Mass evacuations, particularly those at a statewide level, are among the largest single-event highway traffic events. They can last several days, cover thousands of miles of roadway, and include hundreds of thousands of people and vehicles. Often, evacuations are criticized for their inefficiency and poor management. Despite the critical importance and the potential impact on lives and safety, there are no recognized methods systematically to quantify traffic characteristics at statewide scales. This paper documents the development and application of an analytical method to measure statewide mass evacuations. The proposed approach sought to be both practical and cost-effective. The research methods are based on simple, yet widely available, and easily understood traffic count datasets that support both qualitative and quantitative analyses. By spatially and temporally arranging sensor-based statewide traffic volume data from the hurricane Irma (2017) and Michael (2018) evacuations, these methods are applied to describe and answer several key questions about statewide mass evacuations. The methods developed in this research are able to estimate the start and end of the auto-based evacuation, the loading and peaking characteristics of traffic, the total number of vehicles involved in the evacuation, and the effective start and end time of the auto-based reentry. Among the key findings of this work were that the hurricane Irma and Michael evacuations began several days before landfall, peaking two to three days before the storm. It is expected that state departments of transportation and emergency management officials can apply similar methods to assess and better plan future evacuations.


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    Title :

    Methodology to Quantify Statewide Evacuations


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2021-10-10




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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