This study proposes a reliable and efficient real-time forecasting platform for use in an accident-prone large-scale transportation network. We showed the method could be applied to various roadway sections without any loss of performance or efficiency. Due to its robustness, efficiency, and versatility, the method could be implemented in the Seoul Metropolitan Area to provide traffic authorities and road users with future traffic information even under accident conditions. This is the major contribution of this research and contrasts with state-of-the-art techniques proposed by prior studies, which rely heavily on parameter tuning with large historical datasets and produce only site-specific forecasts with limited prediction horizons under recurrent traffic conditions. The proposed method makes no assumptions about the physical structure of the transportation network and can be applied to different roads under different traffic conditions without time constraints.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-Time Traffic Forecast System for the Accident-Prone Large-Scale Transportation Network in the Seoul Metropolitan Area


    Additional title:

    KSCE J Civ Eng


    Contributors:
    Kim, Youngho (author) / Park, Minju (author) / Ka, Dongju (author) / Lee, Chungwon (author)

    Published in:

    Publication date :

    2023-07-01


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    TRAFFIC ACCIDENT FORECAST SYSTEM, AND ACCIDENT FORECAST METHOD

    OBA YOSHIKAZU / TANIMOTO TOMOHIKO / SHIROTA TAKAHIRO et al. | European Patent Office | 2017

    Free access

    Multi-scale traffic accident-prone road section cause analysis method

    CHEN LIANG / LU ZHECHAO / WEI FAN et al. | European Patent Office | 2025

    Free access