Speed plays a significant role in evaluating the evolution of traffic status, and predicting speed is one of the fundamental tasks for the intelligent transportation system. There exists a large number of works on speed forecast; however, the problem of long-term prediction for the next day is still not well addressed. In this paper, we propose a multiscale spatio-temporal feature learning network (MSTFLN) as the model to handle the challenging task of long-term traffic speed prediction for elevated highways. Raw traffic speed data collected from loop detectors every 5 min are transformed into spatial–temporal matrices; each matrix represents the one-day speed information, rows of the matrix indicate the numbers of loop detectors, and time intervals are denoted by columns. To predict the traffic speed of a certain day, nine speed matrices of three historical days with three different time scales are served as the input of MSTFLN. The proposed MSTFLN model consists of convolutional long short-term memories and convolutional neural networks. Experiments are evaluated using the data of three main elevated highways in Shanghai, China. The presented results demonstrate that our approach outperforms the state-of-the-art work and it can effectively predict the long-term speed information.


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    Titel :

    Long-Term Traffic Speed Prediction Based on Multiscale Spatio-Temporal Feature Learning Network


    Beteiligte:
    Zang, Di (Autor:in) / Ling, Jiawei (Autor:in) / Wei, Zhihua (Autor:in) / Tang, Keshuang (Autor:in) / Cheng, Jiujun (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    2482663 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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