In the process of expressway traffic data application, the spatial-semantic-time relationship affects the determination coefficient and spatial load rate of traffic flow data, and it is difficult to identify expressway traffic flow anomalies quickly and accurately. Therefore, this paper proposes an expressway traffic flow anomaly identification algorithm based on improved Mask RCNN. By clustering, the similar traffic flow data are classified into one category, and the traffic flow anomaly identification data of different expressways are divided into spatial relationship, semantic relationship and time relationship, and the missing data are filled, and the traffic flow data are divided into recent period data, daily period data and periodic data to improve, so as to realize the design of expressway traffic flow anomaly identification algorithm. The experimental results show that the average absolute percentage error and root mean square error are lower after the application of this algorithm, and the obtained determination coefficient is higher, which is consistent with the actual identification results, and the expressway traffic flow anomaly identification effect is better.


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    Titel :

    An improved mask RCNN algorithm for abnormal identification of expressway traffic flow


    Beteiligte:
    Jovanovic-Dolecek, Gordana (Herausgeber:in) / Du, Ke-Lin (Herausgeber:in) / Jiao, Hongqiao (Autor:in)

    Kongress:

    Fifth International Conference on Digital Signal and Computer Communications (DSCC 2025) ; 2025 ; Changchun, China


    Erschienen in:

    Proc. SPIE ; 13653 ; 136530T


    Erscheinungsdatum :

    07.07.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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