From cellular attractor selection to adaptive signal control for traffic networks

Tian, Daxin, Zhou, Jianshan, Sheng, Zhengguo, Wang, Yunpeng and Ma, Jianming (2016) From cellular attractor selection to adaptive signal control for traffic networks. Scientific Reports, 6. p. 23048. ISSN 2045-2322

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Abstract

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TA Engineering (General). Civil engineering (General) > TA0329 Engineering mathematics. Engineering analysis
Depositing User: Zhengguo Sheng
Date Deposited: 16 Mar 2016 10:01
Last Modified: 09 Mar 2017 15:54
URI: http://sro.sussex.ac.uk/id/eprint/60059

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