Modern artificial intelligence (AI) and machine learning (ML) traffic prediction models provide accuracy, yet lack interpret-ability. Our approach designs a local neighborhood of segments and time-periods to construct a prediction data set that contain explicit dynamics across properties measured in the spatial temporal frame. The approach performs similarly to a popular TGCN ML model while providing opportunity to understand the relationship between predictor and predictand across the graph structure to which it is applied.


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    Title :

    Traffic Speed Prediction Using Explicit Spatial Temporal Dynamics


    Contributors:


    Publication date :

    2024-09-24


    Size :

    1133602 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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