Enabling Hybrid Distributed Model Predictive Control of Traffic Signals In Urban Arterial Networks

Abstract

Road users still encounter unnecessary delays due to inefficient traffic control in urban traffic networks. These delays are ever-increasing and have large environmental and economic consequences. In the Netherlands, most intersections are controlled using actuated controllers, which respond directly to the current traffic demand obtained from vehicle detections. This causes the signal timing to be short-sighted and coordination between intersections is limited.
In order to reduce vehicle delays in urban traffic networks, a hybrid Decentralised Model Predictive traffic signal Controller (DeMPC) is developed based on a new mixed logical dynamical model of a signalised intersection. Operation constraints are also formulated for on-street application and vehicle arrivals at the intersection are predicted by a long-short term memory neural network. A distributed algorithm is also developed to coordinate the signals of the decentralized controllers in urban traffic networks.
Simulation results of a real-world intersection in North-Holland show that the mean delay time per vehicle of the developed DeMPC is 24% lower than the greedy control method of Yunex and only 8% above the actuated controller. The DeMPC outperforms both actuated and Yunex’s method by 10% and 26% in terms of the total number of stops, respectively. For future work, the distributed coordination algorithm should be evaluated by simulation and the DeMPC should be evaluated in combination with green light optimised speed advice.