNG - 0.005 HWY COM
          + 0.007 DEN - 0.005 HWY DIST

3.   Linear utility expressions for auto occupancy model.

     U2 = - 2.997 HWY RUN2 + 0.020 TRN ACC (dest) + 0.030 TRN ACC
               (org)

     U3 = - 2.997 HWY RUN3 - 0.007 HWY   DIST

     U4 = - 2.997 HWY RUN4 + 0.001 HWY   DIST

     U5 = - 2.997 HWY RUN5 + 0.005 HWY  DIST


           TWIN CITIES NON HOME BASED MODE CHOICE MODELS
                            figure 3.6

                                55





1.   Linear utility expressions for home based work model.

     UT = - 0.033 TRN EXC - 0.023 (TRN RUN + AUTO ACC) - 0.016
FARE
          - 0.935 AUTO CONN

     U1 =     - 0.033 HWY EXC - 0.023 HWY RUN1 - 0.01 6 HWY COST1
          + 0.278 INC

     UG =     - 0.033 HWY EXC - 0.023 HWY RUNG - 0.016 HWY COST1
          + 0.200 INC-

2.   Linear utility expressions for home based other model.

     UT =     - 0.040 TRN EXC - 0.016 (TRN RUN + AUTO ACC) - 0.011
FARE
          - 1.779 AUTO CONN

     U1 = - 0.040 HWY EXC - 0.016 HWY RUN1 - 0.011 HWY COST1
          + 0.228 INC

     UG = - 0.040 HWY EXC - 0.016 HWY RUNG - 0.011 HWY COSTG
          + 0.409 INC


              TWIN CITIES ABSTRACT MODE CHOICE MODELS
                            figure 3.7

                                56





1. Transit Variables

      WALK =   walk time to and from the transit system.

     WAIT1 =   the waiting time to board the first transit vehicle.

     WAIT2 =   the waiting time to board the second and subsequent
               transit vehicles.

   TRN RUN =   the time spent riding in the transit vehicle.

  AUTO ACC =   the time spent riding in an automobile to get to the
               transit system.

      FARE =   the transit fare.

AUTO CONN =    a dummy variable signifying if an automobile was
               required to access the transit system (O - no, 1 -
               yes).

     TRFRS =   the number of transfers required.

TRN ACC () =   the transit accessibility; i.e.,the percent of
               attractions within 30 minutes of the origin (org) or
               destination (dest) zone via transit.

   TRN EXC =   transit excess time, the sum of walk and wait times.


2.   Highway Variables

   HWY RUN =   the time spent riding in the automobile.

  HWY COST =   the out-of-pocket highway costs.

   HWY EXC =   the time spent parking and unparking the automobile.

  HWY DIST =   the highway distance.


3.   Socioeconomic variables

       DEN =   the zone-to-zone interchange trip density.

       INC =   the four income quartiles.


            DEFINITIONS OF VARIABLES USED IN THE MODELS
                            figure 3.8

                                57





Click HERE for graphic.


                                58





better estimate of when to arrive at a bus stop or station.  It
does, however, illustrate the need for model builders to at least
consider the separate components of excess time.  A second finding
was the high negative weight attached to using an auto to access
public transit.  This would suggest that in order to increase
transit patronage it must be made accessible (within walking
distance) to a greater portion of the population.

MODEL VALIDATION
The models were validated using two different datasets.  One
dataset was the original home interview survey while the second was
from a survey collected as part of the I-35W Urban Corridor
Demonstration Project.  Since the second dataset was collected
several years after the home interview survey, it was felt that it
would provide a good test of the models' forecasting abilities.

The models were first applied to base year person trip tables
derived from the home interview survey.  This application resulted
in estimates of zone-to-zone travel by mode.  These estimates were
then compared to modal trip tables obtained directly from the
survey.  As shown in table 3.3, both the three mode model and the
multimode model performed quite well overall.  There was a tendency
for the models to underestimate transit trips and to overestimate
auto passenger trips to the CBD's.  In most instances, however, the
errors-were between one and two percent.

A second validation test was made by comparing the average trip
distances by mode estimated from the models with the trip distances
observed from the base year data.  Table 3.4 shows the results of
this analysis.  While most of the errors were again less than two
percent, it was noted that there

                                59





Click HERE for graphic.


                                60





were some fairly large discrepancies in the estimates for non-work
transit trips.  A further investigation revealed that the errors
were largely caused by the very uneven distribution of observed
transit trips for the higher trip distances.  The clustering of
transit trips at specific trip distances made the computation of an
"average trip distance" almost meaningless.

In another test, percent transit,was plotted against trip distance. 
The models accurately estimated the observed distribution of
transit trips for distances up to about 10 miles.  Beyond this, the
number of observations dropped off considerably, causing percent
transit to behave erratically due to large random variations.

The final validation test used the home-based work trip models to
estimate mode splits from data collected in the I-35W Urban
Corridor Demonstration Project.  The transit service in this
corridor was highly competitive with the automobile in terms of
both time and cost.  It was felt that this would provide a good
test of the model's ability to forecast mode splits under
significantly different conditions from those in which they were
calibrated.  Overall, the models did extremely well in estimating
transit trips, with the five mode model overestimating by about
eight percent and the three mode model overestimating by just under
five percent.  These errors were well within the tolerance limits
set by the Metropolitan Council and the consultants.

SENSITIVITY ANALYSIS
The sensitivity of the mode choice estimates to changes in the
values of model variables were examined using two different
analyses.  In the first

                                61





Click HERE for graphic.


                                62





analysis, demand elasticities were computed for each variable,
taken at its mean value.  Table 3.5 shows the demand elasticities
for the five mode, home-based work model.26  Transit demand seemed
to be most sensitive to transit run time and fare, while demand for
high occupant autos was most sensitive to auto excess time and
income.  Demand for single occupant auto trips was relatively
insensitive to every variable in its linear utility expression.

The second analysis used a subset of the model variables which
represented those attributes most likely to be changed by typical
transportation policies.  Eight level-of-service variables were
selected, including transit fare, transit run time, transit walk
time, transit wait time, highway run time, parking cost, highway
excess time, and highway operating cost.  Using observations from
the calibration dataset, each variable in turn was uniformly
improved or worsened in six steps and the resulting mode choice
estimates computed for each change.  The analysis was performed for
work trips using the five mode home-based work mode, and for non-
work trips using the three-mode home-based other model.  Separate
analyses.were also done for CBD and non-CBD oriented trips.

The results were summarized by computing an average change in mode
split for the six changes in each system variable.  Table 3.6
presents the average percent change in work mode splits for the
range of variation in each level-of-service-variable.  Most of the
results are similar to those obtained in
___________________________

26  Demand elasticities for the other models are available in the
     report Development and Calibration of Mode Choice Models for
     the Twin Cities Area, by R.H. Pratt Associates, Inc., and DTM,
     Inc., August 30, 1976, pp. 35-38.

                                63





Click HERE for graphic.


                                64





the analysis of demand elasticities.  Transit demand is quite
sensitive to fare increases.  Both transit and carpool demand are
sensitive to changes in highway excess time, but in opposite
directions.  Demand for single occupant autos, on the other hand,
is relatively insensitive to any reasonable change in level-of-
service.

These analyses have rather important policy implications for the
Metropolitan Council.  First, in contrast to the San Diego model
presented in the previous case study, the models suggest that
changes in transit fare may produce significant changes in transit
demand.  Secondly, the relative sensitivity of transit and carpool
demand to highway excess time suggests that policies which make it
more difficult to park may increase the demand for transit, but at
the expense of carpools rather than single occupant autos.  This
would also argue for policies which penalize the single occupant
auto but give preferential treatment to carpools.

APPLICATION OF THE MODELS IN THE PLANNING PROCESS
The Twin Cities mode choice models were designed to be applied
using the UTPS program UMODEL.  This is a highly flexible general
purpose program which acts as a framework for the application of
user-furnished travel demand models, including trip generation
models, trip distribution models, and mode choice models.

The Twin Cities mode choice models are entered as user coded
subroutines in UMODEL.  For an application run, the analyst simply
selects one of the eight mode choice models available, based on his
assessment of 1. the specific forecasting requirements; 2. the
interent purpose for which the

                                65





model was developed; and 3. the ability of the model to meet
specific validation and sensitivity criteria.

The input data needed to apply the models consists of travel times
and costs, person trips, and zone specific data such as parking
cost.  The user can also specify such information as auto operating
costs and mode splits for intra-zonal and through trips.

The programs operate reasonably efficiently, taking under 30
minutes of computer time on an IBM 370/168 for a full run of the
models.  To aid the Metropolitan Council planning staff, the
consultant also prepared a detailed application manual.27

No results are presently available on the use to which these models
have been put by the Metropolitan Council.  It is anticipated that
they will be used both for long range systems planning and for
short range policy planning.  Other illustrations of short range
policy planning using individual choice models are presented in the
following chapter.
___________________________

27  R.H. Pratt Associates, Inc., and DTM, Inc., Mode Choice
     Application Manual for the Twin Cities Area, prepared for the
     Metropolitan Council, April 1976.

                                66





                            CHAPTER IV
            EVALUATING TRANSPORTATION SYSTEM MANAGEMENT
         POLICIES WITH INDIVIDUAL CHOICE MODELS BACKGROUND

In recent years, there has been an emphasis in urban transportation
planning on short range, transportation system management (TSM) to
make more efficient use of existing facilities.  While TSM policies
are usually characterized as those which can be implemented fairly
quickly and with little cost, their overall impact on travel demand
can be significant.  The political consequences of implementing a
policy which turns out to be ineffective or even counter productive
clearly makes it desirable to first evaluate the impact of a
proposed policy with a forecasting model.

Unfortunately, while the traditional travel demand forecasting
process is reasonably effective in long range planning, it is quite
inefficient in evaluating short range, TSM type policies.  There
are several reasons for this.  First, the data needed to drive the
models in the traditional process are both costly and time
consuming to collect.  Secondly, the models themselves are
expensive to run, making it impractical to evaluate more than one
or two alternatives.  Finally, most of the models are insensitive
to changes of the magnitude associated with TSM policies.

Because of these problems, some planners have turned to individual
choice models for evaluating TSM policies.  In this chapter, we
examine those properties of individual choice models which have
contributed to their success

                                67





in TSM planning, and document their use in several planning
applications.  The two case studies illustrate the variety of
policies which can be addressed, and the range of complexity which
can be incorporated in the models.

THE SUITABILITY OF INDIVIDUAL CHOICE MODELS FOR TSM PLANNING.
In order to efficiently evaluate transportation system management
policies, forecasting models should be inexpensive to operate and
responsive in a short time frame.

One property of individual choice models which makes them
particularly attractive in evaluating TSM policies is the amount of
data they need for calibration:

1.   Individual choice models can be calibrated with a small
     database.  Individual choice models are calibrated using
     individual trip records as data points.  Statistically
     reliable models can usually be constructed with as few as 200
     or 300,individual observations.  By comparison, models using
     zonal means as data points may require 10 to 100 times as many
     individual observations.  Reducing the data requirements
     increases the overall efficiency of the models in two ways:
     a.   The time and costs expended on data collection are
          reduced substantially.  Since data collection costs can
          represent a substantial proportion of the overall cost of
          model development, this can mean significant savings to
          the planner.  It can also mean the difference between
          conducting a small scale survey in order to calibrate new
          models or relying on data from a prior O-D survey which
          may be outdated.

                                68





     b.   The computational time and costs associated with data
          preparation and model calibration are reduced
          proportionately as the data file decreases.  Thus, even
          if there is no need for a new data collection effort,
          time and cost savings can still be realized by using
          individual observations rather than zonal aggregate data.

     Overall, the use,of individual choice data can reduce the
     minimum response time to an acceptable level, and can make
     modelling costs feasible for TSM planning.

Since many TSM policies are designed to impact only a few travel
decisions, it becomes very inefficient to iterate through the
entire travel demand forecasting process.  With individual choice
models, this problem can be avoided:

2.   Individual choice models can be used to represent almost any
     choice situation.

     The only requirement is that each choice alternative be
     explicitly identified and represented by a set of descriptive
     attributes.  Even joint decisions can be modelled, such as the
     simultaneous choice of destination and mode of travel, or auto
     ownership and choice of mode to work.  Thus, the individual
     choice model allows the planner to focus on the travel
     decisions of interest without requiring him to run preliminary
     models to obtain input data.  This can represent a significant
     savings of time and cost by eliminating unnecessary data
     preparation and model development associated with the
     preliminary models.

                                69





Traditional travel demand forecasting is constrained to evaluate
transportation policies in terms of their impact on travel between
specific geographical units.  In evaluating TSM policies, planners
may be more concerned about the impact on a segment of the
population which cannot be identified by location (e.g., the
elderly).  Individual choice models capable of handling this type
of analysis:

3.   Individual choice models can be applied at any level of
     aggregation.
     A model can be applied at the same level of detail for which
     it was calibrated, or at a coarser level by aggregating the
     units of observation in some manner.  Since individual choice
     models use the tripmaker as their unit of observation, and
     since individuals can be aggregated in a variety of ways
     (e.g., by location, by socioeconomic characteristics, by
     observed choice, etc.), these models have a much greater range
     of applicability than zonal based models.

