Chapter 1 Introduction
1.1 Background
Discrete choice models can be used to analyze and predict a decision maker’s choice of one alternative from a finite set of mutually exclusive and collectively exhaustive alternatives. Such models have numerous applications since many behavioral responses are discrete or qualitative in nature; that is, they correspond to choices of one or another of a set of alternatives.
The ultimate interest in discrete choice modeling, as in most econometric modeling, lies in being able to predict the decision making behavior of a group of individuals (we will use the term “individual” and “decision maker” interchangeably, though the decision maker may be an individual, a household, a shipper, an organization, or some other decision making entity). A further interest is to determine the relative influence of different attributes of alternatives and characteristics of decision makers when they make choice decisions. For example, transportation analysts may be interested in predicting the fraction of commuters using each of several travel modes under a variety of service conditions, or marketing researchers may be interested in examining the fraction of car buyers selecting each of several makes and models with different prices and attributes. Further, they may be interested in predicting this fraction for different groups of individuals and identifying individuals who are most likely to favor one or another alternative. Similarly, they may be interested in understanding how different groups value different attributes of an alternative; for example are business air travelers more sensitive to total travel time or the frequency of flight departures for a chosen destination.
There are two basic ways of modeling such aggregate (or group) behavior. One approach directly models the aggregate share of all or a segment of decision makers choosing each alternative as a function of the characteristics of the alternatives and socio-demographic attributes of the group. This approach is commonly referred to as the aggregate approach. The second approach is to recognize that aggregate behavior is the result of numerous individual decisions and to model individual choice responses as a function of the characteristics of the alternatives available to and socio-demographic attributes of each individual. This second approach is referred to as the disaggregate approach.
The disaggregate approach has several important advantages over the aggregate approach to modeling the decision making behavior of a group of individuals. First, the disaggregate approach explains why an individual makes a particular choice given her/his circumstances and is, therefore, better able to reflect changes in choice behavior due to changes in individual characteristics and attributes of alternatives. The aggregate approach, on the other hand, rests primarily on statistical associations among relevant variables at a level other than that of the decision maker; as a result, it is unable to provide accurate and reliable estimates of the change in choice behavior due changes in service or in the population. Second, the disaggregate approach, because of its causal nature, is likely to be more transferable to a different point in time and to a different geographic context, a critical requirement for prediction. Third, discrete choice models are being increasingly used to understand behavior so that the behavior may be changed in a proactive manner through carefully designed strategies that modify the attributes of alternatives which are important to individual decision makers. The disaggregate approach is more suited for proactive policy analysis since it is causal, less tied to the estimation data and more likely to include a range of relevant policy variables. Fourth, the disaggregate approach is more efficient than the aggregate approach in terms of model reliability per unit cost of data collection. Disaggregate data provide substantial variation in the behavior of interest and in the determinants of that behavior, enabling the efficient estimation of model parameters. On the other hand, aggregation leads to considerable loss in variability, thus requiring much more data to obtain the same level of model precision. Finally, disaggregate models, if properly specified, will obtain un-biased parameter estimates, while aggregate model estimates are known to produce biased (i.e. incorrect) parameter estimates.
1.2 Use of Disaggregate Discrete Choice Models
The behavioral nature of disaggregate models, and the associated advantages of such models over aggregate models, has led to the widespread use of disaggregate discrete choice methods in travel demand modeling. A few of these application contexts below with references to recent work in these areas are: travel mode choice (reviewed in detail later), destination choice (C. Bhat, Govindarajan, and Pulugurta 1998; K. E. Train 1998), route choice (Yai, Iwakura, and Morichi 1997; Cascetta et al. 2002; Erhardt et al. 2003; Gliebe and Koppelman 2002), air travel choices (Proussaloglou and Koppelman 1999), activity analysis (Wen and Koppelman 1999) and auto ownership, brand and model choice (K. Train and others 1994; C. R. Bhat and Pulugurta 1998). Choice models have also been applied in several other fields such as purchase incidence and brand choice in marketing (Kalyanam and Putler 1997; Bucklin, Gupta, and Han 1995), housing type and location choice in geography (Waddell 1993; Evers 1990; William Sermons and Koppelman 1998), choice of intercity air carrier (Proussaloglou and Koppelman 1999) and investment choices of finance firms (Corres, Hajivassiliou, and Ioannides 1993).
