ArticleCommunications physics2023
Understanding congestion propagation by combining percolation theory with the macroscopic fundamental diagram.
Article in Communications physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Forecasting Congestion and Enabling Proactive Management on Expressways During Public Holidays: A Survey of Methods, Open Issues, and Research Directions.Sensors (Basel, Switzerland) · 2026Review
- Event-based spatiotemporal networks for modelling emergent phenomena in complex systems.Nature communications · 2026Article
- Modeling and analysis of the coupling effect for large-scale multi-band optical networks.Scientific reports · 2025Article
- Universality in multispecies urban traffic.Communications physics · 2025Article
- Congestion Transition on Random Walks on Graphs.Entropy (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The science of cities aims to model urban phenomena as aggregate properties that are functions of a system's variables. Following this line of research, this study seeks to combine two well-known approaches in network and transportation science: (i) The macroscopic fundamental diagram (MFD), which examines the characteristics of urban traffic flow at the network level, including the relationship between flow, density, and speed. (ii) Percolation theory, which investigates the topological and dynamical aspects of complex networks, including traffic networks. Combining these two approaches, we find that the maximum number of congested clusters and the maximum MFD flow occur at the same moment, precluding network percolation (i.e. traffic collapse). These insights describe the transition of the average network flow from the uncongested phase to the congested phase in parallel with the percolation transition from sporadic congested links to a large, congested cluster of links. These results can help to better understand network resilience and the mechanisms behind the propagation of traffic congestion and the resulting traffic collapse.
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Registered trials
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