MLOps in freight rail operations

Railways are essential for freight transport due to their operational reliability advantages, but maintaining this advantage requires optimised railway infrastructure. Previous research has developed models to predict freight rail disruptions/disturbances and their associated delay times, in order to better understand the impact of multiple factors on them. However, because these models are built on static datasets, extracting real value from a model in a production environment remains difficult.This paper presents a methodology that demonstrates the potential of MLOps in automating the entire workflow, from data extraction to model deployment for real-time delay predictions in freight rail operations, including good practices of Continuous-Integration, Continuous-Delivery, and Continuous-Training, as well as a tool list for each process.Our research advances the field of railway operations by developing an entire MLOps workflow using data from the freight rail operations of the Luxembourgish National Freight Railway Company over a seventeen-month period. Furthermore, we employed a LightGBM model that had previously performed well in another study. This workflow can be automatically triggered to develop the processes and thus maintain an ML model capable of predicting delay times for CFL Multimodal operations in real-time.Our findings demonstrate that MLOps have the potential to automate the entire process, opening up new avenues for future research in this field. Although the methodology presented is intended to optimise freight rail operations for a specific company, it can be easily transferable to other railway companies or other transportation industries, such as aviation, shipping, and trucking.