Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model
Vessel trajectory prediction is important in maritime traffic safety and emergency management. Vessel trajectory prediction using vessel automatic identification system (AIS) data has attracted wide attention. Deep learning techniques have been widely applied to vessel trajectory prediction tasks du...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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University of Zagreb, Faculty of Transport and Traffic Sciences
2024-12-01
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| Series: | Promet (Zagreb) |
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| Online Access: | https://traffic2.fpz.hr/index.php/PROMTT/article/view/772 |
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| _version_ | 1850122789682413568 |
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| author | Xinyun WU Jiafei CHEN Caiquan XIONG Donghua LIU Xiang WAN Zexi CHEN |
| author_facet | Xinyun WU Jiafei CHEN Caiquan XIONG Donghua LIU Xiang WAN Zexi CHEN |
| author_sort | Xinyun WU |
| collection | DOAJ |
| description | Vessel trajectory prediction is important in maritime traffic safety and emergency management. Vessel trajectory prediction using vessel automatic identification system (AIS) data has attracted wide attention. Deep learning techniques have been widely applied to vessel trajectory prediction tasks due to their advantages in fine-grained feature learning and time series modelling. However, most deep learning-based methods use a unified approach for modelling AIS data, ignoring the diversity of AIS data and the impact of noise on prediction performance due to environmental factors. To address this issue, this study introduces a method consisting of temporal convolutional network (TCN), convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) to predict vessel trajectories, called TCC. The model employs TCN to capture the complex correlation of the time series, utilises CNN to capture the fine-grained covariate features and then captures the dynamics and complexity of the trajectory sequences through ConvLSTM to predict vessel trajectories. Experiments are conducted on real public datasets, and the results show that the TCC model proposed in this paper outperforms the existing baseline algorithms with high accuracy and robustness in vessel trajectory prediction. |
| format | Article |
| id | doaj-art-75d9ad8b75694d9ca4333f053f0b3ec0 |
| institution | OA Journals |
| issn | 0353-5320 1848-4069 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | University of Zagreb, Faculty of Transport and Traffic Sciences |
| record_format | Article |
| series | Promet (Zagreb) |
| spelling | doaj-art-75d9ad8b75694d9ca4333f053f0b3ec02025-08-20T02:34:46ZengUniversity of Zagreb, Faculty of Transport and Traffic SciencesPromet (Zagreb)0353-53201848-40692024-12-013661160117510.7307/ptt.v36i6.772772Vessel Trajectory Prediction Method Based on the Time Series Data Fusion ModelXinyun WU0Jiafei CHEN1Caiquan XIONG2Donghua LIU3Xiang WAN4Zexi CHEN5Hubei University of TechnologyHubei University of TechnologyHubei University of TechnologyChina Waterborne Transport Research InstituteWuhan Second Ship Design & Research InstituteXiaomi Technology (Wuhan) Co., LtdVessel trajectory prediction is important in maritime traffic safety and emergency management. Vessel trajectory prediction using vessel automatic identification system (AIS) data has attracted wide attention. Deep learning techniques have been widely applied to vessel trajectory prediction tasks due to their advantages in fine-grained feature learning and time series modelling. However, most deep learning-based methods use a unified approach for modelling AIS data, ignoring the diversity of AIS data and the impact of noise on prediction performance due to environmental factors. To address this issue, this study introduces a method consisting of temporal convolutional network (TCN), convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) to predict vessel trajectories, called TCC. The model employs TCN to capture the complex correlation of the time series, utilises CNN to capture the fine-grained covariate features and then captures the dynamics and complexity of the trajectory sequences through ConvLSTM to predict vessel trajectories. Experiments are conducted on real public datasets, and the results show that the TCC model proposed in this paper outperforms the existing baseline algorithms with high accuracy and robustness in vessel trajectory prediction.https://traffic2.fpz.hr/index.php/PROMTT/article/view/772automatic identification system (ais) datavessel trajectory predictiondeep learningneural network |
| spellingShingle | Xinyun WU Jiafei CHEN Caiquan XIONG Donghua LIU Xiang WAN Zexi CHEN Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model Promet (Zagreb) automatic identification system (ais) data vessel trajectory prediction deep learning neural network |
| title | Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model |
| title_full | Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model |
| title_fullStr | Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model |
| title_full_unstemmed | Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model |
| title_short | Vessel Trajectory Prediction Method Based on the Time Series Data Fusion Model |
| title_sort | vessel trajectory prediction method based on the time series data fusion model |
| topic | automatic identification system (ais) data vessel trajectory prediction deep learning neural network |
| url | https://traffic2.fpz.hr/index.php/PROMTT/article/view/772 |
| work_keys_str_mv | AT xinyunwu vesseltrajectorypredictionmethodbasedonthetimeseriesdatafusionmodel AT jiafeichen vesseltrajectorypredictionmethodbasedonthetimeseriesdatafusionmodel AT caiquanxiong vesseltrajectorypredictionmethodbasedonthetimeseriesdatafusionmodel AT donghualiu vesseltrajectorypredictionmethodbasedonthetimeseriesdatafusionmodel AT xiangwan vesseltrajectorypredictionmethodbasedonthetimeseriesdatafusionmodel AT zexichen vesseltrajectorypredictionmethodbasedonthetimeseriesdatafusionmodel |