Maritime Traffic Management Research Articles

A Review over AI Methods Developed for Maritime Awareness Systems

Due to their operations against illegal activities, maritime threats or collision prevention analysis, maritime surveillance plays a vital role in maritime traffic security and safety management. Today's maritime surveillance and awareness systems can integrate multiple data sources like: coastal, HFSWR and SAR radars, AIS or satellite imagery; and this process produces massive amounts of data. That available data can be processed, with the use of Artificial Intelligence (AI) methods and algorithms, to automatically monitor the maritime traffic and its implications in safety, security, economy and environment. This paper's purpose is to briefly reveal current AI techniques that have been researched and deployed in the industry, and to seize the opportunity of implementing them.

Prediction Method of Ship's Track Based on Mathematical Model

Liu, J., 2020. Prediction method of ship's track based on mathematical model. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 379-382. Coconut Creek (Florida), ISSN 0749-0208.Real-time and accurate acquisition of ship navigation dynamic information plays an important role in maritime traffic research and management, and ship intelligent collision avoidance. This paper proposes to convert ship automatic identification system (AIS) data into navigation dynamics time series data for training and testing of LSTM network. The prediction results are compared with the prediction results of the traditional track estimation algorithm and the BP (back propagation) neural network method. The results prove that the ship navigation dynamic prediction model based on the mathematical model has high accuracy, strong robustness and good versatility. Features. The prediction results can provide a reference for the supervision of the ship traffic management center, and have high practical application value in early warning of ship collisions and groundings.

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

Low-Light Image Enhancement With Regularized Illumination Optimization and Deep Noise Suppression

Maritime images captured under low-light imaging condition easily suffer from low visibility and unexpected noise, leading to negative effects on maritime traffic supervision and management. To promote imaging performance, it is necessary to restore the important visual information from degraded low-light images. In this article, we propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. In particular, a hybrid regularized variational model, which combines L0-norm gradient sparsity prior with structure-aware regularization, is presented to refine the coarse illumination map originally estimated using Max-RGB. The adaptive gamma correction method is then introduced to adjust the refined illumination map. Based on the assumption of Retinex theory, a guided filter-based detail boosting method is introduced to optimize the reflection map. The adjusted illumination and optimized reflection maps are finally combined to generate the enhanced maritime images. To suppress the effect of unwanted noise on imaging performance, a deep learning-based blind denoising framework is further introduced to promote the visual quality of enhanced image. In particular, this framework is composed of two sub-networks, i.e., E-Net and D-Net adopted for noise level estimation and non-blind noise reduction, respectively. The main benefit of our image enhancement method is that it takes full advantage of the regularized illumination optimization and deep blind denoising. Comprehensive experiments have been conducted on both synthetic and realistic maritime images to compare our proposed method with several state-of-the-art imaging methods. Experimental results have illustrated its superior performance in terms of both quantitative and qualitative evaluations.

Research on Time Characteristics of Near Miss in Bohai Sea

It is an effective method to analyze the maritime traffic risk using near miss model, but there are no works from the temporal perspective. The duration of near miss shows the maritime traffic risk. The longer the duration is, the higher the risk is. The influence of the ship maneuvering capability, speed, size, and Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) on the safety of ship navigation are considered in this article, the navigation safety domain is used to detect near misses, and the duration of a single near miss is defined, which is the time from target ship sailing into the navigation safety domain of own ship to leaving, and then an algorithm for calculating duration of near misses is proposed. To measure the similarity of two ships' lengths, the similarity coefficient model of ships' lengths is defined. Based on the k -means algorithm, different parameters k are selected to cluster the duration of near misses. According to the clustering results, the connections between the duration of near misses and the ship type, length, and speed were analyzed to identify a series of time characteristics of near misses, that is high frequencies of short-duration near misses and the strong connections between the long-duration near misses, ship length, and ship speed. The method is first proposed for doing research of near misses in temporal dimension. The findings of this article help researchers and maritime traffic management agencies to better understand the importance of maritime traffic risk from the temporal perspective.

Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

Abstract The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.

Development of Intelligent Marine Traffic Service Application Software for Haikou Bay

Li, D.-L., 2019. Development of intelligent marine traffic service application software for Haikou Bay. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 490–494. Coconut Creek (Florida), ISSN 0749-0208.Intelligent maritime traffic management platform can greatly improve the service efficiency and the shipping security of maritime transport industries. In fact, it is more significant for the development of maritime traffic industry. This paper devises an intelligent and integrated VTS information platform for marine transport service in Haikou Bay based on the traditional VTS system, builds a GIS-T-based VTS architecture, and proposes an intelligent VTS ship traffic prediction algorithm based on the grey system theory adaptive GM (1,1) model. The findings show that, in relation to the traditional VTS, the GIS-T-based VTS architecture has a better effect in spatial data management and analysis, and the algorithm based on GM (1,1) model renders a higher prediction precision, thereby effectively improving the marine transport intelligence level in Haikou Bay. This study has borne another one theoretical and practical fruit for the intelligent marine transport services and has a certain application value.