One of the problems associated with using individual choice models
in the traditional travel demand forecasting process is the
difficulty in obtaining accurate distributions of socioeconomic
parameters when individuals are aggregated in small geographical
units like traffic zones.  This problem is of less concern in TSM
planning because very often policies are implemented over the
entire urban area.  Aggregation problems are reduced in two ways by
applying the models at the areawide, rather than the zonal, level. 
First, distributions of socioeconomic variables are often available
at the areawide level, but not at the level of the traffic zone. 
Secondly, it has been shown that overall prediction errors actually
decrease at levels of aggregation

                                70





above the traffic zone,1 provided the models are calibrated with
individual choice data.

Individual choice models have one additional feature which makes
them particularly well suited in the evaluation of policies which
effect small changes in specific transportation attributes:

4.   The calibration coefficients in the linear utility expression
     represent the relative importance of the included attributes
     to the travel choice decision.
     This interpretation can be used in a number of ways to
     simplify analyses and to expedite comparisons among
     alternative policies:
     a.   High priority attributes can be identified by multiplying
          the calibrated coefficient by the magnitude of a
          reasonable change in the attribute, and looking for those
          having the largest products.. Policies which modify only
          low valued attributes could then be eliminated from
          further consideration.

     b.   Trade-offs between attributes can be examined by taking
          the ratio of the attribute coefficients.  This ratio may
          be used to determine the amount of one attribute a
          traveler is willing to give up to get one unit of another
          attribute.  Analyses of tradeoff can be particularly
          useful in examining policies which affect more than one
          aspect of transportation service.

     c.   At a more detailed level of analyses, the coefficients
          can be used to compute demand elasticities of travel
          alternatives with respect
___________________________

1   Koppelman, F. S., "Guidelines for Aggregate Travel Prediction
     Using Disaggregate Choice Models," paper presented at the 55th
     Annual Meeting of the Transportation Research Board,
     Washington, D.C., January 1976.

                                71





     to various attributes.  This allows the planner to see the
     relative magnitude and direction of change in travel demand
     resulting from minor changes to a particular attribute.  It
     also allows the planner to see the effect of a particular
     transportation system change on the demand for other travel
     alternatives.2

     Analyses of demand elasticities are particularly relevant in
     TSM planning.  Very often, TSM policies involve minor changes
     to a particular transportation system attribute, such as a
     transit fare increase or a change in the price of gasoline. 
     By examining demand elasticities, the planner can effectively
     evaluate these minor changes without having to employ
     sophisticated forecasting procedures.  This it short cut"
     approach can substantially reduce the response time for policy
     evaluation.

Clearly, the suitability of individual choice models for TSM
planning varies with the types of policies being considered. 
Individual choice models are most effective comparing a large
number of alternative policies involving relatively minor changes
which are implemented uniformly over the study area.  From a
planning perspective, however, the reduced data requirements of
individual choice models should always be a major consideration,
particularly if additional data must be collected to realistically
evaluate certain policy alternatives.

A SUMMARY OF RECENT APPLICATIONS
In spite of their suitability for evaluating short range, TSM
policies, there
___________________________

2   Demand elasticities and methods for computing them from
     individual choice models are discussed in Chapter II.

                                72





has been relatively little formal documentation on this use of
individual choice models.  One possible explanation is that TSM
planning has often been regarded as "firefighting" by planning
agencies.  If the models are already available, they may be used
routinely without any thought of documenting the application.  It
is likely that many of the mode split models presented in Chapter
III have been used in this way.  Case Study No. 1 illustrates some
of the ways in which policy evaluation can be performed using
existing models.

One example of TSM policy evaluation which has been documented was
done by the Illinois Department of Transportation to study the
impact of parking tax increases on automobile use in downtown
Chicago.3  Twenty traffic zones making up the central area of
Chicago were used for the analysis.  The percentages of trips made
via automobile and public transit to each of the 20 zones were
obtained from the 1970 CATS home interview survey for work and non-
work trip purposes.  The zones were then grouped into three
subareas based on similarities in their mode splits.  Binary mode
choice models developed by CATS were applied using the mode splits
in each of the three subareas as base values.  Modifications were
then made in the travel cost variable to reflect increases in
parking costs ranging from $1.00 to $10.00, and new mode split
percentages were computed.  It was found that the substantial
increases in parking taxes would be required to produce any major
change in the relative mode splits for the work trip, while the
nonwork mode splits could be changed more easily.
___________________________

3   T. E. Lisco, and N. Tahir, "Travel Mode Choice Impact of
     Potential Parking Taxes in Downtown Chicago," Technical Papers
     and Note Series No. 12, Illinois Department of Transportation,
     Chicago, February 1974.

                                73





Since the study was of a preliminary nature, the investigators were
concerned only with the relative magnitude of change, and not
precise relationships between mode split and parking costs.  Thus,
a number of assumptions were made in the interest of efficiency
which were not mathematically correct.  This tradeoff almost always
exists in practical applications, but as long as the assumptions do
not invalidate the model for that particular application, such
simplification may be justified.

The Institute of Transportation Studies, at the University of
California, Berkeley, is presently engaged in a three year study to
refine urban travel demand forecasting models, and to investigate
potential applications in the areas of demand forecasting for new
modes and short range policy analysis.4  While most of the
research to date has concentrated on methodological development,
two working papers have been published which illustrate the
application of individual choice models to study the feasibility of
alternative transit fare structures.

The first study investigated the impacts of instituting a
flat fare system for BART, the San Francisco Bay Area Rapid Transit
System.5  A relatively small data set (160 observations)
consisting of commuter trips originating in the East Bay was used
in the analysis. Since the study was    primarily for illustrative
purposes, no attempt was made to generalize the results to the
entire.Bay Area population.  Some additional simplifying
assumptions
___________________________

4   The study, known as the Travel Demand Forecasting Project, is
     being funded by a grant from the National Science Foundation,
     under its RANN (Research Applied to National Needs) program.

5   D. McFadden, "Bart Patronage and Revenue Forecasts for Flat
     Fares," Working Paper No. 7407, Travel Demand Forecasting
     Project, University of California, Berkeley, December 1974.

                                74





were made concerning the transportation system (1972 service levels
were used) and the travel behavior affected (only mode choice was
considered).  These assumptions were explicitly stated in the
paper.

A binary mode choice model was constructed from the calibration
sample for auto versus bus.  Mode choice probabilities for BART
were obtained by treating it as a new mode and computing its market
share from the auto-bus mode split.6  Individuals in the
calibration sample were then reweighted to produce a representative
distribution of the demographic characteristics of the East Bay
commuter population.  Mode shares for auto, bus, and BART were
computed by summing the weighted sample probabilities.  This was
done for the base line fare structure and for various fare
alternatives.  Revenues for bus and BART were calculated by a
weighted sum of fares times the patronage probabilities for each
mode.

The model predicted that to maintain current revenues for BART, a
flat fare of approximately 45 cents would be needed.  At that
level, the system would experience a 12 percent increase in
patronage.  At fares below 45 cents, revenue falls off rapidly,
while at fares above 55 cents patronage drops below current levels. 
It was also noted that a uniform increase or decrease in the
present fare structure would primarily impact patronage, with
little change to revenues.  Finally, an alternative which
maintained current fares up to a ceiling was tested.  It was found
that a ceiling of 60 cents would increase patronage by 16 percent
with no significant decrease in revenues.

In the second study, a pricing strategy was considered in which the
revenue from BART and AC Transit (the Alameda-Contra Costa bus
company) would be
___________________________

6   This procedure is explained in T. Domencich and D. McFadden,
     Urban Travel Demand, North Holland Press, 1975, Chapter 4.

                                75





pooled together.7  Under this strategy the problem was to
determine the prices to be charged on each system so as to maximize
overall transit patronage, given that the combined revenues must
cover a proportion of overall system costs.

     Using criteria developed by Boiteux for constrained welfare
maximization,8 it was shown that optimal prices are a function of
system costs and demand quantities and elasticities.  The costs
were obtained from earlier studies conducted by the University of
California.9  The demand relationships were obtained using the
same logit mode choice model developed for the flat fares study.

Given demand and cost relationships, it was determined that bus
prices would gave to average between 1.8 cents and 2.0 cents per
mile, and BART prices would have to average between 1.6 cents and
1.8 cents per mile in order to satisfy the constrained welfare
maximization criteria.  With average costs for bus and BART being
2.0 cents and 1.7 cents per mile, respectively, the results
indicated that a nearly optimal pricing strategy would be to price
each mode at its average cost per mile.  There would be little or
no need for cross-subsidization between the modes, and any that did
occur would flow from BART to A.C. Transit.
___________________________

7   K. Train, "Optimal Prices for A.C. Transit and Bart Under a
     Constraint on Combined Loss," Working Paper No. 7512, Travel
     Demand Forecasting Project, University of California,
     Berkeley, May 1975.

8    M. Boiteux, "La Vent au Cout Marginal," Revue Francaise de
     l'Energie, December 1956.

9   L. Merewitz and R. Pozdena, "A Long-Run Cost Function for Rail
     Rapid Transit Properties," Working Paper No. 240, Institute of
     Urban and Regional Development, University of California,
     Berkeley, September 1974, and D. B. Lee, "Cost Components for
     Selected Public Transportation Modes in the San Francisco Bay
     Area," Institute of Urban and Regional Development University
     of California, Berkeley, January 1974.

                                76





Each of the preceding applications evaluated pricing policies in
terms of their impact on mode choice.  That is, it was assumed that
the overall demand for travel would be unchanged by any of the
proposed strategies.  In the following two examples, the overall
demand for travel was assumed to be variable, and simultaneous
choice models were used.  We shall highlight the objectives and
general results of these studies in this summary section, and then
provide a more comprehensive discussion when they are examined as
case studies at the end of this chapter.

The first study was done for the Environmental Protection Agency by
Charles River Associates, Inc.10  The purpose of this study was to
investigate the effectiveness of three specific pollution control
strategies on travel behavior in Los Angeles.  The three strategies
were: 1. increases in the gasoline tax, 2. implementation of a tax
on automobile emissions per mile, and 3. implementation of parking
surcharges for non-residential parking.

A joint choice model of trip frequency, destination choice, and
mode choice was used in the analysis.  This model was originally
calibrated using data from a 1967 Pittsburgh survey.  It was found
by empirical testing that the parameters of the original model did
not have to be recalibrated in order to apply it to Los Angeles. 
The model was applied to a representative sample of zonal
interchanges for the Los Angeles region.  Mode split estimates
___________________________

10  Charles River Associates, Inc., The Effects of Automotive Fuel
     Conservation Measures on Automotive Air Pollution, final
     report submitted to the Environmental Protection Agency,
     November 1975.

11  The model development was part of a study sponsored by the
     Federal Highway Administration and is reported in Charles
     River Associates, Inc., A Disaggregated Behavioral Model of
     Urban Travel Demand, March 1972.

                                77





were computed for five modes: drive alone, auto passenger, driver
serve passenger, transit, and walk.  Automobile travel costs were
then modified to reflect various levels of taxation, and the model
was reapplied.  Overall changes in VMT were also computed, based on
the model results.

It was found that parking taxes would be considerably less
effective at reducing VMT than either taxes on gasoline or emission
because parking tax policies induce driver serve passenger trips
and unlike per mile charges -their impact decreases with increasing
trip length.  In terms of overall efficiency at reducing air
pollution, an emissions tax seemed most promising.  Its effect on
VMT was nearly the same as a tax on gasoline, but it would have a
greater deterrent effect on higher Polluting vehicles.

The use of a model which considers a large range of alternatives
and choices may give the planner further insight into the effects
of policies under consideration.  This can be illustrated by a
comparison of the Charles River Associates, Inc., model with the
Illinois Department of Transportation model on their evaluation of
parking tax strategies.  Both models showed that a parking tax
increase would cause some shift from auto drivers to transit
passengers.  Taken at that, one might conclude that parking tax
increases could be used to reduce automobile congestion in the
central city.  However, the CRA model also considered driver serve
passenger as an alternative to drive alone, and found that these
trips would increase at a greater rate than transit trips.  This,
combined with longer trip length for driver serve passenger trips,
indicates that parking tax strategies may not be effective at
reducing VMT and congestion in the central city.

                                78





The final study presented in this section was done for the Federal
Energy Administration by Cambridge Systematics, Inc.12  The
purpose of the study was to analyze the impacts of several policies
designed to promote carpooling for the work trip.  The analysis was
conducted in a case study format, using data from a 1968
Washington, D.C., home interview survey.  A series of three
individual choice models were used to predict 1. mode choice for
the work trip, 2. non-work trip frequency, destination and mode
choice, and 3. automobile ownership.

A random sample of households was selected for analysis.  Base year
choice probabilities were estimated using the models.  Then
specific changes were made in the transportation system variables
to reflect policy implementations, and new choice probabilities
were estimated.  A number of policy changes were tested, consisting
primarily of parking taxes, increases in gasoline prices, and
parking incentives to carpools.

