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ORIGINAL RESEARCH article

Front. Phys.

Sec. Social Physics

Volume 13 - 2025 | doi: 10.3389/fphy.2025.1622316

Dynamical interaction network in urban traffic

Provisionally accepted
Shiyan  LiuShiyan Liu1Guanwen  ZengGuanwen Zeng2Ruiqi  LiRuiqi Li3Linhan  GuoLinhan Guo1Daqing  LiDaqing Li1*
  • 1School of Reliability and Systems Engineering, Beihang University, Beijing, China
  • 2School of Systems Science, Beijing Jiaotong University, Beijing, China
  • 3Hangzhou International Innovation Institute, Beihang University, Beijing, China

The final, formatted version of the article will be published soon.

Urban traffic systems transition dynamically between congestion and free-flow states, driven by local interactions between road segments or regions. Understanding how these interactions contribute to congestion, including system-wide congestion, is crucial for effective traffic management. However, existing research has overlooked the dynamical nature of these interactions, which are essential for capturing the changing behavior of urban traffic. In this study, we use a pairwise maximum entropy model to infer interaction networks from sliding time windows and analyze their dynamics during typical daily periods: morning peak, noon off-peak, and evening peak. We find three main results: (1) interaction networks remain stable within each period but exhibit structural shifts between periods, especially between peak and off-peak periods; (2) stable high-strength edges in dynamical interaction network are characterized by long-range and negative interactions; (3) the proportion and modularity of positive interactions, along with the strength of negative interactions, are important structural features that distinguish peak from off-peak hours. These results provide new insights into how local interaction dynamics drive global state transitions in urban traffic, offering guidance for improving traffic resilience through targeted control strategies.

Keywords: Urban traffic, interaction network, maximum entropy model, dynamical network, network structure

Received: 03 May 2025; Accepted: 27 May 2025.

Copyright: © 2025 Liu, Zeng, Li, Guo and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Daqing Li, School of Reliability and Systems Engineering, Beihang University, Beijing, China

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