# **1. Introduction**

In cities around the world, the COVID-19 pandemic introduced changes in travel patterns and disruptions in urban mobility, such as reduction in trips by public transport and changes in work and study-related trips, as well as increase in e-commerce related trips. While some of these changes were driven by specific regulatory measures, such as capacity limits to public transport use, others were related to changes in personal preferences that may or may not be limited to the specific time period retrospectively referred to as “the pandemic”. The traditional transport planning data sources and methods – such as periodic and timeconsuming household travel surveys – were no longer sufficient to understand and act on the new dynamics associated with the pandemic, such as decrease in public transport use and changes in travel during the hours commonly thought of as “peak”. The pandemic introduced new preferences, restrictions, and incentives for travel that could no longer be explained solely by population and economic growth.

The _Big Bang_ of so-called “big data”, advanced analytics and new technologies are transforming not only mobility but also the ability of transport planners to identify important mobility patterns at new scales and resolutions and to act on these findings in real time. Telecommunications and digital technology provide an opportunity to measure mobility; especially in data poor settings with low smartphone penetration, call detail records (CDR) have the highest coverage. In Argentina, there were over 56.3 million cellular subscriptions registered in 2019,<sup>2</sup> and smartphone adoption is expected to increase from 59 percent in 2019 to 82 percent by 2025, while in Colombia the respective figures are 65 percent and 77 percent.

The current study took advantage of the very high mobile phone penetration rates in Bogota and the Buenos Aires Metropolitan Area (AMBA) and the associated availability of high frequency and spatial resolution mobility data to understand how mobility evolved during the pandemic. While the pandemic impacted numerous mobility indicators that matter for the system’s environmental and financial sustainability, only a subset of these can be considered to have persisted beyond the immediate lockdown period and thus not only represent more permanent changes in preferences and behaviors but are also more relevant for transport policy and planning going forward. The analysis distinguishes between short-term changes in mobility, as observed over the period between October 2019 and October 2020, and changes that also more or less persisted a full year later despite the removal of formal restrictions to mobility in both cities. The four categories of indicators tracked for the three time points are: trip generation, temporal distribution of trips, spatial distribution of trips, and modal split. In turn, indicators under each of these categories are segmented according to traveler (e.g., socioeconomic/ income group) and trip characteristics (e.g., travel purpose), in order to identify variations in their value according to different demand segments.

# **2. Review of the literature**

Since the outbreak of the pandemic, several studies have analyzed mobility changes in cities in the context of the pandemic, including in Latin America. Focusing on Colombia and India, Vallejo-Borda et al. (2023) model the travel choices to understand the changes in mode-choice preference and the frequency of trips, comparing pre-COVID with during-COVID scenarios. They find that in both countries the utility related to active modes (more used) and public transportation (less used) changed during the pandemic, and that perceptions toward government responses had a significant impact on the choices in Colombia, though this was not the case in India. Mejia-Dorantes et al. (2021) analyzed mobility trends and patterns in the metropolitan area of Barcelona before and after the COVID-19 pandemic outbreak using a combination of traditional travel surveys and smartphone data. They found that after the pandemic outbreak, highly educated population groups and those with higher income were more likely to change their mobility patterns compared to others and that, while remote work and studies may shape new mobility patterns for some population segments, not all mobility

> 2 World Bank World Development Indicators.

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