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.


Extended Abstract (pdf, 0.6MB) ,
Full Report (pdf, 1.17MB) , Transportation Research Board 97th Annual Meeting, 2018


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

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