This article was motivated by the urban mobility changes observed at the onset of the COVID-19 pandemic in Brazil. We aim to analyze travel behavior before and during the COVID-19 pandemic in Brazil considering two samples of revealed preference online data, independent samples tests, multinomial logit models (MNL), and mixed logit models (ML). The analysis shows a decrease in Urban Public Transport (UPT) use. Comfort and frequency of the UPT service were important factors to attract users during the pandemic period. Ridesourcing services were used for leisure purposes before the pandemic. During the pandemic, they were used for health purposes. Active modes were used more for shopping and leisure purposes during the pandemic. Regarding car users, such as drivers, it was found that they used ridesourcing less often during the pandemic than before. The main contribution of this research concerns the changes in travel behavior that might remain and how these analyses can shape sustainable transportation public policies in the future. Therefore, for a Brazilian study case, this article suggests an increase in the quality of UPT services, a reform on pricing regulations for UPT, an increase in the infrastructure for active modes, an implementation of car demand management strategies, and more strategies to support teleworking as a form of traffic demand management.
The choice of port is one of the topics that most interest researchers. Port selection behaviour may vary depending on port user perspectives. Prior studies have attempted to determine user preferences for certain port choice factors, but there are only few studies in developing countries, such as Brazil, where there is a lack of studies on this topic. The objectives of this article are to analyse the port selection factors in Brazil for different port users, to discuss the implications for competitiveness among ports and to assist port service providers to formulate strategies. Identifying factors will help port service providers to develop strategies in the Brazilian market. The paper explores the case of the Southeast region port market in Brazil, based on data from the port sector and in-depth interviews with a representative selection of port users and specialists in the port sector. The results suggest that Brazilian Port Administrations need to adopt strategies aimed at increasing connections with the interior of Brazil, but they also need to adopt a highly market-based approach, communicating and synchronizing strategies with different public and private stakeholders.
Freight transportation in Brazil is characterised by the predominance of the road travel mode. This imbalance in the sector suggests the need to develop efficient strategies that can increase competitiveness of alternative modes such as the railway network. However, in Brazil, there are few studies investigating firms' preferences concerning different attributes of travel modes. This study analyses the travel mode choice decision-making process of shippers in the state of Rio de Janeiro, Brazil. The main objectives of this article are related to model travel mode choice and characterise freight transport in a Brazilian context. Discrete choice models were estimated using Stated Preference data to identify shippers' preferences and discuss some possible sustainable policies that could increase the competitiveness of the railway network. Multinomial and mixed logit models were estimated. Elasticities and probability marginal effects were computed, and different scenarios were simulated to predict the possible effects of implementing alternative transport policies. The elasticity results imply that demand is more elastic regarding cost than other variables. A 1% decrease in the cost of rail induces a 2.71% increase in rail demand. Marginal effect values show that a door-todoor service has the highest potential to increase rail demand. However, providing a door-to-door service would likely have huge operational costs, which would increase the rail cost and therefore reduce the overall benefits. Simulation results show that shippers' preferences have low sensitivity to changing factors. Finally, covariates associated with the Brazilian context, how to measure them properly and apply them in freight models of similar regions are also discussed.
Discrete choice models have been used over the years in disaggregated approaches to forecast destination choices. However, there are important constraints in some of these models that pose obstacles to using them, such as the Independence of Irrelevant Alternatives (IIA) property in the Multinomial Logit model, the need to assume specific structures and high calibration times, depending on the complexity of the case being evaluated. However, some of these mentioned constraints could be mitigated using Mixed Models or Nested Logit. Therefore, this paper proposes a comparative analysis between the Artificial Neural Network (ANNs), the Multinomial and Nested Logit models for disaggregated forecasting of urban trip distribution. A case study was conducted in a medium-sized Brazilian city, Santa Maria (RS), Brazil. The data used come from a household survey, prepared for the Urban Mobility Master Plan. For the sake of comparison, hit rates and frequency of trip distribution distances were analyzed, showing that ANNs can be as efficient as the Discrete Choice models for disaggregated forecasting of urban trip destination without, however, assuming some constraints. Finally, based on the results obtained, the efficiency of ANNs is observed for predicting alternatives with a low number of observations. They are important tools for obtaining Origin-Destination matrices from incomplete sample matrices or with a low number of observations. However, it is important to mention that discrete choice models can provide important information for the analyst, such as statistical significance of parameters, elasticities, subjective value of attributes, etc.
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