To improve the efficiency of detecting abnormal traffic incidents on the road network and reduce the false alarm rate, a real-time traffic anomaly detection framework based on a graph spatiotemporal pattern learning (GSTPL) network is proposed. In this framework, a traffic pattern search algorithm based on a fluctuation similarity measure is designed to screen traffic flow data with the same traffic pattern, and a traffic pattern graph tuple is constructed as the input of the network model to avoid the sample imbalance problem and the effect of single-sample randomness for traffic pattern learning. Then the GSTPL network is designed to extract, unsupervised, the traffic spatiotemporal pattern features and make a reasonable prediction of future traffic parameters as the basis for anomaly evaluation. To further restrain the effect of random fluctuations in traffic flow parameters, an abnormal state evaluation method is designed to calculate the anomaly state likelihood by prediction error distribution learning. The overall detection framework realizes stable prediction of network key node traffic parameters by using spatiotemporal pattern features to construct the traffic pattern graph tuple, and gives incident evaluation results in real time by combination with the detection data. The experiment uses I90 and I405 highway traffic data in Seattle, WA, from 2015. Through comparative analysis, the proposed incident detection method based on GSTPL has a higher detection rate and lower false alarm rate, can adaptively learn dynamic changes of the traffic pattern, and has strong adaptability and stability to different traffic environments.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Graph Spatiotemporal Pattern Learning Network for Real-Time Road Network Traffic Abnormal Incident Detection


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Li, Haitao (Autor:in) / Ma, Yongjian (Autor:in) / Wang, Xin (Autor:in) / Li, Zhihui (Autor:in)


    Erscheinungsdatum :

    05.05.2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Road traffic incident real-time prediction system

    JIA QINGRONG / XU ZHAOFENG / LIAO JIANXUN et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network

    Shun Wang / Yimei Lv / Yuan Peng et al. | DOAJ | 2022

    Freier Zugriff

    Abnormal Traffic Incident Detection Based on Hidden Markov Models

    Zhou, Jinglei / Xu, Jin / Liao, Shaoyi | ASCE | 2011


    Real-Time Traffic Incident Detection with Classification Methods

    Li, Linchao / Zhang, Jian / Zheng, Yuan et al. | British Library Conference Proceedings | 2018


    Real-Time Traffic Incident Detection with Classification Methods

    Li, Linchao / Zhang, Jian / Zheng, Yuan et al. | Springer Verlag | 2017