Marisol Castro

A Spatial Generalized Ordered Response Model to Examine Highway Crash Injury Severity

This paper proposes a flexible econometric structure for injury severity analysis at the level of individual crashes that recognizes the ordinal nature of injury severity categories, allows unobserved heterogeneity in the effects of contributing factors, as well as accommodates spatial dependencies in the injury severity levels experienced in crashes that occur close to one another in space. The modeling framework is applied to analyze the injury severity sustained in crashes occurring on highway road segments in Austin, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files from 2009 and includes a variety of crash characteristics, highway design attributes, driver and vehicle characteristics, and environmental factors. The results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects. The most important determinants of injury severity on highways, according to our results, are (1) whether any vehicle occupant is ejected, (2) whether collision type is head-on, (3) whether any vehicle involved in the crash overturned, (4) whether any vehicle occupant is unrestrained by a seat-belt, and (5) whether a commercial truck is involved.

Joint Model of Participation in Nonwork Activities and Time-of-Day Choice Set Formation for Workers

Nonwork activity and travel participation, an important component of overall travel demand, is complex to model because the greater degree of flexibility associated with such travel induces larger variability and randomness in the behavior. This paper aims to offer a framework for modeling workers’ participation in and travel mileage allocated to non-work activities during various time periods of the day. Five time-of-day blocks are defined for workers on the basis of the period of the day in relation to the work schedule. Individuals can choose to pursue nonwork activities in one or multiple time blocks and travel miles to accomplish the activities. A multiple discrete-continuous extreme value modeling approach is used to model this phenomenon. A unique element of the paper is the addition of a latent choice set generation model as a first component in the model system. This choice set generation model can be used to determine the set of time-of-day periods that each individual will consider for the pursuit of nonwork activities, while recognizing that the consideration choice set is not explicitly observed (and is therefore latent) by the analyst. Thus, the model system presented in this paper is capable of modeling nonwork activity engagement and associated travel mileage by time-of-day period while incorporating varying choice sets across individuals. The two-component model system is applied to a survey sample drawn from the San Francisco area in California and shown to perform substantially better than a pure multiple discrete-continuous extreme value model that assumes a constant choice set across the sample.