Summary of Prediction Of Vessel Arrival Time to Pilotage Area Using Multi-data Fusion and Deep Learning, by Xiaocai Zhang et al.
Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning
by Xiaocai Zhang, Xiuju Fu, Zhe Xiao, Haiyan Xu, Xiaoyang Wei, Jimmy Koh, Daichi Ogawa, Zheng Qin
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the use of multi-data fusion and deep learning techniques to predict vessel arrival times at pilotage areas. The approach involves extracting vessel arrival contours using Multivariate Kernel Density Estimation (MKDE) and clustering, followed by fusing multiple data sources including AIS, pilotage booking information, and meteorological data. A Temporal Convolutional Network (TCN) framework is then constructed to learn hidden arrival patterns of vessels. The proposed method outperforms state-of-the-art baseline methods in regression tasks, achieving Mean Absolute Error (MAE) ranging from 4.58 min to 4.86 min. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer programs and big data to predict when boats will arrive at a place where they need help navigating. They use different types of information like the boat’s location, weather forecasts, and schedules for when the boats are supposed to arrive. The team built a special model that can learn patterns in the data and make accurate predictions. Their results show that their method is better than others at predicting arrival times, with an error rate as low as 4-5 minutes. |
Keywords
* Artificial intelligence * Clustering * Convolutional network * Deep learning * Density estimation * Mae * Regression
