A Neural Network Approach for ETA Prediction in Inland Waterway Transport

A Neural Network Approach for ETA Prediction in Inland Waterway Transport

Wenzel, P.A. (TU Delft Transport Engineering and Logistics)
Jovanovic, Raka (Hamad bin Khalifa University)
Schulte, F. (TU Delft Transport Engineering and Logistics)

Daduna, Joachim R. (editor)
Liedtke, Gernot (editor)
Shi, Xiaoning (editor)
Voß, Stefan (editor)

2023

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.

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

http://resolver.tudelft.nl/uuid:0d212c07-8ba6-43a9-bd5f-f35c844c6d60

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

Springer

2024-03-07

978-3-031-43611-6

Computational Logistics: Proceedings of the 14th International Conference, ICCL 2023

Proceedings of the 14th International Conferences on Computational Logistics, ICCL 2023, 2023-09-06 → 2023-09-08, Berlin, Germany

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 14239 LNCS

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 P.A. Wenzel, Raka Jovanovic, F. Schulte