# A Deep Learning Model for Ship Trajectory Prediction Using Automatic Identification System (AIS) Data

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Research Framework

#### 2.2. Data Preprocessing

#### 2.3. Ship Trajectory Prediction Based on CNN-LSTM-SE

#### 2.3.1. Convolutional Neural Network (CNN)

#### 2.3.2. Long Short-Term Memory (LSTM)

#### 2.3.3. Squeeze-and-Excitation Network (SE)

#### 2.3.4. CNN-LSTM-SE Model

#### 2.4. Evaluation Index

## 3. Experiments

#### 3.1. Experimental Setup

^{®}Core™ i5-1135G7 with a CPU frequency of 2.40 GHz. The RAW was 16 GB, and the GPU was NVDIA GeForce GTX 750Ti. The Windows 10 operating system was used. The programming language for prediction experiments was Python 3.8; Pytorch 1.10 was also used.

#### 3.2. Experimental Data

#### 3.3. Results

#### 3.3.1. Analysis of Ship-1 Trajectory Prediction Results

#### 3.3.2. Analysis of Ship-2 Trajectory Prediction Results

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Model | Time Interval | Parameter Name | Optimal Parameters |
---|---|---|---|

CNN-LSTM-SE | 10 s | Kernel size | 2 |

Stride | 2 | ||

LSTM node | 300 | ||

Linear layer node | 100 | ||

Output layer node | 2 | ||

30 s | Kernel size | 2 | |

Stride | 1 | ||

LSTM node | 300 | ||

Linear layer node | 100 | ||

Output layer node | 2 | ||

1 min | Kernel size | 2 | |

Stride | 1 | ||

LSTM node | 150 | ||

Linear layer node | 50 | ||

Output layer node | 2 |

Time Interval | Model | ARMSE | AMAPE | AED | FD | AGD |
---|---|---|---|---|---|---|

10 s | CNN-LSTM-SE | 0.0014 | 0.0031 | 0.0020 | 0.0051 | 0.2141 |

CNN-LSTM | 0.0024 | 0.0033 | 0.0028 | 0.0244 | 0.2816 | |

CNN | 0.0042 | 0.0055 | 0.0050 | 0.0356 | 0.5037 | |

LSTM | 0.0027 | 0.0043 | 0.0032 | 0.0168 | 0.3308 | |

30 s | CNN-LSTM-SE | 0.0022 | 0.0034 | 0.0030 | 0.0070 | 0.3001 |

CNN-LSTM | 0.0043 | 0.0063 | 0.0050 | 0.0334 | 0.5171 | |

CNN | 0.0051 | 0.0065 | 0.0061 | 0.0451 | 0.6077 | |

LSTM | 0.0068 | 0.0112 | 0.0081 | 0.0432 | 0.8504 | |

1 min | CNN-LSTM-SE | 0.0029 | 0.0044 | 0.0037 | 0.0089 | 0.3721 |

CNN-LSTM | 0.0061 | 0.0107 | 0.0074 | 0.0313 | 0.7831 | |

CNN | 0.0125 | 0.0172 | 0.0154 | 0.0749 | 0.9467 | |

LSTM | 0.0095 | 0.0133 | 0.0107 | 0.0529 | 1.0987 |

Time Interval | Model | ARMSE | AMAPE | AED | FD | AGD |
---|---|---|---|---|---|---|

10 s | CNN-LSTM-SE | 0.0012 | 0.0018 | 0.0007 | 0.0043 | 0.0750 |

CNN-LSTM | 0.0018 | 0.0023 | 0.0008 | 0.0228 | 0.0854 | |

CNN | 0.0016 | 0.0020 | 0.0008 | 0.0156 | 0.0781 | |

LSTM | 0.0017 | 0.0032 | 0.0011 | 0.0059 | 0.1179 | |

30 s | CNN-LSTM-SE | 0.0024 | 0.0024 | 0.0010 | 0.0071 | 0.1006 |

CNN-LSTM | 0.0037 | 0.0042 | 0.0015 | 0.0269 | 0.1597 | |

CNN | 0.0029 | 0.0044 | 0.0016 | 0.0166 | 0.1671 | |

LSTM | 0.0024 | 0.0037 | 0.0015 | 0.0204 | 0.1538 | |

1 min | CNN-LSTM-SE | 0.0025 | 0.0030 | 0.0017 | 0.0089 | 0.1682 |

CNN-LSTM | 0.0045 | 0.0084 | 0.0029 | 0.0107 | 0.3002 | |

CNN | 0.0042 | 0.0066 | 0.0023 | 0.0170 | 0.2448 | |

LSTM | 0.0060 | 0.0101 | 0.0034 | 0.0102 | 0.3659 |

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## Share and Cite

**MDPI and ACS Style**

Wang, X.; Xiao, Y.
A Deep Learning Model for Ship Trajectory Prediction Using Automatic Identification System (AIS) Data. *Information* **2023**, *14*, 212.
https://doi.org/10.3390/info14040212

**AMA Style**

Wang X, Xiao Y.
A Deep Learning Model for Ship Trajectory Prediction Using Automatic Identification System (AIS) Data. *Information*. 2023; 14(4):212.
https://doi.org/10.3390/info14040212

**Chicago/Turabian Style**

Wang, Xinyu, and Yingjie Xiao.
2023. "A Deep Learning Model for Ship Trajectory Prediction Using Automatic Identification System (AIS) Data" *Information* 14, no. 4: 212.
https://doi.org/10.3390/info14040212