Table 1.
overview of data collection.
Fig 1.
Vehicle speed time series curves.
Fig 2.
Acceleration time series curves.
Fig 3.
Change lane spacing time series curves.
Fig 4.
Steering angle time series curve.
Fig 5.
Effect of wavelet thresholding denoising.
Fig 6.
Heatmap of Normalized Features.
Table 2.
Comparison of Traffic Accident Risk Prediction Models Based on Different Algorithms.
Table 3.
Comparison of the predictive performance of candidate models.
Fig 7.
Structure of CNN+ LSTM + GNN accident risk prediction model.
Fig 8.
Heatmap of Normalized Features Over Time.
Table 4.
Notation.
Table 5.
Division of the data set.
Fig 9.
Model training curve.
Fig 10.
Time vs. Position Trajectory Prediction with Risk Levels.
Fig 11.
Comparison of the Distribution of Statistical Indicators for Positive and Negative Samples.
Fig 12.
Distribution of key indicators of vehicle trajectory.
Table 6.
Confusion matrix for model prediction.
Fig 13.
Predictive performance of the model in different scenarios.
Table 7.
Detailed comparison of CNN+LSTM+GNN model performance and resource consumption.
Fig 14.
Relationship Between Dataset Size, Training Time, Accuracy, and Memory Usage.
Fig 15.
Real-time Performance of Model with Different Data Sizes.
Fig 16.
Accuracy Comparison: Optimized vs Non-Optimized.
Fig 17.
Loss Comparison: Optimized vs Non-Optimized.
Fig 18.
Comparison of Model Parameters in Different Scenarios.
Fig 19.
Model Performance over 12 Months.