Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning

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Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning
Title:
Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning
Journal Title:
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Publication Date:
13 October 2024
Citation:
Zhang, X., Fu, X., Xiao, Z., Xu, H., Wei, X., Koh, J., Ogawa, D., & Qin, Z. (2023, September 24). Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning. 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). https://doi.org/10.1109/itsc57777.2023.10422495
Abstract:
This paper investigates the prediction of vessels' arrival time to the pilotage area using multi-data fusion and deep learning approaches. Firstly, the vessel arrival contour is extracted based on Multivariate Kernel Density Estimation (MKDE) and clustering. Secondly, multiple data sources, in- cluding Automatic Identification System (AIS), pilotage booking information, and meteorological data, are fused before latent feature extraction. Thirdly, a Temporal Convolutional Network (TCN) framework that incorporates a residual mechanism is constructed to learn the hidden arrival patterns of the vessels. Extensive tests on two real-world data sets from Singapore have been conducted and the following promising results have been obtained: 1) fusion of pilotage booking information and mete- orological data improves the prediction accuracy, with pilotage booking information having a more significant impact; 2) using discrete embedding for the meteorological data performs better than using continuous embedding; 3) the TCN outperforms the state-of-the-art baseline methods in regression tasks, exhibiting Mean Absolute Error (MAE) ranging from 4.58 min to 4.86 min; and 4) approximately 89.41& to 90.61& of the absolute prediction residuals fall within a time frame of 10 min.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the SMI - Maritime Artificial Intelligence (AI) Research Programme
Grant Reference no. : SMI-2022-MTP-06
Description:
ISSN:
2153-0017
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