1.3 Application Context in Current Course
In this self-instructing course, we focus on the travel mode choice decision. Within the travel demand modeling field, mode choice is arguably the single most important determinant of the number of vehicles on roadways. The use of high-occupancy vehicle modes (such as ridesharing arrangements and transit) leads to more efficient use of the roadway infrastructure, less traffic congestion, and lower mobile-source emissions as compared to the use of single-occupancy vehicles. Further, the mode choice decision is the most easily influenced travel decision for many trips. There is a vast literature on travel mode choice modeling which has provided a good understanding of factors which influence mode choice and the general range of trade-offs individuals are willing to make among level-of-service variables (such as travel time and travel cost).
The emphasis on travel mode choice in this course is a result of its important policy implications, the extensive literature to guide its development, and the limited number of alternatives involved in this decision (typically, 3 – 7 alternatives). While the methods discussed here are equally applicable to cases with many alternatives, a limited number of mode choice alternatives enable us to focus the course on important concepts and issues in discrete choice modeling without being distracted by the mechanics and presentation complexity associated with larger choice sets.
1.4 Urban and Intercity Travel Mode Choice Modeling
The mode choice decision has been examined both in the context of urban travel as well as intercity travel.
1.4.1 Urban Travel Mode Choice Modeling
Many metropolitan areas are plagued by a continuing increase in traffic congestion resulting in motorist frustration, longer travel times, lost productivity, increased accidents and automobile insurance rates, more fuel consumption, increased freight transportation costs, and deterioration in air quality. Aware of these serious consequences of traffic congestion, metropolitan areas are examining and implementing transportation congestion management (TCM) policies. Urban travel mode choice models are used to evaluate the effectiveness of TCM policies in shifting single-occupancy vehicle users to high-occupancy vehicle modes.
The focus of urban travel mode choice modeling has been on the home-based work trip. All major metropolitan planning organizations estimate home-based work travel mode choice models as part of their transportation planning process. Most of these models include only motorized modes, though increasingly non-motorized modes (walk and bike) are being included (Lawton 1989; Purvis 1997).
The modeling of home-based non-work trips and non-home-based trips has received less attention in the urban travel mode choice literature. However, the increasing number of these trips and their contribution to traffic congestion has recently led to more extensive development of models for these trip purposes in some metropolitan regions (for example, Iglesias 1997; Marshall and Ballard 1998).
In this course, we discuss model-building and specification issues for home-based work and home-based shop/other trips within an urban context, though the same concepts can be immediately extended to other trip purposes and locales.
1.4.2 Intercity Mode Choice Models
Increasing congestion on intercity highways and at intercity air terminals has raised serious concerns about the adverse impacts of such congestion on regional economic development, national productivity and competitiveness, and environmental quality. To alleviate current and projected congestion, attention has been directed toward identifying and evaluating alternative proposals to improve intercity transportation services. These proposals include expanding or constructing new express roadways and airports, upgrading conventional rail services and providing new high-speed ground transportation services using advanced technologies. Among other things, the a priori evaluation of such large scale projects requires the estimation of reliable intercity mode choice models to predict ridership share on the proposed new or improved intercity service and identify the modes from which existing intercity travelers will be diverted to the new (or improved) service.
Intercity travel mode choice models are usually segmented by purpose (business versus pleasure), day of travel (weekday versus weekend), party size (traveling individually versus group travel), etc. The travel modes in such models typically include car, rail, air, and bus modes (Koppelman and Wen 1998; C. R. Bhat 1998; and Marwick 1993).
This manual examines issues of urban model choice; however, the vast majority of approaches and specifications can and have been used in intercity mode choice modeling.
