Luìs Manuel Nobre de Brito Elvas

• Elvas, Luìs B.; Nunes, Miguel; Ferreira, Joao C.; Francisco, Bruno & Afonso, Jose A. (2023). Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data. Applied Sciences. ISSN 2076-3417. 14(1). doi: 10.3390/app14010362. Show summary

  Urban environments are characterized by a complex soundscape that varies across different periods and geographical zones. This paper presents a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data. Traditional noise-monitoring methods often require specialized equipment and are limited in scope. Our methodology involves gathering audio recordings from city sensors and localization data from mobile phones placed in urban areas over extended periods with a focus on nighttime, when noise profiles shift significantly. By leveraging machine learning techniques, the developed system processes the audio data to extract noise features indicative of different sound sources and intensities. These features are correlated with geographic location data to create comprehensive city noise maps during nighttime hours. Furthermore, this work employs clustering algorithms to identify distinct noise zones within the urban landscape, characterized by their unique noise signatures, reflecting the mix of anthropogenic and environmental noise sources. Our results demonstrate the effectiveness of using mobile phone data for nocturnal noise analysis and zone identification. The derived noise maps and zones identification provide insights into noise pollution patterns and offer valuable information for policymakers, urban planners, and public health officials to make informed decisions about noise mitigation efforts and urban development.

• Ferreira, Joao C.; Francisco, Bruno; Elvas, Luìs B.; Nunes, Miguel & Afonso, Jose A. (2023). Predicting People’s Concentration and Movements in a Smart City. Electronics. ISSN 2079-9292. 13(1). doi: 10.3390/electronics13010096. Show summary

  first_pagesettingsOrder Article Reprints Open AccessArticle Predicting People’s Concentration and Movements in a Smart City by Joao C. Ferreira 1,2,3ORCID,Bruno Francisco 1,Luis Elvas 1,2,3,Miguel Nunes 1 andJose A. Afonso 4,*ORCID 1 Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, 1649-026 Lisboa, Portugal 2 Inov Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal 3 Department of Logistics, Molde University College, 6410 Molde, Norway 4 CMEMS-UMinho/LABBELS—Associate Laboratory, University of Minho, 4800-058 Guimarães, Portugal * Author to whom correspondence should be addressed. Electronics 2024, 13(1), 96; https://doi.org/10.3390/electronics13010096 Submission received: 25 October 2023 / Revised: 18 December 2023 / Accepted: 21 December 2023 / Published: 25 December 2023 (This article belongs to the Special Issue AI Technologies and Smart City) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract With the rapid growth of urbanization and the proliferation of mobile phone usage, smart city initiatives have gained momentum in leveraging data-driven insights to enhance urban planning and resource allocation. This paper proposes a novel approach for predicting people’s concentration and movements within a smart city environment using mobile phone data provided by telecommunication operators. By harnessing the vast amount of anonymized and aggregated mobile phone data, we present a predictive framework that offers valuable insights into urban dynamics. The methodology involves collecting and processing location-based data obtained from telecommunication operators. Using machine learning techniques, including clustering and spatiotemporal analysis, we developed models to identify patterns in people’s movements and concentration across various city regions. Our proposed approach considers factors such as time of day, day of the week, and special events to capture the intricate dynamics of urban activities. The predictive models presented in this paper demonstrate the ability to predict areas of high concentration of people, such as commercial districts during peak hours, as well as the people flow during the time. These insights have significant implications for urban planning, traffic management, and resource allocation. Our approach respects user privacy by working with aggregated and anonymized data, ensuring compliance with privacy regulations and ethical considerations. The proposed models were evaluated using real-world mobile phone data collected from a smart city environment in Lisbon, Portugal. The experimental results demonstrate the accuracy and effectiveness of our approach in predicting people’s movements and concentration. This paper contributes to the growing field of smart city research by providing a data-driven solution for enhancing urban planning and resource allocation strategies. As cities continue to evolve, leveraging mobile phone data from telecommunication operators can lead to more efficient and sustainable urban environments.

• Elvas, Luìs B.; Nunes, Miguel; Francisco, Bruno & Domingues, Nuno (2023). City mobility and night life monitor. In Martins, Ana Lucia; Ferreira, Joao C.; Kocian, Alexander; Tokkozhina, Ulpan; Helgheim, Berit & Bråthen, Svein (Ed.), Intelligent Transport Systems : 7th EAI International Conference, INTSYS 2023, Molde, Norway, September 6-7, 2023, Proceedings. Springer. ISSN 978-3-031-49379-9. p. 126–150. doi: https:/doi.org/10.1007/978-3-031-49379-9_7. Show summary

  Home Intelligent Transport Systems Conference paper City Mobility and Night Life Monitor Luís B. Elvas, Miguel Nunes, Bruno Francisco & Nuno Domingues Conference paper First Online: 12 December 2023 15 Accesses Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 540) Abstract This paper presents an Internet of Things (IoT) system designed to collect and analyse information regarding the travel patterns and movements of individuals in densely populated locations, in the context of smart cities. People’s movements are retrieved from coarse-grained aggregated cellular network data without collecting sensitive information from mobile devices and users. These data were provided by a Portuguese cellular operator to the Lisbon City Council to characterize people movements in the city. In this sense, the mobile phones act as useful sensor devices for collecting rich spatiotemporal information about human movement patterns. The purpose of this research work is to create a machine learning-based data-driven approach that is able to receive anonymised data from telecommunication operators to provide a big picture about citizen mobility in the city and to identify patterns based on the collected data, in order to provide relevant information for city planning and events coordination. Some of the main applications of the proposed system are the coordination of big events and the management and control of commuting traffic.