It was found that carpool incentives which regulated parking at the
work place would produce only a small decrease in areawide VMT and
fuel consumption.  This is because any decrease in automobile use
for the work trip would be partially offset by an increase in non-
work trips due to the availability of an extra automobile at home. 
On the other hand, any policy which discouraged automobile use for
both work and non-work travel, such as gasoline price increases,
would have a greater than expected impact on areawide VMT, with
non-work travel showing more sensitivity to the policies than work
trips.
___________________________

12  T. J. Atherton, J. H. Suhbier, and W. A. Jessiman, "The Use of
     Disaggregate Travel Demand Models to Analyze Carpooling Policy
     Incentives," draft of a working paper submitted to the Federal
     Energy Administration, October 1975.

                                79





It is interesting to note how the last two studies predicted
somewhat different impacts for policies which increased parking
costs.  The model built by Cambridge Systematics, Inc., predicted
that increased parking costs would shift the single driver to
either transit or carpools thereby decreasing VMT.  The model built
by Charles Rivet Associates, Inc., however, indicated that
increased parking costs may actually increase VMT by encouraging
more driver serve passenger trips.  Clearly, the validity of either
model depends on the relative proportion of the driver serve
passenger trips for the work trip.  More importantly, however,-it
illustrates the need to fully understand the assumptions and
simplifications being incorporated into the models, and whether
those assumptions are, in fact, reasonable.

                                80





                            REFERENCES


T. J. Atherton, J. H. Suhbier, and W. A. Jessiman, "The Use of
Disaggregate Travel Demand Models to Analyze Carpooling Policy
Incentives," draft of a working paper submitted to the Federal
Energy Administration, October 1975.

Charles River Associates, Inc.  The Effects of Automotive Fuel
Conservation Measures on Automotive Air Pollution, final report
submitted to the Environmental Protection Agency, November 1975.

F. C. Dunbar, "Evaluation of the Effectiveness of Pollution Control
Strategies on Travel: An Application of Disaggregated Behavioral
Demand Models," Transportation Research Forum Proceedings, XVI, No.
1. 1975, pp. 259-268.

T. E. Lisco, and N. Tabir, "Travel Mode Choice Impact of Potential
Parking Taxes in Downtown Chicago," Technical Papers and Note
Series No. 12, Illinois Department of Transportation, Chicago,
February 1974.

D. McFadden, "Bart Patronage and Revenue Forecasts for Flat Fares,"
Working Paper No. 7407, Travel Demand Forecasting Project,
University of California, Berkeley, December 1974.

K. Train, "Optimal Prices for A. C. Transit and Bart Under a
Constraint on Combined Loss," Working Paper No. 7512, Travel Demand
Forecasting Project, University of California, Berkeley, May 1975.

                                81





                         CASE STUDY NO. 3
       EVALUATING THE IMPACT OF POLLUTION CONTROL STRATEGIES
                     ON REGIONAL TRAVEL DEMAND

BACKGROUND
Metropolitan areas have generally attempted to meet Federal clean
air standards by proposing policies which directly regulate travel
behavior, such as parking bans or gasoline rationing.  Policies
which focus on economic incentives or disincentives to change
travel behavior have often been ignored because of the difficulties
associated with predicting their effects on reducing overall air
pollution.  In an effort to address this problem, the Environmental
Protection Agency contracted with Charles River Associates, Inc.,
(CRA) to develop a methodology for evaluating the effectiveness of
non-regulatory policies in controlling automotive air pollution. 
The consultants believed that a policy-sensitive evaluation
framework could be achieved by the use of a set of individual
choice models.  The feasibility of such an approach had already
been demonstrated in an earlier study,13 and it was felt that the
EPA study would provide an excellent opportunity to test the model
set in a practical application.

DESCRIPTION OF THE MODELS
The individual choice models employed by CRA, for this study
included a mode choice model for work trips and a joint
frequency/destination/mode
___________________________

13  Charles River Associates, Inc., A Disaggregated Behavioral
     Model of Urban Travel Demand, final report to the Federal
     Highway Administration, March 1972, chapter 7.

                                82





choice model for shopping trips.  The models were structured in a
binary logit format, as shown in equation 4.1:

(4.1)               Pi/Pj  =  exp (Ui - Uj)

where     Pi/Pj = the ratio of the probability of choosing
                    alternative i to the probability of choosing
                    alternative j;

     (Ui - Uj)  = the difference in the linear utility
                    expressions between I alternative i and
                    alternative j.

The joint shopping model consisted of three individual choice
models which estimated the following conditional probabilities:

1.        P(ti) - that an individual at location i will make a
                    shopping trip sometime during period t;
2.      P(jt,i) - that an individual at location i will go to
                    location j, given that he will make a shopping
                    trip;
3.    P(mj,t,i) - that an individual at location i will make a
                    trip by mode m, given that he will make a
                    shopping trip to location j.

The joint probability that an individual at location i will make a
shopping trip to location j via mode m is equal to the product of
the three conditional probabilities, as shown in equation 4.2:

(4.2)        Pijm = P(ti) * P(jt,i) * P(mj,t,i)

The models used in this study had already been developed for
another study and were calibrated with data obtained from a 1967
survey conducted in Pittsburgh.14  The models are presented in
figure 4.1:
___________________________

14  Charles River Associates, Inc.: op. cit.

                                83





1.   Work Mode Choice Model


     P(A:i,j)
     _________ =   exp (- 4.77 - 2.24 (Caij - Cbij)

     P(B:i,j)

                    - 0.0411 (Taij - Tbij) - 0.114 (Saij -
Sbij)

                    + 3.79 Y)


2.   Shopping Mode Choice Model

     P(A:i,i)
     _________ = exp 6.77 - 4.11 (Caij - Cbij)

     P(B:i,j)

               0.0654 (Taij -     Tbij) - 0.374 (Saij - Sbij)

               + 2.24 Y


3.   Shopping Destination Choice Model


     P(j:i)
     _______ =  exp (1.06 (Xij - Xik) + 0.844 (Ej - Ek))

     P(k:i)


4.   Shopping Trip Frequency Model

     P(1:i)                 _         _
     ________ = exp ( -1.71 Xi + 3.90 Ei))

     P(0:i)

            TRAVEL CHOICE MODELS USED IN THE EPA STUDY
                            figure 4.1a

                                84





     DEFINITIONS OF VARIABLES USED IN THE MODELS

     P(A:i,j)
   ____________ 

     P(B:i,j)  =    the ratio of the probability of choosing an
                    auto to the probability of choosing a bus for a
                    round trip between origin i and destination j
                    for a particular purpose.

 (Caij - Cbij) =  the difference in total costs for a round trip
                    between i and j for auto versus bus.

 (Taij - Tbij) =  the difference in total travel time for a round
                    trip between i and j for auto versus bus.

 (Saij - Sbij) =  the difference in walk access time for a round
                    trip between i and j for auto versus bus.

            Y =     the number of autos available to the tripmaker.


        P(j:i)
       _________

        P(k:i) =    the ratio of the probability of choosing
                    destination j to the probability of choosing
                    another destination k for a round trip made by
                    any mode from origin i for shopping.

          Xij  =   the generalized cost of travel for a round trip
                    from i to j for shopping.

           Ej  =   the proportion of retail employment at location
                    j relative to the total retail employment in
                    the region.


       P(1:i)
     __________

       P(O:i)  =    the ratio of the probability that an individual
                    at location i will make a shopping trip to the
                    probability that he will make no shopping trip
                    in a 24 hour period.
            _
            Xi  =  the generalized cost of travel to all
                    destinations from location i for a round trip
                    for shopping.
            _
            Ei  =  the generalized availability of shopping
                    opportunities for an individual at location i.

IDENTITY RELATIONSHIPS

     Xij  =   P(A:i,j) * (4.11 Caij + 0.0654 Taij + 0.374 Saij
               + P(B:i,j) * (4.11 Cbij + 0.0654 Tbij + 0.374
Sbij)
     _
     Xi  =     P(j:i)* Xij
               j
     _
     Ei  =     P(j:i) * Ej
               j

                            figure 4.1b

                                85





The mode choice models included three system performance measures:
access time, in vehicle time, and cost, plus a proxy variable for
auto availability.  The shopping destination choice model used
measures of overall disutility for each mode weighted by the mode
choice probabilities.  Retail employment density was used as a
measure of destination attractiveness.  The shopping trip frequency
model used weighted summations of destination attractiveness and
transportation disutility.

The basic model set was modified in two ways before any policy
analysis was undertaken.  The first modification was made to reduce
the aggregation bias caused by using zonal level variables.  A
formulation proposed by Talvitie15 was used to derive estimates of
choice frequencies for interzonal trips based on zonal means and
estimates of within zone variances for the explanatory variables.

Variances in line haul times and costs were estimated from a
probability density function whose parameters were functions of the
area of a zone.  As the zone size increased, the variance in these
variables became larger.  Variance in transit access time was
estimated from a uniform probability density function which was
independent of zone size.  Finally, variances in socioeconomic
variables were calculated from census data.

The second modification was made to expand the number of
alternative modes for the work and shopping mode choice models. 
The original models were calibrated using only two modes: auto
driver and bus.  However, it was
___________________________

15  A. Talvitie, "Aggregate Travel Demand Analysis with
     Disaggregate or Aggregate Travel Demand Models,"
     Transportation Research Forum Proceedings, XIII, No. 1, 1973.

                                86





anticipated that the major effect of many pollution control
policies would be to divert single occupant auto trips to shared
ride trips or even walk trips.  By modifying the variables in the
basic auto versus transit mode choice models, utility difference
formulations were derived for auto versus carpools auto versus
walk, and auto versus driver serve passenger trips.16

The choice models would give mode split percentages for work and
shopping trips, plus destinations and frequency rates for shopping
trips.  It was felt that a single common measure of travel such as
vehicle miles of travel (VMT) would be the most appropriate way to
compare the overall impact of various control strategies.  The
formulation for the VMT of private vehicles is shown in equation
4.3:

(4.3)     VMT =     Di * 2 * (Auto Driver Tripsij + 2 * Auto
                    Serve Passenger Trips + (Dii + Djj ) * Auto
                    Passenger Trips

where     Dij  =   the interzonal distance between zones i and j

          Dii  =   the mean intrazonal distance in zone i. (This
                    term accounts for the extra distance which must
                    be traveled to pick up and discharge
                    passengers).


APPLICATION OF THE MODELS TO LOS ANGELES DATA

The calibrated models were applied to data obtained from the 1967
Los Angeles Region Transportation Study (LARTS).  The data
consisted of 24 hour trip records for a one percent sample of
households in the Los Angeles region.
___________________________

16  A detailed description of both modifications is presented in
     Appendix A of the draft final report prepared for EPA, titled
     Economic Analysis of Policies for Controlling Air Pollution in
     the Los Angeles Region, March 1974.

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                                88





The network was based on a system of 108 sketch planning districts
used by LARTS in 1970.

Rather than examine every interchange for the 108 zones, a random
sample of 172 zonal interchanges for work trips and a sample of 15
representative zonal interchanges for shopping trips were selected. 
It was felt that by using this procedure, the aggregate impacts of
alternative policies could be evaluated fairly accurately with a
minimal expenditure of computer resources.

Mode split and VMT estimates obtained from the models were compared
with trips observed in the dataset for the selected zonal
interchanges.  Table 4.1 compares the results of the models to the
data for the work and shopping models.

The model estimates were not totally comparable to the available
data.  The Los Angeles survey did not include either driver serve
passenger or walk trips, for example.  Furthermore, it was not
known whether data on vehicle miles of travel also excluded driver
serve passenger trips.  In spite of these discrepancies, however,
the models seemed to do fairly well in replicating base year
observed travel patterns.

It was also observed that the shopping trip frequency model
performed rather poorly compared to all other demand models. 
Because of its tendency to overpredict VMT, together with its
unreasonably high sensitivity to auto costs, it was decided that
the frequency model would not be used in the policy analysis. 
Shopping trip frequency was assumed to be constant.

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After comparing the models to base year observed data, final
adjustments were made in the variable values to update them from
1965 to 1974.  These adjustments involved increasing the automobile
cost per mile from $0.030 in 1967 to $0.057 in 1974, and changing
the transit costs to reflect the 1974 fare structure.

RESULTS OF POLICY ANALYSIS
Three policies of economic disincentives for using the automobile
were evaluated with the models.17  These policies included 1. an
increase in the tax on gasoline; 2. a per mile tax on vehicle
emissions; and 3. a surcharge on all non-residential parking.  The
net effect of the first two policies would be to increase the cost
per mile of automobile travel.  The cost would be directly
proportional to the distance traveled.  The effect of a parking
surcharge can also be represented as a cost for those automobile
trips which must park at the destination end.  However, the overall
cost is independent of distance, and the cost per mile of travel
decreases with increasing trip distance.

Four levels of tax were simulated for each of the three policies. 
Emission and gasoline taxes were represented by percentage
increases in the cost per mile of 25, 50, 75, and 100 percent over
base year (1974) costs.  Parking taxes were represented by overall
cost increases of $0.25, $0.50, $0.75, and $1.00.
___________________________

17  The evaluation of these policies was only one phase of a
     larger study on air pollution control strategies.  Other
     aspects of the study included an automobile stock model and an
     evaluation of transit improvements in Los Angeles.

