用卷积网络提升列车速度测量精度，尤其在复杂工况下表现更优。
Convolutional Neural Networks for Accurate Measurement of Train Speed
- 设计多分支CNN模型，从时序与空间特征中提取速度信息
- 在含防滑保护激活的模拟数据上，误差比传统方法降低32%
- 适合铁路安全监控与智能运维系统研发人员参考
本研究探讨卷积神经网络在提升列车速度估计精度中的应用，应对现代铁路系统的复杂挑战。对比了单分支2D、单分支1D和多分支三种CNN架构与自适应卡尔曼滤波器的性能，使用含与不含轮滑保护激活的模拟列车运行数据进行分析。结果表明，基于CNN的方法，尤其是多分支模型，在复杂运行条件下表现出更高的准确性和鲁棒性，显著优于传统方法。这些发现凸显深度学习技术在更有效捕捉复杂交通数据中细微模式方面的潜力，有助于提升铁路安全与运营效率。
原文摘要 · Abstract (English)
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.
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