title: Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach.
abstract: Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.
authors: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China; College of Transportation Engineering, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.
authors: Rongjie
authors: R
authors: Yu
authors: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China; College of Transportation Engineering, Tongji University, 4800 Cao'an Road, Shanghai 201804, China. Electronic address: wangxs@tongji.edu.cn.
authors: Xuesong
authors: X
authors: Wang
authors: The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China; College of Transportation Engineering, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.
authors: Kui
authors: K
authors: Yang
authors: Department of Civil, Environmental and Construction Engineering, University of Central Florida Orlando, FL 32826-2450, United States.
authors: Mohamed
authors: M
authors: Abdel-Aty
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data; D001334:Automobile Driving; D001499:Bayes Theorem; D002681:China; D002947:Cities; D004779:Environment Design; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods; D012449:Safety; D014505:Urban Population
keywords: Bayesian inference; Crash risk analysis; Random effects logistic regression model; Semi-parametric model