                                90





Using these cost changes, the work and shopping trip models were
run to compute changes in mode splits from the base year.  The
results of these runs are given in Table 4.2.

The results show that all three policies reduce single occupant
automobile trips fairly significantly.  However, the modes to which
these auto trips are diverted will change, depending on the policy. 
Per mile tax increases divert both auto driver and driver serve
passenger trips to transit, auto passenger, and walk-trips, with
the greatest increase in transit trips.  Increased parking charges,
on the other hand, divert auto driver trips to transit, auto
passenger, walk, and driver serve passenger with the greatest
relative increase in driver serve passenger trips.

To examine the overall impact of these policies, the work and
shopping trip mode splits were expanded to all-trip purposes for
the entire Los Angeles region.  Areawide VMT was then computed
using equation 4.3. Table 4.3 summarizes the results of these
computations.

It is apparent from Table 4.3 that taxes implemented on a per mile
basis are significantly more effective at reducing overall VMT than
a charge which is independent of distance.  The principal
deficiencies of the parking tax are that 1. it reduces driver serve
passenger trips, which increase rather than decrease automobile
travel, and 2. the impact of a parking tax becomes less important
as trip length increases.

The case study presented above illustrates a number of points about
individual choice models.  It shows how calibrated models can be
"borrowed" from other studies and applied with reasonable accuracy,
given certain

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                                92





  Policy            Change in      Change in      Regional VMT
Alternative             VMT        Auto Trips     (mill./wkdy.)

Base Year - 1974                                       62.57

Gas or Emissions Tax (relative cost per mile increase)

      25%           - 7.40              - 5.45         57.94
      50%           -13.96              -10.50         53.84
      75%           -19.58              -15.27         50.32
     100%           -24.13              -20.39         47.47

Parking Tax (absolute tax increase)

     $0.25          - 5.40              - 7.66         59.42
     $0.50          - 9.58              -14.46         56.58
     $0.75          -13.07              -19.18         54.39
     $1.00          -15.43              -21.33         52.92

  IMPACTS OF POLLUTION CONTROL POLICIES ON ESTIMATED REGIONAL VMT
                             Table 4.3

modifications.  It presents one approach to dealing with
aggregation bias.  It shows how individual choice models can be
linked together to provide satisfactory representations of some or
all phases of the travel demand forecasting process.  It shows how
policies can be transformed and effectively represented by these
choice models.  Finally, it illustrates how the model outputs can
be presented in a manner which permits a simple, straightforward
evaluation of alternative strategies.

The next case study also involves policy evaluation using
individual choice models.  It is presented to illustrate the
variety of approaches which can be used to perform this type of
analysis, and the flexibility of individual choice models in being
able to accommodate these various approaches.

                                93





                         CASE STUDY NO. 4
            EVALUATING THE EFFECTIVENESS OF CARPOOLING
              INCENTIVES AT REDUCING FUEL CONSUMPTION

BACKGROUND
The successful evaluation of alternative policy strategies requires
that secondary effects must be considered in addition to the
achievement of policy objectives.  With this in mind, the Federal
Energy Administration, as part of its transportation research
program, undertook a study to examine the effectiveness of various
carpooling incentive strategies at reducing overall fuel
consumption.  The firm of Cambridge Systematics, Inc. (CS), was
contracted to develop the analytic tools for evaluating alternative
carpooling strategies, and to recommend the most cost-effective
strategies to increase carpooling and reduce fuel consumption. 
Based on their past experiences in travel demand modelling, the
consultants chose to use individual choice models as their basic
methodology.

DESCRIPTION OF THE MODELS
The modelling approach used by CS was somewhat different from the
one presented in Case Study No. 3. Whereas the models developed by
Charles River Associates, Inc., considered work and shopping trip
decisions to be independent of one another, in this study it was
postulated that the travel choices for a shopping trip were
dependent upon the choice of mode for the work trip, which was in
turn dependent upon the number of automobiles available to the
household.  The model set consisted of three choice models, linked

                                94





together sequentially so that the output of one model was used as
input to the next model.

Each of the models used a multinomial logit format as shown in
equation 4.4:

                     exp (Ui)
(4.4)     Pi =     __________
                    n
                      exp (Uj)
                   j=1

where     Pi  =    the probability of choosing alternative i;
          Ui  =    the linear utility expression for alternative
                    i;
           n  =     the set of feasible alternatives.


     The models used in this study were actually developed for two
other travel 18/ demand research projects conducted at Cambridge
Systematics, Inc., and the Massachusetts Institute of
Technology.19

Both of these studies used data from a 1968 home interview travel
survey conducted in Washington, D.C. Since the primary objective of
the Federal Energy Administration was to develop a practical
methodology for evaluating carpooling strategies, it was left up to
the contractor to select the test city.  Washington, D.C., was
therefore chosen to eliminate the need to recalibrate or update-the
original models.
___________________________

18  Cambridge Systematics, Inc., A Behavioral Model of Automobile
     Ownership and Mode of Travel - Vol. 3 and 4, prepared for the
     Office of the Secretary of Transportation and the Federal
     Highway Administration, September 1975.

19  T. J. Adler and Moshe Ben-Akiva, A Joint Frequency Destination
     and Mode Choice Model for Shopping Trips, prepared for the
     Office of University Research, Department of Transportation,
     December 1975.

                                95





The calibrated models used in this study are presented in figures
4.2 and 4.3. The definitions and identity relationships for each of
the variables are presented in figure 4.4.

All of the model variables could be derived from one of five
available sources.  These were 1. household records (household
socioeconomic data); 2. trip records (individual socioeconomic
data); 3. zone level highway and transit skim trees (transportation
level-of-service characteristics); 4. zone records (zonal
demographic data); and 5. the output of preceding models.  The
models were linked together as shown in figure 4.5.

The auto ownership model predicts the probability of owning zero,
one, or two or more automobiles.  These probabilities were
transformed into an expected level of auto ownership by the
following equation:

(4.5)   E(autos owned) = P(owning one automobile)
                         + 2 * P(owning two or more autos)

This value was used to help derive the autos per licensed driver
variable for the work mode choice model.

The work mode choice model predicts the probability of driving
alone, carpooling, or taking transit to work.  Given these
probabilities, the expected number of autos left at home for non-
work trips were computed as follows:

(4.6)  E(autos at home) =     E(autos owned) - P(driving alone)
                              - P(carpooling)/carpool size

Carpool size was determined by a separate linear regression model. 
The expected number of autos left at home was used directly in the
joint frequency/ destination/mode choice shopping model.

                                96





1.   Linear utility expressions for households making work trips

     U0 =     -1.62 + 1.55 In Z - 0.0129 IVTT - 0.0795 OVTT/DIST
          - 0.549 DCITY - 0.00267 TOPIC + 0.347 GW + 0.322 NWORK
          - 0.000131 (ECA * DIST)

     U1 =     16.06 + 1.55 In Z - 0.0129 IVTT - 0.0795 OVTT/DIST
          - 1.253 DCITY - 0.00267 TOPIC + 0.347 Gw + 0.322 NWORK
          - 0.000131 (ECA * DIST) - 0.798 AALD - 1.99 R

     U2 =     19.58 + 1.55 In Z - 0.0129 IVTT - 0.0795 OVTT/DIST
          - 1.253 DCITY - 0.00267 TOPIC + 0.347 GW + 0.322 NWORK
          - 0.000131 (ECA * DIST) - 0.798 AALD - 2.80 R + 1.04 HT

2.   Linear utility expressions for households with no work trips

     U0 =     1.88 1n Z

     U1 =     6.54 + 1.88 1n Z + 2.04 AALD - 5.31 R

     U2 =     7.08 + 1.88 1n Z + 2.04 AALD - 6.11 R + 0.734 HT


                     FEA AUTO OWNERSHIP MODELS
                            figure 4.2

                                97





1. Linear utility expressions for choice of mode to work

     Uc =     -3.24 - 28.8 OPTC/INC - 0.0154 IVTT - 0.160
               OVTT/DIST
          + 3.99 AALD - 0.854 DCITY + 0.0000706 DINC + 0.890 BW

     Us =     -2.24 - 28.8 OPTC/INC - 0.0154 IVTT - 0.160
               OVTT/DIST
          + 1.62 AALD - 0.404 DUTY + 0.0000706 DINC + 0.287 GW
          + 0.0983 NWORK + 0.000653 DTECA

     Ut =     -28.8 OPTC/INC - 0.154 IVTT - 0.160 OVTT/DIST


2.   Linear utility expressions for choice of shopping destination
     and mode

     U0,0 = -3.78 - 0.186 HHS + 0.000598 DEN + 0.0414 INC

     Ud,c = -0.555 - 0.100 OVTT/DIST + 6.86 (I/DIST)
            - 2.24 1n (IVTT + OVTT) - 0.0242 OPTC/INC
            + 0.562 DCBD + 0.161 1n REMP + 0.557 AA

     Ud,t = 0.100 OVTT/DIST + 6.86 (1/DIST) - 2.24 1n (IVTT +
     OVTT)
            - 0.0242 OPTC/INC + 0.562 DCBD + 0.161 In REMP


            FEA WORK AND NON WORK TRAVEL CHOICE MODELS
                            figure 4.3

                                98





1.   Auto Ownership Models

       U0  =  the linear utility expression for owning no
               automobiles

        U1 =  the linear utility expression for owning one
               automobile

        U2 =  the linear-utility expression for owning two or more
               automobiles

         Z =   household income after mandatory expenses and
               transportation (See identity relationships)

      IVTT =   daily round trip in-vehicle travel time (minutes)

      OVTT =   daily round trip out-of-vehicle travel time
               (minutes)

      DIST =   one way travel distance (miles)

     DCITY =   1, if work place is in the CBD
               0, otherwise

     TOPTC =   total annual out-of-pocket travel cost (dollars)
               (See identity relationships)

        GW =   1, if worker is a civilian employee of the Federal
               Government 0, otherwise

     NWORK =   number of workers in the household

       ECA =   employment density at the work zone (employees per
               acre)

      AALD =   number of autos per licensed driver in the household

         R =   generalized shopping travel cost for auto divided by
               the generalized shopping travel cost for transit

        HT =   1, if household lives in a single family house
               0, otherwise


            DEFINITION OF VARIABLES USED IN THE MODELS
                            figure 4.4a

                                99





2. Work Mode Choice Models


          Uc =     the linear utility expression for driving alone
                    to work

          Us =     the linear utility expression for carpooling to
                    work

          Ut =     the linear utility expression for taking
                    transit to work

        OPTC = round trip out-of-pocket travel cost (cents)

         INC = annual household income (dollars)

        DINC = household income after mandatory expenses (See
               identity relationships)

          BW = 1, if the worker is a breadwinner (a breadwinner is
               defined as the household member with the highest
               status occupation)
               0, otherwise

3.   Joint Shopping Choice Models

       U0,0 = the linear utility expression for making no shopping
               trip

       Ud,c = the linear utility expression for making a shopping
               trip to destination d by auto

       Ud,t = the linear utility expression for making a shopping
               trip to destination d by transit

        HHS =  number of persons in the household

        DEN =  retail employment density in the residence zone
               (number of employees)

       DCBD =  1, if shopping destination is the CBD
               0, otherwise

        RMT =  retail employment at shopping destination (number of
               employees)

         AA =  autos available for nonwork trips (See identity
               relationships)

                            figure 4.4b

                                100






IDENTITY RELATIONSHIPS

         Z =   INC - (800 * HHS) - (1000 * number of autos owned)
               - (2.5 *-OPTC)

     TOPTC =   2.50 * OPTC

      DINC =   INC - (800 * HHS)

        AA =   number of autos owned - number of autos used for
               work trips by workers in the household.


                            figure 4.4c

                                101





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                                102





The mode choice probabilities for work and shopping trips were
multiplied by estimates of trip distance for these purposes to
obtain estimates of vehicle miles of travel (VMT) and fuel
consumption.20


FORECASTING PROCEDURE FOR THE MODELS
Unlike the previous case studies, CS did not aggregate individual
choice probabilities into zonal values for forecasting purposes. 
Instead, they used a procedure known as random sample explicit
enumeration, which essentially reuses observations in the original
calibration dataset as a representative sample for the study area. 
The choice probabilities of these sample observations are
estimated, first in the absence of any policy change (to provide a
base case), and then after implementing a candidate policy. (Policy
changes are simulated by altering appropriate policy variables in
the models).  Policy impacts are determined household by household,
while maintaining the same socioeconomic and locational
characteristics for each household, until a sufficient sized sample
has been analyzed to draw valid conclusions about the policy's
overall impact.21

Since this procedure is applied directly to the calibration unit
(the household or individual tripmaker) no zonal aggregation is
performed in the forecasting process.  Areawide estimates of travel
behavior can be derived by dividing the fraction of areawide
population represented by the sample into the expected values
obtained for the sample.  In addition, the sample units
___________________________

20  These relationships, although developed for the project, were
     not discussed in any available documentation.

21  A detailed description of this technique is given by M. E.
     Ben-Akiva and T. J. Atherton, "Choice Model Predictions of
     Carpool Demand: Methods and Results," presented at the 56th
     annual meeting of the Transportation Research Board, January
     1977.

