Spatiotemporal forecasting of track geometry irregularities with exogenous factors

To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with a higher spatiotemporal frequency. For forecasting with a high spatiotemporal frequency, it is necessary to capture spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors. In this study, we propose a method 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 (ConvLSTM). In the experiment, we compared the proposed method with other methods in terms of the forecasting performance. Additionally, we conducted an ablation study on exogenous factors to examine their contribution to the forecasting performance. The results reveal that spatial calculations and maintenance record data improve the forecasting of the vertical alignment. Introduction Tokaido Shinkansen is a high-speed train linking Japan’s major cities: Tokyo, Nagoya, Kyoto, and Osaka. Serious train accidents caused by track geometry irregularities have not occurred because of the railroad operators’ high temporal/spatial frequency of track maintenance. Track maintenance activities can be categorized into two main groups: preventive and corrective maintenance. Preventive maintenance predicts failures before they occur, whereas corrective maintenance rectifies the existing defects. Currently, Tokaido Shinkansen operates under corrective maintenance. The railroad operator performs track maintenance when the track conditions exceed the designed maintenance level. The designed maintenance levels include precautionary and dangerous levels. At the point where the track condition exceeds the precautionary level, railroad operators plan track maintenance for the next few days. At the point where the track condition exceeds the dangerous level, railroad operators immediately perform emergency track maintenance. Emergency track maintenance requires additional personnel and resources, resulting in additional costs. Preventive maintenance can reduce these costs. Suppose that the Tokaido Shinkansen operates on preventive maintenance, in this case, railroad operators forecast whether the track condition will exceed the maintenance level. Subsequently, at points where track conditions are likely to exceed maintenance levels, railroad operators perform track maintenance. Consequently, the number of points where the track conditions exceed the preventive or dangerous levels is reduced. Preventive maintenance guarantees railroad security and reduces costs by smoothing the amount of maintenance. In Tokaido Shinkansen, a track-inspection train, called Doctor Yellow, inspects track geometry irregularities, which are rail deformations. Doctor Yellow inspects five types of track geometry irregularities: vertical alignment, lateral alignment, gauge deviation, cross level, and twist. Table 1 describes these five irregularities. The gauge is the distance between the right and left rails. Doctor Yellow inspects the vertical and lateral alignments of each left and right rail. Vertical alignment is critical among track geometry irregularities because it causes ride-quality deterioration and train accidents. Figure 1 illustrates vertical alignment. In ∗Chiba University †kawa@faculty.chiba-u.jp 1 ar X iv :2 21 1. 03 54 9v 1 [ cs . L G ] 7 N ov 2 02 2 Track geometry irregularity Description Vertical alignment vertical displacement in the longitudinal direction of the rail Lateral alignment lateral displacement in the longitudinal direction of the rail Gauge deviation error between the design and actual measured value of the gauge Cross-level deviation between the top surfaces of the left and right rails at a given location Twist difference between two cross-level measurements taken a specific distance apart Table 1: Track geometry irregularities.