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A Neural Network Approach for ETA Prediction in Inland Waterway Transport

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Computational Logistics (ICCL 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14239))

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Abstract

Ensuring the accuracy of the estimated time of arrival (ETA) information for ships approaching ports and inland terminals is increasingly critical today. Waterway transportation plays a vital role in freight transportation and has a significant ecological impact. Improving the accuracy of ETA predictions can enhance the reliability of inland waterway shipping, increasing the acceptance of this eco-friendly mode of transportation. This study compares the industry-standard approach for predicting the ETA based on average travel times with a neural network (NN) trained using real-world historical data. This study generates and trains two NN models using historical ship position data. These models are then assessed and contrasted with the conventional method of calculating average travel times for two specific areas in the Netherlands and Germany. The results indicate by using specific input features, the quality of ETA predictions can improve by an average of 20.6% for short trips, 4.8% for medium-length trips, and 13.4% for long-haul journeys when compared to the average calculation.

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Authors and Affiliations

  1. Technical University of Delft, Mekelweg 5, 2628, Delft, CD, Netherlands

    Peter Wenzel & Frederik Schulte

  2. Qatar Environment and Qatar Environment and Energy Research Institute, Hamad bin Khalifa University, PO Box 5825, Doha, Qatar

    Raka Jovanovic

Authors
  1. Peter Wenzel
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  2. Raka Jovanovic
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  3. Frederik Schulte
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Corresponding author

Correspondence to Frederik Schulte .

Editor information

Editors and Affiliations

  1. Hochschule für Wirtschaft und Recht Berlin, Berlin, Germany

    Joachim R. Daduna

  2. Deutsches Zentrum für Luft- und Raumfahrt (DLR), Berlin, Germany

    Gernot Liedtke

  3. Deutsches Zentrum für Luft- und Raumfahrt (DLR), Berlin, Germany

    Xiaoning Shi

  4. University of Hamburg, Hamburg, Germany

    Stefan Voß

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Wenzel, P., Jovanovic, R., Schulte, F. (2023). A Neural Network Approach for ETA Prediction in Inland Waterway Transport. In: Daduna, J.R., Liedtke, G., Shi, X., Voß, S. (eds) Computational Logistics. ICCL 2023. Lecture Notes in Computer Science, vol 14239. Springer, Cham. https://doi.org/10.1007/978-3-031-43612-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-43612-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43611-6

  • Online ISBN: 978-3-031-43612-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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