Traffic estimation is imperative for conducting fundamental transportation engineering tasks such as transportation planning and traffic safety studies. Additionally, traffic prediction is vital for many data-driven intelligent transportation system applications. Most traffic estimation and prediction methods rely on infrastructure-based sensors to collect traffic parameters. However, infrastructure-based data collection can be costly and time consuming to set up and maintain. Additionally, the spatial distribution of the collected traffic data is limited by the location of the deployed hardware sensors. Probe vehicle data can be used to overcome these limitations. Traffic modeling for estimation and prediction is a complex task due to the stochastic nonlinear spatiotemporal dependencies exhibited by traffic parameters. In this paper, a deep learning-based sequence-to-sequence architecture called Seq2seq GCN-LSTM was proposed to estimate and predict network-wide traffic volume and speed. The proposed method utilizes short-term historical traffic data collected from a low-penetration rate probe vehicle fleet to estimate and predict traffic parameters up to 60-minutes ahead. The method utilizes Graph Convolutional Networks for spatial dependency extraction and Long Short-Term Memory networks to model temporal dependencies. The proposed method generated superior traffic results compared to the baseline models. Additionally, the model demonstrated robustness against perturbations caused by the low rate. Furthermore, the probe vehicle penetration rate was varied to test its effect on the proposed method’s modeling capability. The model was able to maintain traffic volume and speed estimation and prediction performance within a reasonable margin of error using penetration rates as low as 1.5% and 0.5%, respectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Sequence-to-Sequence Recurrent Graph Convolutional Networks for Traffic Estimation and Prediction Using Connected Probe Vehicle Data


    Beteiligte:
    Abdelraouf, Amr (Autor:in) / Abdel-Aty, Mohamed (Autor:in) / Mahmoud, Nada (Autor:in)


    Erscheinungsdatum :

    2023-01-01


    Format / Umfang :

    2145691 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Extended Multi-Component Gated Recurrent Graph Convolutional Network for Traffic Flow Prediction

    Zhao, Junhui / Xiong, Xincheng / Zhang, Qingmiao et al. | IEEE | 2024



    Traffic flow prediction method and system based on multi-time-sequence convolutional gated graph neural network

    SHI QUAN / ZHANG TENGYUN / SHEN QINQIN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Vehicle Travel Time Estimation Using Sequence Prediction

    Gültekin Gündüz / Tankut Acarman | DOAJ | 2020

    Freier Zugriff

    Attribute-enhanced perceptual sequence diagram convolutional network traffic speed prediction method

    SONG YAOFENG / LUO RUIKANG | Europäisches Patentamt | 2023

    Freier Zugriff