title: Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method.
abstract: Hotspot identification (HSID) is the first and key step of the expressway safety management process. This study presents a new HSID method using the quantitative risk assessment (QRA) technique. Crashes that are likely to happen for a specific site are treated as the risk. The aggregation of the crash occurrence probability for all exposure vehicles is estimated based on the empirical Bayesian method. As for the consequences of crashes, crashes may not only cause direct losses (e.g., occupant injuries and property damages) but also result in indirect losses. The indirect losses are expressed by the extra delays calculated using the deterministic queuing diagram method. The direct losses and indirect losses are uniformly monetized to be considered as the consequences of this risk. The potential costs of crashes, as a criterion to rank high-risk sites, can be explicitly expressed as the sum of the crash probability for all passing vehicles and the corresponding consequences of crashes. A case study on the urban expressways of Shanghai is presented. The results show that the new QRA method for HSID enables the identification of a set of high-risk sites that truly reveal the potential crash costs to society.
authors.affiliation: Department of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China. 1433975@tongji.edu.cn.
authors.forename: Can
authors.initials: C
authors.lastname: Chen
authors.affiliation: Department of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China. 10ironman@tongji.edu.cn.
authors.forename: Tienan
authors.initials: T
authors.lastname: Li
authors.affiliation: Department of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China. sunjian@tongji.edu.cn.|Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China. sunjian@tongji.edu.cn.
authors.forename: Jian
authors.initials: J
authors.lastname: Sun
authors.affiliation: Department of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China. fengchen@tongji.edu.cn.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D002681:China / Q000453:epidemiology; D002947:Cities; D003661:Decision Support Techniques; D004779:Environment Design; D006801:Humans; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D017751:Safety Management / Q000379:methods; D014505:Urban Population
keywords: crash; empirical Bayesian; expressway; hotspot identification; potential crash costs; risk assessment