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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Elvas, L.B., Marreiros, C.F., Dinis, J.M., Pereira, M.C., Martins, A.L., Ferreira, J.C.: Data-driven approach for incident management in a smart city. Appl. Sci. Switz. 10(22), 1–18 (2020). https://doi.org/10.3390/app10228281
Mesquitela, J., Elvas, L.B., Ferreira, J.C., Nunes, L.: Data analytics process over road accidents data—a case study of Lisbon City. ISPRS Int. J. Geo-Inf. 11(2), 143 (2022). https://doi.org/10.3390/ijgi11020143
Elvas, L.B., Mataloto, B.M., Martins, A.L., Ferreira, J.C.: Disaster management in smart cities. Smart Cities 4(2), Art. no. 2 (2021). https://doi.org/10.3390/smartcities4020042
Elvas, L.B., Gonçalves, S.P., Ferreira, J.C., Madureira, A.: Data fusion and visualization towards city disaster management: Lisbon case study. EAI Endorsed Trans. Smart Cities 6(18), e3–e3 (2022). https://doi.org/10.4108/eetsc.v6i18.1374
Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339, b2535 (2009). https://doi.org/10.1136/bmj.b2535
Fekih, M., Bellemans, T., Smoreda, Z., Bonnel, P., Furno, A., Galland, S.: A data-driven approach for origin–destination matrix construction from cellular network signalling data: a case study of Lyon region (France). Transportation 48(4), 1671–1702 (2021). https://doi.org/10.1007/s11116-020-10108-w
Badii, C., Difino, A., Nesi, P., Paoli, I., Paolucci, M.: Classification of users’ transportation modalities from mobiles in real operating conditions. Multimed. Tools Appl. 81(1), 115–140 (2022). https://doi.org/10.1007/s11042-021-10993-y
Carter, E., Adam, P., Tsakis, D., Shaw, S., Watson, R., Ryan, P.: Enhancing pedestrian mobility in smart cities using big data. J. Manag. Anal. 7(2), 173–188 (2020). https://doi.org/10.1080/23270012.2020.1741039
Jeong, J., et al.: Mobility prediction for 5G core networks. IEEE Commun. Stand. Mag. 5(1), 56–61 (2021). https://doi.org/10.1109/MCOMSTD.001.2000046
Ali Haidery, S., Ullah, H., Khan, N.U., Fatima, K., Rizvi, S.S., Kwon, S.J.: Role of big data in the development of smart city by analyzing the density of residents in shanghai. Electron. Switz. 9(5), 837 (2020). https://doi.org/10.3390/electronics9050837
Guo, P., Xiao, K., Ye, Z., Zhu, W.: Route optimization via environment-aware deep network and reinforcement learning. ACM Trans. Intell. Syst. Technol. 12(6), 1–21 (2021). https://doi.org/10.1145/3461645
Li, C., Hu, J., Dai, Z., Fan Z., Wu, Z.: Understanding individual mobility pattern and portrait depiction based on mobile phone data. ISPRS Int. J. Geo-Inf. 9(11), 666 (2020). https://doi.org/10.3390/ijgi9110666
Keawsomnuk, P.: A structural equation model of factors relating to smart cities that affect the management of the world heritage site as well as the quality of life of tourists and villagers in Ayutthaya, Thailand. Humanit. Arts Soc. Sci. Stud. 21(1), 35–42 (2021). https://doi.org/10.14456/hasss.2021.4
Leow, N.X., Krishnaswamy, J.: Smart cities need environmental consciousness and more social responsibilities as an outcome of COVID-19 – reflections from urban road commuters. Foresight 24(2), 276–296 (2022). https://doi.org/10.1108/FS-02-2021-0035
Martins, T.G., Lago, N., Santana, E.F., Telea, A., Kon, F., de Souza, H.A.: Using bundling to visualize multivariate urban mobility structure patterns in the São Paulo Metropolitan Area. J. Internet Serv. Appl. 12(1), 1–32 (2021). https://doi.org/10.1186/s13174-021-00136-9
Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining (2000)
Funding
This work was supported by EEA Grants Blue Growth Programme (Call #5). Project PT-INNOVATION-0069–Fish2Fork. This research also received funding from ERAMUS+ project NEMM with grant 101083048.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-49379-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49378-2
Online ISBN: 978-3-031-49379-9
eBook Packages: Computer ScienceComputer Science (R0)