Impact of Distance Measures on the Performance of AIS Data Clustering

Automatic Identification System(AIS)data stream analysis is based on the AIS data of different vessel’s behaviours,including the vessels’routes.When the AIS data consists of outliers,noises,or are incomplete,then the analysis of the vessel’s behaviours is not possible or is limited.When the data consists of outliers,it is not possible to automatically assign the AIS data to a particular vessel.In this paper,a clustering method is proposed to support the AIS data analysis,to qualify noises and outliers with respect to their suitability,and finally to aid the reconstruction of the vessel’s trajectory.In this paper,clustering results have been obtained using selected algorithms,including k-means,k-medoids,and fuzzy c-means.Based on the clustering results,it is possible to decide on the qualification of data with outliers and on their usefulness in the reconstruction of the vessel trajectory.The main aim of this paper is to answer how different distance measures during a clustering process can influence AIS data clustering quality.The main core question is whether or not they have an impact on the process of reconstruction of the vessel trajectories when the data are damaged.The research question during the computational experiments asked whether or not distance measure influence AIS data clustering quality.The computational experiments have been carried out using original AIS data.In general,the experiment and the results confirm the usefulness of the cluster-based analysis when the data include outliers that are derived from the natural environment.It is also possible to monitor and to analyse AIS data using clustering when the data include outliers.The computational experiment results confirm that the k-means with Euclidean distance has the best performance.

Sensitive Resource and Traffic Density Risk Analysis of Marine Spill Accidents Using Automated Identification System Big Data

This study developed a new methodology for analyzing the risk level of marine spill accidents from two perspectives,namely,marine traffic density and sensitive resources.Through a case study conducted in Busan,South Korea,detailed procedures of the methodology were proposed and its scalability was confirmed.To analyze the risk from a more detailed and microscopic viewpoint,vessel routes as hazard sources were delineated on the basis of automated identification system(AIS)big data.The outliers and errors of AIS big data were removed using the density-based spatial clustering of applications with noise algorithm,and a marine traffic density map was evaluated by combining all of the gridded routes.Vulnerability of marine environment was identified on the basis of the sensitive resource map constructed by the Korea Coast Guard in a similar manner to the National Oceanic and Atmospheric Administration environmental sensitivity index approach.In this study,aquaculture sites,water intake facilities of power plants,and beach/resort areas were selected as representative indicators for each category.The vulnerability values of neighboring cells decreased according to the Euclidean distance from the resource cells.Two resulting maps were aggregated to construct a final sensitive resource and traffic density(SRTD)risk analysis map of the Busan–Ulsan sea areas.We confirmed the effectiveness of SRTD risk analysis by comparing it with the actual marine spill accident records.Results show that all of the marine spill accidents in 2018 occurred within 2 km of high-risk cells(level 6 and above).Thus,if accident management and monitoring capabilities are concentrated on high-risk cells,which account for only 6.45%of the total study area,then it is expected that it will be possible to cope with most marine spill accidents effectively.

基于苹果应用商店大数据的APP词典融媒创新分析

手机终端的APP词典是目前最主流、最贴近融媒辞书发展趋势的数字化词典形态。利用Data.ai平台对苹果应用商店2360个APP词典的调查研究发现,自2008年首个产品发行以来,APP词典在数字开发商科技实力的巨大推动下迅速发展,展现了六大融媒创新变革。侧重词典本体内链“融合”的创新表现为:基于语言加工的多语种服务融合,基于集成整合的多词典类型融合,基于逻辑媒体的多模态内容融合和基于查询场景的多检索手段融合;侧重词典外部世界外链“融通”的创新表现为:数字工具之间的功能融通和词典交际主体之间的身份融通。APP词典要实现融媒辞书的跨越转型,还需要考虑:如何在利用技术追求“美颜”式发展的同时做到内外兼修,如何在编纂传统规范和身份创新融通之间实现编用平衡,如何达到海量内容与精准服务的信息平衡,如何保证泛在服务与知识工具的定位平衡。

An Efficient Long Short-Term Memory and Gated Recurrent Unit Based Smart Vessel Trajectory Prediction Using Automatic Identification System Data

Maritime transportation,a cornerstone of global trade,faces increasing safety challenges due to growing sea traffic volumes.This study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System(AIS)data and advanced deep learning models,including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Bidirectional LSTM(DBLSTM),Simple Recurrent Neural Network(SimpleRNN),and Kalman Filtering.The research implemented rigorous AIS data preprocessing,encompassing record deduplication,noise elimination,stationary simplification,and removal of insignificant trajectories.Models were trained using key navigational parameters:latitude,longitude,speed,and heading.Spatiotemporal aware processing through trajectory segmentation and topological data analysis(TDA)was employed to capture dynamic patterns.Validation using a three-month AIS dataset demonstrated significant improvements in prediction accuracy.The GRU model exhibited superior performance,achieving training losses of 0.0020(Mean Squared Error,MSE)and 0.0334(Mean Absolute Error,MAE),with validation losses of 0.0708(MSE)and 0.1720(MAE).The LSTM model showed comparable efficacy,with training losses of 0.0011(MSE)and 0.0258(MAE),and validation losses of 0.2290(MSE)and 0.2652(MAE).Both models demonstrated reductions in training and validation losses,measured by MAE,MSE,Average Displacement Error(ADE),and Final Displacement Error(FDE).This research underscores the potential of advanced deep learning models in enhancing maritime safety through more accurate trajectory predictions,contributing significantly to the development of robust,intelligent navigation systems for the maritime industry.

