Scale-free Resilience of Real Traffic Jams

Abstract

The concept of resilience can be realized in natural and engineering systems, representing the ability of system to adapt and recover from various disturbances. Although resilience is a critical property needed for understanding and managing the risks and collapses of transportation system, an accepted and useful definition of resilience for urban traffic as well as its statistical property under perturbations is still missing. Here we define city traffic resilience based on the spatio-temporal clusters of congestion in real traffic, and find that the resilience follows a scale free distribution in two-dimensional city road networks and one-dimensional highways, with different exponents, but similar exponents in different days and different cities. The traffic resilience is also revealed to have a novel scaling relation between the cluster size of the spatio-temporal jam and its recovery duration, independent of microscopic details. Our findings of universal traffic resilience can provide indication towards better understanding and designing these complex engineering systems under internal and external disturbances.

arXiv comment6 pages, 4 figures

References in corpus (8)

• Statistical mechanics of complex networks
• Spatial Networks
• Critical phenomena in complex networks
• Resilience and efficiency in transportation networks
• Fluctuation scaling in complex systems: Taylor's law and beyond
• Abrupt transition in the structural formation of interconnected networks
• Operational resilience: concepts, design and analysis
• Power-law distributions in binned empirical data

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• Evidence of Non-Equilibrium Critical Phenomena in a Simple Model of Traffic
• Empirical evidence for a jamming transition in urban traffic
• Vulnerability of Transport through Evolving Spatial Networks
• Spatiotemporal statistical features of velocity responses to traffic congestions in a local motorway network
• Scale-free congestion clusters in large-scale traffic networks: a continuum modeling study
• Observing cities as a complex system
• Identification and prioritization of urban traffic bottlenecks