Estimating urban traffic states using iterative refinement and Wardrop equilibria

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