Mining navigable water current information from ship-based big automated identification system data

He, Zhengwei; Shu, Li; Wang, Senjie; Zhuang, Zhong

doi:10.1016/j.oceaneng.2021.110076

Cited by 2 publications

(1 citation statement)

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“…It uses evidence consequences, a decision tree model, and a back-propagation neural network. He et al [51] introduced Gated Recurrent Unit (GRU) and bidirectional recurrent neural networks (BRNNs) in a Current Information Mining Model (CIMM) using AIS data. A machine learning model for ship-to-ship collision risk based on big (AIS) and hydrometeorological data and current traffic conditions is presented in [52].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Ship Collision Probability Model Based on Monte Carlo Simulation and Bi-LSTM

Vukša

Vidan

Bukljaš

et al. 2022

JMSE

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The efficiency and safety of maritime traffic in a given area can be measured by analyzing traffic density and ship collision probability. Maritime traffic density is the number of ships passing through a given area in a given period of time. It can be measured using vessel tracking systems, such as the Automatic Identification System (AIS). The information provided by AIS is real-time data designed to improve maritime safety. However, the AIS data can also be used for scientific research purposes to improve maritime safety by developing predictive models for collisions in a research area. This article proposes a ship collision probability estimation model based on Monte Carlo simulation (MC) and bidirectional long short-term memory neural network (Bi-LSTM) for the maritime region of Split. The proposed model includes the processing of AIS data, the verification of AIS data, the determination of ports and ship routes, MC and the collision probability, the Bi-LSTM learning process based on MC, the ship collision probability for new or existing routes, and the traffic density. The results of MC, i.e., traffic/vessel route and density, and collision probability for the study area can be used for Bi-LSTM training with the aim of estimating ship collision probability. This article presents the first part of research that includes MC in detail, followed by a preliminary result based on one day of processed AIS data used to simulate MC and propose a model architecture that implements Bi-LSTM for ship collision probability estimation.

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Section: Literature Reviewmentioning

confidence: 99%