Investigating the Spatial-Temporal Variation of Pre-Trip Searching in an Urban Agglomeration

Search engines have been the primary tool for online information search before traveling. Timely detection and the control of peak tourist flows in scenic areas prevent safety hazards and the overconsumption of tourism resources due to excessive tourist clustering. This study focuses on the spatial-...

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Bibliographic Details
Published in:Sustainability 2023-07, Vol.15 (14), p.11423
Main Authors: Zhang, Jianxin, Yan, Yuting, Zhang, Jinyue, Liu, Peixue, Ma, Li
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Big Data
COVID-19
Cultural heritage
Econometrics
Forecasting
Internet/Web search services
Investigations
Literature reviews
Marketing
Medical research
Neural networks
Sales promotions
Search engines
Sustainability
Sustainable development
Time series
Tourism
Travel
Travel industry
Webometrics
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Summary:Search engines have been the primary tool for online information search before traveling. Timely detection and the control of peak tourist flows in scenic areas prevent safety hazards and the overconsumption of tourism resources due to excessive tourist clustering. This study focuses on the spatial-temporal interactions between the pre-trip stage and the after-arrival stage to investigate online information search behavior. Big data obtained from mobile roaming and search engines provide precise data on daytime and city scales, which enabled this paper to examine the relationship between daily tourist arrivals and their pre-trip searching from 40 cities within the Yangtze River Delta urban agglomeration. This study had several original results. First, tourists generally search for tourist information 2–8 days before arriving at destinations, while tourist volume and SVI from source cities show distance attenuation. Second, SVI is a precursor to changes in tourist volume. The precursory time rises with the increase of traffic time spatially. Third, we validated a VAR model and improved its accuracy by constructing it based on the spatial-temporal differentiation of search features. These findings would enhance the management and preservation of tourism resources and promote the sustainable development of tourism destinations.
ISSN:2071-1050
2071-1050
DOI:10.3390/su151411423