## 4 Results and Discussion
In Table 4, in addition to the results on total delays, we present the relative difference, expressed by indices \(\frac{p_{D,WB,c}}{p_{D,NB,ALL}}\) and \(\frac{p_{D,WB,c}}{p_{D,NB,c}}\), where \(p_{D,NB,ALL}\) is the total vehicle delay of the reference simulation, \(p_{D,NB,c}\) is the total delay without traffic control, and \(p_{D,WB,c}\) is the total delay with the traffic control measure in different weather conditions \(c\). Owing to the \"overtaking ban for HGVs\", delays were 2% to 3% lower in fair weather conditions and 5% to 7% lower in adverse weather conditions. With higher traffic flow rates, delays could be reduced by up to 21%, regardless of weather conditions, but only up to a certain threshold. As rates exceeded 4500 pcu/h, the relative differences between the delays under different conditions decreased. From the results, similar to the case of relative delays, it appears that traffic management with truck overtaking bans at lower flow rates was not as effective as at flows above 2800 pcu/h. Through traffic simulations, we confirmed that relative traffic delays depend on road weather conditions. In adverse conditions, these were, on average, 1% to 10% higher than relative delays in dry conditions with the same traffic flow rate, and worst in the case of winter conditions, followed by low visibility and various rain intensities or wetness. For the maximum simulated flow rate of 4000 veh/h with a 10% share of heavy vehicles, i.e., a traffic flow density of slightly more than 20 veh/km, when the characteristics of the traffic flow in the traffic lanes were known, the relative delays were 1% greater in dry weather conditions, 3% to 5% greater in wet conditions, 8% greater in poor visibility, and up to 12% greater in winter conditions than in the case where the traffic flow characteristics were determined independently of weather conditions (_ALL_). By simulating the traffic management measure of the truck overtaking ban, we calculated that relative delays were reduced by an average of 15% to 18% compared to a benchmark without traffic management. The largest difference occurred with medium-sized flow rates or a density of about 12 veh/km and was as high as 25% in snowy weather conditions. We found that the \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \(q_{e,tot,Sc}\) & \(p_{D,NB,ALL}\) & \(q_{e,tot,Sc}\) & \(p_{D,NB,All}\) & \(q_{e,tot,Sc}\) & \(p_{D,NB,All}\) & \(q_{e,tot,Sc}\) & \(p_{D,NB,All}\) & \(q_{e,tot,Sc}\) & \(p_{D,NB,All}\) & \(q_{e,tot,Sc}\) & \(p_{D,NB,All}\) \\ \hline (pcu/h) & (b) & (pcu/h) & (b) & (pcu/h) & (b) & (pcu/h) & (b) & (pcu/h) & (b) & (pcu/h) & (b) & (pcu/h) & (b) \\ \hline
1575 & 0.84 & 1575 & 0.85 & 1575 & 0.73 & 1575 & 0.92 & 1575 & 0.74 & 1575 & 0.79 & 1575 & 0.78 \\
2291 & 1.78 & 2284 & 1.81 & 2291 & 1.98 & 2284 & 2.11 & 1.83 & 2284 & 1.95 & 2284 & 1.85 \\
2873 & 3.03 & 2873 & 3.21 & 2873 & 3.66 & 2880 & 3.56 & 2873 & 3.14 & 2880 & 3.34 & 2880 & 3.45 \\
3448 & 7.05 & 3448 & 6.90 & 3455 & 7.13 & 3441 & 6.98 & 3448 & 7.09 & 3455 & 7.18 & 3455 & 7.24 \\
4093 & 11.11 & 4086 & 10.75 & 4051 & 10.67 & 4051 & 11.75 & 4072 & 11.30 & 4058 & 11.36 & 4044 & 11.75 \\
4526 & 17.91 & 4540 & 17.65 & 4526 & 18.85 & 4547 & 20.84 & 4547 & 18.47 & 4526 & 19.30 & 4526 & 20.58 \\ \hline \(q_{e,tot,Sc}\) & \(p_{D,NB,All}\) & \(p_{D,NB,All}\) & \(p_{D,NB,All}\) & \(p_{D,NB,All}\) & \(p_{D,NB,All}\) & \(p_{D,NB,All}\) & \(p_{D,NB,All}\) & \(p_{D,NB,All}\) & \(p_{D,NB,All}\) & \(p_{D,NB,All}\) & \(p_{D,NB,NB,All}\) \\ \hline
1575 & 1.02 & 0.87 & 1.10 & 0.89 & 0.95 & 0.94 & 0.94 \\
2291 & 1.02 & 1.12 & 1.12 & 1.19 & 1.03 & 1.10 & 1.04 \\
2873 & 1.06 & 1.21 & 1.17 & 1.04 & 1.10 & 1.14 \\
3448 & 0.98 & 1.01 & 0.99 & 1.01 & 1.02 & 1.03 \\
4093 & 0.97 & 0.96 & 1.06 & 1.02 & 1.02 & 1.06 \\
4526 & 0.99 & 1.05 & 1.16 & 1.03 & 1.08 & 1.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Total delays in simulated network depending on \(q_{e,tot,Sc}\) for simulation scenarios _NB_ALL_ and _NB_c_, along with index ratios.