TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port
Abstract
:1. Introduction
- Aiming at the cyclical patterns and dynamic spatiotemporal dependence features of ship traffic flow in the port, a multidimensional spatiotemporal feature fusion model is constructed to tap the spatiotemporal dynamic influence and interdependence of ship traffic flow. The model can accurately capture the spatiotemporal dependencies of different nodes in the port, enabling precise predictions of traffic flow with significant spatiotemporal changes in complex navigational environments.
- The TCN and BiGRU models are employed to extract temporal features from ship traffic flow, significantly enhancing the performance of the model in the time learning module. Based on GAT, spatial features in ship traffic flow are extracted by utilizing the spatial correlation coefficient matrix and traffic flow matrix, enhancing the model’s capability to model node relationships and features.
- Based on the historical AIS data from the port, the performance of the constructed model is evaluated comparatively. The results show that the TG-PGAT model has higher accuracy, robustness, and anti-interference capability, enabling the prediction and analysis of ship traffic flow dynamics in the port.
2. Materials and Methods
2.1. Problem Description
2.1.1. Construction of Ship Traffic Map in the Port
2.1.2. Analysis of Spatiotemporal Features of Ship Traffic Flow in the Port
- Temporal Feature Analysis
- Spatial Feature Analysis
2.2. Model Construction
2.2.1. Model Input
2.2.2. Spatial Feature Extraction Module
- The Calculation of Spatial Attention Coefficient of Ship Traffic Flow in the Port
- Node Correlation Calculation of Ship Traffic Flow in the Port
- Fusion of Local Spatial Attention Features and Global Node Correlation Features
2.2.3. Temporal Feature Extraction Module
- Local Time-series Feature Extraction of Ship Traffic Flow in the Port
- Global Time-domain Feature Extraction of Ship Traffic Flow in the Port
2.2.4. Feature Fusion Module
3. Experiment
3.1. Experimental Data
3.2. Experimental Setup
3.2.1. Evaluation Indicator Setting
3.2.2. Model Optimization and Parameter Setting
3.3. Model Performance Experiment
3.3.1. Overall Prediction Effect of the Model
3.3.2. Comparative Analysis of Model Performance
3.4. Ablation Experiment
3.5. Robustness Experiment
3.6. Visualization Experiment
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Module | Model | Parameter | Value |
---|---|---|---|
Feature Decomposition | VMD | K | 5 |
Temporal Feature Extraction | TCN | L | 4 |
k | 5 | ||
d | 1, 2, 4, 8, 16 | ||
BiGRU | Hidden Layer Neurons | 64 | |
Spatial Feature Extraction | GAT | Number of Layers | 3 |
Number of Attention Heads | 4 |
Prediction Methods | Characteristics | Literature | |
---|---|---|---|
Traditional Statistical Models | ARIMA | It has a good effect on predicting long-term traffic flow with obvious trends and seasonality but cannot handle the nonlinear characteristics of traffic flow. | [6] |
KF | It can effectively address the nonstationary characteristics of traffic flow time series and has good performance in short-term traffic flow prediction. | [1] | |
Classical Machine Learning Methods | SVM | It can capture the complex nonlinear relationships in traffic flow data, but the parameter settings significantly impact the prediction results. | [11] |
KNN | As a nonparametric learning method, it cannot uncover the periodic characteristics of traffic flow. | [9] | |
Deep Learning Models | CNN | It can capture spatial correlations by using multiple layers of convolution and nonlinear activation functions. | [15] |
TCN | It captures local temporal dependencies in time-series data through convolution operations and is sensitive to hyperparameters and data noise. | [45] | |
GRU | It effectively solves the gradient vanishing or explosion problem that traditional RNNs encounter when processing long sequences, thereby being able to capture long-term dependencies in traffic flow data. | [18] | |
Data-Driven Spatiotemporal Fusion Models | CNN-LSTM | It can simultaneously extract the spatial and temporal features of traffic flow data, achieving a deep integration of spatiotemporal features. | [21] |
Semi-Dynamic Spatial–Temporal GNN (SDSTGNN) | It can effectively extract the spatiotemporal features of channel traffic flow by integrating GNN, LSTM, and Transformer. | [24] | |
Spatial-Temporal Attention Bidirectional LSTM (STA-BiLSTM) | It can achieve spatiotemporal traffic flow prediction in inland waters by combining GAT and BiLSTM models. | [25] |
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Ma, J.; Zhou, Y.; Chang, Y.; Zhu, Z.; Liu, G.; Chen, Z. TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port. J. Mar. Sci. Eng. 2024, 12, 1875. https://doi.org/10.3390/jmse12101875
Ma J, Zhou Y, Chang Y, Zhu Z, Liu G, Chen Z. TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port. Journal of Marine Science and Engineering. 2024; 12(10):1875. https://doi.org/10.3390/jmse12101875
Chicago/Turabian StyleMa, Jianwen, Yue Zhou, Yumiao Chang, Zhaoxin Zhu, Guoxin Liu, and Zhaojun Chen. 2024. "TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port" Journal of Marine Science and Engineering 12, no. 10: 1875. https://doi.org/10.3390/jmse12101875
APA StyleMa, J., Zhou, Y., Chang, Y., Zhu, Z., Liu, G., & Chen, Z. (2024). TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port. Journal of Marine Science and Engineering, 12(10), 1875. https://doi.org/10.3390/jmse12101875