Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Wang, Huei-Kang
Master of Applied Science - MASc
University of British Columbia
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.
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 framework. A multi-task learning archi-tecture is implemented based on test results. Through sharing parameters while training, thisarchitecture enables long-term destination predictions by balancing short-term trajectory pre-dictions. The experimental results demonstrate that the LSTM model with an average accuracypercentage of 85.1% outperforms the CNN and DNN with accuracy percentages of 68.3% and78.2% respectively. The LSTM model with multi-task architecture can achieve an accuracyof 87.0%. With the proposed computational framework, AIS data can be utilized for mar-itime predictions, supporting more complex marine applications and covering more expansivegeographic areas.iiiLay SummaryLogistics is transforming into an AI-driven industry. In maritime transportation, maritimestakeholders such as port authorities and cargo inspection businesses can adopt the learning-based approach to predict ship trajectories and destinations with AIS data. However, prac-titioners are facing challenges in manipulating the raw AIS data to derive the insights withartificial intelligent techniques. This study proposed a deep learning framework, which can ex-tract vessel voyages from AIS data and learn directly from them. The method is not limited toport operations but can be adaptable globally with sea region filtering, giving the flexibility toapplications for any regions and ports of interest. The proposed deep learning techniques willbenefit maritime industrial practitioners, port authorities, and other stakeholders in maritimeintelligence transportation who need to deal with the AIS data for decision-making.ivPrefaceThis thesis is based on the research work conducted in the School of Engineering at TheUniversity of British Columbia, Okanagan Campus, under the supervision of Prof. Zheng Liu.Published work is contained in this thesis.The following paper is contained in this thesis and used with the permission of IEEE.• Wells Wang, Junchi Bin, Amirhossein Zaji, Richard Halldearn, Fabien Guillaume, EricLi, and Zheng Liu. A Multi-Task Learning-based Framework for Maritime Trajectoryand Destination with AIS Data IEEE Transactions on Artificial Intelligence, 2021. (Sub-mitted)I am the principle contributor for the work. Prof. Zheng Liu provided me with somesuggestions to improve my research work. Besides, Mr. Richard Halldearn, the CTO at theNavarik Corp., and Mr. Fabien Guillaume, the product manager at Spire Maritime Corp.,helped with the acquisition of the data used in the thesis.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xGlossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xivChapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Research Gaps and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.1 Research Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Chapter 2: Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1 Preprocessing and Knowledge Extraction for AIS Data and Maritime Trajectories 72.1.1 AIS Data and Maritime Trajectory Preprocessing . . . . . . . . . . . . 8viTABLE OF CONTENTS2.1.2 Maritime Knowledge Extraction . . . . . . . . . . . . . . . . . . . . . 112.2 Deep Learning in Maritime Predictions . . . . . . . . . . . . . . . . . . . . . . 122.2.1 Vessel Trajectory Predictions . . . . . . . . . . . . . . . . . . . . . . . 132.2.2 Vessel Destination Predictions . . . . . . . . . . . . . . . . . . . . . . 142.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Chapter 3: AIS-based Vessel Trajectory and Destination Prediction . . . . . . . . 173.1 Overview of the Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2 Knowledge Extraction for Learning with AIS Data . . . . . . . . . . . . . . . 183.2.1 Data Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.2.2 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2.3 Origin-Destination (OD) Recognition . . . . . . . . . . . . . . . . . . 233.2.4 Vessel Trip Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2.5 Region Filters for Selecting Interested Sea Areas . . . . . . . . . . . . 293.3 Deep Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.3.1 Multi-Layer Perceptron (MLP) . . . . . . . . . . . . . . . . . . . . . . 313.3.2 Convolutional Neural Network (CNN) . . . . . . . . . . . . . . . . . . 323.3.3 Recurrent Neural Network (RNN) . . . . . . . . . . . . . . . . . . . . 333.3.4 Multi-Task Learning (MTL) Techniques . . . . . . . . . . . . . . . . . 353.4 AIS-Based Maritime Prediction with Deep Learning . . . . . . . . . . . . . . . 363.4.1 Maritime Predictive Applications with Neural Networks . . . . . . . . 373.4.2 Maritime Predictive Applications with Multi-Task Learning Techniques 383.5 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.5.1 The Criterion of Vessel Position . . . . . . . . . . . . . . . . . . . . . 403.5.2 The Criterion of Port Classification . . . . . . . . . . . . . . . . . . . . 413.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Chapter 4: Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43viiTABLE OF CONTENTS4.1 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.1.1 Model Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.1.2 Multi-Task Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.2.1 Trajectory Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.2.2 Training Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.3.1 Model Comparison Results . . . . . . . . . . . . . . . . . . . . . . . . 474.3.2 Multi-Task Learning Results . . . . . . . . . . . . . . . . . . . . . . . 514.4 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Chapter 5: Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575.2 Limitations and Future Research Directions . . . . . . . . . . . . . . . . . . . 59Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60viiiList of TablesTable 3.1 Variable descriptions of AIS data . . . . . . . . . . . . . . . . . . . . . 19Table 3.2 Comparisons between two types of vessel IDs . . . . . . . . . . . . . . 20Table 3.3 Variable descriptions of OD data . . . . . . . . . . . . . . . . . . . . . 26Table 4.1 The number of selected ports and extracted trips . . . . . . . . . . . . . 46Table 4.2 The evaluation results of trajectory prediction with different neural net-works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Table 4.3 The evaluation results of destination prediction with different neuralnetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Table 4.4 The evaluation results of trajectory prediction with MTL . . . . . . . . 52Table 4.5 The evaluation results of destination prediction with MTL . . . . . . . . 53ixList of FiguresFigure 1.1 Illustration of the research objectives . . . . . . . . . . . . . . . . . . 4Figure 2.1 An example of a noise in a vessel trajectory . . . . . . . . . . . . . . . 9Figure 3.1 The overall architecture of the proposed methodology. The details ofthe maritime data and knowledge extraction approaches are presentedin Chapter 3.2. The details of maritime predictions and the architectureof the MTL model are presented in Chapter 3.4. . . . . . . . . . . . . 17Figure 3.2 The flowchart of the preprocessing procedures . . . . . . . . . . . . . 19Figure 3.3 The illustration of a single vessel trajectory . . . . . . . . . . . . . . . 20Figure 3.4 The illustration of noises and removal process in a vessel trajectory . . 21Figure 3.5 The illustration of detected noises . . . . . . . . . . . . . . . . . . . . 21Figure 3.6 The demo of information loss using DP algorithm as compression . . . 22Figure 3.7 The visualization of port polygons . . . . . . . . . . . . . . . . . . . . 24Figure 3.8 Visualizing port labeling results for a single vessel trajectory . . . . . . 25Figure 3.9 Sample of the generated OD data . . . . . . . . . . . . . . . . . . . . 26Figure 3.10 Sample of the OD data after analysis . . . . . . . . . . . . . . . . . . 27Figure 3.11 The distribution of distance and duration of trips collected in the ODdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Figure 3.12 Visualization of trips extracted from the AIS data . . . . . . . . . . . . 29Figure 3.13 A demo of trip trajectories after region filter in the NWE region . . . . 30Figure 3.14 The architecture of the multi-layer perceptron [1]. . . . . . . . . . . . 32Figure 3.15 The illustration of 1-dimensional convolution for vessel trajectories. . . 32xLIST OF FIGURESFigure 3.16 The illustration of the recurrent neural network unrolled [2]. . . . . . . 34Figure 3.17 The architecture of the MTL in deep neural networks [3]. . . . . . . . 36Figure 3.18 The architecture of the MTL model for trajectory and destination pre-diction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Figure 4.1 Design of our experiments. The training data is generated with theknowledge extraction processes of our framework (see Fig. 3.1). Next,we compared single-task models with different types of neural net-works. Finally, we evaluate the performance of MTL models on ourprediction targets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Figure 4.2 Data distribution for regions . . . . . . . . . . . . . . . . . . . . . . . 46Figure 4.3 Comparisons of the cumulative distribution function of distance erroramong five region and overall distance error for the three models . . . 50Figure 4.4 Comparisons on the cumulative distribution function of overall dis-tance error and each regions’ distance error for three different weights . 54xiGlossaryAIS Automatic Identification System. 1IMO International Maritime Organization. 1ML Machine Learning. 2DL Deep Learning. 2ANN Artificial Neural Network. 11MSA Maritime Situation Awareness. 12OD Origin-Destination. 12LSTM Long Short-Term Memory. 13MLP Multi Layer Perceptron. 13WPI World Port Index. 17MMSI Maritime Mobile Service Identity. 20DBSCAN Density-Based Spatial Clustering Applicationswith Noise. 22DNN Dense Neural Network. 30ST Spatio-Temporal. 30CNN Convolutional Neural Network. 30RNN Recurrent Neural Network. 30RMSE Root Mean Squared Error. 40MAE Mean Absolute Error. 40AUC Area Under Curve. 41GPU Graphics Processing Unit. 47xiiGlossaryCDF Cumulative Distribution Function. 47xiiiAcknowledgementsFirst of all, I would like to present immense gratitude to my supervisor, Professor ZhengLiu, whose guidance and encouragement throughout my graduate study have been indispens-able. I have attained valuable research skills and technical writing skills from his guidance.Additionally, I would also like to thank him for helping with the opportunity to work withindustries, which has brought me precious working experience. I have developed professionalattitude and skills in my graduate study with the help of my supervisor.I would like to thank my co-supervisor, Dr. Eric Li, who was willing to help as a mentorand a friend. I also present my greatest gratitude to him for giving guidance in the perspectiveof marketing throughout my graduate studies. He has been a great role model to work with.I would also like to thank Dr. Liwei Wang and Dr. Mahmudur Fatmi for their willingnessto serve on the advisory committee. I really appreciate their valuable time and constructivecomments on my thesis.In addition, I would like to give my appreciation to all of my colleagues at the IntelligentSensing, Diagnostic and Prognostic Research Lab (ISDPRL), who have encouraged me andtried to offer help with my research. Especially, I would thank Mr. Junchi Bin, Mr. Cheng-KaiZhang, and Mr. Amirhossein Zaji for their support and constant encouragement during the pasttwo years.Finally, I owe my parents the greatest gratitude for their understanding, mental support,patience and love over all the past years. I could not possibly achieve the research work withouttheir continuous encouragement and support, especially their kindness and care to me. I wishthe happiest and healthiest life to my parents.xivChapter 1Introduction1.1 Background and MotivationData being created, captured, and consumed worldwide over the past few years had gonethrough an exponential growth. Exploiting the massive amount of data has become the mainfocus for most of the corporations, regardless of which the domain they are in. In shipping,equipping Automatic Identification System (AIS) is mandatory for almost every ship due tosafety concerns (i.e. collision avoidance). AIS provides information of the ships’ location thatcan be received by satellites for monitoring. Hence, AIS has become a great data source forapplications that benefits the maritime transportation sector.Currently, AIS data is collected for three major endeavors: vessel tracking, monitor mar-itime activities, and avoid collisions. The AIS system is a mandatory equipment for shipsregulated by the International Maritime Organization (IMO). As a major vessel surveillanceand communication system worldwide, AIS data becomes essential to the maritime industry.On that note, historical AIS data stands a ground for maritime stakeholders as it can be utilizedin pattern recognition for global shipping routes.Safe and efficient maritime transportation planning depends greatly on understanding ves-sel travel patterns and deriving vessels’ future positions. Predicting the next positions of au-tonomous vessels help collision avoidance [4]. Vessel voyage’s static information such asdestination port, however, is manually set therefore is unreliable in some extent [5]. Hence,stakeholders such as port authorities and vessel operators often faced challenges to predict avessel’s future position and destination. AIS provides a vast amount of data containing self-11.2. Research Gaps and