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) |
|---|---|
| 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 |
|---|---|
| Volume | 2018-December |
| ISSN (Print) | 0743-1546 |
Conference
| Conference | 57th IEEE Conference on Decision and Control, CDC 2018 |
|---|---|
| Country | United States |
| City | Miami |
| Period | 12/17/18 → 12/19/18 |
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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
On the Induction of Cascading Failures in Transportation Networks. / Kearney, Griffin; Fardad, Makan.
2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1821-1826 8619519 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
AN - SCOPUS:85062181136
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 -