A multi-layer perceptron type of artificial neural network predicts congested freeway data while demonstrating robustness to faulty loop detector data. Test results on historical data from the I-5 freeway in Seattle, Washington demonstrate that a neural network can successfully predict volume and occupancy one minute in advance, as well as fill in the gaps for missing data with an appropriate prediction. The volume and occupancy predictions are used as inputs to a fuzzy logic ramp metering algorithm currently under testing.


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    Title :

    Freeway traffic data prediction using neural networks


    Contributors:
    Taylor, C. (author) / Meldrum, D. (author)


    Publication date :

    1995-01-01


    Size :

    434097 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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