The construction of maritime route networks holds significant importance for autonomous navigation of vessels. In this study, a two-layer maritime route network modeling method based on huge amounts of ship trajectory data was proposed. Firstly, we introduce a novel method for extracting nodes of the marine route network, which identifies feature points in ship trajectories through clustering. Secondly, we use a spatial computing method to transform ship trajectory data into a sequence of waypoint regions and establish a node connection matrix to realize the nodes' connection of the topological layer route network. And routes are extracted between waypoint regions to characterize the connection relationship of the geometric layer network. Finally, by connecting nodes of the topological layer with the support of the connection matrix and waypoint regions of the geometric layer with the route, the two-layer maritime route network that combines topological and geometric layers is constructed. The proposed method was applied to the waters of Vancouver, successfully constructing a topological-geometric two-layer maritime route network. Overall, the proposed method is beneficial for improving the safety and efficiency of autonomous navigation of ships, and has a positive impact on the development of smart shipping industry.@en

A method is proposed for remotely detecting sulfur content in fuel oil used by ships in emission control areas (ECAs) based on the direct collection of SO2 emission data from the plume of the ship and numerical simulation models. Assuming a ship in the ECA uses fuel oil with compliance fuel sulfur content (FSC), activity-based ship emission assessment model is firstly utilized to calculate the SO2 concentration emitted from the ship. Numerical simulation models are then used to estimate the theoretical SO2 diffusion concentration at the location of the monitoring equipment. The observed SO2 concentration at the same time can then be integrated to estimate the FSC of the ship. A sample of eleven ships was selected to verify the proposed method in Yantian port, Shenzhen, China in June 2018. The result illustrates that the method can remotely detect ships using fuel oil with the FSC greater than 0.146%m/m in the ECAs, the relative error is 46%.@en

The purpose of this paper is to evaluate the effect of the seasonal flow field on inland ship emissions and to improve calculation accuracy. Firstly, the flow field model is built through numerical simulation to correct the sailing speed of the ship from the Automatic Identification System (AIS) information in real-time. Then, an optimal emission estimation model for inland ships considering flow field factors is proposed. Finally, the effectiveness of the optimization model is demonstrated by a case study, and the influence of the seasonal flow field on emission calculation is analyzed. It indicates that the calculation results of the model considering the influence of the flow field are more accurate. Without considering the flow field, the results of emission calculations are often underestimated, especially in summer, which shows the importance of incorporating the flow field factors into the calculation of inland ship emissions.@en

The purpose of this paper is to evaluate the effect of the seasonal flow field on inland ship emissions and to improve calculation accuracy. Firstly, the flow field model is built through numerical simulation to correct the sailing speed of the ship from the Automatic Identification System (AIS) information in real-time. Then, an optimal emission estimation model for inland ships considering flow field factors is proposed. Finally, the effectiveness of the optimization model is demonstrated by a case study, and the influence of the seasonal flow field on emission calculation is analyzed. It indicates that the calculation results of the model considering the influence of the flow field are more accurate. Without considering the flow field, the results of emission calculations are often underestimated, especially in summer, which shows the importance of incorporating the flow field factors into the calculation of inland ship emissions.@en

The purpose of this paper is to evaluate the effect of the seasonal flow field on inland ship emissions and to improve calculation accuracy. Firstly, the flow field model is built through numerical simulation to correct the sailing speed of the ship from the Automatic Identification System (AIS) information in real-time. Then, an optimal emission estimation model for inland ships considering flow field factors is proposed. Finally, the effectiveness of the optimization model is demonstrated by a case study, and the influence of the seasonal flow field on emission calculation is analyzed. It indicates that the calculation results of the model considering the influence of the flow field are more accurate. Without considering the flow field, the results of emission calculations are often underestimated, especially in summer, which shows the importance of incorporating the flow field factors into the calculation of inland ship emissions.@en

The purpose of this paper is to evaluate the effect of the seasonal flow field on inland ship emissions and to improve calculation accuracy. Firstly, the flow field model is built through numerical simulation to correct the sailing speed of the ship from the Automatic Identification System (AIS) information in real-time. Then, an optimal emission estimation model for inland ships considering flow field factors is proposed. Finally, the effectiveness of the optimization model is demonstrated by a case study, and the influence of the seasonal flow field on emission calculation is analyzed. It indicates that the calculation results of the model considering the influence of the flow field are more accurate. Without considering the flow field, the results of emission calculations are often underestimated, especially in summer, which shows the importance of incorporating the flow field factors into the calculation of inland ship emissions.@en

