CoLight: Learning Network-level Cooperation for Traffic Signal Control (1905.05717v2)

Abstract: Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.

• The paper introduces CoLight, a novel RL model employing graph attention networks to optimize network-level traffic signal control under dynamic conditions.
• It embeds real-time traffic data and leverages neighbor information to predict Q-values, significantly improving travel times across urban networks.
• Experimental evaluations show CoLight outperforms traditional and other RL methods, demonstrating scalability and robust performance in large-scale scenarios.

CoLight: Learning Network-Level Cooperation for Traffic Signal Control

The paper "CoLight: Learning Network-level Cooperation for Traffic Signal Control" introduces a novel reinforcement learning (RL) model aimed at improving the efficiency of traffic signal control across large-scale road networks. CoLight, short for Cooperative Light, leverages graph attentional networks to dynamically facilitate communication among intersections and decode the influences of both spatially and temporally coupled intersections on traffic flow.

Summary of the Approach

Many existing traffic signal control strategies rely on fixed assumptions, which often fail under real-world, dynamic traffic conditions. Traditional methods typically implement cooperation by setting pre-calculated offsets between traffic signals or solving optimization problems with rigid assumptions. However, such strategies can become inefficient in the presence of diverse and fluctuating traffic patterns.

CoLight addresses these shortcomings by employing deep reinforcement learning with graph attentional networks to model the complexity of network-level cooperation. The model consists of three main components: embedding observations of traffic states, using graph attentional networks (GATs) for neighborhood cooperation to capture dynamic interactions, and predicting Q-values to determine signal control policies.

1. Observation Embedding: Traffic data, including the number of vehicles and the current signal phase, is projected into a latent space of learned features, providing a rich representation for the RL agents.
2. Graph Attention Networks (GATs): These are employed to assess the dynamic importance of neighboring intersections' traffic states. Unlike traditional methods which treat all neighboring information equally, GATs allow CoLight to weigh incoming information based on its relevance to the target intersection’s control strategy. This facilitates effective cooperation by maintaining index-free modeling of neighboring intersections through parameter sharing.
3. Q-value Prediction: Reinforcement learning techniques using Q-learning and the embedded state space predict optimal control actions, improving upon travel completion times across the network. By sharing parameters across intersections, the model remains scalable to large road networks.

Experimental Evaluation

The paper presents a comprehensive experimental analysis across both synthetic datasets and real-world traffic data from cities like New York, Hangzhou, and Jinan. Key findings include:

• Performance: CoLight consistently outperforms both traditional approaches (e.g., MaxPressure) and other RL-based methods, achieving lower average travel times even under realistic urban settings with several hundred traffic signals.
• Scalability: The model demonstrates effective scalability, handling networks up to 196 intersections efficiently—surpassing the ability of methods that are limited to models for individual intersections.
• Attention Mechanism Benefits: The GAT allows intersections to differentially weigh upstream and downstream traffic data, showing adaptability to both spatial traffic anomalies and temporal variations.

Implications and Future Directions

CoLight exemplifies how advancements in deep reinforcement learning, particularly the use of graph attention networks, can solve complex multi-agent decision-making problems like traffic signal control. The method challenges the practicality of traditional models, especially where assumptions on uniform traffic demand and lane capacity are ineffectual.

The implications of this work extend beyond traffic management systems, with potential applications spanning any domain requiring decentralized, scalable coordination among networked nodes—such as distributed computing or even logistics networks.

Future research may explore adaptive neighborhood definitions based on real-time traffic flow data and integrate more diverse input features such as road conditions and environmental factors to further enhance model performance. Additionally, pursuing real-world deployment and validation of CoLight could provide empirical insights and stimulate further refinement and adoption by urban planning authorities. The paper's methodology enriches the existing body of work in smart city infrastructure and underscores the critical role of AI-driven methodologies in transforming urban mobility systems.