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 th...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.展开更多
Urban railways are vital means of public transportation in Korea.More than 30%of metropolitan residents use the railways,and this proportion is expected to increase.To enhance safety,the government has mandated the in...Urban railways are vital means of public transportation in Korea.More than 30%of metropolitan residents use the railways,and this proportion is expected to increase.To enhance safety,the government has mandated the installation of closed-circuit televisions in all carriages by 2024.However,cameras still monitored humans.To address this limitation,we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof.We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway passengers.Detailed data collection was conducted across the Shinbundang Line of the Incheon Transportation Corporation,and the Ui-Shinseol Line.We observed several behavioral characteristics and assigned unique annotations to them.We also considered carriage congestion.Recognition performance was evaluated by camera placement and number.Then the camera installation plan was optimized.The dataset will find immediate applications in domestic railway operations.The artificial intelligence algorithms will be verified shortly.展开更多
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 Iden...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.展开更多
As maritime activities increase globally,there is a greater dependency on technology in monitoring,control,and surveillance of vessel activity.One of the most prominent systems for monitoring vessel activity is the Au...As maritime activities increase globally,there is a greater dependency on technology in monitoring,control,and surveillance of vessel activity.One of the most prominent systems for monitoring vessel activity is the Automatic Identification System(AIS).An increase in both vessels fitted with AIS transponders and satellite and terrestrial AIS receivers has resulted in a significant increase in AIS messages received globally.This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics,of which a pertinent example is the improvement of vessel location predictions.In this paper,we propose a novel strategy for predicting future locations of vessels making use of historic AIS data.The proposed method uses a Linear Regression Model(LRM)and utilizes historic AIS movement data in the form of a-priori generated spatial maps of the course over ground(LRMAC).The LRMAC is an accurate low complexity first-order method that is easy to implement operationally and shows promising results in areas where there is a consistency in the directionality of historic vessel movement.In areas where the historic directionality of vessel movement is diverse,such as areas close to harbors and ports,the LRMAC defaults to the LRM.The proposed LRMAC method is compared to the Single-Point Neighbor Search(SPNS),which is also a first-order method and has a similar level of computational complexity,and for the use case of predicting tanker and cargo vessel trajectories up to 8 hours into the future,the LRMAC showed improved results both in terms of prediction accuracy and execution time.展开更多
This paper presents an air quality simulation model that incorporates shipping activities and weather conditions,with a case study of Hainan Island to examine the impact of ship emissions on air quality.The findings r...This paper presents an air quality simulation model that incorporates shipping activities and weather conditions,with a case study of Hainan Island to examine the impact of ship emissions on air quality.The findings reveal that the density of automatic identification system(AIS)signals is particularly high in the southern coastal regions.The results showed that the annual ship emissions recorded the highest density of 896.7 tons/0.01°,49.8 tons/0.01°,1139.7 tons/0.01°,and 122,000 tons/0.01°for sulfur oxides(SO_(x)),particulate matter(PM),nitrogen oxides(NOx),and carbon dioxide(CO_(2)),respectively.Furthermore,the partial distributions of these emissions were not significantly affected by the seasons.Ships within twelve nautical miles of Hainan coastlines emit approximately 2817.7 tons of SO_(x),14,686.4 tons of NO_(x),630.4 tons of PM_(2.5),and 416.9 tons of hydrocarbons(HC)annually.These emissions are primarily concentrated in the sea areas surrounding the ports of Haikou,Yangpu,Basuo,and Sanya.Ships manufactured between 2000 and 2010 have contributed significantly to air pollution,with SO_(x) and HC emissions accounting for approximately 51%and 56% of total emissions,respectively.However,for shipsmanufactured after 2016,these proportions have dropped to approximately 10%.In terms of air pollutants fromship emissions in Hainan Island,the spatial distribution of their contributions is significantly uneven.The impact of PM2.5 differs significantly depending on the season,with the concentrations being substantially higher during Spring.However,the proportions of O3 and other pollutants do not vary significantly,except during Spring.展开更多
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 K...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.展开更多
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...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.展开更多
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 recurr...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.展开更多
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 ra...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.展开更多
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 environ...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.展开更多
Aim&Scopes.Intelligent Medicine is an open access,peer-reviewed journal sponsored and owned by the Chinese Medical Association and designated to publish high-quality original research and review articles from the ...Aim&Scopes.Intelligent Medicine is an open access,peer-reviewed journal sponsored and owned by the Chinese Medical Association and designated to publish high-quality original research and review articles from the interdisciplinary areas concerning the theory,algorithms,and practice of internet technology,artificial intelligence(AI),big data science,and medical information in the clinical medicine,biomedicine,and public health.Intelligent Medicine appreciates the innovation,science,significance,and practicality.The topics focus on but not limited to the internet technology,Al,or data science enabled intelligent medical system,including clinical decision making(diagnosis,therapy,prediction,and prognosis),telemedicine,guideline management,drug development,and health management.Articles are published quarterly.The journal is available both in print and online.展开更多
文摘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.
基金supported by a Korean Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure and Transport(grant no.RS-2023-00239464).
