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.
Estimating urban traffic states using iterative refinement and Wardrop equilibria
IET Intelligent Transport Systems ; 12 , 8 ; 875-883
07.06.2018
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
convex optimisation programme , Wardrop equilibria , urban traffic , convex programming , metropolitan areas , vehicle traversed paths , nested optimisation process , traffic logistics , real-world GIS data , iterative refinement , network travel time estimation , urban traffic state estimation , iterative methods , city planning , road traffic , GPS traces
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