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.
Abstract
Traffic congestion at urban intersections is commonly associated with non-adaptive Fixed Time Signal Control (FTSC), which cannot respond effectively to variations in vehicle types, traffic demand, and intersection policies. Although deep reinforcement learning (DRL) has been increasingly applied to traffic signal control, comprehensive evaluations under multi-intersection environments with heterogeneous vehicles and different turning policies remain limited. This study evaluates four value-based DRL algorithms, namely DQN, DDQN, Dueling DQN, and Dueling DDQN, for optimizing traffic signal control in a simulated four-intersection network. The simulation incorporates heterogeneous vehicle types, priority-weighted vehicles, and two turning policy scenarios, and the revised evaluation also includes Fixed Time Signal Control, Longest Queue First, and Max Pressure as baseline controllers. Results from repeated training and testing evaluations show that the DRL-based controllers generally outperform FTSC and remain competitive against adaptive baselines across waiting time and speed metrics. Case 2, which allows direct left turns, consistently performs better than Case 1; however, this improvement is interpreted as the combined effect of DRL-based control and a less restrictive traffic policy. In offline testing for Case 2, Dueling DQN reduces ambulance waiting time from 167.6 to 42.2 s, corresponding to a 74.83% reduction. Overall, 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.
Simulation results indicate that the proposed Adaptive Quad-Agent Double Deep Q-Network model effectively supports dynamic signal phase adaptation, minimizes congestion, and provides more accurate queue length estimations under complex traffic conditions.
Bharathi Ramesh Kumar, Sachin Salunkhe, S. Shinde et al.· Frontiers in Future Transpor...· 0 citations
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 novel cooperative MARL-based approach for adaptive traffic signal control in multi-intersection networks that significantly outperforms existing methods in relation to average pheromone intensity, average noise emission, and average waiting time is proposed.
T. Haddad· Transportation Research Reco...· 0 citations
Work zones reduce roadway capacity and create unstable merging, queue spillback, and stop-and-go conditions that degrade traffic operations while elevating crash risk. Conventional fixed-time, actuated, and adaptive controllers are poorly suited to these non-stationary conditions, and most reinforcement learning approaches optimize mobility while treating safety only as a post hoc evaluation measure. This study develops a safety-aware Deep Q-Network framework for adaptive signal control at intersections operating near work zone activity areas. Merge conflict risk, upstream spillback propagation, and stop-and-go instability are embedded directly into both the state representation and the reward formulation, alongside operational objectives. A merge-conflict model based on relative spacing, relative speed, and acceleration characterizes unsafe interactions in the merge region, and a Pareto-based procedure samples reward-weight vectors to identify non-dominated policies. The framework was evaluated in a SUMO microscopic simulation of a signalized intersection under lane closure. Relative to default fixed-time control, the selected policy increased throughput by 24.6–37.3% across vehicle classes (p < 0.001; Cohen’s d = 0.53–1.29), with the largest gains for trucks and buses, and reduced maximum queue length by 39.1% and spillback distance by 45.8%. The findings show that a single controller trained with surrogate safety indicators as learning objectives can improve operational performance while reducing safety-critical instability in work zones.
Israel Afriyie, Kwadwo Amankwah-Nkyi, Percy Agyei-Essiful et al.· Future Transportation· 1 citation