Constructing Trajectory and Predicting Estimated Time of Arrival for Long Distance Travelling Vessels: A Probability Density-based Scanning Approach
This work addresses ETA prediction for maritime logistics, offering a domain-specific incremental improvement.
The study tackled the problem of predicting Estimated Time of Arrival (ETA) for long-distance vessels by constructing trajectories using a probability density-based approach, achieving an average error of 0.106 days (2.544 hours) and 92.08% accuracy for routes between Singapore and Australia ports.
In this study, a probability density-based approach for constructing trajectories is proposed and validated through an typical use-case application: Estimated Time of Arrival (ETA) prediction given origin-destination pairs. The ETA prediction is based on physics and mathematical laws given by the extracted information of probability density-based trajectories constructed. The overall ETA prediction errors are about 0.106 days (i.e. 2.544 hours) on average with 0.549 days (i.e. 13.176 hours) standard deviation, and the proposed approach has an accuracy of 92.08% with 0.959 R-Squared value for overall trajectories between Singapore and Australia ports selected.