## 3 Modelling the Efficiency of Traffic Lane Control and Weather Conditions  
A hypothetical simulation model was created in the PTV Vissim software environment. Using the Wiedemann 99 traffic model, we set up a 2 km long unidirectional highway carriageway link. The scheme is shown in Figure 5, where the positions of the cross-sections P1 and P2 are marked, along with the virtual simulation test points, such as sensor loops [K1, K2, K3, K4] on the individual traffic lanes corresponding to the individual traffic lane \(i=\) {1--right lane; 2--left (overtaking) lane} and measuring points {K5, K6} on the carriageway. The measuring points at the cross-sections were used to observe the lane distribution of traffic flow. The first 600 m of the model was intended to establish a stable traffic flow, the next 1200 m was intended for measurements, and another 200 m was intended for exit vehicles from the simulation model of the road network. In the section between P1 and P2, there were link control sensors T1 and T2 on each lane, from which we obtained the values of traffic flow characteristics on the 1000 m long section, separately by traffic lanes and for the \"average\" lane. The \"average\" lane represents the average value of the simulation results of both traffic lanes. In the selected microsimulation model, traffic flow is distributed among traffic lanes on the basis of parameters describing the behaviour of drivers and the characteristics of vehicles entering the simulation model. In order to use the model in a real environment, one should calibrate the model and make a comparison between the actual characteristics of the traffic flow and the modelled characteristics. The traffic model simulation time was \(T_{s}=\) 60 min and we gradually loaded the model in 10 min time intervals with different values of the flow rate of all vehicles on the carriageway. The total traffic flow rate increased uniformly at each interval \(q_{tot,c}=\) {1500 veh/h, 2000 veh/h, 2500 veh/h, 3000 veh/h, 3500 veh/h, 4000 veh/h). We modelled the situation in which \(LFR_{2,c}\approx LFR_{1,c}\) occurs in the traffic flow, i.e., in the case in which vehicles change lanes intensively and both traffic lanes are approximat  
Figure 5: Schema of unidirectional carriageway in the simulation traffic model. was observed through the viewer interface and control points (Figures 5 and 6a,b). In the simulation, we considered the constant share of heavy vehicles \(P_{hv}=10\%\). We modelled three scenarios, two of them for six weather conditions:  
1. Without traffic control restrictions and without separate consideration of weather conditions \(c\), \"ALL\" weather conditions are considered--\(NB\_ALL\) simulations (Figure 6a);
2. Without traffic control restrictions, but with weather conditions \(c\) considered--\(NB\_c\) simulations (Figure 6a): 1. \(NB\_d\) simulation taking into account the empirical speed distribution functions of light (_F-0178,V\({}_{pc}\),i,d_) and heavy vehicles (_F-0178,V\({}_{lrv}\),i,d_), in traffic lanes \(i=\{1,2\}\), in \"dry\" and fair weather (Figure 3); 2. \(NB\_w1\)-, \(NB\_w2\)-, \(NB\_w3\) simulation by taking into account the empirical speed distribution functions of light (_F-0178,V\({}_{pc}\),i,wv_) and heavy vehicles (_F-0178,V\({}_{lrv}\),i,wv_), in traffic lanes with different precipitation intensities and wetness of road surface (Figure 3); 3. \(NB\_lv\) simulation by taking into account the empirical speed distribution function of light (_F-0178,V\({}_{pc}\),i,lv_) and heavy vehicles (_F-0178,V\({}_{lrv}\),i,lv_) in traffic lanes with \"poor visibility\" (Figure 3); 4. \(NB\_s\) simulation by taking into account the empirical speed distribution function of light (_F-0178,V\({}_{pc}\),i,s_) and heavy vehicles (_F-0178,V\({}_{lrv}\),i,s_) in traffic lanes under \"winter conditions\" (Figure 3);
3. With the traffic control of \"overtaking ban for HGVs\", but with consideration of road-weather conditions \(c\)--\(WB\_c\) simulations (Figure 6b): \(\{WB\_d,WB\_w1,WB\_w2,\)\(WB\_w3,WB\_lv,WB\_s\}\).