Learning Phase Competition for Traffic Signal Control (1905.04722v1)

Abstract: Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions. Among them, improving the urban transportation efficiency is one of the most prominent topics. Recent studies have proposed to use reinforcement learning (RL) for traffic signal control. Different from traditional transportation approaches which rely heavily on prior knowledge, RL can learn directly from the feedback. On the other side, without a careful model design, existing RL methods typically take a long time to converge and the learned models may not be able to adapt to new scenarios. For example, a model that is trained well for morning traffic may not work for the afternoon traffic because the traffic flow could be reversed, resulting in a very different state representation. In this paper, we propose a novel design called FRAP, which is based on the intuitive principle of phase competition in traffic signal control: when two traffic signals conflict, priority should be given to one with larger traffic movement (i.e., higher demand). Through the phase competition modeling, our model achieves invariance to symmetrical cases such as flipping and rotation in traffic flow. By conducting comprehensive experiments, we demonstrate that our model finds better solutions than existing RL methods in the complicated all-phase selection problem, converges much faster during training, and achieves superior generalizability for different road structures and traffic conditions.

• The paper introduces FRAP, a novel RL framework that employs demand-based phase competition and symmetry invariance to improve traffic signal control with travel time reductions of 15%–47%.
• It details a pairwise phase comparison technique that reduces redundant state exploration and accelerates training convergence.
• Experiments on diverse intersections validate FRAP’s scalability and robust adaptation to dynamic urban traffic conditions.

Learning Phase Competition for Traffic Signal Control: An Overview

The paper "Learning Phase Competition for Traffic Signal Control" presents a novel reinforcement learning (RL) framework termed FRAP, which seeks to optimize urban traffic signal control through a model design that embodies symmetry invariance and demand-based phase competition. Notably, the research demonstrates FRAP’s superior performance compared to contemporary RL approaches by fostering efficient exploration of state-action space, adapting to varying traffic dynamics, and achieving empirical scalability.

Core Innovations and Methodology

FRAP emerges from the limitations observed in existing RL models, which often struggle to rapidly converge and adapt in complex traffic scenarios. These challenges stem from an extensive and often inefficiently navigated state space due to variable traffic flows, especially in an intersection with diverse phase options. Addressing these intricacies, FRAP leverages two key principles:

1. Phase Competition: At the heart of the FRAP model is the competition principle, where traffic signal priorities are derived from traffic demands, thus optimizing green signal allocation. This competition is managed through a pairwise comparison framework that contrasts each possible phase against others, evaluated by predictive models trained on vehicle demand data.
2. Invariance to Symmetries: The ability to recognize and manage symmetrical cases such as flipping and rotation in traffic flows serves as a simplifying factor in FRAP's design. By identifying symmetrical scenarios, FRAP reduces redundant exploration and allows for more rapid generalization, adapting seamlessly to changes in traffic patterns.

Experimental Validation and Results

The researchers validated their model using real-world datasets from various locations with differing traffic conditions. The FRAP approach consistently realized improved travel times, showing a 15% to 47% enhancement over other methods such as DRL and IntelliLight. Furthermore, FRAP demonstrated accelerated training convergence and robustness across different intersection configurations and traffic conditions, which include varied road geometries like 3-, 4-, and 5-approach situations. Notably, as this paper is not contingent on a fixed urban setting, its findings suggest adaptability in broader geographical contexts.

Implications and Future Directions

FRAP stands as a significant advancement in RL-based traffic control, providing a methodology that can scale up to complex, real-world intersection systems. By successfully incorporating real-time adaptive modeling of traffic demands and leveraging phase competition principles, it directly supports urban planning and smart city initiatives. The practical implications include reduced travel time and congestion, contributing to economic benefits and improved urban mobility.

Regarding theoretical implications, FRAP deepens reinforcement learning applications within traffic engineering, showcasing the potential of integrating domain-specific complexities into RL frameworks for enhanced real-time learning. Nevertheless, the efficacy of RL models like FRAP in scenarios containing additional urban factors such as pedestrian dynamics and non-motorized traffic remains to be explored.

Future research can build on FRAP by exploring heightened coordination between multiple intersections, considering multimodal traffic data, and extending real-time deployment feasibility studies. Additionally, deploying FRAP in live environments can provide critical feedback for iterative improvements and alignment with dynamic urban transport demands.

By synthesizing principles from traffic management and machine learning, this research underscores the potential for AI-driven interventions in optimizing urban infrastructure, contributing substantially to ongoing smart city endeavors.