Traffic flow prediction, particularly in areas that experience highly dynamic flows such as motorways, is a major issue faced in traffic management. Due to increasingly large volumes of data being generated every minute, deep learning methods have been used extensively in the latest years for both short and long term traffic flow prediction. However, such models, despite their efficiency, need large amounts of historical information to be provided, and they take a considerable amount of time and computing resources to train, validate and test. This paper presents two new spatial-temporal approaches for building accurate short-term predictions along a popular motorway in Sydney Australia, by making use of the graph structure of the motorway network (including exits and entries). Our proposed methods are proximity-based, and they use the most recent available traffic flow information of the upstream counting stations closest to a given target station. Where such information is not available they employ daily historical means instead. We show that for short-term predictions (less than 10 minutes into the future), our proposed graph-based approaches outperform state-of-the-art deep learning models, such as long-term short memory, convolutional neuronal networks or hybrid models.


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    Title :

    Graph modelling approaches for motorway traffic flow prediction




    Publication date :

    2020-09-20


    Size :

    1388439 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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