Research on Ship Collision Risk Calculation in Port Navigation Waters Based on Ising Model and AIS Data
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 2
Abstract
Effectively identifying the distribution pattern and influencing factors of ship collision risk is crucial for ensuring navigation safety, operation, and management efficiency in port waters. First, this paper used the Ising model theory to extract ship data from the automatic identification system (AIS) and investigate the mutual influence mechanism among ships. Second, by applying the Ising model, a calculation model was developed to determine ship collision risk values in port waters. This model takes into account various influential factors, including the number of ship track crossing frequency, density distribution, velocity dispersion, and spacing. By integrating these factors, the model enables a quantitative analysis of the ship collision risk situation in port waters. Finally, to demonstrate the effectiveness of the proposed Ising model, a case study was conducted using Qingdao Port as an example. Through this case study, the paper analyzes the patterns of ship collision risks in the port area. The findings reveal that the Ising model effectively identifies areas with higher collision risk, enhancing the identification of ship navigation risks and contributing to overall navigation safety management in port waters. The results indicate that the proposed collision risk Ising model can quantitatively assess the distribution of collision risks for ships in restricted water areas such as ports. These findings contribute to the identification of ship navigation risks in port waters.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported in part by the Shandong Provincial Natural Science Foundation under Grant ZR2021QG022 and in part by the Shandong Big Data Development Innovation Laboratory for Shipping Safety and Management Financial.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 2 • June 2024
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© 2024 American Society of Civil Engineers.
History
Received: Jul 20, 2023
Accepted: Oct 29, 2023
Published online: Jan 19, 2024
Published in print: Jun 1, 2024
Discussion open until: Jun 19, 2024
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