Citation

Andrews S, Wang H, Ni D, Gao S, Collura J. J. Transp. Saf. Secur. 2010; 2(4): 352-368.

Copyright

(Copyright © 2010, Southeastern Transportation Center, and Beijing Jiaotong University, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19439962.2010.517743

PMID

unavailable

Abstract

The nature of natural disasters is often unpredictable, so it is extremely important that sufficient planning be done to evaluate the preparedness, the system response, and the ability of transportation infrastructures to handle evacuation traffic. This article presents an adapted evacuation planning methodology that essentially incorporates the traditional four-step planning model and dynamic traffic assignment. The authors utilize the transportation network in Western Massachusetts as an example to test the effectiveness of the proposed methodology using two with-notice hurricane evacuation scenarios. The analysis is performed by using the off-the-shelf computer-based planning software package TransCAD to assist the four-step transportation planning process. By altering inputs and using dynamic traffic assignment, TransCAD can be used to predict how the transportation system behaves during an evacuation. Two scenarios are used as the basis of the evacuation modeling. A production-attraction model is presented to replicate the behavior of evacuees. The production-attraction model results are compared with a series of reports, and the model falls within the recommendations of these reports. Several different evacuation speeds are examined for two different study areas. The Results section shows the outputs that can be garnered from the program such as the clearance time, critical locations, V/C ratios, and location-to-location travel times. Finally, recommendations are made on how these results can be used to aid planners. It is worth mentioning that the proposed evacuation modeling methodology can be extended and implemented in other places with minor modifications of network topology and localized travel behavior data.