ABSTRACT
There is a class of transportation-related problems that involve interrelated discrete and continuous data. Examples include consumers’ choice of the type of vehicle to own (discrete) and the number of kilometers to drive it (continuous), choice of route (discrete) and driving speed (continuous), and choice of trip-generating activity (discrete) and duration in the activity (continuous). These interrelated discrete/continuous data can be easily overlooked and sometimes difficult to identify. The transportation literature and the literature in many other fields are strewn with well-known examples of data that have been erroneously modeled as continuous data, when the data are really part of an interrelated discrete/continuous process. The consequences of this oversight are biased estimation results that could significantly alter the inferences and conclusions drawn from the data. This chapter presents the theory behind discrete/continuous models along with examples that demonstrate the application of such models. These important underutilized models have enormous potential for future use in transportation data analyses.
