Traffic flows are key parameters for design, channelization, and signal control of the intersection. Thus, it is important to study how to correct the output of the assignment model so it can be used for the intersection design. This paper uses the assigned and observed traffic flows of some intersections as the input/output factors to train the neural network to obtain an intersection traffic forecast model. In order to train the neural network, the number and location of the sampled intersections are determined, and traffic flows in the intersection are collected. Then, the surveyed and assigned data neural network is trained. Finally, the accuracy of the trained model is examined.


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    Title :

    Estimation of Traffic Flows at Intersections Based on Traffic Assignment and Neural Network Model


    Contributors:
    Fang, Qin (author) / Wu, Shanhua (author) / Yang, Zhognzhen (author)

    Conference:

    11th International Conference of Chinese Transportation Professionals (ICCTP) ; 2011 ; Nanjing, China


    Published in:

    ICCTP 2011 ; 1042-1051


    Publication date :

    2011-07-26




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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