Deep reinforcement learning-based prioritization for rapid post disaster recovery of transportation infrastructure systems

Among various natural hazards that threaten transportation infrastructure, flooding and hurricanes represent a major hazard in Region 6’s states including Louisiana and Texas to roadways as it challenges their design, operation, efficiency, and safety. These catastrophic natural disaster events including flooding and hurricanes generally lead to massive obstruction of traffic, direct damage to highway/bridge structures/pavement, and indirect damages to economic activities and regional communities which may cause loss of many lives. The recent large-scale floods such as 2017/2018 hurricanes and 2016 Baton Rouge devastating flooding reminded how destructive hurricanes and floods are. The observed consequences from these events make evident their ability to generate largescale damages to society, raising the levels of exposure of all transportation infrastructure. For instance, the Hurricane Katrina made landfall on August 29, 2005, providing some of the most plentiful and illustrative empirical evidence of the impact of hurricanes and storm surge on the performance of bridges and the transportation network. There is approximately 3,220 km (2,000 mi) of roadway in the Greater New Orleans area which was submerged in floodwaters for up to 5 weeks. After disasters strike, reconstruction and maintenance of an enormous number of damaged transportation infrastructure systems require each DOT to take extremely expensive and long-term processes. In addition, planning and organizing post-disaster reconstruction and maintenance projects of transportation infrastructures are extremely challenging for each DOT because they entail the massive number and the broad areas of the projects with various considerable factors and multi-objective issues including social, economic, political, and technical factors. Furthermore, decision-makers need to deal with limited federal, state, and local resources in planning sequential and organized reconstruction of affected transportation systems. In particular, since transportation networks play a pivotal role in disaster recovery of communities as primary routes for salvage, evacuation and restoration, their recovery processes should consider short- and long-term logistics and plan with underlying heterogeneous factors. Yet, amazingly, a comprehensive, integrated, data-driven approach for organizing and prioritizing post-disaster transportation reconstruction projects remains elusive. In addition, DOTs in Region 6 need to improve the current practice and system to identify and predict the detailed factors and their impacts affecting post-disaster transportation recovery. The main objective of this proposed research is to develop a deep reinforcement learning-based project prioritization system for rapid post-disaster reconstruction and recovery of damaged transportation infrastructure systems. This project also aims to provide a means for Louisiana and Texas (ultimately to all Region 6’s States) to facilitate the systematic optimization and prioritization of the post-disaster reconstruction and maintenance plan of transportation infrastructure by focusing on social, economic, and technical aspects. As the critical mass of Region 6’s transportation infrastructure has been severely damaged from recent flood and hurricane disasters, this study that concurrently involves the transportation infrastructure systems of Louisiana and Texas has a significant impact on holistic organization and prioritization of Regional 6’s transportation systems affected by natural disasters. In addition, the expected outcomes from this project would assist not only engineers and decision-makers in the Louisiana Department of Transportation and Development (LaDOTD) and the Texas Department of Transportation (TxDOT), but also Region 6’s State administrators in optimizing and sequencing transportation recovery processes at a regional network level and evaluating its long-term impacts after disasters. Thus, it is crucial for transportation agencies to have a comprehensive approach to plan their recovery project and meet the federal regulation of maintaining mobility and safety of the network in an acceptable level as well as fulfilling other objectives including socioeconomic, time, and cost.

• Record URL:
  https://transet.lsu.edu/research-in-progress/
• Supplemental Notes:
  • 20PPLSU14

Language

• English

Project

• Status: Completed
• Funding: $ 150000
• Contract Numbers:

  69A3551747106

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Transportation Consortium of South-Central States (Tran-SET)

  Louisiana State University
  Baton Rouge, LA  United States  70803
• Project Managers:

  Mousa, Momen

• Performing Organizations:

  Louisiana State University

  3660G Patrick F. Taylor Hall
  Civil and Environmental Engineering
  Baton Rouge, LA  United States  70803
• Principal Investigators:

  Lee, Yongcheol

• Start Date: 20200801
• Expected Completion Date: 20220201
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Disaster preparedness; Disaster resilience; Floods; Hurricanes; Infrastructure; Machine learning; Optimization; Reconstruction
• Geographic Terms: Louisiana; Texas
• Subject Areas: Bridges and other structures; Highways; Operations and Traffic Management; Pavements; Planning and Forecasting; Security and Emergencies;

Filing Info

• Accession Number: 01757511
• Record Type: Research project
• Source Agency: Transportation Consortium of South-Central States (Tran-SET)
• Contract Numbers: 69A3551747106
• Files: UTC, RIP
• Created Date: Nov 10 2020 4:16PM