# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Driving Behavior Characteristics Analysis Results
#### 3.1.5 Cluster Analysis of Driving Behavior  
To understand driving behavior patterns identified during sharp turns, we carried out cluster analyses based on the characteristic indicators derived from our driving simulation experiments. For this analysis, we included data from all 30 participants who drove through 12 sharp turns on a simulated X412 road section, resulting in 360 driver-curve pairs. The dataset from the simulation was approached with K-means clustering to determine the four measurements of behavior: average speed through the curves, average acceleration, average time headway, and average steering wheel angle. We used the elbow method to find the optimal number of clusters and compute the within-cluster sum of squares over several clusters that were predetermined. The analysis suggested that four clusters would give a reasonable level of complexity to the model while providing sufficient explanatory power. To determine different driving styles at sharp turns, we performed cluster analysis on the characteristic indicators of speed, acceleration, headway, and steering angle of all drivers over all sharp turns, which resulted in 360 driver-curve pairs. The optimal cluster number of four was achieved through the elbow method, which determines the relative value of several clusters by the variance explained. The K-means algorithm was employed to determine the optimal number of clusters as four using the elbow method. Drivers were categorized into four classes, as shown in Table 7. The first class exhibits a cautious and steady driving style with low speed, moderate acceleration, and steering amplitude. The second class demonstrates an aggressive and adventurous driving style with high speed and frequent steering and acceleration actions. The third class reflects a rough and reckless driving style with moderate speed but intense steering and acceleration, along with short headway distance. The fourth class displays a careful yet sluggish driving style with relatively stable speed and acceleration but delayed steering actions and long headway \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index (deg)** & **Straight-Line Section** & **Curved Section** \\ \hline Average steering angle & 2.5 & 28.6 \\ Deviation of the steering angle & 1.2 & 12.4 \\ Average steering speed & 1.8 & 15.2 \\ Steering-rate standard deviation & 0.9 & 8.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Steering control indicators of different sections. distance. Based on the clustering results, a comparison of accident risks for different driving patterns was conducted to construct an accident risk prediction model:  
\[P_{a}=\frac{e^{-3.27+0.68\text{m}+1.12\text{m}+0.84\text{m}-0.52 THW}}{1+e^{-3.27 +0.68\text{m}+1.12\text{m}+0.84\text{m}-0.52 THW}} \tag{10}\]  
where \(p_{a}\) is the probability of an accident, \(v\) is the speed factor (0-5), a is the acceleration factor (0-5), \(\delta\) is the steering factor (0-5), and THW is the headway factor (0-5). Levels of driving risk, referenced in this analysis, were derived from a composite evaluation model based on the following four components: (1) measurements of deviation from internationally established standards and safety thresholds found in the scholarly literature, including those for safe curve navigation speeds and acceleration limits; (2) expert assessments conducted by three traffic safety engineers who independently evaluated 120 video recordings of driving behaviors at sharp turns, rating each on a defined risk scale; (3) correlation with actual traffic conflict outcomes recorded during our field observations; and (4) statistical analysis using Z-score calculations to quantify how each observed behavior deviated from established norms. For the risk assessment scale of 1 to 5, values were assigned using a weighted scoring technique based on the severity of overspeeding (25%), headway safety margin (25%), steering instability (20%), acceleration volatility (20%), and environmental variables (10%).