A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment

Nima Haghighi, S. Kiavash Fayyaz, Xiaoyue Cathy Liu, Anthony Grubesic, Ran Wei

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The reliability, survivability and vulnerability of critical infrastructure systems has received significant attention of the past several decades. Transportation systems are among the many critical lifelines that urban areas and their associated communities are dependent upon. Disruptions to these systems have the potential to create significant human suffering and severe economic damage. As a result, the ability to proactively assess response and recovery options is critical for emergency preparedness in urban areas. Unfortunately, the vast majority of infrastructure disruption studies are deterministic in nature, exploring the impact of node and arc losses with pre-defined interdiction scenarios and ignoring the underlying dynamism of extreme events. We advance existing knowledge by presenting a probabilistic approach for simulating a range of disruption scenarios and for identifying critical links within the network. Specifically, our approach takes advantage of Monte Carlo simulation, network-wide demand modeling and regression analysis to address the probabilistic nature of disaster effects and the joint impacts of network link failures. Using Salt Lake County, Utah as the study area, the resulting analysis effectively identifies and ranks links based on their vulnerability and criticality. The proposed method is easily transferable to any transportation network, regardless of scale, topology or extreme event.

Original languageEnglish (US)
Pages (from-to)1-23
Number of pages23
JournalNetworks and Spatial Economics
DOIs
StateAccepted/In press - Mar 16 2018

Fingerprint

Risk assessment
Critical infrastructures
Regression analysis
Disasters
Topology
Salts
Recovery
Economics
Monte Carlo simulation

Keywords

  • Critical infrastructure
  • Monte Carlo
  • Network-disruption
  • Regression
  • Simulation

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment. / Haghighi, Nima; Fayyaz, S. Kiavash; Liu, Xiaoyue Cathy; Grubesic, Anthony; Wei, Ran.

In: Networks and Spatial Economics, 16.03.2018, p. 1-23.

Research output: Contribution to journalArticle

Haghighi, Nima ; Fayyaz, S. Kiavash ; Liu, Xiaoyue Cathy ; Grubesic, Anthony ; Wei, Ran. / A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment. In: Networks and Spatial Economics. 2018 ; pp. 1-23.
@article{3680982bb6db47639a4c5b88ea3bd821,
title = "A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment",
abstract = "The reliability, survivability and vulnerability of critical infrastructure systems has received significant attention of the past several decades. Transportation systems are among the many critical lifelines that urban areas and their associated communities are dependent upon. Disruptions to these systems have the potential to create significant human suffering and severe economic damage. As a result, the ability to proactively assess response and recovery options is critical for emergency preparedness in urban areas. Unfortunately, the vast majority of infrastructure disruption studies are deterministic in nature, exploring the impact of node and arc losses with pre-defined interdiction scenarios and ignoring the underlying dynamism of extreme events. We advance existing knowledge by presenting a probabilistic approach for simulating a range of disruption scenarios and for identifying critical links within the network. Specifically, our approach takes advantage of Monte Carlo simulation, network-wide demand modeling and regression analysis to address the probabilistic nature of disaster effects and the joint impacts of network link failures. Using Salt Lake County, Utah as the study area, the resulting analysis effectively identifies and ranks links based on their vulnerability and criticality. The proposed method is easily transferable to any transportation network, regardless of scale, topology or extreme event.",
keywords = "Critical infrastructure, Monte Carlo, Network-disruption, Regression, Simulation",
author = "Nima Haghighi and Fayyaz, {S. Kiavash} and Liu, {Xiaoyue Cathy} and Anthony Grubesic and Ran Wei",
year = "2018",
month = "3",
day = "16",
doi = "10.1007/s11067-018-9392-3",
language = "English (US)",
pages = "1--23",
journal = "Networks and Spatial Economics",
issn = "1566-113X",
publisher = "Kluwer Academic Publishers",

}

TY - JOUR

T1 - A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment

AU - Haghighi, Nima

AU - Fayyaz, S. Kiavash

AU - Liu, Xiaoyue Cathy

AU - Grubesic, Anthony

AU - Wei, Ran

PY - 2018/3/16

Y1 - 2018/3/16

N2 - The reliability, survivability and vulnerability of critical infrastructure systems has received significant attention of the past several decades. Transportation systems are among the many critical lifelines that urban areas and their associated communities are dependent upon. Disruptions to these systems have the potential to create significant human suffering and severe economic damage. As a result, the ability to proactively assess response and recovery options is critical for emergency preparedness in urban areas. Unfortunately, the vast majority of infrastructure disruption studies are deterministic in nature, exploring the impact of node and arc losses with pre-defined interdiction scenarios and ignoring the underlying dynamism of extreme events. We advance existing knowledge by presenting a probabilistic approach for simulating a range of disruption scenarios and for identifying critical links within the network. Specifically, our approach takes advantage of Monte Carlo simulation, network-wide demand modeling and regression analysis to address the probabilistic nature of disaster effects and the joint impacts of network link failures. Using Salt Lake County, Utah as the study area, the resulting analysis effectively identifies and ranks links based on their vulnerability and criticality. The proposed method is easily transferable to any transportation network, regardless of scale, topology or extreme event.

AB - The reliability, survivability and vulnerability of critical infrastructure systems has received significant attention of the past several decades. Transportation systems are among the many critical lifelines that urban areas and their associated communities are dependent upon. Disruptions to these systems have the potential to create significant human suffering and severe economic damage. As a result, the ability to proactively assess response and recovery options is critical for emergency preparedness in urban areas. Unfortunately, the vast majority of infrastructure disruption studies are deterministic in nature, exploring the impact of node and arc losses with pre-defined interdiction scenarios and ignoring the underlying dynamism of extreme events. We advance existing knowledge by presenting a probabilistic approach for simulating a range of disruption scenarios and for identifying critical links within the network. Specifically, our approach takes advantage of Monte Carlo simulation, network-wide demand modeling and regression analysis to address the probabilistic nature of disaster effects and the joint impacts of network link failures. Using Salt Lake County, Utah as the study area, the resulting analysis effectively identifies and ranks links based on their vulnerability and criticality. The proposed method is easily transferable to any transportation network, regardless of scale, topology or extreme event.

KW - Critical infrastructure

KW - Monte Carlo

KW - Network-disruption

KW - Regression

KW - Simulation

UR - http://www.scopus.com/inward/record.url?scp=85044033981&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044033981&partnerID=8YFLogxK

U2 - 10.1007/s11067-018-9392-3

DO - 10.1007/s11067-018-9392-3

M3 - Article

AN - SCOPUS:85044033981

SP - 1

EP - 23

JO - Networks and Spatial Economics

JF - Networks and Spatial Economics

SN - 1566-113X

ER -