Open AccessEstimating urban traffic states using iterative refinement and Wardrop equilibria
Author(s) -
Li Weizi,
Jiang Meilei,
Chen Yaoyu,
Lin Ming C.
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.0007
Subject(s) - global positioning system , beijing , metropolitan area , transport engineering , computer science , field (mathematics) , process (computing) , operations research , geography , engineering , mathematics , telecommunications , archaeology , pure mathematics , china , operating system
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.