Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 117, Issue 30, Pages 17528-17534Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1907493117
Keywords
resilience; urban traffic; percolation; multiple states; tipping point
Categories
Funding
- National Natural Science Foundation of China [U1811463, 71621001, 71822101, 71890973/71890970, 61961146005, 71771009, 91846202]
- Israel Ministry of Science and Technology (MOST)
- Italian Ministry of Foreign Affairs, MOST
- Japan Science Foundation
- Binational Israel-China Science Foundation [3132/19]
- National Science Foundation
- Office of Naval Research
- Defense Threat Reduction Agency [HDTRA-1-19-1-0016]
- Binational Science Foundation-National Science Foundation
- Bar-Ilan University Center for Cyber Security and Applied Cryptography
- ONR [N00014-15-1-2640]
- NSF [PHY-1505000, CMMI-1125290, CHE-1213217]
- DTRA [HDTRA1-14-1-0017]
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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 sepa-rating 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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