The Road Not Taken : Representing Expert Knowledge for Route Similarities in Sustainable Tourism using Machine Learning

Title data

Bollenbach, Jessica ; Rebholz, Dominik ; Keller, Robert:
The Road Not Taken : Representing Expert Knowledge for Route Similarities in Sustainable Tourism using Machine Learning.
In: Electronic Markets. Vol. 35 (2025) . - 72.
ISSN 1422-8890
DOI: https://doi.org/10.1007/s12525-025-00816-5

Official URL:

Abstract in another language

As recreational tourism in rural areas rises in popularity, overtourism, and crowding pose growing challenges, impacting both society and the environment. To support sustainable smart tourism, an information system for visitor management offers a valuable approach. A significant challenge in this context is the identification of suitable alternatives to congested areas. This paper proposes a method to calculate route similarities with distance-based algorithms and machine learning models using descriptive data to redirect visitors to less-crowded paths. A case study in a nature park validates the approach, using realworld hiking data from an online outdoor platform. Expert surveys on route similarity are used to train the models and evaluate the results. Machine learning significantly outperforms traditional similarity algorithms, achieving up to 117% higher R2 values (0.448 vs. 0.206), 26% lower MSE values (0.530 vs. 0.719), and 40% higher Spearman correlations (0.699 vs. 0.498). The random forest regression model yields the best results. This research provides a foundation for future efforts to enhance sustainable tourism by offering a data-driven approach to identifying alternative routes that align with visitor preferences.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	Visitor management; Route similarity; Machine learning
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration Research Institutions Research Institutions > Affiliated Institutes Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT Research Institutions > Affiliated Institutes > FIM Research Center for Information Management
Result of work at the UBT:	No
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 330 Economics
Date Deposited:	31 Oct 2025 08:37
Last Modified:	31 Oct 2025 08:37
URI:	https://eref.uni-bayreuth.de/id/eprint/95044