Abstract Based on the analysis of the field traffic flow time series, we found that there is self-similarity and periodic similarity in the traffic flow of different observation scales, which makes the short-term traffic flow prediction a meaningful work. For the purpose of finding the smallest prediction cycle, fractal analysis was conducted in the relation between the observation scale and the prediction cycle by using both the field data and the simulated data. We calculate the fractal dimension and the scaling region of traffic flow time series by using the G-P algorithm. If the scaling region can be found in the traffic flow time series at some observation scale, it means that there is self-similarity in the time series at that observation scale. The minimum observation scale at which there is self-similarity in the traffic flow is the smallest prediction cycle. This observation scale is a prerequisite for judging whether the traffic flow can be predicted or not. This research provides a reference for the short-term traffic flow prediction on expressway and urban road.


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    Title :

    Fractal Analysis of the Relation between the Observation Scale and the Prediction Cycle in Short-Term Traffic Flow Prediction


    Contributors:


    Publication date :

    2018-01-16


    Size :

    8 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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