Distributed MPC-based Coordination of Traffic Perimeter and Signal Control: A Lexicographic Optimization Approach
By: Viet Hoang Pham, Hyo-Sung Ahn
Potential Business Impact:
Cleverly manages city traffic to stop jams.
This paper introduces a comprehensive strategy that integrates traffic perimeter control with traffic signal control to alleviate congestion in an urban traffic network (UTN). The strategy is formulated as a lexicographic multi-objective optimization problem, starting with the regulation of traffic inflows at boundary junctions to maximize the capacity while ensuring a smooth operation of the UTN. Following this, the signal timings at internal junctions are collaboratively optimized to enhance overall traffic conditions under the regulated inflows. The use of a model predictive control (MPC) approach ensures that the control solution adheres to safety and capacity constraints within the network. To address the computational complexity of the problem, the UTN is divided into subnetworks, each managed by a local agent. A distributed solution method based on the alternating direction method of multipliers (ADMM) algorithm is employed, allowing each agent to determine its optimal control decisions using local information from its subnetwork and neighboring agents. Numerical simulations using VISSIM and MATLAB demonstrate the effectiveness of the proposed traffic control strategy.
Similar Papers
Adaptive model predictive control for traffic signal timing with unknown demand and parameters
Systems and Control
Smart traffic lights learn to fix traffic jams.
Integrated Strategy for Urban Traffic Optimization: Prediction, Adaptive Signal Control, and Distributed Communication via Messaging
Systems and Control
Makes traffic lights smarter to cut car waits.
Distributed Mixed-Integer Quadratic Programming for Mixed-Traffic Intersection Control
Systems and Control
Makes traffic lights and self-driving cars work together.