Prediction of Port Recovery Time after a Severe Storm Project

The abundant oil and gas deposit and the convenience of shipping have made Gulf Coast the center of a major center of economic activity and the petrochemical industry in the United States. The port of Houston solely contributes to the local economy of about 330 billion dollars and employment of about 3.2 million. However, adverse storm events, such as hurricanes and tropical cyclones, significantly impact regional economies and port operations. For example, Hurricane Ike in 2008 caused more than 25 million dollars in damage along the coast and made hundreds of thousands of people homeless. Many ports on Gulf Coast have closed part or all operations to withstand Hurricane Laura in 2020. The impact or closure period of a port operation mainly depends on weather prediction from National Oceanic and Atmospheric Administration (NOAA), but the port recovery period may relate to multiple factors, including but not limited to weather conditions, port infrastructure, and road connectivity. Existing studies have been focused on performance evaluation and resilience measurement of the port under different hazard events. However, the relationship between the aforementioned factors and port recovery is not well investigated yet. Moreover, recovery prediction of the port under extreme weather is also critical for stakeholders to ensure the port's resilient and sustainable operations. This study aims to explore the relationship of potential influencing factors on port recovery under adverse storm events. Besides, by using multi-source data and applying machine learning algorithms, a model is built up to predict the port recovery after adverse storm events. This study intends to benefit transportation agencies and port administrators by enhancing the resilience, safety, and efficiency of maritime transportation. Project Objective(s): The objective of the proposed research project is to develop a stochastic integer programming model and algorithms aimed for exact optimal solution to the dredging project optimization problem. The model and algorithms will be tested on select waterway system with historical data about their optimality and computational efficiency.

Language

• English

Project

• Status: Completed
• Funding: $105000
• Contract Numbers:

  69A3551747130

• 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:

  Maritime Transportation Research and Education Center

  University of Arkansas
  Fayetteville, AR  United States  72701
• Performing Organizations:

  Texas A&M Transportation Institute, College Station

  Texas A&M University System
  3135 TAMU
  College Station, TX  United States  77843-3135
• Principal Investigators:

  Wang, Bruce

  Yunlong, Zhang

• Start Date: 20220801
• Expected Completion Date: 20230831
• Actual Completion Date: 20230831
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Algorithms; Disaster resilience; Forecasting; Hurricanes; Machine learning; Ports; Storms
• Geographic Terms: Gulf Coast (United States)
• Subject Areas: Marine Transportation; Planning and Forecasting; Security and Emergencies; Terminals and Facilities;

Filing Info

• Accession Number: 01863185
• Record Type: Research project
• Source Agency: Maritime Transportation Research and Education Center
• Contract Numbers: 69A3551747130
• Files: UTC, RIP
• Created Date: Oct 31 2022 9:31PM