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

arXiv: https://arxiv.org/abs/2403.09969 · HTML: https://mlsanity.com/paper/2403.09969/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting](https://mlsanity.com/paper/2109.01657/) (2021)
- [Grand Challenge: Real-time Destination and ETA Prediction for Maritime Traffic](https://mlsanity.com/paper/1810.05567/) (2018)

## Built on by (most cited first, 3 of 3)

- [Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2602.02959/) (2026)
- [A Modular, Data-Free Pipeline for Multi-Label Intention Recognition in Transportation Agentic AI Applications](https://mlsanity.com/paper/2511.03363/) (2025)
- [Beyond the Next Port: A Multi-Task Transformer for Forecasting Future Voyage Segment Durations](https://mlsanity.com/paper/2601.08013/) (2026)

## Abstract (arXiv)

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.