Traffic speed prediction is among the foundations of advanced traffic management and the gradual deployment of internet of things sensors is empowering data-driven approaches for the prediction. Nonetheless, existing research studies mainly focus on short-term traffic prediction that covers up to one hour forecast into the future. Previous long-term prediction approaches experience error accumulation, exposure bias, or generate future data of low granularity. In this paper, a novel data-driven, long-term, high-granularity traffic speed prediction approach is proposed based on recent development of graph deep learning techniques. The proposed model utilizes a predictor-regularizer architecture to embed the spatial-temporal data correlation of traffic dynamics in the prediction process. Graph convolutions are widely adopted in both sub-networks for geometrical latent information extraction and reconstruction. To assess the performance of the proposed approach, comprehensive case studies are conducted on real-world datasets and consistent improvements can be observed over baselines. This work is among the pioneering efforts on network-wide long-term traffic speed prediction. The design principles of the proposed approach can serve as a reference point for future transportation research leveraging deep learning.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Long-Term Urban Traffic Speed Prediction With Deep Learning on Graphs


    Contributors:


    Publication date :

    2022-07-01


    Size :

    1587764 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic speed prediction using deep learning method

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


    Short-term urban traffic prediction based on deep learning: A systematic map

    Chen, Mingong / Huang, Yuxiang / Liang, Zhihong et al. | IEEE | 2021


    Long-Term Ship Speed Prediction for Intelligent Traffic Signaling

    Gan, Shaojun / Liang, Shan / Li, Kang et al. | IEEE | 2017



    Traffic Speed Prediction with Deep Learning Methods - Best results

    Ganske, Anette | ORKG Comparisons | 2022

    Free access