Capacity Analysis for Bifurcated Estuaries Based on Ship Domain Theory and its Applications

2017 ◽  
Vol 2611 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Xingjian Zhang ◽  
Junmin Mou ◽  
Jianfeng Zhu ◽  
Pengfei Chen ◽  
Rongfang (Rachel) Liu
Keyword(s):  
Traffic Management ◽  
River Estuary ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Water Levels ◽  
Domain Theory ◽  
Yangtze River Estuary ◽  
Automatic Identification ◽  
Identification System ◽  
Estimation Model

The bifurcated estuary is an important segment of marine transportation systems that are themselves becoming increasingly important. Because of branching channels, the cyclical change of water levels, and sophisticated operating rules in many large bifurcated estuaries, it is often difficult to estimate the traffic capacity and simulate ships’ motions, even though it is critically important for traffic management and efficiency. In recent years, the increasing number of ships that collect and contribute to the Automatic Identification System (AIS) have made it possible to monitor traffic flow along waterways, including bifurcated estuaries. This study developed a typical capacity estimation model based on ship domain theory. By using AIS data collected in the Yangtze River estuary, a typical bifurcated estuary system, the study analyzed various physical characteristics, weather conditions, and vessel characteristics to derive related impacts of each on overall capacity of the bifurcated estuary. Validated with practical observations, the method can be applied to similar estuary channel systems to improve waterway operations and management.

scholarly journals Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

2021 ◽  
Vol 10 (11) ◽  
pp. 757
Author(s):  
Pin Nie ◽  
Zhenjie Chen ◽  
Nan Xia ◽  
Qiuhao Huang ◽  
Feixue Li
Keyword(s):  
Traffic Management ◽  
Similarity Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Motion Direction ◽  
Maritime Traffic ◽  
A Cell ◽  
Robustness To Noise ◽  
Trajectory Similarity

Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.

scholarly journals Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

2019 ◽  
Vol 11 (18) ◽  
pp. 4989 ◽  
Author(s):  
Wei Yu ◽  
Hua Bai ◽  
Jun Chen ◽  
Xingchen Yan
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Rapid Development ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Space Time ◽  
Smart Cards ◽  
The Sustainable Development ◽  
Passenger Flow ◽  
Challenges And Opportunities

The rapid development of cities has brought new challenges and opportunities to traditional traffic management. The usage of smart cards promotes the upgrading of intelligent transportation systems, and also produces considerable big data. As an important part of the urban comprehensive transportation system, Nanjing metro has more than 1 million inbound and outbound records of traffic smart cards used by residents every day. How to process these traffic data and present them visually is an urgent problem in modern traffic management. In this study, five working days with normal weather conditions in Nanjing were selected, and the swiping records of the smart cards were extracted, and the space–time characteristics were analyzed. In terms of time analysis, this research analyzed the 24-h fluctuation of daily average passenger flow, peak hour coefficient of passenger flow, 24-h fluctuation of passenger flow on different metro lines, passenger flow intensity on different metro lines and passenger flow comparison at different stations. In spatial analysis, this study uses thermodynamic charts to represent the inflow and outflow of passengers at different stations during early and evening peak periods. The analysis results and visualized images directly reflect the area where Nanjing metro congestion is located, and also shows the commuting characteristics of residents. It can solve the problem of urban congestion, carry out the rational layout of urban functional areas, and promote the sustainable development of people and cities.

AIS Database Powered by GIS Technology for Maritime Safety and Security

2008 ◽  
Vol 61 (4) ◽  
pp. 655-665 ◽  
Author(s):  
Ziqiang Ou ◽  
Jianjun Zhu
Keyword(s):  
Traffic Management ◽  
Surveillance Program ◽  
Automatic Identification ◽  
Identification System ◽  
Gis Technology ◽  
Aerial Surveillance ◽  
Maritime Traffic ◽  
Land Control ◽  
Maritime Domain Awareness ◽  
Fundamental Requirement

The Automatic Identification System (AIS) is an efficient tool to exchange positioning data among participating naval units and land control centres. It was developed primarily as an advanced tool for assistance to sailors during navigation and for the safety of the life at sea. Maritime security has become a major concern for all coastal nations, especially after September 11, 2001. The fundamental requirement is maritime domain awareness via identification, tracking and monitoring of vessels within their waters and this is exactly what an AIS could bring. This paper will be focused on how the AIS-derived information could be used for coastal security, maritime traffic management, vessel tracking and monitoring with the help of GIS technology. The AIS data used in this paper was collected by the Canadian national aerial surveillance program.

scholarly journals Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8133
Author(s):  
Clara I. Valero ◽  
Enrique Ivancos Pla ◽  
Rafael Vaño ◽  
Eduardo Garro ◽  
Fernando Boronat ◽  
...  
Keyword(s):  
Traffic Management ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Legacy System ◽  
Cognitive Component ◽  
Cognitive Services ◽  
Oceanographic Data ◽  
Reducing Costs ◽  
Easy Integration

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

scholarly journals A Hybrid Method for Inland Ship Recognition Using Marine Radar and Closed-Circuit Television

2021 ◽  
Vol 9 (11) ◽  
pp. 1199
Author(s):  
Xinglong Liu ◽  
Yicheng Li ◽  
Yong Wu ◽  
Zhiyuan Wang ◽  
Wei He ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Weather Conditions ◽  
Calibration Method ◽  
Closed Circuit ◽  
Automatic Identification ◽  
Identification System ◽  
Closed Circuit Television ◽  
Multi Scale ◽  
Marine Radar ◽  
Ship Recognition

