Data-Driven Method for Predicting Future Evacuation Zones in the Context of Climate Change

The determination of evacuation zones where inhabitants are prone to hurricane-related risk is helpful in estimating the demand of evacuees. The evacuation zones defined currently cannot remain the same in the future, since the long-term climate change such as the rise of sea level would have major impacts on hurricane-related risks. Traditional methods for the prediction of future evacuation zones rely heavily on the storm surge models and could be time-consuming and costly. This study aims to develop a novel data-driven method which can promptly predict future evacuation zones in the context of climate change. The map of Manhattan, which is the central area of New York City (NYC), was uniformly split into 150×150 feet² grid cells as the basic geographical units of analysis. A decision tree and a random forest were used to capture the relationship between grid cell-specific features such as geographical features, historical hurricane information, evacuation mobility, and demographic features and current zone categories which could reflect the risk levels during hurricanes. Ten-fold cross-validation was used to evaluate model performance and it was found that the random forest outperformed the decision tree in term of the accuracy and Kappa statistic. The random forest was used to predict the delineation of evacuation zones in the 2050s and 2090s, in the context of sea level rises. Compared with the current zoning, the areas with need of evacuation are expected to expand in the future. The proposed algorithm could be used to estimate evacuation demand in the future and thus support decision-making in the evacuation planning and the management of emergency resources.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABR30 Standing Committee on Emergency Evacuations.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Xie, Kun
  • Ozbay, Kaan
  • Zhu, Yuan
  • Yang, Hong
• Conference:
  • Transportation Research Board 95th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2016-1-10 to 2016-1-14
• Date: 2016

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 16p
• Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Algorithms; Climate change; Decision making; Emergency management; Evacuation; Hurricanes; Mathematical prediction; Sea level; Zoning
• Uncontrolled Terms: Random forest algorithm; Zones
• Geographic Terms: Manhattan (New York, New York)
• Subject Areas: Planning and Forecasting; Security and Emergencies; Transportation (General); I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01590074
• Record Type: Publication
• Report/Paper Numbers: 16-3667
• Files: TRIS, TRB, ATRI
• Created Date: Feb 10 2016 9:43AM