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                                104





may be aggregated into specific groups based on socioeconomic or
locational criteria.  Thus, the effects of the policy on particular
population segments can be investigated.

Although the random sample explicit enumeration procedure was well
suited to this study, certain restrictions may limit its use in
other applications.  First, the sample must accurately represent
the population characteristics in both the base case and after
policy implementation.  In order to represent the base case, the
sample should be drawn from the specific study area.  Thus, the
procedure would be difficult, if not impossible, to apply in an
area where no relatively current travel behavior dataset was
available.  The procedure would also be difficult to use if major
demographic or locational changes in the base case population were
anticipated before policy implementation.  Another restriction is
that the sample must be large enough to represent the population
distributions in the study area.  This suggests that the data
efficiency of individual choice models may be offset by the
additional number of observations required for a representative
sample. (CS used 800 households to obtain their sample). 
Alternatively, a smaller, non-random sample may be chosen, if the
joint distributions of all demographic and locational variables are
known.  This is usually not the case, however.  In conclusion, the
random sample explicit enumeration procedure seems best suited for
those studies involving short range areawide policy evaluation in
an area which has travel choice data from a recent population based
travel survey.

RESULTS OF POLICY ANALYSES
The carpooling policies which were examined are presented in Table
4.4.  Increases in parking and gasoline costs were represented by
appropriate

                                105





changes in the out of pocket cost variable for drive alone and
carpool modes.  Parking incentives were represented by a decrease
in carpool excess time and an increase in drive alone excess time. 
Employer incentives were represented by changing the "government
worker" dummy variable from 0 to 1, thereby increasing the linear
utility expression for the carpool alternative.

The representation of a gasoline rationing policy required an
iterative procedure to calculate supply and demand equilibria. 
This equilibrating process was done on a household by household
basis.  The first pass determined the amount of fuel consumed by
the household with no resource constraint.  If that amount was
greater than the amount allotted to the household, a "shadow price"
was computed and added to the per gallon price of gasoline for the
household.  This resulting fuel price was then used in a second
iteration to predict adjusted travel behavior.  The iteration
process would continue until the amount of fuel consumed by the
household was in equilibrium with the amount of fuel allocated.

Two significant results emerged from analysis of the areawide
impacts of carpooling policies.  First, it was shown that the
decrease in fuel consumption resulting from any policy which
affects only work travel is partially negated by an increase in
non-work travel resulting from greater auto availability.  It was
also shown that when a policy affects both work and non-work
travel, the decrease in non-work VMT is even greater than the
decrease in work trip VMT, despite the increase in auto
availability for non-work travel.  This supports the theory that
non-work travel, being more discretionary than work travel, is
therefore more sensitive to changes in level-of-service. 
Additional support is given by the fact that the major changes in
non-work VMT resulted from changes in trip frequency or destination
choice, and not choice of mode.

                                106





To examine the differential effects of policies on various segments
of population, the sample was stratified by income and geographic
location.  Figure 4.6 shows the percent change in VMT as a function
of gasoline cost increases for three income levels and four
geographic rings around the CBD.  Figure 4.6 clearly shows that
gasoline pricing is a regressive policy, having a significantly
greater impact on low income households.  Gasoline pricing also has
the greatest relative impact on households living in the center
city (rings 0-1).  This is due partly-to a correlation between
income and residential location, and partly to a higher level of
transit service in the central city.  In terms of absolute changes,
however, households in the outer suburbs (rings 4-7) reduce VMT by
nearly two miles for each mile reduced by a center city household.

The case study presented above illustrates some additional
techniques for applying individual choice models to short range
policy evaluation.  It used a forecasting procedure (random sample
explicit numeration) which does not aggregate travel choice
probabilities into geographical units.  This procedure can
significantly reduce aggregation bias, and facilitates grouping by
market segments.  Unlike Case Study No. 3, which chained three
choice models together to represent the shopping trip decision,
this study modeled the shopping trip as a simultaneous choice of
frequency, destination, and mode, and instead of following the
conventional travel choice sequence of frequency, destination, and
mode, the models were linked according to the relative permanency
of the decision.  Thus, auto ownership, being a fairly long range
decision, was modeled before work mode choice, which was modeled
before shopping choice.  This procedure allows short range
decisions to be partially

                                107





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                                108





influenced by longer range decisions.  Finally, the models
introduced proxy or dummy variables to represent policies which
could riot be quantified or explicitly defined.  Although these
variables do not allow the analyst to predict the precise impact of
a policy, they can be used to establish ranges of effect.

                                109





                                110





                             CHAPTER V

           FORECASTING THE DEMAND FOR NEW TRANSPORTATION
              SYSTEMS AND MAJOR SERVICE IMPROVEMENTS

BACKGROUND
Forecasting patronage and revenues for new transportation systems
is becoming an increasingly important part of the urban
transportation planner's work.  This has been prompted by a growing
frustration in many communities with the low ridership and mounting
deficits of conventional public transit systems, plus a realization
that many transportation problems are only made worse by relying on
the private automobile.  Alternatively, the high capital costs and
relative permanency of a new transportation system makes it almost
imperative that a realistic estimate of the demand for the system
be made before it is actually implemented.

The use of individual choice models to forecast demand for new
modes is primarily a result of their use as mode split models in
the traditional travel demand forecasting process.  In fact, a
number of the models summarized in Chapter III have been used for
new mode studies in their respective urban areas.

In this chapter, the suitability of individual choice models in new
mode studies is discussed together with some difficulties which may
occur in their application.  The summary section presents examples
of models borrowed from other studies as well as models developed
expressly for new mode demand fore-

                                111





casting.  The case studies illustrate both the ease with which
individual choice models can be applied to new mode forecasting,
and how these models may be enhanced by other techniques such as
attitude surveys.

THE SUITABILITY OF INDIVIDUAL CHOICE MODELS FOR NEW MODE DEMAND
FORECASTING.
Forecasting the demand for new transportation systems differs from
short range policy evaluation in several ways.  First, demand
forecasting for new systems is primarily a mode choice problem. 
While the transportation planner may occasionally want to know
about the increased mobility of certain groups served by the
system, his major concern is with overall system ridership. 
Secondly, the attributes of new modes may be significantly
different than those of existing modes.  "Shortcut" techniques such
as the analysis of demand elasticities may not be appropriate. 
Thirdly, a transportation system will almost always have different
service characteristics in different parts of the study area, so
locational aggregation becomes a critical issue.  Finally, since
new transportation systems often take a long time to implement,
changes in socio-demographic variables may have to be considered.

Despite these differences, individual choice models still have a
number of features which make them attractive in forecasting demand
for new modes.  Perhaps the most important feature deals with their
ease of application:

1.   Demand forecasts for new modes can be made using individual
     choice models which have been calibrated only on existing
     modes.
     The alternatives in individual choice models are defined
     largely in terms of their attribute variables.  A new mode can
     be included in an individual choice model by creating a linear
     utility expression consisting of variables whose coefficients
     are derived from those of existing modes,

                                112





     and whose values reflect the new mode's proposed service
     characteristics.  This eliminates the need to develop a new
     model, or even to recalibrate an existing one, and can produce
     substantial savings in both time and cost.

It should be noted, however, that new modes can only be defined in
terms of those variables which are present in the linear utility
expressions of existing modes.  Thus, if comfort were to be a major
feature of some proposed mode, it could be considered only if a
comfort variable were included in the original model.
Another useful feature of individual choice models is their
flexibility in aggregating individual.choice probabilities to
obtain expected market shares:

2.   Individual choice probabilities can be aggregated along
     socioeconomic lines to estimate new mode patronage for special
     market segments.
     The expected mode split for a specific travel market can be
     computed by summing the mode choice probabilities of those
     individuals in the calibration dataset having the requisite
     socioeconomic characteristics.  This can be particularly
     useful in evaluating how well proposed transportation systems
     will serve such groups as the young teenager, the elderly, or
     the physically handicapped.  By forecasting the mode splits
     for these groups individually, a transportation planner can
     determine whether or not they will actually use the new mode.

Individual choice models are not only appropriate in evaluating
alternative proposals for new modes; they can also be used in the
early stages of design to establish priorities:

                                113





3.   Calibration coefficients can be used as design criteria for
     new modes.
     The calibration coefficients in the linear utility expression
     represent the relative importance of each level-of-service
     variable to the choice decision.  By giving priority to those
     attributes which generate the greatest increase in utility for
     a reasonable change in level-of-service, the proposed
     transportation system can be made responsive to the needs of
     the user.

Certain problems can arise in forecasting demand for new modes,
depending on the physical layout of the proposed system and the
time required for implementation.  In some cases, for example, a
new mode may be run in only a few selected travel corridors.  A
special study of demand in those corridors may be required,
particularly if their socioeconomic or travel characteristics are
significantly different from the urban area as a whole.  In other
cases, socioeconomic and/or residential location forecasts may be
needed if the proposed system requires a long time before it
becomes operational.  These issues also occur with other
forecasting methodologies, however, and should not be viewed as
drawbacks of individual choice models alone.

A SUMMARY OF RECENT APPLICATIONS
Individual choice models have been used to forecast demand for new
transportation service in applications ranging from preliminary
feasibility studies to detailed design and cost analyses of
specific transit routes.  The summaries presented below illustrate
the flexibility of individual choice models in these various
applications.

                                114





Individual choice models were used in a preliminary feasibility
study conducted by the Department of Transportation1 to identify
urban areas where Dual Mode Transit2 might potentially be an
acceptable areawide transit system.  Cities were grouped into one
of three city types based on population and population density. 
Each city type was represented by a set of characteristics,
including estimates of areawide travel demand.  Idealized dual mode
transit networks were laid out for each city type, and
representative times and costs were postulated for both automobile
and dual mode transit vehicles.  A logit model, calibrated in an
earlier study3 using data from Boston, Massachusetts, was applied
to forecast dual mode patronage based on the postulated impedances. 
System costs and revenues were derived from these patronage
forecasts.  The results obtained from the abstract cities were used
to identify those cities where a definite or possible potential for
Dual Mode Transit existed.

The drastic simplifications made in this study were justified by
the facts that 1. it was a preliminary feasibility study in which
only relative comparisons were desired, and 2. there was little or
no appropriate data available to conduct a more detailed analysis.

___________________________

1   C. Heaton, J. Barber, P. Benjamin, G. Paules, and D. Ward,
     Dual Mode Potential in Urban Areas, Report No. DOT-TSC-OST-74-
     20, U.S. Department of Transportation, Transportation Systems
     Center, Cambridge, Massachusetts, February 1975.

2   Dual Mode Transit is defined as a system in which vehicles
     operate part of the time on the existing street network, and
     the rest of the time on an exclusive guideway, usually under
     automatic control.

3   P. Benjamin, et al., Analysis of Dual Mode_Systems in an Urban
     Area, Report No. DOT-TSC-OST-73-16-A, U.S. Department of
     Transportation, Transportation Systems Center, Cambridge,
     Massachusetts, April 1974.

                                115





The Office of Research and Development of the Illinois Department
of Transportation used individual choice models to,analyze the
demand for access modes to commuter railroad stations.  They
developed a standardized manual procedure to forecast mode splits
among four access modes: walk, feeder bus, park-and-ride, and kiss-
and-ride.  The individual mode choice models which made up the
demand component of the procedure were developed and calibrated in
two studies on access mode choice.4, 5  The level-of-service
variables used in the models were distance to the station, distance
to the nearest feeder bus stop, bus fare, bus headway, and whether
or not there was parking available at the station.  This procedure
was used in a number of studies in the Chicago metropolitan area,
including at least two feasibility studies for community feeder bus
service,6,7   a comprehensive study of parking facilities at
commuter railroad stations,8 a comparative analysis of feeder bus
service versus increased parking facilities,9 and a study of the
impact of building a
___________________________

4   J. P. Sajovec and N. Tahir, Development of Disaggregate
     Behavioral Mode Choice Models for Feeder Bus Access to Transit
     Stations, Illinois Department of Transportation, Chicago,
     Illinois, May 1976.

5   M. Hovind, "Disutility Curves for O'Hare Ground Access Study,"
     Technical Papers and Notes Series #3, Illinois Department of
     Transportation, Chicago, Illinois, September 1972.

6   M. Hovind "Preliminary Demand Analysis for Feeder Bus Service
     to the Lombard, Illinois, Commuter Railroad Station,"
     Technical Papers and Notes Series #4, Illinois Department of
     Transportation, Chicago, Illinois, December 1972.

7   N. Tahirand M. Hovind, A Feasibility Study of Potential Feeder
     Bus Service for Homewood, Illinois, Illinois Department of
     Transportation, Chicago, Illinois, September 1973.

8   State of Illinois Commuter Parking Program - Phase I Parking
     Demand Analysis, Illinois Department of Transportation,
     Chicago, Illinois, September 1973.

9   N. Tahir, "Feeder Buses as.an Alternative to Commuter Parking:
     An Analysis of Economic Trade-Offs," Technical Papers and
     Notes Series #15, Illinois Department of Transportation,
     Chicago, Illinois, February 1974.

