ARTÍCULO
TITULO

Interpolation-Based Inference of Vessel Trajectory Waypoints from Sparse AIS Data in Maritime

Bakht Zaman    
Dusica Marijan and Tetyana Kholodna    

Resumen

The availability of automatic identification system (AIS) data for tracking vessels has paved the way for improvements in maritime safety and efficiency. However, one of the main challenges in using AIS data is often the low quality of the data. Practically, AIS-based trajectory data of vessels are available at irregular time intervals; consequently, large temporal gaps often exist in the historical AIS data. Meanwhile, certain tasks such as waypoint detection using historical data, which involves finding locations along the trajectory where the vessel changes its course (and possibly speed, acceleration, etc.), require AIS messages with a high temporal resolution. High-resolution AIS data are especially required for waypoint detection in critical areas where vessels maneuver carefully because of, e.g., narrow pathways or the presence of islands. One possible solution to address the problem of insufficient AIS data in vessel trajectories is interpolation. In this paper, we address the problem of detecting waypoints in a single representative trajectory with insufficient data using various interpolation-based methods. To this end, a two-step approach is proposed, in which the trajectories are first interpolated, and then the waypoint detection method is applied to the merged trajectory containing both interpolated and observed AIS messages. The numerical results demonstrate the effectiveness of exploiting various interpolation methods for waypoint detection. Moreover, the results of the numerical experiments show that the proposed methodology is effective for waypoint detection in envisaged settings with insufficient data, and outperforms the competing algorithm.

 Artículos similares

       
 
João N. Ribeiro da Silva, Tiago A. Santos and Angelo P. Teixeira    
This paper develops a methodology to estimate ship emissions using Automatic Identification System data (AIS). The methodology includes methods for AIS message decoding and ship emission estimation based on the ship?s technical and operational characteri... ver más

 
Zhaojin Yan, Guanghao Yang, Rong He, Hui Yang, Hui Ci and Ran Wang    
Automatic identification systems (AIS) provides massive ship trajectory data for maritime traffic management, route planning, and other research. In order to explore the valuable ship traffic characteristics contained implicitly in massive AIS data, a sh... ver más

 
Zhiyuan Wang, Yong Wu, Xiumin Chu, Chenguang Liu and Mao Zheng    
Collision risk identification is an important basis for intelligent ship navigation decision-making, which evaluates results that play a crucial role in the safe navigation of ships. However, the curvature, narrowness, and restricted water conditions of ... ver más

 
Weihua Zhu, Shoudong Wang, Shengli Liu, Libo Yang, Xinrui Zheng, Bohao Li and Lixiao Zhang    
Maritime accidents, such as ship collisions and oil spills, directly affect maritime transportation, pollute the water environment, and indirectly threaten life and property safety. Predicting the maritime accident susceptibility and taking measures in a... ver más

 
Xuhang Xu, Chunshan Liu, Jianghui Li, Yongchun Miao and Lou Zhao    
Vessel trajectory prediction is an important step in route planning, which could help improve the efficiency of maritime transportation. In this article, a high-accuracy long-term trajectory prediction algorithm is proposed for oil tankers. The proposed ... ver más