Position weighted backpressure intersection control for urban networks

Abstract

Decentralized intersection control techniques have received attention in the literature as tools that address scalability issues of network intersection control. Chief among these techniques are backpressure (BP) control algorithms, which were originally developed of for large wireless networks. In addition to being light-weight computationally, they come with guarantees of performance at the network level, specifically network-wide stability. The dynamics in backpressure control are represented using networks of point queues and this also applies to all of the applications to traffic control. As such, BP in traffic fail to capture the spatial distribution of vehicles along the intersection links and, consequently, spill-back dynamics. This paper derives a position weighted backpressure (PWBP) control policy for network traffic applying continuum modeling principles of traffic dynamics and thus capture the spatial distribution of vehicles along network roads and spill-back dynamics. PWBP inherits the computational advantages of traditional BP. To prove stability of PWBP, (i) a Lyapunov functional that captures the spatial distribution of vehicles is developed; (ii) the capacity region of the network is formally defined in the context of macroscopic network traffic; and (iii) it is proved, when exogenous arrival rates are within the capacity region, that PWBP control is network stabilizing. We conduct comparisons against a real-world adaptive control implementation for an isolated intersection. Comparisons are also performed against other BP approaches in addition to optimized fixed timing control at the network level. These experiments demonstrate the superiority of PWBP over the other control policies in terms of capacity region, network-wide delay, congestion propagation speed, recoverability from heavy congestion (outside of the capacity region), and response to incidents.

References in corpus (5)

• Self-Control of Traffic Lights and Vehicle Flows in Urban Road Networks
• Multi-scale Perimeter Control Approach in a Connected-Vehicle Environment
• Traffic flow on realistic road networks with adaptive traffic lights
• Traffic state estimation using stochastic Lagrangian dynamics
• Sparse Travel Time Estimation from Streaming Data

Cited by in corpus (17)

• EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System
• Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control
• Traffic Data Imputation using Deep Convolutional Neural Networks
• A Real-Time Dispatching Strategy for Shared Automated Electric Vehicles with Performance Guarantees
• A Novel Max Pressure Algorithm Based on Traffic Delay
• Backpressure Control with Estimated Queue Lengths for Urban Network Traffic
• Pay for Intersection Priority: A Free Market Mechanism for Connected Vehicles
• OCC-MP: A Max-Pressure framework to prioritize transit and high occupancy vehicles
• Power laws and phase transitions in heterogenous car following with reaction times
• Noticeability Versus Impact in Traffic Signal Tampering
• Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation
• Learning Traffic Speed Dynamics from Visualizations
• A Max Pressure Algorithm for Traffic Signals Considering Pedestrian Queues
• Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning
• Generalized Adaptive Smoothing Using Matrix Completion for Traffic State Estimation
• Mobility as a Resource (MaaR) for resilient human-centric automation: a vision paper
• A CAV-based perimeter-free regional traffic control strategy utilizing existing parking infrastructure