Constructing Trajectory and Predicting Estimated Time of Arrival for Long Distance Travelling Vessels: A Probability Density-based Scanning Approach
Systems and Control
2022-05-18 v1 Artificial Intelligence
Computational Engineering, Finance, and Science
Machine Learning
Systems and Control
Abstract
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.
Keywords
Cite
@article{arxiv.2205.07945,
title = {Constructing Trajectory and Predicting Estimated Time of Arrival for Long Distance Travelling Vessels: A Probability Density-based Scanning Approach},
author = {Deqing Zhai and Xiuju Fu and Xiao Feng Yin and Haiyan Xu and Wanbing Zhang and Ning Li},
journal= {arXiv preprint arXiv:2205.07945},
year = {2022}
}