AI Article Synopsis

  • Research shows that abrupt changes in traffic patterns, similar to those in natural systems like ecology or climate, have rarely been documented.
  • Using percolation techniques, the study investigates urban traffic in megacities like Beijing and Shanghai, revealing multiple traffic states that fluctuate over time.
  • The study identifies critical tipping points that mark transitions between different traffic performance regimes, which could help improve traffic management and resilience strategies in urban environments.

Article Abstract

While abrupt regime shifts between different metastable states have occurred in natural systems from many areas including ecology, biology, and climate, evidence for this phenomenon in transportation systems has been rarely observed so far. This limitation might be rooted in the fact that we lack methods to identify and analyze possible multiple states that could emerge at scales of the entire traffic network. Here, using percolation approaches, we observe such a metastable regime in traffic systems. In particular, we find multiple metastable network states, corresponding to varying levels of traffic performance, which recur over different days. Based on high-resolution global positioning system (GPS) datasets of urban traffic in the megacities of Beijing and Shanghai (each with over 50,000 road segments), we find evidence supporting the existence of tipping points separating three regimes: a global functional regime and a metastable hysteresis-like regime, followed by a global collapsed regime. We can determine the intrinsic critical points where the metastable hysteresis-like regime begins and ends and show that these critical points are very similar across different days. Our findings provide a better understanding of traffic resilience patterns and could be useful for designing early warning signals for traffic resilience management and, potentially, other complex systems.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7395445PMC
http://dx.doi.org/10.1073/pnas.1907493117DOI Listing

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