Convolutional Neural Networks for Accurate Measurement of Train Speed
This work addresses railway safety and operational efficiency by improving speed measurement, but it is incremental as it applies existing deep learning techniques to a specific domain problem.
The study tackled train speed estimation by comparing CNN architectures with an Adaptive Kalman Filter, finding that CNN-based methods, especially a multiple-branch model, achieved superior accuracy and robustness, particularly under challenging conditions like Wheel Slide Protection activation.
In this study, we explore the use of Convolutional Neural Networks for improving train speed estimation accuracy, addressing the complex challenges of modern railway systems. We investigate three CNN architectures - single-branch 2D, single-branch 1D, and multiple-branch models - and compare them with the Adaptive Kalman Filter. We analyse their performance using simulated train operation datasets with and without Wheel Slide Protection activation. Our results reveal that CNN-based approaches, especially the multiple-branch model, demonstrate superior accuracy and robustness compared to traditional methods, particularly under challenging operational conditions. These findings highlight the potential of deep learning techniques to enhance railway safety and operational efficiency by more effectively capturing intricate patterns in complex transportation datasets.