A Novel Approach for Forecasting Tourist Arrivals Using Web Search Data and Artificial Intelligence
The development of economic activity has been matched by growth in the tourism industry. According to information, the tourism industry is growing and both the number of domestic and international tourists visiting each year is expanding. Because of this quick expansion, there are now critical co...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2020/1/jods2024_40.pdf http://eprints.intimal.edu.my/2020/2/562 http://eprints.intimal.edu.my/2020/ http://ipublishing.intimal.edu.my/jods.html |
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| Summary: | The development of economic activity has been matched by growth in the tourism industry.
According to information, the tourism industry is growing and both the number of domestic and
international tourists visiting each year is expanding. Because of this quick expansion, there are
now critical complications with the management of tourism, such as predicting the arrivals for
travel, particularly when a lot of people are visiting appealing locations for particular periods. The
proposed Artificial Fish Swarm Optimized Dynamic Gated Recurrent Unit (AFSO-DGRU)
approach transforms the forecasting of demand for tourism by utilizing intelligence from swarms
to improve forecasts and strategically adapting to fluctuating visitor structures. It ensures accurate
and dynamic responses even during times of uncertainty when demand is high. The study used
Google Trends to collect data from searches on the web and examine trends in tourist’s interest
and demand for travel. By combining innovative artificial intelligence (AI) algorithms with realtime
online search data, this study presents a novel way to improve the accuracy of visitor arrival
predictions. The proposed method performs better than the existing methods to utilize the
parameters such as mean absolute deviation called MAE (42.01), mean square error denoted by
MSE (3059.85), mean absolute percentage error defined MAPE (1.34), and RMSPE or root mean
square percentage error (1.43). This research utilizes web search data and AI to improve the
accuracy of forecasting tourist arrivals, offering valuable insights for understanding tourism trends. |
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