The intelligent transportation system (ITS) has been proven capable of effectively addressing traffic congestion issues. For vehicles to perform effectively and improve mobility under the intelligent driving environment, real-time prediction of traffic speed is undoubtedly essential. Considering the complex spatiotemporal dependency inherent in traffic data, conventional prediction models encounter many limitations. To improve the prediction performance and investigate the temporal features, this study focuses on emerging deep neural networks (DNNs) using the Caltrans Performance Measurement System (PeMS) data. This research also establishes an intelligent driving environment in the simulation and compares the traditional car-following model with deep learning methods in terms of multiple performance metrics. The results indicate that both supervised learning and unsupervised learning are superior to the simulation-based model on the freeway, and the two deep learning networks are almost identical to one another. Besides, the result reveals that all models have their latent features for different time dimensions under the low traffic loads, transition states, and heavy traffic loads. This is critical in the application of prediction technologies in ITS. The findings can assist transportation researchers and traffic engineers in both traffic operation and management, such as bottleneck identification, platooning control, and route planning.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach


    Beteiligte:
    Chengying Hua (Autor:in) / Wei (David) Fan (Autor:in)


    Erscheinungsdatum :

    2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    A Hybrid Machine Learning Approach for Freeway Traffic Speed Estimation

    Zhang, Zhao / Yuan, Yun / Yang, Xianfeng (Terry) | Transportation Research Record | 2020


    Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

    Shabarek, Abdullah / Chien, Steven / Hadri, Soubhi | Transportation Research Record | 2020


    Analyzing Freeway Traffic under Congestion: Traffic Dynamics Approach

    Nam, D. H. / Drew, D. R. | British Library Online Contents | 1998


    Random Forests for Freeway Short-Term Traffic Speed Prediction

    Zheng, Hongyu / Chen, Xiaowei / Chen, Xiqun (Michael) | ASCE | 2018