HighlightsA generalized statistical traffic assignment (GESTA) model is developed to estimate the probability distributions of link and path flows.Route choice models are consistent with the settings of random O-D demands and random path/link flow and costs.GESTA has a clear multi-level structure based on three types of variations: demand variation, route choice variation and measurement errors.The theory of variance analysis for path/link flow is established.The atomic and non-atomic games in statistical traffic equilibrium are differentiatedGESTA is tested on a large-scale real network to gain insights; numerical experiments are also used to discuss the non-uniqueness of path flow distributions in GESTA.

    AbstractThis paper generalizes and extends classical traffic assignment models to characterize the statistical features of Origin-Destination (O-D) demands, link/path flow and link/path costs, all of which vary from day to day. The generalized statistical traffic assignment (GESTA) model has a clear multi-level variance structure. Flow variance is analytically decomposed into three sources, O-D demands, route choices and measurement errors. Consequently, optimal decisions on roadway design, maintenance, operations and planning can be made using estimated probability distributions of link/path flow and system performance. The statistical equilibrium in GESTA is mathematically defined. Its multi-level statistical structure well fits large-scale data mining techniques. The embedded route choice model is consistent with the settings of O-D demands considering link costs that vary from day to day. We propose a Method of Successive Averages (MSA) based solution algorithm to solve for GESTA. Its convergence and computational complexity are analyzed. Three example networks including a large-scale network are solved to provide insights for decision making and to demonstrate computational efficiency.


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    Title :

    On the variance of recurrent traffic flow for statistical traffic assignment


    Contributors:
    Ma, Wei (author) / Qian, Zhen (Sean) (author)


    Publication date :

    2017-05-14


    Size :

    26 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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