Remote Sensing Decision Support System for Optimal Access Restoration in Post Disaster Environments

Access restoration is an extremely important part of disaster response. Without access to the site, critically important emergency functions like search and rescue, emergency evacuation, and relief distribution, cannot commence. Frequently, roads are opened in descending order of functional class. While this may work in simple cases, it does not take into account the urgency of the needs at the demand nodes, which represent population and economic centers or critical infrastructure that need access restored quickly. The key to optimal allocation of resources is proper prioritization. Such prioritization is possible only with state-of-the-art computing power that supports a Decision Support System (DSS) based on Commercial Remote Sensing (CRS), given the large area coverage, the complexity of the decision process, the need for rapid action, and the spatial nature of the impacts. This project has developed a DSS based on the following principles: (1) that CRS is key to estimating both network conditions and disaster impacts; (2) that Access Restoration Plans (ARPs) are more effective when they explicitly take into account priority rules/metrics and the resource constraints faced by responders; (3) that such priority rules/metrics must consider the impacts on population, economic centers, and critical facilities; (4) that cutting-edge optimization algorithms—using CRS inputs and priority rules/metrics—are the best way to reach sound decisions; and (5) that the proposed DST, with modifications, could play a key role during various phases of the disaster cycle, including response and effective recovery. The DST is comprised of 4 modules. The first module assesses the impacts on the transport network, using a CRS multi-modal data collection/processing at its core. The second module identifies the resource constraints (e.g., number of trucks available at time t). The third module specifies the rules/metrics that define the level of importance of the competing needs. Finally, an optimization procedure uses the other modules’ outputs to estimate the optimal Access Restoration Path.

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  • Supplemental Notes:
    • Appendices are a separate document (255p.) available at the provided URL.
  • Corporate Authors:

    Rensselaer Polytechnic Institute

    Center for Infrastructure, Transportation and the Environment
    Troy, NY  United States 

    Rochester Institute of Technology

    Rochester, NY  United States  14623

    New York City Department of Transportation

    2 Rector Street
    New York, NY  United States  10006-1819

    Department of Transportation

    Office of the Assistant Secretary for Research and Technology
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Authors:
    • Holguin-Veras, Jose
  • Publication Date: 2017


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Appendices; Figures; References; Tables;
  • Pagination: 85p

Subject/Index Terms

Filing Info

  • Accession Number: 01670161
  • Record Type: Publication
  • Contract Numbers: Cooperative Agreement No. OASRTRS-14-H-RPI
  • Created Date: Apr 25 2018 10:31AM