# Demand Forecasting Model for Airline Flights Based on Historical Passenger Flow Data
## 4 Discussion  
The evaluation results indicate that the RMSE and MAE metrics are within an acceptable range. However, to enhance the quality of the modeling results, it is necessary to achieve lower scores, which represents a potential avenue for further research. A MAPE exceeding 30% for both directions indicates a substantial degree of percentage deviation, which may not be deemed acceptable for tasks that necessitate a high degree of accuracy. RMSE measures the root mean square deviation between actual and predicted values, indicating the absolute average error in units of passenger demand. In this case, the RMSE for route A-B is 24.839, and for B-A it is 27.955. Values below 25-30 may be acceptable for models with large datasets, but in this case, the high RMSE suggests the potential for significant forecasting errors in individual time slots. MAE measures the mean absolute error, reflecting how much the model deviates from actual values on average. The MAE for A-B is 20.379 and for B-A is 25.179, indicating the typical deviation of the model from the actual data. Unlike RMSE, MAE is less sensitive to outliers. A low MAE value may indicate stable forecasting performance, but its increase for the B-A route may point to higher variability in passenger demand or lower model quality for this direction. MAPE expresses the mean absolute percentage error as a percentage of actual values, making it convenient for interpretation. For the obtained forecast, values of 35.705% for A-B and 31.435% for B-A show that, on average, the model deviates by more than 30% from actual demand. MAPE above 30% is considered high for forecasting tasks with strict accuracy requirements, but in the context of forecasting passenger demand for air travel, this could be attributed to anomalies in the data, insufficient consideration of external factors, or limitations of the model itself. When compared to accuracy metrics from other forecasting methods, it is worth noting that hybrid approaches often outperform classical methods by accounting for both linear and nonlinear components. Research indicates that such models achieve MAPE levels of 10-15%, which is significantly better than the current results [22,44]. Thus, when compared to alternative methods, it was found that the presented model provides a baseline sufficient level of forecasting but falls short in performance compared to modern methods (hybrid approaches, neural network models, and transformers) that can significantly improve forecasting accuracy. For tasks requiring higher precision and shorter forecasting horizons, it is recommended to use alternative approaches to achieve a MAPE below 20% and improve RMSE/MAE. At this juncture in the model development process, the upper-level calibration of the forecast results is being conducted, with due consideration given to the impact of the dynamics of macroeconomic indicators until the conclusion of the forecast period. In order to enhance the precision of the model, prospective avenues for advancement include the incorporation of the influence of competitor airline flight demand and alternative transportation modalities (public road and rail) to destinations analogous to the airline's route network. The incorporation of supplementary attributes would facilitate the estimation of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics Results** & **Direction A–B** & **Direction B–A** \\ \hline RMSE & 24.839 & 27.955 \\ MAE & 20.379 & 25.179 \\ MAPE & 35.705 & 31.435 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of passengers demand forecasting. the prospective demand for flights, accounting for the probability that passengers may opt to fly with competing companies or alternative modes of transportation [45]. In order to account for the functional dependence of the demand estimate, it is necessary to collect the minimum dataset required for competitor flights and alternative modes of transport.