Water Traffic Safety Research Articles

A seafarers’ cognitive error mining model based on IDA and TRACEr and mitigation measures for ship collision accidents

Human factor is the main cause of maritime accidents. However, the in-depth study on the mechanism of cognitive errors in seafarers’ human error needs to be supplemented. This study empirically analyzes 116 collision cargo ships along the Yangtze River trunk line. To explore the relationship between seafarers’ task errors and the cognitive process of accidents, a seafarers’ cognitive error mining model is proposed, which integrates the Information, Decision, and Action (IDA) cognitive model into the Technique for the Retrospective and Predictive Analysis of Cognitive Errors (TRACEr). According to the approach of mining from the outside to the inside, firstly, the error mode in the cognitive stage of seafarer tasks are identified. Then the fine-grained main manifestation of the task errors is mined by K-means, and Apriori algorithm is employed to mine the hidden features between the external and internal error modes. The results show that there are four main problems of Yangtze River ship accidents, and seafarers have four to seven corresponding causal chains from psychological error mechanism to performance shaping factors for each major issue in the cognitive stage. On the basis of the results, this study summarizes the guiding principles of accident prevention and seafarer management measures to provide decision support for water traffic safety.

The Impact of Digital Development on Port Security Resilience—An Empirical Study from Chinese Provinces

As the world transitions into the post-pandemic era, ports in various countries are experiencing increased activity, leading to significant challenges in ensuring traffic safety within port jurisdictions. It is essential to explore methods to improve port security resilience to maintain regular economic and trade exchanges. This article examines the influence of digital development on port security resilience. Firstly, the provincial digital development level score is objectively evaluated from the perspective of digital infrastructure, digital application, and digital industry development. Secondly, the port safety resilience score is assessed considering water traffic safety, rescue and recovery capabilities, and operational efficiency. Lastly, the focus is on 16 provinces in China’s coastal and Yangtze River Economic Belt, establishing provincial panel data from 2010 to 2019, and empirically analyzing the direct impact of digital development on the resilience of port security. The results indicate that digital development enhances the resilience of port security, with significant heterogeneity and long-term effects observed.

Identifying the Causes of Ship Collisions Accident Using Text Mining and Bayesian Networks

Under the backdrop of the robust growth of the global economy, the water transport industry is experiencing rapid development, resulting in an increase in ship collisions and a critical water traffic safety situation. This study uses text mining techniques to gather a corpus of data. The corpus includes human factors, ship factors, natural environmental factors, and management factors, which are used as target data to obtain a high-dimensional sparse original feature vector space set comprising eigenvalues and eigenvalue weight attributes. Chi-square statistics are utilised to reduce dimensionality, resulting in a final set of 33-dimensional text feature items that determine the causal factors of ship collision risk. Taking the four steps involved in the collision process as the primary focus, a Bayesian network structure for ship collision risk is constructed based on the “human-ship-environment-management” system. By incorporating existing ship collision accident/danger reports, conditional probability tables are computed for each node in the Bayesian network structure, enabling the modelling of ship collision risk. The model is validated through an example, revealing that, under relevant conditions, the probability of collision exceeds 90 %. This finding demonstrates the validity of the model and allows one to deduce the primary cause of ship collision accidents.

Prediction of Ship Traffic Flow and Congestion Based on Extreme Learning Machine with Whale Optimization Algorithm and Fuzzy c-Means Clustering

Accurately predicting short-term congestions in ship traffic flow is important for water traffic safety and intelligent shipping. We propose a method for predicting the traffic flow of ships by applying the whale optimization algorithm to an extreme learning machine. The method considers external environmental uncertainty and complexity of ships navigating in traffic-intensive waters. First, the parameters of ship traffic flow are divided into multiple modal components using variational mode decomposition and extreme learning machine. The machine and the whale optimization algorithm constitute a hybrid modelling approach for predicting individual modal components and integrating the results of individual components. Considering a map between ship traffic flow parameters and congestion, fuzzy c-means clustering is used to predict the level of ship traffic congestion. To verify the effectiveness of the proposed method, ship traffic flow data of the Yangtze River estuary were selected for evaluation. Results from the proposed method for predicting ship traffic flow parameters are consistent with measurements. Specifically, the prediction accuracy of the ship traffic congestion reaches 76.04%, which is reasonable and practical for predicting ship traffic congestion.

