A Neural Network Approach for ETA Prediction in Inland Waterway Transport

Peter Wenzel; Raka Jovanovic; Frederik Schulte

doi:10.1007/978-3-031-43612-3_13

A Neural Network Approach for ETA Prediction in Inland Waterway Transport

Peter Wenzel, Raka Jovanovic, Frederik Schulte^*

^*Corresponding author for this work

QEERI - Energy Center

Delft University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Computational Logistics - 14th International Conference, ICCL 2023, Proceedings
Editors	Joachim R. Daduna, Gernot Liedtke, Xiaoning Shi, Stefan Voß
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	219-232
Number of pages	14
ISBN (Print)	9783031436116
DOIs	https://doi.org/10.1007/978-3-031-43612-3_13
Publication status	Published - 2023
Event	Proceedings of the 14th International Conferences on Computational Logistics, ICCL 2023 - Berlin, Germany Duration: 6 Sept 2023 → 8 Sept 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14239 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Proceedings of the 14th International Conferences on Computational Logistics, ICCL 2023
Country/Territory	Germany
City	Berlin
Period	6/09/23 → 8/09/23

Keywords

Estimated Time of Arrival Prediction
Inland Waterway Transport
Machine Learning
Neural Networks

Access to Document

10.1007/978-3-031-43612-3_13

Cite this

Wenzel, P., Jovanovic, R., & Schulte, F. (2023). A Neural Network Approach for ETA Prediction in Inland Waterway Transport. In J. R. Daduna, G. Liedtke, X. Shi, & S. Voß (Eds.), Computational Logistics - 14th International Conference, ICCL 2023, Proceedings (pp. 219-232). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14239 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43612-3_13

Wenzel, Peter ; Jovanovic, Raka ; Schulte, Frederik. / A Neural Network Approach for ETA Prediction in Inland Waterway Transport. Computational Logistics - 14th International Conference, ICCL 2023, Proceedings. editor / Joachim R. Daduna ; Gernot Liedtke ; Xiaoning Shi ; Stefan Voß. Springer Science and Business Media Deutschland GmbH, 2023. pp. 219-232 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Neural Network Approach for ETA Prediction in Inland Waterway Transport",

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.",

keywords = "Estimated Time of Arrival Prediction, Inland Waterway Transport, Machine Learning, Neural Networks",

author = "Peter Wenzel and Raka Jovanovic and Frederik Schulte",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Proceedings of the 14th International Conferences on Computational Logistics, ICCL 2023 ; Conference date: 06-09-2023 Through 08-09-2023",

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doi = "10.1007/978-3-031-43612-3_13",

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Wenzel, P, Jovanovic, R & Schulte, F 2023, A Neural Network Approach for ETA Prediction in Inland Waterway Transport. in JR Daduna, G Liedtke, X Shi & S Voß (eds), Computational Logistics - 14th International Conference, ICCL 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14239 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 219-232, Proceedings of the 14th International Conferences on Computational Logistics, ICCL 2023, Berlin, Germany, 6/09/23. https://doi.org/10.1007/978-3-031-43612-3_13

A Neural Network Approach for ETA Prediction in Inland Waterway Transport. / Wenzel, Peter; Jovanovic, Raka; Schulte, Frederik.
Computational Logistics - 14th International Conference, ICCL 2023, Proceedings. ed. / Joachim R. Daduna; Gernot Liedtke; Xiaoning Shi; Stefan Voß. Springer Science and Business Media Deutschland GmbH, 2023. p. 219-232 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14239 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Neural Network Approach for ETA Prediction in Inland Waterway Transport

AU - Wenzel, Peter

AU - Jovanovic, Raka

AU - Schulte, Frederik

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Estimated Time of Arrival Prediction

KW - Inland Waterway Transport

KW - Machine Learning

KW - Neural Networks

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AN - SCOPUS:85172405110

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 232

BT - Computational Logistics - 14th International Conference, ICCL 2023, Proceedings

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A2 - Liedtke, Gernot

A2 - Shi, Xiaoning

A2 - Voß, Stefan

PB - Springer Science and Business Media Deutschland GmbH

T2 - Proceedings of the 14th International Conferences on Computational Logistics, ICCL 2023

Y2 - 6 September 2023 through 8 September 2023

ER -

Wenzel P, Jovanovic R, Schulte F. A Neural Network Approach for ETA Prediction in Inland Waterway Transport. In Daduna JR, Liedtke G, Shi X, Voß S, editors, Computational Logistics - 14th International Conference, ICCL 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 219-232. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43612-3_13

A Neural Network Approach for ETA Prediction in Inland Waterway Transport

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