Abstract Travel time plays an important role in many ITS application such as traffic control and trip guidance. However the travel time information is acquired after the real driving. This makes travel time prediction an important way to estimate the real-travel time before actual traveling. In this paper, we focus on predicting the travel time of a road segment using deep learning methods. In our work, the historical travel time information collected by Fuzhou taxicab is extracted. Different recurrent networks architectures are applied. Experimental result shows that the deep learning models considering the temporal relation work well on travel time prediction.


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    Title :

    Predicting the Travel Time in Using Recurrent Neural Networks: A Case Study of Fuzhou


    Contributors:
    Li, Luming (author) / Jiang, Xinhua (author)


    Publication date :

    2017-11-03


    Size :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English