Efficiency and Reliability Analysis of Self-Adaptive Two-Stage Fuzzy Control System in Complex Traffic Environment

Mingzhi Wang,Liuqing Ding,He Tian,Xianyu Wu,Jie Lin ,Miao He

doi:10.1155/2022/6007485

Abstract

In this paper, a self-adaptive, two-stage fuzzy controller is established to realize the real-time online optimization of traffic signal timing plan, which takes multimodels of transportation as the research object to analyze the reliability of the control system at the isolated urban intersection. In this system, the first stage calculates traffic urgency degree for all red phases and selects the red phase with maximum traffic urgency degree as the next green phase. The second stage determines whether to extend or terminate the current signal phase. Aiming at the problems of the parameters of membership functions empirical settings and insufficient response to the real-time fluctuation in traffic flow, the controller introduces an improved hybrid genetic algorithm to solve it and enable the controller to self-learn. Finally, a microsimulation platform is constructed based on the VISSIM and Python language to evaluate the efficiency and reliability of the controller under complex actual traffic conditions. Results showed that the average delay time per vehicle is reduced by 14.59%, while the average number of stops per vehicle is reduced by 0.71% compared with the traditional control method. Results indicate that the traffic signal timing plan generated by the controller can efficiently improve the intersection traffic capacity and has good efficiency and reliability under the condition of medium saturation and unsteady flow.

Highlights

At present, with the development of shared mobility and traffic detection techniques theories, many scholars in the field of urban traffic engineering are paying more attention to the collaborative optimization of multi-transport models in traffic safety [1,2,3], traffic operation efficiency and reliability [4, 5], and environmental protection [6,7,8]
Compared with fixed-time control (FTC), the average delay time per vehicle of hybrid genetic algorithm fuzzy control (HGAFC) is reduced by 14.59%, and the average number of stops per vehicle is reduced by 0.71%; the average delay time per vehicle of traditional fuzzy control (TFC) is reduced by 11.08%, and the average number of stops per vehicle is reduced by 0.33%
E following inferences can be drawn based on Figures 5–7: (1) In the early morning, the effect of the control algorithm is the same. e reason is that when the traffic flow of each approach of the intersection is low, the vehicle queue length is short. e fuzzy control’s main influence factor is each phase’s red/ green light duration, and the fuzzy control is in a fixed-time control state

Summary

Introduction

With the development of shared mobility and traffic detection techniques theories, many scholars in the field of urban traffic engineering are paying more attention to the collaborative optimization of multi-transport models in traffic safety [1,2,3], traffic operation efficiency and reliability [4, 5], and environmental protection [6,7,8]. Shared mobility has led to more walking, and cycling trips significantly changed intersections’ traffic flow structure and brought new challenges to the intersection signal timing optimization. With the emergence of new detection technologies such as microwave radar and high point video, it is possible to analyze the efficiency and reliability of signal timing control systems in the real environment

Methods

Results

Conclusion