THE USE OF ARTIFICIAL INTELLIGENCE TO FORECAST PASSENGER AIR TRAVEL DEMAND
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Abstract
This study aimed to 1) investigate the factors affecting air travel demand among passengers, and 2) examine the capability of artificial intelligence (AI) in forecasting passenger air travel demand. This research employed a quantitative research approach using a questionnaire to collect data from a sample of 400 passengers traveling on both domestic and international flights. Data analysis included descriptive statistics, such as mean and standard deviation (Mean & SD), as well as Pearson correlation to examine the relationships between various factors and air travel demand.
The findings revealed that:
For Objective 1) The results for the first objective revealed that digital information and technology had a mean score of 4.42 (SD = 0.53), passenger behavior 4.35 (SD = 0.57), economic factors 4.21 (SD = 0.62), and seasonal and temporal factors 4.08 (SD = 0.65). All factors showed a statistically significant positive correlation with air travel demand (p < 0.05), indicating that passengers consider digital information, personal behavior, economic conditions, and timing as important in planning air travel.
For Objective 2) For the second objective, the findings indicated that AI can forecast air travel demand effectively. Accuracy scored 4.31 (SD = 0.55), processing speed 4.46 (SD = 0.50), support for flight planning 4.38 (SD = 0.52), and managerial decision support 4.29 (SD = 0.58). These results demonstrate that the integration of AI with digital information and passenger behavior enhances forecasting accuracy and speed, supports flight planning, and facilitates strategic decision-making in airline operations.
In conclusion, applying AI in conjunction with digital data and passenger behavioral insights can significantly improve the effectiveness of air travel demand forecasting and support operational and strategic management in the airline industry.
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References
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