Artificial transportation system is critical to parallel transportation management. At its kernel is the agent-based computation which simulates individual's travel behaviors via disaggregated models and “grows” complex traffic scenarios for computational experiments. However, as a common problem, there still lacks a general calibration method for its agent travel behavioral models. Motivated by this, the paper proposes a calibration method for agent-based travel model in artificial system, which correlates macro traffic data with micro behavioral model parameters. The behavioral model is calibrated by two data sources from real urban transportation: link traffic counts and Automatic Vehicle Identification data. Our results indicate that the proposed method can help receive a reasonable model and be applied in general calibration problems.


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    Title :

    Hybrid calibration of agent-based travel model using traffic counts and AVI data


    Contributors:
    Ye, Peijun (author) / Wang, Fei-Yue (author)


    Publication date :

    2017-10-01


    Size :

    372594 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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