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A novel framework of real-time regional collision risk prediction based on RNN approach
| Content Provider | Scilit |
|---|---|
| Author | Liu, Dapei Cai, Yao Wang, Xin Liu, Zihao Liu, Zhengjiang |
| Copyright Year | 2021 |
| Description | Regional collision risk assessment 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. 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 prediction. Book Name: Developments in Maritime Technology and Engineering |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003216582-15&type=chapterpdf |
| Ending Page | 149 |
| Page Count | 11 |
| Starting Page | 139 |
| DOI | 10.1201/9781003216582-15 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-07-08 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Developments in Maritime Technology and Engineering Marine Engineering Optimized Neural Proposes a Framework Vessel Dbscan Regional Collision Risk Shapley |
| Content Type | Text |
| Resource Type | Chapter |
