title: From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.
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.
authors.affiliation: Beihang University, School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing, 100191, China.|Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, No. 2 SiPaiLou, Nanjing, 210096, China.
authors.forename: Daxin
authors.initials: D
authors.lastname: Tian
authors.affiliation: Beihang University, School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing, 100191, China.
authors.forename: Jianshan
authors.initials: J
authors.lastname: Zhou
authors.affiliation: University of Sussex, Department of Engineering and Design, Brighton, BN1 9RH, United Kingdom.
authors.forename: Zhengguo
authors.initials: Z
authors.lastname: Sheng
authors.affiliation: Beihang University, School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beijing, 100191, China.|Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, No. 2 SiPaiLou, Nanjing, 210096, China.
authors.forename: Yunpeng
authors.initials: Y
authors.lastname: Wang
authors.affiliation: The Texas Department of Transportation, 10016 Liriope Cove, Austin, TX 78750, United States.
authors.forename: Jianming
authors.initials: J
authors.lastname: Ma
mesh_terms: D000222:Adaptation, Physiological / Q000235:genetics / Q000502:physiology*; D000465:Algorithms; D002947:Cities; D019295:Computational Biology / Q000379:methods; D004059:Diffusion of Innovation; D004777:Environment; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism / Q000502:physiology*; D053263:Gene Regulatory Networks; D008962:Models, Theoretical; D015398:Signal Transduction / Q000235:genetics / Q000502:physiology*; D014186:Transportation