The purpose of this paper is to evaluate the effect of the seasonal flow field on inland ship emissions and to improve calculation accuracy. Firstly, the flow field model is built through numerical simulation to correct the sailing speed of the ship from the Automatic Identification System (AIS) information in real-time. Then, an optimal emission estimation model for inland ships considering flow field factors is proposed. Finally, the effectiveness of the optimization model is demonstrated by a case study, and the influence of the seasonal flow field on emission calculation is analyzed. It indicates that the calculation results of the model considering the influence of the flow field are more accurate. Without considering the flow field, the results of emission calculations are often underestimated, especially in summer, which shows the importance of incorporating the flow field factors into the calculation of inland ship emissions.@en

The purpose of this paper is to evaluate the effect of the seasonal flow field on inland ship emissions and to improve calculation accuracy. Firstly, the flow field model is built through numerical simulation to correct the sailing speed of the ship from the Automatic Identification System (AIS) information in real-time. Then, an optimal emission estimation model for inland ships considering flow field factors is proposed. Finally, the effectiveness of the optimization model is demonstrated by a case study, and the influence of the seasonal flow field on emission calculation is analyzed. It indicates that the calculation results of the model considering the influence of the flow field are more accurate. Without considering the flow field, the results of emission calculations are often underestimated, especially in summer, which shows the importance of incorporating the flow field factors into the calculation of inland ship emissions.@en

This study contributes to addressing the challenge of quickly obtaining an effective and accurate nonparametric model for describing ship maneuvering motion in three degrees of freedom (3-DOF). To achieve this, an intelligent ship dynamics nonparametric modeling method named improved PER-DDPG is proposed. This method leverages the deep deterministic policy gradient algorithm (DDPG) and prioritized experience replay mechanism (PER) and analyzes the characteristics between the goal of deep reinforcement learning (DRL) and the modeling process of the nonparametric model. The PER mechanism is utilized to enhance the agent's understanding of the overall mechanism of ship motion by improving the utilization of samples. The meaning of target value is redefined due to transforming DRL aiming at maximizing cumulative rewards into maximizing the set of immediate rewards at each time step. To validate the performance of the proposed modeling method, we conduct simulation studies using a benchmark ship model i.e., a Mariner cargo ship dynamic model, and experimental studies using a real unmanned surface vehicle (USV). In the simulation test, we demonstrate the effectiveness and generalization of the proposed method through zigzag and turning circle tests. Furthermore, we verify the robustness and applicability of the proposed method by using datasets with uncertain environmental disturbances and datasets with different sampling frequencies. Additionally, the experimental tests conducted on the USV indicate the consistency of the proposed approach.@en

Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method to detect fishing vessels’ fishing activities based on the defined local dynamic parameters and global statistical characteristics of vessel trajectories. On a local scale, the stop points and points of interest (POIs) in the vessel trajectory are extracted. Voyage extraction can then be conducted on this basis. After that, multiple characteristics based on motion and morphology on a global scale are defined to construct a logistic regression model for fishing behavior detection. To verify the effectiveness and feasibility of the method, vessel trajectory data, and fishing log data collected from Chinese ocean squid fishing vessels in Argentine waters in 2020 are integrated for fishing operation detection. Multiple evaluation metrics show that the proposed method can provide robust and accurate recognition results. Moreover, further analysis of the temporal and spatial distribution and seasonal changes in squid fishing activities in Argentine waters has been performed. A more refined assessment of the fishing activities of individual fishing vessels can also be provided quantitatively. All the results above can benefit the regulation of fishing activities.@en

Dynamic collision avoidance between multiple vessels is a task full of challenges for unmanned surface vehicle (USV) movement, which has high requirements on real-time performance and safety. The difficulty of multi-obstacle collision avoidance is that it is hard to formulate the optimal obstacle avoidance strategy when encountering more than one obstacle threat at the same time; a good strategy to avoid one obstacle sometimes leads to threats from other obstacles. This paper presents a dynamic collision avoidance algorithm for USVs based on rolling obstacle classification and fuzzy rules. Firstly, potential collision probabilities between a USV and obstacles are calculated based on the time to the closest point of approach (TCPA). All obstacles are given different priorities based on potential collision probability, and the most urgent and secondary urgent ones will then be dynamically determined. Based on the velocity obstacle algorithm, four possible actions are defined to determine the basic domain in the collision avoidance strategy. After that, the Safety of Avoidance Strategy and Feasibility of Strategy Adjustment are calculated to determine the additional domain based on fuzzy rules. Fuzzy rules are used here to comprehensively consider the situation composed of multiple motion obstacles and the USV. Within the limited range of the basic domain and the additional domain, the optimal collision avoidance parameters of the USV can be calculated by the particle swarm optimization (PSO) algorithm. The PSO algorithm utilizes both the characteristic of pursuance for the population optimal and the characteristic of exploration for the individual optimal to avoid falling into the local optimal solution. Finally, numerical simulations are performed to certify the validity of the proposed method in complex traffic scenarios. The results illustrated that the proposed method could provide efficient collision avoidance actions.@en