Objectivesreporting positioning messages of vessels [6]. With proper handling, these AIS data can beutilized to achieve the endeavor to enhance maritime safety and efficiency by inferring vessels’future positions.Beyond the original endeavor of monitoring vessel positions, AIS data has transformed intoa crucial source for the stakeholders in the maritime industry, including diverse sub-sectorssuch as supply chain, finance, and logistics. As a conservative and uncomputerized industry,many maritime segments were operating through complex manual process, which cost a lot oftime and money. Recently, as service providers in the maritime industries offer data solutions,firms in supply chain and logistics can digitalize their applications. Predictive analytic andDeep Learning (DL) models can also be derived from historical AIS data to forecast futurepositions of the vessels, giving ship operators a thorough sense of navigation, and leveragingthe power of data to guide decision making. As a result, this thesis ought to investigate theprediction of vessels’ future positions, including trajectories and destinations, utilizing deeplearning models.1.2 Research Gaps and ObjectivesAIS data is currently being adopted beyond its original use, given the progressive increaseof satellite-based receiver networks [7]. With the expansion of AIS data received, numerousmaritime transportation researches apply ML and Deep Learning (DL) to vessel trajectoryprediction (e.g. [8–10]) and vessel destination prediction (e.g. [11–13]). However, as themaritime industry transform digitally, certain gaps are discovered to cope with new needs. Theremain of this section discusses the research gaps and the objectives of this thesis.1.2.1 Research GapsGiven the research background and previous literature, several gaps evolved regarding thecurrent field. The research gaps are listed as follows:21.2. Research Gaps and Objectives1. Maritime predictions that aim at avoiding collisions were restricted in terrestrialarea, which may not be adequate for cargo inspection applications that took placein broader ocean regions. AIS data was originally envisioned as a collision avoidancesystem to ensure waterways safety. Traditional AIS is only available within the terrestrialarea. However, the use of satellite to capture AIS brought the coverage of data to aglobal basis. This revolutionizes what we know about the vessels in the middle of theocean. Hence, progressing beyond its original usage, satellite AIS data plays a big rolein accelerating the digitization of global maritime trade, maritime cargo inspection, andsupply chain management.Modeling within terrestrial area may meet the needs of predicting vessel future trajectoryto avoid collisions. However, from a supply chain perspective, inspections of maritimecargo happen for on the way vessels all around the globe. The area of interest wouldsometimes go beyond the terrestrial area. In this case, predictions on an ocean level,where positions of vessels can range from one port to another (trip-based), is requiredfrom the maritime stakeholders.Aiming at solving collision problems, previous studies investigated vessel movementprediction methods on numerous area that are smaller in scale. Due to the nature of theirobjectives, extant studies have been focusing on patterns of a single vessel, few vesselstraveling between two ports, or vessels within port areas. As far as we know from theliterature, there has not been a study investigating trip-based vessel trajectory predictionon a global basis with AIS data.On top of that, deep learning techniques were used for vessel movement predictionswithin terrestrial area. However, in terms of ocean-level trip-based maritime prediction,multiple combinations of ports should be considered when vessel trajectory were ex-tracted from raw AIS data for model training. To our knowledge, there has not been astudy investigating the extraction of knowledge and trip-based trajectories from raw AISdata tailoring for supervised learning tasks.31.2. Research Gaps and Objectives2. The prediction of vessel trajectory and destination were viewed separately. Theprediction of vessel trajectory and destination are related to each other since they areboth on the vessel’s future path. However, as far as we know from the literature, therehas not been a study making a model learn to predict both targets (vessel trajectory andvessel destination) at once, leveraging the relationship between them while lowering theloss when training, will benefit the model with higher performance.1.2.2 Research ObjectivesFigure 1.1: Illustration of the research objectivesAs shown in Fig. 1.1, considering the research gaps, the main research question of thisthesis is to investigate whether deep learning prediction methods using trip-based trajectoriesextracted from the AIS data can successfully predict vessel future trajectory and destinationwithin ocean level region with the aim to support maritime cargo inspection. The objectiveincorporates the following three questions:1. Can trip-based trajectories be extracted through an automation process from raw AISdata?2. Can deep learning models achieve general results for vessel trajectory and destinationprediction in broader ocean-level territory?41.3. Thesis Outline3. Would learning to predict vessel trajectory and destination together benefits the perfor-mance?To address the main research question, we identify five research objectives that are ad-dressed in this thesis. The objectives are listed as the following:1. Develop a global-based automated system that extract knowledge and trip-based trajec-tories from raw AIS data.2. Develop deep learning prediction models to predict vessel trajectory and destination us-ing the extracted knowledge and trip-based trajectories.3. Develop deep learning architectures that can learn and optimize the prediction of vesseltrajectory and destination together.4. Evaluate the prediction models with different types of neural networks5. Evaluate the effect of prediction vessel trajectory and destination separately and together.1.3 Thesis OutlineThe thesis is organized into five chapters.Chapter 1 presents the background of the vessel route estimation (trajectory prediction anddestination prediction), the current challenges, the objectives, and the brief introduction to theproposed solution in this thesis.Chapter 2 reviews the existing AIS-based maritime predictive applications. The learningapproaches for vessel trajectory prediction and vessel destination prediction in the previousresearches are reviewed. The main contribution, research gaps, and potential opportunities arealso discussed in this chapter.Chapter 3 presents the methodologies of the proposed framework that deal with the dis-covered research gaps mentioned in chapter 2. The framework includes a general automation51.3. Thesis Outlineprocess to prepare AIS data for training, the trajectory and destination prediction methodolo-gies for pre-processed AIS trajectories, and the MTL architectures that enhance the generalprediction results for vessel destination prediction.Chapter 4 presents the experiments and results designed based on the proposed framework.The performances of vessel trajectory prediction and vessel destination prediction on differentdeep neural networks are being compared. The MTL architecture is also being implemented tosee if enhancement on the prediction performance exists. The comparisons are done validatingthe vessel trajectories over five filtered ocean-level regions.Chapter 5 concludes this thesis and highlights the key theoretical and practical implications.This chapter also addresses the research limitations and future research directions.6Chapter 2Literature ReviewThis thesis proposes DL solutions for AIS-based vessel trajectory and destination predic-tion. As mentioned in the introduction, raw AIS data requires certain processing and knowl-edge extraction before it can be feed into a DL model. Regarding AIS-based DL predictionscovering larger region, the essence of establishing steps for turning raw AIS data into trainableindices becomes clearer. Hence, this research reviews the literature concerning topics relatedto the mining of AIS data and maritime trajectories, followed by the literature concerning ap-plying DL on maritime predictions.Section 2.1 gives an overview of preparing and utilizing AIS data for retrieving maritimeknowledge. Reviews on data preprocessing and knowledge extraction are presented in Sec-tion 2.1.1 and Section 2.1.2, respectively. Subsequently, Section 2.2 provides a review of DLpredictive applications for the maritime domain. Vessel trajectory prediction and vessel desti-nation prediction researches are reviewed in Section 2.2.1 and Section 2.2.2, respectively.2.1 Preprocessing and Knowledge Extraction for AIS Dataand Maritime TrajectoriesThe quality of the AIS data depends on the vessels’ navigational state, the AIS transceiverson board, and the receivers coverage [14]. In other words, AIS data prone to have unreasonablepoints which requires preprocessing. With the nature of reporting positions, the AIS data thatare filtered by vessels can also be viewed as a trajectory. Maritime trajectories are also proneto have noises, which needs further cleaning. With proper handling, useful knowledge can be72.1. Preprocessing and Knowledge Extraction for AIS Data and Maritime Trajectoriesextracted from these AIS-based maritime trajectories. This section reviews previous researchesregarding the topic of AIS data preprocessing and knowledge extraction for vessel trajectories.2.1.1 AIS Data and Maritime Trajectory PreprocessingA spatial trajectory is the trace of a moving object in geographical spaces. It is usually rep-resented by series of chronologically ordered points. While AIS data consist of self-reportingposition for numerous different vessels, extracting data of a single vessel can form a trajectory.Trajectory data can be categorized into four major categories: mobility of people, mobility oftransportation vehicles, mobility of animals, and mobility of natural phenomena [15]. Vesseltrajectory falls into the category of the mobility of transportation vehicles. However, it is fardifferent from the trajectory of automobile in terms of road segments, duration, and geograph-ical coverage. Y. Zheng classified trajectory preprocessing techniques into four dimensions,consisting of noise filtering, stay point detection, trajectory compression, and trajectory seg-mentation [15]. The following context of this section reviews the four category of trajectorypreprocessing and maritime researches that falls into these fields.Noise FilteringPoor positioning signals and sensor noises may result in undesirable outliers for vesseltrajectories derived from raw AIS data. Fig. 2.1 was part of a trajectory of a single vessel, wevisualized the trajectory with Tableau. As shown in Fig. 2.1, the trajectory extracted from theAIS data has a noise at the top right corner, where the longitude of the observation had changesdramatically. Hence, filtering such noise point is necessary before starting tasks like mining ortraining a model.In maritime trajectory preprocessing, several researches have been done regarding thistopic, where vessel trajectory reconstruction is conducted. L. Zhang et al. [16] proposed anovel trajectory reconstruction model for the vessel trajectory inferred from the AIS data. Themodel first conducted an outliers removal to filter out abnormal observations. Then, it catego-82.1. Preprocessing and Knowledge Extraction for AIS Data and Maritime TrajectoriesFigure 2.1: An example of a noise in a vessel trajectoryrized the trajectory into different states according to the location and speed. Finally, the modelperform different fitting procedures for trajectories within each states. The research focusedon preprocessing high-frequency AIS data located within the port water area. Regarding thislimitation, filtering noises for ship trajectories beyond the port water area, where time intervalbetween two AIS data point in a extracted trajectory is generally higher, could be explored infurther studies. This would help construct vessel trajectory in a port-to-port base, and could po-tentially benefit maritime stakeholders who focus on large-scale maritime transportation (e.g.maritime cargo inspection business).L. Zhao et al. [17] describes noises in vessel trajectories as a problem of spacial logicalintegrity, and suggests a method that can detect outliers in a track using illogical speeds calcu-lated from two consecutive positions. Then, the detected outliers can be removed. Althoughthe research was conducted in port waters (Zhoushan Islands), the method could be expandedto larger ocean reagions.Stay Point DetectionIn a trajectory, locations where subjects stay for a while are called ”stay points.” For ex-ample, tourist attractions in human mobility trajectories and gas stations in vehicle trajectoriescan both be called stay points [15]. With stay points detected, trajectories of time-stamped92.1. Preprocessing and Knowledge Extraction for AIS Data and Maritime Trajectoriessequences can be turned into sequences of locations and support further applications [18]. Twotypes of stay points can be denoted in terms of positioning records. For the first type of staypoints, the position of the observation remains stationary for a while. On the other hand, theobservation of the second type of stay points remains stationary but with the position readingshifting around [19]. In general, the stay points in vessel trajectories occurs as the second type.For vessel trajectories, port or dock for vessel to stay can be denoted as stay points. How-ever, these information is not available in AIS