The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

The member states of International Maritime Organization (IMO) have been leading in and enforcing the use of automatic identification systems (AIS) in the analysis of ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. This study will help non-federal stakeholders understand the AIS data and contribute to future research by assessing difficulties and improving access to data and applications. This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. The literature revealed that using AIS data will be beneficial to the public as well as to business and public agencies.

Multiagent Decision Making For Maritime Traffic Management

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale up to real world problems. Empirical results on synthetic and real world problems show that our approach can significantly reduce congestion while keeping the traffic throughput high.

Assessment of the Influence of Offshore Wind Farms on Ship Traffic Flow Based on AIS Data

This paper proposes a framework to assess the influence of Offshore Wind Farms (OWFs) on maritime traffic flow based on raw Automatic Identification System (AIS) data collected before and after the installation of the offshore wind turbines. The framework includes modules for data acquisition, data filtering and statistical analysis. The statistical analysis characterises the influence of an OWF on maritime traffic in terms of minimum passing distances and lateral distribution of the ship trajectories near the OWF. The framework is applied to a specific route for which AIS data is available before and after an OWF installation. The impacts of the OWF on marine traffic are diverse and depend on the ship type categories. This paper quantitatively characterises an OWF's influence on a specific route that is probabilistically modelled, which is important for further studies on OWF site selection and maritime traffic risk assessment and management.

Traffic Pattern Mining and Forecasting Technologies in Maritime Traffic Service Networks: A Comprehensive Survey

Maritime traffic service networks and information systems play a vital role in maritime traffic safety management. The data collected from the maritime traffic networks are essential for the perception of traffic dynamics and predictive traffic regulation. This paper is devoted to surveying the key processing components in maritime traffic networks. Specifically, the latest progress on maritime traffic data mining technologies for maritime traffic pattern extraction and the recent effort on vessels’ motion forecasting for better situation awareness are reviewed. Through the review, we highlight that the traffic pattern knowledge presents valued insights for wide-spectrum domain application purposes, and serves as a prerequisite for the knowledge based forecasting techniques that are growing in popularity. The development of maritime traffic research in pattern mining and traffic forecasting reviewed in this paper affirms the importance of advanced maritime traffic studies and the great potential in maritime traffic safety and intelligence enhancement to accommodate the implementation of the Internet of Things, artificial intelligence technologies, and knowledge engineering and big data computing solution.

Mining maritime traffic conflict trajectories from a massive AIS data

The growing volume of maritime traffic is proving a hindrance to navigational safety. Researchers have sought to improve the safety of maritime transportation by conducting statistical analysis on historical collision data in order to identify the causes of maritime collisions. However, this approach is hindered by the limited number of incidents that can be collected in a given area over a given period of time. Automatic Identification System (AIS) has made available enormous quantities of maritime traffic data. Trajectory data are collected through the electronic exchange of navigational data among ships and terrestrial and satellite base stations. Due to a massive AIS data of recording ship movement, such data provide great opportunity to discover maritime traffic knowledge of movement behavior analysis, route estimation, and the detection of anomalous behaviors. Our objective in this paper was to identify potential between-ship traffic conflicts through the discovery of AIS data. Traffic conflict refers to trajectories that could lead to a collision if the ships do not take any evasive action. In other words, conflicting trajectories can be treated as a near-collision cases for analysis. The prevention of collisions requires an efficient method by which to extract conflicting trajectories from a massive collection of AIS data. To this end, we developed a framework CCT Discovery that allows the automated identification of clusters of conflicting trajectories (CCTs) from AIS data without supervision. Experiments based on real-world data demonstrate the efficacy of the proposed framework in terms of accuracy and efficiency. For improvement in the navigational traffic safety, the discovered data of conflict trajectory can contribute to numerous applications, such as collision situation awareness and prediction, anti-collision behaviors modeling and recommendation, and conflict area analysis for maritime traffic flow management.

GIS-Based Simulation Methodology for Evaluating Ship Encounters Probability to Improve Maritime Traffic Safety

Evaluating ship encounters probability efficiently is significant for maritime transportation risk analysis as well as improving maritime traffic safety. In practice, ship encounters probability estimate by using traditional observation or field experiments is nearly impossible due to the expensive cost. In this paper, we propose a methodology framework for evaluating ship encounters probability based on an agent-based simulation paradigm. Individual ship’s navigation behavior is modeled as an agent, and their encounters are detected by an innovative geographic information system (GIS)-based intelligent algorithm. We then design and implement a prototype simulator based on the methodology framework and apply it in the mid-western waters of Bohai Sea of China to demonstrate its functionalities and utility. The findings show that the framework proposed in this paper has potential to provide decision support for guiding future marine transportation planning. This paper offers new insights and valuable demonstration on promoting maritime traffic safety management with the integration of GIS and emerging complex system simulation technology.

