A recent scientific study published in Scientific Reports, an international peer-reviewed journal, uses artificial intelligence and machine learning models within the framework of Saudi Vision 2030 to reveal advanced results predicting tourism growth in the Kingdom of Saudi Arabia until 2034.
The study, produced by a research team from Saudi and international universities, was based on official data on tourist movements in Saudi cities for the period 2021-2023 and focused on analyzing tourism demand in light of the major transformations seen in the tourism sector, one of the pillars of Saudi economic diversification.
The study presents long-term projections showing that the Kingdom’s tourism growth will continue to grow at an accelerated pace over the next decade, supported by domestic tourism growth, as well as major tourism projects, entertainment seasons, and international events.
flow of tourists
This study showed that the expansion of recreational, sports, and cultural heritage tourism has made accurate forecasting of tourist flows an essential planning tool that contributes to improved infrastructure decisions, service development, and increased efficiency of tourism policies.
The results showed that the aggregate machine learning model succeeded in capturing seasonal variations and the complex relationships between tourism expenditure, time, city, and key events, achieving the highest level of predictive accuracy compared to traditional statistical methods.
Request and use
The analysis showed that tourism expenditure factors were the most influential in determining demand, followed by time factors, and then geographic location, reflecting the central role of economic factors in shaping tourist movements.
The researchers emphasized that the results of this study will be a practical tool for decision makers by supporting hotel planning, improving the allocation of tourism services, determining peak periods, more accurately directing promotional campaigns, etc., and will contribute to maximizing the economic benefits of tourism.
Extend your data
This study highlighted the importance of expanding tourism databases in the future to incorporate additional variables such as climate factors, migration patterns, and changes in economic conditions to increase the reliability of long-term forecasts.
The study concluded that artificial intelligence has become a crucial element in tourism decision-making, and that investments in predictive analytics and advanced data are fundamental pillars to achieving the goals of Saudi Vision 2030 and building a globally competitive and sustainable tourism sector.
A recent scientific study published in Scientific Reports, an international peer-reviewed journal, has revealed advanced results using artificial intelligence and machine learning models to predict the growth of tourism in the Kingdom of Saudi Arabia until 2034, within the framework of Saudi Vision 2030.
The study, produced by a research team from Saudi Arabia and international universities, was based on official data on tourist movements in Saudi cities for the period 2021-2023 and focused on analyzing tourism demand in light of the major changes taking place in the tourism sector as one of the pillars of Saudi economic diversification.
The study provides long-term projections for the Kingdom’s tourism growth to continue at an accelerated pace over the next decade, supported by domestic tourism growth, as well as large-scale tourism projects, entertainment seasons and international events.
flow of tourists
This study reveals that with the expansion of recreational, sports, and cultural heritage tourism, accurate forecasting of tourist flows has become an important planning tool, contributing to improved infrastructure decision-making, service development, and tourism policy efficiency.
The results showed that the ensemble machine learning model achieved the highest level of predictive accuracy compared to traditional statistical methods and was able to better capture seasonal variations and the complex relationships between tourism spending, time, cities, and key events.
demand and spending
The analysis shows that tourism expenditure factors are the most influential in determining demand scale, followed by time and geographic location, which reflects the central role of economic factors in shaping tourism movements.
The researchers confirmed that the results of this study will be a practical tool for decision-makers by supporting hotel planning, improving the allocation of tourism services, identifying peak periods, and more accurately directing promotional campaigns, contributing to maximizing the economic benefits of tourism.
data expansion
This study highlighted the importance of expanding tourism databases in the future and integrating additional variables such as climatic factors, migration patterns, and changes in economic conditions to increase the reliability of long-term forecasts.
The study concluded that artificial intelligence has become a crucial element in tourism decision-making, and that investments in predictive analytics and advanced data are fundamental pillars to achieving the goals of Saudi Vision 2030 and building a globally competitive and sustainable tourism sector.