From Diaries to Digital:The Role of AI in Web-Mediated Documentary Analysis

This paper explores how artificial intelligence(AI)can support social researchers in utilizing web-mediated documents for research purposes.It extends traditional documentary analysis to include digital artifacts such as blogs,forums,emails and online archives.The discussion highlights the role of AI in different stages of the research process,including question generation,sample and design definition,ethical considerations,data analysis,and results dissemination,emphasizing how AI can automate complex tasks and enhance research design.The paper also reports on practical experiences using AI tools,specifically ChatGPT-4,in conducting web-mediated documentary analysis and shares some ideas for the integration of AI in social research.

Study of narrow waterways congestion based on automatic identification system(AIS)data:A case study of Houston Ship Channel

Using automatic identification system(AIS)data,this article first has extended the definition of three widely used roadway congestion indices to maritime transportation systems(MTS),traffic speed index(TSI),traffic rate index(TRI),and dwell time index(DTI).Next,a methodology is developed to measure the indices based on AIS data,considering various factors,including path geometry,time of day,and the type and size of vessels,and finally the method has been applied to the AIS data of the Houston Ship Channel(HSC)to evaluate the applicability in real cases.The results show that although average TSI and TRI cannot represent waterway congestion,the real-time values(rather than the average)at the micro level can help finding location,time,and severity of traffic congestion.Besides,while TSI and TRI have shortcomings,both average and real-time dwell time index(DTI)can quantify traffic congestion and highlight severity in different waterway segments for different types of vessels.When congestion happens at some narrow waterways,vessels need to wait at sea buoy or docks,thus dwell time index(DTI)can quantify traffic congestion better than in-transit indices such as travel speed,TSI.According to HSC DTI,most tankers experience long waiting times at the sea buoy and Galveston Bay,while cargo vessels experience delays at Bayport and Barbour’s Cut terminals.This paper helps the decision-makers quantify congestion in different sections of a waterway and provides measures to compare congestion for national competing projects at different waterways.

A Port Ship Flow Prediction Model Based on the Automatic Identification System and Gated Recurrent Units

Water transportation today has become increasingly busy because of economic globalization.In order to solve the problem of inaccurate port traffic flow prediction,this paper proposes an algorithm based on gated recurrent units(GRUs)and Markov residual correction to pass a fixed cross-section.To analyze the traffic flow of ships,the statistical method of ship traffic flow based on the automatic identification system(AIS)is introduced.And a model is put forward for predicting the ship flow.According to the basic principle of cyclic neural networks,the law of ship traffic flow in the channel is explored in the time series.Experiments have been performed using a large number of AIS data in the waters near Xiazhimen in Zhoushan,Ningbo,and the results show that the accuracy of the GRU-Markov algorithm is higher than that of other algorithms,proving the practicability and effectiveness of this method in ship flow prediction.

Elucidation of Latent Risk of Navigation Using an Actual Ship Behavior Analysis

In recent years, maritime transportation has played an important role in global economy development. As a result, ship traffic has become more congested. Moreover, ship navigation is susceptible to weather and environmental conditions, and in some cases, it may become dangerous. Therefore, vessels are subjected to high-risk navigation conditions. To understand the latent risk of ship navigation, this study focused on the actual ship behavior. Thus, an analysis of ship behavior was carded out using historical ship navigation based on automatic identification system data. Consequently, a dynamic analysis of the speed and encounter situation was performed. One of the main results of this work was the understanding of the latent risk involved in ships navigating the Seto Inland Sea, which is one of the most congested routes in Japan. Moreover, the risk areas were obtained, and visualized using a geographical information system. The obtained results can be applied to ensure safe navigation and the development of a safe and efficient navigation model.

China's Economic Growth:The“Two-Dimensional Driving Effect”of Data Factors

Data factors have become one of the five essential production factors,but their role in economic growth has always been ambiguous.Starting from AI technologies,this paper establishes an endogenous growth model of data factors affecting economic growth,constructs the generation path and value path of data factors,and estimates the value of new data factors at the provincial level in China from 1999 to 2018 accordingly.Based on theoretical analyses and empirical tests,it clarifes that data factors have a“two-dimensional driving effect”on China's economic growth,that is,data factors can drive growth both directly through its own economic growth effect and indirectly by promoting technological progress.Furthermore,this paper makes three extended discussions,aiming to make a trial study on the impacts of local government big data transaction platforms on data factors and their growth effects,discuss whether it is possible to reduce the uncertainties of local economic policy based on the nature of data factors,and make a preliminary survey of the output elasticity of data factors between 1999 and 2018.