types can be replaced by information technologies. Shaer and Haghshenas (2021) identified the factors affecting older adults’ travel mode choices specifically, focusing on the case of Isfahan, Islamic Republic of Iran. Because older adults are more vulnerable to bacteria and viruses, restrictions on using modes such as a private vehicle and public transportation to prevent the spread of COVID-19 led to a significant reduction in their mobility. The main purpose of the study was to understand how older adults’ travel had changed since the COVID-19 outbreak. The results show that, despite the decrease in the average frequency of travel, there was an increase in the share of walking and cycling modes, shopping and recreational travel on foot, and cycling, indicating the resilience of walking and cycling in critical situations.

Several studies have proposed forward-looking mobility scenarios. These include a post-pandemic scenario analysis of lifestyle changes in developed and developing country societies (Echegaray 2021) which identifies four scenarios depending on whether social relationships tend to be more virtual or not and whether quality of life remains closely related to consumption or not; analyses of sustainable development scenarios of the LAC region in the post-pandemic environment (Morea 2020; Zhang and Engelke 2021) that identify possible evolutions of the socioeconomic context of the region, depending on whether pre-pandemic patterns continue, whether the pandemic generates controlled transformations in the economy and society, or whether the pandemic generates rapid paradigm shifts. In the European context, a similar analysis was conducted by Cuhls et al. (2022), while Trivers et al. (2021) implemented a smaller spatial scale analysis focused on the city of London.

Most studies consider that some changes in habits brought about by the pandemic may persist over time. In general, they agree that the pandemic has led to an acceleration of the virtualization of some activities (e.g. telecommuting, e-commerce, etc.), but also that there is a possibility that some of this adoption may be reversed. In addition, studies with longer-term scenarios tend to associate this virtualization with a societal reliance on technology to solve the challenge of natural resource scarcity and climate change (Cuhls et al. 2022). Reflecting this trend in mobility would be an increased adoption of new transportation technologies and services (e.g., electric vehicles, shared mobility, etc.) (Echegaray 2021). On the other hand, the pandemic may also have awakened a greater awareness of vulnerability, both at the social level (greater sensitivity to socioeconomic inequality) and at the ecosystem level (greater sensitivity to the ecosystem deterioration). If this awareness were to persist, it could lead to a search for community and proximity values, reflected in concepts such as "the 15-minute city". Scenarios in which concerns about social inequality and climate change are more evident would lead to greater efforts to improve accessibility and reduce emissions from urban mobility. A recent comprehensive study covering several countries of different income levels concludes that future travel habits remain uncertain, with several other crises coinciding with recovery from the pandemic (see ITF 2023); it also points out the possible trade-offs involved in the mobility changes introduced by the pandemic, such as less work travel at the expense of more non-work travel. The studies on the topic tend to agree that changes in habits and the socio-economic context will influence the city and mobility. The study of post-pandemic scenarios in the city of London (Travers et al. 2021) reflects well how lifestyle and socioeconomic context constraints impact mobility. This study includes a scenario in which proximity urbanism concepts are prioritized, in line with a greater awareness of vulnerability in cities; as well as a scenario in which the virtualization of work leads to a decline of the urban center and of the attractor areas of forced mobility.

# **3. Methodology**

Mobile phone data refers to anonymized Call Detail Record (CDR) data (metadata), observed for every call/text made or received with timestamp, with GPS coordinates of the tower from where the call is made. Such data allows measuring mobility across entire cities or even countries at very fine spatial and temporal levels. Example outputs from mobile data that are relevant for understanding the changes in urban mobility introduced by the COVID-19 pandemic include: (i) dynamic population mapping – _How many people are at_ 3