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Mining Tourists’ Movement Patterns in a City

  • Conference paper
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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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References

  1. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of Things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015). https://doi.org/10.1109/COMST.2015.2444095

    Article  Google Scholar 

  2. Jino Ramson, S.R., Moni, D.J.: Applications of wireless sensor networks — A survey. in 2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT), pp. 325–329 (2017). https://doi.org/10.1109/ICIEEIMT.2017.8116858

  3. Gerla, M., Kleinrock, L.: Vehicular networks and the future of the mobile internet. Comput. Netw. 55(2), 457–469 (2011). https://doi.org/10.1016/j.comnet.2010.10.015

    Article  Google Scholar 

  4. Khan, W.Z., Xiang, Y., Aalsalem, M.Y., Arshad, Q.: Mobile phone sensing systems: a survey. IEEE Commun. Surv. Tutor. 15(1), 402–427 (2013). https://doi.org/10.1109/SURV.2012.031412.00077

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. Elvas, L.B., Mataloto, B.M., Martins, A.L., Ferreira, J.C.: Disaster management in smart cities. Smart Cities 4(2), 819–839 (2021). https://doi.org/10.3390/smartcities4020042

    Article  Google Scholar 

  7. 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

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. LCG, Toursim numbers in Portugal (2022). https://travelbi.turismodeportugal.pt/turismo-em-portugal/turismo-em-numeros-marco-2022/. Accessed May 13 2022

  10. 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

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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. Electronics 9(5), 837 (2020). https://doi.org/10.3390/electronics9050837

    Article  Google Scholar 

  15. 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

    Article  Google Scholar 

  16. 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

    Article  Google Scholar 

  17. Guo, P., Xiao, K., Ye, Z., Zhu, W.: Route optimization via environment-aware deep network and reinforcement learning. ACM Trans. Intell. Syst. Technol. (TIST) 12(6), 1–21 (2021). https://doi.org/10.1145/3461645

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. 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, 1–32 (2021). https://doi.org/10.1186/s13174-021-00136-9

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. Cheshmehzangi, A., et al.: A hierarchical study for urban statistical indicators on the prevalence of COVID-19 in Chinese city clusters based on multiple linear regression (MLR) and polynomial best subset regression (PBSR) analysis. Sci. Rep. 12(1), 1964 (2022). https://doi.org/10.1038/s41598-022-05859-8

    Article  Google Scholar 

  22. Šauer, M., Vystoupil, J., Novotná, M., Widawski, K.: Central European tourist flows: intraregional patterns and their implications. Morav. Geogr. Rep. 29(4), 278–291 (2021). https://doi.org/10.2478/mgr-2021-0020

    Article  Google Scholar 

  23. Lao, X., Deng, X., Gu, H., Yang, J., Yu, H., Xu, Z.: Comparing intercity mobility patterns among different holidays in china: a big data analysis. Appl. Spat. Anal. Policy (2022). https://doi.org/10.1007/s12061-021-09433-z

    Article  Google Scholar 

  24. Li, X., et al.: Position prediction system based on spatio-temporal regularity of object mobility. Inf. Syst. 75, 43–55 (2018). https://doi.org/10.1016/j.is.2018.02.004

    Article  Google Scholar 

  25. Türk, U., Östh, J., Kourtit, K., Nijkamp, P.: The path of least resistance explaining tourist mobility patterns in destination areas using Airbnb data. J. Transp. Geogr. 94, 103130 (2021). https://doi.org/10.1016/j.jtrangeo.2021.103130

    Article  Google Scholar 

  26. Mizzi, C., et al.: Unraveling pedestrian mobility on a road network using ICTs data during great tourist events. EPJ Data Sci. 7(1), 44 (2018). https://doi.org/10.1140/epjds/s13688-018-0168-2

    Article  Google Scholar 

  27. Wirth, R., Hipp, J.: CRISP-DM: Towards a standard process model for data mining, p. 11

    Google Scholar 

  28. “Lisbon Administrative Reform - First Quarterly Monitoring Report.” https://www.am-lisboa.pt/451600/1/008910,000505/index.htm. Accessed Oct 27 2022

  29. INE, “Tourism Statistics - 2020.” https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_publicacoes&PUBLICACOESpub_boui=280866098&PUBLICACOESmodo=2. Accessed Oct 21 2022

  30. Healy, K.: Data Visualization: A Practical Introduction. Princeton University Press, Princeton (2018)

    Google Scholar 

  31. “Tripadvisor,” Tripadvisor. https://www.tripadvisor.pt/. Accessed Aug 09 2022

  32. “Web Summit: Lisbon hosting the largest tech event in the world - Portugal - Portuguese American Journal.” https://portuguese-american-journal.com/web-summit-lisbon-hosting-the-largest-tech-event-in-the-world-portugal/.Accessed May 13 2022

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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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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

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