Traffic Model Based Predictive Control: A Piecewise-Affine using METANET - M. Wakhid Musthofa (InPrime, 2020)
Abstract Traffic congestion on the freeway is a serious problem for modern society. Dynamic traffic management is a good alternative solution to improve efficiency on congestion problems. This article aims to analyze parts of freeway traffic network by using METANET model which is part of macroscopic traffic flow model that describes a set of parameters such as mean speed, traffic flow, and density of a traffic system. The piecewise-affine (PWA) approximation on METANET model is used to design traffic predictive controls and test them on a traffic model structure. This approach guarantees more intensive calculation for METANET traffic flow model in nonlinear form in the context of model predictive control (MPC). Some equations in the METANET model will be approximated by PWA function. With PWA-MPC approximation as direct calculation, equation of PWA model can be transformed into mixed-integer linear programming (MILP). Furthermore, to see the control of the model with MPC control, numerical simulations will be carried out on mean speed, traffic density, traffic flow, queue length, and MPC control. We use time 0 – 2.5 hours. Simulation result shows that the density of traffic, traffic flow, and queue length decreased in this time period, while the mean speed increased.