Cira Souza Pitombo

O avanço das Tecnologias da Informação e Comunicação (TICs) e a popularização do teletrabalho, com a pandemia de COVID-19, alteraram a dinâmica da mobilidade nos últimos anos. Em relação às distâncias e ao teletrabalho existem relações de causalidade importantes. A relação investigada no presente trabalho está atrelada à escolha de teletrabalho devido às distâncias atuais a serem percorridas, principalmente em cidades maiores. Assim, este artigo analisou relações entre distâncias urbanas percorridas e o teletrabalho e, de forma secundária, investigou relações entre os diferentes regimes, frequências de viagem e uso de tecnologias. Para isso, foi usado um método de três etapas, com uso sequencial das seguintes ferramentas: (1) Algoritmo CART (Classification And Regression Tree); (2) Testes qui-quadrado e (3) Modelo logit multinomial. Os dados foram obtidos por meio de uma pesquisa online, realizada entre abril e junho de 2022, com 247 respondentes, de diversas cidades brasileiras. Os resultados mostraram que teletrabalhadores parciais percorrem distâncias maiores para viagens com motivo trabalho (entre 15,5 e 37,5 km), enquanto teletrabalhadores integrais têm menor frequência de deslocamento para as mesmas viagens. Adicionalmente, trabalhadores presenciais se deslocam com menor frequência para viagens por outros motivos.

A mobilidade urbana é um dos fatores de maior preocupação para a qualidade de vida da população urbana. Poucos estudos têm se dedicado a avaliar as condições atuais de acessibilidade e segurança no transporte em municípios brasileiros. O objetivo deste estudo foi realizar o diagnóstico da situação atual de alguns municípios brasileiros com relação ao atendimento da meta proposta pelo Objetivo do Desenvolvimento Sustentável (ODS) 11.2, que trata do acesso aos sistemas de transportes de forma sustentável, segura e acessível. Foram utilizados dados de questionários oriundos de fontes oficiais e de domínio público e de uma amostra de 64 municípios. O método contemplou análises qualitativas, estatísticas descritivas e aplicação de técnicas de análise multivariada. Dos municípios estudados, 45,3% estão na região Sudeste, 20,3% na região Nordeste, 18,7% na região Sul, 9% na região Centro-oeste, e 6,2% na região Norte. Foi identificado seis conjuntos de informações referentes a Legislação, Acessibilidade, Política participativa, Infraestrutura, Tarifas e Veículos. O estudo evidenciou a necessidade de aprimoramento dos questionários existentes para a melhoria das informações reportadas e para possibilitar a avaliação das metas do ODS e de ações junto aos municípios. Observatórios urbanos podem contribuir para melhorar disseminação de dados e compartilhamento de informações.

Spatial effects are intrinsic to studies on travel behavior, including modal choice problems. Among spatial dependence and heterogeneity, the resulting spatial variation of estimated coefficients is one of the most significant gains in local spatial models. Thus, this article aims to analyze a modal choice problem, considering spatial dependence and variability simultaneously. Additionally, a test is proposed to evaluate and validate the spatial variability, obtaining disaggregated results. A database adapted from the household origin-destination survey was used, which was carried out in 2007/2008 in the city of São Carlos - São Paulo, Brazil. The proposed spatial variability test uses the estimated parameters of the GWLR model (main database and 200 spatially randomized databases), compared to the confidence intervals of the coefficients in the non-spatial logit model for the spatial variability hypothesis. The results of the proposed test are similar to the reference test in the case study. The disaggregated results can be used to verify if there are certain subgroups that are more likely to be spatially stationary and if these groups exhibit a spatial pattern. Moreover, it can be observed that the local spatial model provides a better fit and estimates when compared to the non-spatial model.

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.

