Estimating urban traffic states using iterative refinement and Wardrop equilibria. Issue 8 (7th June 2018) - Li, Weizi; Jiang, Meilei; Chen, Yaoyu; Lin, Ming C.
Abstract : 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.