What is Transport Chain

The aim of the article is to demonstrate the use and benefit of machine learning (ML) in logistics by means of a significant, practice-relevant application: the prediction of estimated times of arrival (ETA) in intermodal transport chains. Based on a real use case, the article first provides an approach for the methodical procedure for the implementation of ETA predictions and a description of essential development phases. Subsequently, a cross-actor prediction approach for the combined road-rail transport of containers in the port hinterland is designed, and ML-based prediction models for specific logistics processes are prototypically implemented and evaluated. Finally, an outlook on future research directions is given.