EXP-DDQN algorithm.

With the increasing integration of Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs) in urban traffic systems, along with highly variable pedestrian crossing demands, traffic management faces unprecedented challenges. This study introduces an improved adaptive signal control approach using an enhanced dual-layer deep Q-network (EXP-DDQN), specifically tailored for intelligent connected environments. The proposed model incorporates a comprehensive state representation that integrates CAV-HDV car-following dynamics and pedestrian flow variability. Additionally, it features an improved MC Greedy exploration strategy and prioritized experience replay, enabling efficient learning and adaptability in highly dynamic traffic scenarios. These advancements allow the system to dynamically adjust green light durations, phase switches, and pedestrian phase activations, achieving a fine balance between efficiency, safety, and signal stability. Experimental evaluations underscore the model’s distinct advantages, including a 26.9% reduction in vehicle-pedestrian conflicts, a 31.83% decrease in queue lengths, a 32.52% reduction in delays compared to fixed-time strategies, and a 35.17% reduction in pedestrian crossing wait times. Furthermore, EXP-DDQN demonstrates significant improvements over traditional DQN and DDQN methods across these metrics. These results underscore the method’s distinct capability to address the complexities of mixed traffic scenarios, offering valuable insights for future urban traffic management systems.

随着网联自动驾驶汽车（Connected and Automated Vehicles, CAVs）与人驾驶车辆（Human-Driven Vehicles, HDVs）在城市交通系统中的融合程度持续加深，加之行人过街需求呈现高度动态多变的特征，交通管理正面临前所未有的挑战。本研究提出一种改进的自适应信号控制方法，采用增强型双层深度Q网络（EXP-DDQN），专为智能网联交通环境量身打造。所提出的模型整合了全面的状态表征体系，融合了CAV与HDV的跟驰动力学特性以及行人流的动态变化特征。此外，该方法还引入了改进的MC贪婪探索策略与优先经验回放机制，能够在高度动态的交通场景中实现高效学习与自适应调整。上述技术改进使得系统可以动态调节绿灯时长、相位切换以及行人相位激活，在通行效率、交通安全与信号稳定性之间实现精细平衡。实验评估结果凸显了该模型的显著优势：相较于固定配时信号控制策略，其人车冲突数降低26.9%、排队长度减少31.83%、通行延误降低32.52%，行人过街等待时间缩短35.17%。此外，相较于传统深度Q网络（DQN）与双层深度Q网络（DDQN）方法，EXP-DDQN在上述各项指标上均实现了显著提升。上述结果充分证明，该方法能够有效应对混合交通场景的复杂挑战，为未来城市交通管理系统的优化提供了极具价值的参考思路。