Urban Intersection Signal Control Based on Time-Space Resource Scheduling

Lili Zhang,Li Wang,Qi Zhao,Lingyu Zhang

doi:10.1109/access.2021.3059496

Abstract

This paper improves the level of urban traffic control by creasing the dimension of control variables. It focuses on roads rather than vehicles. A new space-time resource scheduling model and a bi-level optimization control method for urban intersections are developed in this study. In traditional concept, the properties of lane are fixed. Nowadays, it changes with the development of new technologies, which increase the dimension of the control variables in the control model and expand the control capability. To this end, the space-time resource scheduling model for intersections includes spatial variables (lane genes, phases, and phase sequences) and time variables (green light time of phases). Then, a new bi-level optimization control method is developed, in which there are an upper layer for lane control based on reinforcement learning and a lower layer is a two-layer optimal control method of phase control based on the model predictive control idea. Finally, the proposed method is proved more efficient than traditional methods after comprehensive experiments.

Highlights

The essence of traffic flow change is that the traffic demand matches the space-time resources [1]
A two-level optimization method is designed for lane control based on reinforcement learning and phase control based on model predictive control
The upper layer is lane control based on reinforcement learning and the lower layer is phase control based on model predictive control ideas

Summary

INTRODUCTION

The essence of traffic flow change is that the traffic demand matches the space-time resources [1]. The new technology has been widely used in the field of traffic control [14], [15], it is still mainly based on the allocation of time resources in the traditional theoretical framework of traffic control, and no one has tried to increase the dimension and further research on extended control capabilities. We propose a time-space resource scheduling model for future city intersections. A two-level optimization method is designed for lane control based on reinforcement learning and phase control based on model predictive control.

RELATED WORK

OPTIMIZATION METHOD

LANE CONTROL BASED ON REINFORCEMENT LEARNING

PHASE CONTROL BASED ON MODEL PREDICTIVE CONTROL IDEAS

CONCLUSION