• Elvas, Luìs B.; Nunes, Miguel; Afonso, José Augusto; Helgheim, Berit & Francisco, Bruno (2023). Mining tourists’ movement patterns in a city. In Martins, Ana Lucia; Ferreira, Joao C.; Kocian, Alexander; Tokkozhina, Ulpan; Helgheim, Berit & Bråthen, Svein (Ed.), Intelligent Transport Systems : 7th EAI International Conference, INTSYS 2023, Molde, Norway, September 6-7, 2023, Proceedings. Springer. ISSN 978-3-031-49379-9. p. 105–125. doi: https:/doi.org/10.1007/978-3-031-49379-9_6. Show summary

  Although tourists generate a large amount of data (known as “big data”) when they visit cities, little is known about their spatial behavior. One of the most significant issues that has recently gained attention is mobile phone usage and user behavior tracking. A spatial and temporal data visualization approach was established with the purpose of finding tourists’ footprints. This work provides a platform for combining multiple data sources into one and transforming information into knowledge. Using Python, we created a method to build visualization dashboards aiming to provide insights about tourists’ movements and concentrations in a city using information from mobile operators. This approach can be replicated to other smart cities with data available. Weather and major events, for instance, have an impact on the movements of tourists. The outputs from this work provide useful information for tourism professionals to understand tourists’ preferences and improve the visitors’ experience. Management authorities may also use these outputs to increase security based on tourists’ concentration and movements. A case study in Lisbon with 4 months data is presented, but the proposed approach can also be used in other cities based on data availability. Results from this case study demonstrate how tourists tend to gather around a set of parishes during a specific time of the day during the months under study, as well as how unusual circumstances, namely international events, impact their overall spatial behavior.

• Elvas, Luìs B.; Nunes, Miguel; Francisco, Bruno; Gonçalves, Frederica; Martins, Ana Lucia & Ferreira, Joao C. (2023). Points of interest in smart cities and visitor behavior. In Martins, Ana Lucia; Ferreira, Joao C.; Kocian, Alexander; Tokkozhina, Ulpan; Helgheim, Berit & Bråthen, Svein (Ed.), Intelligent Transport Systems : 7th EAI International Conference, INTSYS 2023, Molde, Norway, September 6-7, 2023, Proceedings. Springer. ISSN 978-3-031-49379-9. p. 69–91. doi: 10.1007/978-3-031-49379-9_4. Show summary

  Home Intelligent Transport Systems Conference paper Points of Interest in Smart Cities and Visitor Behavior Luís B. Elvas, Miguel Nunes, Bruno Francisco, Frederica Gonçalves, Ana Lucia Martins & Joao Carlos Ferreira Conference paper First Online: 12 December 2023 15 Accesses Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 540) Abstract Smart cities leverage technology and data to enhance the quality of urban life, including the management of points of interest (POIs) and visitor experiences. This paper explores the relationship between POIs and visitor behavior in smart cities, examining the impact of technology-driven solutions on understanding, analyzing, and optimizing visitor experiences. It highlights the importance of data-driven approaches in identifying and managing POIs, enhancing visitor satisfaction, and driving economic growth. The paper reviews existing literature, discusses key concepts, and presents case studies to illustrate the role of POIs in smart cities and their influence on visitor behavior. Our major contribution is a data driven approach to extract useful information from real data to municipality decisions and understand the problem. It concludes with recommendations for future research and practical implications for city planners, policymakers, and tourism authorities.

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• Elvas, Luìs B. & Mawrides, Emmanuel Kafti (2024). Empowering patient-centric health data exchange : a blockchain-based framework for secure and efficient data sharing . Molde University College - Specialized University in Logistics. ISSN 978-82-7962-371-7. Show summary

  In the healthcare sector, the sharing of sensitive health data poses substantial privacy and security challenges that must be navigated with utmost care. With health data recognized by the European Commission as a unique and valuable resource for both retrospective and prospective research, and the Organisation for Economic Co-operation and Development (OECD) advocating for robust health data governance, there is a clear directive to harmonize individual privacy with the need for data accessibility. This paper proposes a conceptual blockchain framework designed to empower patients in the management and sharing of their medical information. Through this framework, patients would assume the role of data custodians, with the authority to grant access to their health records as they see fit. By leveraging blockchain technology's inherent qualities of decentralization, immutability, and transparency, our framework ensures that data sharing is secure, patient-centered, and conducted under the patient's consent. This approach not only upholds the privacy and autonomy of individuals but also streamlines the data sharing process. It is particularly advantageous for research centers, which can access necessary data without the procedural delays associated with traditional ethical approval processes. Our conceptual framework aims to redefine health data exchange, prioritizing patient control and trust, while fostering a more efficient and collaborative research environment.

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