Abstract--Efficient traffic signal control (TSC) is crucial for reducing congestion, travel delays, pollution, and for ensuring road safety. Traditional approaches, such as fixed signal control and actuated control, often struggle to handle dynamic traffic patterns. In this study, we propose a novel adaptive TSC framework that leverages Reinforcement Learning (RL), using the Proximal Policy Optimization (PPO) algorithm, to minimize total queue lengths across all signal phases. The challenge of efficiently representing highly stochastic traffic conditions for an RL controller is addressed through multiple state representations, including an expanded state space, an autoencoder representation, and a K-Planes-inspired representation. The proposed algorithm has been implemented using the Simulation of Urban Mobility (SUMO) traffic simulator and demonstrates superior performance over both traditional methods and other conventional RL-based approaches in reducing queue lengths. The best performing configuration achieves an approximately 29% reduction in average queue lengths compared to the traditional Webster method. Furthermore, comparative evaluation of alternative reward formulations demonstrates the effectiveness of the proposed queue-based approach, showcasing the potential for scalable and adaptive urban traffic management. I. INTRODUCTION Traffic signal control (TSC) is a crucial problem that needs to be addressed to manage traffic flows, ensure road safety, reduce delays, and increase efficiency and social benefits.

In traffic engineering, the fixed-time traffic signal control remains widely used for its low cost, stability, and interpretability. However, its design depends on hand-crafted formulas (e.g., Webster) and manual re-timing by engineers to adapt to demand changes, which is labor-intensive and often yields suboptimal results under heterogeneous or congested conditions. This paper introduces the EvolveSignal, a large language models (LLMs) powered coding agent to automatically discover new traffic signal control algorithms. We formulate the problem as program synthesis, where candidate algorithms are represented as Python functions with fixed input-output structures, and iteratively optimized through external evaluations (e.g., a traffic simulator) and evolutionary search. Experiments on a signalized intersection demonstrate that the discovered algorithms outperform Webster's baseline, reducing average delay by 20.1% and average stops by 47.1%. Beyond performance, ablation and incremental analyses reveal that EvolveSignal modifications-such as adjusting cycle length bounds, incorporating right-turn demand, and rescaling green allocations-can offer practically meaningful insights for traffic engineers. This work opens a new research direction by leveraging AI for algorithm design in traffic signal control, bridging program synthesis with transportation engineering.

Signal control in urban corridors faces the dual challenge of maintaining arterial traffic progression while adapting to demand variations at local intersections. We propose a hierarchical traffic signal coordination and control scheme that integrates model-based optimization with reinforcement learning. The system consists of: (i) a High-Level Coordinator (HLC) that selects coordination strategies based on observed and predicted demand; (ii) a Corridor Coordinator that derives phase constraints from the selected strategy-either Max-Flow Coordination (MFC) or Green-Wave Coordination (GWC); and (iii) Hybrid Signal Agents (HSAs) that determine signal phases via reinforcement learning with action masking to enforce feasibility. Hierarchical reinforcement learning with Proximal Policy Optimization (PPO) is used to train HSA and HLC policies. At the lower level, three HSA policies-MFC-aware, GWC-aware, and pure agent control (PAC) are trained in conjunction with their respective coordination strategies. At the higher level, the HLC is trained to dynamically switch strategies using a multi-objective reward balancing corridor-level and network-wide performance. The proposed scheme was developed and evaluated on a SUMO-RLlib platform. Case results show that hybrid MFC maximizes throughput under heavy demand; hybrid GWC consistently minimizes arterial stops and maintains progression across diverse traffic conditions but can reduce network-wide efficiency; and PAC improves network-wide travel time in moderate demand but is less effective under heavy demand. The hierarchical design enables adaptive strategy selection, achieving robust performance across all demand levels.

