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Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors

Author

Listed:
  • Yuhuan Zhang

    (Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China)

  • Huapu Lu

    (Institute of Transportation Engineering and Geomatics, Tsinghua University, Beijing 100084, China)

  • Wencong Qu

    (Road Transport Books Center, China Communications Press Co., Ltd., Beijing 100011, China)

Abstract

The purpose of this paper is to investigate the existence of stratification heterogeneity in traffic accidents in Shenzhen, what factors influence the casualties, and the interaction of those factors. Geographical detection methods are used for the analysis of traffic accidents in Shenzhen. Results show that spatial stratification heterogeneity does exist, and the influencing factors of fatalities and injuries are different. The traffic accident causes and types of primary responsible party have a strong impact on fatalities and injuries, followed by zones and time interval. However, road factors, lighting, topography, etc., only have a certain impact on fatalities. Drunk driving, speeding over 50%, and overloading are more likely to cause more casualties than other illegal behaviors. Speeding over 50% and speeding below 50% have significant different influences on fatalities, while the influences on injuries are not obvious, and so do drunk driving (Blood Alcohol Concentration ≥ 0.08) and driving under the influence of alcohol (0.08 > Blood Alcohol Concentration ≥ 0.02). Both pedestrians and cyclists violating the traffic law are vulnerable to fatality. Heavy truck overloading is more likely to cause major traffic accidents than minibuses. More importantly, there are nonlinear enhanced interactions between the influencing factors, the combination of previous non-significant factors and other factors can have a significant impact on the traffic accident casualties. The findings could be helpful for making differentiated prevention and control measures for traffic accidents in Shenzhen and the method selection of subsequent research.

Suggested Citation

  • Yuhuan Zhang & Huapu Lu & Wencong Qu, 2020. "Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors," IJERPH, MDPI, vol. 17(2), pages 1-17, January.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:2:p:572-:d:309289
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    References listed on IDEAS

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    1. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    2. Zhiyuan Sun & Jianyu Wang & Yanyan Chen & Huapu Lu, 2018. "Influence Factors on Injury Severity of Traffic Accidents and Differences in Urban Functional Zones: The Empirical Analysis of Beijing," IJERPH, MDPI, vol. 15(12), pages 1-16, December.
    3. Dong, Chunjiao & Nambisan, Shashi S. & Richards, Stephen H. & Ma, Zhuanglin, 2015. "Assessment of the effects of highway geometric design features on the frequency of truck involved crashes using bivariate regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 30-41.
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    Cited by:

    1. Qing Ye & Yi Li & Wenzhe Shen & Zhaoze Xuan, 2023. "Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    2. Yuquan Zhou & Yingzhi Wang & Feng Zhang & Hongye Zhou & Keran Sun & Yuhan Yu, 2023. "GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network," IJERPH, MDPI, vol. 20(4), pages 1-18, February.
    3. Rishuang Sun & Chi Zhang & Yujie Xiang & Lei Hou & Bo Wang, 2022. "Identification Method for Crash-Prone Sections of Mountain Highway under Complex Weather Conditions," Sustainability, MDPI, vol. 14(22), pages 1-16, November.

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