# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 5 Dataset Description
### Quantitative Analysis
The results of these configurations, as shown in Table 1, provide insight into the trade-offs between precision, recall, and overall classification performance. Each configuration was evaluated based on the following metrics:  
* ** Precision: ** Measures the accuracy of positive predictions. * ** Recall: ** Assesses the ability of the model to correctly identify all relevant instances of each event. * ** F1-Score: ** The harmonic mean of precision and recall, providing a balance between the two. * ** Accuracy: ** The overall accuracy of the model. * ** AUC (Area Under the Curve): ** Provides a measure of the model's ability to discriminate between classes. The results are summarized below in Table 1, showing performance metrics for the three configurations. The results of the different configurations show the following key insights:  
* Configuration A achieved the highest AUC value (0.98106), indicating excellent general model discrimination between event types. However, while precision and recall were balanced, there was no clear optimization for specific event classes. The model performed well in detecting both event and non-event classes but was not specifically tuned for rare event detection. * Configuration B, incorporating class balancing adjustments, improved recall (0.75) for the \"No events detected\" class, which is the dominant class in the dataset. However, this came at the expense of AUC (0.91358), reflecting a trade-off in the ability to distinguish between event and nonevent classes. This adjustment was beneficial for better detecting the non-event class, but may have caused slight underperformance in event detection. * Configuration C provided the best overall balance of precision (0.80) and recall (0.75), while also improving the AUC to 0.94682. This configuration demonstrated the effectiveness of hyperparameter tuning, balance of model complexity, and generalization. It showed the highest overall robustness and is considered the most reliable configuration for event detection, as it optimized the model for both types of events and non-events. * Comparison with Alternative Approaches. To further evaluate the performance of our proposed method, we compared it with a rule-based approach. The rule-based model, which was primarily used for event labeling, relies on predefined thresholds for vibration and acceleration patterns. Although this approach achieves a high precision of 0.92 and accuracy of 0.910 (see Table 1), these results are **highly dependent on the adjustment of the manual parameters**. A fundamental limitation of the rule-based approach is its lack of generalization. For each dataset and even for different segments of the trajectory within the same dataset**, continuous manual adjustments** are required to maintain its precision. This makes the approach impractical for real-time deployment in large-scale dynamic environments, where road conditions, vehicle types, and sensor variations can significantly affect detection performance. Additionally, the rule-based approach does not produce a **probabilistic output**, meaning it cannot compute an AUC score. Unlike machine learning models, which can adapt to unseen data distributions, the rule-based model strictly follows predefined conditions, making it highly sensitive to variations in road events. In contrast, our proposed Random Forest-based approach provides a more **scalable and adaptive** solution. As shown in Table 1, despite a slight trade-off in precision,