Optimal Transportation and Shoreline Infrastructure Investment Planning Under a Stochastic Climate Future

This paper considers best planning for investment to protect transportation in coastal areas from the effects of climate change. Solutions derived from optimization modeling can aid in deciding whether there a need for costly infrastructure to combat sea level rise and prevent coastal flooding, or whether it would be better to wait until after such an incident. The authors developed a recursive noisy genetic algorithm (RNGA), designed to seek the best combination of investment decisions to be taken now, based on the probability of long-term sea level rise and the extreme weather that might arise over the planning period. The application of the algorithm in a case study of part of the Washington, DC area by the Potomac River showed the expected long-term benefits and indirect effects on social well being. The methodology can be integrated in a decision support system to help government, infrastructure owners and workers effectively assess the threats from sea level rise and the flooding that will occur with increase in extreme weather. The technique can be used to examine investment performance over a chosen period, and with minor modification it can consider a fixed budget for taking protective or mitigative actions. The tool then can inform decision makers on the best investment of funds across systems, regions and proposed projects. Through optimized investment, the transportation network will be more resistent to flooding, with improved reliability, safety and resilience.

• Record URL:
  • http://ntc.umd.edu/sites/default/files/documents/research/NTC2014-SU-R-14%20Miller%20Hooks.pdf
• 
• Summary URL:
  • http://ntc.umd.edu/node/33
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Office of the Assistant Secretary for Research and Technology

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

  National Transportation Center at Maryland

  1124 Glenn Martin Hall
  University of Maryland
  College Park, MD  United States  20742

  University of Maryland, College Park

  Department of Civil and Environmental Engineering
  College Park, MD  United States  20742
• Authors:
  • Miller-Hooks, Elise
  • Asadabadi, Ali
• Publication Date: 2016-4-21

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Maps; References; Tables;
• Pagination: 57p

Subject/Index Terms

• TRT Terms: Case studies; Climate change; Coastal engineering; Genetic algorithms; Highway engineering; Infrastructure; Investments; Mathematical models; Methodology; Sea level; Stochastic processes; Transportation planning
• Geographic Terms: Washington (District of Columbia)
• Subject Areas: Economics; Environment; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01667371
• Record Type: Publication
• Report/Paper Numbers: NTC2014-SU-R-14
• Files: UTC, TRIS, ATRI, USDOT
• Created Date: Apr 25 2018 11:16AM