messages. Hence, process is needed in staypoint detection for vessel trajectories. C. Zhang [20] implemented the DBSCAN clusteringalgorithm to form stay points cluster and identify the clusters within the range of the port co-ordinates as a ”port stay point.” The port stay points detected in the research supported thesegmentation of the trajectories as inputs for training the destination prediction model, whichwould be discussed in Chapter 2.1.1:trajectory segmentation.Trajectory CompressionConsidered as a measure of data reduction, trajectory compression aims to reduce redun-dancy in data by discarding the number of location points by forming an approximated trajec-tory from the original trajectory [21]. For AIS-based data mining tasks, trajectory compressioncould be beneficial as the position readings is recorded every few minutes.Douglas-Peucker algorithm [22] is a well-known algorithm in trajectory compression, whichviews the task as a line generalization problem. The Douglas-Peucker algorithm aims to keepdirectional trends by approximating the original trajectory with line segments, under a thresh-old of perpendicular Euclidean distance error. For vessel trajectory compression, the Douglas-Peucker algorithm is well adopted in traffic flow pattern recognition [23] and clustering vesselroutes [24].As literature suggest, this method could be implemented on pattern recognition tasks formaritime traffic. However, it neglects the temporal dimension of a ship’s route [17]. WhenSpatio-Temporal (ST) [25] research is conducted using vessel trajectories, other data reduction102.1. Preprocessing and Knowledge Extraction for AIS Data and Maritime Trajectoriesmethods should be considered. For example, with regards to predict vessels’ future trajectories,R. Chen et al. [26] and N. Forti et al. [27] resamples the trajectory with fixed time intervalas a data reduction measure. This approach can also unify the format of the input trajectorysegments and the prediction results.Trajectory SegmentationDividing a trajectory into segments enables us to extract more knowledge, such as sub-trajectory patterns, beyond the knowledge that can be inferred from a whole trajectory. Seg-mentation techniques can be categorized into 3 types, namely, time-interval based [28], trajec-tory shape based [29, 30], and semantic meaning based [31].For the maritime domain, AIS records contains historical trajectories of vessels that visitedmultiple ports. With these ports recognized (with stay point detection techniques) as points ofsemantic meanings in a trajectory, the trajectory can be segmented into sub-trajectories whichrepresents voyages for a vessel. C. Zhang et al. [32] segmented the vessel trajectories with”port stay point” detected to support the training process of vessel destination prediction. Onthe other hand, K. I. Kim et al. [33] proposed a method that can preprocess AIS data for trainingArtificial Neural Networks (ANNs) that can predict ship traffic flow. The method categorizeships’ speed features as thresholds to segment ship routes into different gate lines, which canbe used as inputs for predicting ship flows. However, as the literature suggests this methoddoes not accurately predict multiple ship movements.2.1.2 Maritime Knowledge ExtractionWith preprocessed AIS-based vessel trajectories, further knowledge could be determinedproviding information for maritime stakeholders and support prediction tasks, such as route es-timation [34]. Knowledge extracted from vessel trajectories can be ranging from global tradepatterns [35] to traffic route patterns [11]. In this section, knowledge extraction literature thataims at or could potentially support vessel trajectory or destination prediction is reviewed. V. F.112.2. Deep Learning in Maritime PredictionsArguedas et al. [36] proposed a method on extracting representation of vessel routes from his-torical AIS positioning data. The experiment done within the area of Dover Strait definitivelydemonstrates the effectiveness of the knowledge extraction process. The result of knowledgediscovery can potentially support the Maritime Situation Awareness (MSA) applications, suchas track reconstruction from missing information, situation/destination prediction, and detec-tion of anomalous behaviour. As the author suggest, the experiment was done with a specificarea of interest, hence, further development of the study lie in scaling the knowledge extrac-tion process to a global scale. With another approach to extract zones for maritime traffic, L.Zhang et al. [37] proposed an analytical approach to analyze ship traffics in the Singaporeport waters. The study categorize areas near port into maritime traffic zone and constructedan Origin-Destination (OD) matrix based on the zones for ship demand analysis. Moreover,the study transform speed distribution into spatial grids for exploring traffic density spatial-temporal dynamics. The experimental results show the method is capable of gaining insightsfor ship traffics in the port water, which may potentially support port operators’ decision mak-ing. Future opportunity for the study would be applying the analysis results to other maritimetasks, such as ship route estimation.2.2 Deep Learning in Maritime PredictionsAIS was made initially for collision avoidance. However, it has become the source formany researchers and stakeholders to understand more about marine transportation patternsand further predicts future maritime activities [34]. In recent years, with the rise of compu-tational power and the amount of data being collected, ML and DL have transformed data inmany domains. In terms of AIS data, this section reviews the learning methods for vessel tra-jectory and destination prediction that has been done in previous researches and identifies theunexplored domains and potential opportunities for this research area.122.2. Deep Learning in Maritime Predictions2.2.1 Vessel Trajectory PredictionsRecent researches on vessel trajectory prediction with learning models have succeeded incapturing patterns and long-term dependencies. J. Liu et al. [8] proposed a Support VectorRegression model optimized by Adaptive Chaos Differential Evolution algorithm for its suit-ability on small sample training. The results show that the model can be trained with a singlevessel and predict its ship trajectory with low losses over the Tianjin port. However, as stated inthe literature, the prediction error becomes large if the target trajectory pattern is different (e.g.,trajectories of different vessels, destination ports, etc.). Also, by training historical trajectoriesnear the Tianjin port, H. Tang et al. [9] presented an Long-Short Term Memory (LSTM) modelthat can predict the trajectory of the next 10 mins for ships with different patterns. However,as the author suggest, the model failed to predict the trajectory for specific ships due to theirunique patterns.W. Li et al. [10] clustered vessel trajectory patterns and modeled these motion patternsvia LSTM networks for predictions up to 15 minutes. Training trajectories within the SouthChannel of the Yangtze River Estuary, the results show the capability of LSTM networks indistinguishing vessel patterns for predictions. N. Forti et al. [27] proposed a sequence tosequence model with encoder-decoder LSTM architecture to capture long-term dependenciesfor AIS trajectories from two ports (Piombino to Portoferraio). The LSTM-based networkin the experiment shows improved performance at the vessels’ turning waypoints. R. Chenet al. [26] combined the Mixture Density Network and LSTM to learn long-term contextualinformation from the historical trajectory. The experiment is done with incomplete trajectorytraveling towards the port of Virginia, predicting the future path of the vessel for up to an hour.[38] explored ML techniques and Multi-Layer Perceptron (MLP) for trajectory prediction taskswith ship trajectories over the Aegean Sea. The work demonstrated the possibility of modelingmultiple vessel routes in a single model and claimed the generalization of trajectory predictionwith ML approaches.These researches focused on fitting a learning model to predict vessel trajectories pertain132.2. Deep Learning in Maritime Predictionsto regions with smaller scales, i.e., trajectories in a port, between 2 ports, and within a smallocean division. In this case, the model’s capability of predicting trajectories that travel beyondthe regions remains unknown. Hence, investigations of utilizing DL models for vessel routeestimation on ocean-levels, where more diverse ship paths and travel patterns are involved,remains unexplored. Moreover, long-term trajectory prediction is more crucial in some cir-cumstances, e.g., prevent collisions for certain types of vessels with restricted maneuverability,port arrangements, and traffic route guidance. This also requires more general models trainedwith historical trajectory data covering regions with larger scales.This thesis ought to investigate how DL models perform in predicting vessel trajectorieswhen a more complex scenario is introduced, where the scale of the regions is expanded, andmore combinations of ports are involved. In the meantime, this research also explores thegeneralization of DL models in different ocean divisions globally.2.2.2 Vessel Destination PredictionsPredicting maritime destinations with AIS data is much more complex than purely predict-ing trajectories since most AIS data that can be acquired is lacking information of vessels’port information[32]. Thus raw AIS data requires specific preprocessing and feature extractionprocedures, especially for neural network-based methods[17].In terms of clustering-based approach, G. Pallotta et al. [11] is a classic research utiliz-ing AIS data for vessel destination prediction. The proposed traffic route anomaly detectionmethod in the research uses route clustering techniques to extract maritime movement patternswithin any predefined bounding box, which represents area that were surrounded by an imag-inary rectangle. The route classification method affects vessel destination prediction withincertain bounding box limits such as the Strait of Gibraltar. However, as the regions of interestexpand in the Indian Ocean, the lack of constraint and density would lower the percentage oftraffic that can be clustered.C. Zhang et al. [32] proposed a random forest-based similarity measurement method for a142.3. Summarygeneral vessel destination prediction. The research established a global prediction for vesseldestination, where the predictive boundary is not restricted to a specific region. However, themodel can only predict with around 70 percent accuracy, which is considered a limit to thisresearch.As for neural network-based approaches, D.-D. Nguyen et al. [12] proposed a sequence-to-sequence model base on LSTM for vessel destination predictions. The model maps vesseltrajectories to sequence of spatial grids’ within the Mediterranean Sea as the regions that thegiven AIS data falls in. Inspired by recent success in natural language processing techniques,the research discretized the vessels’ coordinates into spatial grids to predict their arrival ports.With the same region of interests among 40 ports in the Mediterranean Sea, C.-X. Lin et al.[13] used neural network classifiers in prediction of arrival ports. The AIS data in the neuralnetworks based researches stated above came from the DEBS challenge of the Hobbit project[39], where the port information is given, and the data sets are preprocessed as different trips.However, in most cases, the port information is not given in the AIS messages. On top of that,historical trajectories were used purely as inputs instead of as an auxiliary task for destinationprediction in these researches. Whether considering the loss of trajectory can improve accuracyin predicting vessel destination remains unexplored.This thesis visits the limitations and gaps mentioned in the above literature by proposinga framework for vessel trajectory and destination prediction. The first part of the frameworkprovides a procedure to extract information from raw AIS data for DL. In the second part, aMTL architecture is proposed to improve accuracy in predicting vessel destination.2.3 SummaryThis chapter provides a comprehensive review on exploiting AIS data for knowledge extrac-tion and maritime predictive applications. For maritime predictions, learning based approachis garnering more interest for vessel trajectory and destination prediction. However, these re-152.3. Summarysearches mainly focus on port water area. The expansion of satellite AIS coverage enables thepositioning data to be not restricted near the port. To better predict vessel trajectory and des-tination with regions in larger scale globally, this thesis proposed a DL framework that takesraw AIS data and produce predictive models for vessel trajectory and destination.16Chapter 3AIS-based Vessel Trajectory andDestination Prediction3.1 Overview of the MethodologyThe overall architecture of the proposed methodology takes maritime data as inputs, ana-lyze and generate trip data for each vessels in the knowledge extraction process, and outputsvessels’ future trajectories and destinations in the prediction process (Fig. 3.1). AIS data andWorld Port Index (WPI) data enables the extraction of vessel trajectories segmented by tripsregarding several regions of interest. The extracted trajectories can then be the inputs for pre-dicting ships’ future trajectories and destinations. By utilizing the framework, one can conductprediction on vessels’ future paths within their areas of interest.Figure 3.1: The overall architecture of the proposed methodology. The details of the maritimedata and knowledge extraction approaches are