srep23048.pdf (6.3 MB)
From cellular attractor selection to adaptive signal control for traffic networks
journal contribution
posted on 2023-06-09, 00:35 authored by Daxin Tian, Jianshan Zhou, Zhengguo ShengZhengguo Sheng, Yunpeng Wang, Jianming MaThe 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.
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Publication status
- Published
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- Published version
Journal
Scientific ReportsISSN
2045-2322Publisher
Nature Publishing GroupExternal DOI
Volume
6Page range
23048Department affiliated with
- Engineering and Design Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2016-03-16First Open Access (FOA) Date
2016-03-16First Compliant Deposit (FCD) Date
2016-03-16Usage metrics
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