A Machine Learning Based Methodology for Broken Rail Prediction on Freight Railroads: A Case Study in the United States

•In this paper, a framework consisting of a pipeline of methodologies is proposed for short-term prediction of broken rails. The effectiveness of the proposed framework is validated using data from one Class I railroad.•Feature-based track segmentation is proposed to delineate the railroad network, which is able to improve the performance of the proposed model by reducing variations in data records.•This paper extracts more features and uses them as input variables for the machine learning models. While previous studies did not include them, the feature importance identifies the top influencing variables, such as minimum temperature and crossing angles of turnouts, for broken rail prediction.