{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"High-frequency forecasting from mobile devices’ bigdata: an application to tourism destinations’ crowdedness","author":[{"@type":"Person","name":"Vicente Ramos"},{"@type":"Person","name":"Woraphon Yamaka"},{"@type":"Person","name":"B. Alorda"},{"@type":"Person","name":"Songsak Sriboonchitta"}],"datePublished":"2021","isPartOf":{"@type":"Periodical","name":"International Journal of Contemporary Hospitality Management"},"identifier":"https://doi.org/https://doi.org/10.1108/ijchm-10-2020-1170","description":"PurposeThis paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.Design/methodology/approachThis study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.FindingsThe results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).Practical implicationsA discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.Originality/valueHigh-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.Plain Language SummaryThis research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.","url":"https://innscience.ca/en/community/literature/35d8df55-25db-43be-a7ca-90ca3fa2fabf"}