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Cascading traffic jamming in a two-dimensional Motter and Lai model
Gabriel Cwilich1,2, Sergey V Buldyrev1
1Department of Physics, Yeshiva University, 500 West 185th Street, New York, New York 10033, USA.
Physical Review. E
|September 16, 2022
Summary
We investigated cascading traffic jamming on random geometric graphs. A critical attack size causes complete network jamming, dependent on graph properties and the Motter and Lai model
Area of Science:
- Network science
- Complex systems analysis
- Traffic flow dynamics
Background:
- Cascading failures are critical in network robustness.
- Understanding traffic jamming dynamics is essential for network resilience.
- The Motter and Lai model provides a framework for studying cascading effects.
Purpose of the Study:
- To investigate cascading traffic jamming on two-dimensional random geometric graphs.
- To determine the critical attack size leading to complete network jamming.
- To analyze the influence of average degree, system size, and tolerance on jamming.
Main Methods:
- Utilizing the Motter and Lai model for simulation.
- Analyzing localized (circular/line) and dispersed node attacks.
- Examining network behavior under varying attack sizes and parameters.
Main Results:
- A critical attack size was identified, triggering complete network jamming.
- The critical size is influenced by the average degree (〈k〉) of the graph.
- The number of nodes (N) and tolerance parameter (α) significantly affect the jamming threshold.
Conclusions:
- Network jamming can be triggered by attacks of a critical size.
- Network topology and model parameters dictate vulnerability to cascading failures.
- Findings inform strategies for enhancing network resilience against targeted disruptions.
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