# Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data
## 6 Conclusions and Discussion  
This paper constructs the capacity drop analysis using video image processing technology to extract high-precision vehicle trajectory data. The article analyzes the characteristics of the evolution process of traffic congestion at the bottleneck in a tunnel section. The capacity drop is captured by plotting the cumulative vehicle count curves. The capacity at the merge bottleneck is found to be 6700 veh/h, while the magnitude of the capacity drop is estimated to be 12.7%. We also explored the driving characteristics related to the occurrence of the capacity drop. The results show that the time headway for different driving speeds ranges from 2.04 s to 2.52 s in synchronous flow and from 2.09 s to 3.76 in the wide moving jam, which indicates a clear difference between the two traffic phases. Based on the analysis, the main findings are summarized as follows: (1) we captured the dynamic process of tunnel traffic capacity drop involving free flow and congested flow; (2) the capacity drop in the diagrams shows the difference between congested flow and non-congested flow, and deep analysis shows the different driving characteristics of drivers in the two states; (3) the influence of lane-changing behavior on the capacity drop is captured by analyzing the driving behaviors. In the free flow state, the disturbance caused by lane-changing can be quickly eliminated. With the increase in vehicle count, frequent lane-changing behavior will accumulate disturbance. When the disturbance reaches a certain degree, congestion will occur, and the speed of vehicles will drop sharply, resulting in a capacity drop. The study's findings can provide evidence of explanations of causes of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Lane 1**} & \multicolumn{2}{c}{**Lane 2**} & \multicolumn{2}{c}{**Lane 3**} & \multicolumn{2}{c}{**Lane 4**} \\ \hline  & \begin{tabular}{c} **Sample** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Mean (S.D.)** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Sample** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Mean (S.D.)** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Mean (S.D.)** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Mean (S.D.)** \\ **Size** \\ \end{tabular} & \
\begin{tabular}{c} **Sample** \\ **Size** \\ \end{tabular} &
\begin{tabular}{c} **Mean (S.D.)** \\ **Size** \\ \end{tabular} \\ \hline \(a_{LC}\) & 34 & 0.042 (11.2) & 100 & \(-\)0.103 (0.400) & 125 & \(-\)0.098 (0.501) & 259 & \(-\)0.083 (0.451) \\ \hline \(a_{FO}\) & 11 & 0.101 (0.221) & 42 & \(-\)0.014 (0.241) & 50 & 0.153 (0.371) & 103 & 0.079 (0.267) \\ \hline \(a_{FT}\) & 17 & \(-\)0.002 (0.283) & 49 & \(-\)0.057 (0.332) & 69 & \(-\)0.060 (0.328) & 135 & \(-\)0.052 (0.294) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Speed change of vehicles on different travel lanes. capacity in tunnel bottlenecks in urban areas, supporting real-time traffic control strategies to preserve tunnel capacity. The algorithm proposed in the study has the potential for applications in engineering. For example, the traffic control center can use the algorithm to accurately monitor and predict traffic conditions. Once the capacity drop is captured or predicted, we can quickly inform the drivers about the upcoming congestions within the tunnel via roadside variable message signs or vehicle onboard displays to take some precautions. In addition, we can apply active traffic management (variable speed limit, ramp metering, lane control, etc. ), techniques to prevent the drop in tunnel capacity further and maintain a high flow rate [43]. Finally, as we explored the vehicle driving behaviors that trigger the capacity drop, we can apply some vehicle behavior control measures (such as lane change regulations) to prevent the disturbance that causes capacity drop. Those are some efforts that can be conducted for engineering applications based on the algorithms in our research.