A Contextual Hybrid Model for Vessel Movement Prediction

Saeed Mehri,Ali Asghar Alesheikh,Anahid Basiri

doi:10.1109/access.2021.3066463

Abstract

Predicting the movement of the vessels can significantly improve the management of safety. While the movement can be a function of geographic contexts, the current systems and methods rarely incorporate contextual information into the analysis. This paper initially proposes a novel context-aware trajectories' simplification method to embed the effects of geographic context which guarantees the logical consistency of the compressed trajectories, and further suggests a hybrid method that is built upon a curvilinear model and deep neural networks. The proposed method employs contextual information to check the logical consistency of the curvilinear method and then, constructs a Context-aware Long Short-Term Memory (CLSTM) network that can take into account contextual variables, such as the vessel types. The proposed method can enhance the prediction accuracy while maintaining the logical consistency, through a recursive feedback loop. The implementations of the proposed approach on the Automatic Identification System (AIS) dataset, from the eastern coast of the United States of America which was collected, from November to December 2017, demonstrates the effectiveness and better compression, i.e. 80% compression ratio while maintaining the logical consistency. The estimated compressed trajectories are 23% more similar to their original trajectories compared to currently used simplification methods. Furthermore, the overall accuracy of the implemented hybrid method is 15.68% higher than the ordinary Long Short-Term Memory (LSTM) network which is currently used by various maritime systems and applications, including collision avoidance, vessel route planning, and anomaly detection systems.

Highlights

According to the International Maritime Organization (IMO), the shipping industry plays a fundamental role in the world economy [1]
Due to the fact that the performance of learning methods for trajectory prediction significantly depends on the quality of the data [3], and the quality of spatial data can have different aspects [50], this paper focuses on the logical consistency of compressed trajectories
While the movement can be a function of geographic contexts, the current systems and methods rarely incorporate contextual information into the analysis

Summary

INTRODUCTION

According to the International Maritime Organization (IMO), the shipping industry plays a fundamental role in the world economy [1]. They rely on the learning models to mimic the movement behavior of vessels based on the historical movement dataset [10] In this class, artificial neural networks are gaining more attention in predicting vessels’ movement trajectories i.e., regarded as complex, dynamic, and technical systems [30]. In order to consider the environmental factors into the prediction models, Vijverberg [21] et al built upon the work of Vemula [34] et al and added a feature vector for the Attention-LSTM At first, they used a linear extrapolation to predict vessels’ future positions in 10-second time intervals and trained the LSTM model to calculate the linear displacement or the offset of the vessels from the predicted path. This paper combines the curvilinear model [35] and the deep learning LSTM network [36] by taking contextual information as well as vessels’ data, which is expected to retain logical consistency and increase prediction accuracy. Where (xi, yi) is a position of the vessel at time ti

PROBLEM FORMULATION

DATA SOURCES AND PREPROCESSES

CONTEXTUAL CONDITION

TRAJECTORY COMPRESSION

CONCLUSION