Abstract:
Road intersections have always caused traffic congestion, but an efficient transportation system can benefit society. Real-time traffic signal timing may reduce such traffic congestion. Most traffic signal control methods require a lot of road data, like vehicle positions. This paper reduces average waiting time at a road intersection. An end-to-end off-policy deep reinforcement learning (deep RL) agent with background removal residual networks powers our traffic signal control (TSC) system. Real-time road intersection images feed the agent. The agent can signal real-time traffic conditions (near-) optimally after training. We test TSC methods in intersection scenarios. Our end-to-end deep RL approach adapts to dynamic traffic and outperforms other TSC methods.
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