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