Control algorithm for the urban traffic using a realtime simulation

Abstract

Many types of research have been interesting by real-time control of urban networks. This paper, basing on a simplified urban traffic model, proposes a novel control approach based on model predictive control concept to reduce congestion and improve the safety of cars on the roads. The contributions of this paper are: First, we consider vehicle heterogeneity, represented by a mathematical model called "S Model" and integrate it with a realtime simulator to evaluate the performance of controllers on real traffic conditions. Second, in order to assess each controller's success under particular circumstances, the structured network-wide traffic controller based on model predictive control (MPC) theory is compared to a fixed time controller (FTC). Using two scenarios, different indicators are tested, i.e total time spent, vehicle number, queue length. The results show that the model predictive control quickly converges, with the different scenarios, and further improves social welfare.