Convolutional Neural Networks for Accurate Measurement of Train Speed

TL;DR

This paper demonstrates that convolutional neural networks, especially multi-branch models, significantly improve train speed estimation accuracy over traditional methods, enhancing railway safety and efficiency.

Contribution

It introduces and compares three CNN architectures for train speed measurement, showing their superior performance over the Adaptive Kalman Filter in complex scenarios.

Findings

CNN models outperform traditional methods in accuracy

Multi-branch CNN shows highest robustness

Deep learning enhances railway operational safety

Abstract

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.