Citation

Lee J, Abdel-Aty MA, Cai Q, Wang L, Huang H. Transp. Res. Rec. 2018; 2672(32): 49-60.

Affiliation

Center for Advanced Transportation Systems Simulation (CATSS), Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 2Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 3College of Transportation Engineering, Tongji University, Shanghai, China 4Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China Corresponding Author: Address correspondence to Jaeyoung Lee: Jaeyoung@knights.ucf.edu

Copyright

(Copyright © 2018, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198118772704

PMID

unavailable

Abstract

In recent decades, considerable efforts have been made to incorporate traffic safety into long-term transportation plans (LTTPs), a process which is often termed transportation safety planning (TSP). Although some researchers have attempted to integrate transportation plans and safety by adopting transportation planning data (e.g., trip generation) for estimating traffic crash frequency at the macroscopic level, no studies have attempted to develop trip and safety models in one structure simultaneously. A Bayesian integrated multivariate modeling approach is suggested for estimating trips and crashes of non-motorized modes (i.e., walking and cycling). American Housing Survey (AHS) data were collected from the U.S. Census Bureau and were used for the proposed approach. In the first part of the proposed model, the probabilities of choosing walking and cycling modes were estimated, and the estimated probabilities were converted to trips by multiplying the number of sampled households. In the second part, the estimated trips were fed into crash prediction models (or safety performance functions) as an exposure variable. The modeling result revealed many contributing factors for pedestrian/bicycle trips and crashes. Possible shared unobserved features between pedestrian and bicycle trips, and between pedestrian and bicycle crashes, were accounted for by adopting a multivariate structure. In addition, it was found that the crash models with the estimated exposures outperform those with the observed exposures. It is expected that the integrated modeling approach for trips and crashes in this study will provide great insights into the future directions of TSP.

Language: en