Estimating Traffic Conditions At Metropolitan Scale Using Traffic Flow Theory

Weizi Li, Meilei Jiang, Yaoyu Chen, and Ming C. Lin
University of North Carolina at Chapel Hill


Traffic has become a major problem in metropolitan areas around the world. It is of a great importance to understand the complex interplay of road networks and traffic conditions. We propose a novel framework to estimate traffic conditions at the metropolitan scale using GPS traces. Our approach begins with an initial estimation of network travel times by solving a convex optimization program based on traffic flow theory. Then, we iteratively refine the estimated network travel times and vehicle traversed paths. Last, we perform a bilevel optimization process to estimate traffic conditions on road segments that are not covered by GPS data. The evaluation and comparison of our approach over two state-of-the-art methods show up to 96.57% relative improvements. We have further conducted field tests by coupling road networks of San Francisco and Beijing with real-world GIS data, which involve 128,701 nodes, 148,899 road segments, and over 26 million GPS traces.


Full Paper (pdf, 1.17MB), IET Intelligent Transport Systems, 2018


The authors would like to thank the National Science Foundation and US Army Research Office.

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