WalkingStreet: understanding human mobility phenomena through a mobile application

Understanding human mobility patterns requires access to timely and reliable data for an adequate policy response. This data can come from several sources, such as mobile devices. Additionally, the wide availability of communications networks enables applications (mobile apps) to generate data anytime and anywhere thanks to their general adoption by individuals. Although data is generated from personal devices, if a relevant set of metrics is applied to it, it can become useful for the authorities and the community as a whole. This paper explores new methods for gathering and analyzing location-based data using a mobile application called WalkingStreet. The article also illustrates the great potential of human mobility metrics for moving spatial measures beyond census units, key measures of individual, collective mobility and a mix of the two, investigating a range of important social phenomena, the heterogeneity of activity spaces and the dynamic nature of spatial segregation.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia through project UIDB/04728/2020

Smart human mobility in smart cities

Nowadays, society has challenged the scienti c community to nd solutions able to use technology to solve the gentri cation3 of city centers. Within this context, smart cities have had an important role because they view each citizen as a data source. In the same way, the Internet of Things network increases the number of physical devices generating peta-bytes of information into a Smart city architecture. Thus an appropriate Machine Learning approach is required to process and analyze collected data. In this paper, we apply three di erent Machine Learning techniques such as Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and a combined architecture, which we call CNN-LSTM, to the data generated by LinkNYC Kiosks devices | based on the city of New York |, and come to the conclusion the combined model gets better results in predicting human mobility

IoT architecture proposal from a survey of pedestrian-oriented applications

The significant improvement in the field of Internet of Things (IoT) has made human life more sophisticated. In fact, the IoT is covering devices and appliances that support one or more common ecosystems, and can be controlled via devices associated with that ecosystem. This control is only possible by building an architecture. Whether in indoor or outdoor environments, IoT services are only available to individuals or pedestrians because a IoT architecture enables that the quality of its components (i.e. Cloud/Fog servers, protocol communication and IoT devices) and the way they interact are directly correlated in terms of effectiveness and applicability. This paper aims to provide a comprehensive overview of an architecture in pedestrian-oriented applications in an IoT environment. Moreover, our survey has taken into account the main challenges and limitations of each component of IoT technology

Representing human mobility patterns in urban spaces

Human mobility is important in understanding urban spaces. Citizens interact with urban spaces using the available infrastructures, not just in the mobility sector but in public services, and in Information and Communications Technology (ICT) services, that simultaneously record their footprints. Besides, the number of mobile users is increasing very rapidly in the Internet of Things (IoT) era. These additional devices will produce a great amount of data and create new big challenges for network infrastructure. Because of this new connectivity platform, and the fast growth of wireless communication, it’s important to discuss the arrival of 5G systems. They will have a large impact on coverage, spectral efficiency, data rate of global mobile traffic, and IoT devices, and in turn it will be possible to analyze the lifestyle and understand the mobility of people, such as the most frequently visited urban spaces. Therefore, this paper is relevant in the context of smart cities and will allow for an easy connection between citizens and technology innovation hub, acquiring detailed data on human movements. Based on the analysis of generated data we try to widen this view and present an integrated approach to the analysis of human mobility using LinkNYC kiosks and 311 Service Requests in New York city

Urban human mobility modelling and prediction: impact of comfort and well-being indicators

There are increasingly more discussions on and guidelines about different levels of indicators surrounding smart cities (e.g., comfort, well-being and weather conditions). They are an important opportunity to illustrate how smart urban development strategies and digital tools can be stretched or reinvented to address localised social issues. Thus, multi-source heterogeneous data provides a new driving force for exploring urban human mobility patterns. In this work, we forecast human mobility data using LinkNYC kiosks and Metropolitan Transportation Authority (MTA) Wi-Fi in New York City to study how comfort and well-being indicators influence people's movements. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead have shown their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters.This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia through project UIDB/04728/2020

Analyzing metrics to understand human mobility phenomena: challenges and solutions