To fulfill the modern concept of sustainable construction, the civil engineering community has shown increased interest in alternative options to replace natural backfills for engineering purposes. Since Recycled Construction and Demolition Waste (RCDW) has proven to be attractive in environmental, economic, and technical aspects, its behavior should be assessed considering its interaction with other construction materials, such as geosynthetics. Bearing in mind that the backfill affects the durability of geosynthetic materials, this study aims to assess the damage caused to geogrids by RCDW dropped by transportation (dump) trucks. Moreover, this study aimed to obtain an equation to predict the reduction factor caused by the backfill drop process. In an experimental facility, six RCDW materials (with different grain size distributions) were dropped (using a backhoe loader) from 1.0 m and 2.0 m heights over three distinct geogrids; the geogrid samples were exhumed and then tested under tensile loading. The results provided a database subjected to machine learning (Artificial Neural Network—ANN) to predict the reduction factor caused by the induced damage. The results demonstrate that the increase in drop height or potential energy cannot be directly associated with the damage. However, the damage increases as the maximum grain size of uniform gradation backfill increases, which is different from the results obtained from the fall of continuous gradation backfill. Moreover, since ANNs do not have any of the traditional constraints that multiple linear regression has, this method is an attractive solution to predict the geosynthetic reduction factors, providing relative errors lower than 8% compared to the experimental investigation reported in the study.

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.

In light of the unavailability of traffic volume data for all road segments, the scientific literature proposes estimating this variable using spatial interpolators. However, most of the methods found use the Euclidean distance between the database points as a proximity measure, in addition to ignoring the anisotropy of the phenomenon. Thus, the objective of the present study was to apply Ordinary Kriging (OK) with network distances and anisotropic OK in traffic volume data on highways in the state of São Paulo (Brazil), comparing its results to the traditional isotropic approach with Euclidean distances. Goodness-of-fit measures confirmed the good performance and better suitability of OK with network distances over the analyses that use Euclidean distances. Addressing the anisotropy of the traffic volume data also helped to improve the results. The proposed method can effectively support estimating traffic volume in segments without flow data.

The main aims of this article are: (1) to evaluate the quality of cycling infrastructures in the city of João Pessoa (PB) using the Analytic Hierarchy Process (AHP) and (2) to extend the proposed assessment (using quality indicators) to the entire cycling network of the municipality applying geostatistical spatial interpolators. Therefore, a two-step approach was proposed. Initially, the physical and operational factors that affect the quality of cycling infrastructures were identified to formulate the hierarchy. Afterwards, five specialists in cycling infrastructures and cycle path users were asked to fill in a form, aiming to weight the criteria. Next, we used a database collected in the field from 27 sections of cycle paths. After obtaining the scores for each section, they were classified, and maps were created to locate the critical sections. Having verified if there was spatial dependence, related to the cycling quality indicator, geostatistical modeling was carried out and the quality indicator was estimated for 32 cycle path sections, not previously collected in field. Therefore, the complete cycle network map, which was properly evaluated, can be used as a low-cost tool to support decision-making and implementation of transport policies.

Em termos gerais, a calibração do modelo de escolha discreta se dá através de dados obtidos por pesquisas de Preferência Revelada (PR) e Declarada (PD). No planejamento de transportes, uma das principais fontes de dados é a Pesquisa O/D, que é uma pesquisa de PR e descreve as escolhas e comportamentos reais dos indivíduos. Entretanto, não é possível, através desta fonte, caracterizar as alternativas não escolhidas. Este trabalho possui dois objetivos associados: (1) propor um critério para caracterizar, de forma agregada, as alternativas modais, utilizando dados de PR e (2) testar o aprimoramento de estimativas de escolha modal a partir da inclusão das características agregadas das alternativas. Primeiramente, foi utilizado o algoritmo CART (Classification And Regression Tree) para caracterizar os tempos de viagem dos modos de transporte disponíveis na área de estudo (Cidade de São Paulo, Brasil). As viagens foram agrupadas, segundo variáveis independentes selecionadas pelo algoritmo, e foram obtidos valores médios de tempos de viagens para cinco alternativas modais – informação anteriormente não disponível na pesquisa de PR. Finalmente, o aprimoramento da modelagem de escolha discreta, a partir da inclusão dos tempos de viagens médios, é testado através de uma amostra de validação e métricas de desempenho, tais como Percentual de Acertos e Valor do log da Verossimilhança. Observou-se um incremento das estimativas a partir da inclusão das durações de viagens, sendo o método proposto uma contribuição acadêmica para a modelagem a partir de dados de PR.