Prediction of population behavior in hurricane evacuations

Anyidoho, PK and Davidson, RA and Rambha, T and Nozick, LK (2022) Prediction of population behavior in hurricane evacuations. In: Transportation Research Part A: Policy and Practice, 159 . pp. 200-221.

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Abstract

This study advances prediction of population evacuation behavior during hurricanes by comprehensively comparing five different models based on their practical utility for future hurricanes. The models�participation rate (PR-S), logistic regression (LR-S), random parameter logit (RPL), time-dependent Cox (TD-Cox), and dynamic discrete choice (DDC)�were fitted using population survey and hurricane data collected in a consistent format across four different hurricanes (Florence 2018, Michael 2018, Dorian 2019, and Barry 2019). Out-of-sample predictive power was evaluated in terms of prediction of total evacuation rates, spatial distribution of evacuees, evacuation timing, and individual behavior. The final set of predictors can be obtained for a whole region and applied in the future for prediction. The results suggest that if only an estimate of the total evacuation rate for the whole region is required, the LR-S is easiest to implement and provides good predictive power. However, if spatial and/or timing predictions are required, the DDC is recommended. The results suggest that in general, for future hurricanes, the best models currently available can estimate total evacuation rate within one to nine percentage points; evacuation rate for each county within 10 to 15 percentage points; and departure curve within several hours. Results also indicate that errors become smaller as geographic granularity increases. © 2022 Elsevier Ltd

Item Type:	Journal Article
Publication:	Transportation Research Part A: Policy and Practice
Publisher:	Elsevier Ltd
Additional Information:	The copyright for this article belongs to Elsevier Ltd
Keywords:	Hurricanes; Population statistics, Discrete choice; Dynamic discrete choice; Evacuation; Hurricane evacuation; Logistics regressions; Model-based OPC; Participation rate; Percentage points; Predictive power; Random parameters logit, Forecasting
Department/Centre:	Division of Interdisciplinary Sciences > Center for Infrastructure, Sustainable Transportation and Urban Planning (CiSTUP) Division of Mechanical Sciences > Civil Engineering
Date Deposited:	15 May 2022 08:57
Last Modified:	15 May 2022 08:57
URI:	https://eprints.iisc.ac.in/id/eprint/71756

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