United States Department of Transportation
i
Real‐time Distributed Optimization of Traffic Signal Timing
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2021-12-01
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[PDF-10.90 MB]
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Edition:February 2019 – December 2021
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Abstract:Leveraging recent advancements in distributed optimization and reinforcement learning, and the growing connectivity and computational capability of vehicles and infrastructure, we propose to advance real-time adaptive signal control via distributed control and optimization. This report consists of three parts. Part 1 develops distributed algorithms for solving a traffic signal timing optimization problem, which is formulated as a mixed-integer programming model. Specifically, the alternating direction method of multipliers (ADMM) is employed, and a two-stage stochastic cell transmission model (CTM) that considers the uncertainty of traffic demand and vehicle turning ratios is considered. Part 2 proposes a framework that utilizes reinforcement learning to optimize a max pressure controller considering the phase switching loss. The max pressure control is modified by introducing a switching curve, and the proposed control method is proved throughput-optimal in a store-and-forward network. Then the theoretical control policy is extended by using a distributed approximation and position-weighted pressure so that the policy-gradient reinforcement learning algorithms can be utilized to optimize the parameters in the policy network including the switching curve and the weigh curve. Part 3 applies reinforcement learning to traffic signal control in a multi-agent scheme, considering the data availability and implementability. The information extracted from traffic cameras is used to define the state of the agents; the action design is aligned with the NEMA dual-ring convention and bounded by a safety constraint, and the coordination is achieved by a shared reward structure among agents.
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