Investigations on AIS-based vessel trajectory and destination prediction with deep learning
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Wang, Huei-Kang
issued: 2021-09-01T19:55:34Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Automatic Identification System (AIS) is initially developed for tracking ships to avoid collisions. As numerous small satellites were launched in recent years, millions of AIS messages are captured by these satellite AIS providers every day. The massive amount of data allow shipping firms and port operators to better predict vessels' movement. In this study, a computational framework is developed to predict future trajectories and destinations of vessels by applying deep learning to the satellite AIS messages containing self-reporting positioning data. This study employs deep learning approaches for large-scale maritime prediction and identifies the most suitable deep neural networks for this specific application. The framework is validated globally with the experiments covering five large-scale sea areas. Three prediction models, Convolutional Neural Networks (CNN), Dense Neural Networks (DNN), and Long Short-Term Memory (LSTM) have been tested in the framework. A multi-task learning architecture is implemented based on test results. Through sharing parameters while training, this architecture enables long-term destination predictions by balancing short-term trajectory predictions. The experimental results demonstrate that the LSTM model with an average accuracy percentage of 85.1% outperforms the CNN and DNN with accuracy percentages of 68.3% and 78.2%, respectively. The LSTM model with multi-task architecture can achieve an accuracy of 87.0%. With the proposed computational framework, AIS data can be utilized for maritime predictions, supporting more complex marine applications and covering more expansive geographic areas.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/79578
note: INVESTIGATIONS ON AIS-BASEDVESSEL TRAJECTORY ANDDESTINATION PREDICTION WITHDEEP LEARNINGbyHuei-Kang WangB.S., Fu Jen Catholic University, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinTHE COLLEGE OF GRADUATE STUDIES(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)September 2021© Huei-Kang Wang, 2021The following individuals certify that they have read, and recommend to the College ofGraduate Studies for acceptance, a thesis entitled:INVESTIGATIONS ON AIS-BASED VESSEL TRAJECTORY AND DESTINATION PREDICTIONWITH DEEP Learningsubmitted by Huei-Kang Wang in partial fulfilment of the requirements of the degree ofMaster of Applied ScienceDr. Zheng Liu, School of EngineeringSupervisorDr. Eric Li, Faculty of ManagementCo-supervisorDr. Liwei Wang, School of EngineeringSupervisory Committee MemberDr. Mahmudur Fatmi, School of EngineeringSupervisory Committee MemberDr. Ayman Elnaggar, School of EngineeringUniversity ExamineriiAbstractAutomatic Identification System (AIS) is initially developed for tracking ships to avoidcollisions. As numerous commercial satellites were launched by numerous firms in recentyears, millions of AIS messages are captured by these satellite AIS providers every day. Themassive amount of data allow shipping firms and port operators better predict vessels’ move-ment. In this study, a computational framework is developed to predict future trajectories anddestinations of vessels by applying deep learning to the satellite AIS messages containing self-reporting positioning data.This study employs deep learning approaches for large-scale maritime prediction and iden-tifies the most suitable deep neural networks for this specific application. The framework isvalidated globally with the experiments covering five large-scale sea areas. Three predictionmodels, Convolutional Neural Networks (CNN), Dense Neural Networks (DNN), and LongShort-Term Memory (LSTM) have been tested in the f…
has type: Thesis/Dissertation
date issued: 2021-09
is shown at: 10.14288/1.0401849
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-ShareAlike 4.0 International
rights URI: http://creativecommons.org/licenses/by-sa/4.0/
scholar level: Graduate
contributor: Liu, Zheng (Professor of engineering)
contributor: Li, Eric
type: Text
identifier URI: http://hdl.handle.net/2429/79578