A Hybrid Machine Learning Approach for Accurate Railway Traffic Forecasting

For efficient operations and efficient traffic management, railway traffic forecasting is essential. Conventional forecasting systems frequently have trouble representing nonlinear relationships and temporal dependencies, which results in subpar accuracy and reliability. These methods are based on traditional machine learning algorithms and statistical techniques like linear regression and ARIMA. Furthermore, these models are unable to account for seasonality and traffic variations that occur in the actual world. To tackle these limitations, the study suggests a novel hybrid approach called Gradient-Boosted Recurrent Neural Network (GBRNN), which combines Gradient-Boosted Machines (GBMs) and Recurrent Neural Networks (RNNs). The GBRNN combines RNNs more especially, Long Short-Term Memory (LSTM) units for temporal dependency capture with GBMs for advanced feature extraction. With a prediction accuracy of 89.2%, the proposed method outperforms the existing techniques, as shown by its lower Mean Absolute Error (MAE) of 4.32 and Root Mean Square Error (RMSE) of 6.78. Conversely, the greater MAE and RMSE values of existing systems indicate less accurate forecasts. Moreover, traffic volume is shown to be the most significant component by the feature importance analysis of the GBRNN model, outperforming other variables such as weather and train delays. The results highlight how well the GBRNN performs in improving operational decision-making and forecasting accuracy in railway traffic management. The continuing improvement of the model and its adaptation to changing traffic patterns will be the main goals of future studies.