Traffic has become a major problem in metropolitan areas across the world. It is critical to understand the complex interplay of a road network and its traffic states so that researchers and planners can improve the city planning and traffic logistics. The authors propose a novel framework to estimate urban traffic states using GPS traces. Their approach begins with an initial estimation of network travel times by solving a convex optimisation programme based on Wardrop equilibria. Then, they iteratively refine the estimated network travel times and vehicle traversed paths. Lastly, using the refined results as input, they perform a nested optimisation process to derive traffic states in areas without data coverage to obtain full traffic estimations. The evaluation and comparison of their approach over two state-of-the-art methods show up to 96% relative improvements. In order to study urban traffic, the authors have further conducted field tests in Beijing and San Francisco using real-world GIS data, which involve 128,701 nodes, 148,899 road segments, and over 26 million GPS traces.


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    Title :

    Estimating urban traffic states using iterative refinement and Wardrop equilibria


    Contributors:
    Li, Weizi (author) / Jiang, Meilei (author) / Chen, Yaoyu (author) / Lin, Ming C. (author)

    Published in:

    Publication date :

    2018-06-07


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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