Traffic accidents seriously threaten people’s lives and property all over the world. Therefore, it is of great significance to human society to have a long-term traffic accidents data with detail temporal and geographic information in a specific space, which can be used for traffic accident hotspots identification to reduce the incidence of traffic accidents. Here, we obtain a one-year dataset of traffic accidents of the city center in Changchun, Northeast China, in 2017. In this paper, we analyze the risk of traffic accident in urban area, and then discover the characteristics of traffic accidents at the temporal and spatial aspect. We construct a traffic network, which takes crossings as nodes and road sections as edges and weighted by the total number of traffic accidents. In addition, we integrate road structure data and meteorological data to explore the characteristics of the traffic network.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    The Spatiotemporal Traffic Accident Risk Analysis in Urban Traffic Network


    Additional title:

    Lect.Notes Social.Inform.


    Contributors:
    Li, Wuyungerile (editor) / Tang, Dalai (editor) / Zhang, Chijun (author) / jin, Jing (author) / Huang, Qiuyang (author) / Du, Zhanwei (author) / Yuan, Zhilu (author) / Tang, Shengjun (author) / Liu, Yang (author)

    Conference:

    International Conference on Mobile Wireless Middleware, Operating Systems, and Applications ; 2020 ; Hohhot, China July 11, 2020 - July 11, 2020



    Publication date :

    2020-11-05


    Size :

    6 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Urban road network traffic accident risk studying, judging and visualizing method

    XU PENGPENG / LIU XIAOYAN / WANG QIANFANG et al. | European Patent Office | 2025

    Free access

    Risk, especially risk of traffic accident

    Haight, Frank A. | Elsevier | 1986


    A Dynamic Spatiotemporal Prediction Method for Urban Network Traffic

    Li, Zhenyu / Fu, Yuchuan / Zhao, Pincan et al. | IEEE | 2022


    Urban Railway Network Traffic Prediction with Spatiotemporal Correlations Matrix

    Shao, Weijuan / Li, Man | British Library Conference Proceedings | 2016