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Profiling tourists' use of public transport through smart travel card data

Author

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  • Gutiérrez, Aaron
  • Domènech, Antoni
  • Zaragozí, Benito
  • Miravet, Daniel

Abstract

Data collected through smart travel cards in public transport networks have become a valuable source of information for transport geography studies. During the last two decades, a growing body of literature has used this sort of data source to study the behaviour of public transport users in cities and regions around the world. However, its use has been scarce in contexts where public transport demand is highly influenced by the activities of the tourist sector. Therefore, it remains to be seen whether these data can be leveraged to optimize the supply of public transport. In this article, data drawn from the Camp de Tarragona automated fare collection system extracted during 2018 are used to study tourists' use of public transport in Costa Daurada (Catalonia, Spain). This is a popular coastal destination with a high concentration of visitors during the summer period. The analysis focuses on the use of the T-10, a multipersonal transport fare with no time limitations on its use which makes it appealing for tourists. Model-based clustering has been applied to identify different clusters of passengers according to their activity and spatial profiles. Differences between profiles are significant and, as a result, this study allowed the validation of a method that could be replicated in other contexts, as it provides highly useful information for public transport policy and mobility management.

Suggested Citation

  • Gutiérrez, Aaron & Domènech, Antoni & Zaragozí, Benito & Miravet, Daniel, 2020. "Profiling tourists' use of public transport through smart travel card data," Journal of Transport Geography, Elsevier, vol. 88(C).
    • Handle: RePEc:eee:jotrge:v:88:y:2020:i:c:s0966692320302283
    DOI: 10.1016/j.jtrangeo.2020.102820
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    References listed on IDEAS

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    1. Albalate, Daniel & Bel, Germà, 2010. "Tourism and urban public transport: Holding demand pressure under supply constraints," Tourism Management, Elsevier, vol. 31(3), pages 425-433.
    2. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    3. Aaron Gutiérrez & Daniel Miravet, 2016. "The Determinants of Tourist Use of Public Transport at the Destination," Sustainability, MDPI, vol. 8(9), pages 1-16, September.
    4. Ed Manley & Chen Zhong & Michael Batty, 2018. "Spatiotemporal variation in travel regularity through transit user profiling," Transportation, Springer, vol. 45(3), pages 703-732, May.
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    Cited by:

    1. Sun, Li & Zhao, Juanjuan & Zhang, Jun & Zhang, Fan & Ye, Kejiang & Xu, Chengzhong, 2024. "Activity-based individual travel regularity exploring with entropy-space K-means clustering using smart card data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    2. Kar, Manaswinee & Sadhukhan, Shubhajit & Parida, Manoranjan, 2022. "Assessing commuters’ perceptions towards improvement of intermediate public transport as access modes to metro stations," Transport Policy, Elsevier, vol. 129(C), pages 140-155.
    3. Benito Zaragozí & Sergio Trilles & Aaron Gutiérrez & Daniel Miravet, 2021. "Development of a Common Framework for Analysing Public Transport Smart Card Data," Energies, MDPI, vol. 14(19), pages 1-22, September.
    4. Türk, Umut & Östh, John & Kourtit, Karima & Nijkamp, Peter, 2021. "The path of least resistance explaining tourist mobility patterns in destination areas using Airbnb data," Journal of Transport Geography, Elsevier, vol. 94(C).

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