Linearly Solvable Mean-Field Traffic Routing Games

• 成果类型: Article
• 署名作者: Tanaka, Takashi; Nekouei, Ehsan; Pedram, Ali Reza; Johansson, Karl Henrik
• 署名单位: University of Texas System; University of Texas Austin; City University of Hong Kong; Royal Institute of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.2986195
• 发表日期: 2021
• 页码: 880-887
• 关键词: Intelligent transportation systems; multiagent systems; terative learning control mean field games

摘要

We consider a dynamic traffic routing game over an urban road network involving a large number of drivers in which each driver selecting a particular route is subject to a penalty that is affine in the logarithm of the number of drivers selecting the same route. We show that the mean-field approximation of such a game leads to the so-called linearly solvable Markov decision process, implying that its mean-field equilibrium (MFE) can be found simply by solving a finite-dimensional linear system backward in time. Based on this backward-only characterization, it is further shown that the obtained MFE has the notable property of strong time-consistency. A connection between the obtained MFE and a particular class of fictitious play is also discussed.