presented in Chapter 3.2. The details of maritimepredictions and the architecture of the MTL model are presented in Chapter 3.4.173.2. Knowledge Extraction for Learning with AIS Data3.2 Knowledge Extraction for Learning with AIS DataSince the early 2000s, AIS data have become a major source for tracking marine trafficfor safety concerns. This section present a procedure that uses AIS data to develop cleaneddata sets that are categorized by vessels of interests and segmented by different trips of thosevessels. This section also introduced the background of AIS data and WPI data, the proceduresof preprocessing them into cleaned trajectories, the algorithm that recognized start and end portfor vessels, the extraction method of vessel trajectories segmented by trips, and the filteringmethod that can be implemented in this framework for regions of interests. Finally, with theprocedures above, AIS data can be turned into training inputs for the prediction model in thenext section.3.2.1 Data DescriptionsIn terms of maritime safety and avoiding collision, equipping AIS devices was made oblig-atory by the International Maritime Organization (IMO) for most ships [6]. Since the AIS mes-sages transmit through non-secure channels, they are widely adopted by vessel traffic services[40]. With the nature of monitoring marine traffic, AIS data can also be used in the perspectiveof maritime traffic researches, such as collision avoidance [41]. This study focus on extractingvessel voyages for future paths and destination prediction with AIS data. Hence, among the 27types of AIS messages, the position type of AIS data was adopted as the variable descriptionshown in Table 3.1. The original parameter descriptions of type 1 AIS messages (the positiontype of AIS messages) can be found in the regulations of International TelecommunicationsUnion[42].Since the AIS data are just positions for different ships, port information is needed to extractvoyages. Hence the WPI data is adopted for reference of port positions [43]. Port name,latitude, and longitude of ports is the primary concern in this research. Since 3 months timespan could reflect vessel patterns for a season, in this thesis, AIS data within the three months183.2. Knowledge Extraction for Learning with AIS DataTable 3.1: Variable descriptions of AIS dataParameter DescriptionUser ID Unique identifier such as MMSI number.LongitudeLongitude in 1/10000 min (±180◦, East = positive(as per2’s complement), West = negative (as per 2’s complement).)LatitudeLatitude in 1/10000 min (±90◦, North = positive(as per2’s complement), South = negative (as per 2’s complement).)Time stampUTC second when the report was generated by the electronicposition system (EPFS).SOG Speed over ground in 1/10 knot steps (0-102.2 knots)time span between Oct.1, 2018 and Dec.31, 2018 are adopted.3.2.2 Data PreprocessingThis thesis proposed a flowchart of preprocessing procedures specifically tailored for theglobal-based AIS trajectory referencing general trajectory preprocessing procedures [15] andAIS preprocessing procedures [17]. General trajectory preprocessing methods include noisefiltering, stay point detection, and trajectory compression [15]. However, AIS data requiresfurther cleaning regarding the validity of AIS trajectories and the spatial context within tra-jectories [17]. Considering both the nature of AIS messages and trajectories, the flowchart ofpreprocessing procedures for the AIS trajectory is shown in Fig. 3.2.Figure 3.2: The flowchart of the preprocessing procedures193.2. Knowledge Extraction for Learning with AIS DataTrajectory Extraction and AIS Validity FilteringThe preprocessing procedures starts with extracting data with MMSI and IMO number,which is the form of vessel identity in AIS messages, to different trajectories. Consideringthe completeness of AIS tracks, the AIS validity filtering exclude situations where 2 or morevessels share the same MMSI number using IMO as a cross reference. In other words, thevessels with the same MMSI but with multiple IMO number will be excluded, since there isa conflict with the vessel ID and the extracted trajectory would be biased. The comparisonsbetween the two types of IDs are shown in Table 3.2. Each vessel defined by IMO (since MMSIcould be duplicated) would be stored in a file as a trajectory. Then, the following process couldbe done with vessel (MMSI and IMO) as a unit. Fig. 3.3 shows a demo of a single extractedvessel trajectory.Figure 3.3: The illustration of a single vessel trajectoryTable 3.2: Comparisons between two types of vessel IDsID Type AIS Type Reassign PossibilityMMSI Position, Static YesIMO Static No203.2. Knowledge Extraction for Learning with AIS DataNoise FilteringAs mentioned in Section 2.1.1, poor positioning signals may lead to outliers in AIS data.Hence, noise filtering needs to be done for all extracted vessel trajectories. Fig. 3.4(a) showsthe noises in a vessel trajectory, while Fig. 3.4(b) shows the same trajectory after noise filtering.(a) Noises in a vessel trajectory (b) Vessel trajectory after noise filteringFigure 3.4: The illustration of noises and removal process in a vessel trajectoryFigure 3.5: The illustration of detected noisesThe process of noise filtering is to first identify the outliers, then remove them. To detectthe outliers, clustering method is adopted. A single trajectory is detected as a cluster, then213.2. Knowledge Extraction for Learning with AIS Datapoints that does not belong to the cluster is recognized as noises. The implementation is donewith Density-Based Spatial Clustering Applications with Noise (DBSCAN) clustering method.Fig. 3.5 shows detected noise (red) in a trajectory.Trajectory ResamplingTaking the idea of trajectory compression mentioned in Section 2.1.1, this thesis resampledresampled trajectories into hourly bases as a sliding window based algorithm of compression,which reduced the computational power and set off the data for further analysis. Though insome other literature for trajectory data mining, compression was done based on the DouglasPeuker (DP) algorithm, the results would lose too many information about the trajectory aswe examined. The algorithm search for drastic turning points in a trajectory but ignores thepositions in the middle, which in the case of a maritime trajectory, the change of directionsare often mild. Fig. 3.6 shows a simple demo of the information loss with the algorithm (thered points are the original trajectory and the blue points are the trajectory after compression).Since we want to capture the whole shape of the trajectory but meanwhile have certain amountof data deduction, we selected the time-based resampling approach.Figure 3.6: The demo of information loss using DP algorithm as compression223.2. Knowledge Extraction for Learning with AIS Data3.2.3 Origin-Destination (OD) RecognitionObtaining OD matrices has been a common way of dealing with spatiotemporal data indifferent types of trajectories such as vehicle and human mobility [44]. G. Spiliopoulos et al.[35] presented a similar method with the port calls data since the AIS messages don’t providetrustworthy information of the ports. In this thesis, a port-based OD recognition was conductedwith the combination of AIS and WPI data. We proposed the port-based OD recognition tech-nique with the endeavor of extracting vessel trips. In this thesis, the definition of a vessel tripis based on the time and position when a vessel departs from a port (origin) and the time andposition when a vessel arrives at another port (destination). The process of OD recognition canbe categorized into three parts: port labeling, OD data generation, and OD data analysis.Port LabelingKnowing the information of the ports is essential to the process because it stands for the de-parture and destination points, which is part of our input and output in the maritime prediction.The port labeling process aims to label the positions where the vessels remain stationary at portarea. As mentioned in Section 3.2.1, the WPI data provides various information about portsaround the globe. By combining the knowledge of the AIS and the WPI data, port labeling isdone in the following steps:1. Detecting ”stay points”: As mentioned in Section 2.1.1, stay points occurs when objectsin a trajectory stays at a certain location for a while. Detecting stay points in a trajectoryhelps narrow down the target so that positions where vessels are ported can be furtherlocated. To detect stay points, clustering method is adopted. Positions that remainsstationary but shifting around is detected as a clustered stay point, then positions thatdoes not belong to the cluster is recognized as moving vessels. The implementation isalso done with the DBSCAN clustering method given the radius of 4 miles and minimumsamples of 4.233.2. Knowledge Extraction for Learning with AIS Data2. Generate port polygons: In order to detect whether a vessel’s stay point is at port, weneed to create and area representing ports (docks) so that we can know if the vesselstay point is within that area or not. The process is done by generating circles aroundall ports in the WPI data, and take the union of circles that intersects with each other.The implementation is done with the python geopandas package. Fig. 3.7 visualizes thegenerated port polygons.(a) Port polygons (b) Zoom in view of port polygonsFigure 3.7: The visualization of port polygons3. Label port positions: With the port labeling method, we can determine whether a staypoint is a port. This process cross check port polygons and the positions of the staypoints, then determine whether it should be consider as a port. Next, for the stay pointsdetected in the previous steps, this process detect if each points are within the generatedport polygons. The stay points within the port polygons would be labeled as ”ported”.Fig. 3.8 visualizes the port labeling results, where the red points are vessel positions thatare labeled as ”ported”. With the port labeled trajectory data, the further OD data generationprocess could then proceed. This would establish a data of the origin (departure port) and thedestination (arrival port) as extracted knowledge from the AIS data.243.2. Knowledge Extraction for Learning with AIS DataFigure 3.8: Visualizing port labeling results for a single vessel trajectoryOD Data GenerationWith the ported points being labeled in the previous port labeling process, a port-based ODmatrices can be generated through the recognition process, which includes 2 parts: check timegaps and Retriece OD indices. During the research it has been found that some AIS data isbound to have missing time gaps even though most of the observations are re-sampled into 1point per hours after preprocessing. Hence, program that can check for and deal with time gapslike this are added to the AIS to OD process. The process are as the following:1. Check time gaps: The process check through observations and see if there are time gapsbetween one position and the following one. The threshold of the program see the dataas time gap is 13 hours. Then, the data is grouped by time gaps into groups for the nextstep.2. Retrieve OD indices: The process recognize origin and destination of each voyage forvessels. This uses the data saved from port labeling process (the variable that indicateswhether a position is ”ported”).Fig. 3.9 shows a sample of the generated OD data. The definition of the variables areshown in Table 3.3. Each observations from data retrieved represents a voyage, which containsthe time, latitude, and longitude when the vessel departed/arrived.253.2. Knowledge Extraction for Learning with AIS DataFigure 3.9: Sample of the generated OD dataTable 3.3: Variable descriptions of OD dataParameters DescriptionsMMSI Unique identifiers for vesselsSTime The start time of the tripSLng The longitude of vessels’ departure positionsSLat The latitude of vessels’ departure positionsETime The end time of the tripELng The longitude of vessels’ destination positionsELat The latitude of vessels’ destination positionsOD Data AnalysisSome analysis could be done with the generated OD data, to gain more knowledge aboutthe vessel voyages and initiate some filtering process to improve the quality of our trajectorydata in the following process. With the positions and time of the origin and destination ofa voyage, distance and duration of the voyage could be derived. Moreover, referencing thecoordinates back to WPI data, the port names and the country of start and end point can alsobe added as the metadata for each voyages.Fig. 3.10 shows the OD data with added information after the analysis. The distributionof duration and distance of the voyages in the OD data are shown in Fig. 3.11. With the ODdata being generated, vessel voyages (trips) could be extracted through combining it with thepreprocessed trajectories data.263.2. Knowledge Extraction for Learning with AIS DataFigure 3.10: Sample of the OD data after analysis(a) Distance distribution (b) Duration distributionFigure 3.11: The distribution of distance and duration of trips collected in the OD data273.2. Knowledge Extraction for Learning with AIS Data3.2.4 Vessel Trip ExtractionIn order to initiate destination prediction for the vessel, a trip-based trajectory need tobe extracted as the input for the model. Much like the semantic segmentation of the vesseltrajectory by port, the proposed trip extraction process queries back to the preprocessed AIStrajectories using vessel voyages information in the OD data. In the process, the AIS pointsbetween the start and end port in the OD data were extracted as trajectory segments. Thealgorithm of the trip extraction procedures is presented in Algorithm. 