Data-Driven Highway Infrastructure Resilience Assessment - Phase II

The transportation systems sector, one of the most critical infrastructure sectors in the US, has a subsector of highway and motor carrier industries that supports daily activities and emergency actions by providing services to other critical infrastructure segments such as healthcare and public health, emergency services, manufacturing, food and agriculture, etc. However, transportation networks face risks from natural and human-made events such as hurricanes, tsunamis, earthquakes, bridge collapse, and terrorist attacks. Thus, to improve the reliability of the components in interconnecting networks, it is necessary to consider these unpredictable failures in the network design. Resilient network design ensures that the network functionality is at an acceptable level of service in the presence of all probabilistic failures. In this study, the authors addressed uncertainty in a transportation network by proposing a trilevel optimization model, which improves the resiliency of the network against uncertain disruptions. The link capacities are uncertain parameters and the origin-destination demands are deterministic. The goal was to minimize the total travel time under uncertain disruptions by designing a resilient transportation network. The trilevel optimization model has three levels. The lower level determines the network flow, the middle level assesses the resiliency of the network by identifying the worst-case scenario disruptions that could lead to a maximal travel time, and the upper level uses the system perspective to expand the existing transportation network to enhance the network’s resiliency. In addition, the authors propose a new formulation for the network flow problem that will significantly reduce the number the number of variables and constraints. The results of solving the trilevel optimization model can improve the resiliency of the network. However, this study was subject to some limitations, which suggested future research directions. In reality, transportation demands are not consistent, but the proposed model considers origin-destination demands as deterministic parameters. Relaxing this assumption requires a more complicated model to reflect uncertain demands. Other possible future work would be designing an exact algorithm to find the optimal solution.

• Record URL:
  http://www.intrans.iastate.edu/mtc/index.cfm/research/project/project/-697559922
• Record URL:
  http://intrans.iastate.edu/research/projects/detail/?projectID=-697559922
• Record URL:
  http://www.intrans.iastate.edu/research/documents/research-reports/data_driven_hwy_infra_resilience_w_cvr.pdf
• 

Language

• English

Project

• Status: Completed
• Funding: $199998
• Contract Numbers:

  DTRT13-G-UTC37

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Midwest Transportation Center

  Iowa State University
  2711 S Loop Drive, Suite 4700
  Ames, IA  United States  50010-8664
• Performing Organizations:

  Iowa State University, Ames

  Institute for Transportation
  2711 South Loop Drive, Suite 4700
  Ames, Iowa  United States  50010-8664
• Principal Investigators:

  Hu, Guiping

  Dong, Jing

  Wang, Lizhi

  Xuesong, Zhou

• Start Date: 20150815
• Expected Completion Date: 20170930
• Actual Completion Date: 20180629

Subject/Index Terms

• TRT Terms: Cost effectiveness; Disasters; Highways; Infrastructure; Optimization; Threats
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Security and Emergencies;

Filing Info

• Accession Number: 01578120
• Record Type: Research project
• Source Agency: Midwest Transportation Center
• Contract Numbers: DTRT13-G-UTC37
• Files: UTC, RIP
• Created Date: Oct 13 2015 2:07PM