Bus speed modeling is essential for effective operation and management of public transit systems. Space-time interaction patterns are being ignored when modeling bus speed, and this would lead to biased statistical inferences. This paper proposed a spatiotemporal Bayesian model to characterize space-time interaction patterns among road segments using large-scale bus GPS data and to further develop the bus speed prediction model based on that. Results showed that a type II interaction pattern existed in the data, and the mean absolute percentage errors (MAPEs) of the test sets were 11.3% for the AM peak and 22.5% for the PM peak. Results were further compared with existing work. It was found that the proposed model presented a superior predictive performance while keeping the interpretability of contributing factors and space-time interaction patterns.


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    Titel :

    A Bayesian Spatiotemporal Approach for Bus Speed Modeling*


    Beteiligte:
    Hu, Bin (Autor:in) / Xie, Kun (Autor:in) / Cui, Haipeng (Autor:in) / Lin, Hangfei (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    523771 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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