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

IF 6.3 3区 管理学 Q1 HOSPITALITY, LEISURE, SPORT & TOURISM
Jessica Bollenbach, Stefan Neubig, Andreas Hein, Robert Keller, Helmut Krcmar
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Abstract

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.

Abstract Image

实现积极的游客管理:旅游景点的本地短期入住率预测
在经历了 Covid-19 大流行病的暂时冲击之后,旅游业的快速恢复和恢复增长加速了不可持续的旅游业,导致当地(过度)拥挤、环境破坏、排放增加以及旅游接受度降低。要应对这些挑战,就需要在景点(POI)建立积极的游客管理系统,这就需要对景点的具体占用率进行及时的本地预测,以预测和缓解拥挤状况。因此,我们提出了一种新方法,用于测量开放空间、可自由进入的兴趣点的游客流动情况,并评估多个占用率和游客人数机器学习预测模型的预测性能。我们分析了空间粒度、时间粒度和预测时间范围的多种情况组合。通过对 SHAP 值的分析,我们确定了最重要的特征对预测的影响,并为缺乏游客流动数据的类似地区提取了可转移的知识。结果表明,针对特定 POI 的预测是可以实现的,与占用率预测的关系适中,与游客数量预测的关系较强。在所有情况下,XGBoost 和随机森林都优于其他模型,预测准确率随着预测时间范围的缩短而提高。为了有效地对游客进行主动管理,将具有不同空间聚合和预测时间范围的多个模型结合起来,为确定适当的引导措施提供了最佳的信息基础。这一数字技术的创新应用促进了目的地管理机构与游客之间的信息交流,推动了目的地的可持续发展,提升了旅游体验。
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来源期刊
Information Technology & Tourism
Information Technology & Tourism HOSPITALITY, LEISURE, SPORT & TOURISM-
CiteScore
18.10
自引率
5.40%
发文量
22
期刊介绍: Information Technology & Tourism stands as the pioneer interdisciplinary journal dedicated to exploring the essence and impact of digital technology in tourism, travel, and hospitality. It delves into challenges emerging at the crossroads of IT and the domains of tourism, travel, and hospitality, embracing perspectives from both technical and social sciences. The journal covers a broad spectrum of topics, including but not limited to the development, adoption, use, management, and governance of digital technology. It supports both theory-focused research and studies with direct relevance to the industry.
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