Boundary Control of Traffic Congestion Modeled as a Non-stationary Stochastic Process

TL;DR

This paper presents a novel boundary control method for traffic congestion using a stochastic process model and model predictive control, integrated with SUMO and MATLAB for simulation, demonstrating effective congestion reduction.

Contribution

Introduces a conservation-based traffic model combined with MPC for boundary control, utilizing linear temporal logic and quadratic programming, with practical implementation in SUMO and MATLAB.

Findings

Effective congestion reduction demonstrated in simulations

Integration of SUMO and MATLAB enables practical testing

Model predictive control effectively manages boundary traffic flows

Abstract

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 congestion is reduced. We establish an interface between the Simulation of Urban Mobility (SUMO) software and MATLAB to define a network of interconnected roads (NOIR) as a directed graph, and present traffic congestion management as a network control problem. By formally specifying the traffic feasibility conditions, and using the linear temporal logic, we present the proposed MPC-based boundary control problem as a quadratic programming with linear equality and inequality constraints. The success of the proposed traffic boundary control is demonstrated by simulation of traffic congestion control in Center City Philadelphia.