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Abstract

Methods, systems, and processor-readable media for adaptive cycle-level traffic signal control are described. An adaptive cycle-level traffic signal controller and control method that operate within a continuous action space. A reinforcement learning algorithm called Proximal Policy Optimization (PPO), which is a type of actor-critic model for reinforcement learning, may be used to generate signal cycle phase durations selected from a continuous range of values. The controller thus does not treat the action space as discrete, but instead produces continuous values as output. The generated phase durations may define a full traffic signal cycle. The inputs to the controller may indicate current and past states of the traffic environment. The average duration of delay of vehicles in the traffic environment may be used to calculate the reward for the reinforcement learning model that drives the behavior of the controller.

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