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Predicting Estimated Arrival Times in Logistics Using Machine Learning
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Author(s): Peter Poschmann (Technische Universität Berlin, Germany), Manuel Weinke (Technische Universität Berlin, Germany)and Frank Straube (Technische Universität Berlin, Germany)
Copyright: 2023
Pages: 19
Source title:
Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch160
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Abstract
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.
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