Determination of Ship Collision Avoidance Timing Using Machine Learning Method

Yu Zhou, Weijie Du, Jiao Liu, Haoqing Li, Manel Grifoll, Weijun Song () and Pengjun Zheng ()
Additional contact information
Yu Zhou: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
Weijie Du: Ningbo Pilot Station, Ningbo 315040, China
Jiao Liu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
Haoqing Li: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
Manel Grifoll: Barcelona School of Nautical Studies, Universitat Politècnica de Catalunya (UPC—BarcelonaTech), 08034 Barcelona, Spain
Weijun Song: Ningbo Liwan New Material Co., Ltd., Ningbo 315812, China
Pengjun Zheng: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China

Sustainability, 2024, vol. 16, issue 11, 1-23

Abstract: The accurate timing for collision avoidance actions is crucial for preventing maritime collisions. Traditional methods often rely on collision risk assessments, using quantitative indicators like the Distance to the Closest Point of Approach (DCPA) and the Time to the Closest Point of Approach (TCPA). Ship Officers on Watch (OOWs) are required to execute avoidance maneuvers once these indicators reach or exceed preset safety thresholds. However, the effectiveness of these indicators is limited by uncertainties in the maritime environment and the human behaviors of OOWs. To address these limitations, this study introduces a machine learning method to learn collision avoidance behavior from empirical data of ship collision avoidance, particularly in cross-encounter situations. The research utilizes Automatic Identification System (AIS) data from the open waters around Ningbo Zhoushan Port. After data preprocessing and applying spatio-temporal constraints, this study identifies ship trajectory pairs in crossing scenarios and calculates their relative motion parameters. The Douglas–Peucker algorithm is used to identify the timing of ship collision avoidance actions and a collision avoidance decision dataset is constructed. The Random Forest algorithm was then used to analyze the factors affecting the timing of collision avoidance, and six key factors were identified: the distance, relative speed, relative bearing, DCPA, TCPA, and the ratio of the lengths of the giving-way and stand-on ships. These factors serve as inputs for the XGBoost algorithm model, which is enhanced with Particle Swarm Optimization (PSO), and thus constructing a ship collision avoidance decision model. In addition, considering the inherent errors in any model and the dynamic nature of the ship collision avoidance process, an action time window for collision avoidance is introduced, which provides a more flexible time range for ships to make timely collision avoidance responses based on actual conditions and the specific encounter environment. This model provides OOWs with accurate timing for taking collision avoidance decisions. Case studies have validated the practicality and effectiveness of this model, offering new theoretical foundations and practical guidance for maritime collision avoidance.

Keywords: maritime transportation; collision avoidance time window; AIS data; PSO-XGBoost; COLREGS; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/11/4626/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/11/4626/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:11:p:4626-:d:1404830

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().