Traffic density estimation plays a crucial role in traffic management. This paper introduces a novel approach for accurately estimating traffic density using a graph-based density estimation algorithm applied to video data. Our proposed method leverages an advanced object detection model capable of real-time vehicle detection across various sizes and types. By processing video frames, the system detects and counts vehicles, which are then used to construct a graph representation of the traffic. Combining the strengths of vehicle detection algorithms with graph-based density estimation techniques, our approach effectively estimates traffic density in real-world scenarios. Experimental evaluations conducted on real-world video data validate the effectiveness of our integrated method. The results demonstrate that our approach accurately identifies congested areas, providing valuable insights into traffic flow dynamics. This facilitates timely interventions, such as signal optimization or rerouting strategies, ultimately alleviating congestion and enhancing overall traffic efficiency.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Graph-Based Density Estimation for Real-Time Traffic Congestion Monitoring


    Contributors:


    Publication date :

    2024-12-12


    Size :

    1062626 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Real-Time Traffic Optimization for Urban Traffic Congestion

    Korde, Mridula / Gharote, Nishad / Mishra, Rahul et al. | IEEE | 2025


    TRAFFIC CONGESTION ESTIMATION

    YAMAMOTO GAKU / MIZUTA HIDEYUKI / WATANABE SHOICHIRO | European Patent Office | 2020

    Free access

    Monitoring traffic congestion

    LIU JING / ZHANG QIWEI / LIU JUN et al. | European Patent Office | 2018

    Free access

    MONITORING TRAFFIC CONGESTION

    LIU JING / ZHANG QIWEI / LIU JUN et al. | European Patent Office | 2018

    Free access