In this paper we present a novel approach called STAMP for predicting the ETA of a query trip under the condition that the route to be taken is unknown. Unlike most of the existing approaches which compute a plausible route for the query trip and predict ETA based on the computed route, STAMP does not need to compute any route at the time of prediction. Instead, STAMP partitions a road network offline, into ETA homogeneous zones, and pre-computes k-shortest paths between each zone pair to accommodate various route choices of travelers. It then builds a machine learning model using expressive features created based on the k-shortest paths. These features capture the road characteristics and traffic patterns between a zone pair, without concerning the exact origin/destination location within a zone. We compare STAMP with two existing approaches using industrial scale real-world data. The results show that STAMP provides better prediction accuracy and is able to generalize across space (unseen location pairs) and time (unseen departure times).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    STAMP: An Approach to ETA Prediction by Spatio-temporal Discretization and Machine Learning


    Beteiligte:
    Xu, Bo (Autor:in) / Jonietz, David (Autor:in) / Gupta, Rohit (Autor:in) / Soleymani, Ali (Autor:in) / Malm, Kevin (Autor:in) / Kohn, Reinhard (Autor:in)


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    749025 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    A Spatio-Temporal Deep Learning Approach for Short-Term Prediction of Passenger Demand

    KE, Jintao / ZHENG, Hongyu / YANG, Hai et al. | TIBKAT | 2017


    SHEET HOLDER FOR STAMP MAKING MACHINE WITH STAMP

    Europäisches Patentamt | 2015

    Freier Zugriff

    Traffic Congestion Prediction Based on Spatio-Temporal Graph Structure Learning

    Bannur, Charvi / Bhat, Chaitra / Goutham, Gagan et al. | IEEE | 2023


    Spatio-Temporal Prediction of Gaseous and Liquid Spray Fields using Machine Learning (AIAA 2018-4760)

    Ganti, Himakar / Khare, Prashant | British Library Conference Proceedings | 2018