Distributed Traffic Signal Control for Maximum Network Throughput (1205.5938v1)

Abstract: We propose a distributed algorithm for controlling traffic signals. Our algorithm is adapted from backpressure routing, which has been mainly applied to communication and power networks. We formally prove that our algorithm ensures global optimality as it leads to maximum network throughput even though the controller is constructed and implemented in a completely distributed manner. Simulation results show that our algorithm significantly outperforms SCATS, an adaptive traffic signal control system that is being used in many cities.

• The paper presents a novel distributed algorithm that adapts backpressure routing to achieve global optimality in network throughput.
• Simulation studies using MATLAB and MITSIMLab demonstrate significant reductions in queue lengths and improved responsiveness compared to SCATS.
• The approach uses local traffic data to enable scalable and real-time traffic signal management in complex urban networks.

Distributed Traffic Signal Control for Maximum Network Throughput

Introduction

The paper "Distributed Traffic Signal Control for Maximum Network Throughput" (1205.5938) introduces a novel algorithm for traffic signal management, inspired by backpressure routing strategies commonly applied within communication and power networks. Traffic management serves as a critical component in urban mobility, with adaptive traffic signal control systems like SCATS and SCOOT presently deployed globally. Despite various existing methodologies, the issue of scalability persists, restricting their effective application to extensive road networks. By leveraging a distributed approach, this research aims to address these limitations and enhance network throughput.

Algorithm Development and Theoretical Foundations

The paper adapts concepts from backpressure routing to develop a distributed traffic signal control algorithm, characterized by its simplicity and scalability. This adaptation involves key modifications due to differences between the original application domains and traffic control scenarios. Notably, traffic signal controllers operate without comprehensive knowledge of traffic arrival rates and lack control over the vehicular flow path once a signal phase is activated. The algorithm dynamically selects phases based on queue differences across links, demonstrating that global optimality can be achieved despite decentralized decision-making at each junction.

The researchers formally prove that, under specific conditions, their algorithm ensures maximum throughput and network stability. This is significant as existing distributed algorithms fail to guarantee global optimality. The system is applicable to large-scale networks due to its reliance on local information, inherently enabling real-time operations across complex urban environments.

Simulation and Performance Evaluation

Simulation results affirm the efficacy of the proposed algorithm over SCATS, showcasing substantial reductions in queue lengths and improved responsiveness. Utilizing both MATLAB and MITSIMLab simulations, which account for macroscopic and microscopic traffic modeling respectively, the distributed traffic signal control algorithm demonstrates markedly superior queue management capabilities.

Results highlight its ability to sustain higher arrival rates without exceeding queue capacities, with simulations yielding reductions by an order of magnitude in maximum queue lengths and significant improvements in average metrics. Additionally, the algorithm mitigates issues such as queue spillback, indicating fewer occurrences of vehicles spreading across multiple links upstream, which is prevalent under existing systems.

Conclusion and Future Directions

The paper conclusively proves the practical viability of integrating backpressure routing principles into traffic signal control, effectively enhancing urban traffic management systems. The research promises improvements in scalability and network throughput without necessitating precise traffic rate estimations, thus representing a robust alternative to conventional adaptive systems like SCATS.

Future research may explore fairness constraints, such as guaranteed service intervals for each traffic flow. Additionally, addressing transient delays caused by non-periodic phase switching will be vital to fully harness driver predictability. Coordination issues, including the synchronization of signals to form "green waves," constitute another prospective advancement that could amplify the algorithm's effectiveness in real-world deployments. The methodological innovations presented herein offer promising avenues for the evolution of intelligent transportation systems, promoting enhanced urban mobility and sustainability.