Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data
With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most res...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6622862 |
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| _version_ | 1832550900227375104 |
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| author | He Ma Yi Zuo Tieshan Li |
| author_facet | He Ma Yi Zuo Tieshan Li |
| author_sort | He Ma |
| collection | DOAJ |
| description | With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively. |
| format | Article |
| id | doaj-art-499d0b304c214ce68c8127d4928d947b |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-499d0b304c214ce68c8127d4928d947b2025-02-03T06:05:31ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6622862Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS DataHe Ma0Yi Zuo1Tieshan Li2Navigation CollegeNavigation CollegeNavigation CollegeWith the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.http://dx.doi.org/10.1155/2022/6622862 |
| spellingShingle | He Ma Yi Zuo Tieshan Li Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data Journal of Advanced Transportation |
| title | Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data |
| title_full | Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data |
| title_fullStr | Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data |
| title_full_unstemmed | Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data |
| title_short | Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data |
| title_sort | vessel navigation behavior analysis and multiple trajectory prediction model based on ais data |
| url | http://dx.doi.org/10.1155/2022/6622862 |
| work_keys_str_mv | AT hema vesselnavigationbehavioranalysisandmultipletrajectorypredictionmodelbasedonaisdata AT yizuo vesselnavigationbehavioranalysisandmultipletrajectorypredictionmodelbasedonaisdata AT tieshanli vesselnavigationbehavioranalysisandmultipletrajectorypredictionmodelbasedonaisdata |