1.Algorithm 1: Trip ExtractionInput: OD data D = [D1, D2, ..., Dn],AIS trajectories A = [A1, A2, ..., Am]Output: Vessel Trip Dataforeach i ∈ [1,n] doj = index of A where Di{mmsi}=Ai{mmsi};Read A j;stime = Di{STime};etime = Di{ETime};srow = index of A j where A j{timestamp}=stime;erow = index of A j where A j{timestamp}=etime;tripi = A j[srow:erow];Save tripi;endThe extracted trajectory segments are defined as trip trajectories, which are then feed intothe model training process for the prediction in the framework. The trips trajectories extractedwithin the 3 months time span between Oct.1, 2018 and Dec.31, 2018 are stored in separatecsv files. Then, by pushing the coordinates and time of each trip trajectories into geojsonformat, the trip trajectories could be visualized on a world map using kepler.gl [45], a geospatialvisualization framework open sourced by Uber. Fig. 3.12 visualizes the trips extraction resultsgenerated from the AIS data.283.2. Knowledge Extraction for Learning with AIS DataFigure 3.12: Visualization of trips extracted from the AIS data3.2.5 Region Filters for Selecting Interested Sea AreasDefining regions of interest brings focus to areas where the research lies in and reducesredundant data. The region filter process is designed to filter the extracted trip trajectoriesinto different sea regions. [46] divided the world into polygons and conducted a vessel ca-pacity count within each polygons using ray casting algorithms [47]. Since the frameworkin this study aims to predict maritime future paths on a global bases, this thesis adopted themethod and implemented the framework in different sea regions around the globe. The ex-tracted trip trajectories were categorized into the following regions for further predictions(themodel training process): the Atl(Atlantic), EstP(East Pacific), NWE(North West Europe),Med(Mediterranean), and ER (Arabian Gulf, Indian Ocean, and Far East). Figure 3.13 showsa demo of trip trajectories within the NWE region after the region filter process. For the latertraining process, this study focused the interest separately on the trip trajectories within thesefive predefined regions.293.3. Deep Learning AlgorithmsFigure 3.13: A demo of trip trajectories after region filter in the NWE region3.3 Deep Learning AlgorithmsDL is a subset of ML that learns representations from the raw data using ANNs, whichis inspired by the biological neural networks in human brains. In the bigger picture, in com-parison with traditional ML, DL forms a higher level of representation in the data without theneed for a human expert to extract features. With the aim of taking this advantage of DL, theframework proposed in this research takes maritime predictive analytic closer to the raw AISdata. This thesis explores the implementation of DL with AIS data-driven vessel trajectory anddestination prediction.In terms of DL, the simplest form of ANN is MLP, which can also be referred to as DenseNeural Networks (DNN). Moreover, as trajectories can be categorized as ST data, two ad-vanced networks, Convolutional Neural Network (CNN) and RNN, can be used to capturecorrelations in the spatial domain and learn temporal dependencies, respectively [48]. On theother hand, as mentioned in Chapter 2, this research brought together the prediction of ves-sel trajectory and destination, instead of learning them separately. To achieve this endeavor, a303.3. Deep Learning AlgorithmsMTL technique is introduced. Hence, this section goes through the framework of MLP, CNN,RNN, and the MTL technique. Then, the implementation of DL with AIS data is introduced inthe next section.3.3.1 Multi-Layer Perceptron (MLP)A perceptron, also called a neuron (resonated with the biological neuron of the human brain[49]), is the fundamental building blocks of DL. Hence, MLP is neural networks in it’s simplestform. MLP can be formed by three components, an input layer, hidden layers, and the outputlayer. Each layer consists of a number of neurons. Since each neuron is densely connectedwith all the neurons in the previous layers, these layers are called dense layers. The functionof MLP process with trajectory inputs is defined in Equation. 3.1:zi(x) = g(w0,i+m∑j=1x jw j,i) (3.1)where x = {x1, ...,xm} is the set of trajectory data; Z = {z1, ...,zd1} are the neurons inthe next layer of the network (d1 is the number of neurons in the next layer); w are weightsassociated with each input neurons, and w0 are the biases; g is the activation function, whichintroduces non-linearity to the networks.Fig. 3.14 shows the architecture of MLP. The layer that takes inputs from the data set isthe input layer, which is the network’s visible part. Each neuron in the layer passes through afeature in the data as the input value to the next layer. The following layers are called hiddenlayers, which process the information from the input layer and extract representations from thelast hidden layer. Although there is only one hidden layer in the example show in Fig. 3.14,there could be multiple hidden layers in a neural network, the number of which determines thedepth of the network. Finally, the output layer outputs the vector of values that corresponds tothe problems to be solved, which in the case of trajectory prediction, is the future position ofthe vessel.313.3. Deep Learning AlgorithmsFigure 3.14: The architecture of the multi-layer perceptron [1].3.3.2 Convolutional Neural Network (CNN)The CNN is developed for learning with two-dimensional representations internally duringthe feature learning process, which achieve significant performances in image classificationtasks [50], thus supporting the rise of computer vision in real-world applications. The sameprocess can be applied to the one-dimensional sequence of data, which corresponds to the AIS-based trip trajectories for vessel trajectory and destination prediction. The one-dimensionalCNN can learn spatial properties from the raw time series data directly without the need ofdomain expertise to conduct feature engineering. Thus, this characteristic of CNN can supportsequence classification by learning an internal representation from the time series input. The1-dimensional convolution process for vessel trajectory is illustrated in Fig. 3.15.Figure 3.15: The illustration of 1-dimensional convolution for vessel trajectories.323.3. Deep Learning AlgorithmsThe process connects patches in the input layer to single neurons in the subsequent layer.This allows the network to preserve spatial structures that are inherent to the input. To detectthese spatial features, the kernel with weights is applied to each patch in the input with it’sequal size, weighting the connections between the patches and the neurons. As a result, theneurons in the next layer is defined by applying this patch with kernel of its equal size and it’slearned weights. The calculation of the convolution is defined in Equation. 3.2:hp =m∑i=1k∑j=1wi jxi+p−1, j +b (3.2)where hp is the p-th neuron in the next hidden layer; m and k is the kernel size, whichin the case of Fig. 3.15, both equals 2; w is the corresponding weights of the kernel; x arethe corresponding neurons of the trajectory inputs; b are the biases. After each convolutionoperation, the non-linear activation function is applied to the output volume of the layer [51].With the input features being captured, the next part of the CNN directly apply the dense layersto the features to perform the prediction tasks [2], which in the case of this thesis, the predictionof vessel trajectory and destination.3.3.3 Recurrent Neural Network (RNN)To deal with sequential data, sequence modeling takes in the consideration of the timecomponent, which expands the range of possibilities to situations that can involve temporalinputs. The RNN allows the network to deal with sequential data by introducing recurrence tothe architecture of the neurons. The endeavor is done by applying a recurrent relation at eachtime steps to process sequences.Fig. 3.16 illustrates the process of RNN. The key to the network that allows temporaldimension to be added is the update of the state, ht , at each time steps as the sequence isprocessed, linking the information and the computation of the network at different time stepsto each other [1]. Hence, the output at the time step, t, is defined not only by the input but by333.3. Deep Learning AlgorithmsFigure 3.16: The illustration of the recurrent neural network unrolled [2].it’s combination with the hidden cell state ht . The calculation of the update of the hidden stateht and the output vector yˆt is defined in Equation. 3.3 and Equation. 3.4, respectively:ht = f (W Thhht−1+WTxhxt) (3.3)yˆt =W Thyht (3.4)where W Txh is the weight that transform the input in the hidden state computation; WThh is theweight that defines the relationship between the prior hidden state and the current hidden state;W Thy is the weight that transform the hidden state to the output.Conventional RNN faces practical difficulties of capturing long-term dependencies whentraining sequences with long intervals of temporal contingencies [52]. Therefore, a specialform of the RNN, called LSTM, is proposed to solve the problem [2]. The core idea behindthe LSTM is the cell state. By removing and adding information to the cell state, the networkcan remember relevant information for long periods of time. Gates are a way to optionally letinformation through. Each cell states, Ct , are computed through three gates: input gate, forgetgate, and output gate. The calculation is defined as follows:it = σ(W ixXt +Whi ht−1+Wicct−1+bi) (3.5)343.3. Deep Learning Algorithmsft = σ(W fx Xt +Wfh ht−1+Wfc ct−1+b f ) (3.6)ct = ft⊗ ct−1+ it tanh(W cx Xt +W ch ht−1+bc) (3.7)ht = yˆt⊗ tanh(ct) (3.8)yˆt = σ(W yˆyˆ Xt +Wyˆh ht−1+Wyˆc ct +byˆ) (3.9)In the equations above, i, f , yˆ, and c corresponds to the parameters of the input gate, theforget gate, the output gate, and the cell state, respectively[2]. The W is the set of weights foreach gate in each neuron. First, the input gate with sigmoid activation σ decides which valueswill be updated. Next, the old state is multiplied by ft , determining the things that shouldbe forgotten, and add by new candidate values created with a tanh activation function. Thisupdates the cell state, ct . Finally, the output is computed based on the cell state. The details ofthe equations are given in [49]. In this thesis, vessel trips are considered as sequential inputsfor training. Hence, LSTM were considered as a candidate for modeling trajectory sequences.3.3.4 Multi-Task Learning (MTL) TechniquesIntuitively, the motivation behind MTL is inspired by learning of human [53]. When learn-ing new tasks, the knowledge that a person acquired by learning related tasks are often applied,such as applying the knowledge of running when playing tennis. In the view of applyingANNs, MTL can be viewed as an inductive transfer [3]. Inductive transfer enables the modelto perform in favor of certain hypotheses over others, which is caused by the introduction of theinductive bias. The auxiliary task in MTL provides the inductive bias, which cause the modelto prefer hypotheses that explain more than one task. This could also be viewed as a way of353.4. AIS-Based Maritime Prediction with Deep Learningregularizing the model, ensuring it to generalize better.Figure 3.17: The architecture of the MTL in deep neural networks [3].Fig. 3.17 shows the architecture of MTL in the neural networks. The process is done bysharing parameters for all tasks in the shared layers. In the meantime, the task-specific layersare also preserved to deal with each individual tasks. The model optimizes the losses of all thetasks together. Hence, a representation that fits more to all the tasks are captured. This wouldsignificantly lower the risk of overfitting the model [53].This thesis dealt with two prediction tasks for maritime application, vessel trajectory pre-diction and vessel destination prediction. In a sense, the two task are somewhat related since thevessel would eventually get to it’s destination passing through the future trajectory. Therefore,this thesis implemented the MTL technique to see whether this would improve the predictionof the tasks. The implementation of the MTL is presented in Section 3.4. The comparisons ofthe models with and without the MTL technique is presented in Chapter 4.3.4 AIS-Based Maritime Prediction with Deep LearningThis section focuses on constructing prediction models with the data generated from theknowledge extraction process. With the trip trajectories generated in the previous phase, the363.4. AIS-Based Maritime Prediction with Deep LearningDL models can then be constructed. To enable neural networks to predict the target, the slidingwindow technique can be applied for turning time series data (trip trajectories) into a supervisedstructure [54]. For each timestamp t in a trip trajectory, the input trajectory of the model isx(t−m)λ ,φ , ...,x(t)λ ,φ , where m is the window, xλ ,φ is the vessel’s position, λ and φ is the latitude andlongitude of the vessel. For each input trajectory, there are two targets:• Trajectory target y(t+1)λ ,φ : The position of the vessel at next timestamp during the trip.