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Open AccessArticle

Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms

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Bosch Connected Industry, Robert Bosch Manufacturing Solutions GmbH, Leitzstrasse 47, 70469 Stuttgart, Germany
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BIBA—Bremer Institut für Produktion und Logistik GmbH, University of Bremen, Hochschulring 20, 28359 Bremen, Germany
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Faculty of Production Engineering, University of Bremen, Badgasteiner Straße 1, 28359 Bremen, Germany
*
Author to whom correspondence should be addressed.
Logistics 2020, 4(1), 1; https://doi.org/10.3390/logistics4010001
Received: 18 November 2019 / Revised: 17 December 2019 / Accepted: 23 December 2019 / Published: 25 December 2019
Accurate travel time prediction is of high value for freight transports, as it allows supply chain participants to increase their logistics quality and efficiency. It requires both sufficient input data, which can be generated, e.g., by mobile sensors, and adequate prediction methods. Machine Learning (ML) algorithms are well suited to solve non-linear and complex relationships in the collected tracking data. Despite that, only a minority of recent publications use ML for travel time prediction in multimodal transports. We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times. Using different combinations of features derived from the data, we have built several models for travel time prediction. Tracking data from a real-world multimodal container transport relation from Germany to the USA are used for evaluation of the established models. We show that SVR provides the best prediction accuracy, with a mean absolute error of 17 h for a transport time of up to 30 days. We also show that our model performs better than average-based approaches. View Full-Text
Keywords: logistics; supply chain management; multimodal freight transports; travel time prediction; machine learning logistics; supply chain management; multimodal freight transports; travel time prediction; machine learning
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MDPI and ACS Style

Servos, N.; Liu, X.; Teucke, M.; Freitag, M. Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms. Logistics 2020, 4, 1.

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