Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Xinqiang Chen,Octavian Postolache,Yongsheng Yang,Hailin Zheng,Pengwen Xiong,Yong Xiong,Jun Ling

doi:10.1155/2020/7191296

Abstract

Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

Highlights

Maritime transportation occupies over 90% of global trade in terms of goods delivering volume
For any given ship trajectories, the prediction accuracy is quantified with the above-mentioned statistical indicators (see equations (13) to (16)). e smaller root mean square error (RMSE), mean absolute error (MAE), Frechet distance (FD), and average Euclidean distance (AED)
E proposed framework firstly identified different ship trajectories via time interval between neighboring data samples, which is the first substep in the data quality control procedure of the proposed framework. en, the data outliers in the raw Automatic Identification System (AIS) data were determined with a group of constraints, which were further corrected by the moving average method

Summary

Introduction

Maritime transportation occupies over 90% of global trade in terms of goods delivering volume. Enhancing traffic safety attracts huge attention considering that maritime traffic incident can cause significant loss of human life, navigation environment damage, etc. To avoid potential maritime accidents, various maritime surveillance data are collected for the purpose of navigation environment awareness, which provides accurate early-warning information to maritime traffic participants [3]. E AIS data involves meaningful spatial-temporal maritime traffic information which supports various navigation operation decisions. The AIS data is a popular data source for analyzing ship trajectory variation tendency. Note that AIS is a type of selfreporting system originally designed for preventing potential accident, which is a mandatory facility for cargo ships (i.e., ship with gross tonnage larger than 300) [4,5,6,7,8]. Fishing boats with length longer than 15 m are required to install AIS equipment in the European Union Member States [9,10,11,12]

Objectives

Methods

Results

Conclusion