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The COVID-19 pandemic has caused significant changes around the world. The circumstances resulted in a radical shift in people's lives, including the way they move around the cities and/or carry out their activities. This study carries out a travel behavior analysis using commuting panel data collected over 7 days using smartphones. The study focuses on the Maceió Metropolitan Area (MMA), which is in the state of Alagoas in the northeast region of Brazil. Cluster analysis, using the k-means algorithm, divided the sample into three groups of travel behavior: Group A ("Infrequent travelers, for work or shopping trip purposes and very prone to do remote work"), Group B ("Intermediate travelers, for work or shopping trip purposes and prone to do remote work"), and Group C ("Frequent travelers, for work or meal purchases and not likely to do remote work"). Groups B and C are predominantly formed by individuals who carry out activities that are less likely to do remote work. By analyzing the groups, it is possible to understand the changes that occurred during the period studied (September/October 2020) and what are the expectations for a post-pandemic scenario, associated with each behavioral group. It was observed that "Working" was the main trip purpose during the pandemic and that the possibility of teleworking depends on the type of activity carried out. Making a scale of the resilience of activities considering the replacement of out-of-home activities by in-home remote activities, it can be observed that Group A was the most resilient, followed by Group B and C, respectively. For the post-pandemic scenario, Groups A and B are also the most likely to use Information and Communication Technologies (ICTs) and continue carrying out other remote activities, such as grocery shopping and meals, replacing, in the future, predominantly trips using ICTs.

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INTRODUCTION: Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data. RESULTS: The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.

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OBJECTIVE: To carry out a spatial analysis of the occurrence of acts of violence (specifically robberies) in public transportation, identifying the regions of greater incidence, using geostatistics, and possible causes with the aid of a multicriteria analysis in the Geographic Information System. METHODS: The unit of analysis is the traffic analysis zone of the survey named Origem-Destino, carried out in Salvador, state of Bahia, in 2013. The robberies recorded by the Department of Public Security of Bahia in 2013 were located and made compatible with the limits of the traffic analysis zones and, later, associated with the respective centroids. After determining the regions with the highest probability of robbery, we carried out a geographic analysis of the possible causes in the region with the highest robbery potential, considering the factors analyzed using a multicriteria analysis in a Geographic Information System environment. RESULTS: The execution of the two steps of this study allowed us to identify areas corresponding to the greater probability of occurrence of robberies in public transportation. In addition, the three most vulnerable road sections (Estrada da Liberdade, Rua Pero Vaz, and Avenida General San Martin) were identified in these areas. In these sections, the factors that most contribute with the potential for robbery in buses are: F1 - proximity to places that facilitate escape, F3 - great movement of persons, and F2 - absence of policing, respectively. CONCLUSIONS: Indicator Kriging (geostatistical estimation) can be used to construct a spatial probability surface, which can be a useful tool for the implementation of public policies. The multicriteria analysis in the Geographic Information System environment allowed us to understand the spatial factors related to the phenomenon under analysis.

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ABSTRACT OBJECTIVE To carry out a spatial analysis of the occurrence of acts of violence (specifically robberies) in public transportation, identifying the regions of greater incidence, using geostatistics, and possible causes with the aid of a multicriteria analysis in the Geographic Information System. METHODS The unit of analysis is the traffic analysis zone of the survey named Origem-Destino, carried out in Salvador, state of Bahia, in 2013. The robberies recorded by the Department of Public Security of Bahia in 2013 were located and made compatible with the limits of the traffic analysis zones and, later, associated with the respective centroids. After determining the regions with the highest probability of robbery, we carried out a geographic analysis of the possible causes in the region with the highest robbery potential, considering the factors analyzed using a multicriteria analysis in a Geographic Information System environment. RESULTS The execution of the two steps of this study allowed us to identify areas corresponding to the greater probability of occurrence of robberies in public transportation. In addition, the three most vulnerable road sections (Estrada da Liberdade, Rua Pero Vaz, and Avenida General San Martin) were identified in these areas. In these sections, the factors that most contribute with the potential for robbery in buses are: F1 - proximity to places that facilitate escape, F3 - great movement of persons, and F2 - absence of policing, respectively. CONCLUSIONS Indicator Kriging (geostatistical estimation) can be used to construct a spatial probability surface, which can be a useful tool for the implementation of public policies. The multicriteria analysis in the Geographic Information System environment allowed us to understand the spatial factors related to the phenomenon under analysis.

RESUMO OBJETIVO Realizar uma análise espacial da ocorrência de atos de violência (específicamente roubos) em transporte público, identificando as regiões de maior incidência, por meio da geoestatística, e possíveis causas com auxílio de análise multicritério em Sistema de Informação Geográfica. MÉTODOS A unidade de análise é a zona de tráfego da pesquisa Origem-Destino, realizada em Salvador, BA, Brasil, em 2013. Os roubos registrados pela Secretaria de Segurança Pública da Bahia, no mesmo ano, foram localizados e compatibilizados aos limites das zonas de tráfego e, posteriormente, associados aos respectivos centroides. Após determinação das regiões de maior probabilidade de ocorrências, foi feita uma análise geográfica de possíveis causas na região de maior potencial, considerando fatores analisados por meio de uma análise multicritério em ambiente de Sistema de Informação Geográfica. RESULTADOS A execução das duas etapas deste trabalho permitiu identificar áreas correspondentes à maior probabilidade de ocorrência de roubo em transporte público. Além disso, foram identificados três trechos viários (Estrada da Liberdade, Rua Pero Vaz e Avenida General San Martin) mais vulneráveis, localizados nessas áreas. Nesses trechos, os fatores que mais contribuem para o potencial de ocorrência de roubo em ônibus são: F1 - proximidade a locais que facilitam a fuga, F3 - grande movimentação de pessoas e F2 - ausência de policiamento, respectivamente. CONCLUSÕES Com o uso da Krigagem indicativa (estimação geoestatística), é possível a construção de uma superfície espacial de probabilidades de ocorrências, que pode ser uma ferramenta útil para implementação de políticas públicas. A análise multicritério no ambiente do Sistema de Informação Geográfica permitiu a compreensão dos fatores espaciais relacionados ao fenômeno em análise.

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