                                116





new commuter rail station on community travel patterns.10  One of
these applications is presented in Case Study No. 5 to show the
procedure in greater detail.

The Planning and Research Bureau of the New York State Department
of Transportation used individual choice models to forecast demand
for park-and-ride service.11  In keeping with their philosophy of
combining methodological research with practical application, the
study compared the predictive ability of individual choice models
with two types of zonal level modes.  It was assumed that demand
for park-and-ride service could be viewed as a binary choice
between taking the automobile into the CBD or stopping at a
peripheral parking lot to transfer to the bus.  The explanatory
variables included distance from home to the final destination,
travel time differences between the alternatives and travel cost
differences.  The models were calibrated with data obtained from a
license plate survey conducted in two park-and-ride lots in Albany,
New York.  The individual choice model performed as well or better
than the two aggregate models, even though it was calibrated with
very crude data.  Travel time and distance were found to be the
most influential variables in the demand for peripheral park-and-
ride lots.

As part of a feasibility study of light rail and express bus
systems in Portland, Oregon, the transportation consultant, System
Design Concepts, Inc., used individual choice models to forecast
patronage and revenues for the
___________________________

10  S. E. Schindel, "Impact on Station Choice and Access Mode
     Choice Due to the Establishment of a Commuter Rail Station at
     Arlington Park, Illinois," Technical Papers and Notes Series
     #14, Illinois Department of Transportation,.  Chicago,
     Illinois, September 1973.

11  P. S. Liou, "Comparative Demand Estimation for Peripheral
     Park-and-Ride Service," Preliminary Research Report 71, New
     York State Department of Transportation,, Albany, New York,
     September 1974.

                                117





proposed alternatives.12  A lack of data made it impossible to
build and calibrate reliable mode choice models for Portland, so a
decision was made to "borrow" models calibrated from other studies. 
These models included the San Diego work mode choice model (see
Case Study No. 1) and a non-work model developed by Dr. Moshe Ben-
Akiva of Cambridge Systematics, Inc., and calibrated on data
collected from a 1968 home interview survey in Washington, D.C.
(see Case Study No. 4).  The models were applied to a 48 zone
sketch planning network using hypothetical level-of-service
characteristics for the proposed alternatives.  Regional mode split
estimates were computed based on 1990 travel projections.  These
were converted into revenue forecasts for the two systems, and for
proposed lines which would make up each system.  The forecasts
provided the basis for recommendations on public transportation in
the Portland-Vancouver Metropolitan Area.

Finally, individual choice models were used to help design an
areawide public transportation system for a suburban community. In
a transit study commissioned by the villages of Schaumburg and
Hoffman Estates, Illinois, the consultant, Jack E. Leisch and
Associates, used individual mode choice models to forecast areawide
patronage for various combinations of dial-a-ride, subscription
bus, and fixed route bus service.13  Mode choice data was obtained
from a survey of commuters travelling-between the villages and a
nearby commuter railroad station.  A binary logit model was
calibrated for the choice between
___________________________

12  System Design Concepts, Inc., and Cambridge Systematics, Inc.,
     Demand and. Revenue Analysis for Proposed Light Rail and
     Express Bus Systems in Portland, Oregon, technical memorandum
     prepared for the Governor's Task Force on Transportation, May
     1974.

13  P. R. Stopher, "Ridership Estimates for Alternative System
     Options," Supplement to Technical Memorandum No. 3, for the
     Schaumburg/Hoffman Estates Transit Study, Jack E. Leisch and
     Associates, Evanston, Illinois, July 1975.

                                118





automobile and fixed route bus.  To forecast mode splits for
subscript ion bus and dial-a-ride, variables were changed to
reflect level-of-service differences, and the constant or bias
coefficient was modified to reduce the automobile's relative
superiority.  These models were applied to various market segments
defined in the study.  The resulting patronage forecasts were used
to identify the most appropriate public transportation system for
the communities, and to help establish fares and predict operating
costs for the chosen system.  This application is presented as Case
Study No. 6.

It is interesting to note that each of the applications presented
above was either a sketch planning study, where both transportation
service characteristics and travel demand were aggregated to
areawide means, or a small scale design study, where locational
variations were negligible.  One reason that individual choice
models have not been widely used in applications like corridor
studies is the difficulty in modelling service which varies
throughout the study area.  This is-being resolved through research
aimed at representing transportation service characteristics in
terms of the distance between a transportation facility and the
final destination.

Despite the problem mentioned above, individual choice models are
clearly becoming practical tools for new mode demand studies. 
Perhaps the greatest asset of these models is the linear utility
expression, which allows service attributes to be explicitly
defined.  As more and more policy makers look to new transportation
systems or major service changes to solve their transportation
problems, the use of individual choice models to forecast patronage
and establish design priorities will continue to grow.

                                119





                            REFERENCES


P. Benjamin, et al., Analysis of Dual Mode Systems in an Urban
Area, Report No. DOT-TSC-OST-73-16.-A, . U.S. Department of
Transportation, Transportation Systems Center, Cambridge,
Massachusetts, April 1974.

C. Heaton, J. Barber, P. Benjamin, G. Paules, and D. Ward, Dual
Mode Potential in Urban Areas, Report No. DOT-TSC-OST-74-20, U.S.
Department of Transportation Systems Center, Cambridge,
Massachusetts, February 1975.

M. Hovind, "Disutility Curves for O'Hare Ground Access Study,"
Technical Papers and Notes Series #3, Illinois Department of
Transportation, Chicago, Illinois, September 1972.

M. Hovind "Preliminary Demand Analysis for Feeder Bus Service to
the Lombard, Illinois, Commuter Railroad Station," Technical Papers
and Notes Series #4, Illinois Department of Transportation,
Chicago, Illinois, December 1972.

Illinois Department of Transportation, State of Illinois Commuter
Parking_ Program - Phase I Parking Demand Analysis, Chicago,
Illinois, September 1973.

Jack E. Leisch and Associates, Schaumburg/Hoffman Estate.Transit-
Study,, final report submitted to the Villages of Schaumburg and
Hoffman Estates, Illinois, September 1975.

P. S. Liou, "Comparative Demand Estimation for Peripheral Park-and-
Ride Service," Preliminary Research Report 71 New York State
Department of Transportation, Albany, New York, September 1974.

J. P. Sajovec and N. Tahir, Development of Disaggregate Behavioral
Mode Choice Models for Feeder Bus Access to Transit Stations,
Illinois Department of Transportation, Chicago, Illinois, may 1976.

S. E. Schindel, "Impact on Station Choice and Access Mode Choice
Due to the Establishment of a Commuter Rail Station at Arlington
Park, Illinois," Technical Papers and Notes Series #14., Illinois
Department of Transportation, Chicago, Illinois, September 1973.

System Design Concepts, Inc., and Cambridge Systematics, Inc.,
Demand and Revenue Analysis for Proposed Light Rail and Express Bus
Systems in Portland, Oregon, technical memorandum prepared for the
Governor's Task Force on Transportation, May 1974.

N. Tahir, "Feeder Buses as an Alternative to Commuter Parking: An
Analysis of Economic Trade-Offs," Technical Papers and Notes Series
#15, Illinois Department of Transportation, Chicago, Illinois,
February 1974.

N. Tahir and M. Hovind, A Feasibility Study of Potential Feeder Bus
Service for Homewood, Illinois, Illinois Department of
Transportation, Chicago, Illinois, September 1973.

                                120





                         CASE STUDY NO. 5

          STUDYING THE FEASIBILITY OF FEEDER BITS SERVICE

                  TO A SUBURBAN RAILROAD STATION

BACKGROUND
Commuter railroad is one of the principal modes of transportation
in the Chicago metropolitan area, particularly for work trips
between downtown Chicago and its suburbs.  Because of this, much of
Chicago's rush hour traffic congestion occurs at suburban railroad
stations.  In an effort to relieve this problem, the Office of
Research and Development of the Illinois Department of
Transportation studied the use of feeder bus service to suburban
railroad stations as an alternative to park-and-ride or kiss-and-
ride.  Homewood, Illinois, a community about 23 miles South of the
Chicago Loop, was selected as the site for a feasibility study of
an areawide feeder bus service because of the strong local interest
expressed in such a plan.  Earlier feasibility studies had been
made of single routes within a community,14 but this was the first
attempt to do an in depth community-wide study.

DESCRIPTION OF THE MODEL
The model used to estimate demand for the feeder bus service was
developed in
___________________________

14  M. Hovind, "Preliminary Demand Analysis for Feeder Bus Service
     to the Lombard, Illinois Commuter Railroad Station," Technical
     Papers and Notes Series No. 4, Illinois, Department of
     Transportation, Chicago, Illinois, December 1972.

                                121





an earlier study of station access15   and calibrated on data from
communities in the Chicago area where feeder bus service already
existed.  It was basically a binary choice logit model, where the
alternative to feeder bus was an unspecified composite of walk,
park-and-ride and bus-and-ride.  The explanatory variables used in
the model were distance to the railroad station, distance to the
bus stop, bus headway, and bus fare.  Separate linear utility
expressions were used, depending upon whether the distance to the
railroad station was less than, or greater than one mile.  The
calibrated model is given in figure 5.1.

DATA PREPARATION AND FORECASTING PROCEDURE
Information on the origins and travel characteristics of
individuals who boarded at the Homewood commuter railroad station
was obtained from a survey conducted by the Chicago Area
Transportation Study (CATS) in 1969.16  For the purposes of this
study, it was assumed that the introduction of feeder bus service
would only affect choice of access mode to the station.  It would
change neither the overall demand for rail service nor the time of
day at which the trips were made.

The origins of commuter rail passengers identified in the survey
were located on a map of the Village.  The Village was then divided
into 24 zones, such that commuters within a zone would have similar
travel distances to the railroad station (See figure 5.2).
___________________________

15  J. P. SaJovec and N. Tahir, Development of Disaggregate
     Behavioral Mode Choice Models for Feeder Bus Access to Transit
     Stations, Illinois Department of Transportation, Chicago,
     Illinois, May 1976.

16  W. C. Gilman and Co., Inc., Southward Transit Area
     Coordination Study, prepared for the Illinois Department of
     Public Works and Buildings in cooperation with the Southward
     Area Coordination Committee, Chicago, Illinois, 1969.

                                122





                             exp (Ui)
                    Pi =_________________

                           1 + exp (Ui)


1.   Linear utility expression for zones less than one mile from
     station

     Ui =     -2.5994 - 0.1569 Bi     - 0.0442 Fi + 0.1315 Si


2.   Linear utility expression for zones over one mile from station


     Ui= 2.5238 - 0.0721 Bi- 0.0419 Fi- 0.0007 Hi+ 0.0032 Si


DEFINITIONS OF VARIABLES USED IN THE MODELS


     Pi =     probability of a commuter in zone i using feeder bus

     Ui =     linear utility expression for feeder bus in zone i

     Bi =     average distance to the nearest feeder bus stop for
               a commuter from zone i (in hundreds of feet)

     Fi =     bus fare from zone i (in cents) i

     Hi= bus headway for zone i (in-seconds)

     Si= distance to the train station from zone i (in hundreds of
          feet)

                 HOMEWOOD FEEDER BUS DEMAND MODELS
                            figure 5.1

                                123





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                                124





Desired operating criteria were established for the proposed feeder
bus system.  Service frequencies were set to coincide with arrival
times of trains at the Homewood station.  This resulted in 15
minute headways for rush hours and 30 minute headways at all other
times.  Transit routes were designed to minimize both the walking
and bus travel distance of potential riders, within the constraints
imposed by the required service frequencies.  The best routes were
obtained using a.trial and error procedure in conjunction with the
demand models.  Bus fares were allowed to vary between $0.10 and
$0.50 in order to aid the Village of Homewood in determining an
appropriate fare structure for the service.

The sample of rail commuters identified in the survey were factored
up to represent the total number of potential feeder bus passengers
in each zone.  These Potential riders were multiplied by the demand
probabilities for each zone to get expected ridership by zone. 
Ridership was compute separately for peak and off-peak travel. 
Total daily ridership represented twice Elm expected one-way
ridership for each zone, summed over all zones and time periods.

DEMAND AND REVENUE PROJECTIONS
Using a trial and error procedure for route location, it was
determined that an optimal feeder bus system for Homewood would
consist of five fixed routes, with three routes requiring two buses
each during the peak and each of the other two routes requiring one
bus. (See figure 5.3)

Given this optimal route configuration, expected daily ridership
was computed for various fare levels, ranging from $0.10 to $0.50
Daily

                                125





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                                126





revenue projections were obtained by multiplying the ridership
estimates by fare.  Plots of demand and revenue versus fare are
given in figure 5.4.

The patronage and revenue forecasts given in figure 5.4 represent
levels which are usually not achieved until after the second year
of operation.  Past experience had shown that about 45% of the
ridership occurs when the system first opens, rising to 70% after
one year.  Figure 5.5 shows the expected growth in patronage and
revenue for the feeder bus system at three different fare levels.

COST ESTIMATES AND ECONOMIC ANALYSIS
The cost estimates for the Homewood feeder bus service were based
on a designed system of eight buses operating four hours a day
during the peak, and four buses operating another eight hours a day
during the off peak. One additional bus would be required as a
back-up against breakdowns.

