Digitales Besuchermanagement im Tourismus – Konzeptioneller Rahmen und Gestaltungsmöglichkeiten

Schmücker, Dirk; Keller, Robert M.; Reif, Julian; Schubert, Johannes; Sommer, Guido

doi:10.1007/978-3-658-37545-4_13

Cited by 1 publication

(2 citation statements)

References 35 publications

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“…Instead, preventive measures with close monitoring and active visitor management are required specifically at open-spaced natural POIs. Schmücker et al (2022) distinguish active visitor management from general visitor guidance by emphasizing that it should rely on soft measures. Combined with digital technologies, these soft measures such as recommendation systems provide often untapped potential to effectively prevent overcrowding before it even occurs (Spenceley et al 2015;Veiga et al 2018).…”

Section: Overtourism and Overcrowdingmentioning

confidence: 99%

“…The United Nations already demanded in 2016 the transformation towards more sustainable tourism as part of the 17 sustainable development goals to address these issues (DESA 2016). Consequently, POIs must actively embrace transformation towards more sustainable and smart tourism (Tauber and Bausch 2022), focusing on implementing active visitor management strategies (Schmücker et al 2022). Effective active visitor management entails the spatial distribution of visitors among nearby POIs and strategically times arrivals to prevent overcrowding at any single location (Hall and McArthur 1996;Mason 2005).…”

Section: Introductionmentioning

confidence: 99%

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Enabling active visitor management: local, short-term occupancy prediction at a touristic point of interest

Bollenbach,

Neubig,

Hein

et al. 2024

Inf Technol Tourism

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After the temporary shock of the Covid-19 pandemic, the rapid recovery and resumed growth of the tourism sectors accelerates unsustainable tourism, resulting in local (over-)crowding, environmental damage, increased emissions, and diminished tourism acceptance. Addressing these challenges requires an active visitor management system at points of interest (POI), which requires local and timely POI-specific occupancy predictions to predict and mitigate crowding. Therefore, we present a new approach to measure visitor movement at an open-spaced, and freely accessible POI and evaluate the prediction performance of multiple occupancy and visitor count machine learning prediction models. We analyze multiple case combinations regarding spatial granularity, time granularity, and prediction time horizons. With an analysis of the SHAP values we determine the influence of the most important features on the prediction and extract transferable knowledge for similar regions lacking visitor movement data. The results underline that POI-specific prediction is achievable with a moderate relation for occupancy prediction and a strong relation for visitor count prediction. Across all cases, XGBoost and Random Forest outperform other models, with prediction accuracy increasing as the prediction time horizon shortens. For effective active visitor management, combining multiple models with different spatial aggregations and prediction time horizons provides the best information basis to identify appropriate steering measures. This innovative application of digital technologies facilitates information exchange between destination management organizations and tourists, promoting sustainable destination development and enhancing tourism experience.

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Section: Overtourism and Overcrowdingmentioning

confidence: 99%