Highlights High resolution traffic speed temporospatial distribution and propagation dynamics prediction Deep learning framework integrating traffic flow dynamics and topological dependency Dynamic programming capturing traffic flow interdependency

    Abstract The information of network-wide future traffic speed distribution and its propagation is beneficial to develop proactive traffic congestion management strategies. However, predicting network-wide traffic speed propagation is non-trivial. This study develops a traffic flow dependency and dynamics based deep learning aided approach (TD2-DL), which predict network-wide high resolution traffic speed propagation by explicitly integrating temporal-spatial flow dependency, traffic flow dynamics with deep learning method techniques. Specifically, we first develop a graph theory-based method to identify the local temporal-spatial traffic dependency of each road among neighboring roads adaptive to the prediction horizon and traffic delay. Then, traffic speed propagation on every road is mathematically described by v-CTM based on traffic initial and boundary conditions. Next, the long short-term memory (LSTM) model is employed to predict boundary conditions factoring the traffic temporal-spatial dependency and historical data predicted by v-CTM. In this way, we well couple the physical models (traffic dependency and v-CTM) with the deep learning approach, and further make them coevolution under this framework. Last, an EKF is used to assimilate predicted traffic speed predicted by v-CTM coupled with the LSTMs and the field traffic data; an FNN is introduced to impute missing and corrupted data for improving the traffic speed prediction accuracy. The numerical experiments indicated that the TD2-DL predicted the network-wide traffic speed propagation in 30 minutes with accuracy varying from 85%-98%. It outperformed the tested models recently developed in literature. The ablation experimental results confirmed the significance of factoring traffic dependency and integrating data imputation and assimilation techniques for improving the prediction accuracy.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Traffic Flow Dependency and Dynamics based Deep Learning Aided Approach for Network-Wide Traffic Speed Propagation Prediction


    Beteiligte:
    Yang, Hanyi (Autor:in) / Du, Lili (Autor:in) / Zhang, Guohui (Autor:in) / Ma, Tianwei (Autor:in)


    Erscheinungsdatum :

    2022-11-18


    Format / Umfang :

    19 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Traffic speed prediction method based on space-time dependency enhancement network

    PAN SU / LI XINXIN / WEN YAN | Europäisches Patentamt | 2024

    Freier Zugriff


    Traffic speed prediction using deep learning method

    Yuhan Jia, / Jianping Wu, / Yiman Du, | IEEE | 2016


    Highway traffic flow speed prediction method based on traffic factor state network

    ZHANG WEIBIN / FENG YAOYAO | Europäisches Patentamt | 2020

    Freier Zugriff