Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness.

@en

The development of advanced ship positioning and intelligent sensing technologies has transformed navigation at sea, moving beyond reliance on captains’ experience and standard routes. The trajectories traversed by ships at sea contain valuable data that can be mined to map maritime transportation networks and inform intelligent navigation systems. Ship trajectory data at scale enables discovery of the underlying network of maritime routes, providing key insights for applications like intelligent navigation, abnormal behavior detection, trajectory prediction, and maritime traffic pattern analysis. This study reviews the development of research on maritime route networks (MRNs) derived from ship trajectory data. It summarizes the technical process to construct a MRN, contrasting approaches for identifying waypoints, extracting routes, and representing the overall maritime traffic network structure. Finally, this study explores potential applications of MRNs and anticipates promising future research directions in this domain.

@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

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

Spatiotemporal companion pattern (STCP) mining is one of the means to identify and detect group behavioral activities. To detect the spatiotemporal traveling pattern of ships from massive spatiotemporal trajectory data and to understand the movement law of group ships, this article proposes a feature-driven approach for STCP mining that consists of (1) generating the grid index via the rasterizing of geospace and characterizing trajectory points via the spatiotemporal trajectory grid sequences (STTGSs) of ships; (2) designing filtering rules with the constraints of range, time and distance to construct a candidate set for ship STCP mining; and (3) measuring the STTGS similarity of the associated ships and setting the confidence threshold to realize spatiotemporal companion mining. The effectiveness of the proposed method is practically validated on a real trajectory dataset which is collected from the Taiwan Strait waters. The experimental results are as follows: 825 pairs of associated ships and 225 pairs of accompanying ships are mined when the grid size is 0.05° and the confidence is 0.5. Larger grid sizes can increase the inclusiveness of the associated ship trajectory similarity measurement, which can result in an increase in confidence of pattern. A large number of pseudo-accompaniment ships are extracted to the result set, resulting in a more dispersed distribution of pattern confidence. By verifying the proposed method, accompanying behavioral activities such as ship cooperative operation, companion navigation method, and so on, can be detected. These results can provide a reference for the research of ship group behavior identification and have an important application value for water transportation management.@en

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

Identification of parameters involved in the linear response model with high precision is a highly cost-effective, as well as a challenging task, in developing a suitable model for the verification and validation (V+V) of some key techniques for autonomous vessels in the virtual testbed, e.g., guidance, navigation, and control (GNC). In order to deal with this identification problem, a novel identification framework is proposed in this paper by introducing the extended state observer (ESO), and the well-evaluated robust weighted least square support vector regression algorithm (RW-LSSVR). A second-order linear response model is investigated in this study due to its wide use in controller designs. Considering the highly possible situation that only limited states could be measured directly, the required but immeasurable states in identifying parameters contained in the response model are approximately estimated by the ESO. Theoretical analysis of the stability is given to show and improve the applicability of the ESO. Simulation studies based on linear response models with predefined parameter values of a cargo vessel and a patrol vessel maneuvering in an open water area are carried out, respectively. Results show that the proposed approach not only estimates immeasurable states with high accuracy but also ensures good performance on the parameter identification of the response model with very close values to the nominal ones. The proven identified approach is economic because it only requires limited kinds of low-cost sensors.@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

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

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

The prediction of ship location has become an increasingly popular research hotspot in the field of maritime transportation engineering, which benefits maritime safety supervision and security. Existing methods of ship location prediction based on motion characteristics have a large uncertainty and cannot guarantee trajectory prediction accuracy of the target ship. An improved method of location prediction using k-nearest neighbor (KNN) is proposed in this paper. An expanded circle area of the latest point of the target ship is first generated to find the reference points with similar movement characteristics in the constraints of distance and time intervals. Then, the top k-nearest neighbors are determined based on the degree of similarity. Relationships between the reference point of each neighbor and the latest points of the target ship are calculated. The predicted location of the target ship can then be determined by a weighted calculation of the locations of all neighbors at the predicted time and their relationships with the target ship. Experiments of ship location prediction in 10 min, 20 min, and 30 min were conducted. The correlation coefficient of the location prediction error for the three experiments was 0.992, 0.99, and 0.9875, respectively. The results show that ship location prediction with reference to multiple nearest neighbors with similar movements can provide better accuracy.@en

