Analyzing pedestrian crash injury severity under different weather conditions

被引:46
|
作者
Li, Duo [1 ]
Ranjitkar, Prakash [2 ]
Zhao, Yifei [1 ]
Yi, Hui [1 ]
Rashidi, Soroush [3 ]
机构
[1] Changan Univ, Highway Sch, Room 208, Xian 710000, Shaanxi, Peoples R China
[2] Univ Auckland, Dept Civil & Environm Engn, Auckland, New Zealand
[3] Opus Int Consultants Ltd, Auckland, New Zealand
关键词
Injury severity; weather; classification and regression trees (CART); random forests; ACCIDENT; RISK;
D O I
10.1080/15389588.2016.1207762
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Objective: Pedestrians are the most vulnerable road users due to the lack of mass, speed, and protection compared to other types of road users. Adverse weather conditions may reduce road friction and visibility and thus increase crash risk. There is limited evidence and considerable discrepancy with regard to impacts of weather conditions on injury severity in the literature. This article investigated factors affecting pedestrian injury severity level under different weather conditions based on a publicly available accident database in Great Britain.Method: Accident data from Great Britain that are publicly available through the STATS19 database were analyzed. Factors associated with pedestrian, driver, and environment were investigated using a novel approach that combines a classification and regression tree with random forest approach.Results: Significant severity predictors under fine weather conditions from the models included speed limits, pedestrian age, light conditions, and vehicle maneuver. Under adverse weather conditions, the significant predictors were pedestrian age, vehicle maneuver, and speed limit.Conclusions: Elderly pedestrians are associated with higher pedestrian injury severities. Higher speed limits increase pedestrian injury severity. Based on the research findings, recommendations are provided to improve pedestrian safety.
引用
收藏
页码:427 / 430
页数:4
相关论文
共 50 条
  • [11] Weather impacts on single-vehicle truck crash injury severity
    Naik, Bhaven
    Tung, Li-Wei
    Zhao, Sharishan
    Khattak, Aemal J.
    JOURNAL OF SAFETY RESEARCH, 2016, 58 : 57 - 65
  • [12] Analyzing influencing factors of crash injury severity incorporating FARS data
    Zhang, Zhijian
    Jiang, Yubin
    Chen, Zhijun
    Xiong, Yubing
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 41 (04) : 5053 - 5063
  • [13] Crossing locations, light conditions, and pedestrian injury severity
    Siddliqui, Navd A.
    Chu, Xuehao
    Guttenplan, Martin
    PEDESTRIANS AND BICYCLES, 2006, (1982): : 141 - +
  • [14] Investigating risk factors associated with pedestrian crash occurrence and injury severity in Texas
    Rahman, Mashrur
    Kockelman, Kara M.
    Perrine, Kenneth A.
    TRAFFIC INJURY PREVENTION, 2022, 23 (05) : 283 - 289
  • [15] Exploring the Effect of Visibility Factors on Vehicle-Pedestrian Crash Injury Severity
    Harris, Laura
    Ahmad, Numan
    Khattak, Asad
    Chakraborty, Subhadeep
    TRANSPORTATION RESEARCH RECORD, 2023, 2677 (11) : 24 - 35
  • [16] Urban crash-related child pedestrian injury incidence and characteristics associated with injury severity
    Koopmans, Joy M.
    Friedman, Lee
    Kwon, Soyang
    Sheehan, Karen
    ACCIDENT ANALYSIS AND PREVENTION, 2015, 77 : 127 - 136
  • [17] Application of partial proportional odds model for analyzing pedestrian crash injury severities in Switzerland
    Sasidharan, Lekshmi
    Menendez, Monica
    JOURNAL OF TRANSPORTATION SAFETY & SECURITY, 2019, 11 (01) : 58 - 78
  • [18] Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models
    Cheng, Wen
    Gill, Gurdiljot Singh
    Sakrani, Taha
    Dasu, Mohan
    Zhou, Jiao
    ACCIDENT ANALYSIS AND PREVENTION, 2017, 108 : 172 - 180
  • [19] Partial proportional odds model-An alternate choice for analyzing pedestrian crash injury severities
    Sasidharan, Lekshmi
    Menendez, Monica
    ACCIDENT ANALYSIS AND PREVENTION, 2014, 72 : 330 - 340
  • [20] Analyzing injury severity of bus passengers with different movements
    Li, Duo
    Zhao, Yifei
    Bai, Qiang
    Zhou, Bei
    Ling, Hongbiao
    TRAFFIC INJURY PREVENTION, 2017, 18 (05) : 528 - 532