Análise de dados e Machine Learning na mobilidade urbana

A mobilidade tornou-se num dos desafios mais difíceis que as cidades têm de enfrentar. Mais de metade da população mundial reside em áreas urbanas e com o contínuo aumento da população é imperativo que as cidades usem os seus recursos de forma eficiente. Exige-se por isso, que cada vez mais, a gestão e o planeamento da oferta de transportes, tenha de ser realizada de uma forma racional e eficaz de modo a satisfazer as necessidades dos cidadãos. Obter e reunir dados a partir de diferentes fontes de dados pode ser extretamente importante para apoiar novas soluções que podem ajudar a construir uma melhor mobilidade. O crowdsensing tornou-se uma conhecida forma de partilhar dados extraídos por dispositivos, que capturam dados através dos seus sensores, como o smartphone com o objetivo de atingir um bem comum. Nesta tese de mestrado é proposta uma metodologia que analisa dos dados extraídos, identifica áreas de maior procura e as possíveis razões pra este fenómeno. Esta metodologia pretende auxiliar o melhoramento da gestão e oferta da rede de transportes de uma dada cidade em estudo, neste caso a área metropolitana do Porto, considerando dados recolhidos da utilização da técnica de crowdsensing

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Investigating the accessibility of crowdwork tasks on Mechanical Turk

Funding Information: This work was supported by the EPSRC (grants EP/R004471/1 and EP/S027432/1). Supporting data for this publication is available at https://doi.org/10.17863/CAM.62937.Crowdwork can enable invaluable opportunities for people with disabilities, not least the work fexibility and the ability to work from home, especially during the current Covid-19 pandemic. This paper investigates how engagement in crowdwork tasks is affected by individual disabilities and the resulting implications for HCI. We first surveyed 1,000 Amazon Mechanical Turk (AMT) workers to identify demographics of crowdworkers who identify as having various disabilities within the AMT ecosystem-including vision, hearing, cognition/mental, mobility, reading and motor impairments. Through a second focused survey and follow-up interviews, we provide insights into how respondents cope with crowdwork tasks. We found that standard task factors, such as task completion time and presentation, often do not account for the needs of users with disabilities, resulting in anxiety and a feeling of depression on occasion. We discuss how to alleviate barriers to enable effective interaction for crowdworkers with disabilities.Publisher PD

Selected Papers from the 5th International Electronic Conference on Sensors and Applications

This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications