ADAPTIVE TRAFFIC SIGNAL CONTROL BASED ON THE NEURAL NETWORK PREDICTION OF THE WEIGHTED TRAFFIC FLOW
A.A. Agafonov, A.S. Yumaganov, V.V. Myasnikov
Samara National Research University, Samara, Russia
Keywords: Traffic signal control, artificial neural network, reinforcement learning, connected vehicles
Abstract
A two-stage method for adaptive traffic signal control based on an estimate of the predicted weighted traffic flow passing through an intersection is proposed. At the first stage, we estimate the travel time required for each vehicle to pass the intersection using an artificial neural network model and estimate the predicted traffic flow through the intersection for a given phase of the traffic signal cycle. At the second step, a weighted flow estimate is formed, which takes into account the waiting time of vehicles. The proposed method for choosing the traffic signal phase is based on maximizing the weighted traffic flow. The results of experimental studies allow us to conclude that the proposed approach outperforms the classical approaches and state-of-the-art methods of traffic signal control based on reinforcement learning.
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