A Sidelobe-Aware Small Ship Detection Network for Synthetic Aperture Radar Imagery

Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique challenges. SAR image characteristics, such as the sidelobe effect and blurred outline induced by the special imaging mechanism, as well as the small ship size, are the primary factors that lower the detection accuracy. This paper provides a sidelobe-aware small ship detection network for synthetic aperture radar imagery. First, considering the sidelobe effect and blurred outline, dual-pooling, i.e., average pooling and max pooling, was utilized to build a feature extraction module that lowered the effects of strong scattering points outside of the ship body and enhanced the ship body information. Second, as the bipartition process of the average pooling and maximum pooling caused some loss of original data information, different feature maps in the network were concatenated to construct a new network structure to compensate for the information lost and enrich the small ship features. Third, because the traditional loss function based on centroid distance and aspect ratio may result in the same loss function value for different prediction box sizes, a novel loss function based on the dual Euclidean distances of the corner point coordinates between the prediction box and the real box was proposed, which could accurately describe various overlapping box situations. Experiments using the Large-Scale SAR Ship Detection Dataset (LS-SSDD), SAR Ship Detection Dataset (SSDD), and AIR-SARShip dataset validated the efficacy and state-of-the-art performance.

Research on Unmanned Ship Path Planning based on RRT Algorithm

The increase of water shipping density, coupled with the increasing complexity of the waterway, the ship's intelligent collision avoidance system plays a pivotal role in the transportation safety of shipping. Therefore, it has gradually become the focus of water traffic safety research. In recent years, China is still facing a period of great development in navigation technology, safety, green, digital, intelligent will become the development trend. Although the waterway environment has been improving, the increase of shipping vessels has led to frequent accidents in the waterway, and the traditional management methods and means lack real-time monitoring and scheduling of vessels, and the management lacks intelligence and information technology. In this paper, we solve the ship path problem from the point of path planning of intelligent ships. RRT* algorithm is a common method for path planning, but the traditional RRT* algorithm has problems in ship path planning, slow convergence speed, many path turns and large curvature fluctuations. For these problems, an improved fast extended random tree algorithm (Bi-RRT) is proposed. The improved RRT* algorithm is simulated and verified by MATLAB simulation platform. The simulation results show that the research algorithm can shorten the planning time, reduce the number of iterations, and has better feasibility and effectiveness.

Ship Target Detection Algorithm Based on Improved YOLOv3 for Maritime Image

Accurate identification of ships is the key technology of intelligent transportation in water. At the same time, it also provides a judgment basis for water traffic safety control. This paper proposed a detection method of ships in water based on improved You Only Look Once version 3 (YOLOv3), which is called Feature Attention, Feature Enhancement YOLOv3 (AE-YOLOv3). The feature attention module was constructed by introducing the attention mechanism, which was embedded in Darknet-53 for feature recalibration, which improved the feature extraction ability of the model in the complex navigable background. For the problem of insufficient semantic information of low-level features in the feature fusion process, a feature enhancement module was constructed and applied to the feature fusion part to enhance the receptive field size of the corresponding feature layer and the correlation degree of feature extraction network. Experiments were carried out on the public SeaShips dataset. Experiments show that the detection accuracy is as high as 98.72%, which is better than that of other mainstream ship identification models, fully verifying the superiority of this method in the detection of waterborne traffic ships.

Study on the Temporal and Spatial Distribution Characteristics of Aquatic Traffic Accidents

The study of the characteristic of aquatic traffic accidents is significant for avoiding accidents effectively and guiding the supervision of water traffic safety and the search and rescue of water accidents. In this paper, the temporal and spatial characteristics of aquatic accidents from the aspects of accident occurrence and accident consequences were studied. The results indicated that the total number of verified distress incidents has significant temporal and spatial distribution characteristics; the number of verified distress incidents in coastal areas is significantly higher than that of inland rivers; the main causes of water accidents include collisions, stranding, fires and windstorm, all of which have a significant temporal distribution. In terms of accident consequences, the number of general danger is significantly higher than that of other levels of risk, and there is a significant temporal distribution characteristics on four danger levels. The main types of ships in distress are transporting ships, agricultural vessels, fishing boats and official ships. The number of transporting ships and fishing boats both fluctuated greatly in some months; there was no difference on the temporal distribution for the number of agricultural vessels and official ships in a year. There is no significant linear relationship between the number of distressed people and ships; the number of people in distress on the four types of ships all showed a certain time distribution characteristics; the number of deaths and disappearances in the main rivers rose sharply in June. By analyzing the temporal and spatial distribution characteristics of accidents and their consequences, this paper provides a basis for the relevant authorities to optimize the prevention and control of water accidents, the layout of water search and rescue forces and the timeliness of target searches.

Risk evaluation system of navigation security based on coupled wind and wave model: a case of study of Qiongzhou strait

With the development of water transport, there is a rapid increase in maritime traffic density. Therefore, navigation security should be given adequate attention. In this study, a risk evaluation system combining wind field model, wave model and standards of risk grades was proposed and applied in the Qiongzhou Strait (QS), China. Based on meteorological data, a wind filed model was established by Fujita-Takahashi formula and was verified using observed data set of typical typhoon process. Then, a simulation wave nearshore wave model which was used to simulate the significant wave height (SWH) was established with bathymetry data. In addition, through the analysis of the types and quantities of wrecks and ships in the research area, the typical ship was selected and classified by tonnage. Combining the as low as reasonably practicable with wind speed and SWH, the risk evaluation system for navigation security was proposed. Applications on QS showed that the system could provide an hourly reference of risk grades to water traffic safety early warning. Also, the conclusions of this study could serve as recommendations for Maritime Administration to manage navigation safety.

