Citation

Yu R, Abdel-Aty MA. J. Saf. Res. 2013; 46: 91-97.

Affiliation

Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, USA; School of Transportation Engineering, Tongji University, 4800 Cao'an Road, 201804 Shanghai, China. Electronic address: rongjie.yu@gmail.com.

Copyright

(Copyright © 2013, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2013.05.002

PMID

23932690

Abstract

INTRODUCTION: This study provides a systematic approach to investigate the different characteristics of weekday and weekend crashes. METHOD: Weekend crashes were defined as crashes occurring between Friday 9p.m. and Sunday 9p.m., while the other crashes were labeled as weekday crashes. In order to reveal the various features for weekday and weekend crashes, multi-level traffic safety analyses have been conducted. For the aggregate analysis, crash frequency models have been developed through Bayesian inference technique; correlation effects of weekday and weekend crash frequencies have been accounted. A multivariate Poisson model and correlated random effects Poisson model were estimated; model goodness-of-fits have been compared through DIC values. In addition to the safety performance functions, a disaggregate crash time propensity model was calibrated with Bayesian logistic regression model. Moreover, in order to account for the cross-section unobserved heterogeneity, random effects Bayesian logistic regression model was employed. RESULTS: It was concluded that weekday crashes are more probable to happen during congested sections, while the weekend crashes mostly occur under free flow conditions. Finally, for the purpose of confirming the aforementioned conclusions, real-time crash prediction models have been developed. Random effects Bayesian logistic regression models incorporating the microscopic traffic data were developed. Results of the real-time crash prediction models are consistent with the crash time propensity analysis. Furthermore, results from these models would shed some lights on future geometric improvements and traffic management strategies to improve traffic safety. IMPACT ON INDUSTRY: Utilizing safety performance to identify potential geometric improvements to reduce crash occurrence and monitoring real-time crash risks to pro-actively improve traffic safety.

Language: en