# Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data
## 5 Conclusions  
The study yielded a predictive model for estimating airline flight demand based on the analysis of historical data to identify the seasonality of demand. The proposed model provides a medium-term forecast using machine learning and multiple linear regression methods, accounting for the impact of changes in macroeconomic factors on the final distribution of passenger demand by flight destination. The developed model was validated using historical data on demand and flight departure times from 2019 to the first half of 2024 for a revolving airline route comprising two flight destinations with independently determined demand: from A to B and from B to A. The values of the metrics for assessing the accuracy of the model results for the demand forecast for the two turnaround flight directions were 24.839 and 27.955, 20.379 and 25.179, 35.705, and 31.435 for RMSE, MAE, and MAPE, respectively.  
The key advantages of the model include the consideration of macroeconomic factors, high flexibility for various forecasting horizons with appropriate preprocessing of data for the selected horizon, and demonstrated accuracy compared to baseline methods. In the future, the model can be compared with neural networks and other modern approaches to confirm its competitiveness.  
One of the key limitations of the developed model is its dependency on the quality and completeness of the input data. For instance, gaps or errors in historical data on demand and flight schedules can negatively impact forecast accuracy. Another limitation is the model's restricted ability to account for changes in the competitive environment and the potential shift in passenger demand for air travel toward alternative transportation modes. Currently, the model does not include detailed data on competitor actions, such as price reductions, route changes, or increased flight frequencies. Additionally, the model may be less effective in forecasting demand for new routes or in regions with insufficient historical information, as these cases lack adequate data for training.  
The potential for enhancement of the presented model lies in the advancement of forecast precision and the incorporation of a function elucidating the impact of the competitive environment (demand for flights by competitor airlines and alternative modes of transport) on the final distribution of passenger demand. The presented model can be used as a decision-making tool for production resource planning in an airline. The integration of the model with internal decision support systems enables the development of a comprehensive system for the estimation and optimization of passenger revenue under a variety of flight schedule planning scenarios.  
Conceptualization, K.N.P. and A.M.G.; methodology, Z.A.S.; software, Z.A.S.; validation, K.A.L., Z.A.S. and K.N.P.; formal analysis, Z.A.S.; investigation, K.A.L.; resources, K.N.P.; data curation, A.M.G.; writing--original draft preparation, K.A.L.; writing--review and editing, K.N.P.; visualization, Z.A.S.; supervision, A.M.G.; project administration, A.M.G.; funding acquisition, A.M.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Higher Education of the Russian Federation, grant number 075-03-2024-004.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.