The Ship Driving Safety Control System Based on Visual Effect Analysis

Fu, S., 2019. The ship driving safety control system based on visual effect analysis. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 88–91. Coconut Creek (Florida), ISSN 0749-0208.With the increase in the number of ships in China, the incidence of water traffic accidents has risen remarkably, and research on the control systems that drive the ship safety has become a necessity. Based on the principle of visual effect analysis, this article analyzes the influence of eye-movement characteristics on driving safety by means of experimental research and SPSS data statistics and proposes taking three eye-movement parameters—the fixation-point distribution ratio, the fixation-duration ratio, and pupil change—as indicators to analyze the safe driving of ship drivers. The research results show that the complexity of the ship's navigation situation will make the driver's fixation points more widely distributed, and the driver's pupil will become larger. Under conditions of a multiship encounter, the driver's fixation-duration ratio is the largest, and his or her attention is highly concentrated; during the midday period, the driver's concentration declines significantly. Based on visual analysis, this article studies the safe driving of ships, which has certain guiding significance for the driver's behavior in the process of ship operation and control, and meanwhile, the results are conducive to the establishment of a water traffic safety system.

Analysis of Black Spot of Traffic Accident in Wuhan Port

The proposal to build the “Yangtze River Golden Waterway” strategy has enabled the shipbuilding volume of the Yangtze River trunk to grow rapidly. With the increase of ship traffic flow, the Yangtze River water traffic safety is facing severe challenges. This paper draws on the road traffic black spots and existing research methods of water traffic accidents and the definition of water traffic black spots. The DBSCAN algorithm and the accident-level weighted average method are used to establish the blackpoint identification model for waterborne traffic. The model is applied to the Yangtze River mid-stream Wuhan Port 2013-2017 water traffic safety accident as an example. Ten black spots and corresponding boundaries are identified. The black point analysis method used in this paper can effectively quantitatively analyze the spatial distribution and characteristics of water traffic black spots, which can provide a new theoretical basis for ensuring the safety of water traffic.

A Classification Method for Inland Navigable and Substandard Waterways

Classification of inland navigable and substandard waterways is essential for optimal allocation of safety administration resources. It is stipulated in “Regulations on the Administration of Water Traffic Safety in Zhejiang Province” that Maritime Safety Administration (MSA) should be responsible for the Navigable waterways using professional resources whilst Local Transportation Authorities should be responsible for the safety management of the Substandard waterway. It is therefore important to classify waterways into navigable and substandard areas in order to apply for different safety administration measures. At present, the division of inland navigable and substandard waterways is not clear, and there are no systematic methods for the classification of waterways. This paper investigated the present conditions of the inland waterways in Ningbo, and a classification method is proposed which can classify waterways into navigable and substandard areas based on C2R model of data envelopment analysis (DEA). A case study was also carried out using the proposed method and the results were compared with those obtained using fuzzy comprehensive evaluation (FCE) method. It is found that unlike the FCE, the proposed method does not need to determine the exactly weights of the indicators which influence the classification of inland waterways and does not need to determine the membership function of the indicators, thus the results are more objective and reliable.

Water Traffic Safety Evaluation Based on the Grey Correlation Grade Analysis

Aiming at the deficiency of the water traffic safety evaluation method in our country, the water traffic safety evaluation model was established after full discussing, which is based on grey relational grade analysis. The water traffic safety status of shipping companies was evaluated through their correlation, which is on the basis of the Five index method, after the original data in a reasonable manner of dimensionless processing. Through a case of a shipping group, it is proved that the grey correlation greed analysis method can evaluate enterprise's security situation simply and effectively on the basis of original index of death toll, the number of accidents above General level, the number of ship destroyed and direct economic loss. Additionally, the evaluation is more objective and scientific.

Information System Design for Early Warning Management of Yangtze River Water Traffic Safety

The paper discusses the signification of early warning information management of water traffic safety, and concludes that information deficiency is the main reason to lead to traffic accident. The purpose of Early Warning Management (EWM) is to solve the problem of information deficiency during navigation. The core is navigation early warning information collecting, processing and transpiring in time. In response to the features of water traffic system, the research designs an Early Warning Management Information System (EWMIS) for water traffic safety, including information process progress, and function and logical structure of the system. The EWMIS process progress covers collection, handling and publishing of early warning information; the EWMIS Functional Structural includes the early warning information integrated platform, the receiving warning information processing subsystem, the early warning decision acid subsystem and the pre-control measures management subsystem. Language: en