Maritime traffic probabilistic prediction based on ship motion pattern extraction

H. Rong, A.P. Teixeira and C. Guedes Soares

Reliability Engineering and System Safety, 2022, vol. 217, issue C

Abstract: This paper proposes a novel maritime traffic prediction method based on ship motion pattern extraction, considering ship destination prediction and ship trajectory prediction within a specific route. To extract ship motion patterns from historical Automatic Identification System data, traffic departure-arrival areas are determined based on the Order Points to Identify the Clustering Structure algorithm and ship trajectories following the same itinerary are clustered. A maritime traffic network abstraction consisting of nodes that represent waypoint areas and navigational legs is constructed to represent the maritime traffic at a larger scale. Multinomial Logistic Regression and Gaussian Process regression models are developed and applied for predicting probabilistically the ships’ destinations and their trajectories along the ship route, respectively. Based on these models, the uncertainty on the ship's future position can be estimated given its current state. The proposed method is capable of long-term ship position prediction and provides information on the maritime traffic 10, 30 and 60 min ahead when the method is applied to all ships navigating in a study area. The presented method may assist maritime authorities to improve the efficiency of maritime traffic surveillance and to develop strategies to improve navigation safety.

Keywords: AIS data; Maritime traffic; Ship motion pattern; Maritime traffic prediction; Gaussian Process regression models; Multinomial Logistic Regression models (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832021005639
Full text for ScienceDirect subscribers only

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:eee:reensy:v:217:y:2022:i:c:s0951832021005639

DOI: 10.1016/j.ress.2021.108061

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().