The invention provides a road section safety risk state cognition method based on trajectory data. The method comprises the following steps: firstly, selecting a certain road section of a road as a research area, acquiring vehicle types and trajectory data of all vehicles passing through the research area in a certain period of time, meshing the research area, distributing motion parameters of the vehicles to each grid, performing conflict analysis based on the trajectory data and alternative safety measures, and acquiring a conflict index TIT; and carrying out risk label division on the TIT distribution of all samples by adopting a quantile method, finally constructing a time-space sequence deep learning model which takes grid chart data as input and risk labels as output to identify a risk state, and carrying out influence analysis on time and space levels. According to the invention, potential traffic safety risks on the road can be identified in real time, and support is provided for realizing a safe, efficient and intelligent road traffic system.

    本发明提供了一种基于轨迹数据的公路路段安全风险状态认知方法。该方法首先选定公路某一路段为研究区域,获取某一段时间内经过该研究区域所有车辆的车辆类型和轨迹数据,对研究区域网格化后将车辆的运动参数分配至各个网格中,基于轨迹数据和替代安全措施进行冲突分析并获取冲突指标TIT,对所有样本的TIT分布采用分位数法进行风险标签划分,最后构建以网格图数据为输入,风险标签为输出的时空序列深度学习模型对风险状态进行识别,并在时间和空间层面上进行影响分析。本发明有助于实时识别道路上的潜在交通安全风险,为实现安全、高效、智能的公路交通系统提供支撑。


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    Title :

    Road section safety risk state cognition method based on trajectory data


    Additional title:

    一种基于轨迹数据的公路路段安全风险状态认知方法


    Contributors:
    JIN SHENG (author) / JIANG YANG (author) / ZHOU MENGTAO (author) / BAI CONGCONG (author) / GUO WENTONG (author)

    Publication date :

    2024-03-26


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



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