智能交通预测 深度强化学习
Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in
improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. Rapidly advancing vehicular communication
and edge cloud computation technologies provide key enablers for smart traffic
management. However, operating viable real-time actuation mechanisms on a
practically relevant scale involves formidable challenges, e.g., policy iteration and
conventional Reinforcement Learning (RL) techniques suffer from poor scalability
due to state space explosion. Motivated by these issues, we explore the potential
for Deep Q-Networks (DQN) to optimize traffic light control policies. As an
initial benchmark, we establish that the DQN algorithms yield the “thresholding”
policy in a single-intersection. Next, we examine the scalability properties of
DQN algorithms and their performance in a linear network topology with several
intersections along a main artery. We demonstrate that DQN algorithms produce
intelligent behavior, such as the emergence of “greenwave” patterns, reflecting
their ability to learn favorable traffic light actuations.
