Estimation of crowding factors for public transport during the COVID-19 pandemic in Santiago, Chile.

A sharp decrease in public transport demand has been observed during the COVID-19 pandemic around the world. In this context, it is relevant to understand how mode preferences have changed since the surge of COVID-19. In order to better understand how the pandemic changed mode choice, particularly regarding the impact of crowding and face mask use in public transport, we conducted a stated preference on-line and on-street survey in Santiago, Chile. Our sample is balanced in gender but has a higher proportion of individuals with college degrees and those under 45 years of age than the population of Santiago. The data collected was then used to estimate two multinomial mode choice models, a latent class model and a mixed logit model with latent variables. The models yielded a value of travel time in crowded conditions (4 pax/m2) and low face mask use (50%) of 3.0-5.1 times higher than the case with low crowding (0.5 pax/m2) and 100% face mask use. Moreover, women tend to be more sensitive than men to the use of face masks in public transport. Besides, young and low-income people are relatively less sensitive to crowding. The crowding penalization obtained is higher than in pre-pandemic models calibrated for Santiago for similar passenger densities. Also, as we expected, it grows non-linearly with passenger density. Disinfection of vehicles, as well as the perception of health risk, cleanliness, safety and comfort, were also relevant in explaining mode choice. Further research shall discuss how the change of mode preferences together with new demand patterns influence the operational design of public transport services.

Characterising public transport shifting to active and private modes in South American capitals during the COVID-19 pandemic.

During the year 2020, the COVID-19 pandemic affected mobility around the world, significantly reducing the number of trips by public transport. In this paper, we study its impact in five South American capitals (i.e., Bogotá, Buenos Aires, Lima, Quito and Santiago). A decline in public transport patronage could be very bad news for these cities in the long term, particularly if users change to less sustainable modes, such as cars or motorbikes. Notwithstanding, it could be even beneficial if users selected more sustainable modes, such as active transport (e.g., bicycles and walking). To better understand this phenomenon in the short term, we conducted surveys in these five cities looking for the main explanation for changes from public transport to active and private modes in terms of user perceptions, activity patterns and sociodemographic information. To forecast people's mode shifts in each city, we integrated both objective and subjective information collected in this study using a SEM-MIMIC model. We found five latent variables (i.e., COVID-19 impact, Entities response, Health risk, Life related activities comfort and Subjective well-being), two COVID-19 related attributes (i.e., new cases and deaths), two trip attributes (i.e., cost savings and time), and six socio-demographic attributes (i.e., age, civil status, household characteristics, income level, occupation and gender) influencing the shift from public transport to other modes. Furthermore, both the number of cases and the number of deaths caused by COVID-19 increased the probability of moving from public transport to other modes but, in general, we found a smaller probability of moving to active modes than to private modes. The paper proposes a novel way for understanding geographical and contextual similarities in the pandemic scenario for these metropolises from a transportation perspective.

Impact of COVID-19 on the number of days working from home and commuting travel: A cross-cultural comparison between Australia, South America and South Africa.

The COVID-19 pandemic has changed the way we go about our daily lives in ways that are unlikely to return to the pre-COVID-19 levels. A key feature of the COVID-19 era is likely to be a rethink of the way we work and the implications on commuting activity. Working from home (WFH) has been the 'new normal' during the period of lockdown, except for essential services that require commuting. In recognition of the new normal as represented by an increasing amount of WFH, this paper develops a model to identify the incidence of WFH and what impact this could have on the number of weekly commuting trips. Using data collected in eight countries (Argentina, Australia, Brazil, Chile, Colombia, Ecuador, Peru and South Africa), we developed a Poisson regression model for the number of days individuals worked from home during the pandemic. Simulated scenarios quantify the impact of the different variables on the probability of WFH by country. The findings provide a reference point as we continue to undertake similar analysis at different points through time during the pandemic and after when restrictions are effectively removed.