With the more and more serious problems such as urban traffic accident and jam etc, how to finish short-term traffic forecast is becoming the premise and key of achieving traffic control and inducement. A new forecast method for city short-term traffic flows is presented, based on neuro-fuzzy decision tree method which is different from traditional models and improves FDT's classification accuracy and extracts more accuracy human interpretable classification rules. The results indicate that this method is valid for short-term traffic flows prediction and it will have a good application prospect in this area.


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    Title :

    Urban Short-Term Traffic Flow Prediction Based on Neuro-FDT


    Contributors:

    Conference:

    First International Conference on Transportation Engineering ; 2007 ; Southwest Jiaotong University, Chengdu, China



    Publication date :

    2007-07-09




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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