Reducing Urban Traffic Congestion Using Deep Learning and Model Predictive Control
Zhun Yin, Tong Liu, Chieh Wang, Hong Wang, Zhong‐Ping Jiang
Abstract
This article proposes a deep learning (DL)-based control algorithm-DL velocity-based model predictive control (VMPC)-for reducing traffic congestion with slowly time-varying traffic signal controls. This control algorithm consists of system identification using DL and traffic signal control using VMPC. For the training process of DL, we established a modeling error entropy loss as the criteria inspired by the theory of stochastic distribution control (SDC) originated by the fourth author. Simulation results show that the proposed algorithm can reduce traffic congestion with a slowly varying traffic signal control input. Results of an ablation study demonstrate that this algorithm compares favorably to other model-based controllers in terms of prediction error, signal varying speed, and control effectiveness.