The majority of road accidents occur as a result of driver irresponsibility. Utilizing deep learning models to analyze traffic CCTV can effectively mitigate traffic accidents. This paper aims to identify and monitor vehicles and assess traffic conditions by comparing and categorizing the similarities between vehicle trajectories. Three similarity measurement methods are employed: cosine similarity, Jensen-Shannon divergence, and Euclidean distance similarity. The results show that lanes and traffic patterns can be effectively identified using the presented approach. Through our approach, one can further develop traffic monitoring and analysis advancements, thereby enhancing road safety and traffic management


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-Time Traffic Analysis Using Vehicle Trajectory Similarity in Edge Computing


    Beteiligte:
    Jang, Jae-Geun (Autor:in) / Jung, Jin-Uk (Autor:in) / Kim, Seonhyeong (Autor:in) / Choi, Ayoung (Autor:in) / Kwon, Young-Woo (Autor:in)


    Erscheinungsdatum :

    18.02.2025


    Format / Umfang :

    5215310 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Self‐Similarity of Real Time Traffic

    Sheluhin, Oleg I. / Smolskiy, Sergey M. / Osin, Andrey V. | Wiley | 2007



    Vehicle trajectory calculation method based on space-time similarity

    JING GUOSHENG / ZHOU ZHIHUA / HU JINGSONG et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Real-Time Vehicle Trajectory Prediction for Traffic Conflict Detection at Unsignalized Intersections

    Qianxia Cao / Zhongxing Zhao / Qiaoqiong Zeng et al. | DOAJ | 2021

    Freier Zugriff