• Destination target yd: The destination information from the original trip trajectory.Intuitively, each pair of input trajectory and it’s targets can be viewed as an observation ofour supervised input data set. Hence, the input data of the model can be denoted as Equation.3.10, where N is the total observations in the data set, Xλ ,φ ∈ Rm×2 is the input trajectorytensor, Yp ∈ R2 is the vessel position of the next timestamp, and Yd ∈ RK is the destinationcorresponding to each trip as K is the number of ports in D . With the input data and targetsbeing prepared and defined, the application of neural network methods for each single-taskprediction (Section 3.4.1) and the approach for utilizing them for combined prediction as aMTL model (Section 3.4.2) are being explained in the following of this section.D = (x(i)λ ,φ ,y(i)p ,y(i)d )Ni=1(3.10)3.4.1 Maritime Predictive Applications with Neural NetworksIn the concept of DL, the model can be derived as fθ (yp|x) in the case of trajectory predic-tion, and fθ (yd|x) in the case of destination prediction. θ is the parameters of the models. Theobjective then, would be to minimize the loss function L for the corresponding prediction taskswith respect to θ . For trajectory prediction models, the objective can be denoted as Equation.3.11, whereJT (θ) is the cost function for trajectory prediction, J is the number of neurons inthe output layer, hθ ,T (x) ∈ R2 is the output layer computation, and (hθ ,T (x))i is the ith output373.4. AIS-Based Maritime Prediction with Deep Learningof the neural network.minθJT (θ) =1N[N∑i=1J∑j=1(y(i)j −hθ ,T (x(i)) j)2] (3.11)For the destination prediction models, the objective can be denoted as, Equation. 3.12,where JD(θ) is the cost function for the destination prediction, K is the class number (totalports), hθ ,D(x) ∈ RK is the output layer computation, and (hθ ,D(x))i is the ith output of theneural network.minθJD(θ) =− 1N [N∑i=1K∑k=1y(i)k log(hθ ,D(x(i))k)] (3.12)The models are constructed with multiple neural network layers. In terms of the selectionsof neural networks, [48] mentioned that trajectories can be represented as sequence of locationsas well as matrix so thus DL models such as LSTM and CNN can be applied. Hence, inthis study, LSTM, CNN, and DNN were selected for training the trip trajectories. The neuralnetwork with the optimal performances would be selected as the core network of the multi-tasklearning architecture.3.4.2 Maritime Predictive Applications with Multi-Task LearningTechniquesMTL enables the model to learn a more general representation by sharing parameters be-tween tasks [3]. In the MTL model, the prediction of trajectory and destination are consideredtogether. Hence, the objective of the model is denoted as:minθ[JT (θ)+JD(θ)] (3.13)383.4. AIS-Based Maritime Prediction with Deep LearningFigure 3.18: The architecture of the MTL model for trajectory and destination prediction393.5. Evaluation MetricsAs a result, the loss function of the MTL model should consider both targets at once, which isdenoted as:JM = wTJT +wDJD, (3.14)where wT and wD are the weights being assigned to each loss function.Fig. 3.18 shows the architecture of the MTL model. Our activation of all the hidden layersare set as ReLU [51]. The input of the model is the input trajectory, while the port information isfed in as metadata inputs. The shared layer in the model architecture allows parameter sharingbetween the two tasks. The network then splits off into different heads for the prediction ofeach tasks, where the fully connect layer linked to each task can be defined as task-specificlayers. The fully connected layer is formulated as:hW,b(x) = f (W T x+b), (3.15)where x is the input vector; hW,b is the output vector; b is the corresponding bias; W is thecorresponding weights; and f is the activation function. The activation for the position outputis linear, while softmax is selected as the activation for arrival port output.3.5 Evaluation MetricsThis thesis consider two aspects of criteria when exploiting the performance of the experi-ments. Regarding trajectory prediction, the focus was on the criterion of position. In terms ofdestination prediction, our focus lies in the criterion of port classification.3.5.1 The Criterion of Vessel PositionRoot Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are metrics used toevaluate regression problems. In this thesis, since the criterion of predicting vessel position isa regression problem (predicting the latitude and longitude of the vessels), we adopted them to403.5. Evaluation Metricsevaluate the errors of the predictions and the amount of deviations from the actual positions.The lower values of the RMSE and MAE indicates the better performance of the model. Equa-tion 3.16 and 3.17 show the definitions of the metrics, where N denotes the total number oftesting trajectories, y denotes the vector of actual position and yˆ denotes the vector of predictedpositions.RMSE =√∑Ni=1(yi− yˆi)2N(3.16)MAE =1NN∑i=1|yi− yˆi| (3.17)Furthermore, the geographic distance between the predicted positions and actual positionsis also being evaluated. Since the Haversine formula is generally used for navigation purposes[55], it is used in this research to calculate the distance error. The Haversine formula canbe defined as Equation 3.18. Thus, the Haversine distance between two coordinates can becalculated as defined in Equation 3.19. The distance error is computed for each observationin the testing data and calculate the mean distance error DistM and the standard deviation ofdistance error DistS for evaluation.Haversine(θ) = sin2θ2(3.18)d = 2r sin−1√sin2(θ2−θ12)+ cos(θ1)cos(θ2)sin2(λ2−λ12)(3.19)3.5.2 The Criterion of Port ClassificationRegarding the accuracy of destination prediction, this study explore whether the outputof each testing observation are classified as the actual port of destination. The destinationaccuracy is defined in Equation 3.20, where N denotes the total number of testing trajectories,y denotes the vector of actual destinations, and yˆ denotes the vector of predicted destinations.In the meantime, the Area Under Curve (AUC) is also included as a measure of evaluating the413.6. Summarymodels’ performance [56].Accuracy(Acc) =|y∩ yˆ||y∪ yˆ| =|y∩ yˆ|N(3.20)3.6 SummaryThis chapter introduced the methodology for AIS-based vessel trajectory and destinationprediction as a framework. The methodology tailored for deep learning, is mainly consist oftwo phase, knowledge extraction and maritime prediction. In the knowledge extraction phase,raw AIS data was taken through a series of steps including preprocessing, OD recognition, tripextraction, and region filter. This produced the OD data, containing all the voyage informationthat can be derived from the AIS data, and the trip trajectory data, which can be used as theinput for the maritime prediction phase. In the maritime prediction phase, this chapter identifiesthree deep learning networks and a learning technique suitable for this research. Moreover,the architecture of incorporating these networks and techniques with maritime prediction isintroduced. Finally, the evaluation metrics for the two criteria of maritime prediction are given.42Chapter 4ExperimentsIn this section, we first introduce the design of our experiments. Next, we describe the ex-periment setup including trajectory data and training parameters that were used. Furthermore,the evaluation approach, including 2 interested criteria are discussed. Finally, the detailed ex-perimental results are presented.4.1 Experimental DesignFigure 4.1 shows the design of our experiment. In general, the experiment is designed totest the effectiveness of the proposed model over the 5 predefined regions. Specifically, wetargeted at the followings:• Model comparison. Compare the performances of different types of neural networkmodels (single-task) for separate predictions of vessel trajectories and destinations.• Multi-task prediction. Construct MTL models for combined predictions of vessel tra-jectories and destinations and compare the results with the single-task models.4.1.1 Model ComparisonThree neural network models, including LSTM, CNN, and DNN, are being compared byevaluating their performances on trajectory prediction. Each model takes in the preprocessedtrajectory data as inputs to predict the vessels next hour trajectory. In this step, we aim toexplore the effectiveness of predicting trajectory with certain baseline DL methods within the434.1. Experimental DesignFigure 4.1: Design of our experiments. The training data is generated with the knowledgeextraction processes of our framework (see Fig. 3.1). Next, we compared single-task modelswith different types of neural networks. Finally, we evaluate the performance of MTL modelson our prediction targets.444.2. Experimental Setupregions of interests. Moreover, with the same trajectory as inputs, the model takes departureports of each trajectory as another type of input for destination port classification. Similarly,the performances of the three types of neural networks were being compared. The results of thecomparisons were taken into consideration for the construction of MTL model in the followingphase.4.1.2 Multi-Task PredictionThere are two targets of interest in the MTL prediction phase: trajectory prediction anddestination prediction. We select the best neural network model from the model comparisonphase and embed it into the MTL model for a one-hour trajectory prediction and destinationport prediction. Both the loss of the one-hour trajectory and destination were taken into accountwhen training the model. In this phase, we aims to evaluate the effectiveness of destinationprediction with the MTL model and compare the performance of the model when we assigndifferent weights to the loss of two prediction outputs.4.2 Experimental SetupThis section introduce the data set used for the experiment, which is generated from theknowledge extraction phase of our study. Next, this section introduce some parameters thatwas used in our experiment.4.2.1 Trajectory DataFigure 4.2 shows the data distribution over different sea regions in this study. The AIS dataregarding this project is global-based. We filtered them into different regions for more focusedand robust research results. Nonetheless, the scale of the prediction regions is still larger thanrelative researches on maritime trajectory predictions mentioned in Chapter 2.454.2. Experimental SetupFigure 4.2: Data distribution for regionsEach arc line in Fig. 4.2 represents a link between the start and end port of a trip that thevessel has traveled. The trajectories are extracted from these port-to-port connections. Table4.1 shows the number of selected ports and extracted trips for each sea region. When processingthe data for training, we transformed trips into 12-hour trajectories using sliding-window. Eachtrajectory is given a next hour position and a destination as targets of prediction.Table 4.1: The number of selected ports and extracted tripsRegions Ports TripsNWE 39 1168Atl 39 306EstP 14 198Med 21 179ER 15 7104.2.2 Training ParametersFor each sea region experiments in the model comparison phase and the MTL predictionphase, we split the data into training sets (70%) and testing sets (30%). The training sets aresampled with Synthetic Minority Over-sampling Technique (SMOTE) under Imbalanced-learn464.3. Experimental ResultsAPI [57]. We set the training epochs of the neural networks to 500 with batch sizes of 256.All training sessions are implemented under Keras with a server configured with an NVIDIAGP104 GPU.4.3 Experimental ResultsTwo parts of the experiment results were presented. As shown in the experiment design,the model comparison results correspond to single-task predictions done with different types ofneural networks. Next, the MTL model results presents results for MTL prediction done withthe best model selected from the model comparison phase. Both parts include experimentalresults of five predefined regions.4.3.1 Model Comparison ResultsWe compare the performance of the selected neural network models under two dimensions.The results for the experiments are shown as follows:• Trajectory prediction results. The performance comparison among different modelson one-hour trajectory prediction is presented in Table 4.2. The evaluation approachesfor these results falls in the criterion of vessel position stated in Section 3.5.1. We’vealso plotted Cumulative Distribution Function (CDF) plots of the distance error withineach regions, which is shown in Fig. 4.3. In the meantime, we also included a CDF plot(Fig. 4.3 (a)) for the overall distance error.