# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 3 Results
### Association Rule Mining Results
#### 3.2.4 Key Driving Behavior Pattern Recognition  
Based on the association rules mined by the FP-Growth algorithm and combined with the enhancement degree and significance test, this paper identifies a series of key driving behavior patterns highly correlated with traffic conflicts, which mainly include the following:  
(1) In the mode of speeding + short headway + frequent lane change (TS\(\uparrow\) + LK\(\downarrow\) + LC\(\uparrow\)), the probability of lane-change conflict is 3.72 times the average level, and the severity is also high. This indicates that frequent lane changes are very likely to cause vehicle track interleaving and lateral collision under the conditions of overspeeding and short-distance following. (2) In the mode of overspeed + long front time + sudden braking (TS\(\uparrow\) + LK\(\uparrow\) + HS\(\uparrow\)), the probability of rear-end collision is 3.15 times the average level, and most of these collisions are of moderate severity. This indicates that even if the time distance is long, the sudden braking when speeding easily causes the rear car to have no time to react, resulting in a rear-end collision. (3) In the mode of short headway + rapid acceleration (LK\(\downarrow\) + AS\(\uparrow\)), the probability of cross collision is 2.58 times the average level, and most of the collisions are serious. This indicates that rapid-acceleration overtaking is more likely to cause serious collisions at intersections under the condition of short following. (4) In the mode of speeding + frequent lane change (TS\(\uparrow\) + LC\(\uparrow\)), the increase degree in this mode is relatively low (1.86), but it is still the mode associated with a high incidence of rear-end collisions, mostly of a slight degree of seriousness. These results indicate that speeding and lane changing are common causes of traffic flow disorder and potential accidents, and they significantly reduce traffic efficiency, even if no accidents are caused. Figure 12 shows the comparison of traffic conflict risks for the above four types of key driving behavior patterns. It can be seen that the mode of \"TS\(\uparrow\) + LK\(\downarrow\) + LC\(\uparrow\)\" has the highest comprehensive risk and that the probability and severity of traffic conflicts are significantly higher than for the other modes, such that it is the illegal behavior that traffic management departments should focus on monitoring and severely crack down on. Other modes, such as \"TS\(\uparrow\) + LK\(\uparrow\) + HS\(\uparrow\)\" and \"LK\(\downarrow\) + AS\(\uparrow\)\", also have higher comprehensive risks, which are especially likely to lead to accidents of higher severity, which should cause drivers' vigilance and strengthen legal constraints. Although the \"TS\(\uparrow\) + LC\(\uparrow\)\" mode \begin{table}
\begin{tabular}{c c c c c c} \hline
**Rules** & **Support** & **Confidence** & **Lift** & **Confidence Intervals** & **Test Results** \\ \hline [TS\(\uparrow\), LK\(\downarrow\), LC\(\uparrow\)] \(\rightarrow\) [CL, Ser] & 0.025 & 0.85 & 3.72 & [0.62–0.78] & Significant \\ [TS\(\downarrow\), LK\(\uparrow\)] + [CW, Mod] & 0.018 & 0.78 & 3.15 & 0.55–0.72 & Significant \\ [LK\(\downarrow\), AS\(\uparrow\)] \(\rightarrow\) [CO, Ser] & 0.021 & 0.67 & 2.58 & [0.48–0.65] & Significant \\ [TS\(\uparrow\), LK\(\downarrow\)] \(\rightarrow\) [CW, Min] & 0.052 & 0.63 & 1.86 & [0.46–0.62] & Non-significant \\ \hline \end{tabular}
\end{table} 
Table 9: Significance test of key association rules. mostly causes minor collisions, due to its high incidence, it should also be targeted through publicity and education, traffic organization optimization, and other means.