This article is on the basis of the classical speed-density model which combines with the complex road traffic flow, to raise a way that using the weighted regression to calibrate speed-density model parameters in mesoscopic traffic simulator. After processing detector data, the densities are taken as the variables; locally weighted regression is used to build a nonparameter relationship for the number of the traffic flow, to calibrate the vehicle speed. The test with a huge amount of factual data shows that the methods proposed in this paper outperforms the common optimal algorithm: simplex method in the vehicles speed estimation precision, and can accurately descript the dynamic change regularity of the road traffic flow.


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    Title :

    Novel Algorithm for Calibrating Speed-Density Model Parameters in Mesoscopic Traffic Simulator



    Published in:

    Applied Mechanics and Materials ; 599-601 ; 1891-1896


    Publication date :

    2014-08-11


    Size :

    6 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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