Adaptive Traffic Signal Control With Deep Reinforcement Learning and High Dimensional Sensory Inputs

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Despite the constant rise in global urban populations and subsequent rise in transportation demand, significant expansion of infrastructure has been hampered by the constraints of space, cost, and environmental concerns. Therefore, optimizing the efficiency of existing infrastructure is becoming increasingly important. Baher Abdulhai discusses adaptive traffic signal controllers that aim to provide demand-responsive strategies to minimize motorists’ delay and achieve higher throughput at signalized intersections.

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