Defining basic metrics that analyze human motion is important for urban planning and population mobility forecasting. These metrics are applied to understand extensive human mobility data generated from multiple sources. This means that our understanding of the basic metrics governing human motion is conditioned by integrating different data sources available. To the best of our knowledge, this article is a comprehensive study of the characteristics and metrics of human mobility patterns. Initially, it focuses on understanding common metrics in human mobility research. Then, it compares metrics such as resilience, displacement, interval and duration in different data types such as Wi-Fi, Call Detail Records (CDRs), Global Positioning System (GPS) and Social Media collected from two individuals. Comparing the results, a variation in movement patterns in both individuals is found in our study. Finally, we uncover a few interesting phenomena that lay a solid foundation for future research.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/04728/2020

Mobile networks and internet of things: contributions to smart human mobility

Nowadays, our society can be considered as a “connected society” thanks to a heterogeneous network and the growth of mobile technologies. This growth has meant new devices are now supporting Internet of Things (IoT) architecture. Consequently, a new look at the current design of wireless communication systems is needed. Smart mobility concerns the massive movement of people and requires a complex infra-structure that produces a lot of data, creating new interesting challenges in terms of network management and data processing. In this paper, we address classic generations of mobile technology until the latest 5G implementation and its alternatives. This analysis is contextualized for the problem of smart mobility services and people-centric services for the internet of things that have a wide range of application scenarios within smart cities.This work has been supported by FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been sup ported by national funds through FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia through project UIDB/04728/2020

Explainable artificial intelligence on smart human mobility: a comparative study approach

Explainable artificial intelligence has been used in several scientific fields to understand how and why a machine learning model makes its predictions. Its characteristics have allowed for greater transparency and outcomes in AI-powered decision-making. This building trust and confidence can be useful in human mobility research. This work provides a comparative study in terms of the explainability of artificial intelligence on smart human mobility in the context of a regression problem. Decision Tree, LIME, SHAP, and Seldon Alibi are explainable approaches to describe human mobility using a dataset generated from New York Services. Based on our results, all of these approaches present relevant indicators for our problem.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/04728/2020

Sentiment analysis based on smart human mobility: a comparative study of ML models

The great social development of the last few decades has led more and more to free time becoming an essential aspect of daily life. As such, there is the need to maximize free time trying to enjoy it as much as possible and spending it in places with positive atmospheres that result in positive sentiments. In that vein, using Machine Learning models, this project aims to create a time series prediction model capable of predicting which sentiment a given place cause on the people attending it over the next few hours. The predictions take into account the weather, whether or not an event is happening in that place, and the history of sentiment in that place over the course of the previous year. The extensive results on dataset illustrate that Long Short-Term Memory model achieves the state-of-the-art results over all models. For example, in multivariate model, the accuracy performance is 80.51% when it is applied on the LinkNYC Kiosk dataset.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been sup ported by national funds through FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia through project UIDB/04728/202

Questões ambientais versus economia em Sistemas de Gestão Ambiental: avanços e perspectivas

Um Sistema de Gestão Ambiental (SGA) pode ser entendido como o conjunto dos procedimentos necessários para administrar uma empresa garantindo que as suas atividades gerem o menor impacto negativo possível sobre o ambiente. As empresas que buscam produzir sem por em risco a qualidade do ambiente garantem para si um diferencial, que pode destacá-las no mercado e garantir novos consumidores. Com o SGA também se pode reduzir o custo de produção, por diminuir os gastos com insumos, e gerar benefícios sociais e ambientais. Toda a sociedade se beneficia do SGA, através da qualidade ambiental que é mantida ou melhorada. Do mesmo modo, os recursos ambientais são conservados e a biodiversidade é protegida. A ISO 14000 foi criada em 1996, quando a International Organization for Standardization criou as normas de gestão ambiental de âmbito internacional. A partir de então, empresas de diversos países podem adotar as normas e certificar seus serviços e produtos. Os empresários vêm tomando consciência das vantagens da adoção de um SGA, pois o número de certificações vem aumentando em todo o mundo. Pode-se concluir que o SGA é uma ferramenta importante para as empresas que visam ganhar mercado e lucratividade, mas também para a obtenção de um ambiente ecologicamente equilibrado