Aug 2026· Advanced Electromagnetics· Vol 15, pp. 5498-5509· 0 citations
TL;DR
A traffic signal dynamic optimization algorithm, the Cross-Attention Mechanism and Dueling Double DQN (CAM-D3QN), which utilizes a novel crisscross attention module to dynamically model spatial dependencies between intersections and incorporates the Dueling Double DQN architecture for robust Q-value estimation.
Abstract
With the acceleration of urbanization, traffic congestion at multiple intersections has become a core bottleneck restricting urban operational efficiency. To address this, this paper proposes a traffic signal dynamic optimization algorithm, the Cross-Attention Mechanism and Dueling Double DQN (CAM-D3QN). This method utilizes a novel crisscross attention module to dynamically model spatial dependencies between intersections and incorporates the Dueling Double DQN architecture for robust Q-value estimation. Validated on CityFlow using Grid-4×4 and Hangzhou-real networks, CAM-D3QN significantly outperforms the state-of-the-art baseline, GPLight, achieving relative improvements of approximately 10.5% in average vehicle delay, 11.3% in average queue length, 3.3% in throughput, alongside notable reductions in stops (12.1%) and fuel consumption (7.2%). Ablation experiments further demonstrate that removing the cross-attention module increases queue length by 30.6% in sudden congestion scenarios. The proposed method achieves superior performance to the baseline across four typical traffic scenarios on the Hangzhou-real road network, demonstrating its generalization capabilities. By leveraging the coordinated optimization of dynamic spatial perception and robust value assessment, this paper provides an effective solution for efficient and robust coordinated traffic signal control. The framework can be combined with traffic states acquired from radar, roadside sensors or wireless communication units in intelligent transportation systems.
This paper introduces a traffic signal control system using deep reinforcement learning to solve the congestion problems at signalized intersections under dynamic traffic conditions. The proposed framework is simulated with the help of MATLAB-SUMO co-simulation framework, the traffic signal control is modeled as a Markov Decision Process (MDP). State space includes traffic density, the queue length, vehicles waiting time, and the actual signal phase whereas the action space comprises of the possible selections of the signal phase. A Deep Q-Network (DQN) is utilized to estimate the optimal state-action value function so that green times can be dynamically allocated based on the changing traffic demand. Multi-objective reward functionality is based on the combined minimization of vehicle delay, queue length, and waiting time and maximization of traffic throughput. Experience replay and target network updates are used to stabilize the learning process. Simulation experiments are conducted in low, medium, and high traffic demand conditions to compare the work of the suggested framework with fixed-time control, actuated control, and classical tabular Q-learning methods. Experimental results demonstrate that the proposed framework reduces average delay by up to 33.9%, decreases queue length by 47.1%, and increases throughput by 25.5% compared to fixed-time control under high-demand conditions. Finally, scalability studies involving networks of up to 16 isolated signalized intersections were conducted to assess computational feasibility and robustness even when the size of the network grows. Comprehensively, the results prove the usefulness of deep reinforcement learning in the creation of intelligent and adaptive traffic signal control services in the city.
Manisha Aeri, K. Purohit, Lata Nautiyal et al.· Service Oriented Computing a...· 0 citations
A critical review of the available literature underscores the potential of DL to improve congestion management and provides important pointers for future development in order to make it more applicable to sustainable and intelligent transportation systems.
Al Ani Mohammed Nsaif Mustafa, Mohd Murtadha Bin Mohamad, F. Muchtar· Acta Universitatis Sapientia...· 0 citations
The findings demonstrate the potential of DRL-based traffic signal control in controlled simulation conditions and highlight that algorithm performance is strongly influenced by traffic policy design and environmental complexity.
D. Prastiyanto, A. A. Manaf, Muhammad Ahnaf Maulana et al.· Scientific Reports· 0 citations
Urban traffic congestion poses a growing challenge to efficient mobility, and existing forecasting and routing methods often struggle to support timely decisions in dynamic road networks. To address this issue, this study proposes a hybrid framework that combines a spatiotemporal graph attention network with the Quantum Approximate Optimization Algorithm (QAOA) for joint congestion prediction and path optimization. In the prediction stage, a multi-scale spatiotemporal encoder is developed to capture short-term, intra-day, and multi-day traffic variation patterns, while an adaptive graph learning mechanism is introduced to model hidden spatial correlations among road segments. In the optimization stage, the routing problem is formulated as a QUBO model and solved by an improved deep QAOA with hierarchical parameter sharing, which helps stabilize training and improve solution quality. The predicted congestion probabilities are further incorporated into the routing objective, enabling the optimization module to avoid highly congested areas. Experiments on the PeMS-BAY and METR-LA datasets show that the proposed method reduces the RMSE of 15-min traffic prediction by 5.4% compared with the strongest baseline. For 50-node routing tasks, it achieves an optimality ratio of 0.952, outperforming standard QAOA. In congestion-aware routing, the average travel time is further reduced by 13.7%. These results indicate that the proposed framework is effective for integrated traffic prediction and dynamic path planning.
Overall, the proposed Improved Dolphin Swarm‐optimized Dynamic Recurrent Neural Network shows promising potential for supporting intelligent traffic management and reducing traffic congestion; however, further validation using larger and more diverse datasets is required to confirm its generalizability and reliability.
Mao-Sheng Yan, Yi-Han Wang, Qingfeng Dong et al.· Concurrency and Computation· 0 citations
This comprehensive review examines recent advances in intelligent traffic management systems that integrate deep learning-based vehicle detection with adaptive signal control mechanisms, specifically focusing on emergency vehicle prioritization to provide a comprehensive framework for researchers and practitioners developing next-generation intelligent transportation systems.
P. T. H. Pathirana, R. J. Wellassa, M. Karunarathna· International journal of re...· 0 citations
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