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- Beanstalk: a community based passive wi-fi tracking system for analysing tourism dynamicsPublication . Nunes, Nuno; Ribeiro, Miguel; Prandi, Catia; Nisi, ValentinaThis paper presents Beanstalk, an interactive platform to assist communities in easily running systematic analysis of mobility patterns of tourists at their destinations, contributing in new ways in visualizing spatio-temporal mobility data for forecasting, tracking trends, detecting patterns and noticing anomalies. The approach takes advantage of a combination of passive Wi-Fi tracking and ground truth data provided by tourism authorities. By analyzing a large dataset for a medium sized European island, we provide evidence of the accuracy and effectiveness of this low-cost method in inferring topological characteristics of tourist behavior and relevant typologies of trip itineraries. This helps decision makers in the touristic sector to plan and manage actions geared towards improving the sustainability and competitiveness of their touristic regions. In particular, we argue that in a world where sensing data is becoming inexpensive, there is an opportunity to use this approach to deliver data back to local communities which are empowered to act and leverage this information.
- Enhancing sustainable mobility awareness by exploiting multi-sourced data: the case study of the Madeira islandsPublication . Prandi, Catia; Nunes, Nuno; Ribeiro, Miguel; Nisi, ValentinaIn this paper we present a low-cost infrastructure to collect a variety of location-based multi-sourced data with the aim of providing personalized services and raising awareness for sustainable mobility solutions. The gathered data can be used to provide: (i) citizens and tourists with personalized location-based services to increase sustainability awareness; (ii) local authorities and tourism boards with a tool to identify and prevent mobility issues; and (iii) transport companies with an instrument to support urban mobility planning decisions. To collect data, we exploited a low-cost Wi-Fi passive tracking system and we augmented this infrastructure using sensors for detecting environmental conditions. To achieve this, we provided 60 points of interest and 20 buses with our solution, to spread out the sensors over the entire Madeira Island. Using the gathered data, we developed different scenarios to prove that in a world where sensing data is becoming inexpensive, there are opportunities to use our approach to deliver data back to the citizens, empowering local communities, with the goal of promoting sustainable mobility and tourism.
- Modeling Adoption, Security, and Privacy of COVID-19 Apps: Findings and Recommendations From an Empirical Study Using the Unified Theory of Acceptance and Use of TechnologyPublication . Nunes, Nuno; Adamo, Greta; Ribeiro, Miguel; Gouveia, Bruna R.; Gouveia, Élvio Rúbio; Teixeira, Pedro; Nisi, ValentinaBackground: The global health crisis caused by COVID-19 has drastically changed human society in a relatively short time. However, this crisis has offered insights into the different roles that such a worldwide virus plays in the lives of people and how those have been affected, as well as eventually proposing new solutions. From the beginning of the pandemic, technology solutions have featured prominently in virus control and in the frame of reference for international travel, especially contact tracing and passenger locator applications. Objective: The objective of this paper is to study specific areas of technology acceptance and adoption following a unified theory of acceptance and use of technology (UTAUT) research model. Methods: We presented a research model based on UTAUT constructs to study the determinants for adoption of COVID-19–related apps using a questionnaire. We tested the model via confirmatory factor analysis (CFA) and structural equation modeling (SEM) using travelers’ data from an insular tourist region. Results: Our model explained 90.3% of the intention to use (N=9555) and showed an increased understanding of the vital role of safety, security, privacy, and trust in the usage intention of safety apps. Results also showed how the impact of COVID-19 is not a strong predictor of adoption, while age, education level, and social capital are essential moderators of behavioral intention. Conclusions: In terms of scientific impact, the results described here provide important insights and contributions not only for researchers but also for policy and decision makers by explaining the reasons behind the adoption and usage of apps designed for COVID-19.
- Passive Wi-Fi monitoring in the wild: a long-term study across multiple location typologiesPublication . Ribeiro, Miguel; Nunes, Nuno; Nisi, Valentina; Schöning, JohannesIn this paper, we present a systematic analysis of large-scale human mobility patterns obtained from a passive Wi-Fi tracking system, deployed across different location typologies. We have deployed a system to cover urban areas served by public transportation systems as well as very isolated and rural areas. Over 4 years, we collected 572 million data points from a total of 82 routers covering an area of 2.8 km2. In this paper we provide a systematic analysis of the data and discuss how our low-cost approach can be used to help communities and policymakers to make decisions to improve people’s mobility at high temporal and spatial resolution by inferring presence characteristics against several sources of ground truth. Also, we present an automatic classification technique that can identify location types based on collected data.