In this study, a method for dynamically establishing ship domain in inland waters is proposed to help make decisions about ship collision avoidance. The surrounding waters of the target ship are divided to grids and then calculating the grid densities of ships in each moment to deter-mine the shape and size of ship domain of different types of ships. At last, based on the spatiotem-poral statistical method, the characteristics of ship domains of different types of ship in different navigational environments were analyzed. The proposed method is applied to establish ship domains of different types of ship in Wuhan section of the Yangtze River in January, February, July, and August in 2014. The results show that the size of ship domain increases as the ship size increases in each month. The domain size is significantly influenced by the water level, and the ship domain size in dry seasons is larger than in the wet seasons of inland waters.@en

Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.@en

At present, many maritime applications, such as ship traffic statistics, require fast spatiotemporal query of ship traj ectory data. The existing research is weak in supporting the trajectory spatiotemporal query, and can only realize the spatiotemporal query based on circular and rectangular regions. Moreover, complex spatial indexes and computer clusters are usually required to achieve efficient spatiotemporal query, which undoubtedly increases the storage cost. To solve above problems, this article designs the storage structure and partition strategy of ship trajectory data, and builds attributes multilevel index and H3 spatial index on ship traj ectory data based on ClickHouse database, which not only achieves spatiotemporal query based on the oval, round, and arbitrary polygon area, and improve the efficiency of the spatiotemporal query; At the same time, the data storage space is reduced, so that a common server can manage 10 billion ship trajectory data. The empirical study on a large number of data shows that the storage scheme designed in this paper not only enhances the ability of spatiotemporal query, improves the efficiency of data storage and spatiotemporal query, but also reduces the cost of data storage to a large extent.@en

Formal expression of ship behavior is the basis for developing autonomous navigation systems, which supports the scene recognition, the intention inference, and the rule-compliant actions of the systems. The Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) offers experience-based expressions of ship behavior for human beings, helping the humans recognize the scene, infer the intention, and choose rule-compliant actions. However, it is still a challenge to teach a machine to interpret the COLREGs. This paper proposed an ontological ship behavior model based on the COLREGs using knowledge graph techniques, which aims at helping the machine interpret the COLREGs rules. In this paper, the ship is seen as a temporalspatial object and its behavior is described as the change of object elements in time spatial scales by using Resource Description Framework (RDF), function mapping, and set expression methods. To demonstrate the proposed method, the Narrow Channel article (Rule 9) from COLREGs is introduced, and the ship objects and the ship behavior expression based on Rule 9 are shown. In brief, this paper lays a theoretical foundation for further constructing the ship behavior knowledge graph from COLREGs, which is helpful for the complete machine reasoning of ship behavior knowledge in the future.@en

The remote monitoring method based on air-quality monitoring sensors is a common way to monitor the FSC (fuel sulfur content) of oils for ships. Considering the influences of geographical environments, atmospheric conditions, regional ship traffic flow, emission characteristics of ships, and height of monitoring sensors on the monitor station chosen, a new method was proposed to optimize the site selection for monitoring the FSC of fuel oils used by ships in waters of the port. SO2 numeric simulation and observation from sensors were integrated to estimate the FSCs. The proposed method was utilized to recommend the sites of the fixed sniffing monitoring stations in Yantian port, Shenzhen, China from June and July 2018. The results showed that the experimental stations could monitor FSCs, and the relative difference between the estimated and actual FSCs of ships was 16.34%. The proposed method for recommending sites of FSC monitoring sensors contributed to intelligently supervising air pollutants emitted from ships and fuel oils of ships in the emission control areas of China.@en

