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This paper presents the results of a new deep learning model for trafficsignal control. In this model, a novel state space approach is proposed tocapture the main attributes of the control environment and the underlyingtemporal traffic movement patterns, including time of day, day of the week,signal status, and queue lengths. The performance of the model was examinedover nine weeks of simulated data on a single intersection and compared to asemi-actuated and fixed time traffic controller. The simulation analysis showsan average delay reductions of 32% when compared to actuated control and 37%when compared to fixed time control. The results highlight the potential fordeep reinforcement learning as a signal control optimization method.

Adaptive Traffic Signal Control with Deep Reinforcement Learning An Exploratory Investigation