ABSTRACT
Discrete or nominal scale data often play a dominant role in transportation because many interesting policy-sensitive analyses deal with such data. Examples of discrete data in transportation include the mode of travel (automobile, bus, rail transit), the type or class of vehicle owned, and the type of a vehicular accident (run-off-road, rear-end, head-on, etc.). From a conceptual perspective, such data are classified as those involving a behavioral choice (choice of mode or type of vehicle to own) or those simply describing discrete outcomes of a physical event (type of vehicle accident). The methodological approach used to model these conceptual perspectives statistically is often identical. However, the underlying theory used to derive these models is often quite different. Discrete models of behavioral choices are derived from economic theory, often leading to additional insights in the analysis of model estimation results, whereas models of physical phenomena are derived from simple probabilistic theory.
