| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.13 MB | Adobe PDF |
Orientador(es)
Resumo(s)
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.
Descrição
Copyright © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Palavras-chave
Sustainable smart cities Decision-support systems Urban population forecasting Aggregated mobile network data Deep learning Urban analytics Smart city governance Privacy-preserving data Machine learning Big data Mobility patterns IoT
Contexto Educativo
Citação
Elvas, L.B., Nunes, M., Francisco, B., Domingues, N. (2024). City Mobility and Night Life Monitor. In: Martins, A.L., Ferreira, J.C., Kocian, A., Tokkozhina, U., Helgheim, B.I., Bråthen, S. (eds) Intelligent Transport Systems. INTSYS 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 540. Springer, Cham. https://doi.org/10.1007/978-3-031-49379-9_7
Editora
Springer, Cham
Licença CC
Sem licença CC