Comparison of selected regression models in predicting railway traffic noise levels
Artykuł w czasopiśmie
MNiSW
70
Lista 2024
| Status: | |
| Autorzy: | Wrótny Marcin |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 1 |
| Wolumen/Tom: | 37 |
| Numer artykułu: | 2026105 |
| Strony: | 1 - 10 |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 czerwca 2026 |
| Abstrakty: | angielski |
| The aim of this study was to compare the effectiveness of selected regression methods in predicting the noise level generated by railway traffic. The analysis was based on measurement data collected at ten locations in Poland, taking into account technical and environmental parameters as well as train passage characteristics. Linear regression (OLS), Weighted Least Squares (WLS), LASSO, Ridge and Elastic Net were used in the modelling, and their effectiveness was assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The results showed that the noise level depends primarily on train speed, passage time, and rolling stock type, while meteorological variables had a marginal impact. The best fit was obtained for the WLS model, with effectively solving the problem of heteroscedasticity. Regularized models made it possible to reduce the number of predictors without losing the quality of the fit. The study confirms that modern regression techniques can be a valuable tool for assessing the impact of railways on the acoustic environment. |
