## 5 Conclusions  
The research presented in this article included an analysis of traffic flow characteristics in different highway traffic lanes with an emphasis on the influence of weather conditions. With the results of the analyses, we confirmed that weather conditions have different effects on traffic flow and lane distribution. This may be important when monitoring capacity utilisation and other performance criteria of highway traffic control services for the future. In general, we found that, in certain cases, bad weather has a positive effect on the homogeneity of the traffic flow speed and that, under such circumstances, vehicles do not change lanes, which can reduce delays. Using a traffic microsimulation on a highway section, we determined vehicle delays as a function of traffic demand in different weather conditions using the example of the traffic  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \(q_{e,out,Sc}\) & \(p_{D,NB,all}\) & \(q_{e,out,Sc}\) & \(p_{D,NB,d}\) & \(q_{e,net,Sc}\) & \(p_{D,NB,br}\) & \(q_{e,net,Sc}\) & \(p_{D,NB,br}\) & \(q_{e,net,Sc}\) & \(p_{D,NB,br}\) & \(q_{e,net,Sc}\) & \(p_{D,NB,br,br}\) & \(q_{e,net,Sc}\) & \(p_{D,NB,br,br}\) \\ \hline
**(pcu/h)** & **(h)** & **(pcu/h)** & **(h)** & **(pcu/h)** & **(h)** & **(pcu/h)** & **(h)** & **(pcu/h)** & **(h)** & **(pcu/h)** & **(h)** & **(pcu/h)** & **(h)** \\ \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 \(q_{e,out,Sc}\) & \(p_{D,NB,all}\) & \(p_{D,NB,d}\) & \(p_{D,NB,d}\) & \(p_{D,NB,br,all}\) & \(p_{D,NB,br,br,all}\) & \(p_{D,NB,br,br,all}\) & \(p_{D,NB,br,br,all}\) & \(p_{D,NB,br,br,all}\) & \(p_{D,NB,br,br,all}\) & \(p_{D,NB,br,br,all}\) & \(p_{D,NB,br,br,all}\) & \(p_{D,NB,br,br,all}\) \\ \hline
1575 & 1.02 & 0.87 & 0.87 & 1.089 & 0.95 & 0.94 & 0.94 \\
2291 & 1.02 & 1.12 & 1.19 & 1.03 & 1.10 & 1.04 & 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 & 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 \(q_{e,out,Sc}\) & \(p_{D,NB,d}\) & \(p_{D,NB,d}\) & \(p_{D,NB,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,br,brmanagement scenario \"overtaking ban for HGVs\". The delays calculated with the traffic model were compared with a reference scenario without traffic management in simulated different weather conditions but with controlled traffic input data. If the effectiveness of the AHTMS measures is measured by the reduction in delays, it would be important to know the result that the same value of delay is achieved for different values of traffic flow density in different weather situations. In practice, the techniques for empirically determining delays can be complex; hence, we can expect the introduction of online traffic models soon.