## 7 Quantitative Risk Analysis
### Research Findings  
With reference to the research findings of our work, it is to be said that in general a comparison is very difficult with other studies of the literature for different modeling and parameters implemented, human behavior of users during the evacuation process, different safety countermeasures and control systems. Full-scale experiments on the other hand are more useful, but they are often expensive and/or dangerous and require the disruption of the traffic flow through the tunnel with the use of alternative itineraries on open roads. However, it is to be stressed that the authors of the present paper preliminary validated the FDS code by means of a comparison with the experimental data reported in [PERSON] et al. (2017). Therefore, the authors are confident that by carrying out numerical simulations based on an appropriate fluid-dynamic analysis, as well as by using a suitable probabilistic approach for individualizing the F-N curves, they have calibrated and applied the modeling process quite accurately, so that the research results can provide an interesting contribution to  
Figure 10: F-N curves related to escaping users (downstream of the fire in direction of the portal B) of the investigated tunnel tube, when is used for bi-directional traffic, during the day and the night, respectively; with d equal to the distance between the vehicles in the queue.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Potential**} & \multicolumn{2}{c}{**Annual Cumulative Frequency (F)**} & \multirow{2}{*}{**Reduction**} \\ \cline{2-2} \cline{4-4}
**Fatalities (N)** & & & **Day** \\ \hline
1 & \(1.88\times 10^{-4}\) & \(3.86\times 10^{-5}\) (\(3.86\times 10^{-5}\)) & 80\% (80\%) \\
5 & \(1.75\times 10^{-4}\) & \(3.07\times 10^{-5}\) (\(3.41\times 10^{-5}\)) & 83\% (81\%) \\
10 & \(1.65\times 10^{-4}\) & \(9.26\times 10^{-6}\) (\(1.46\times 10^{-5}\)) & 94\% (92\%) \\
15 & \(1.48\times 10^{-4}\) & \(5.63\times 10^{-6}\) (\(9.26\times 10^{-6}\)) & 96\% (94\%) \\
20 & \(9.56\times 10^{-5}\) & \(7.51\times 10^{-7}\) (\(1.69\times 10^{-6}\)) & 99\% (98\%) \\
25 & \(9.47\times 10^{-5}\) & \(1.88\times 10^{-7}\) (\(7.51\times 10^{-7}\)) & 100\% (99\%) \\
30 & \(5.75\times 10^{-5}\) & Not applicable (\(7.51\times 10^{-7}\)) & \(-\)(99\%) \\
35 & \(5.51\times 10^{-5}\) & Not applicable (\(1.88\times 10^{-7}\)) & \(-\)(100\%) \\
40 & \(3.63\times 10^{-5}\) & Not applicable & \\
50 & \(3.65\times 10^{-6}\) & Not applicable & – \\
60 & \(1.82\times 10^{-6}\) & Not applicable & – \\
65 & \(9.11\times 10^{-7}\) & Not applicable & – \\ \hline \hline \end{tabular}
\end{table}
Table 3: Risk level of escaping users (downstream of the fire in direction of portal B) in the investigated one-way tube when used for bi-directional traffic. In brackets are reported the values of the annual cumulative frequency, as well as the reduction of the risk level, when d = 3 m.  
the state-of-the-art by showing the effects of hourly traffic volumes on the risk level of tunnel users in the event of fire. However, future research might be also focused on the uncertainties analysis for making a more consolidated verify of the findings obtained.