Mode and Shelter Choice Planning During Evacuation: A Multinomial Logistic Regression Analysis of COVID-19-Induced Migration in India

Abstract

Background: The COVID-19 pandemic triggered unprecedented mobility disruptions worldwide as governments imposed strict lockdowns to contain the spread of the virus. In India, prolonged restrictions severely affected economic activity, particularly for migrant workers, leading to a large-scale and unplanned exodus from urban employment centres to native places. This sudden population movement undermined containment efforts and contributed to the spatial diffusion of infections. Understanding evacuees’ behavioural responses during such crises is therefore critical for effective emergency logistics and evacuation planning. Methods: This study examines the determinants of transport mode and shelter choice decisions made by migrants during the COVID-19-induced evacuation in India. Using primary survey data, a multinomial logistic regression model is developed to analyze how socio-economic characteristics influence evacuees’ choices of travel mode and shelter type. Results: The results reveal significant heterogeneity in decisionmaking, highlighting the role of economic vulnerability and accessibility constraints in shaping evacuation behaviour. Conclusions: The findings offer actionable insights for policymakers and emergency planners to design inclusive evacuation strategies, improve crisis-responsive transportation planning, and enhance shelter provisioning in future pandemics or large-scale disruptions. The study contributes to the logistics and humanitarian operations literature by providing empirical evidence on evacuation behaviour under public health emergencies.