Traffic Signal Timing Optimization at Intersections Using an Improved Q-Learning Algorithm

In order to address the limitations of the conventional traffic signal control methods with regard to adapting to dynamic traffic scenarios and the challenges associated with multi-objective collaborative optimization, this study proposes a Firefly-guided Hybrid Deep Q-Network (FH-DQN) algorithm for optimizing the intersection signal timing. The proposed framework synergistically integrates firefly algorithm-based swarm intelligence with deep reinforcement learning, employing a brightness-driven hierarchical exploration strategy for enhancing the action selection efficiency. A multi-objective dynamic reward function incorporating vehicle delay, queue length, carbon emissions, and traffic throughput is developed in order to achieve a balanced optimization of traffic efficiency and environmental sustainability. The algorithm architecture includes the following innovative components: a dual-network collaborative structure that enhances the vehicle responsiveness to sudden congestion at individual intersections, an adaptive brightness update rule derived from the firefly algorithm-based optimization principles and the multi-objective dynamic reward function which achieves the dynamic adjustment of the intersection traffic signals. The extensive experiments conducted on the Simulation of Urban Mobility (SUMO) platform demonstrate that the FH-DQN algorithm achieves a superior performance in comparison with the fixed-time and conventional DQN approaches in typical cross-intersection scenarios. Specifically, the average queue waiting time for vehicles when applying the proposed method is reduced by 26.09% in comparison with the DQN-based approach. The ablation experiment confirms the individual contributions of the dynamic reward function and global network components to enhancing the overall performance of the FH-DQN algorithm. To sum up, this study provides a novel framework for collaborative traffic signal optimization in complex urban road networks, significantly improving both the adaptive capability and multi-objective optimization performance of intelligent transportation systems. Future work could focus on adapting the algorithm to metropolitan-scale networks and on integrating multimodal traffic data for enhancing the operational robustness of the proposed model.