--Recently, learning-based approaches, have achieved significant success in automatically devising effective traffic signal control strategies. In particular, as a powerful evolutionary machine learning approach, Genetic Programming (GP) is utilized to evolve human-understandable phase urgency functions to measure the urgency of activating a green light for a specific phase. However, current GP-based methods are unable to treat the common traffic features of different traffic signal phases consistently. T o address this issue, we propose to use a symmetric phase urgency function to calculate the phase urgency for a specific phase based on the current road conditions. This is represented as an aggregation of two shared subtrees, each representing the urgency of a turn movement in the phase. We then propose a GP method to evolve the symmetric phase urgency function. We evaluate our proposed method on the well-known cityflow traffic simulator, based on multiple public real-world datasets. The experimental results show that the proposed symmetric urgency function representation can significantly improve the performance of the learned traffic signal control policies over the traditional GP representation on a wide range of scenarios. Further analysis shows that the proposed method can evolve effective, human-understandable and easily deployable traffic signal control policies. RAFFIC signals, located at signalized intersections, manage traffic flow in various directions, thereby significantly contributing to the improvement of both transportation efficiency and road safety [1]. Poorly designed traffic signal plans result in commuters wasting valuable time on the roads. The majority of existing traffic signal control systems do not operate based on decisions tailored to the dynamic traffic conditions. For instance, the Sydney Coordinated Adaptive Traffic System [2], which relies on a predetermined cycle time plan, remains extensively utilized in real signalized intersections worldwide. The emergence of Deep Reinforcement Learning (DRL) as a solution to the Traffic Signal Control (TSC) problem is driven by advancements in deep learning [3] and the increasing accessibility of transportation infrastructure components such as surveillance cameras, road sensors, and the internet of vehicles [4]. This trend is exemplified by recent research efforts [5]-[7].

The very few studies that have attempted to formulate multi-agent reinforcement learning (RL) algorithms for adaptive traffic signal control have mainly used value-based RL methods although recent literature has shown that policy-based methods may perform better in partially observable environments. Additionally, because of the simplifying assumptions on signal timing made almost universally across previous studies, RL methods remain largely untested for real-world signal timing plans. This study formulates a multi-agent proximal policy optimization (MA-PPO) algorithm to implement adaptive and coordinated traffic control along an arterial corridor. The formulated MA-PPO has centralized critic architecture under the centralized training and decentralized execution framework. All agents are formulated to allow selection and implementation of up to eight signal phases as commonly implemented in the field controllers. The formulated algorithm is tested on a simulated real-world corridor with seven intersections, actual/complete traffic movements and signal phases, traffic volumes, and network geometry including intersection spacings. The performance of the formulated MA-PPO adaptive control algorithm is compared with the field implemented coordinated and actuated signal control (ASC) plans modeled using Vissim-MaxTime software in the loop simulation (SILs). The speed of convergence for each agent largely depended on the size of the action space which in turn depended on the number and sequence of signal phases. Compared with the currently implemented ASC signal timings, MA-PPO showed a travel time reduction of about 14% and 29%, respectively for the two through movements across the entire test corridor. Through volume sensitivity experiments, the formulated MA-PPO showed good stability, robustness and adaptability to changes in traffic demand.

Adaptive Traffic Signal Control (ATSC) system is a critical component of intelligent transportation, with the capability to significantly alleviate urban traffic congestion. Although reinforcement learning (RL)-based methods have demonstrated promising performance in achieving ATSC, existing methods are still prone to making unreasonable policies. Therefore, this paper proposes a novel Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for multi-intersection signal control (BCT-APLight). In BCT-APLight, the Critique-Tune (CT) framework, a two-layer Bayesian structure is designed to refine the excessive trust of RL policies. Specifically, the Bayesian inference-based Critique Layer provides effective evaluations of the credibility of policies; the Bayesian decision-based Tune Layer fine-tunes policies by minimizing the posterior risks when the evaluations are negative. Meanwhile, an attention-based Adaptive Pressure (AP) mechanism is designed to effectively weight the vehicle queues in each lane, thereby enhancing the rationality of traffic movement representation within the network. Equipped with the CT framework and AP mechanism, BCT-APLight effectively enhances the reasonableness of RL policies. Extensive experiments conducted with a simulator across a range of intersection layouts demonstrate that BCT-APLight is superior to other state-of-the-art (SOTA) methods on seven real-world datasets. Specifically, BCT-APLight decreases average queue length by \textbf{\(\boldsymbol{9.60\%}\)} and average waiting time by \textbf{\(\boldsymbol{15.28\%}\)}.