文摘Urban railways are vital means of public transportation in Korea.More than 30%of metropolitan residents use the railways,and this proportion is expected to increase.To enhance safety,the government has mandated the installation of closed-circuit televisions in all carriages by 2024.However,cameras still monitored humans.To address this limitation,we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof.We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway passengers.Detailed data collection was conducted across the Shinbundang Line of the Incheon Transportation Corporation,and the Ui-Shinseol Line.We observed several behavioral characteristics and assigned unique annotations to them.We also considered carriage congestion.Recognition performance was evaluated by camera placement and number.Then the camera installation plan was optimized.The dataset will find immediate applications in domestic railway operations.The artificial intelligence algorithms will be verified shortly.
基金the“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-004)Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.RS-2022-00155857,Artificial Intelligence Convergence Innovation Human Resources Development(Chungnam National University)).
文摘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.
文摘As maritime activities increase globally,there is a greater dependency on technology in monitoring,control,and surveillance of vessel activity.One of the most prominent systems for monitoring vessel activity is the Automatic Identification System(AIS).An increase in both vessels fitted with AIS transponders and satellite and terrestrial AIS receivers has resulted in a significant increase in AIS messages received globally.This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics,of which a pertinent example is the improvement of vessel location predictions.In this paper,we propose a novel strategy for predicting future locations of vessels making use of historic AIS data.The proposed method uses a Linear Regression Model(LRM)and utilizes historic AIS movement data in the form of a-priori generated spatial maps of the course over ground(LRMAC).The LRMAC is an accurate low complexity first-order method that is easy to implement operationally and shows promising results in areas where there is a consistency in the directionality of historic vessel movement.In areas where the historic directionality of vessel movement is diverse,such as areas close to harbors and ports,the LRMAC defaults to the LRM.The proposed LRMAC method is compared to the Single-Point Neighbor Search(SPNS),which is also a first-order method and has a similar level of computational complexity,and for the use case of predicting tanker and cargo vessel trajectories up to 8 hours into the future,the LRMAC showed improved results both in terms of prediction accuracy and execution time.
基金supported by the National Key Research and Development Program of China(No.2022YFC3704200)the National Natural Science Foundation of China(No.52306128)+5 种基金the Major Program of Science and Technology of Hainan Province,China(No.ZDKJ202007)the Innovation Platform for Academicians of Hainan Province(No.YSPTZX202205)the Youth Innovation Foundation of Hainan Research Academy of Environmental Sciences,China(No.QNCX2021002)the Central Guiding Local Science and Technology Development Fund Projects(No.236Z4001G)the Natural Science Basic Research Program of Shaanxi(No.2023-JC-QN-0517)the support from Energy Foundation China.
文摘This paper presents an air quality simulation model that incorporates shipping activities and weather conditions,with a case study of Hainan Island to examine the impact of ship emissions on air quality.The findings reveal that the density of automatic identification system(AIS)signals is particularly high in the southern coastal regions.The results showed that the annual ship emissions recorded the highest density of 896.7 tons/0.01°,49.8 tons/0.01°,1139.7 tons/0.01°,and 122,000 tons/0.01°for sulfur oxides(SO_(x)),particulate matter(PM),nitrogen oxides(NOx),and carbon dioxide(CO_(2)),respectively.Furthermore,the partial distributions of these emissions were not significantly affected by the seasons.Ships within twelve nautical miles of Hainan coastlines emit approximately 2817.7 tons of SO_(x),14,686.4 tons of NO_(x),630.4 tons of PM_(2.5),and 416.9 tons of hydrocarbons(HC)annually.These emissions are primarily concentrated in the sea areas surrounding the ports of Haikou,Yangpu,Basuo,and Sanya.Ships manufactured between 2000 and 2010 have contributed significantly to air pollution,with SO_(x) and HC emissions accounting for approximately 51%and 56% of total emissions,respectively.However,for shipsmanufactured after 2016,these proportions have dropped to approximately 10%.In terms of air pollutants fromship emissions in Hainan Island,the spatial distribution of their contributions is significantly uneven.The impact of PM2.5 differs significantly depending on the season,with the concentrations being substantially higher during Spring.However,the proportions of O3 and other pollutants do not vary significantly,except during Spring.
基金This research was supported by a grant[KCG-01-2017-01]through the Disaster and Safety Management Institute funded by the Ministry of Public Safety and Securitythe National Research Foundation of Korea(NRF)grant[No.2018R1D1A1B07050208]funded by the Ministry of Science and ICT of Korea Government.
文摘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.
文摘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.
文摘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.
基金partially supported by the Center for Advances in Port Management,Lamar University.
文摘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.
文摘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.
文摘Aim&Scopes.Intelligent Medicine is an open access,peer-reviewed journal sponsored and owned by the Chinese Medical Association and designated to publish high-quality original research and review articles from the interdisciplinary areas concerning the theory,algorithms,and practice of internet technology,artificial intelligence(AI),big data science,and medical information in the clinical medicine,biomedicine,and public health.Intelligent Medicine appreciates the innovation,science,significance,and practicality.The topics focus on but not limited to the internet technology,Al,or data science enabled intelligent medical system,including clinical decision making(diagnosis,therapy,prediction,and prognosis),telemedicine,guideline management,drug development,and health management.Articles are published quarterly.The journal is available both in print and online.
