文章基本信息

标题：A Novel Framework of Real-Time Regional Collision Risk Prediction Based on the RNN Approach
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作者：Dapei Liu ; Xin Wang ; Yao Cai 等
期刊名称：Journal of Marine Science and Engineering
电子版ISSN：2077-1312
出版年度：2020
卷号：8
期号：3
页码：224
DOI：10.3390/jmse8030224
语种：English
出版社：MDPI AG
摘要：Regional collision risk identification and prediction is important for traffic surveillance in maritime transportation. This study proposes a framework of real-time prediction for regional collision risk by combining Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique, Shapley value method and Recurrent Neural Network (RNN). Firstly, the DBSCAN technique is applied to cluster vessels in specific sea area. Then the regional collision risk is quantified by calculating the contribution of each vessel and each cluster with Shapley value method. Afterwards, the optimized RNN method is employed to predict the regional collision risk of specific seas in short time. As a result, the framework is able to determine and forecast the regional collision risk precisely. At last, a case study is carried out with actual Automatic Identification System (AIS) data, the results show that the proposed framework is an effective tool for regional collision risk identification and prediction.