Vessel recognition plays important role in ensuring navigation safety. However, existing methods are mainly based on a single sensor, such as automatic identification system (AIS), marine radar, closed-circuit television (CCTV), etc. To this end, this paper proposes a coarse-to-fine recognition method by fusing CCTV and marine radar, called multi-scale matching vessel recognition (MSM-VR). This method first proposes a novel calibration method that does not use any additional calibration target. The calibration is transformed to solve an N point registration model. Furthermore, marine radar image is used for coarse detection. A region of interest (ROI) area is computed for coarse detection results. Lastly, we design a novel convolutional neural network (CNN) called VesNet and transform the recognition into feature extraction. The VesNet is used to extract the vessel features. As a result, the MVM-VR method has been validated by using actual datasets collected along different waterways such as Nanjing waterway and Wuhan waterway, China, covering different times and weather conditions. Experimental results show that the MSM-VR method can adapt to different times, different weather conditions, and different waterways with good detection stability. The recognition accuracy is no less than 96%. Compared to other methods, the proposed method has high accuracy and great robustness.

Freight Distribution Analysis and Modelling of Inland Waterway Transport along the Yangtze River Economic Belt using Big Data

2022 ◽  
pp. 1-36
Author(s):  
Guihua Deng ◽  
Ming Zhong ◽  
Mo Lei ◽  
John Douglas Hunt ◽  
Wanle Wang ◽  
...  
Keyword(s):  
Yangtze River ◽  
Length Distribution ◽  
Water Levels ◽  
Automatic Identification ◽  
Identification System ◽  
Distribution Analysis ◽  
The Yangtze River ◽  
Distribution Models ◽  
Inland Waterway ◽  
Different Types

The Yangtze River Economic Belt (YREB) serves as the main east-west axis of China to promote economic development and environmental protection along the Yangtze River. This paper analyses the factors that affect the freight distribution of major types of cargo transported through the Yangtze River, using data from the automatic identification system (AIS) and ship visa data. First, a set of freight impedance functions are developed for different types of links of the waterway network, by considering a number of factors such as cargo types, delays at ship locks, water levels and flows at different waterway segments and upstream and downstream shipping speeds. Both the distance- and time-based impedance matrices of different types of cargo are computed, respectively. After that, gravity model (GM) and intervening opportunity model (IOM) are estimated to simulate the distribution of different types of cargo based on the computed impedance matrices. Meanwhile, a trip length distribution (TLD) method is applied to validate the estimated distribution models. The results indicate that GM with a power term outperforms other models, and the time-based models are superior to the distance-based ones for the prediction of freight distributions over large geographies like the YREB. This work offers an in-depth understanding of the freight characteristics of inland waterways and therefore it should be helpful for relevant authorities in formulating their port and inland waterway plans and policies.

scholarly journals Using Deep Learning to Forecast Maritime Vessel Flows

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1761 ◽  
Author(s):  
Xiangyu Zhou ◽  
Zhengjiang Liu ◽  
Fengwu Wang ◽  
Yajuan Xie ◽  
Xuexi Zhang
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Transportation Systems ◽  
Traffic Information ◽  
Automatic Identification ◽  
Identification System ◽  
System Data ◽  
Lstm Network

Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best.

scholarly journals Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

2021 ◽  
Vol 9 (6) ◽  
pp. 566
Author(s):  
Lianhui Wang ◽  
Pengfei Chen ◽  
Linying Chen ◽  
Junmin Mou
Keyword(s):  
Traffic Management ◽  
Performance Metrics ◽  
Spatial Clustering ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Clustering Methods ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Shape Characteristics

The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.

Early Adoption Technology Performance Impact: AIS on the St. Lawrence Seaway

2005 ◽  
Vol 58 (1) ◽  
pp. 17-30
Author(s):  
Martha Grabowski ◽  
Hemil Dhami
Keyword(s):  
Traffic Management ◽  
System Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Performance Impact ◽  
Critical System ◽  
Early Adoption ◽  
State Evaluation ◽  
Traffic Management System ◽  
The Impact

An Automatic Identification System (AIS) was implemented in the St. Lawrence Seaway during 2003. This paper reports the results of a trial conducted pre- and post-AIS implementation to examine the impact of AIS adoption in a safety-critical system. Analysis of the impact on three types of operators, ship's masters, mates and shore-based traffic management system operators showed that overall AIS significantly improved voyage plan monitoring, contributed to improved monitoring vigilance and offered significant aid to decision making. Recommendations include follow-on studies to include a steady state evaluation of the technology impact once the system is mature and a broadening of the pool of subjects to include a less experienced, more international and less well educated group of operators.

scholarly journals Assessment of AIS Vessel Position Report Under the Aspect of Data Reliability

2012 ◽  
Vol 19 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Paweł Banyś ◽  
Thoralf Noack ◽  
Stefan Gewies
Keyword(s):  
Data Analysis ◽  
Baltic Sea ◽  
Traffic Management ◽  
Automatic Identification ◽  
Identification System ◽  
Detailed Data ◽  
Data Reliability ◽  
The Baltic Sea ◽  
Data Set ◽  
The Baltic

Abstract Since its introduction the Automatic Identification System (AIS) has played an important part in improving safety at sea, making bridge watchkeeping duties more comfortable and enhancing vessel traffic management ashore. However the analysis of a AIS data set describing the vessel traffic of the Baltic Sea came to conclusion, that specific parameters with relevance to navigation seemed to be defective or implausible. Essentially, it concerned the true heading (THDG) and the rate of turn (ROT) parameters. With the paper we are trying to clarify, which parameters of the AIS position report and to what extent, are affected. The detailed data analysis gives answers on how reliable the AIS data in different traffic areas is.

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