Exploring machine learning for adaptive traffic signal control

Under conditions of severe local congestion on urban road networks, such as might be caused by major events, incidents or construction activity, existing traffic signal control and coordination methods may not generate optimum control strategies. Unless managed effectively, traffic queues can cause gridlock, bringing traffic flow to a virtual half. In this paper, we explore the application of reinforcement-learning (RL) agents to the task of …