AIS data-driven ship trajectory prediction modelling and analysis based on machine learning and deep learning methods

Maritime transport faces new safety challenges in an increasingly complex traffic environment caused by large-scale and high-speed ships, particularly with the introduction of intelligent and autonomous ships. It is evident that Automatic Identification System (AIS) data-driven ship trajectory prediction can effectively aid in identifying abnormal ship behaviours and reducing maritime risks such as collision, stranding, and contact. Furthermore, trajectory prediction is widely recognised as one of the critical technologies for realising safe autonomous navigation. The prediction methods and their performance are the key factors for future safe and automatic shipping. Currently, ship trajectory prediction lacks the real performance measurement and analysis of different algorithms, including classical machine learning and emerging deep learning methods. This paper aims to systematically analyse the performance of ship trajectory prediction methods and pioneer experimental tests to reveal their advantages and disadvantages as well as fitness in different scenarios involving complicated systems. To do so, five machine learning methods (i.e., Kalman Filter (KF), Support Vector Progression (SVR), Back Propagation network (BP), Gaussian Process Regression (GPR), and Random Forest (RF)) and seven deep learning methods (i.e., Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (Bi-LSTM), Sequence to Sequence (Seq2seq), Bi-directional Gate Recurrent Unit (Bi-GRU), and Transformer) are first extracted from the state-of-the-art literature review and then employed to implement the trajectory prediction and compare their prediction performance in the real world. Three AIS datasets are collected from the waters of representative traffic features, including a normal channel (i.e., the Chengshan Jiao Promontory), complex traffic (i.e., the Zhoushan Archipelago), and a port area (i.e., Caofeidian port). They are selected to test and analyse the performance of all twelve methods based on six evaluation indexes and explore the characteristics and effectiveness of the twelve trajectory prediction methods in detail. The experimental results provide a novel perspective, comparison, and benchmark for ship trajectory prediction research, which not only demonstrates the fitness of each method in different maritime traffic scenarios, but also makes significant contributions to maritime safety and autonomous shipping development

