Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.

Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.

New technologies applied to transportation services in the city, enable the shift to sustainable transportation modes making bike-sharing systems (BSS) more popular in the urban mobility scenario. This study focuses on understanding the spatiotemporal station and trip activity patterns in the Lisbon BSS, based in 2018 data taken as the baseline, and understand trip rate changes in such system, that happened in the following years of 2019 and 2020. Furthermore, our paper aims to understand the COVID-19 pandemic impact in BSS mobility patterns. In this paper, we analyzed large datasets adopting a CRISP-DM data mining method. By studying and identifying spatiotemporal distribution of trips through stations, combined with weather factors, we looked at BSS improvements more suitable to accommodate users' demand. Our major contribution was a new insight on how people move in the city using bikes, via a data science approach using BSS network usage data. Major findings show that most bike trips occur on weekdays, with no precipitation, and we observed a substantial growth of trip count, during the observed time frame, although cut short by the pandemic. We believe that our approach can be applied to any city with available urban mobility data.

O estudo apresenta os resultados de uma pesquisa realizada com professoras de línguas adicionais para crianças, de um contexto mato-grossense, com o intuito de compreender os desafios enfrentados nesse momento de distanciamento social e as estratégias usadas para promover às crianças, na medida do possível, uma educação de qualidade social. O trabalho pauta-se nos pressupostos da Linguística Aplicada e discute os conceitos de línguas adicionais (SCHLATTER; GARCEZ, 2009) para crianças (SANTOS, 2009, 2005), tecnologias digitais e os desafios no ensino de línguas na pandemia (MENDONÇA, 2020), e insere-se no método de pesquisa qualitativa, de cunho interpretativista, baseada em Bauer, Gaskell e Allum (2002) e Gil (2007). Para a geração de dados, realizou-se entrevistas semiestruturadas, por meio da ferramenta Whatsapp, com cinco professoras de uma especialização Lato Sensu, ofertada por uma universidade pública situada ao norte de Mato Grosso. Os dados da pesquisa sinalizam que as professoras têm vivenciado dias desgastantes e enfrentado alguns desafios, tais como: dificuldade de adaptação ao uso das ferramentas digitais, invasão de privacidade devido a todo momento receberem mensagens de pais e alunos, falta de internet e, principalmente, de material pedagógico para esse novo contexto. Além disso, afirmam que o ensino de uma língua para crianças exige o contato que as aulas remotas não podem ofertar. Por fim, pontuam que a educação nunca foi tão excludente, tendo em vista que a aprendizagem de seus pequenos aprendizes se encontra prejudicada e as lacunas apresentadas, possivelmente, não serão preenchidas.

The development of the Internet of Things and mobile technology is connecting people and cities and generating large volumes of geolocated and space-time data. This paper identifies patterns in the Lisbon GIRA bike-sharing system (BSS), by analyzing the spatiotemporal distribution of travel distance, speed and duration, and correlating with environmental factors, such as weather conditions. Through cluster analysis the paper finds novel insights in origindestination BSS stations, regarding spatial patterns and usage frequency. Such findings can inform decision makers and BSS operators towards service optimization, aiming at improving the Lisbon GIRA network planning in the framework of multimodal urban mobility.