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Analyzing Risk Factors for Fatality in Urban Traffic Crashes: A Case Study of Wuhan, China

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  • Zhensheng Wang

    (Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
    Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China
    College of Information Engineering, Shenzhen University, Shenzhen 518060, China
    Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China)

  • Yang Yue

    (Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
    Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China)

  • Qingquan Li

    (Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
    Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China)

  • Ke Nie

    (Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China)

  • Wei Tu

    (Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
    Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation, Shenzhen University, Shenzhen 518060, China)

  • Shi Liang

    (Shenzhen Prevention and Treatment Center for Occupational Diseases, 2019 Buxin Road, Luohu District, Shenzhen 518010, China)

Abstract

How to maintain public transit safety and sustainability has become a major concern for the department of Road Traffic Administration. This study aims to analyze the risk factors that contribute to fatality in road traffic crashes using a 5-year police-reported dataset from the Wuhan Traffic Management Bureau. Four types of variables, including driving experience, environmental factor, roadway factor and crash characteristic, were examined in this research by a case-control study. To obtain a comprehensive understanding of crash fatality, this study explored a detailed set of injury-severity risk factors such as impact direction, light and weather conditions, crash characteristic, driving experience and high-risk driving behavior. Based on the results of statistical analyses, fatality risk of crash-involved individuals was significantly associated with driving experience, season, light condition, road type, crash type, impact direction, and high-risk driving behavior. This study succeeded in identifying the risk factors for fatality of crash-involved individuals using a police-reported dataset, which could provide reliable information for implementing remedial measures and improving sustainability in urban road network. A more detailed list of explanatory variables could enhance the accountability of the analysis.

Suggested Citation

  • Zhensheng Wang & Yang Yue & Qingquan Li & Ke Nie & Wei Tu & Shi Liang, 2017. "Analyzing Risk Factors for Fatality in Urban Traffic Crashes: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 9(6), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:897-:d:99718
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    References listed on IDEAS

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    Cited by:

    1. Maosheng Li & Hui Xie & Panpan Shu, 2021. "Study on the Impact of Traffic Accidents in Key Areas of Rural Roads," Sustainability, MDPI, vol. 13(14), pages 1-17, July.
    2. Guofa Li & Yuan Liao & Qiangqiang Guo & Caixiong Shen & Weijian Lai, 2021. "Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016," IJERPH, MDPI, vol. 18(3), pages 1-24, January.
    3. Min Li & Wuhong Wang & Zhen Liu & Mingjun Qiu & Dayi Qu, 2022. "Driver Behavior and Intention Recognition Based on Wavelet Denoising and Bayesian Theory," Sustainability, MDPI, vol. 14(11), pages 1-12, June.
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    5. Zhensheng Wang & Ke Nie, 2019. "Measuring Spatial Patterns of Health Care Facilities and Their Relationships with Hypertension Inpatients in a Network-Constrained Urban System," IJERPH, MDPI, vol. 16(17), pages 1-22, September.
    6. Yaxin Fan & Xinyan Zhu & Bing She & Wei Guo & Tao Guo, 2018. "Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.

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