# Factors Affecting Rear-End Collisions in Underground Road Junctions Using VISSIM
## 5 Conclusions  
Logistic regression analysis was used to examine accident risk factors in the VISSIM network. This is a proactive method for safety management and operation of underground \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Coefficient** & **Odds Ratio** & \(p\)**-Value** \\ \hline Intercept & 0.0213 & 1.022 & \textless{}0.01 \\ AccelerationMean & 0.0742 & 1.077 & \textless{}0.01 \\ SpeedMean & \(-\)0.0106 & 0.989 & \textless{}0.01 \\ PosLatMean & \(-\)0.469 & 0.626 & \textless{}0.01 \\ SpacingMean & \(-\)0.0134 & 0.987 & \textless{}0.01 \\ DensityMean & 0.0114 & 1.011 & \textless{}0.01 \\ AccelerationStd & 0.6599 & 1.935 & \textless{}0.01 \\ SpeedStd & \(-\)0.0032 & 0.997 & \textless{}0.01 \\ PosLatStd & 2.2134 & 9.147 & \textless{}0.01 \\ SpacingStd & \(-\)0.0075 & 0.993 & \textless{}0.01 \\ DensityStd & \(-\)0.0034 & 0.997 & \textless{}0.01 \\ R1\_C & 0.0321 & 1.033 & \textless{}0.01 \\ R1\_D & 0.0595 & 1.061 & \textless{}0.01 \\ R1\_E & 0.0638 & 1.066 & \textless{}0.01 \\ R2\_C & \(-\)0.0028 & 0.997 & \textless{}0.01 \\ R2\_D & \(-\)0.0362 & 0.964 & \textless{}0.01 \\ R2\_E & \(-\)0.0474 & 0.954 & \textless{}0.01 \\ \hline \hline \end{tabular} (Pseudo R-square: 0.267, log-likelihood: \(-\)131,270,000).  
\end{table} 
Table 7: Statistic results of logistic regression models. road junctions that do not exist yet. We aimed to simulate the underground road environment by reflecting the speed and traffic volume of an actual underground road. To realize the underground road environment, we specifically configured the simulation based on VDS data of Korean underground roads. To predict the risk of accidents in a large-depth environment, the analysis was conducted using VISSIM data, and the speed and traffic volume of the Shinwol-Yeoeui underground road were reflected in the speed distribution. Time to collision (TTC) was used as the surrogate safety measure (SSM) to determine whether there was a conflict, and each datum was aggregated by segment to collect the traffic information of the surrounding segments depending on whether there was an accident in the target segment. Speed, acceleration, and density decreased when underground road speed information was applied and when the level of service (LOS) of the access road was worsened in the underground road environment. In addition, the simulation results show that the proportion of conflicts is higher than that of non-conflicts in scenarios that reflect the speed distribution of underground roads. Thus, it is necessary to manage accidents in underground road junctions. The analysis revealed that the acceleration standard deviation and lateral standard deviation were the main contributing factors to the accident. Variables such as acceleration and lateral position are affected when driving in a tunnel environment similar to an underground road, so management of these variables is particularly necessary when operating on an underground road. For example, it is expected that improving lane markings in underground roads to encourage drivers to keep their lane, and maintaining uniform speeds through speed enforcement, will reduce the risk of accidents in underground roads. In the future, a more detailed analysis will be required to analyze the effects of different management strategies in underground roads. In addition, when the LOS of road R1 with a classifier increases from B to C and from C to D, the accident risk increases considerably, so it seems that traffic operation management should be performed before LOS D. In addition, the probability of an accident decreases when driving close to the left on a junction with a right-hand curve, so installing a sufficiently wide shoulder is expected to have a positive effect on accident reduction. Notwithstanding, this study has some limitations. Due to data acquisition difficulties, only two months of VDS data were used to adjust the simulation parameters, making it difficult to understand long-term traffic patterns and accident risk.