Using a Deep Reinforcement Learning Agent for Traffic Signal Control (1611.01142v1)

Abstract: Ensuring transportation systems are efficient is a priority for modern society. Technological advances have made it possible for transportation systems to collect large volumes of varied data on an unprecedented scale. We propose a traffic signal control system which takes advantage of this new, high quality data, with minimal abstraction compared to other proposed systems. We apply modern deep reinforcement learning methods to build a truly adaptive traffic signal control agent in the traffic microsimulator SUMO. We propose a new state space, the discrete traffic state encoding, which is information dense. The discrete traffic state encoding is used as input to a deep convolutional neural network, trained using Q-learning with experience replay. Our agent was compared against a one hidden layer neural network traffic signal control agent and reduces average cumulative delay by 82%, average queue length by 66% and average travel time by 20%.

• The paper introduces a novel deep Q-network agent using discrete traffic state encoding (DTSE) for improved intersection management.
• It reports significant performance improvements with reductions in cumulative delay (82%), queue length (66%), and travel time (20%).
• The methodology leverages CNN-based feature extraction and Q-learning with experience replay within the SUMO microsimulator.

Advanced Deep Reinforcement Learning for Traffic Signal Management

The paper presents a comprehensive paper on the application of deep reinforcement learning techniques to traffic signal control, employing modern artificial intelligence paradigms to enhance the efficiency of intersection management. In the context of increasing urbanization and vehicular congestion, the paper addresses the critical challenge of optimizing traffic signal phases to minimize delays and improve traffic flow without the extensive infrastructure investments typically required for capacity enhancement.

Core Methodology

The authors introduce a novel approach utilizing a deep Q-network traffic signal control agent (DQTSCA) implemented in the traffic microsimulator SUMO. The innovation lies in the proposed discrete traffic state encoding (DTSE), a dense information representation capturing significant elements of traffic states, including vehicle positions and speeds, alongside current signal phases. This representation is processed by a convolutional neural network (CNN), leveraging its ability to perform complex feature extraction and pattern recognition with minimal preprocessing.

The training of the CNN is conducted through reinforcement learning, specifically Q-learning with experience replay, allowing the agent to iteratively refine its policy by maximizing cumulative rewards associated with traffic signal actions. This architecture showcases an improved form of decentralized decision-making for intersection management, contrasting traditional fixed-time and reactive control strategies.

Significant Results

Quantitative evaluations indicate the superiority of the DQTSCA over traditional one-layer neural network models, with reported reductions in average cumulative delay, queue length, and travel time by 82%, 66%, and 20% respectively. These figures reflect a substantial enhancement in both the fluidity and efficiency of traffic flow at controlled intersections.

The agent's robustness is indicated by its performance in learning policies that balance exploration and exploitation effectively over training epochs. The performance improvements are evidenced through distinct patterns of reward stabilization, indicating convergence towards optimal traffic management strategies.

Implications and Future Directions

The findings illustrate the practical viability of integrating AI-driven solutions within traffic management systems, with the potential for broader applications in real-world transportation networks. The approach anticipates advancements in sensor technology—such as vehicular communication systems—which will furnish the requisite real-time data needed for these methods to achieve their full potential.

Furthermore, the DTSE and CNN framework is notably adaptable, offering the prospect of controlling various intersection geometries with minimal retraining. This adaptability, coupled with the efficient utilization of information-rich state encodings, suggests a scalable solution that could be extended to more complex and interconnected urban traffic networks.

Future work could explore the optimization of reward structures to accommodate diverse traffic management goals, including fairness and robustness to dynamic traffic patterns. Additionally, extending the agent's control capabilities to encompass all traffic phases, including transition phases, will further streamline intersection management and enhance overall traffic safety and throughput.

In summary, the authors contribute significantly to the domain of intelligent transportation systems by demonstrating how deep reinforcement learning can transform traditional traffic signal control frameworks into sophisticated, adaptive systems capable of addressing the growing challenges of urban traffic management.