Understanding travel behaviour patterns and their dynamics: Applying fuzzy clustering and age-period-cohort analysis on longterm data of German travellers

Authors

  • Elisabeth Bartl Department of Geography, LMU Munich, Germany. Email: elisabeth.bartl@lmu.de https://orcid.org/0000-0002-5112-3719
  • Maximilian Weigert Department of Statistics, Statistical Consulting Unit StaBLab, LMU Munich, Germany
  • Alexander Bauer Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Germany https://orcid.org/0000-0003-3495-5131
  • Jürgen Schmude Department of Geography, LMU Munich, Germany https://orcid.org/0000-0002-1449-1889
  • Marion Karl School of Hospitality and Tourism Management, University of Surrey, United Kingdom
  • Helmut Küchenhoff Department of Statistics, Statistical Consulting Unit StaBLab, LMU Munich, Germany

DOI:

https://doi.org/10.54055/ejtr.v39i.3862

Keywords:

Travel behaviour, tourist segmentation, longitudinal study, fuzzy clustering, cluster analysis, APC analysis

Abstract

This study examines how travel behaviour patterns change over time. It addresses the limitations of traditional segmentation studies, which often focus on static snapshots of travel behaviour. A comprehensive approach is proposed, integrating multi-dimensional segmentation and temporal analysis. Based on a large, repeated, cross- sectional dataset (1983–2018) from Germany, the study employs a research design that combines fuzzy clustering, to identify distinct tourist types including their heterogeneous behaviour, and additive logistic regression analysis, to analyse temporal changes in travel behaviour patterns. The findings reveal five tourist types based on their travel behaviour. These tourist types differ in sociodemographic characteristics and are related to each other. The chance of belonging to those tourist types changes over a tourists' life cycle (age), over time due to external factors (period) and across generations (cohort), providing insights into evolving travel behaviour. The findings from this study can help tourism stakeholders to adapt their strategies to changing tourist behaviour and improve destination management and marketing efforts.

Author Biographies

Elisabeth Bartl, Department of Geography, LMU Munich, Germany. Email: elisabeth.bartl@lmu.de

Department of Geography

Maximilian Weigert, Department of Statistics, Statistical Consulting Unit StaBLab, LMU Munich, Germany

Department of Statistics, Statistical Consulting Unit StaBLab

Alexander Bauer, Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Germany

Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center

Jürgen Schmude, Department of Geography, LMU Munich, Germany

Department of Geography

Marion Karl, School of Hospitality and Tourism Management, University of Surrey, United Kingdom

School of Hospitality and Tourism Management

Helmut Küchenhoff, Department of Statistics, Statistical Consulting Unit StaBLab, LMU Munich, Germany

Department of Statistics, Statistical Consulting Unit StaBLab

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Published

2025-01-15

How to Cite

Bartl, E., Weigert, M., Bauer, A., Schmude, J., Karl, M., & Küchenhoff, H. (2025). Understanding travel behaviour patterns and their dynamics: Applying fuzzy clustering and age-period-cohort analysis on longterm data of German travellers. European Journal of Tourism Research, 39, 3914. https://doi.org/10.54055/ejtr.v39i.3862