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Study on the Impact of Traffic Accidents in Key Areas of Rural Roads

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  • Maosheng Li

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Hui Xie

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Panpan Shu

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

Abstract

In recent years, the proportion of rural road fatalities in the country is gradually increasing, among which the traffic safety problems are particularly prominent in the town-rural area and the town-center area. Based on the relevant accident data in Hunan Province in recent years, the chi-square test was conducted to obtain the correlation degree between each risk factor and accident severity based on gender stratification. Then, a binary logistic model was established to obtain the significant factors that affect the accident severity in the town-rural area and the town-center area, respectively. Based on the significant factors, relevant safety improvement measures were proposed for the key areas. The results show that severe accidents were significantly related to single-vehicle factors, motorcycle factors, and intersections factors in the town-rural area. In the town-center area, severe accidents were significantly related to elderly age, single-vehicle factors, and nighttime factors. The study obtained the risk factors in key areas, which can provide a reference for the improvement of traffic safety in key areas of rural roads, to ensure the safety and sustainability of rural traffic.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7802-:d:593200
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    References listed on IDEAS

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    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.

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