Maritime shipping is essential to the global economy, while waterway transportation is recognized as a high-risk industry. Additionally, maritime accidents are frequently caused by human errors, and with the rapid improvement of science and technology, the improvement of autonomous ships has been technically feasible, which attracts the wide attention of researchers in academia and industry. However, the knowledge acquisition and representation methods are mainly based on knowledge-based research methods, while the existing research for automatically achieving the autonomous ships’ maneuvering decision-making by acquiring the seafarers’ operation characteristics is still scanty. In addition, it also lacks the appropriate theoretical methods to explore the problem of autonomous ship human-like maneuvering decision-making modeling. Therefore, the research on ship maneuvering decision-making methods still needs to be improved and further developed. This thesis focuses on the problem of modeling seafarers’ navigational decision-making in a typical scenario for autonomous ships’ safety. We propose the method to prioritize safety influencing factors of autonomous ships’ maneuvering decisions and a series of ship maneuvering knowledge learning models to give the autonomous ship the ability to make decisions like a human. The autonomous ship human-like maneuvering decision-making problem has been considered as a machine learning problem, and we translate the problem into learning the maneuvering decision characteristics of the officer on watch (OOW) using various decision tree algorithms. By constructing autonomous ship human-like decision-making maneuvering decision recognition models under multiple constraints in the specific scenarios, the decision-making mechanism of the OOW’s maneuvering behavior under specific water traffic safety influencing factors in the inbound scenario is analyzed, and the OOW’s decisionmaking knowledge is automatically acquired and represented...@en

Short-term traffic flow prediction is a core branch of intelligent traffic systems (ITS) and plays an important role in traffic management. The graph convolution network (GCN) is widely used in traffic prediction models to efficiently handle the graphical structural data of road networks. However, the influence weights among different road sections are usually distinct in real life and are difficult to analyze manually. The traditional GCN mechanism, which relies on a manually set adjacency matrix, is unable to dynamically learn such spatial patterns during training. To address this drawback, this study proposes a novel location graph convolutional network (location-GCN). The location-GCN solves this problem by adding a new learnable matrix to the GCN mechanism, using the absolute value of this matrix to represent the distinct influence levels among different nodes. Subsequently, long short-term memory (LSTM) is employed in the proposed traffic prediction model. Moreover, trigonometric function encoding was used in this study to enable the short-term input sequence to convey long-term periodic information. Finally, the proposed model was compared with the baseline models and evaluated on two real-world traffic flow datasets. The results show that our model is more accurate and robust than the other representative traffic prediction models.@en

Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection.

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For a long time, the development of maritime communication has been restricted by the low data rate, high-latency and high cost of the current communication systems. The upgrade of new generation mobile communication technologies is attracting more and more attention to conduct a shore-based broadband mobile communication network with high-latency and high reliability to serve the maritime industries. This paper presents a solution by means of building a ship-to-infrastructure (S2I) and a ship-to-ship (S2S) wireless communication networks for an offshore region. We characterize the S2I and S2S channels at 5.9 GHz band based on the channel measurements in realistic environments. The channel characteristics, including power delay profile, delay spread, propagation path loss, are extracted and analyzed. In view of the difference between marine and terrestrial communications, we analyze the influencing factors of the offshore water, including effective reflection, divergence and shadowing from the water surface, and diffraction loss caused by the earth curvature. We also predict the power coverage range and the channel capacity for S2I and S2S wireless communications. Finally, the communication performance is evaluated according to the channel measurement and characterization analysis. The research results can be a reference for the construction of maritime communication networks.

