Multiple metastable network states in urban traffic.

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.