@inproceedings{ddbd690c262f4788a60c3cb9f9de8362,
title = "Using Machine Learning to Predict POI Occupancy to Reduce Overcrowding",
abstract = "Due to the rapid growth of the tourism industry, associated effects like overcrowding, overtourism, and increasing greenhouse gas emissions lead to unsustainable development. A prerequisite for avoiding those adverse effects is the prediction of occupancy. The present study elaborates on the applicability and performance of various prediction models by taking a case study of beach occupancy data in Scharbeutz, Germany. The case study compares different machine learning models once as supervised machine learning models and once as time series models with a persistence model. XGBoost and Random Forest as time series demonstrate the most accurate prediction, followed by the supervised XGBoost model. However, the short prediction span of time series models is a disadvantage for longer-term visitor management to avoid the explained unsustainable effects through steering measures, so depending on the use case, the XGBoost model is to be favoured.",
keywords = "Beach Occupancy, Random Forest, SARIMA, Support Vector Regression, Time series Forecast, Tourism Demand, XGBoost",
author = "Jessica Bollenbach and Stefan Neubig and Andreas Hein and Robert Keller and Helmut Krcmar",
note = "Publisher Copyright: {\textcopyright} 2022 Gesellschaft fur Informatik (GI). All rights reserved.; 2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 Computer Science in the Natural Sciences, INFORMATIK 2022 ; Conference date: 26-09-2022 Through 30-09-2022",
year = "2022",
doi = "10.18420/inf2022_34",
language = "English",
series = "Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)",
publisher = "Gesellschaft fur Informatik (GI)",
pages = "393--408",
editor = "Daniel Demmler and Daniel Krupka and Hannes Federrath",
booktitle = "INFORMATIK 2022 - Informatik in den Naturwissenschaften",
}