To improve the ability of ship behavior detection in maritime safety surveillance, a new ship behavior of frequent turning within a certain spatial range, called ship loitering, was found under the ship behavior framework of "anchored-off, straight-sailing, turning". The concept of trajectory redundancy is defined as the quantitative representation of trajectory characteristics of ship loitering. A multi-scale sliding window-based ship loitering detection method is designed to process ship trajectories with different spatial ranges and time duration. Further, a shape recognition model based on a convolutional neural network is constructed and trained to identify four typical shapes of loitering trajectories. To verify the effectiveness of the proposed method, experiments were conducted using the AIS trajectory data collected from the sea off the Strait of Juan de Fuca in the eastern North Pacific Ocean in 2017 and validated against the manual labeling results. The results illustrate that six general categories of ships produce loitering behaviors with different intentions. The characteristics of the spatial-temporal distribution of ship loitering behaviors can be represented as different shapes. The average precision of ship loitering detection reaches 96.5% and the average accuracy of four typical loitering trajectory shapes reaches 86.2%.@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

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

Future emission reductions can be guided by a timely evaluation of the impact of emission reduction initiatives in the marine sector. To forecast carbon emission reductions and assess the consequences of various emission reduction techniques, we offer a generic framework for a ship carbon emission prediction model based on several emission reduction scenarios. Three factors—port and ship electrification (PSE), clean fuel substitution (CFS), and ship speed optimization (SSO)—are combined to create emission reduction scenarios with various intensities (lenient, intensive, and aggressive). In the case of Wuhan Port, the effect of carbon reduction was evaluated by comparing various measurements with the baseline scenario. The results indicate that the aggressive PSE and CFS scenarios will reach the carbon peak in 2029 and 2025, respectively. Finally, the possibility of reducing emissions offered by various solutions is examined, and recommendations for port ships' carbon footprint reduction are provided.@en

This study develops a system named Wuhan University of Technology Intelligent Tracer (WUT-iTracer) for identifying ships using high-fuel-sulfur-content (FSC) fuel oil in port waters. The system integrates a land-based sniffing monitoring module and a numerical simulation module. The land-based sniffing monitoring module collects and processes simultaneous several parameters from the automatic identification system (AIS) and micrometeorological monitoring system, which include the concentrations of gaseous pollutants emitted from ships, marine meteorological parameters, and dynamic and static information of ships. The numerical simulation module was developed based on a mobile ship emissions diffusion model, which estimates the concentration of SO2 at the location of the WUT-iTracer assuming the ship using compliant fuel. The relative difference between the measured and simulated concentration of SO2 is used to detect if the ship uses high-FSC fuel oil. The WUT-iTracer was installed on the shore of Yantian Port, Shenzhen, China, during June and August 2018. Ten ships were selected as samples to verify the efficacy of the WUT-iTracer. Based on these data, it was found that the ship can be treated as using high-FSC (>0.5%) fuel oil if the relative difference between the measured and simulated SO2 concentration is larger than 44%. The field test shows that the WUT-iTracer works well for identifying ships with high-FSC fuel oil. However, further tests and developments are needed for the system to work in different ports and weather conditions.@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

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

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

China Railway Express (CR Express) plays an important role in the China international trade logistics system. CR Express has always adopted a point-to-point direct transportation (DT) mode since its first opening in 2011, which contributed to the rapid development of China CR Express networks in the early stage. However, with the reducing of subsidies, the transit transportation (TT) mode appears which performs better with respect to the improvement of efficiency of CR Express transportation networks, and its high-quality and sustainable development. Therefore, a CR Express with mixed transportation modes of both DT and TT modes will be inevitable in the future. This paper proposes a layout optimization model considering both the two transportation modes. It takes the existing cities in CR Express as candidates and allows hub nodes to choose either DT or TT mode. A case study reveals that the DT mode is more suitable for cities with sufficient goods supply, while the TT mode is more suitable for those with insufficient supply of cargo. At the same time, Zhengzhou, Xi'an, Jinan, Urumqi and other cities in key geographical locations are important hub nodes of the network. The proposed layout optimization model of multiple transportation modes not only considers the current conditions of CR Express, but also takes into account the needs of high-quality development in future, which is believed to be able to provide helpful insight into the layout optimization of CR Express transportation networks and its sustainable development.@en