A Neural Network Approach for ETA Prediction in Inland Waterway Transport

Wenzel, Peter; Jovanovic, Raka; Schulte, Frederik

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

Lecture Notes in Computer Science

2023

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

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A Neural Network Approach for ETA Prediction in Inland Waterway Transport

Peter Wenzel,

Raka Jovanovic,

Frederik Schulte

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2024

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Enhancing Prediction Accuracy of Vessel Arrival Times Using Machine Learning

Evmides,

Aslam,

Ramez

et al. 2024

JMSE

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Marine transportation accounts for approximately 90% of the total trade managed in international logistics and plays a vital role in many companies’ supply chains. However, en-route factors like weather conditions or piracy incidents often delay scheduled arrivals at destination ports, leading to downstream inefficiencies. Due to the maritime industry’s digital transformation, smart ports and vessels generate vast amounts of data, creating an opportunity to use the latest technologies, like machine and deep learning (ML/DL), to support terminals in their operations. This study proposes a data-driven solution for accurately predicting vessel arrival times using ML/DL techniques, including Deep Neural Networks, K-Nearest Neighbors, Decision Trees, Random Forest, and Extreme Gradient Boosting. This study collects real-world AIS data in the Eastern Mediterranean Sea from a network of public and private AIS base stations. The most relevant features are selected for training and evaluating the six ML/DL models. A comprehensive comparison is also performed against the estimated arrival time provided by shipping agents, a simple calculation-based approach, and four other ML/DL models proposed recently in the literature. The evaluation has revealed that Random Forest achieves the highest performance with an MAE of 99.9 min, closely followed by XGBoost, having an MAE of 105.0 min.

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Enhancing Prediction Accuracy of Vessel Arrival Times Using Machine Learning

Evmides,

Aslam,

Ramez

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

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A Neural Network Approach for ETA Prediction in Inland Waterway Transport

Cited by 1 publication

References 23 publications

Enhancing Prediction Accuracy of Vessel Arrival Times Using Machine Learning

Enhancing Prediction Accuracy of Vessel Arrival Times Using Machine Learning

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