Scale-free resilience of real traffic jams
Abstract
Traffic congestion has become the most stubborn disease for the health of a city. Like the self-healing ability of a biological unit from diseases, transportation can also recover spontaneously from various disturbances. To describe this recovery, we define the resilience metric as the spatiotemporal congestion cluster, which can be used for other network systems. Based on large-scale GPS datasets, we reveal that the recovery behavior of transportation from congestion is governed by three scaling laws for all of the congestion scales. These scaling laws are found independent of microscopic details, including fluctuation of traffic demand and corresponding management. Our results of resilience scaling can help to better characterize and improve the adaptation and recovery of city traffic from various perturbations.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 12, 2019
- Source ID
- 10.1073/pnas.1814982116
Entities
People
- Daqing Li
- Guanwen Zeng
- H. Eugene Stanley
- Hai-Jun Huang
- Limiao Zhang
- Shlomo Havlin
Organizations
- Bar-Ilan University
- Beihang University
- Boston University
- Defense Threat Reduction Agency
- National Natural Science Foundation of China
- Program 973