• Destination prediction results. The performance comparison among different modelson vessel destination prediction is presented in Table 4.3. The evaluation for approachesfor these results falls in the criterion of port classification state in Section 3.5.2. TheTop1-Acc indicates the model’s accuracy of successfully predicting the destination port,while the Top5-Acc indicates the model’s accuracy of correctly includes the destinationport within its top five predictions.474.3. Experimental ResultsTable 4.2: The evaluation results of trajectory prediction with different neural networksRegion Model RMSE MAE DistM DistSNWE CNN 0.044 0.021 0.652 0.391NWE DNN 0.051 0.027 0.568 0.446NWE LSTM 0.047 0.023 0.537 0.440Atl CNN 0.052 0.030 0.430 0.326Atl DNN 0.081 0.042 0.688 0.608Atl LSTM 0.037 0.018 0.326 0.296EstP CNN 0.134 0.079 0.825 0.532EstP DNN 0.132 0.070 0.655 0.534EstP LSTM 0.120 0.063 0.572 0.531Med CNN 0.063 0.041 4.141 2.955Med DNN 0.068 0.042 4.738 3.552Med LSTM 0.055 0.038 4.071 2.606ER CNN 0.087 0.066 5.247 1.956ER DNN 0.098 0.071 6.217 3.616ER LSTM 0.047 0.035 3.167 1.752Avg CNN 0.076 0.047 2.259 1.232Avg DNN 0.086 0.050 2.573 1.751Avg LSTM 0.061 0.035 1.735 1.125Several observations were obtained from the experiment results. In Table 4.2, we cansee that RMSE, MAE, and the mean and standard deviation of distance error, hold positivecorrelations among the three models. The LSTM outperforms the others in general, especiallyfor the NWE, Atl, and ER regions. Moreover, we can observe lower distance errors for theLSTM in all regions. The average of five regions also corresponds with the observations statedabove. Regarding the performance among regions, we can see higher losses within the Medand ER regions.On the other hand, relatively accurate predictions occur within the NWE and Atl regions.In terms of distance error shown in Fig. 4.3, we can see from Fig. 4.3 (a) that LSTM slightlysurpasses the others while CNN and DNN achieve comparable results. Furthermore, it can be484.3. Experimental ResultsTable 4.3: The evaluation results of destination prediction with different neural networksRegion Model Top1-Acc Top5-Acc AUCNWE CNN 0.350 0.810 0.776NWE DNN 0.611 0.959 0.959NWE LSTM 0.831 0.995 0.990Atl CNN 0.735 0.984 0.942Atl DNN 0.723 0.998 0.974Atl LSTM 0.870 0.999 0.998EstP CNN 0.894 1.000 0.974EstP DNN 0.850 1.000 0.965EstP LSTM 0.956 1.000 0.998Med CNN 0.891 1.000 0.995Med DNN 0.881 1.000 0.994Med LSTM 0.905 1.000 0.999ER CNN 0.545 0.926 0.821ER DNN 0.575 0.912 0.815ER LSTM 0.691 0.997 0.974Avg CNN 0.683 0.944 0.902Avg DNN 0.728 0.974 0.941Avg LSTM 0.851 0.998 0.992494.3. Experimental Results(a) Overall distance error (b) Distance error of NWE (c) Distance error of Atl(d) Distance error of EstP (e) Distance error of Med (f) Distance error of ERFigure 4.3: Comparisons of the cumulative distribution function of distance error among fiveregion and overall distance error for the three modelsseen that LSTM yields better performance in most regions except for Med, where the distri-butions are overlapping. Nevertheless, the LSTM model still surpass the other two in terms ofDistM and DistS for Med.As for destination prediction comparison shown in Table 4.3, the results are diverse amongdifferent sea regions of the world in terms of accuracy. The accuracy for EstP and Med arerelatively higher than the others, while predicting the correct destination port seems to be harderin the ER region for the model. However, the models in all the regions are able to achieve 90%accuracy for Top5-Acc except for the CNN model in NWE region. Regarding the comparisonof the neural networks, the LSTM model outperforms the other two for all regions, with anaverage of 0.851 in Top1-Acc. The LSTM model performs relatively lower accuracy in the ERregion. On the other hand, the AUC of the LSTM model seems to have lower variation overdifferent regions, with an average of 0.992. This indicates high discriminancy and consistencyof the models.504.3. Experimental ResultsAccording to observations made from the results, we conclude that LSTM is more adequatefor maritime trajectory and destination prediction in this experiment. Hence, we selected theLSTM model and embedded it in the MTL procedure for trajectory and destination prediction.4.3.2 Multi-Task Learning ResultsTwo outputs of the MTL prediction model were evaluated in this section. The modelswere given different proportion of weights W for the loss of each task. The weights W can bedefined as W = (WT ,WD), where WT and WD are defined in Section 3.4.2. The results for theexperiments are shown as follows:• Trajectory prediction results. The performances of trajectory prediction for the MTLmodel are listed in Table 4.4. Similarly, the CDF plots for distance error are shown inFig. 4.4.• Destination prediction results. The MTL models’ performances on destination predic-tion are presented in Table 4.5.In terms of trajectory prediction results, we can see that the models generally perform attheir best when the weights are tuned to (10,1). Exceptions occurs in the EstP region, where theperformance is very close between the weights at (10,1) and (20,1) and the ER region, whereweights at (1,1) performs better than the other two. Fig. 4.4 reflects this observation with theblue line dominating the other two in the NWE, Atl, and Med region, the lines overlapping inthe EstP region, and the red line performing slightly better for the ER region.As for the destination prediction results, observations can be made that performance wouldpeak in general when the weights are set as (10,1) except for ER region. The performance ofthe destination prediction aligns with the performance of the trajectory prediction. Comparedto the single-tasks results in Section 3.5.2, the multi-task model yields better performance interms of destination prediction for all the regions, especially in the ER region, the performance514.3. Experimental ResultsTable 4.4: The evaluation results of trajectory prediction with MTLRegion W RMSE MAE DistM DistSNWE 1,1 0.081 0.045 1.372 0.867NWE 10,1 0.069 0.037 0.902 0.740NWE 20,1 0.089 0.057 1.993 1.011Atl 1,1 0.359 0.188 4.436 3.855Atl 10,1 0.075 0.036 0.728 0.669Atl 20,1 0.182 0.104 2.560 2.109EstP 1,1 0.314 0.174 2.140 1.872EstP 10,1 0.303 0.165 1.869 1.257EstP 20,1 0.307 0.166 1.797 1.382Med 1,1 0.168 0.121 12.591 7.172Med 10,1 0.146 0.081 7.975 7.017Med 20,1 0.207 0.134 13.641 8.669ER 1,1 0.112 0.059 4.992 4.960ER 10,1 0.143 0.089 6.919 5.272ER 20,1 0.125 0.079 6.231 4.414Avg 1,1 0.207 0.117 5.106 3.745Avg 10,1 0.198 0.114 4.137 3.155Avg 20,1 0.182 0.108 5.245 3.517524.3. Experimental ResultsTable 4.5: The evaluation results of destination prediction with MTLRegion W Top1-Acc Top5-Acc AUCNWE 1,1 0.815 0.990 0.985NWE 10,1 0.831 0.994 0.990NWE 20,1 0.826 0.994 0.989Atl 1,1 0.797 1.000 0.995Atl 10,1 0.888 1.000 0.988Atl 20,1 0.839 0.998 0.993EstP 1,1 0.955 1.000 0.997EstP 10,1 0.987 1.000 1.000EstP 20,1 0.988 1.000 1.000Med 1,1 0.919 1.000 0.997Med 10,1 0.934 1.000 0.995Med 20,1 0.930 1.000 0.997ER 1,1 0.771 0.999 0.981ER 10,1 0.761 0.995 0.978ER 20,1 0.750 0.998 0.984Avg 1,1 0.851 0.998 0.991Avg 10,1 0.870 0.997 0.993Avg 20,1 0.867 0.998 0.993534.4. Discussions(a) Overall distance error (b) Distance error of NWE (c) Distance error of Atl(d) Distance error of EstP (e) Distance error of Med (f) Distance error of ERFigure 4.4: Comparisons on the cumulative distribution function of overall distance error andeach regions’ distance error for three different weightsincreased by 7%. However, trade offs exist that the multi-task model performs with relativelyhigher loss for trajectory prediction.4.4 DiscussionsTrajectory and destination prediction have proven their importance in the area of maritimetransportation. Currently, researches that focus on the naval trajectory and destination predic-tions had performed well on specific regions of the world (e.g. [26], [38]). However, predictionmethods on the predefined area of interests might not be applicable when implemented on dif-ferent scales or places. Applying DL models on global bases while segmenting the predictionsin regions may help researchers investigate and replicate the methods in their sea regions ofinterests. Furthermore, identifying the links between trajectory prediction and destination pre-diction may also help in researches related to maritime spatiotemporal data mining.544.5. SummaryThis study examined and compared DL architectures for maritime trajectory prediction indifferent parts of the world to test their generality. We identified LSTM as an adequate DLmodel for generalized trajectory prediction in different regions of the globe. A previous studydemonstrated that the time-series model would require more data in terms of maritime trajec-tory forecasting in general [38]. This report is consistent with our finding that regions withmore trips extracted tend to have better performance (NWE). In the article, the author used aMulti-Layer Perceptron (MLP) with time series that achieved a mean distance error of 4.61kilometers for 30 minutes interval. The DNN model in our study is of the same architecturewith the MLP model in the article. Among the five regions, the model achieved 2.573 kilo-meters in mean distance error for a 1-hour interval. The LSTM model did even better withan average mean distance error of 1.735 kilometers. Though direct comparisons between thetwo results cannot be made since they have different training and testing instances, the rela-tionship between two distance error demonstrates the effectiveness of the models in trajectoryprediction.Predicting vessel trajectory as an auxiliary task enables the destination prediction modelfocus attention on vessels’ future path with the MTL architecture. On top of that, we comparedthe prediction results of destination prediction with and without MTL. We can see from Table4.3 and Table 4.5 that models with MTL can generally perform better in vessel destinationprediction. Regarding the relatively poorer prediction for ER region in the single-task learning,the MTL method dominated single-task prediction with a 10 percent increase on accuracy.4.5 SummaryIn this chapter, the proposed framework is evaluated with a two-phase experimental design,using the processed trip trajectory input with the proposed knowledge extraction module topredict vessel’s future trajectory and destination. In the first part of the experiment, the perfor-mance of three neural networks, DNN, CNN, and LSTM, which are suitable for ST predictive554.5. Summaryanalytic are compared. The experiment results show that the LSTM method outperforms theother in both vessel trajectory prediction and vessel destination prediction. In the second part,this thesis examined the proposed MTL technique in the DL model of vessel trajectory anddestination prediction. The experimental results shows that the MTL performs significantlybetter in specific regions for vessel destination prediction, while having certain improvementscompared to models without the MTL architecture.56Chapter 5ConclusionsGlobal maritime trade is continuously expanding, while digitalization and automation aretransforming the shipping sector [58]. As AIS is being investigated beyond its original useof preventing collision (e.g., vessel tracking for cargo inspection coordination businesses), theprediction process and scale should change to better suit stakeholders’ needs.This chapter concludes the research by summarizing the major contributions and namingthe limitation and future works of this research. Chapter 5.1 listed the four major contributionsand how they bridge the research gaps mentioned in the introduction. Chapter 5.2 discuss thelimitation of this research and possible future directions that may contribute even more to thefield of maritime intelligent transportation beyond this research.5.1 Summary of ContributionsThe objective of this thesis has been to investigate whether deep learning prediction meth-ods using trip-based trajectories extracted from the AIS data can successfully predict vesselfuture trajectory and destination within ocean level region with the aim to support maritimecargo inspection. In this thesis, computational DL solutions for AIS-based maritime predictionare presented. The contributions of this thesis can be summarized as the following:1. Developed a global-based automated system to extract knowledge and trip-basedtrajectories from raw AIS data tailored for DL. With the aim to predict vessel’s futuretrajectory and destination, trip-based trajectories should be extracted within ocean-levelregion. This thesis developed a trip-based knowledge extraction process that deal with575.1. Summary of Contributionsscope beyond patterns of a single vessel, vessels between two ports, or vessels withinport area.The knowledge extraction process includes AIS data preprocessing, OD recognition,vessel trip extraction, and region filters. With the process, raw AIS data can be extractedas OD indices and vessel voyages tailored for DL. The automation of these steps connectwith DL can bring us even closer to the raw data, and lower the cost and need of humanexpert involvement in terms of maritime intelligent transportation.The development of the knowledge extraction system supported the evidence that AIS-based supervised deep learning for vessel trajectory prediction and destination predictionon a global basis can be achieved through the extraction of trip-based vessel trajectories.2. Proposed a multi-task learning framework that can learn and optimize both theprediction of vessel trajectory and destination together The motivation behind theMTL is inspired by learning of human. When learning new tasks, the knowledge thata person acquired by learning related tasks are often applied. The prediction of vesseltrajectory and destination are related to each other since they are both on the vessel’sfuture path. With the incentive, this research investigates whether learning the loss oftwo targets at once in the MTL architecture may benefits one another. The experimentresults shows evidence that learning with vessel trajectory helps the model to generalizeover the destination prediction task, and thus improve the destination prediction accuracyby an average of 3 percent.The proposed solutions provide processes that transforms raw AIS data into trainable in-dices for DL in vessel route estimation. In this way, the AIS data can be utilized in a moreintuitive way, where the process is more reproducible, and the prediction is more general.Maritime inspection focuses on a broader range of vessel voyages than collision avoidance interms of vessel trajectory prediction. The expansion of the prediction scale in this researchshowcases deep neural networks’ capability in vessel route estimation with wider ranges. Fi-585.2. Limitations and Future Research Directionsnally, the lack of accurate information regarding vessels’ destination may result in inefficientport arrangements and traffic route guidance [32]. This study proposed a MTL architecturein our framework, which improves the destination prediction accuracy compared to the mod-els without the architecture. This proposed framework can assist in maritime tasks coveringmore expansive geographic areas and provide knowledge about the future marine status to thestakeholders more intuitively and robustly.5.2 Limitations and Future Research DirectionsA limitation exists in destination prediction of the MTL model in our study. We constructedthe model in the condition with predefined areas and lists of ports. This method is designed tobe replicated by other researchers with their areas of interest. However, as more ports are beingconsidered in the selected regions, the prediction accuracy is likely to drop since some classeswould be highly confusable when classified [59]. Otherwise, the method can be replicated forresearchers that already know their ports of interest. On top of that, only spatial features wereconsidered for the prediction of this research, metadata such as weather conditions were notconsidered.Based on the experiment results, follow up studies such as comparisons of predictionswithin different scales of regions is recommended. Moreover, metadata like weather and shiptype can have an impact on predicting vessel trajectories and destinations. To what extentdo these features affect prediction results is also worth researching. Finally, as a baseline,researchers can replicate this method to predict destinations within their ports of interest. Then,the predicted destinations can become inputs for estimating other port-related activities such ascommodities exchange volumes.59Bibliography[1] A. Amini and A. Soleimany, “6.S191 Introduction to deep learning,” MassachusettsInstitute of Technology: MIT OpenCourseWare, January 2020. [Online]. Available:https://ocw.mit.edu → pages x, 32, 33[2] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp.436–444, May 2015. → pages xi, 33, 34, 35[3] S. Ruder, “An overview of multi-task learning in deep neural networks,” ArXiv e-printsarXiv:1706.05098, 2017. → pages xi, 35, 36, 38[4] S. Hexeberg, A. L. Fla˚tent, B.