Affiliation:
1. Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China
2. The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Abstract
The transportation control infrastructure serves as the foundation for regional traffic signal control. However, in practice, this infrastructure is often imperfect and complex, characterized by factors such as heterogeneity and uncertainty, which pose significant challenges to existing methods and systems. Therefore, this paper proposes a novel approach to coordinated traffic signal control that emphasizes flexibility. To achieve this flexibility, we combine the flexible model of complex networks with robust fuzzy control methods. This approach enables us to overcome the complexity of the transportation control infrastructure and ensure efficient management of traffic signals. Additionally, to ensure long-term operational ease, we develop a regional traffic signal control system using steam computing technology, which provides high scalability and compatibility. Finally, computational experiments are performed to validate adaptability and performance of our proposed approach.
Funder
National Natural Science Foundation of China
Guangdong S&T project
The State Key Laboratory for Management and Control of Complex Systems (
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference45 articles.
1. Review of Urban Traffic Signal Control;Tian;Road Traffic Saf.,2016
2. Chu, T., Qu, S., and Wang, J. (2016, January 6–8). Large-Scale Traffic Grid Signal Control with Regional Reinforcement Learning. Proceedings of the 2016 American Control Conference (ACC), Boston, MA, USA.
3. Study of Urban Traffic Signal Control System;Li;Compr. Transp.,2015
4. Concepts and Frameworks of Artificial Transportation Systems;Wang;Complex Syst. Complex. Sci.,2004
5. A Robust Traffic Control Model Considering Uncertainties in Turning Ratios;Liu;IEEE Trans. Intell. Transp. Syst.,2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献