Traffic congestion in metropolitan areas has become more and more serious. Over the past decades, many academic and industrial efforts have been made to alleviate this problem, among which providing accurate, timely and predictive traffic conditions is a promising approach. Nowadays, online open data have rich traffic related information. Typical such resources include official websites of traffic management and operations, web-based map services (like Google map), weather forecasting websites, and local events (sport games, music concerts, etc.) websites. In this paper, online open data are discussed to provide traffic related information. Traffic conditions collected from web based map services are used to demonstrate the feasibility. The stacked long short-term memory model, a kind of deep architecture, is used to learn and predict the patterns of traffic conditions. Experimental results show that the proposed model for traffic condition prediction has superior performance over multilayer perceptron model, decision tree model and support vector machine model.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Long short-term memory model for traffic congestion prediction with online open data


    Beteiligte:
    Yuan-yuan Chen (Autor:in) / Lv, Yisheng (Autor:in) / Li, Zhenjiang (Autor:in) / Wang, Fei-Yue (Autor:in)


    Erscheinungsdatum :

    01.11.2016


    Format / Umfang :

    588955 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic Congestion Prediction Based on Long-Short Term Memory Neural Network Models

    Chen, Min / Yu, Guizhen / Chen, Peng et al. | ASCE | 2018



    Long Short Term Memory Based Traffic Prediction Using Multi-Source Data

    Leinonen, Matti / Al-Tachmeesschi, Ahmed / Turkmen, Banu et al. | Springer Verlag | 2025

    Freier Zugriff

    Long Short Term Memory Based Traffic Prediction Using Multi-Source Data

    Leinonen, Matti / Al-Tachmeesschi, Ahmed / Turkmen, Banu et al. | Springer Verlag | 2025

    Freier Zugriff