1.5 Description of the Course
This self-instructing course (SIC) is designed for readers who have some familiarity with transportation planning methods and background in travel model estimation. It updates and extends the previous SIC Manual (Horowitz, Koppelman, and Lerman 1986) in a number of important ways. First, it is more rigorous in the mathematical details reflecting increased awareness and application of discrete choice models over the past decade. The course is intended to enhance the understanding of model structure and estimation procedures more so than it is intended to introduce discrete choice modeling (readers with no background in discrete choice modeling may want to work first with the earlier SIC). Second, this SIC emphasizes “hands-on” estimation experience using data sets obtained from planning and decision-oriented surveys. Consequently, there is more emphasis on data structure and more extensive examination of model specification issues.
Update: this manual uses the same models and data as the 2006 Bhat / Koppelman SIC, but the models are executed in R, with the data available for download and the estimation code exposed to the reader.
Third, this SIC extends the range of travel modes to include non-motorized modes and discusses issues involved in including such modes in the analysis. Fourth, this SIC includes detailed coverage of the nested logit model which is being used more commonly in many metropolitan planning organizations today.
1.6 Organization of the Course Structure
This course manual is divided into twelve chapters or modules. CHAPTER 1, this chapter, provides an introduction to the course. CHAPTER 2 describes the elements of the choice process including the decision maker, the alternatives, the attributes of the alternative, and the decision rule(s) adopted by the decision maker in making his/her choice. CHAPTER 3 introduces the basic concepts of utility theory followed by a discussion of probabilistic and deterministic choice concepts and the technical components of the utility function.
CHAPTER 4 describes the Multinomial Logit (MNL) Model in detail. The discussion includes the functional form of the model, its mathematical properties, and the practical implications of these properties in model development and application. The chapter concludes with an overview of methods used for estimating the model parameters.
In [CHAPTER 5][#estimation-chapter], we first discuss the data requirements for developing disaggregate mode choice models, the potential sources for these data, and the format in which these data need to be organized for estimation. Next, the data sets used in this manual, i.e., the San Francisco Bay Area 1990 work trip mode choice (for urban area journey to work travel) and the San Francisco Bay Area Shop/Other 1990 mode choice data (for non-work travel), are described. This is followed by the development of a basic work mode choice model specification. The estimation results of this model specification are reviewed with a comprehensive discussion of informal and formal tests to evaluate the appropriateness of model parameters and the overall goodness-of-fit statistics of the model.
[CHAPTER 6][#specification-chapter] describes and demonstrates the process by which the utility function specification for the work mode choice model can be refined using intuition, statistical analysis, testing, and judgment. Many specifications of the utility function are explored for both data sets to demonstrate some of the most common specification forms and testing methods. Starting from a base model, incremental changes are made to the modal utility functions with the objective of finding a model specification that performs better statistically, and is consistent with theory and our a priori expectations about mode choice behavior. The appropriateness of each specification change is evaluated using judgment and statistical tests. This process leads to a preferred specification for the work mode choice MNL model.
CHAPTER 7 parallels [CHAPTER 6][Model Specification Refinement] for the shop/other mode choice model.
CHAPTER 8 introduces the Nested Logit (NL) Model. The Chapter begins with the motivation for the NL model to address one of the major limitations of the MNL. The functional form and the mathematical properties of the NL are discussed in detail. This is followed by a presentation of estimation results for a number of NL model structures for the work and shop/other data sets. Based on these estimation results, statistical tests are used to compare the various NL model structures with the corresponding MNL.
CHAPTER 9 describes the issues involved in formulating, estimating and selecting a preferred NL model. The results of statistical tests are used in conjunction with our a priori understanding of the competitive structure among different alternatives to select a final preferred nesting structure. The practical implications of choosing this preferred nesting structure in comparison to the MNL model are discussed.
CHAPTER 11 describes how models estimated from disaggregate data can be used to predict a aggregate mode choice for a group of individuals from relevant information regarding the altered value (due to socio-demographic changes or policy actions) of exogenous variables. The chapter also discusses issues related to the aggregate assessment of the performance of mode choice models and the application of the models to evaluate policy actions.
CHAPTER 12 provides an overview of the motivation for and structure of advanced discrete choice models. The discussion is intended to familiarize readers with a variety of models that allow increased flexibility in the representation of the choice behavior than those allowed by the multinomial logit and nested logit models. It does not provide the detailed mathematical formulations or the estimation techniques for these advanced models. Appropriate references are provided for readers interested in this information.