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.
Highlights
Traffic congestion has become a world-concerned problem all over the world
Based on the three key parameters of cycle length, arterial coordination signal 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
Based on the three key parameters of cycle, arterial coordination signal 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
Summary
Traffic congestion has become a world-concerned problem all over the world. With the increasing number of vehicles, traffic congestion has deeply affected people’s daily life and the development of social economy. Traffic signal control problem has a longtime congestion phenomenon at peak time, and has obvious ability of grooming in peak time. The second-generation traffic signal control system dynamically adjusts the parameters of the signal timing scheme (cycle length, split, offset). The third-generation control system uses similar idea to the second-generation to dynamically adjust the signal timing parameter in response to the fluctuation of the time-varying traffic flow at the intersection. Meneguzzer C presented two alternative deterministic, discrete-time DP models of the interaction between signal control and route choice, which are proposed and compared with the conventional iterative optimization and assignment (IOA) method for network traffic signal setting [7]. The fifth-generation traffic signal control system is based on the abilities of artificial intelligence and self-learning
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