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The water level fluctuations in the upper reach of dam reservoirs may result in increasing occurrence probabilities of ship grounding accidents, which is especially prominent in the Three Gorges Reservoir. Grounding accidents may not only cause congestion of the traffic but also lead to loss of properties and pollution to the maritime environment. An analytical model incorporating Bayesian Network is proposed to estimate the occurrence likelihood of a ship being grounded in the fluctuating backwater zone. The proposed model comprehensively considers the characteristics of the ship's properties, organizational factors, hydrological conditions and human factors from a systematic perspective. Historical data collected from the Chongqing Maritime Safety Administration, together with incident reports, are used to develop a quantitative model. The developed model in this paper concludes that out of twenty-six factors, the area of the fluctuating backwater zone, the month, and water level are the predominant factors for the occurrence of grounding accidents in the Three Gorges Reservoir. The results can be used by maritime stakeholders to take mitigation measures for grounding accident reduction.

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This paper presents a bibliometric overview of the publications in the principal international journal Process Safety and Environmental Protection (PSEP) from 1990 to 2020 retrieved in the Web of Science (WoS) database to explore the evolution in safety and environmental engineering design and practice, as well as experimental or theoretical innovative research. Therefore, based on the WoS database and the visualization of similarities (VOS) viewer software, the bibliometric analysis and scientometric mapping of the literature have been performed from the perspectives of doc-ument types, publication and citation distribution over time, leading authors, countries (regions), institutions, the corresponding collaboration networks, most cited publications and references, fo-cused research fields and topics, research trend evolution over time, etc. The paper provides a comprehensive and quantitative overview and significant picture representation for the journal’s leading and evolutionary trends by employing specific aforementioned bibliometric analysis factors. In addition, by reviewing the evolutionary trends of the journal and the proposed investigated factors, such as the influential works, main research topics, and the research frontiers, this paper reveals the scientific literature production’s main research objectives and directions that could be addressed and explored in future studies.

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Frequent water traffic accidents pose severe threats to human life and property safety, the water environment, and adverse effects on social stability. Understanding historical accidents is essential for accident prevention and risk mitigation. However, at present, research on the comprehensive statistical analysis of characteristics and causes of ship accidents that occurred in the inland water areas of the Yangtze River, especially in the fluctuating backwater area (FBA) of Three Gorges Reservoir (TGR) region, is still scanty, even less a hierarchical and systematic analysis framework. Therefore, this paper proposes a comprehensive ship accident characteristics and causes analysis framework, and summarizes and visualizes the characteristics of ship accidents through the statistical and comparative analysis of historical data in terms of categories and severity of accidents, ship types involved in accidents, spatial and temporal distribution characteristics, ship accident losses, and root causes and lessons learned from the related accidents. In order to demonstrate the value of the proposed framework, based on the official sources, ten years of ship accident data from 2009 to 2018 in the FBA of TGR region are collected and analyzed in detail. On the basis of the results, this paper summarizes accident prevention and supervision guidelines to provide decision support for maritime safety. This research is of significance to the water traffic accident precaution and risk mitigation in this region and can be useful for other similar specific scenarios worldwide.

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The Yangtze River is the main artery of the inland water transportation of China, connecting the resource-rich southwest hinterland, the Central China region and the economically prosperous southeast coast of East China. Chongqing, located in the fluctuating backwater of the Three Gorges Project, is the mainstream of the Yangtze River and the Jialing River. It is not only the industrial and commercial center of China but also the most abundant water and intermodal inland hub and material distribution center in the southwest. Based on the ten years of statistical data of ship accidents from 2009 to 2018 of the Chongqing Maritime Safety Administration (MSA), this paper takes the navigation section of the fluctuating backwater area of the Three Gorges Reservoir as the research object, and comprehensively uses statistical analysis, comparative analysis, and data visualization methods. The temporal and spatial distribution characteristics of ship accidents in the fluctuating backwater area and the economic losses in this area are studied. The research results are helpful for strengthening maritime management, improving navigation conditions, and reducing water traffic accidents in the Three Gorges Reservoir area and other similar reservoir areas all over the world. In addition, the research gives detailed guidance for stakeholders and mangers for the prevention of water traffic accidents.@en