Space Traffic Management at the National and International Levels

ABSTRACTThe number of objects in Earth’s orbit has continued to grow since the space race between the United States and the Soviet Union. The historical “few objects, large space” principle left satellite operations a relatively benign environment, but that construct has shifted significantly due to the advent of the commercial space sector, the small satellite industry boom, and the resurgence of the desire for human space flight. The regulations for space operations both at the international and national level have always been sparse, but there is a large desire for more now that commercial capital is at stake. The purpose of this article is to bring to the surface available regulations for on-orbit operations to identify the gaps to fill and highlight the change for the Department of Defense. The international community and commercial sector recognize the need for a state like the United States to set a standard to influence the international community. This national-to-international framework is a technique used in other arenas, such as maritime traffic management and air traffic management, and the lessons learned from these fields are applicable to the space domain.

Resource-Constrained Scheduling for Maritime Traffic Management

We address the problem of mitigating congestion and preventing hotspots in busy water areas such as Singapore Straits and port waters. Increasing maritime traffic coupled with narrow waterways makes vessel schedule coordination for just-in-time arrival critical for navigational safety. Our contributions are: 1) We formulate the maritime traffic management problem based on the real case study of Singapore waters; 2) We model the problem as a variant of the resource-constrained project scheduling problem (RCPSP), and formulate mixed-integer and constraint programming (MIP/CP) formulations; 3) To improve the scalability, we develop a combinatorial Benders (CB) approach that is significantly more effective than standard MIP and CP formulations. We also develop symmetry breaking constraints and optimality cuts that further enhance the CB approach's effectiveness; 4) We develop a realistic maritime traffic simulator using electronic navigation charts of Singapore Straits. Our scheduling approach on synthetic problems and a real 55-day AIS dataset results in significant reduction of the traffic density while incurring minimal delays.

Need for RADAR system utilisation for maritime traffic management: a case of Congo River Basin

Maritime traffic management has emerged as a new challenge along with the developments going on in the world. The present study concentrated on such measures for the maritime traffic management on the Congo River Basin with the help of RADAR technology. The study collected the opinion of the personnel of maritime department as primary data and the secondary data is collected from the records of CICOS and SCTP departments. The data is analysed using the analysis of variance technique to know whether there is increase in the untoward incidents and the traffic among the three countries connected to this basin. The results of the analysis showed the role of human errors in the occurrence of the mishaps and the personnel also opined to install the RADAR system for control purpose.

Need for RADAR system utilisation for maritime traffic management: a case of Congo River Basin

Need for RADAR system utilisation for maritime traffic management: a case of Congo River Basin

Comparing prediction methods for maritime accidents

ABSTRACTThe purpose of maritime accident prediction is to reasonably forecast an accident occurring in the future. In determining the level of maritime traffic management safety, it is important to analyze development trends of existing traffic conditions. Common prediction methods for maritime accidents include regression analysis, grey system models (GM) and exponential smoothing. In this study, a brief introduction is provided that discusses the aforementioned prediction models, including the associated methods and characteristics of each analysis, which form the basis for an attempt to apply a residual error correction model designed to optimize the grey system model. Based on the results, in which the model is verified using two different types of maritime accident data (linear smooth type and random-fluctuation type, respectively), the prediction accuracy and the applicability were validated. A discussion is then presented on how to apply the Markov model as a way to optimize the grey system model. This method, which proved to be correct in terms of prediction accuracy and applicability, is explored through empirical analysis. Although the accuracy of the residual error correction model is usually higher than the accuracy of the original GM (1,1), the effect of the Markov correction model is not always superior to the original GM (1,1). In addition, the accuracy of the former model depends on the characteristics of the original data, the status partition and the determination method for the status transition matrix.

A Study on Maritime Traffic Characteristics according to Water Time(Multte)

This study seeks to analyze ships traffic characteristics according to water time in order to provide the necessary data for efficient traffic management development. To analyze maritime traffic volume according to water time, 1 year amount of solar calendar data were converted into lunar calendar, and then applied the traditional water time system of West Sea by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. As a result, it was found herein that the number of outbound ships was larger on the 2 nd -3 rd water times than the 7 th water times by 23-24 %. And the number of inbound ships was higher on the 12 th -13 th water times than the 9 th water time by 29-33 %. The hourly variation index of inbound and outbound ships according to time, in particular, was found to change in the form of sine function model. This study is expected to serve as a necessary basic material for development of maritime traffic management according to water time.

E-navigation을 활용한 해상교통관리체계 개선방안에 관한 연구

This study proposes the improvement of maritime traffic management for domestic ships and fishing vessels by introducing the e-navigation environment. This study discusses the development of present Vessel Traffic service (VTS) in a systematic aspect and a fuctional aspect. The concept and architecture of e-navigation operation system based on the General Information Center on Maritime Safety and Security (GICOMS) are proposed as a solution for the improvement of maritime traffic management in Korean coastal waters. Especially, means of data exchange between ships and ship-shore based on the Maritime Cloud and regional e-Data Center are discussed. This study will help to the implementation of the Korean e-navigation project which focuses on the safety of small ships and fishing vessels. In the future, it is needed to study for the development and operation of accident prevention system under the e-navigation environment.