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Visualizing Temporal and Spatial Distribution Characteristic of Traffic Accidents in China

Author

Listed:
  • Yingliu Yang

    (Department of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China)

  • Lianghai Jin

    (Department of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China
    Center of Standardization Evaluation for Production Safety, China Three Gorges University, Yichang 443002, China
    Safety & Environment Science and Technology Ltd., Yichang 443002, China)

Abstract

The interaction among social economy, geography, and environment leads to the occurrence of traffic accidents, which shows the relationship between time and space. Therefore, it is necessary to study the temporal and spatial correlation and provide a theoretical basis for formulating traffic accident safety management policies. This paper aims to explore the traffic accident patterns in 31 provinces of China by using statistical analysis and spatial clustering analysis. The results show that there is a significant spatial autocorrelation among traffic accidents in various provinces and cities in China, which means that in space, the number of traffic accidents and deaths is high with high aggregation and low with low aggregation. Positive spatial autocorrelation is primarily concentrated in the southeast coastal areas, while negative spatial autocorrelation is mainly concentrated in the western areas. Jiangsu, Anhui, Fujian, and Shandong are typical areas of traffic accidents, which deviate from the overall positive spatial autocorrelation trend. Traffic accidents in Sichuan are much more serious than those in neighboring provinces and cities; however, in recent years, this situation has disappeared.

Suggested Citation

  • Yingliu Yang & Lianghai Jin, 2022. "Visualizing Temporal and Spatial Distribution Characteristic of Traffic Accidents in China," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13706-:d:950356
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    References listed on IDEAS

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    Cited by:

    1. Xi Zhang & Shouming Qi & Ao Zheng & Ye Luo & Siqi Hao, 2023. "Data-Driven Analysis of Fatal Urban Traffic Accident Characteristics and Safety Enhancement Research," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    2. Jianjun Wang & Chicheng Ma & Sai Wang & Xiaojuan Lu & Dongyi Li, 2022. "Risk Assessment Model and Sensitivity Analysis of Ordinary Arterial Highways Based on RSR–CRITIC–LVSSM–EFAST," Sustainability, MDPI, vol. 14(23), pages 1-19, December.
    3. Junfeng Yao & Longhao Yan & Zhuohang Xu & Ping Wang & Xiangmo Zhao, 2023. "Collaborative Decision-Making Method of Emergency Response for Highway Incidents," Sustainability, MDPI, vol. 15(3), pages 1-23, January.

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