Spatiotemporal forecasting of vertical track alignment with exogenous factors

TL;DR

This paper presents a convolutional LSTM-based method for forecasting vertical track alignment irregularities by incorporating spatial correlations and exogenous factors, enhancing safety in railroad operations.

Contribution

It introduces a novel approach that embeds exogenous factors and captures spatiotemporal correlations for improved track irregularity forecasting.

Findings

Spatial calculations improve forecasting accuracy.

Maintenance record data enhances prediction performance.

The method outperforms other forecasting approaches.

Abstract

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.