Shipping emissions have been considered a significant source of air pollution in the cities along the Yangtze River, with severe impacts on the climate and human health. This study created a complete annual ship emission inventory for the middle reaches of the Yangtze River and assessed its impact on air quality on a regional scale. To estimate the complete emissions, 9 main engine power regression models for different ship types were created to handle those vessels with absent main power data, and a high spatial-temporal resolution annual emission inventory was developed with the activity-based method combined with Automatic Identification System (AIS) data of the full year of 2018. The total emissions of CO2, CO, SO2, NOX, PM2.5, PM10 and HC in middle reaches of the Yangtze River were 5.67 × 105, 1.02 × 103, 5.41 × 102, 1.06 × 104, 2.43 × 102, 2.45 × 102 and 3.52 × 102 tons respectively. Then, the Weather Research and Forecasting with Chemistry (WRF-Chem) model was used to study the dispersion of the ship pollutants in the atmosphere and quantize the impact on the urban area. This research will provide services for the maritime authorities to develop green shipping and emission supervision.@en

To simplify the micro-level CO2 (carbon dioxide) emission calculation model, reduce the dataset quality requirement of the model, and cut down the volume of calculation, a meso-level voyage-based emission model (MeVEM) for inland ships is proposed with their navigation characteristics considered. The navigation characteristics and the main influencing factors of inland ship emissions are analyzed. The main engine power and average speed of the ships are selected as the model inputs. Accurate CO2 emissions are calculated by the use of the micro-level emission model. With that, first-order and second-order polynomial regression models are employed to establish the fitting formula to estimate the emissions per kilometer. To validate the proposed model, the Junshan segment in the middle reaches of the Yangtze River is selected as the study area, and the model parameters are determined to estimate the CO2 emissions. It is found that the model of emission per kilometer (ej, k) established by second-order polynomial regression is more accurate. The results show that the percentage error in the total amount (PETA) of the results estimated by the four proposed models (CO2 emission estimation model for the upstream cargo ships, the downstream cargo ships, the upstream oil tankers, and the downstream oil tankers) are all within ±5%, which verifies the feasibility and applicability of the model. The proposed meso-level model allows us to use a smaller input dataset which is easier to obtain, and estimate CO2 emissions from ships simply and accurately.@en

Unmanned Surface Vessels (USV) may interfere with ships in the same lane during navigation and may be affected by ships that actively collide or intercept the USV. Evidently, the latter poses a greater threat than the former. This paper proposes the concept of active and inactive interference. It is necessary for the USV to accurately classify the vessels’ intentions to conduct a suitable collision avoidance strategy. However, the existing research on collision avoidance of USV focuses on avoiding moving obstacles and rarely considers the interference intention of dynamic obstacles for USV when choosing collision avoidance strategies. This paper proposes an algorithm to recognize the interference intention of ships by combining visual classification with a Support Vector Machine (SVM). First, a Convolutional Neural Network (CNN) is proposed to distinguish the merchant ship from a high-performance ship that actively interferes with the USV with high probability. A high-dimensional feature dataset was designed to illustrate the navigation characteristics of an obstacle, and Principal Component Analysis (PCA) was then employed to reduce the dimensionality of the dataset for obstacle classification. A modified SVM is presented to classify obstacles into active and inactive interference intention categories. Moreover, an escape algorithm based on an improved artificial potential field method is proposed for vessels with active interference. The simulation and experimental results clearly show that the USV can successfully identify the interference intention of obstacles and adopt a specific collision avoidance strategy for obstacles with active interference intentions. Using this algorithm, the USV can safely avoid obstacles with different interference intentions.@en

Unmanned Surface Vessels (USV) may interfere with ships in the same lane during navigation and may be affected by ships that actively collide or intercept the USV. Evidently, the latter poses a greater threat than the former. This paper proposes the concept of active and inactive interference. It is necessary for the USV to accurately classify the vessels’ intentions to conduct a suitable collision avoidance strategy. However, the existing research on collision avoidance of USV focuses on avoiding moving obstacles and rarely considers the interference intention of dynamic obstacles for USV when choosing collision avoidance strategies. This paper proposes an algorithm to recognize the interference intention of ships by combining visual classification with a Support Vector Machine (SVM). First, a Convolutional Neural Network (CNN) is proposed to distinguish the merchant ship from a high-performance ship that actively interferes with the USV with high probability. A high-dimensional feature dataset was designed to illustrate the navigation characteristics of an obstacle, and Principal Component Analysis (PCA) was then employed to reduce the dimensionality of the dataset for obstacle classification. A modified SVM is presented to classify obstacles into active and inactive interference intention categories. Moreover, an escape algorithm based on an improved artificial potential field method is proposed for vessels with active interference. The simulation and experimental results clearly show that the USV can successfully identify the interference intention of obstacles and adopt a specific collision avoidance strategy for obstacles with active interference intentions. Using this algorithm, the USV can safely avoid obstacles with different interference intentions.@en