Touristic travel behaviour in a spatio-temporal context: Statistical analyses for the identification and development of tourists’ behavioural patterns
Final Report Abstract
The research project "Travel behavior in a spatio-temporal context: Statistical analysis for the identification and development of behavioral patterns", conducted by an interdisciplinary team from the departments of geography and statistics at LMU Munich, analyzed the extent to which travel behavior of German tourists has changed in a temporal and spatial context in the period from 1971 to 2018. For this purpose, annually available survey data on travel behavior from almost 50 years were evaluated. Overall, considered data comprised more than 50 relevant travel characteristics for approximately 230,000 surveyed travelers. The large database made it possible to distinguish between three temporal dimensions: Changes over the life cycle (age effect), overarching changes over time triggered by external factors (period effect) and changes between generations (cohort effect). Analyzing temporal changes in tourism demand due to internal and external factors revealed that making shorthaul trips is primarily associated with the travelers’ age, while the increasing implementation of long-haul trips is mainly due to the period effect. Developments regarding participation, frequency and expenses could be explained by the life cycle theory, generation theory and the concept of "travel importance". Age and period were identified as main factors for participation, cohort and age for frequency. In contrast, travel expenses showed no major differences regarding the temporal dimensions. The second main topic of the project involved the identification of tourist types and their development over time. A cluster analysis resulted in five characteristic tourist types. The most important features for the classification were the destination of the main trip and common traveling with family members. Clear differences between individual tourist types were also found regarding sociodemographic characteristics. Overall, the results illustrate that different age groups and generations show different patterns in travel behavior. These findings could be used by political decision-makers and the tourism industry to specifically adapt tourism offers and structures to travelers’ needs. Apart from insights for tourism science, several statistical-methodological developments were achieved. To adequately separate temporal changes into age, period and cohort effects, an age-period-cohort approach based on non-linear regression was adapted and supplemented by novel, descriptive visualizations. Combining the CLARA clustering algorithm with a fuzzy clustering approach made it possible to include hybrid characteristics of tourists in a computationally efficient way. All methodological extensions were published through freely accessible software packages to make them available to the public and potential users from other research projects within and outside tourism science.
Publications
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Consumer Behavior in Tourism Symposium CBTS 2020 - (2020). Spatiotemporal changes in travel behavior: Analyzing external and internal temporal effects on destination choices.
Schmude, J., Weigert, M., Bauer, A., Karl, A., Gernert, M., Küchenhoff, H. & Bartl, E.
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DGT-Jahrestagung 2021 (1. Preis) - (2020). TourIST - Tourism In Space and Time, Conference Poster.
Bartl, E., Weigert, M., Bauer, A., Karl, M., Küchenhoff, H. & Schmude, J.
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International Workshop on Statistical Modelling IWSM 2021 - (2020). Visualization techniques for semiparametric APC analysis: Using Generalized Additive Models to examine touristic travel distances, Conference Poster.
Weigert, M., Bauer, A., Gernert, A., Karl, M., Küchenhoff, H. & Schmude, J.
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Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances. Tourism Economics, 28(5), 1377-1400.
Weigert, Maximilian; Bauer, Alexander; Gernert, Johanna; Karl, Marion; Nalmpatian, Asmik; Küchenhoff, Helmut & Schmude, Jürgen
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APCtools: Descriptive and Model-based Age-Period-Cohort Analysis. Journal of Open Source Software, 7(73), 4056.
Bauer, Alexander; Weigert, Maximilian & Jalal, Hawre
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Tagung der Deutschen Arbeitsgemeinschaft Statistik DAGStat 2022 - (2022): APCtools: An R package for Descriptive and Model-based Age-Period-Cohort Analysis, Conference Poster.
Bauer, A., Weigert, M., Bauer, M., Küchenhoff & Jalal, H.
