Spatial Markov Chain simulation model of accident risk for marine traffic

Xuan, Shaoyong; Xi, Yongtao; Huang, Changhai; Hu, Shenping; Zhang, L.

doi:10.1109/ictis.2017.8047838

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…As maritime activities become increasingly complex, the management of maritime traffic and the protection of maritime infrastructure face serious challenges. The complex navigational environment poses potential obstacles and challenges to the stable development of the maritime shipping industry [1][2][3][4][5]. Ship trajectory prediction can provide reliable data support to ensure the safety and efficiency of maritime traffic management and the protection of offshore infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Research into Ship Trajectory Prediction Based on An Improved LSTM Network

Zhang

Wang

Cui

et al. 2023

JMSE

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The establishment of ship trajectory prediction is critical in analyzing trajectory data. It serves as a critical reference point for identifying abnormal behavior and potential collision risks for ships. Accurate and real-time ship trajectory prediction is essential during navigation. Since the timing of automatic identification system (AIS) data is irregular, traditional methods usually use time calibration to simulate the data of uniform sequencing before analysis. Inevitably, this increases the chances of error and time delays. To address this issue, we propose a time-aware LSTM (T-LSTM) single-ship trajectory model combined with the generative adversarial network (GAN) to predict multiple ship trajectories. These analysis methods are capable of directly analyzing AIS data and have demonstrated better performance in both single-ship and multi-ship trajectories. Our experimental results show that the proposed method achieves high accuracy and can meet the practical navigation requirements of ships.

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Section: Introductionmentioning

confidence: 99%

Research into Ship Trajectory Prediction Based on An Improved LSTM Network

Zhang

Wang

Cui

et al. 2023

JMSE

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“…This type of data can be analyzed using different methods, including statistical approaches such as Markov logic or the Bayesian networks approach [10][11][12], those based on GIS [13,14] or Multi-Agent models [15][16][17][18]. Applications can vary widely, but much of the literature is dedicated to the monitoring of traffic for greater security, such as monitoring in order to avoid collisions [19][20][21], or the individuation of atypical boat behavior by different methods [22][23][24]. Other work has focused on environmental protection [25,26] or on MSP [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Marine Traffic from Sémaphore Data: A Python-GIS Procedure to Build Synthetic Navigation Routes and Analyze Their Temporal Variation

Minelli

Berre

Peuziat

et al. 2021

JMSE

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Originally designed as a mode of telecommunication, the network of French sémaphore is now dedicated to the continuous monitoring and recording of marine traffic along the entire French coast. Although the observation data collected by sémaphores cover 7/7 days and 24/24 h and could provide precious information regarding marine traffic, they remain underexploited. Indeed, these data concern all types of traffic, including leisure boating and smaller craft that are not usually recorded by the most common means of observation, such as AIS, radar and satellite. Based on sémaphore data, traffic pressure and its spatiotemporal distribution can be fully measured to better analyze its interactions with human activities and the environment. One drawback of these data is their initially semantic nature, which requires the development of an original processing method. The protocol developed to analyze the marine traffic of the Iroise Sea and its first results are presented in this article. It is based on a semi-automatic method aimed to clean the original data and quantify the marine traffic along synthetic routes. It includes a procedure that takes into account the temporal evolution of the traffic based on the Allen’s time framework. The results proved interesting as they provide an overview of marine traffic, including all types of vessels, and may be defined for different time periods and granularity. A description of the numerical and geographic instruments created is given; all the written code is released as Open Source software and freely available for download and testing.

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Prediction of the Ship Collision Point—A Review

Roy

2020

Artificial Intelligence Techniques for Advanced Computing Applications

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Spatial Markov Chain simulation model of accident risk for marine traffic

Cited by 6 publications

References 8 publications

Research into Ship Trajectory Prediction Based on An Improved LSTM Network

Research into Ship Trajectory Prediction Based on An Improved LSTM Network

Reconstruction of Marine Traffic from Sémaphore Data: A Python-GIS Procedure to Build Synthetic Navigation Routes and Analyze Their Temporal Variation

Prediction of the Ship Collision Point—A Review

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