Distributed Stochastic Model Predictive Control for an Urban Traffic Network
In this paper, we design a stochastic Model Predictive Control (MPC) traffic signal control method for an urban traffic network when the uncertainties in the estimation of the exogenous (in/out)-flows and the turning ratios of downstream traffic flows are taken into account. Assuming that the traffic model parameters are random variables with known expectations and variance, the traffic signal control and coordination problem is formulated as a quadratic program with linear and second-order cone constraints. In order to reduce computational complexity, we suggest a way to decompose the optimization problem corresponding to the whole traffic network into multiple subproblems. By applying Alternating Direction Method of Multipliers (ADMM), the optimal stochastic traffic signal splits are found in distributed manner. The effectiveness of the designed control method is validated via some simulations using VISSIM and MATLAB.
Code (0)
등록된 구현이 없습니다.
Tasks
Model Predictive ControlTraffic Signal ControlSimilar Papers 제목 키워드 기반
Integrated Strategy for Urban Traffic Optimization: Prediction, Adaptive Signal Control, and Distributed Communication via Messaging
This work introduces an integrated approach to optimizing urban traffic by combining predictive modeling of vehicle flow, adaptive traffic signal control, and a modular integration architecture through distributed messag…
Traffic Signal ControlDistributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon
Human-leading truck platooning systems have been proposed to leverage the benefits of both human supervision and vehicle autonomy. Equipped with human guidance and autonomous technology, human-leading truck platooning sy…
Model Predictive ControlData-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust formulations that are amenable to distribu…
Model Predictive ControlMultistage Stochastic Model Predictive Control for Urban Automated Driving
Trajectory planning in urban automated driving is challenging because of the high uncertainty resulting from the unknown future motion of other traffic participants. Robust approaches guarantee safety, but tend to result…
Model Predictive ControlMotion PlanningTrajectory PlanningBoundary Control of Traffic Congestion Modeled as a Non-stationary Stochastic Process
In this paper, we introduce a new conservation-based approach to model traffic dynamics, and apply the model predictive control (MPC) approach to control the boundary traffic inflow and outflow, so that the traffic conge…
ManagementModel Predictive Control