0000-0001-8320-5295Knowledge Discovery from Urban Mobility Data in Guimarães
Urban mobility has been a challenge for policymakers in several cities, including Guimarães. This scenario is the result of population growth and the increasing number of vehicles per person, making it imperative to adapt urban planning to this expansion. This study aimed to examine urban mobility in Guimarães, considering factors such as the time of day, temperature and whether it is a working day. The results indicated that the flow of pedestrians is lower than that of cars, with considerable differences. The number of pedestrians and cars was also quite different under different conditions, such as seasonality, time of day, type of day and type of avenue. The final contribution of this study, based on the analysis of mobility on each avenue and its profiles, demonstrated not only the usefulness of the results for optimising the resources of municipal authorities, but also made it possible to identify the territorial conditions and characteristics that promote more sustainable mobility.
Impacts of Augmented Reality in E-commerce: Advances and Trends in 2024—A Literature Review
This literature review explores recent advancements in the application of augmented reality (AR) within e-commerce, with a specific focus on consumer behavior and technological integration in 2024. AR has emerged as a transformative technology, enabling immersive shopping experiences that allow consumers to virtually interact with products, enhancing their decision-making process. The review analyzed 11 studies published in 2024, highlighting key trends such as the personalization of recommendations through the integration of artificial intelligence (AI), the impact of sensory perceptions on impulsive buying, and the barriers to AR adoption in developing markets. The methodology followed a systematic approach, with articles sourced exclusively from the SCOPUS database and filtered by relevance based on keywords and open-access criteria. The results indicate that AR positively influences purchase intentions boosting consumer confidence through enhanced visual and interactive experiences. However, challenges remain in terms of usability, accessibility, and the need for simplified interfaces to broaden adoption. Furthermore, the studies point to the potential of combining AR with AI to analyze consumer emotions and dynamically tailor experiences based on real-time emotional responses. This review concludes that although AR has the potential to revolutionize e-commerce, further research is needed to address the long-term impact of AR on consumer behavior, explore its adoption in diverse geographical contexts, and develop more intuitive and accessible AR solutions. Investigating the role of AI in emotion analysis and the personalization of AR experiences also presents a promising area for future research.
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Behaviour of Machine Learning algorithms in the classification of energy consumption in school buildings
Abstract The significance of energy efficiency in the development of smart cities cannot be overstated. It is essential to have a clear understanding of the current energy consumption (EC) patterns in both public and private buildings. One way to achieve this is by employing machine learning classification algorithms, which offer a broader perspective on the factors influencing EC. These algorithms can be applied to real data from databases, making them valuable tools for smart city applications. In this paper, our focus is specifically on the EC of public schools in a Portuguese city, as this plays a crucial role in designing a Smart City. By utilizing a comprehensive dataset on school EC, we thoroughly evaluate multiple ML algorithms. The objective is to identify the most effective algorithm for classifying average EC patterns. The outcomes of this study hold significant value for school administrators and facility managers. By leveraging the predictions generated from the selected algorithm, they can optimize energy usage and, consequently, reduce costs. The use of a comprehensive dataset ensures the reliability and accuracy of our evaluations of various ML algorithms for EC classification.
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