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Mining Tourists’ Movement Patterns in a City

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Intelligent Transport Systems (INTSYS 2023)

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Abstract

Although tourists generate a large amount of data (known as “big data”) when they visit cities, little is known about their spatial behavior. One of the most significant issues that has recently gained attention is mobile phone usage and user behavior tracking. A spatial and temporal data visualization approach was established with the purpose of finding tourists’ footprints. This work provides a platform for combining multiple data sources into one and transforming information into knowledge. Using Python, we created a method to build visualization dashboards aiming to provide insights about tourists’ movements and concentrations in a city using information from mobile operators. This approach can be replicated to other smart cities with data available. Weather and major events, for instance, have an impact on the movements of tourists. The outputs from this work provide useful information for tourism professionals to understand tourists’ preferences and improve the visitors’ experience. Management authorities may also use these outputs to increase security based on tourists’ concentration and movements. A case study in Lisbon with 4 months data is presented, but the proposed approach can also be used in other cities based on data availability. Results from this case study demonstrate how tourists tend to gather around a set of parishes during a specific time of the day during the months under study, as well as how unusual circumstances, namely international events, impact their overall spatial behavior.

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Acknowledgment

We thank the Lisbon City Council for providing us with the data necessary for this study, namely Mr. António Costa (Lisbon City Council), Mrs. Helena Martins (Lisbon City Council) and Mrs. Paula Melicias (Lisbon City Council). We also thank IPMA for providing us with the data necessary to complement our study.

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

  1. Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisboa, Portugal

    Luís B. Elvas, Miguel Nunes & Bruno Francisco

  2. Department of Logistics, Molde University College, 6410, Molde, Norway

    Luís B. Elvas & Berit Irene Helgheim

  3. CMEMS–UMinho, University of Minho, 4800-058, Guimarães, Portugal

    José Augusto Afonso

  4. LABBELS –Associate Laboratory, Braga/Guimarães, Portugal

    José Augusto Afonso

Authors
  1. Luís B. Elvas
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  2. Miguel Nunes
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  3. José Augusto Afonso
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  4. Berit Irene Helgheim
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  5. Bruno Francisco
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Contributions

C.C. conducted all data mining processes and the system development; L.B.E. wrote the state of the art; C.C. performed all the management interface and contributed to the writing of the article; A.L.M contributed with the writing and review; J.C.F. coordinated the research and contributed to the article’s writing; J.A.A contributed with the writing on review and editing. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Luís B. Elvas .

Editor information

Editors and Affiliations

  1. ISCTE-University Institute of Lisbon, Lisbon, Portugal

    Ana Lucia Martins

  2. ISCTE-University Institute of Lisbon, Lisbon, Portugal

    Joao C. Ferreira

  3. University of Pisa, Pisa, Italy

    Alexander Kocian

  4. University of Lisbon, Lisbon, Portugal

    Ulpan Tokkozhina

  5. Molde University College, Molde, Norway

    Berit Irene Helgheim

  6. Molde University College, Molde, Norway

    Svein Bråthen

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Elvas, L.B., Nunes, M., Afonso, J.A., Helgheim, B.I., Francisco, B. (2024). Mining Tourists’ Movement Patterns in a City. 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_6

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  • DOI: https://doi.org/10.1007/978-3-031-49379-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49378-2

  • Online ISBN: 978-3-031-49379-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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