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Stop-level ridership data serve as a basis for various studies toward increasing bus patronage and promoting sustainable land use planning. To address limitations found in previous studies, this study proposes a novel approach based on Geographically Weighted Principal Component Analysis (GWPCA) and Ordinary Kriging to predict the stop-level boarding or alighting data along bus lines in São Paulo (Brazil), considering four different sampling methods. The main contributions are as follows: by accounting for the spatial heterogeneity of the predictor dataset, the GWPCA can identify the most important factor affecting transit ridership even in bus stops with no information on boarding and alighting; the spatial modeling of stop-level ridership data using GWPCA components as explanatory variables allows visualizing the spatially varying effects from predictors on ridership, supporting the land use planning at a local level; GWPCA coupled with kriging simultaneously addresses the multicollinearity of predictor data, its spatial heterogeneity, and the spatial dependence of the stop-level ridership variable, thus enhancing the goodness-of-fit measures of the transit ridership prediction in unsampled stops; and a balanced sample on predictor data and well-spread in the geographic space might be preferred to accurately estimate missing stop-level ridership data. In addition to solve the lack of stop-level ridership data, supporting a reliable bus system planning, the proposed method indicates what predictors should be addressed by policymakers to stimulate a transit-oriented development. The method can be successfully applied to other travel demand variables facing a lack of data such as traffic volume in road segments and mode choice at the household level., Author(s): Samuel de França Marques (corresponding author) [1]; Cira Souza Pitombo [1]; J. Jaime Gómez-Hernández [2] 1. Introduction Stop-level boarding and alighting data are important pieces of information for decisions [...]

The COVID-19 pandemic caused a significant shift towards teleworking, resulting in changes in travel patterns. The relationship between teleworking and individual travel behavior is crucial for transportation planning and policymaking. Thereby, this article investigates the relationships between teleworking, COVID-19, and mobility patterns in Brazil during two different periods of the COVID-19 pandemic. Given the exceptional nature of recent events, it is crucial to conduct studies related to teleworking during the pandemic crisis to contribute relevant information and evidence to the literature on this phenomenon. Such research has the potential to provide new insights and perspectives that enhance our understanding of the challenges and opportunities associated with teleworking. Two different approaches were used in the methodological procedure. The first approach used exploratory analysis to study public aggregated data related to the beginning of the pandemic, followed by a cluster analysis and a multinomial logit model. The second approach, relative to a longer relaxation period of sanitary/health measures, collected disaggregated self-reported data using an online survey, which were analyzed through non-parametric tests. The results show a strong relationship between the teleworking regime and the frequency of trips, especially for work purposes. It also highlights the influence of economic development and the region of Brazil in the kind of work regime. The continuation of studies, such as this one, in current periods is important for analyzing possible impacts, such as the reduction in congestion, vehicle emissions, and to enable the use of teleworking as a demand management policy. [ABSTRACT FROM AUTHOR]

Freight transportation in Brazil is characterized by the predominance of the road mode. This imbalance in the sector suggests the need to develop efficient strategies that can increase competitiveness of alternative modes. 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 characterize 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. Elasticities and probability marginal effects were computed, and different scenarios were simulated to predict the possible effects of implementing alternative transport policies. Simulation results show that shippers' preferences have low sensitivity to changing factors. [ABSTRACT FROM AUTHOR]