Marina Leite de Barros Baltar

The high number of deaths and serious injuries in traffic crashes can be considered a silent global epidemic, as it is still understood by part of society as an inherent consequence of road traffic. There are several risk factors that can increase the occurrence or severity of crashes on roads, acting alone or in combination. Road safety diagnoses based on facts and evidence are essential for improving public policies to reduce victims. With the aim of assisting in these diagnoses and since the official database on these victims is not made available in detail to the public, this work investigates the relationship between seven indicators, collected in field research and in public databases, and the occurrence and fatality of traffic victims in the City of Rio de Janeiro. Linear regression models are developed for each approach and the one with the best statistical parameters is chosen. The model with greater robustness demonstrated that helmet non-use, the density of traffic enforcement cameras, and illiteracy together explain a significant portion of the variation in the fatality rate. The results are considered satisfactory, since a limited number of existing risk factors for road safety were used.

Metro systems are high-capacity urban rail networks designed to provide fast, reliable, and efficient transportation. This article presents a comparative benchmarking study of six leading metro systems in Brazil and six prominent international cases, aiming to identify best practices and recurring challenges based on key operational, planning, design, governance, and performance indicators. The Brazilian systems analyzed are located in Rio de Janeiro, São Paulo, Belo Horizonte, Fortaleza, Recife, and Salvador, while the international cases include London, Paris, Tokyo, Berlin, New York, and Madrid. The methodology combined documentary research with technical analysis of public data sources, institutional reports, and performance indicators. The results reveal significant contrasts in network scale, operational efficiency, governance models, funding mechanisms, and integration with urban planning. São Paulo’s system stands out for its network robustness, automation, and consolidated monitoring framework, while other Brazilian cities face limitations in service coverage and financial sustainability. The international cases offer valuable insights into fare integration, the use of emerging technologies, and the application of performance metrics to foster more sustainable and efficient high-capacity urban transit systems. The findings provide relevant evidence to support policymakers, transport authorities, and urban planners in improving the planning, management, and sustainability of high-capacity urban transit systems.

Despite the ending of social distancing and post-pandemic return to normal activities, the impacts of COVID-19 on urban mobility persist, particularly with regard to public transport. Consequently, this study looks into the post-pandemic changes in travel patterns, seeking to comprehend the effects on the municipalities and suggest helpful measures geared to achieving more sustainable patterns following the lockdown isolation. The chosen case study is the Metropolitan Area of Rio de Janeiro, Brazil. The study was divided into four distinct phases, namely understanding the problems, choosing the study area, field research and making recommendations based not only on the literature but on consultation with operators and experts. The methodological process involves the analysis of data from the field research, to assess any changes in patterns that may have been identified. The results show the changes in the means of travel, with emphasis on a preference for app-based transport services, to the detriment of sustainable transport. The study also shows the challenges facing public transport and the relationship between the choice of travel means and the socioeconomic profile of the passenger.

Um Sistema de Gerenciamento de Incidentes de Tráfego (SGIT) constitui-se em uma importante estratégia para redução dos impactos do setor de transportes rodoviário no desenvolvimento sustentável. Isso porque os incidentes de tráfego são eventos que geram congestionamentos não recorrentes que causam redução da capacidade viária, aumento do tempo de viagem, aumento da emissão de poluentes do ar e Gases de Efeito Estufa, redução da qualidade de vida da população urbana etc. Dessa forma, este estudo busca destacar, por meio de revisão da literatura e desenvolvimento de um fluxograma lógico, como um SGIT eficaz pode contribuir para o alcance de muitos Objetivos de Desenvolvimento Sustentável (ODS) e suas metas da Agenda 2030 para o Desenvolvimento Sustentável. Os resultados mostram que um bom SGIT é capaz de impactar positivamente 9 ODS e 25 metas da Agenda 2030, demostrando assim que os governos devem investir em ações e políticas públicas que possam melhorar os SGIT.

Purpose: Analysis of the production and storage capacity of soybeans and corn in Mato Grosso (Brazil) and its projections until the year 2022. Methodology/Approach: Data regarding Mato Grosso's soy and corn production, area planted, and storage and yield capacity were organized for the years 1995 to 2018. Exponential smoothing models were then used to preview the grain storage balance for the period between 2019 and 2022. Findings: Grain production is expected to grow 11.9%, while the increase in storage capacity is expected to be 9.7%. Therefore, the results showed that the gap between production and storage may increase by 118%, between the harvests of 2017/18 and 2021/22. Moreover, the deficit in storage capacity will represent 53% of the total harvest in 2021/22. Research Limitation/implication: Interferences in the analyzed variables, such as economic, environmental, or technological events, may affect the forecasts' outcomes. Originality/Value of paper: The estimation combines exponential smoothing models with a non-parametric resampling technique. KEYWORD: agribusiness, logistics bottlenecks, agriculture infrastructure. PREVISÃO DA CAPACIDADE DE ARMAZENAMENTO USANDO O MÉTODO DE SUAVIZAÇÃO EXPONENCIAL RESUMO Objetivo: Análise da produção e capacidade de armazenamento de soja e milho em Mato Grosso (Brasil) e suas projeções até o ano de 2022. Metodologia/Abordagem: Organizou-se os dados da produção área plantada, produtividade de soja e milho para o estado de Mato Grosso, bem como a capacidade de armazenamento, para o período entre 1995 e 2018. Posteriormente, modelos de suavização exponencial foram utilizados para prever o saldo de armazenagem de grãos no estado de 2019 a 2022. Conclusões: Espera-se um crescimento de 11,9% na produção de grãos e apenas 9,7% de incremento da capacidade de armazenagem, conforme projeções. Nesse sentido, os resultados evidenciaram que o distanciamento entre produção e armazenagem poderá crescer 118% entre a safra de 2017/18 e 2021/22. Ademais, o déficit será de 53% da safra total em 2021/22. Limitação/implicação da pesquisa: Intervenções nas variáveis analisadas, tais como eventos econômicos, ambientais, ou tecnológicos, poderão afetar o resultado das previsões. Originalidade/valor do trabalho: O trabalho combina os modelos de suavização exponencial com a técnica não paramétrica de reamostragem bootstrap.