Analysis and prediction of railway track longitudinal level using multiple machine learning methods

Abstract

With the fast development of computer science, many prediction models based on machine learning methods have been used in the railway industry, which can better predict the random characteristics in track degradation, plan maintenance activities, and eventually meet the requirement of railway transportation. However, the applicability and generality of these models are unclear and the comparative analysis of these models on the random railway track is rare, which makes it hard for railway engineers to choose the most suitable prediction models in practice. In this paper, the track longitudinal level of a section of a railway measured monthly by the rail infrastructure alignment acquisition system for 1.5 years in the Netherlands has been analysed using multiple mathematical methods. After that, three machine learning-based prediction models were developed to predict the future development of the track longitudinal level, using support vector machine, grey model and deep neural network. The prediction performance of different prediction models is compared and discussed. Recommendations for choosing prediction models and further development are provided.