Fleeing from hurricane Irma: Empirical analysis of evacuation behavior using discrete choice theory
| Autor: | Adam J. Pel, Caspar G. Chorus, Stephen D. Wong, Susan Shaheen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Dependency (UML)
Operations research Computer science 020209 energy Population Transportation Sample (statistics) 02 engineering and technology Latent class choice model 0502 economics and business 0202 electrical engineering electronic engineering information engineering education Evacuations General Environmental Science Civil and Structural Engineering 050210 logistics & transportation Class (computer programming) Choice set education.field_of_study 05 social sciences Mode (statistics) Hurricane Irma Risk perception Portfolio Evacuee behavior Portfolio choice model |
| Zdroj: | Transportation Research. Part D: Transport & Environment, 79 Transportation Research Part D: Transport and Environment, vol 79 |
| ISSN: | 1361-9209 |
| Popis: | This paper analyzes the observed decision-making behavior of a sample of individuals impacted by Hurricane Irma in 2017 (n = 645) by applying advanced methods based in discrete choice theory. Our first contribution is identifying population segments with distinct behavior by constructing a latent class choice model for the choice whether to evacuate or not. We find two latent segments distinguished by demographics and risk perception that tend to be either evacuation-keen or evacuation-reluctant and respond differently to mandatory evacuation orders. Evacuees subsequently face a multi-dimensional choice composed of concurrent decisions of their departure day, departure time of day, destination, shelter type, transportation mode, and route. While these concurrent decisions are often analyzed in isolation, our second contribution is the development of a portfolio choice model (PCM), which captures decision-dimensional dependency (if present) without requiring choices to be correlated or sequential. A PCM reframes the choice set as a bundle of concurrent decision dimensions, allowing for flexible and simple parameter estimation. Estimated models reveal subtle yet intuitive relations, creating new policy implications based on dimensional variables, secondary interactions, demographics, and risk-perception variables. For example, we find joint preferences for early-nighttime evacuations (i.e., evacuations more than three days before landfall and between 6:00 pm to 5:59 am) and early-highway evacuations (i.e., evacuations more than three days before landfall and on a route composed of at least 50% highways). These results indicate that transportation agencies should have the capabilities and resources to manage significant nighttime traffic along highways well before hurricane landfall. |
| Databáze: | OpenAIRE |
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