### Abstract

We examine the effect of malicious attacks in disrupting optimal routing algorithms for transportation networks. We model traffic networks using the cell transmission model, which is a spatiotemporal discretization of kinematic wave equations. Here, vehicles are modeled as masses and roads as cells, and traffic flow is subject to conservation of mass and capacity constraints. At time zero a resource-constrained malicious agent reduces the capacities of cells so as to maximize the amount of time mass spends in the network. For the resulting set of capacities the network router then solves a linear program to determine the flow configuration that minimizes the amount of time mass spends in the network. Our model allows for the outright or partial failure of road cells at time zero, the effects of which can cause cascading failure in the network due to irreversible blockages resulting from congestion. This two-player problem is written as a max-min optimization and is reformulated to an equivalent nonconvex optimization problem with a bilinear objective and linear constraints. Linearization techniques are applied to the optimization problem to find local solutions. Analyzing illustrative examples shows that attackers with relatively small resource budgets can cause widespread failure in a traffic network.

Original language | English (US) |
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Title of host publication | 2018 IEEE Conference on Decision and Control, CDC 2018 |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 1821-1826 |

Number of pages | 6 |

ISBN (Electronic) | 9781538613955 |

DOIs | |

State | Published - Jan 18 2019 |

Event | 57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States Duration: Dec 17 2018 → Dec 19 2018 |

### Publication series

Name | Proceedings of the IEEE Conference on Decision and Control |
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Volume | 2018-December |

ISSN (Print) | 0743-1546 |

### Conference

Conference | 57th IEEE Conference on Decision and Control, CDC 2018 |
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Country | United States |

City | Miami |

Period | 12/17/18 → 12/19/18 |

### Fingerprint

### Keywords

- cascading failures
- flow networks
- network interdiction
- Traffic network

### ASJC Scopus subject areas

- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization

### Cite this

*2018 IEEE Conference on Decision and Control, CDC 2018*(pp. 1821-1826). [8619519] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8619519

**On the Induction of Cascading Failures in Transportation Networks.** / Kearney, Griffin; Fardad, Makan.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2018 IEEE Conference on Decision and Control, CDC 2018.*, 8619519, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 1821-1826, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 12/17/18. https://doi.org/10.1109/CDC.2018.8619519

}

TY - GEN

T1 - On the Induction of Cascading Failures in Transportation Networks

AU - Kearney, Griffin

AU - Fardad, Makan

PY - 2019/1/18

Y1 - 2019/1/18

N2 - We examine the effect of malicious attacks in disrupting optimal routing algorithms for transportation networks. We model traffic networks using the cell transmission model, which is a spatiotemporal discretization of kinematic wave equations. Here, vehicles are modeled as masses and roads as cells, and traffic flow is subject to conservation of mass and capacity constraints. At time zero a resource-constrained malicious agent reduces the capacities of cells so as to maximize the amount of time mass spends in the network. For the resulting set of capacities the network router then solves a linear program to determine the flow configuration that minimizes the amount of time mass spends in the network. Our model allows for the outright or partial failure of road cells at time zero, the effects of which can cause cascading failure in the network due to irreversible blockages resulting from congestion. This two-player problem is written as a max-min optimization and is reformulated to an equivalent nonconvex optimization problem with a bilinear objective and linear constraints. Linearization techniques are applied to the optimization problem to find local solutions. Analyzing illustrative examples shows that attackers with relatively small resource budgets can cause widespread failure in a traffic network.

AB - We examine the effect of malicious attacks in disrupting optimal routing algorithms for transportation networks. We model traffic networks using the cell transmission model, which is a spatiotemporal discretization of kinematic wave equations. Here, vehicles are modeled as masses and roads as cells, and traffic flow is subject to conservation of mass and capacity constraints. At time zero a resource-constrained malicious agent reduces the capacities of cells so as to maximize the amount of time mass spends in the network. For the resulting set of capacities the network router then solves a linear program to determine the flow configuration that minimizes the amount of time mass spends in the network. Our model allows for the outright or partial failure of road cells at time zero, the effects of which can cause cascading failure in the network due to irreversible blockages resulting from congestion. This two-player problem is written as a max-min optimization and is reformulated to an equivalent nonconvex optimization problem with a bilinear objective and linear constraints. Linearization techniques are applied to the optimization problem to find local solutions. Analyzing illustrative examples shows that attackers with relatively small resource budgets can cause widespread failure in a traffic network.

KW - cascading failures

KW - flow networks

KW - network interdiction

KW - Traffic network

UR - http://www.scopus.com/inward/record.url?scp=85062181136&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062181136&partnerID=8YFLogxK

U2 - 10.1109/CDC.2018.8619519

DO - 10.1109/CDC.2018.8619519

M3 - Conference contribution

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 1821

EP - 1826

BT - 2018 IEEE Conference on Decision and Control, CDC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -