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