Method for prediction of ship traffic behaviour and encounter frequency

Itoh, Hibiki

doi:10.1017/s0373463321000771

Cited by 6 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An improved K-means clustering method is then applied to assign weights to datapoint errors. In another study, the authors analyzed parameters affecting frequencies of ship encounters [63]. They introduced a method for predicting traffic-flow behaviors and ship-encounter frequencies with a time constraint, which finds applications in areas like offshore wind farms and fisheries.…”

Section: Ship Navigation Behavior Traffic Flow Modeling and Predictionmentioning

confidence: 99%

“…At the level of maritime navigation organization, modeling traffic flows will impact ships Combining historical data with algorithms such as random number generation, probability space modeling, spatial clustering, CNN, DBSCAN, and LSTM to model traffic flow. In the short term, onboard sensors predict and anticipate ship trajectories, which serve as inputs for navigation decisions [41,45,63,64,70].…”

Section: Ship Navigation Behavior Traffic Flow Modeling and Predictionmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

View full text Add to dashboard Cite

In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

show abstract

Section: Ship Navigation Behavior Traffic Flow Modeling and Predictionmentioning

confidence: 99%

Section: Ship Navigation Behavior Traffic Flow Modeling and Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…From the standpoint of water trafc spatial correlation [38,39], adjacent sections or channels are impacted by nearby vessel trafc fow. Ship trafc fow exhibits a particular time correlation in the close time distance [40]. So, it makes sense to look at how to create a suitable congestion risk model for ship trafc fow.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ship Traffic Flow and Congestion Based on Extreme Learning Machine with Whale Optimization Algorithm and Fuzzy c-Means Clustering

Chen

Huang

Song

et al. 2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

Accurately predicting short-term congestions in ship traffic flow is important for water traffic safety and intelligent shipping. We propose a method for predicting the traffic flow of ships by applying the whale optimization algorithm to an extreme learning machine. The method considers external environmental uncertainty and complexity of ships navigating in traffic-intensive waters. First, the parameters of ship traffic flow are divided into multiple modal components using variational mode decomposition and extreme learning machine. The machine and the whale optimization algorithm constitute a hybrid modelling approach for predicting individual modal components and integrating the results of individual components. Considering a map between ship traffic flow parameters and congestion, fuzzy c-means clustering is used to predict the level of ship traffic congestion. To verify the effectiveness of the proposed method, ship traffic flow data of the Yangtze River estuary were selected for evaluation. Results from the proposed method for predicting ship traffic flow parameters are consistent with measurements. Specifically, the prediction accuracy of the ship traffic congestion reaches 76.04%, which is reasonable and practical for predicting ship traffic congestion.

show abstract

Calculation of the number of ship collision candidates using mesh-based estimation method for ship traffic data

2022

View full text Add to dashboard Cite

Predicting and assessing the ship–ship collision possibility in waters are important for discussions on ship traffic safety. The geometric number of collision candidates is one of the most commonly used indexes for representing the frequency of multi-ship encounters that have potential collisions. It has often been estimated for ship traffic in a delimited area based on existing concepts. However, to discuss ship traffic safety in wide-congested waters where ships navigate along various courses and various ship encounters occur, a comprehensive understanding of potential collisions corresponding to all encounter characteristics, such as the encounter angle and location, is necessary. This paper proposes a method, called a “mesh-based estimation method,” to calculate the geometric number of collision candidates. It can deal with various encounter angles by introducing a computational mesh to comprehensively assess potential collisions in wide-congested waters. The validation of the method is conducted by applying it to simple ship traffic and comparing the calculated result with a result calculated based on a conventional approach. In addition, the method is applied to traffic data of AIS-equipped ships navigating in Tokyo Bay in Japan to show locations that have potential collisions based on the encounter angles.

show abstract

Method for prediction of ship traffic behaviour and encounter frequency

Cited by 6 publications

References 28 publications

A Review on Motion Prediction for Intelligent Ship Navigation

A Review on Motion Prediction for Intelligent Ship Navigation

Prediction of Ship Traffic Flow and Congestion Based on Extreme Learning Machine with Whale Optimization Algorithm and Fuzzy c-Means Clustering

Calculation of the number of ship collision candidates using mesh-based estimation method for ship traffic data

Contact Info

Product

Resources

About