A paradigm shift towards agile and adaptive traffic signal control empowered with the massive growth of Big Data and Internet of Things (IoT) technologies is emerging rapidly for Intelligent Transportation Systems. Generally, an adaptive signal control system fine-tunes signal timing parameters based on pre-defined control hyperparameters using instantaneous traffic detection information. Once traffic pattern changes, those hyperparameters (e.g., maximum and minimum green times) need to be adjusted according to the evolution of traffic dynamics over a very short-term period. Such adjustment processes are usually conducted by professional and experienced traffic engineers. Here we present a human-in-the-loop parallel learning framework and its utilization in an end-to-end recommendation system that mimics and enhances professional signal control engineers’ behaviors. The system has been deployed into a real-world application for an extended period in Hangzhou, China, where signal control hyperparameters are recommended based on large-scale multidimensional traffic datasets. Experimental evaluations demonstrate significant improvements in traffic efficiency through the use of our signal recommendation system.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An End-to-End Recommendation System for Urban Traffic Controls and Management Under a Parallel Learning Framework


    Beteiligte:
    Jin, Junchen (Autor:in) / Guo, Haifeng (Autor:in) / Xu, Jia (Autor:in) / Wang, Xiao (Autor:in) / Wang, Fei-Yue (Autor:in)


    Erscheinungsdatum :

    2021-03-01


    Format / Umfang :

    3976346 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Urban Traffic Management using Machine Learning

    Bharti, Ankit / Hasnani, Rohit / Priyadarshan, Manish et al. | IEEE | 2022


    A survey of urban traffic signal control for agent recommendation system

    Chen, Cheng / Zhu, Fenghua / Ai, Yunfeng | IEEE | 2012