High-frequency forecasting from mobile devices' bigdata: An application to tourism destinations' crowdedness
International Journal of Contemporary Hospitality Management
; 33(6):1977-2000, 2021.
Article
in English
| APA PsycInfo | ID: covidwho-2277691
ABSTRACT
Purpose:
This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two researchobjectives:
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/approach:
This 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.Findings:
The 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 (Naive I model). Practical implications A 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/value High-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 LanguageSummary:
This 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. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
Full text:
Available
Collection:
Databases of international organizations
Database:
APA PsycInfo
Language:
English
Journal:
International Journal of Contemporary Hospitality Management
Year:
2021
Document Type:
Article
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