  Existing ineffective and inflexible traffic light control at urban
intersections can often lead to congestion in traffic flows and cause numerous
problems, such as long delay and waste of energy. How to find the optimal
signal timing strategy is a significant challenge in urban traffic management.
In this paper, we propose PDlight, a deep reinforcement learning (DRL) traffic
light control algorithm with a novel reward as PRCOL (Pressure with Remaining
Capacity of Outgoing Lane). Serving as an improvement over the pressure used in
traffic control algorithms, PRCOL considers not only the number of vehicles on
the incoming lane but also the remaining capacity of the outgoing lane.
Simulation results using both synthetic and real-world data-sets show that the
proposed PDlight yields lower average travel time compared with several
state-of-the-art algorithms, PressLight and Colight, under both fixed and
dynamic green light duration.

Machine Learning;Signal Processing