Smart City Gnosys

Smart city article details

Title	Analyzing Urban Mobility Based On Smartphone Data: The Lisbon Case Study
ID_Doc	9563
Authors	Leal D.; Albuquerque V.; Dias M.S.; Ferreira J.C.
Year	2023
Published	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 486 LNICST
DOI	http://dx.doi.org/10.1007/978-3-031-30855-0_3
Abstract	Our paper addresses the mobility patterns in Lisbon in the vicinity of historical and transportation points of interest, with a case study conducted in the parish of Santa Maria Maior, a vibrant touristic neighborhood. We propose a data science-based approach to analyze such patterns. Our dataset includes five months of georeferenced mobile phone data, collected during late 2021 and early 2022, provided by the municipality of Lisbon. We performed a systematic literature review, using the PRISMA methodology and adopted the CRISP-DM methodology, to perform data curation, statistical and clustering analysis, and visualization, following the recommendations of the literature. For clustering we used the DBSCAN algorithm. We found eight clusters in Santa Maria Maior, with outstanding clusters along 28-E tram and Lisbon Cruise Terminal, where mobility is high, particularly for non-roaming travelers. This paper contributes to the digital transformation of Lisbon into a smart city, by improving improved understanding of urban mobility patterns. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
Author Keywords	CRISP-DM; DBSCAN; point of interest; PRISMA; smartphone data; urban mobility; visualisation

Similar Articles

Id	Similarity	Authors	Title	Published
49856	0.893	Albuquerque, V; Oliveira, A; Barbosa, JL; Rodrigues, RS; Andrade, F; Dias, MS; Ferreira, JC	Smart Cities: Data-Driven Solutions To Understand Disruptive Problems In Transportation-The Lisbon Case Study	ENERGIES, 14, 11 (2021)
20487	0.883	Butron-Revilla C.; Suarez-Lopez E.; Laura-Ochoa L.	Discovering Urban Mobility Patterns And Demand For Uses Of Urban Spaces From Mobile Phone Data	2021 2nd Sustainable Cities Latin America Conference, SCLA 2021 (2021)
6647	0.881	Rosa M.O.; Fonseca K.V.O.; Kozievitch N.P.; De-Bona A.A.; Curzel J.L.; Pando L.U.; Prestes O.M.; Lüders R.	Advances On Urban Mobility Using Innovative Data-Driven Models	Handbook of Smart Cities (2021)
46504	0.875	Ghosh N.; Sarkar U.; Nagesh P.	Review On Application Of Call Details Records (Cdrs) Data To Understand Urban Mobility Scenarios For Future Smart Cities	Springer Geography (2023)
42827	0.872	Rocha, NP; Dias, A; Santinha, G; Rodrigues, M; Queirós, A; Rodrigues, C	Prediction Of Mobility Patterns In Smart Cities: A Systematic Review Of The Literature	TRENDS AND INNOVATIONS IN INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 1, 1159 (2020)
59029	0.871	Ku W.K.; Kou K.P.; Lam S.H.; Wong K.I.	Trip-Pair Based Clustering Model For Urban Mobility Of Bus Passengers In Macao	Transportmetrica A: Transport Science, 19, 3 (2023)
37102	0.869	Elvas L.B.; Nunes M.; Afonso J.A.; Helgheim B.I.; Francisco B.	Mining Tourists’ Movement Patterns In A City	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 540 LNICST (2024)
36873	0.867	Salas P.; Ramos V.; Ruiz-Pérez M.; Alorda-Ladaria B.	Methodological Proposal For The Analysis Of Urban Mobility Using Wi-Fi Data And Artificial Intelligence Techniques: The Case Of Palma	Electronics (Switzerland), 12, 3 (2023)
61317	0.867	Sobral T.; Galvão T.; Borges J.	Visualization Of Urban Mobility Data From Intelligent Transportation Systems	Sensors (Switzerland), 19, 2 (2019)
16755	0.865	Zheng Y.A.; Lakhdari A.; Abusafia A.; Tony Lui S.T.; Bouguettaya A.	Crowdweb: A Visualization Tool For Mobility Patterns In Smart Cities	Proceedings - International Conference on Distributed Computing Systems, 2023-July (2023)