Modelling the effects of COVID-19 on travel mode choice behaviour in India

Abstract

The COVID-19 pandemic has resulted in unprecedented changes in the activity patterns and travel behaviour around the world. Some of these behavioural changes are in response to restrictive measures imposed by the Government (e.g. full or partial lock-downs), while others are driven by perceptions of own safety and/or commitment to slow down the spread (e.g. during the preceding and following period of a lock-down). Travel behaviour amidst the stricter of these measures is quite straightforward to predict as people have very limited choices, but it is more challenging to predict the behavioural changes in the absence of restrictive measures. The limited research so far has demonstrated that different socio-demographic groups of different countries have changed travel behaviour in response to COVID-19 in different ways. However, no studies to date have either (a) investigated the changes in travel behaviour in the context of the Global South, or (b) modelled the relationship between changes in transport mode usage and traveller characteristics in order to quantify the associated heterogeneity. In this paper, we address these two gaps by developing mathematical models to quantify the effect of the socio-demographic characteristics of the travellers on the mode-specific trip frequencies before (January 2020) and during the early stages of COVID-19 spread in India (March 2020). Primary data collected from 498 respondents participating in online surveys have been used to estimate multiple discrete choice extreme value (MDCEV) models in this regard. Results indicate – a) significant inertia to continue using the pre-COVID modes, and b) high propensity to shift to virtual (e.g. work from home, online shopping, etc.) and private modes (e.g. car, motorcycle) from shared ones (e.g. bus and ride-share options). The extent of inertia varies with the trip purpose (commute and discretionary) and trip lengths. The results also demonstrate significant heterogeneity based on age, income, and working status of the respondents. The findings will be directly useful for planners and policy-makers in India as well as some other countries of the Global South in better predicting the mode-specific demand levels and subsequently, making better investment and operational decisions during similar disruptions.