Traffic simulations are commonly used to optimize traffic flow, with reinforcement learning (RL) showing promising potential for automated traffic signal control. Multi-agent reinforcement learning (MARL) is particularly effective for learning control strategies for traffic lights in a network using iterative simulations. However, existing methods often assume perfect vehicle detection, which overlooks real-world limitations related to infrastructure availability and sensor reliability. This study proposes a co-simulation framework integrating CARLA and SUMO, which combines high-fidelity 3D modeling with large-scale traffic flow simulation. Cameras mounted on traffic light poles within the CARLA environment use a YOLO-based computer vision system to detect and count vehicles, providing real-time traffic data as input for adaptive signal control in SUMO. MARL agents, trained with four different reward structures, leverage this visual feedback to optimize signal timings and improve network-wide traffic flow. Experiments in the test-bed demonstrate the effectiveness of the proposed MARL approach in enhancing traffic conditions using real-time camera-based detection. The framework also evaluates the robustness of MARL under faulty or sparse sensing and compares the performance of YOLOv5 and YOLOv8 for vehicle detection. Results show that while better accuracy improves performance, MARL agents can still achieve significant improvements with imperfect detection, demonstrating adaptability for real-world scenarios.

Traffic signal control is crucial for managing congestion in urban networks. Existing myopic pressure-based control methods focus only on immediate upstream links, leading to suboptimal green time allocation and increased network delays. Effective signal control, however, inherently requires a broader spatial scope, as traffic conditions further upstream can significantly impact traffic at the current location. This paper introduces a novel concept based on the Markov chain theory, namely multi-hop upstream pressure, that generalizes the conventional pressure to account for traffic conditions beyond the immediate upstream links. This farsighted and compact metric informs the deep reinforcement learning agent to preemptively clear the present queues, guiding the agent to optimize signal timings with a broader spatial awareness. Simulations on synthetic and realistic (Toronto) scenarios demonstrate controllers utilizing multi-hop upstream pressure significantly reduce overall network delay by prioritizing traffic movements based on a broader understanding of upstream congestion.

Model free reinforcement learning (RL) provides a potential alternative to earlier formulations of adaptive transit signal priority (TSP) algorithms based on mathematical programming that require complex and nonlinear objective functions. This study extends RL - based traffic control to include TSP. Using a microscopic simulation environment and connected vehicle data, the study develops and tests a TSP event-based RL agent that assumes control from another developed RL - based general traffic signal controller. The TSP agent assumes control when transit buses enter the dedicated short-range communication (DSRC) zone of the intersection. This agent is shown to reduce the bus travel time by about 21%, with marginal impacts to general traffic at a saturation rate of 0.95. The TSP agent also shows slightly better bus travel time compared to actuated signal control with TSP. The architecture of the agent and simulation is selected considering the need to improve simulation run time efficiency.

This paper introduces MoveLight, a novel traffic signal control system that enhances urban traffic management through movement-centric deep reinforcement learning. By leveraging detailed real-time data and advanced machine learning techniques, MoveLight overcomes the limitations of traditional traffic signal control methods. It employs a lane-level control approach using the FRAP algorithm to achieve dynamic and adaptive traffic signal control, optimizing traffic flow, reducing congestion, and improving overall efficiency. Our research demonstrates the scalability and effectiveness of MoveLight across single intersections, arterial roads, and network levels. Experimental results using real-world datasets from Cologne and Hangzhou show significant improvements in metrics such as queue length, delay, and throughput compared to existing methods. This study highlights the transformative potential of deep reinforcement learning in intelligent traffic signal control, setting a new standard for sustainable and efficient urban transportation systems.