-O. H. Eriksen, and E. F. Brekke, “AIS-based vessel trajec-tory prediction,” in 2017 20th International Conference on Information Fusion (Fusion),Xi’an, China, 2017, pp. 1–8. → pages 1[5] M. Baldauf, K. Benedict, and F. Motz, “Aspects of technical reliability of navigationsystems and human element in case of collision avoidance,” in Navigation Conferenceand Exhibition, London, UK, 2008, pp. 1–11. → pages 1[6] “Revised guidelines for the onboard operational use of shipborne Automatic IdentificationSystems (AIS),” Res. A.1106(29), International Maritime Organization, London, UK,2016. → pages 2, 18[7] V. F. Arguedas, G. Pallotta, and M. Vespe, “Automatic generation of geographical net-works for maritime traffic surveillance,” in 17th International Conference on InformationFusion (FUSION), Salamanca, Spain, 2014, pp. 1–8. → pages 260Bibliography[8] J. Liu, G. Shi, and K. Zhu, “Vessel trajectory prediction model based on AIS sensordata and adaptive chaos differential evolution support vector regression (ACDE-SVR),”Applied Sciences, vol. 9, no. 15, p. 2983, 2019. → pages 2, 13[9] H. Tang, Y. Yin, and H. Shen, “A model for vessel trajectory prediction based on longshort-term memory neural network,” Journal of Marine Engineering & Technology,vol. 0, no. 0, pp. 1–10, 2019. → pages 13[10] W. Li, C. Zhang, J. Ma, and C. Jia, “Long-term vessel motion predication by modelingtrajectory patterns with ais data,” in 2019 5th International Conference on TransportationInformation and Safety (ICTIS), Liverpool, UK, 2019, pp. 1389–1394. → pages 2, 13[11] G. Pallotta, M. Vespe, and K. Bryan, “Vessel pattern knowledge discovery from AISdata: A framework for anomaly detection and route prediction,” Entropy, vol. 15, no. 6,pp. 2218–2245, 2013. → pages 2, 11, 14[12] D.-D. Nguyen, C. Le Van, and M. I. Ali, “Vessel trajectory prediction using sequence-to-sequence models over spatial grid,” in Proceedings of the 12th ACM InternationalConference on Distributed and Event-Based Systems, Hamilton, NZ, 2018, pp. 258–261.→ pages 15[13] C.-X. Lin, T.-W. Huang, G. Guo, and M. D. F. Wong, “Mtdetector: A high-performancemarine traffic detector at stream scale,” in Proceedings of the 12th ACM InternationalConference on Distributed and Event-Based Systems, Hamilton, NZ, 2018, pp. 205–208.→ pages 2, 15[14] Lapinski, Anna-Liesa S. and Isenor, Anthony W., “Estimating reception coverage char-acteristics of AIS,” Journal of Navigation, vol. 64, no. 4, p. 609–623, 2011. → pages7[15] Y. Zheng, “Trajectory data mining: An overview,” ACM Transactions on Intelligent Sys-tems and Technology, vol. 6, no. 3, 2015. → pages 8, 9, 1961Bibliography[16] L. Zhang, Q. Meng, Z. Xiao, and X. Fu, “A novel ship trajectory reconstruction approachusing AIS data,” Ocean Engineering, vol. 159, pp. 165 – 174, 2018. → pages 8[17] L. Zhao, G. Shi, and J. Yang, “Ship trajectories pre-processing based on AIS data,” Jour-nal of Navigation, vol. 71, no. 5, pp. 1210–1230, 2018. → pages 9, 10, 14, 19[18] L. Xiang, M. Gao, and T. Wu, “Extracting stops from noisy trajectories: A sequenceoriented clustering approach,” ISPRS International Journal of Geo-Information, vol. 5,no. 3, 2016. → pages 10[19] Z. Fu, Z. Tian, Y. Xu, and C. Qiao, “A two-step clustering approach to extract locationsfrom individual gps trajectory data,” ISPRS International Journal of Geo-Information,vol. 5, p. 166, 9 2016. → pages 10[20] C. Zhang, “AIS data-driven general vessel destination prediction: A trajectory similarity-based approach,” Ph.D. dissertation, University of British Columbia, 2019. → pages 10[21] W.-C. Lee and J. Krumm, Trajectory Preprocessing. New York, NY: Springer NewYork, 2011, pp. 3–33. → pages 10[22] D. H. DOUGLAS and T. K. PEUCKER, “Algorithms for the reduction of the numberof points required to represent a digitized line or its caricature,” Cartographica: TheInternational Journal for Geographic Information and Geovisualization, vol. 10, no. 2,pp. 112–122, 1973. → pages 10[23] L. Etienne, T. Devogele, and A. Bouju, “Spatio-temporal trajectory analysis of mobileobjects following the same itinerary,” in Joint International Conference on Theory, DataHandling and Modelling in GeoSpatial Information Science, Hong Kong, Hong KongSAR China, May 2010, pp. 86–91. → pages 10[24] G. K. D. de Vries and M. van Someren, “Machine learning for vessel trajectories us-62Bibliographying compression, alignments and domain knowledge,” Expert Systems with Applications,vol. 39, no. 18, pp. 13 426–13 439, 2012. → pages 10[25] X. Shi and D.-Y. Yeung, “Machine learning for spatiotemporal sequence forecasting: Asurvey,” ArXiv e-prints arXiv:1808.06865, 2018. → pages 10[26] R. Chen, M. Chen, W. Li, and N. Guo, “Predicting future locations of moving objectsby recurrent mixture density network,” ISPRS International Journal of Geo-Information,vol. 9, no. 2, p. 116, 2020. → pages 11, 13, 54[27] N. Forti, L. M. Millefiori, P. Braca, and P. Willett, “Prediction oof vessel trajectories fromAIS data via sequence-to-sequence recurrent neural networks,” in ICASSP 2020 - 2020IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),Barcelona, Spain, 2020, pp. 8936–8940. → pages 11, 13[28] J.-G. Lee, J. Han, X. Li, and H. Gonzalez, “Traclass: Trajectory classification usinghierarchical region-based and trajectory-based clustering,” Proc. VLDB Endow., vol. 1,no. 1, pp. 1081–1094, Aug. 2008. → pages 11[29] N. J. Yuan, Y. Zheng, L. Zhang, and X. Xie, “T-finder: A recommender system for findingpassengers and vacant taxis,” IEEE Trans. on Knowl. and Data Eng., vol. 25, no. 10, p.2390–2403, Oct. 2013. → pages 11[30] J. Yuan, Y. Zheng, X. Xie, and G. Sun, “T-drive: Enhancing driving directions with taxidrivers’ intelligence,” IEEE Trans. on Knowl. and Data Eng., vol. 25, no. 1, p. 220–232,Jan. 2013. → pages 11[31] R. S. P. Gaonkar, M. Xie, and H.-Z. Huang, “Optimizing maritime travel time reliability,”Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineeringfor the Maritime Environment, vol. 227, no. 2, pp. 167–176, 2013. → pages 1163Bibliography[32] C. Zhang, J. Bin, W. Wang, X. Peng, R. Wang, R. Halldearn, and Z. Liu, “AIS data drivengeneral vessel destination prediction: A random forest based approach,” TransportationResearch Part C: Emerging Technologies, vol. 118, 2020, Art. no. 102729. → pages 11,14, 59[33] K. I. Kim and K. M. Lee, “Preprocessing ship trajectory data for applying artificial neuralnetwork in harbour area,” in 2017 European Conference on Electrical Engineering andComputer Science (EECS), 2017, pp. 147–149. → pages 11[34] E. Tu, G. Zhang, L. Rachmawati, E. Rajabally, and G. Huang, “Exploiting AIS data forintelligent maritime navigation: A comprehensive survey from data to methodology,”IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 5, pp. 1559–1582,2018. → pages 11, 12[35] G. Spiliopoulos, D. Zissis, and K. Chatzikokolakis, “A big data driven approach to ex-tracting global trade patterns,” in Mobility Analytics for Spatio-Temporal and Social Data,C. Doulkeridis, G. A. Vouros, Q. Qu, and S. Wang, Eds. Cham: Springer InternationalPublishing, 2018, pp. 109–121. → pages 11, 23[36] V. F. Arguedas, G. Pallotta, and M. Vespe, “Automatic generation of geographical net-works for maritime traffic surveillance,” in 17th International Conference on InformationFusion (FUSION), 2014, pp. 1–8. → pages 12[37] L. Zhang, Q. Meng, and T. Fang Fwa, “Big AIS data based spatial-temporal analysesof ship traffic in Singapore port waters,” Transportation Research Part E: Logistics andTransportation Review, vol. 129, pp. 287–304, 2019. → pages 12[38] A. Valsamis, K. Tserpes, D. Zissis, D. Anagnostopoulos, and T. Varvarigou, “Employingtraditional machine learning algorithms for big data streams analysis: The case of objecttrajectory prediction,” Journal of Systems and Software, vol. 127, pp. 249–257, 2017. →pages 13, 54, 5564Bibliography[39] V. Gulisano, Z. Jerzak, P. Smirnov, M. Strohbach, H. Ziekow, and D. Zissis, “The DEBS2018 grand challenge,” in Proceedings of the 12th ACM International Conference onDistributed and Event-Based Systems, ser. DEBS ’18, Hamilton, NZ, 2018, pp. 191–194.→ pages 15[40] C. Iphar, A. Napoli, and C. Ray, “Detection of false AIS messages for the improvement ofmaritime situational awareness,” in Oceans 2015 - MTS/IEEE Washington, Washington,USA, 2015, pp. 1–7. → pages 18[41] W. Zhang, F. Goerlandt, P. Kujala, and Y. Wang, “An advanced method for detectingpossible near miss ship collisions from AIS data,” Ocean Engineering, vol. 124, pp. 141–156, 2016. → pages 18[42] “Technical characteristics for an automatic identification system using time-division mul-tiple access in the VHF maritime mobile band,” Rec. ITU-R M.1371-5, InternationalTelecommunications Union, Geneva, Switzerland, Feb. 2014. → pages 18[43] “World port index (PUB 150) - Maritime safety information,” National Geospatial-Intelligence Agency, 2019. [Online]. Available: https://msi.nga.mil/Publications/WPI →pages 18[44] L. Liu, Z. Qiu, G. Li, Q. Wang, W. Ouyang, and L. Lin, “Contextualized spatial–temporalnetwork for taxi origin-destination demand prediction,” IEEE Transactions on IntelligentTransportation Systems, vol. 20, no. 10, pp. 3875–3887, 2019. → pages 23[45] “kepler.gl,” Urban Computing Foundation, 2018. [Online]. Available: https://kepler.gl→ pages 28[46] P. Næss, “Investigation of multivariate freight rate prediction using machine learning andAIS data,” Master’s thesis, Norwegian University of Science and Technology, 2018. →pages 2965Bibliography[47] I. E. Sutherland, R. F. Sproull, and R. A. Schumacker, “A characterization of ten hidden-surface algorithms,” ACM Computing Surveys, vol. 6, no. 1, pp. 1–55, 1974. → pages29[48] S. Wang, J. Cao, and P. S. Yu, “Deep learning for spatio-temporal data mining: A survey,”ArXiv e-prints arXiv:1906.04928, 2019. → pages 30, 38[49] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016. → pages31, 35[50] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep con-volutional neural networks,” Commun. ACM, vol. 60, no. 6, p. 84–90, 2017. → pages32[51] V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,”in Proceedings of the 27th International Conference on International Conference on Ma-chine Learning, ser. ICML’10, Madison, WI, USA, 2010, pp. 807–814. → pages 33,40[52] Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradientdescent is difficult,” IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 157–166,1994. → pages 34[53] R. Caruana, “Multitask learning: A knowledge-based source of inductive bias,” in Pro-ceedings of the Tenth International Conference on Machine Learning. Morgan Kauf-mann, 1993, pp. 41–48. → pages 35, 36[54] M. Vafaeipour, O. Rahbari, M. A. Rosen, F. Fazelpour, and P. Ansarirad, “Applicationof sliding window technique for prediction of wind velocity time series,” InternationalJournal of Energy and Environmental Engineering, vol. 5, no. 2, p. 105, 2014. → pages3766Bibliography[55] H. Mahmoud and N. Akkari, “Shortest path calculation: A comparative study forlocation-based recommender system,” in 2016 World Symposium on Computer Appli-cations Research (WSCAR), Cairo, Egypt, 2016, pp. 1–5. → pages 41[56] A. P. Bradley, “The use of the area under the ROC curve in the evaluation of machinelearning algorithms,” Pattern Recognition, vol. 30, no. 7, pp. 1145–1159, 1997. → pages42[57] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Syntheticminority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16,no. 1, pp. 321–357, 2002. → pages 47[58] V. F. Valentine, H. Benamara, and J. Hoffmann, “Maritime transport and internationalseaborne trade,” Maritime Policy & Management, vol. 40, no. 3, pp. 226–242, 2013. →pages 57[59] M. R. Gupta, S. Bengio, and J. Weston, “Training highly multiclass classifiers,” TheJournal of Machine Learning Research, vol. 15, no. 1, pp. 1461–1492, 2014. → pages 5967
Thesis/Dissertation
eng
Electrical Engineering
Vancouver : University of British Columbia Library
Attribution-ShareAlike 4.0 International
Graduate
Liu, Zheng (Professor of engineering)
Li, Eric
Investigations on AIS-based vessel trajectory and destination prediction with deep learning
Text