Abstract
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.
Highlights
Maritime transport is the backbone of international trade and the global economy
The results obtained from prediction framework and actual value of regional collision risk obtained from historical Automatic Identification System (AIS) data are shown in Table 5 and Figure 15
A regional collision risk prediction framework based on real-time AIS data is proposed in this paper
Summary
Maritime transport is the backbone of international trade and the global economy. In recent decades, the rapid development and great volume of marine transportation [1] lead to higher marine traffic density and complexity which trigger vessel collision accidents . First of all, based on the historical statistical data, the number of collision accidents per unit time in a certain water area was first considered to describe the regional collision risk by researchers. Some models require a large amount of historical data of vessel accidents to build a database for predicting the risk of regional vessel collisions, which has a random effect on the real-time quantification and short-term prediction of vessel collision risks in water areas. The real-time and short-term regional collision risk prediction in water areas with limited data is a shortcoming of current research. To identify and predict regional collision risk precisely, a prediction framework for regional collision risk is proposed in this article This framework uses limited non-accident data from selected water area to achieve the prediction of regional collision risk more accurately and effectively.
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