To improve regional travel demand models, transportation engineers and planners desire appropriate representation of subpopulations. University students are a relatively neglected group of the population, often missed in regional behavioral surveys and not well represented in travel demand models. Many students attending a university reside, take classes, work, and perform other activities in the university environment, which is often mixed use, alternative mode friendly, higher density, and livable. The purpose of this paper is to understand the travel behavior of university students and to model associations with their attributes that include personal characteristics, residential location (residing on campus or off campus), and academic status. The data used in this study are from a unique Internet-based survey (N = 1,468) of students at Old Dominion University in Virginia. This effort was conducted in 2010 and was part of the Virginia University Student Travel Survey (USTS) supplement. With USTS data combined with spatial data, rigorous statistical models of automobile and walk–bicycle trip rates are estimated to explore associated factors. Results showed that students living on campus or near campus were significantly more likely to walk and bicycle and less likely to drive automobiles and indicated the value of living in a campus environment with greater accessibility to activities and a walk-and bicycle-friendly network. The behavioral models provide helpful information that can be used to represent better the behavior of university students in regional travel demand models and to improve strategic transportation planning.
Incidents impose substantial social and personal costs on drivers. Some of the larger incidents that cause delays are also associated with secondary incidents. However, we do not fully know the nature of interdependence between primary and secondary incidents. The objective of this study is to understand how primary incident duration and secondary incident occurrence are related. Specifically, secondary incidents are more likely to occur if the primary incident lasts long; at the same time, the durations of primary incidents are expected to be longer if secondary incidents occur. After obtaining traffic incident and road inventory data in the Hampton Roads area, we proceeded by identifying secondary incidents, defined as incidents occurring on the same roadway segment (which average 1 mile in length) as the primary incident and within the actual duration of the primary incident. If the primary incident blocked lanes, then the actual duration plus 15 minutes was used as the threshold. Models for primary incident durations and whether or not a secondary incident occurs are estimated. The interdependence is modeled by considering incident duration as endogenous in the secondary incident occurrence models. The results show statistical evidence for interdependence, but when it is taken into account, no substantial differences in the magnitudes and statistical significance for the estimated independent variables are found (compared to when the interdependence is not accounted for). Statistically significant correlations are found between secondary incident occurrence and other variables, allowing us to recommend specific operational strategies.
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