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      Evaluation and Application of Urban Traffic Signal Optimizing Control Strategy Based on Reinforcement Learning

      1 , 2 , 1 , 2 , 1 , 3 , 1 , 2
      Journal of Advanced Transportation
      Hindawi Limited

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          Abstract

          Reinforcement learning method has a self-learning ability in complex multidimensional space because it does not need accurate mathematical model and due to the low requirement for prior knowledge of the environment. The single intersection, arterial lines, and regional road network of a group of multiple intersections are taken as the research object on the paper. Based on the three key parameters of cycle, arterial coordination offset, and green split, a set of hierarchical control algorithms based on reinforcement learning is constructed to optimize and improve the current signal timing scheme. However, the traffic signal optimization strategy based on reinforcement learning is suitable for complex traffic environments (high flows and multiple intersections), and the effects of which are better than the current optimization methods in the conditions of high flows in single intersections, arteries, and regional multi-intersection. In a word, the problem of insufficient traffic signal control capability is studied, and the hierarchical control algorithm based on reinforcement learning is applied to traffic signal control, so as to provide new ideas and methods for traffic signal control theory.

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          Most cited references13

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          Store-and-forward based methods for the signal control problem in large-scale congested urban road networks

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            Lane-Based Saturation Degree Estimation for Signalized Intersections Using Travel Time Data

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              Estimating Maximum Queue Length for Traffic Lane Groups Using Travel Times from Video-Imaging Data

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                Author and article information

                Journal
                Journal of Advanced Transportation
                Journal of Advanced Transportation
                Hindawi Limited
                0197-6729
                2042-3195
                December 26 2018
                December 26 2018
                : 2018
                : 1-9
                Affiliations
                [1 ]Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China
                [2 ]Intelligent Transportation System Research Center of Tongji University, 4801 Cao’an Road, Shanghai 201804, China
                [3 ]Hangzhou Hikvision Digital Technology Co., Ltd., No. 555 Qianmo Road, Binjiang District, Hangzhou 310052, China
                Article
                10.1155/2018/3631489
                13ecc291-2e4a-4490-90cc-0015ba84fd99
                © 2018

                http://creativecommons.org/licenses/by/4.0/

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