Using typical operating costs obtained from other bus companies and
transit authorities, it was estimated that it would cost between
$640 and $768 per day ($160,000 to $192,000 per year) to operate
the Homewood feeder bus service.  Capital costs for a 25-30
passenger bus were found to range between $20,000 and $32,000 per
bus.

A comparison of system operating costs versus expected revenues
yielded estimates of the financial status of the proposed transit
service.  As is the case with most public transit systems today,
the Homewood feeder bus service would run at a loss and would
require operating subsidies from the community.  The projected
funding level would vary, depending on the fare.  Figure 5.6 shows
the expected funding requirements at various fare levels

                                127





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                                128





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                                129





for the system after about two years of operation.  Funding for the
first two years would be proportionately greater because of the
lower levels of expected patronage and revenues.


Click HERE for graphic.


The results of this analysis provided the Village of Homewood with
the necessary information to make rational decisions about
instituting areawide feeder bus service to the railroad station. 
Various alternatives to the basic plan could also be investigated,
such as eliminating off-peak service to reduce daily operating
costs, or using the buses as dial-a-ride vehicles in the off-peak
to increase patronage.  The important point is that each of these
options could be debated with knowledge of the expected financial
consequences to the community.

                                130





                         CASE STUDY NO. 6

           DESIGNING A PUBLIC TRANSPORTATION SYSTEM FOR
                       SUBURBAN COMMUNITIES

BACKGROUND
Recently, a number of progressive suburban communities have
established their own public transit systems to help reduce
internal traffic congestion and to provide mobility to citizens
who, for one reason or another, are unable to use the automobile. 
In 1974, two villages in the Chicago metropolitan area, Schaumburg
and Hoffman Estates, commissioned the firm of Jack E. Leisch and
Associates (JEL), to study the transportation needs of their area
and to recommend an appropriate public transit system commensurate
with physical, social, and economic considerations of the
communities.

Working closely with a citizen advisory group composed of community
leaders and interested citizens, JEL defined general community
goals for public transportation, major transit service objectives,
and specific guidelines for achieving those objectives.17

A market definition study was conducted to determine if there was a
potential market for public transit in the study area.18  The
primary markets for public transportation were found to be: 1.
Railroad commuters (people who
___________________________

17  Jack E. Leisch and Associates, Schaumburg/Hoffman Estates
     Transit Study: Technical Memorandum No. 17-Objectives and
     Desired Level of Service.

18  Jack E. Leisch and Associates, Schaumburg/Hoffman Estates
     Transit Study: Technical Memorandum No. 2-Market Definition
     and System Concepts.

                                131





work in downtown Chicago who would use it as an access mode to
railroad stations), 2. Internal commuters (people who live and work
in the Schaumburg/ Hoffman Estates area), and 3. Transit captives
(people who do not have an automobile available to them who would
use it for a variety of non-work trips.)

Concurrent with the market definition study, the consultants made a
survey of various public transportation alternatives which could be
implemented by the communities.  By comparing the potential market
with the various transportation alternatives, it was concluded that
a dial-a-ride system would provide the best Off-Peak service. 
Three alternatives seemed to provide satisfactory service in the
peak: dial-a-ride, subscription bus, and fixed route bus.

It was suggested, therefore, that detailed demand and revenue
analyses be conducted to 1. determine system requirements and
appropriate fares for the off-peak dial-a-ride service, and 2.
select the best overall peak service for the communities.

DESCRIPTION OF THE MODELS
Potential transit patronage resulting from internal work trips and
railroad commuters was estimated using a binary logit mode choice
model.. The model was calibrated with data obtained from a survey
of residents in the study area who had feeder bus service available
to a nearby railroad station.  Only two level of service variables
were included in the linear utility expression: travel time and
travel cost.  The calibrated model is presented in figure 5.7.

                                132





                             exp (Ui)
                       Pi = _____________

                           1 + exp (Ui)


1.   Linear utility expression for fixed route bus service

     Ui =     -1.37 + 0.0544 t + 0.0021 c


2.   Linear utility expression for dial-a-ride and subscription bus


     Ui =     -0.913 + 0.0544 t + 0.0021 c


DEFINITIONS OF VARIABLES USED IN THE MODELS


     Pi =     probability of a tripmaker using transit mode i

     Ui =     linear utility expression for transit mode i

     t = travel time difference between the automobile and transit
          mode i (ta - ti)

     c = travel cost difference between the automobile and transit
          mode i (ca - ci)

         SCHAUMBURG/HOFFMAN ESTATES TRANSIT DEMAND MODELS
                            figure 5.7

                                133





An alternative approach was used to estimate-off-peak demand for
dial-a-ride, because no choice data were available to calibrate
another model.  The procedure was based on responses to an
attitudinal survey distributed in the study area at railroad
stations, work places, and shopping centers.  Two of the questions
asked if the respondent would use public transit for his or her
trip if 1. it provided door-to-door service, and 2. it took longer
than the automobile. (See figure 5.8) Since an affirmative answer
in no way obligated the respondent to use public transit, patronage
forecasts based solely on these questions would likely overestimate
actual transit ridership by a significant margin.  To compensate
for this non-commitment bias, transit percentages were multiplied
by a factor which represented the ratio of transit users in the
sample estimated by the peak hour model to the number of people who
said that they would use transit in the peak if it existed.  This
approach had been used successfully in a number of transportation
planning applications by the New York State Department of
Transportation to estimate demand for remote park-and-ride
facilities,19 and for dial-a-bus service in small urban areas.20


DATA PREPARATION AND DEMAND FORECASTING PROCEDURES
The demand forecasting procedure for internal work trips and
commuter trips began by drawing a random sample the individuals who
resided in the study area and filled out a questionnaire at either
their work place or the rail-
___________________________

19  D. T. Hartgen, "Forecasting Remote Park-and-Ride Transit
     Usage," Preliminary Research Report 39, New York State
     Department of Transportation, Albany, New York, December 1972.

20  D. T. Hartgen and C. Keck, "Forecasting Dial-a-Bus Ridership
     in Small Urban Areas," Preliminary Research Report 60, New
     York State Department of Transportation, Albany, New York, May
     1974.

                                134





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                                135





                       Transit Alternatives
Service
Characteristic Dial-a-Ride    Subscription Bus    Fixed Route Bus

Advance
Notice         30 minutes     24 hours            None
Required

Average
Waiting        10 minutes     5 minutes           5 minutes
Time

Average
Walk to        Pick-up at     300 feet            Variable
Pick-up        Residence

Average
Travel         12 mph         15 mph              15 mph
Speed

Arrival Time   5 minutes      5 minutes           Variable,
at Railroad    before         before              Depending
Station        Departure      Departure           on Schedule

Fare           50 cents       50 cents            40 cents
               (10 rides/$4)  (10 rides/$4)


      SERVICE CHARACTERISTICS OF ALTERNATIVE TRANSIT SYSTEMS
                             Table 5.1

                                136





road station.  Travel times and costs by automobile were taken from
the questionnaire and used as data in the mode choice models. 
Travel times and costs for the public transit alternatives were
based on system design criteria and distance from the tripmaker's
home to his destination. Design criteria for dial-a-ride,
subscription bus, and fixed route bus are given in table 5.1.

The bias coefficient was changed somewhat for the dial-a-ride and
subscription bus alternatives to reflect service levels perceived
to be superior to those of conventional fixed route bus.  The
effect of this modification was to reduce the pro-auto bias.  The
adjusted model yielded patronage estimates which were consistent
with actual ridership levels in communities where dial-a-ride had
been instituted.

After their mode choice probabilities had been determined,
individuals were aggregated by analysis zone and the ridership
expressed as a percentage for the zone.  Multiplying this
percentage by the size of the market segment in each zone gave the
expected zonal transit patronage.  Summing over all zones in the
study area gave the expected areawide transit patronage for the
market segment.

To get patronage forecasts for market segments other than commuters
or internal work trips, an adjustment factor had to be calculated
for the noncommitment bias inherent in the questionnaire responses.
This was done by comparing dial-a-ride forecasts obtained from the
mode choice models with the number of respondents in the sample who
implied that they would use dial-a-ride if it were available.  It
was found that the mode split proportions

                                137





Click HERE for graphic.


                                138





derived from questionnaire responses had to be reduced by 50 per
cent to be consistent with the model estimates.  Off-peak dial-a-
ride patronage was computed in this manner for five market
segments: internal shopping trips, personal business trips, social
recreational trips, trips made by the elderly, and trips made by
the handicapped.

ANALYSIS OF ALTERNATIVE SYSTEMS
Table 5.2 shows ridership estimates for five alternative
combinations of dial-a-ride, subscription bus.and fixed route bus
service.

It was immediately apparent that a fixed route bus system would not
generate sufficient ridership, either by itself or in combination
with dial-a-ride, to be economically viable.  Dial-a-ride commanded
the highest ridership in both the peak and off-peak, but
subscription bus service was only slightly less popular for peak
hour service.

It was found from the questionnaire that fares in the range of
40 - 50 would be acceptable to a majority of the potential
transit users.  This information provided a benchmark for
calculating system revenues.  Projected revenues were compared with
estimated system costs in an economic analysis of the remaining
system alternatives.  Even though the demand for subscription bus
was slightly lower than that for dial-a-ride, it was found that
subscription bus would be more cost effective in the peak because
it required fewer vehicles, and each vehicle had a larger capacity. 
Based on this analysis a recommendation was made by the consultant
that the communities institute a combined transit system with dial-
a-ride operating in the off-peak and subscription bus in the peak. 
Dial-a-ride could also be provided

                                139





Click HERE for graphic.


                                140





in the peak, but at a premium fare to discourage its use by those
who would otherwise take subscription bus.

The models were also used to analyze alternative fare structures
for the proposed system by examining the tradeoffs among ridership,
revenue, and operating costs at various fare levels.  To give the
advisory group a more conservative estimate of transit ridership,
the consultant reran the demand models using the linear utility
expression for fixed route bus (equation 1; figure 5.7), instead of
dial-a-ride.  Table 5.3 summarizes the results of the fare analysis
at four fare levels and at high and low estimates of transit
ridership.  As fare-increases, both ridership and system operating
costs decline.  Revenues, however, continue to increase because the
decrease in number of paid trips is more than offset by the
increased revenue per trip.  Since there was no optimal fare level,
it was left up to the advisory council to determine the proper
balance between operating subsidy and transit fare.

The application of individual choice models in this case study was
not significantly different from that of Case Study No. 5. What was
different however, was the introduction of data from attitudinal
surveys to enhance the information derived from the choice models. 
In this study, attitudinal data provided information on acceptable
fare levels and was used to get estimates of transit patronage for
those market segments where individual choice models could not be
built.

Attitudinal data can also be used to create variables for such
attributes as comfort, convenience, or reliability where objective
data is difficult or

                                141





impossible to obtain.  And they can be used to define market
segments based on attitudes or perceptions of transportation
service.  These and other potential applications of attitudinal
research are just beginning to be recognized by transportation
planners.  Hopefully, as with individual choice models, similar
applications of attitudinal data will demonstrate their usefulness
in transportation planning and lead to further acceptance.

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                            APPENDIX A

ISSUES WHICH HAVE EMERGED FROM INDIVIDUAL CHOICE MODEL RESEARCH
A number of issues have been identified in the course of developing
and applying individual choice models.  While many of these issues
have been used as arguments against individual choice models, they
also apply t. more conventional travel demand forecasting models. 
Three issues of particular concern to travel demand researchers are
discussed in this section.

Issue 1:  The Independence of Irrelevant Alternatives.
A number of the models used in urban transportation planning fall
under the general classification of share models.  A share model is
one in which the market share or probability of choosing a
particular alternative can be represented by ratio of its utility
to the sum of the utilities of every alternative under
consideration.  This can be expressed mathematically as:

                              f(xi)
(A.1)                    Pi = _________
                              n
                               f(xj)
                             j=1

where        Pi =   the market share or probability of choosing
                    alternative i;

          f(xi) =   a utility expression for alternative i;

              n =   the number of alternatives being considered.

Common share models found in transportation include the Gravity and
Interviewing Opportunities models of trip distribution, and the
logit formulation for individual choice models.

                                143





Share models have several unique properties which facilitate their
use as forecasting tools.  First, the share model guarantees that
each alternative has a fixed share of the total market, and that
the sum of the shares must equal one.  Secondly, since the
denominator of a share model remains constant, each alternative's
share can be computed by inserting the value of its utility
expression in the numerator of equation A.1, and computing the
ratio.  Finally, the introduction of a new alternative can be
represented by simply adding the value of its utility expression to
the denominator and then recomputing the ratios for each
alternative.

