This paper aims to incorporate the throttle handle level prediction in high speed train(HST) operation prediction problem to enable the prediction of HST drivers’ activities, in which the key instructions available to HST driver are difficult to determine. Specifically, we consider an end-edge-cloud orchestration system to capture the real-time responses for driver state changes. By adding edge computing nodes, the real-time performance of data collection, transmission, and processing is improved. Our ultimate goal is to guide and regulate train drivers’ activities in the same way, regardless of uncertain factors affecting HST dynamic or kinematic performance. We formulate the problem as a physical-based and data-driven deep learning-aided prediction model and solve it using a novel long short-term memory (LSTM) deep neural network which combines: () an off-line approximate training model to learn the time series data in the cloud layer, and () an online prediction process to determine driving strategies in the real-time windows, more in general expressed as driving skill level constraints. To evaluate the performance of our approach, some case studies using the real-world railway infrastructure and HST data have been conducted. The results show that the proposed models produce higher prediction accuracy for both speed and throttle handle level prediction tasks. Compared to the conventional HST operation prediction problem, which considers speed sequences only without throttle handle level consideration, this study finds that jointly modeling speed and throttle handle level actually improves the next operation prediction performance itself, potentially because throttle handle level observations capture the information on HST control dynamics, which may affect operators’ driving choices.

中文翻译：

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使用 LSTM 进行高速列车运行的端边云协作学习辅助预测

本文旨在将油门手柄水平预测纳入高速列车（HST）运行预测问题中，以实现对HST驾驶员活动的预测，其中HST驾驶员可用的关键指令难以确定。具体来说，我们考虑使用端边缘云编排系统来捕获驾驶员状态变化的实时响应。通过增加边缘计算节点，提高数据采集、传输、处理的实时性。我们的最终目标是以同样的方式指导和调节列车驾驶员的活动，无论影响 HST 动态或运动性能的不确定因素如何。我们将问题表述为基于物理和数据驱动的深度学习辅助预测模型，并使用新颖的长短期记忆（LSTM）深度神经网络来解决它，该网络结合了：（）离线近似训练模型来学习云层中的时间序列数据，以及（）在实时窗口中确定驾驶策略的在线预测过程，更一般地表示为驾驶技能水平约束。为了评估我们方法的性能，我们使用现实世界的铁路基础设施和 HST 数据进行了一些案例研究。结果表明，所提出的模型对于速度和油门手柄水平预测任务都能产生更高的预测精度。与仅考虑速度序列而不考虑油门手柄水平的传统 HST 操作预测问题相比，本研究发现，联合建模速度和油门手柄水平实际上提高了下一个操作预测性能本身，可能是因为油门手柄水平观测捕获了以下信息： HST 控制动态，可能会影响操作员的驾驶选择。