Data Fusion and Visualization towards City Disaster Management: Lisbon Case Study

Elvas, Luís B.; Gonçalves, Sandra P.; Ferreira, Joao C Ferreira or Joao; Madureira, Ana

doi:10.4108/eetsc.v6i18.1374

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The results were positive, but given that the dataset provided by the city council does not allow the tracking of each individual terminal for privacy reasons, it was not possible to replicate such a method in this paper. Other similar works, such as Li et al [6], also used data collected from mobile phones where each user was individually identified.…”

Section: Statistical Modelsmentioning

confidence: 99%

“…The subsequent sections of this paper are structured as follows: Section 2 reviews the related literature concerning the prediction of people's concentration and movements within a smart city. In Section 3, we detail the methodology employed in this work, focusing on the identification of mobility patterns through the CRISP-DM framework [4], used by others in similar problems [5,6]. Section 4 is dedicated to the experimental results and the evaluation of the prediction process.…”

Section: Introductionmentioning

confidence: 99%

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Predicting People’s Concentration and Movements in a Smart City

Ferreira,

Francisco,

Elvas

et al. 2023

Electronics

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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.

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Section: Statistical Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%