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From cellular attractor selection to adaptive signal control for traffic networks

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posted on 2023-06-09, 00:35 authored by Daxin Tian, Jianshan Zhou, Zhengguo ShengZhengguo Sheng, Yunpeng Wang, Jianming Ma
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.

History

Publication status

  • Published

File Version

  • Published version

Journal

Scientific Reports

ISSN

2045-2322

Publisher

Nature Publishing Group

Volume

6

Page range

23048

Department affiliated with

  • Engineering and Design Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2016-03-16

First Open Access (FOA) Date

2016-03-16

First Compliant Deposit (FCD) Date

2016-03-16

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