论文标题
与外源因子对垂直轨道对齐的时空预测
Spatiotemporal forecasting of vertical track alignment with exogenous factors
论文作者
论文摘要
为了确保铁路行动的安全性，监视和预测跟踪几何不规则的重要性很重要。更高的安全性需要较高的时空频率进行预测，这又需要捕获空间相关性。另外，轨道的几何不规则性受到多种外源因素的影响。在这项研究中，提出了一种方法，以预测一种通过合并空间和外源性因子计算来预测一种类型的轨道几何不规则，垂直比对。该方法使用卷积长的短期记忆嵌入外源因素并捕获时空相关性。就预测性能而言，还将提出的方法与其他方法进行了比较。此外，进行了有关外源因素的消融研究，以检查其对预测性能的个人贡献。结果表明，空间计算和维护记录数据可以改善垂直比对的预测。
To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with higher spatiotemporal frequencies, which in turn requires capturing spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors. In this study, a method is proposed to forecast one type of track geometry irregularity, vertical alignment, by incorporating spatial and exogenous factor calculations. The proposed method embeds exogenous factors and captures spatiotemporal correlations using a convolutional long short-term memory. The proposed method is also experimentally compared with other methods in terms of the forecasting performance. Additionally, an ablation study on exogenous factors is conducted to examine their individual contributions to the forecasting performance. The results reveal that spatial calculations and maintenance record data improve the forecasting of vertical alignment.
