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Licensed Unlicensed Requires Authentication Published by De Gruyter March 14, 2015

A Two-level Agent-Based Model for Hurricane Evacuation in New Orleans

  • Wei Liang , Nina S.-N. Lam EMAIL logo , Xiaojun Qin and Wenxue Ju

Abstract

Mass evacuation of urban areas due to hurricanes is a critical problem in emergency management that requires extensive basic and applied research. Previous research uses agent-based models to simulate individual vehicle and driver behavior, and is limited mostly to a small study area due to the complexity of the models and the computational time needed. To better understand evacuation behavior, simulating the evacuation traffic in a larger region is needed. This paper develops a two-level regional disaster evacuation model by coupling two agent-based models. The first model uses each census block centroid, weighted with its corresponding number of vehicles, as an agent to simulate the local road network traffic. The second model, developed on the platform of a commercial software program called VISSIM, treats each vehicle as an agent to simulate the interstate highway traffic. This two-level agent-based model was used to simulate hurricane evacuation traffic in New Orleans. Validation results with the real Hurricane Katrina’s evacuation data confirm that the proposed model performs well in terms of high model accuracy (i.e., close agreement between the real and simulated traffic patterns) and short model running time. The modeling results show that the average root-mean-square error (RMSE) for the three major evacuation directions was 347.58. Under a simultaneous evacuation strategy, and with 240,251 vehicles in 17,744 agents (census blocks), it would take at least 46.3 hours to evacuate all residents from the New Orleans metropolitan area. This two-level modeling approach could serve as a practical tool for evaluating mass evacuation strategies in New Orleans and other similar urban areas.


Corresponding author: Nina S.-N. Lam, Louisiana State University – Department of Environmental Sciences, Baton Rouge, LA, USA, e-mail:

Acknowledgement

This material is based upon work supported by a National Science Foundation Doctoral Dissertation Research Award (BCS-0802593) and two research awards from the National Science Foundation (SES-0729259, BCS-1211112). Any opinions, findings, and conclusions or recommendations expressed in this research are those of the authors and do not necessarily reflect the views of the funding agencies.

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Published Online: 2015-3-14
Published in Print: 2015-6-1

©2015 by De Gruyter

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