A simulation model for ship navigation in the “Xiazhimen” waterway based on statistical analysis of AIS data

Xin, Xuri; Liu, Kezhong; Yang, Xing; Yuan, Zhitao; Zhang, Jinfen

doi:10.1016/j.oceaneng.2019.03.052

Cited by 56 publications

(16 citation statements)

References 45 publications

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“…The optimization methods are over constrained to "unusual" situations. For example, some ships change speed or alter course for some reasons [11]. Besides, the rate of arriving ships also fluctuates during different periods.…”

Section: Self-organizing Scheduling Methodsmentioning

confidence: 99%

A Hybrid Self-Organizing Scheduling Method for Ships in Restricted Two-Way Waterways

Wang¹,

Tian²,

Zhang³

et al. 2020

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Traffic conflicts between ships are one of the most important reasons causing delays in restricted waterways. Aiming to improve the traffic efficiency, a hybrid self-organizing scheduling (HSOS) method for restricted two-way waterways is proposed. Ship transportation system is treated as a distributive and self-organized system under uncertainties. Each ship makes the decision on when to enter the waterway and how to keep the safe distance between them, while the VTS center could manage the direction of traffic flow according to the navigation situations. In order to reduce the traffic conflict between the opposite directions, small ships are given higher priority than the large ships in the same direction. When the large ships are accumulating, they are given higher priority than small ships in the same direction. The large ships are delayed while small ships decrease the waiting time. The trade-off between small and large ships can enhance efficiency by accumulating the large ships. Comparing the results from HSOS with First Come First Served (FCFS), it can effectively reduce the average delays brought by large ships, especially at high arrival rates.

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Section: Self-organizing Scheduling Methodsmentioning

confidence: 99%

A Hybrid Self-Organizing Scheduling Method for Ships in Restricted Two-Way Waterways

Wang¹,

Tian²,

Zhang³

et al. 2020

brod

Self Cite

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“…Rong et al [30] adopted the kernel density estimation-based method for mining ships' behaviors from the AIS data. Xin et al [31] proposed a ship traffic simulation model for in the Xiazhimen waterway for analyzing the statistical characteristics of ship traffic. Zhang et al [32] analyzed the spatial-temporal dynamic of traffic in port waterways while using the AIS data.…”

Section: Related Workmentioning

confidence: 99%

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

et al. 2020

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Timely and accurate depth estimation of a shallow waterway can improve shipping efficiency and reduce the danger of waterway transport accidents. However, waterway depth data measured during actual maritime navigation is limited, and the depth values can have large variability. Big data collected in real time by automatic identification systems (AIS) might provide a way to estimate accurate waterway depths, although these data include no direct channel depth information. We suggest a deep neural network (DNN) based model, called DDTree, for using the real-time AIS data and the data from Global Mapper to predict waterway depth for ships in an accurate and timely way. The model combines a decision tree and DNN, which is trained and tested on the AIS and Global Mapper data from the Nantong and Fangcheng ports on the southeastern and southwestern coast of China. The actual waterway depth data were used together with the AIS data as the input to DDTree. The latest data on waterway depths from the Chinese maritime agency were used to verify the results. The experiments show that the DDTree model has a prediction accuracy of 91.15%. Therefore, the DDTree model can provide an accurate prediction of waterway depth and compensate for the shortage of waterway depth monitoring means. The proposed hybrid DDTree model could improve marine situational awareness, navigation safety, and shipping efficiency, and contribute to smart navigation.

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“…Studies on shipping traffic flow have been conducted in the Baltic Sea [8,9], in the Japan Strait [10], in the Adriatic Sea [11], in the port of Lisbon [12], off the coast of India [13], in the Singapore Strait [14]. and in China: the Dagusha Channel [15] and the Ningbo-Zhoushan Port [16]. Two comprehensive studies on traffic analysis in the Istanbul Strait have been published [17,18].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Vessel Traffic Flow for Waterway Design–Port of Świnoujście Case Study

et al. 2021

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The paper presents an analysis of ship traffic using the port of Świnoujście and the problems associated with modelling vessel traffic flows. Navigation patterns were studied using the Automatic Identification System (AIS); an analysis of vessel traffic was performed with statistical methods using historical data; and the paper presents probabilistic models of the spatial distribution of vessel traffic and its parameters. The factors that influence the spatial distribution were considered to be the types of vessels, dimensions, and distances to hazards. The results show a correlation between the standard deviation of the traffic flow, the vessel sizes, and the distance to the hazard. These can be used in practice to determine the safety of navigation and the design of non-existing waterways and to create a general model of vessel traffic flow. The creation of the practical applications is intended to improve navigation efficiency, safety, and risk analysis in any particular area.

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A simulation model for ship navigation in the “Xiazhimen” waterway based on statistical analysis of AIS data

Cited by 56 publications

References 45 publications

A Hybrid Self-Organizing Scheduling Method for Ships in Restricted Two-Way Waterways

A Hybrid Self-Organizing Scheduling Method for Ships in Restricted Two-Way Waterways

DDTree: A Hybrid Deep Learning Model for Real-Time Waterway Depth Prediction and Smart Navigation

Modeling of Vessel Traffic Flow for Waterway Design–Port of Świnoujście Case Study

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