The Yangtze River is the first of China and the third-longest river in the world. It is the most developed inland water transportation system in China. Chongqing, located in the upstream of the Three Gorges Project, is not only the gateway of the industrial and commercial center of China but also the most abundant water and intermodal inland hub and material distribution center in the southwest. Frequent water traffic accidents pose severe threats to the safety of human life and property and the water environment as well as bring adverse effects on social stability. Therefore, based on the ten years of statistical data of ship accidents in 2009-2018 from the Chongqing Maritime Safety Administration (MSA), this paper summarizes the characteristics of the spatiotemporal distribution of accidents through statistical analysis of the historical data. Moreover, it proposes accident prevention and supervision methods to provide decision support for maritime safety. This research is of great significance to the prevention and control of water traffic accidents in this region.

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The representation and discrimination of various traffic states play an essential role in solving traffic accidents and congestion as the foundation of traffic state prediction. However, the existing representation of the traffic state usually only considers the road congestion layer and divides the traffic state into congested and unblocked. Representation only at the congestion layer is difficult to reflect the road traffic state comprehensively. Therefore, we select three indicators from the layers of road congestion, road safety, and road stability, respectively, then utilizing K-means to cluster the traffic state. The clustering results can be regarded as a new type for the representation of a traffic state. As a result, the traffic states are divided into four classes, which comprehensively reflects the level of road congestion, safety, and stability. Using the four traffic states obtained from the clustering results as class labels, we applied a multi-layer perceptron (MLP) to classify the different traffic states, and the receiver operating characteristic (ROC) curve is assessed to verify the superiority of the classification results. Finally, a visual display of the real-time traffic state in a city’s central area was given.

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To date, automatic driving technology has become a hotspot in academia. It is necessary to provide a personalization of automatic driving decision for each passenger. The purpose of this paper is to propose a self-learning method for personalized driving decisions. First, collect and analyze driving data from different drivers to set learning goals. Then, Deep Deterministic Policy Gradient algorithm is utilized to design a driving decision system. Furthermore, personalized factors are introduced for some observed parameters to build a personalized driving decision model. Finally, compare the proposed method with classic Deep Reinforcement Learning algorithms. The results show that the performance of the personalized driving decision model is better than the classic algorithms, and it is similar to the manual driving situation. Therefore, the proposed model can effectively learn the human-like personalized driving decisions of different drivers for structured road. Based on this model, the smart car can accomplish personalized driving.

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Ship maneuvering decisions are influenced by several factors, and it is essential to prioritize the main influencing factors for efficient selection of the corresponding maneuvering decisions. Meanwhile, the autonomous ship maneuvering decision-making influencing factors constitute a typical grey system, which is suitable for research by grey relational analysis. Furthermore, in the fuzzy approach, linguistic assessment of factors is evaluated to obtain priorities numbers. Therefore, this study mainly focuses on the concept of human-like maneuvering for autonomous ships. Based on experimental data of experienced seafarers and using a simulation platform under the scenario of the Shanghai Waigaoqiao wharf, an inference model utilizing grey and fuzzy theories is proposed. The proposed model combined with expert linguistic terms in order to select the ship maneuvering decision-making main influencing factors from multi-source influencing factors (in overall and separated categories of natural environment, ship motion, force parameters, draft, and position), and to study the decision-making prioritization for maritime traffic safety for specific ship maneuvering scenarios. This method can prioritize the main factors which affect maneuvering decisions as well as guide an autonomous ship-assisted or automatic maneuvering evaluation system for the research of human-like maneuvering behavior. This study provides a new perspective on the identification of main ship maneuvering decision-making influencing factors in theory and in practice. It can be utilized for better decision-making concerning maritime traffic safety of autonomous ship maneuvering, which in turn makes shipping safer and promote the application and spreading of autonomous ships.

