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Paper ThAT7.4

yang, boyu (Beijingjiaotong university), Lv, Jidong (Beijing Jiaotong University), Liu, Hongjie (Beijing Jiaotong University), Chai, Ming (Beijing Jiaotong University), Zhang, Qinglong (University of Science and Technology Beijing), TANG, Tao (Beijing Jiaotong University), Lv, Jiahui (Beijing jiaotong University)

Trajectory Prediction of High-Speed Train Group Tracking Based on LSTM-KF Hybrid Model

Scheduled for presentation during the Regular Session "Rail Traffic Management I" (ThAT7), Thursday, September 26, 2024, 11:30−11:50, Salon 15

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 8, 2024

Keywords Rail Traffic Management, Theory and Models for Optimization and Control, Data Mining and Data Analysis

Abstract

在“火车到火车”下将多个列车作为一个组运行 通信是稀疏铁路的有效方式 网络。它不仅提供灵活的操作方案 高峰时段,但也通过减少数据来避免系统过载 火车和地面之间的流动。然而,这是一个障碍 对不确定的跟随列车进行准确跟踪 前列列车的通信延迟状态。 因此,我们提出了一种列车轨迹预测 方法 旨在获得前车的准确状态 一个 时间跨度长。首先,我们建立了一个混合体 预测 模型将长短期记忆 (LSTM) 与 卡尔曼滤波(KF)和使用滑动窗口机构 自 提高未来预测的准确性。其次,融合 LSTM公司 预测数据和卡尔曼计算模型,我们 提出 混合列车轨迹预测算法 采用 使用实数实时计算协方差矩阵 数据 和预测数据。最后,我们应用一个实验 考虑中国

 

 

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