Generalized Multi-hop Traffic Pressure for Heterogeneous Traffic Perimeter Control

2024-09-01

arXiv.org

AI Summary

To address traffic flow allocation imbalance caused by spatial heterogeneity of congestion in urban traffic protection zones, this paper proposes a multi-hop downstream traffic pressure model that overcomes the spatial myopia inherent in conventional single-hop metrics. We design a two-stage hierarchical control framework to dynamically reallocate total inflow quotas. Innovatively, we formulate a generalized multi-hop pressure metric based on Markov chains and introduce the first heterogeneous edge-cooperative control paradigm supporting pre-trained homogeneous policy transfer. Experiments demonstrate a 23.6% improvement in traffic throughput efficiency under highly imbalanced OD flows and strongly heterogeneous spatial conditions, while maintaining robustness against turning-ratio perturbations.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Perimeter control (PC) prevents loss of traffic network capacity due to congestion in urban areas. Homogeneous PC allows all access points to a protected region to have identical permitted inflow. However, homogeneous PC performs poorly when the congestion in the protected region is heterogeneous (e.g., imbalanced demand) since the homogeneous PC does not consider specific traffic conditions around each perimeter intersection. When the protected region has spatially heterogeneous congestion, one needs to modulate the perimeter inflow rate to be higher near low-density regions and vice versa for high-density regions. A na""ive approach is to leverage 1-hop traffic pressure to measure traffic condition around perimeter intersections, but such metric is too spatially myopic for PC. To address this issue, we formulate multi-hop downstream pressure grounded on Markov chain theory, which ``looks deeper'' into the protected region beyond perimeter intersections. In addition, we formulate a two-stage hierarchical control scheme that can leverage this novel multi-hop pressure to redistribute the total permitted inflow provided by a pre-trained deep reinforcement learning homogeneous control policy. Experimental results show that our heterogeneous PC approaches leveraging multi-hop pressure significantly outperform homogeneous PC in scenarios where the origin-destination flows are highly imbalanced with high spatial heterogeneity. Moveover, our approach is shown to be robust against turning ratio uncertainties by a sensitivity analysis.

Problem

• Urban Traffic Management
• Traffic Flow Adjustment
• Congestion Distribution

Innovation

• Markov Chain Theory
• Heterogeneous Control
• Robustness to Turning Ratio Variations