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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Multiple metastable network states in urban traffic.

Guanwen Zeng1,2, Jianxi Gao3, Louis Shekhtman4

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Researchers found multiple traffic states in urban networks, revealing tipping points and a metastable regime. This offers insights into traffic resilience and early warning systems for traffic management.

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Area of Science:

  • Complex Systems Science
  • Transportation Network Analysis
  • Urban Mobility Studies

Background:

  • Abrupt regime shifts are common in natural systems but rarely observed in transportation networks.
  • Lack of methods to identify and analyze multiple network states hinders understanding of traffic system dynamics.
  • Previous studies have not fully explored metastable states and tipping points in large-scale traffic systems.

Purpose of the Study:

  • To identify and analyze metastable network states in urban traffic systems.
  • To investigate the existence of tipping points and hysteresis-like behavior in traffic networks.
  • To understand traffic resilience patterns and develop potential early warning signals.

Main Methods:

  • Utilized percolation approaches to analyze traffic network behavior.
  • Analyzed high-resolution global positioning system (GPS) datasets from Beijing and Shanghai.
  • Examined over 50,000 road segments in each megacity to identify network states and critical points.

Main Results:

  • Observed multiple metastable network states with varying traffic performance that recur daily.
  • Identified tipping points separating three distinct traffic regimes: global functional, metastable hysteresis-like, and global collapsed.
  • Determined intrinsic critical points for the metastable regime, showing consistency across different days.

Conclusions:

  • Evidence supports the existence of metastable states and tipping points in urban traffic systems.
  • Findings enhance understanding of traffic resilience and network dynamics.
  • The study provides a foundation for developing early warning systems for traffic resilience management.