A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models

Abstract

This course manual is divided into twelve chapters or modules. CHAPTER 1 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. CHAPTER 5 first discusses 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. CHAPTER 6 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. CHAPTER 7 parallels CHAPTER 6 for the shop/other mode choice model. CHAPTER 8 introduces the Nested Logit (NL) Model. CHAPTER 9 describes the issues involved in formulating, estimating and selecting a preferred NL model. CHAPTER 10 presents multiple maxima in the estimation of nested logit models. 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. CHAPTER 12 provides an overview of the motivation for and structure of advanced discrete choice models.