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With the further development of marine and information technologies, ship intelligence, green policies and automation will become mainstream with global cargo ships. Ship labor costs increase every year, so for the foreseeable future, the number of experienced crew members will be greatly reduced as smart ship emergence accelerates. At present, there is no mature research system for the human-like piloting of smart ships. In this paper, we use an improved decision tree, which could address problems of fuzziness and uncertainty. This will allow us to study the decision mechanisms of different piloting behaviors in order to realize the automatic acquisition and representation of the pilot's decision-making knowledge in inbound ship analysis as well as the simulated reproduction of the pilot's behavior. The simulation results show that the piloting decision recognition model, based on the fuzzy Iterative Dichotomiser 3 (ID3) decision tree, possesses a high reasoning speed and can accurately identify current piloting behavior. This provides theoretical guidance and a feasibility basis for research into human-like piloting behavior and the realization of automatic smart ship piloting systems.

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The natural environmental factors that typically affect the safe environment of maritime traffic are weather conditions and ocean conditions, specifically, wind, current, and waves. The environmental influence factors affect the ship's navigation and the crew's decisions by affecting the ship's maneuverability, along with the skill and mentality of the crew. In addition, autonomous ship maneuvering decisions are influenced by several factors, and it is especially important to distinguish the crucial influencing factors for efficient selection of the corresponding maneuvering decisions. At the same time, the autonomous ship maneuvering decision factors are a typical 'grey system which is suitable for the research by grey relational analysis. Therefore, this study mainly focuses on the concept of human-like maneuvering for the autonomous ship. Based on the actual crews' operational data from full-task handling simulation platform, we proposed a grey relational analysis model to prioritize the environmental influencing factors, thus to analyze the decision-making mechanisms for specific ship maneuvering scenario. This method can mine the key factors which affect maneuvering decisions and provide guidance for an autonomous ship-assisted or automatic maneuvering system and promotes the application of autonomous ships.

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To date, the increasing density of water traffic has caused the ship's navigation environment to deteriorate, resulting in frequent water traffic accidents. In addition, a majority of maritime accidents are caused by human factors, and one of the important ways to solve the ship accidents caused by human factors is to utilize intelligent maneuvering of ships. Based on the actual crews' operational data from full-task handling simulation platform, this study combines a 30,000-ton bulk carrier inbound navigation scenario and uses the decision tree method to propose a knowledge learning model under multiple environmental constraints to give intelligent ships the ability to make decisions like a human: An intelligent ship Human-like Decision-making Maneuvering Decision Recognition (HDMDR) model. The decision-making mechanism for the maneuvering behavior of Officer On Watch (OOW) under the influence of the specific water traffic environment in the inbound scenario is analyzed, and the OOW's decision-making knowledge is automatically acquired and represented. The validation tests and the comparative analysis with the classic classification algorithms of k-Nearest Neighbours (k-NN) and Support Vector Machine (SVM) are performed to demonstrate the accuracy of the proposed HDMDR model. This paper provides a feasible basis for the human-like decision-making analysis of intelligent ships.

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With the development of terrestrial networks and satellite constellations, vessel movement information can be effectively collected based on Automatic Identification System (AIS) receivers. Vessel motion pattern classification using AIS plays an important role in maritime monitoring and management. However, classifying vast amounts of vessel motion information is prohibitive workload. The aim of this study is to develop effective methods that can aid in automatic vessel motion pattern classification in inland waterways. First, the Least-squares Cubic Spline Curves Approximation (LCSCA) technique is used to represent the vessel motion trajectory. Then, a traditional classification model based on Lp-norm (0 < p < 1) sparse representation is improved to classify vessel motion patterns. And a Matching Pursuit - Fletcher Reeves (MPFR) method is developed to find the sparse solutions of the proposed model. To validate the performance of the proposed model, two AIS datasets from the Yangtze River are collected and applied in our experiment. According to the results, we can know that the proposed model can effectively classify vessel motion pattern in inland waterways. And the effectiveness of the proposed model is superior to those of other representative classification methods.@en