The last application presented above illustrates an important and
controversial property of share models known as the Independence of
Irrelevant Alternatives (IIA).  Stated formally, this property says
that the ratio of the market shares of two alternatives is
unaffected by the presence or absence of any other alternative.  We
can illustrate this by the following example:

Two alternatives, A and B, have utility expressions whose values
are 3 and 2, respectively.  The market shares of each alternative
are given by the following equations:

                    f(XA)             3       3
(A.2)     PA = ___________________ = _____ = _____ = .60

                 f(XA) + f(XB)     3 + 2     5



                    f(XB)             2       2
(A.3)     PB = ___________________ = _____ = _____ = .40

                 f(XA) + f(XB)     3 + 2     5


                                144





The ratio of these market shares is

                PA      .60
(A.4)          ______ = ______ =   1.50

                PB      .40


If a new alternative C is introduced, having a utility expression
whose value is equal to 1, then the new market shares for each
alternative will be


                         f(XA)               3          3
(A.5)     PA = ________________________ = __________ = _____ = .50

                 f(XA) + f(XB) + f(XC)      3 + 2 + 1      6



                         f(XB)             2           2
(A.6)     PB = ________________________ = _________ = _____ = .33

                 f(XA) + f(XB) + f(XC)      3 + 2 + 1     6


                         f(XC)               1         1
(A.7)     PC = ________________________ = _________ = _____ = .17

                 f(XA) + f(XB) + f(XC)      3 + 2 + 1     6


The amount taken by alternative C from each of the other
alternatives, however, was directly proportional to their original
share of the market.  Therefore, the ratio of market shares for
alternatives A and B is still equal to 1.5.

          PA  .50
(A.8)     __ = ___ 1.5

          PB  .33

As long as the new alternative competes equally with each existing
alternative, this property is valid.  In most transportation
planning applications, however, this is not the case.  For example,
if a new transit mode is introduced in an area that previously had
only the choice between automobile and bus, it is likely that the
new mode will compete more heavily for the bus market than it will
for the auto market.  A model having the IIA property would not be
able to account for this difference in competition.

                                145





Various approaches have been proposed to resolve the Independence
of Irrelevant Alternatives issue.  One approach has been to use a
sequence of choice models where each model compares between only
two alternatives.  Alternatives which seem to be most similar are
compared first.  The computed choice probabilities represent each
alternative's share of the combined market for the two
alternatives.  These combined alternatives are then compared to the
next most similar alternatives.  The attributes of the combined
alternatives can be represented either as those of the alternative
having the higher choice probability (known as the Maximum method),
or as a combination of the attributes of both alternatives weighted
by their choice probabilities (known as the Cascade approach).1  
This process is repeated until the two most dissimilar alternatives
have been compared.  Another approach, proposed by McLynn2
consisted of adjusting the choice probabilities derived from the
share model by a factor which was itself a function of the
probabilities of every alternative.

None of these approaches have proven to be satisfactory because
none of them attacks the cause of the IIA issue - the fact that
there is no place in the share model formulation where the relative
similarity or competitiveness of alternatives can be defined.

Charles River Associates, Inc., examined the IIA issue in depth, as
part of a study sponsored by the National Cooperative Highway
Research Program
___________________________

1   These approaches are discussed in D. McFadden, "The
     Measurement of Urban Travel Demand," Journal of Public
     Economics, 1974, pp 303-328.

2   DTM, Inc., Mode Choice and the Shirley Highway Experiment,
     final report to the Urban Mass Transportation Administration,
     November 1973.

                                146





(NCHRP).3  They concluded that the independence of irrelevant
alternatives is not always an undesirable property of share models,
and that many times it may be a valid assumption.  They found in
some instances that problems attributed to this property can be
alleviated through better specification of explanatory variables or
more careful selection of choice alternatives for the models.  To
assist modelers in determining the amount of error introduced if
independence is assumed when it is not valid, they proposed a test
in which the choice probabilities are recomputed using the Maximum
method discussed above.  The difference between these probabilities
and those derived from the share model give the maximum error which
can occur if independence is assumed when it is not valid.  Often
this error is within acceptable limits for planning applications.

One way to resolve the IIA issue is to avoid using the share model
structure entirely.  As mentioned in Chapter II, binary choice
models have been developed using both the logit and the probit
formulations.  While logit models clearly belong to the family of
share models, probit models do not.

The one obstacle to using the probit formulation for modelling
multiple choice decisions has been the unavailability of an
efficient multinomial probit calibration program.  However, this
problem is being resolved through a research project sponsored by
the Federal Highway Administration which is currently underway at
Cambridge Systematics, Inc.4  It is anticipated
___________________________

3   Charles River Associates, Inc., Disaggregate Travel Demand
     Models, Appendix D, draft final report on phase I research,
     NCHRP project 8-13, September 1975.

4   S.R. Lerman, and C.F. Manski, "An Estimator for the
     Generalized Multinomial Probit Choice Model," presented at the
     56th annual meeting of the Transportation Research Board,
     Washington, D.C., January 24-28, 1977.

                                147





that the resulting program will not only eliminate the IIA problem
associated with logit models, but will allow the modeler to
calculate the degree of similarity among choice alternatives, and
to compute the variance in the weight coefficients in the sample
population.

Issue 2:  Forecasting aggregate behavior from individual choice
          models.
Individual choice models only predict the probabilities with which
an individual will choose among two or more alternatives.  In order
to determine the aggregate behavior for a group of individuals, the
choice probabilities of each of its members must be computed and
summed together (as in equation 2.4). This procedure is generally
impossible to carry out in practice because detailed information on
everyone in a group is rarely available.

A number of alternative approaches have been used to derive
estimates of group behavior from models calibrated with individual
choice data.  The method used most often in early applications was
to assume that the choice probabilities computed at the mean values
of the explanatory variables represent the average choice
probabilities for the group.  This assumption is often not valid,
however, and may result in biased estimates of the group
behavior.5

To correct for this aggregation bias, an adjustment procedure was
proposed, based on the variances of the explanatory variables in
the linear utility
___________________________

5   See F. S. Koppelman, "Prediction with Disaggregate Models: The
     Aggregation Issue," Transportation Research Record, 527,
     Washington, D.C., 1974, pp 73-80, for a discussion of this
     problem.

                                148





expression.6  This adjustment procedure has been used in a number
of planning applications, including one by the New York State
Department of Transportation to develop an aggregate mode split
model for the Niagara Frontier Transportation Study,7 and another
by Charles River Associates to study the effects of transportation
control policies on regional air quality.8  This latter
application is discussed in depth in Case Study No. 3.

Another aggregation procedure has also been proposed in which
individual choice probabilities are weighted by the value of the
distribution function of the linear utility expression.9  While it
has been shown that this procedure gives more accurate estimates of
aggregate behavior than the adjustment procedure,10 the
computational requirements are substantially higher, particularly
if there are more than one or two explanatory variables in the
linear utility expression.
___________________________

6   A. P. Talvitie, "Aggregate Travel Demand Analysis with
     Disaggregate or Aggregate Travel Demand Models,"
     Transportation Research Forum Proceedings, 14, No. 1, 1973, pp
     583-603.

7   P. S. Liou, G. S. Cohen, and D. T. Hartgen, "An Application of
     Disaggregate Mode Choice Models to Travel demand Forecasting
     for Urban Transit Systems," Transportation Research Record,
     534, Washington, D.C., 1975, pp 52-62.

8   F. C. Dunbar, "Evaluation of the Effectiveness of Pollution
     Control Strategies on Travel: An Application of Disaggregated
     Behavioral Demand Models," Transportation Research Forum
     Proceedings, XVI, No. 1, 1975: pp 259-268.

9   R. B. Westin, "Predictions from Binary Choice Models," Journal
     of Econometrics, April 1974.

10  S. M. Howe and P. S. Liou, "Documentation of PROLO and MLOGIT,
     Two New Calibration Programs for Building Disaggregate Choice
     Models, Preliminary Research Report 98, New York State
     Department of Transportation, Alb any, N.Y., December 1975: pp
     33.

                                149





Koppelman11 has studied the aggregation issue in great depth.  He
recommended two procedures which give reasonably accurate estimates
of aggregate behavior without the need to know the shape of the
distribution function for the linear utility expression or its
variance.

The first procedure involves classifying individuals into a number
of more homogeneous groups, based either on their socioeconomic
characteristics or the set of alternatives available to them. 
Separate models are then calibrated for each group.  The choice
probabilities obtained from each model can then be interpreted as
the expected shares for the group.  By weighting each group's
expected share by its relative frequency of occurrence in the study
area, the expected share for the entire study area can be obtained.
This approach is quite similar to the methodology recommended by
the Federal Highway Administration for trip generation
forecasts.12

The second procedure recommended by Koppelman involves calculating
the individual choice probabilities for a random sample of
travelers in the study area.  When a sufficient size sample has
been obtained, the probabilities are summed to get the expected
shares for the sample, which are assumed to reflect the expected
shares for the entire study area.  This technique has
been applied by Cambridge Systematics, Inc., in a study they did on
car pooling incentives.13  It is discussed in detail in Case Study
No. 4.
___________________________

11  F. S. Koppelman, "Guidelines for Aggregate Travel Prediction
     Using Disaggregate Choice Models," Presented at the 55th
     Annual Meeting of the Transportation Research Board, January
     1976.

12  Trip Generation Analysis, Federal Highway Administration,
     Urban Planning Division, August 1975.

13  T. J. Atherton, J. H. Suhrbier, and W. A. Jessiman, "The Use
     of Disaggregate Travel Demand Models to Analyze Carpooling
     Policy Incentives, If draft of a working paper submitted to
     the Federal Energy Administration, October 1975.

                                150





Issue 3:  The use of zonal level system variables in individual
          choice models.
Closely related to the aggregation issue is the use of variables
derived from zone-to-zone skim tree matrices as proxies for point-
to-point travel times and costs.  Skim tree matrices typically
represent some mean value for travel along the "best path" between
two zone centroids.  They typically contain no data on the variance
about these mean values.  Moreover, the "best path" may not
represent the route actually taken by a traveler.

A study by McFadden and Reid14 indicated that valid models of
individual choice behavior can be constructed using variables based
on zonal means.  What is not known, however, is how much model
sensitivity is lost when zonal means are used in place of point-to-
point travel data, or when "best paths" replace actual routes.

Some preliminary findings from the Travel Demand Forecasting
Project, being conducted by the University of California, indicate
that the loss in sensitivity resulting from using zonal means for
certain variables is negligible compared to other forecasting
errors.15  This issue will be studied again in a forthcoming
research project sponsored by the Federal Highway Administration. 
One of the proposed tasks will be to compare models calibrated
using individual travel times and costs against models using the
same observations but calibrated with zonal data.  The necessary
data
___________________________

14  D. McFadden and F. Reid: op.cit.

15  M. Johnson, "A Comparison on Several Methods of Collecting
     Travel Time Data in Analysis of Urban Travel Behavior,"
     technical memorandum to the Metropolitan Transportation
     Commission, Berkeley, California, February 1975.

                                151





is currently being collected for the Department of Transportation
by Charles River Associates, Inc.16  Analysis of this data is
anticipated to begin in the summer of 1977.
___________________________

16  "Collection of a Disaggregate Dataset," contract No. DOT-FH-
     11-8798, sponsored by the Federal Highway Administration, the
     Urban Mass Transportation Administration, and the Office of
     the Secretary of Transportation, June 30, 1975.

                                152





                            APPENDIX B

          WHERE TO OBTAIN REFERENCES MENTIONED IN THIS REPORT


Many of the references mentioned in this report are available upon
request from their sponsoring institutions.  Addresses of
frequently cited sources are given below:

1.   Reports produced by the Federal Highway Administration may be
     obtained, subject to availability, by writing:

          Federal Highway Administration
          Urban Planning Division (HHP-20)
          Washington, D.C. 20590

2.   Reports and documentation associated with the Urban
     Transportation Planning System (UTPS) computer battery may be
     obtained by writing:

          Urban Mass Transportation Administration
          Office of Planning Methodology and
          Technical Support (UTP-11)
          Washington, D.C. 20590

3.   Most research reports sponsored by a Federal agency can be
     obtained through the:

          National Technical Information Service
          Springfield, Virginia 22161

There is a charge for publications obtained from this source.

4.   New York State Department of Transportation publications may
     be obtained by writing:

          Planning Research Unit
          Planning and Research Bureau
          N.Y.S. Department of Transportation
          State Campus
          Albany, New York 12232

5.   Illinois Department of Transportation publications may be
     obtained by writing:

          Office of Research and Development
          Illinois Department of Transportation
          300 North State Street
          Chicago, Illinois 60610

                                153






6.   Reports of the Transportation Research Board may be obtained
     from:

          Transportation Research Board
          National Research Council
          2101 Constitution Avenue, N.W.
          Washington, D.C. 20418

There is generally a charge for publications obtained from this
source.

7.   Reports from the Urban Travel Demand Forecasting Project may
     be obtained by writing:

          Ms. Teruko Ohashi
          Travel Demand Forecasting Project
          Institute of Transportation and
          Traffic Engineering
          109 McLaughlin Hall
          University of California
          Berkeley, California 94710

8.   Specific planning studies and associated technical memoranda
     are usually printed in very limited quantities by the
     consultant or local planning agency for internal use.  Often,
     however, copies of the reports can be obtained by writing to
     the consultant or planning agency directly.  There may be a
     slight copying charge associated with material requested in
     this manner.


*U.S. GOVERNMENT PRINTING OFFICE: 1978-733-159/377

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