Hotspot Identification for Shanghai Expressways Using the Quantitative Risk Assessment Method

被引:6
|
作者
Chen, Can [1 ,2 ]
Li, Tienan [1 ,2 ]
Sun, Jian [1 ,2 ,3 ]
Chen, Feng [1 ,2 ]
机构
[1] Tongji Univ, Minist Educ, Dept Traff Engn, 4800 Caoan Rd, Shanghai 201804, Peoples R China
[2] Tongji Univ, Minist Educ, Key Lab Rd & Traff Engn, 4800 Caoan Rd, Shanghai 201804, Peoples R China
[3] Jiangsu Prov Collaborat Innovat Ctr Modern Urban, SiPaiLou 2, Nanjing 210096, Jiangsu, Peoples R China
关键词
expressway; hotspot identification; crash; risk assessment; potential crash costs; empirical Bayesian; SAFETY; SEVERITY; MODEL;
D O I
10.3390/ijerph14010020
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] A note on hotspot identification for urban expressways
    Qu, Xiaobo
    Meng, Qiang
    SAFETY SCIENCE, 2014, 66 : 87 - 91
  • [2] Risk identification and quantitative assessment method of offshore platform equipment
    Ni S.
    Tang Y.
    Wang G.
    Yang L.
    Lei B.
    Zhang Z.
    Energy Reports, 2022, 8 : 7219 - 7229
  • [3] Risk identification and quantitative assessment method of offshore platform equipment
    Ni, Shentong
    Tang, Yang
    Wang, Guorong
    Yang, Liu
    Lei, Bo
    Zhang, Zhidong
    ENERGY REPORTS, 2022, 8 : 7219 - 7229
  • [4] Quantitative risk assessment on a gaseous hydrogen refueling station in Shanghai
    Li Zhiyong
    Pan Xiangmin
    Ma Jianxin
    INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2010, 35 (13) : 6822 - 6829
  • [5] Construction risk identification and assessment of a deep foundation pit in Shanghai
    Shanghai Jianke Project Management Co., Ltd., Shanghai 200032, China
    Yantu Gongcheng Xuebao, 2006, SUPPL. (1912-1915):
  • [6] Risk assessment model of bottlenecks for urban expressways using survival analysis approach
    Zheng, Li-yong
    Chang, Yun-tao
    WORLD CONFERENCE ON TRANSPORT RESEARCH - WCTR 2016, 2017, 25 : 1544 - 1555
  • [7] Quantitative Risk Assessment of Trichloroethylene for a Former Chemical Works in Shanghai, China
    Geng, Chunnu
    Luo, Qishi
    Chen, Mengfang
    Li, Zhongyuan
    Zhang, Changbo
    HUMAN AND ECOLOGICAL RISK ASSESSMENT, 2010, 16 (02): : 429 - 443
  • [8] A Risk Exposure Method of Tunnel Management for Expressways
    Suzuki, Toshio
    Moriyama, Mamoru
    Hira, Toshikatsu
    Kimura, Sadao
    LIFE-CYCLE OF STRUCTURAL SYSTEMS: DESIGN, ASSESSMENT, MAINTENANCE AND MANAGEMENT, 2015, : 645 - 648
  • [9] Quantitative mission risk assessment of the satellite propulsion subsystem using PRA method
    Li X.-P.
    Li F.-Q.
    Huang H.-Z.
    International Journal of Information and Management Sciences, 2018, 29 (04): : 415 - 424
  • [10] Quantitative assessment of landslide risk using Monte Carlo material point method
    Zhou, Xiaomin
    Sun, Zheng
    ENGINEERING COMPUTATIONS, 2020, 37 (05) : 1577 - 1596