The Design and Application of Deep Learning in Urban Intelligent Traffic Signal Planning

Abstract

With the acceleration of urbanization, traffic congestion has become increasingly prominent. Lacking dynamic adaptability, traditional methods of traffic signal control struggle to handle complex traffic flows. This article discusses the potential application of deep learning technologies in intelligent traffic signal planning by focusing on reinforcement learning (RN) and supervised learning. The reinforcement learning framework optimizes signal sequence through dynamic modeling and real-time decision-making, significantly reducing vehicle waiting time and travel time in both single-intersection and multi-intersection coordinated control scenarios. Supervised learning models provide data-driven support for control strategies via high-precision traffic flow prediction. Experimental results demonstrate that these technologies improve traffic efficiency (e.g., average vehicle speed increased by 4.2%) and adapt to sudden traffic incidents. However, challenges such as high data dependency and insufficient model generalizability remain unresolved.