Spatio-temporal forecasting for railway track degradation detection using convolutional LSTM

Abstract

This paper addresses the problem of forecasting railway track irregularity, which is essential to secure the safety and to manage maintenance planning and scheduling. We propose a track degradation model using convolutional long short-term memory (ConvLSTM) for forecasting vertical irregularity of Shinkansen rail lines. The ConvLSTM architecture is designed to be trained on three types of data: spatio-temporal series data on the rail lines measured by a high-speed inspection train, static categorical data such as track structure and foundation, and binary time series data of maintenance workday records. We evaluate forecasting performance in terms of classification accuracy. Experimental results with real data show that the ConvLSTM model provides better forecasting performance when using static categorical data such as track structure and foundation, and binary time series data of maintenance workday records, at the critical points where the track degradation is progressing.