The COVID-19 emergency forced cities worldwide to adopt measures to restrict travel and implement new urban public transport solutions. The discontinuity and reduction of services made users recognize public transport systems as contamination vectors, and the decrease in the number of passengers can already be seen in several places. Thus, this study assessed the impact of the COVID-19 pandemic on urban public transport. We used hybrid choice models (HCMs) to identify the new barriers and potential solutions to increase users’ perception of safety, considering preexistent perceptions of public transportation quality. We used data from an online survey with users of public transportation in a metropolitan area in southern Brazil. We identified that the main barriers to using public transport during virus transmission are related to the system characteristics that force constant interaction with other passengers. Crowded vehicles and crowded stops/stations were considered the most detrimental factor in feeling safe while riding in the COVID-19 outbreak. Countermeasures that reduce the contact with other passengers—directly (limit the number of passengers in vehicles) or indirectly (operate with large vehicles)—and increase offers are possible solutions to make users feel safe while riding. The results of this research might help reduce passenger evasion and migration to more unsustainable transport modes.

The local built environment characteristics deeply influence pedestrians’ behavior, favoring or imposing barriers on walking trips. However, identifying micro-scale built environment data is challenging and time consuming, and in developing countries there is a general lack of reliable information at street level. The recent development of machine learning and image recognition algorithms is helping researchers to collect data quickly, automatically, and on a large scale. Therefore, this study aims to test the application of an existing semantic segmentation algorithm to represent urban scenes in the city of São Paulo. A confirmatory multivariate technique (structural equation model [SEM]) is used to test if a combination of the predetermined categories derived from machine learning algorithms helps to understand which type of environment (urban scenes) represents barriers or incentives to walk. The impacts of the urban scenes on the walking behavior mediated by the walkability perceptions were tested using the aforementioned SEM model. Car-oriented and unoccupied areas, with a high presence of heavy vehicles and a large presence of vegetation, are considered detrimental to the walkability perception and consequently to the walking frequency. On the other hand, densified areas, proximity to public transportation routes, and the presence of lighting and other pedestrians are considered friendlier for pedestrians, encouraging residents to walk.

Walkable neighborhoods are known for bringing social and economic benefits to their residents. One of these benefits is the real estate premium associated with the neighborhood's walkability, which has been explored in studies worldwide. Here, we extend the available evidence by proposing the evaluation of the walkability premium in a new context. We use multigroup structural equation models to evaluate the direct and indirect effects on low-income neighborhoods in two Brazilian metropolitan cities, São Paulo and Rio de Janeiro. The walkability perception is modeled, mediating the impact of the built urban environment elements on real estate pricing. The results confirm that we can expect a premium for walkability investments, even in poor neighborhoods under non-ideal conditions for walking, and this result is invariant between cities. The effects of the built environment on the real estate price are mediated by latent perceptions, showing the importance of incorporating subjective measures when assessing the walkability premium. Additionally, Latin American cities' socioeconomic and environmental conditions provide new interpretations of the studied phenomenon, highlighting the importance of considering the local context.

Global South cities are vastly underrepresented in the literature that analyzes the relationships between location choice, land-use patterns and travel behavior. This paper aims to reduce that underrepresentation by bringing new evidence from a metropolitan region in the Global South. We estimate a Structural Equation Model to study the relationships between land-use patterns, location choice, car ownership and travel behavior, while controlling for self-selection, in the metropolitan region of São Paulo, Brazil. The model structure is adapted from previous applications to include variables related with specific aspects of the studied region, with the inclusion of informal work and people working two jobs, while simultaneously controlling for cohort effects associated with being a millennial. The results support the claim that land-use patterns influence travel behavior, even in a metropolitan area showing strong income-based spatial segregation levels. More specifically, commuting distance and car ownership act as important mediators in the relationships between the total amount of travel by mode and land-use patterns. In contrast to previous applications of this model framework, income plays a stronger role, an indication of relevant income-based residential sorting. Cohort effects are also visible, as millennials prefer to live in central, accessible, and mixed areas, own fewer cars, travel less by car, and use public transit and non-motorized modes more frequently.

This study identifies the most valued factors for potential bicycle users on work trips and evaluate the impact of implementing different strategies to improve bicycle use. We applied an online stated-preference survey from 127 Companhia Riograndense de Saneamento (CORSAN) employees in Porto Alegre (Brazil). Then, we formulated hybrid ordered logit models to estimate the probability of bicycle use under different hypothetical scenarios. The results showed that locker rooms, bicycle parking, bike-sharing systems (bike loaner programs run by the company), training for employees in bicycle use and the presence of bike paths for access to CORSAN all encourage bicycle use. The availability of locker rooms proved to be the most important variable. The joint implementation of locker rooms, bicycle parking and bike-sharing systems (loaner programs) would increase demand by 66%. The strategies discussed in this article can easily be adapted to other contexts where there is a desire to increase shared bicycle commuting to workplaces.