Focused on urban traffic flow issue, the dynamic traffic flow forecasting model is established based on the improved particle optimization algorithm and nerve network theory. Taking the macro dynamic traffic flow as the model, the paper analyzes primarily the features of traffic flow by means of stage-distinguishing method, researches the improved particle optimization algorithm and nerve network theory further and establishes the dynamic traffic flow forecasting model. Finally, it utilizes this model to forecast the traffic flow on North Binghe Road in Lanzhou City. All the results demonstrate that this forecasting model is of higher prestige and proper availability.


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    Title :

    Research on Dynamic Traffic Flow Forecasting Based on Improved Particle Swarm Optimization Algorithm and Neural Network Theory



    Published in:

    Applied Mechanics and Materials ; 178-181 ; 2686-2689


    Publication date :

    2012-05-14


    Size :

    4 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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