The invention belongs to the field of intelligent traffic, and provides an accidental traffic anomaly detection method based on a fusion graph convolution gated neural differential equation for a traffic accidental anomaly problem. Comprising the following steps: step 1, generating time embedding based on data set time period information, generating space embedding based on node space information and generating historical information embedding based on adjacent historical information, and obtaining heterogeneous node space-time embedding after splicing; and the like. By applying the traffic anomaly detection method provided by the invention, an urban traffic management department can identify and cope with an emergent traffic event more efficiently, so that the strain capacity and adaptability of an urban traffic system are greatly improved. The research result lays a solid foundation for a future intelligent urban traffic management system, and has a wide application prospect and important social significance.

    本发明属于智能交通领域,针对交通偶发性异常问题提出一种基于融合图卷积门控神经微分方程的偶发性交通异常检测方法。包括以下步骤:步骤1、基于数据集时间周期信息生成时间嵌入,节点的空间信息生成空间嵌入以及邻近的历史信息生成历史信息嵌入,拼接之后得到异质性的节点时空嵌入;等等。通过应用本发明提出的交通异常检测方法,城市交通管理部门可以更加高效地识别和应对突发交通事件,从而大幅提升城市交通系统的应变能力和适应性。本研究的成果将为未来的智能城市交通管理系统奠定坚实的基础,具有广泛的应用前景和重要的社会意义。


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    Title :

    Accidental traffic anomaly detection method based on fusion graph convolution gated neural differential equation


    Additional title:

    一种基于融合图卷积门控神经微分方程的偶发性交通异常检测方法


    Contributors:
    ZANG DI (author) / ZHAO JIAYI (author) / LONG BAICHAO (author) / CUI ZHE (author) / ZHU HONG (author) / ZHANG JUNQI (author) / CHENG JIUJUN (author) / TANG KESHUANG (author)

    Publication date :

    2024-12-17


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

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





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