This chapter presents a methodology of bus travel time prediction, which is driven by the state-of-the-art machine learning technologies and involves real-time bus GPS location data collection and processing. Public transport plays a vital role in the development of our societies, providing the mobility to access to jobs, education, housing, services and recreation. Due to the rapid global urbanization trend, public transport suffers from the increasing traffic congestion and delay. The proposed methodology can predict bus travel time in real time to help mitigate the impact of traffic congestion by providing timely information of bus arrival time and delay. A case study of prediction of bus travel time in an area of Sydney has been carried out to evaluate our approach. The results show that our approach can effectively predict bus travel time and consistently outperforms the benchmark methods in a variety of scenarios. This research work demonstrates the power of AI technologies to promote productivity in traffic congestion management.


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    Titel :

    AI for Real-Time Bus Travel Time Prediction in Traffic Congestion Management


    Beteiligte:
    Chen, Fang (Herausgeber:in) / Zhou, Jianlong (Herausgeber:in) / Ou, Yuming (Autor:in)

    Erschienen in:

    Humanity Driven AI ; Kapitel : 4 ; 63-84


    Erscheinungsdatum :

    2021-12-02


    Format / Umfang :

    22 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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