Environmental impact of passenger ships in port

The environmental impact of ships can be of different types. This thesis covers air pollution due to chemicals and concentrates on local effects due to compounds emitted in the exhaust gases of internal combustion engines and acoustic pollution. The attention has been focused on the consequences of the presence of many ships in ports located close to inhabited zones. For port-scale analyzes, the case study is the port of Naples for which traffics, geographic conformation, meteorological conditions, results of experimental campaigns both in the field of acoustic and environmental impact are available. In the field of polluting emissions, the case study for the simulations is a catamaran in service at the port of Naples for which experimental measurements at sea and bench tests are available. For the simulation of acoustic emissions, the case study is a passenger ship for which experimental measurements and forecast data are available. Experimental campaigns and simulations have been carried out on the port of Naples and most of the applications concern passenger ships, but methods and procedures can be applied to a general case. The thesis consists of six chapters, briefly introduced here. Each chapter contains a first subsection named "aims and scope" precisely to describe its main purposes in a more extended way than the summary presented here. The theme is first framed in the more general context of the environmental impact of anthropogenic activities and of marine transportation in particular assessment studies and documents issued by international bodies reporting targets for limiting the global environmental impact of the shipping sector are briefly summarized. Recalls on the main mechanisms of formation and reduction of pollutants are exposed. The second chapter describes the bottom-up method aimed at estimating the emissions of passenger ships in port. To obtain an estimation of all the emissions a series of very specific steps are necessary. The main information to be collected and produced concerns: traffic, routes, arrival and departure schedules, engine loads, emissions, heights, and diameters of the funnels. The technique of data collection and its use was gradually deepened (from simple cruise calendar to AIS data). The main application on the entire port sees the use of AIS data. The starting AIS data have been processed through an "ad hoc" MATLAB code capable of managing a relevant amount of data and returning a complete calendar of all the movements of every ship arriving and operating in the port. The use of AIS data has brought about improvements in the calculation methodology for emissions as well, allowing for example a more accurate analysis of average speeds in port and idle times. The port of Naples, where all the analysis were developed, is presented next. The traffics for the years and reference periods chosen in the subsequent analyzes are presented (2012, 2016, and 2018). A comprehensive study of the environmental impact of ships cannot be separated from the creation of atmospheric dispersion models. These models require the flow of pollutants emitted in the main operational phases in port (navigation, maneuvering, and mooring) as the main inputs. The results allow to estimate the weight that the passenger branch has on air quality also thanks to cross-comparisons with port measurements and ARPAC (Regional Agency for Environmental Protection in Campania) data. After the analysis of the environmen0tal impact on a port scale, the problem of emissions has been approached by applying a designated simulation, with the aim to overcome the use of emission factors. The first part of the chapter describes a state of the art of simulation model and an in-depth analysis of the main emission simulation methodologies. An engine model has been created in RICARDO WAVE environment; this engine model was validated and calibrated on an engine installed onboard a passenger ship operating in the port of Naples. Bench test results in terms of power, torque, consumption, and rpm have been used to calibrate the model while experimental measurements validated it. In the dissertation, a description of the case study (ship, engine, bench tests, and sea trials), a description of the model, and an interpretation of the results are presented. The validation on sea trials shows the effectiveness of the model both in terms of main engine parameters and emissions. At the end of the chapter, a comparison between the three emission estimation methodologies (EMEP-EEA, with AIS data, simulations, and experimental campaign) has been carried out. The next chapter of the thesis concerns the assessment of the acoustic impact of passenger ships in port. The structure of the research is typically the same: simulation and experimental results. The first part shows some experimental surveys made on a passenger ship in port that served as validation of a simulation model built in the TERRAIN OLIVE TREE LAB SUITE environment. The second and last part presents the methodology and results obtained in the context of a collaborative research project between the Universities of Naples, Genoa, and Trieste. The project aimed at characterizing the acoustic impact of a ship in light of the new additional class notation published by the Lloyds Register "Procedure for the Determination of Airborne Noise Emissions from Marine Vessels Airborne Noise Emissions from Marine Vessels". The last chapter sets out three applications in order to keep the problem set in a global scale context. The first presents an analysis of the possible countermeasures that can be applied to the cruise ship fleet aimed at environmental safeguarding (DNV Appraisal Tool), in the wake of the EEOI and EEDI. Furthermore, in the context of the environmental impact on a port scale, preliminary measurements of polluting emissions using remote measurement instruments (LIDAR) were carried out with the aim of allowing an indirect estimate of the concentrations of pollutants in the exhausts of ships, thus significantly reducing the uncertainties related to ground-level measurements with active or passive samplers. The last application, on the other hand, concerns the ports and the possible activities and initiatives to be implemented in order to host fleet of increasingly green and eco-sustainable ships (Environmental Ship Index)

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

A novel data condition and performance hybrid imputation method for energy effcient operations of marine systems

Datasets with missing values can adversely affect the accuracy of any subsequent decision making, for instance in condition- and performance-monitoring for energy efficient operations of ship systems. Missing data imputation is therefore, a necessary step as it ensures that the data can reach their full knowledge extracting potential. This paper aims at developing a novel hybrid imputation method, which can be employed to condition data acquired from marine machinery systems, thus increasing the quality of the original dataset and improving the decision making for ship efficient operations. The paper includes of all necessary imputation preparatory steps and further post-imputation processes. The developed method employs a hybrid k-NN and MICE imputation algorithm which combines data mining with first-principle knowledge. The proposed hybrid approach is compared with the individual performance of k-NN and MICE algorithms and is implemented in a dataset acquired from the main engine system of an oceangoing vessel. It is shown that the hybrid approach performs best, exhibiting an average error of 2.2% compared to the k-NN and MICE algorithms with errors 5.6% and 3.3%, respectively, highlighting that the small error of the proposed novel method improves the quality of data used in condition- and performance-monitoring