title: Analysis of safety factors for urban expressways considering the effect of congestion in Shanghai, China.
abstract: Urban expressways are the key components of the urban traffic network. The traffic safety situation on expressways directly influences the efficiency of the whole network. A total of 48,325 crashes were recorded by Shanghai Expressway Surveillance System in a three-year period. Considering the different crash mechanisms under different congestion levels, models for the total crashes, non-congested-flow crashes and congested-flow crashes were respectively formulated based on the real-time traffic condition corresponding to each crash. Moreover, considering the potential spatial correlation among segments, the adjacent-correlated spatial and distance-correlated spatial models were formulated and compared to the traditional non-spatial-correlated model. A Bayesian approach was employed to estimate the parameters. The results showed that the congestion index, merging ratio, ramp density, and average daily traffic significantly affect the crash frequency. The safety factors in non-congested flow and congested flow are different; diverging behavior is more risky in non-congested flow, more lanes tend to increase the risk of crashes in congested flow, and horizontal curves tend to decrease the crash risk in congested flow but cause high risk in non-congested flow. In addition, the distance-correlated spatial model is found to be the best-fitting model. The results of this study suggested that dedicated safety countermeasures can be designed for different traffic situations on urban expressways.
authors.affiliation: Department of Traffic Engineering and Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, No. 4800, Cao'an Road, Shanghai 201804, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China. Electronic address: sunjian@tongji.edu.cn.
authors.forename: Jian
authors.initials: J
authors.lastname: Sun
authors.affiliation: Department of Traffic Engineering and Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, No. 4800, Cao'an Road, Shanghai 201804, China. Electronic address: 10ironman@tongji.edu.cn.
authors.forename: Tienan
authors.initials: T
authors.lastname: Li
authors.affiliation: Key Laboratory of Road Structure & Material Research, Institute of Highway, Ministry of Transport, No. 8 Xitucheng Road, Haidian District, Beijing 100088, China. Electronic address: f.li@rioh.cn.
authors.forename: Feng
authors.initials: F
authors.lastname: Li
authors.affiliation: Department of Traffic Engineering and Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, No. 4800, Cao'an Road, Shanghai 201804, China. Electronic address: fengchen@tongji.edu.cn.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
mesh_terms: D000063:Accidents, Traffic; D001334:Automobile Driving; D001499:Bayes Theorem; D002681:China; D002947:Cities; D003441:Crowding; D004779:Environment Design; D006801:Humans; D008962:Models, Theoretical; D012307:Risk Factors; D012449:Safety; D014505:Urban Population
keywords: Bayesian approach; Congestion index; Safety; Spatial correlation; Traffic situation; Urban expressway