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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
PubMed

Insights

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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