Modelling the effects of COVID-19 on travel mode choice behaviour in India
Modelling the effects of COVID-19 on travel mode choice behaviour in India
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DOI：
10.1016/j.trip.2020.100273
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发表时间：
2020-11-01
影响因子：
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通讯作者：
Choudhury, Charisma F.
中科院分区：
文献类型：
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作者：
Bhaduri, Eeshan;Manoj, B. S.;Choudhury, Charisma F.
COVID-19疫情导致全球活动模式及旅游行为出现前所未有的变化。其中一些行为变化是为了应对政府实施的限制性措施（例如全面或部分封锁），而另一些行为变化则是出于对自身安全的认识和（或）对减缓蔓延的承诺（例如在封锁前后）。由于人们的选择非常有限，因此在这些措施更加严格的情况下预测旅行行为非常简单，但在没有限制措施的情况下预测行为变化更具挑战性。到目前为止，有限的研究表明，不同国家的不同社会人口群体以不同的方式改变了旅行行为，以应对COVID-19。然而，迄今为止还没有任何研究（a）调查全球南方背景下旅行行为的变化，或（B）模拟运输方式使用变化与旅行者特征之间的关系，以量化相关的异质性。在本文中，我们通过开发数学模型来解决这两个差距，以量化旅行者的社会人口特征在COVID-19在印度传播之前（2020年1月）和早期阶段（2020年3月）对特定模式旅行频率的影响。从498名参与在线调查的受访者中收集的原始数据已被用于估计这方面的多个离散选择极值（MDCEV）模型。结果表明- a）继续使用新冠肺炎前模式的惯性很大，以及B）转向虚拟模式的倾向很高（例如在家工作、网上购物等）和私人模式（例如汽车、摩托车）与共享模式（例如公共汽车和乘车共享选项）。惯性的程度随旅行目的（通勤和自由支配）和旅行长度而变化。研究结果还表明，基于受访者的年龄，收入和工作状况的显着异质性。研究结果将直接有助于印度以及全球南部其他一些国家的规划者和政策制定者更好地预测特定模式的需求水平，并随后在类似的中断期间做出更好的投资和运营决策。
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.