Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data
  Fusion and Deep Learning
Xiaocai Zhang Xiuju Fu Zhe Xiao Haiyan Xu Xiaoyang Wei Jimmy Koh Daichi Ogawa Zheng Qin
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, including
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 meteorological 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.