## 4 Results and Discussion
The criterion of traffic control on highways when traffic flow is becoming unstable is usually determined by the total equivalent flow rate \(q_{e,tot,ALL}\) and the equivalent flow rate in the overtaking lane \(q_{e,2,ALL}\), as well as the standard deviation of speed in the overtaking lane [34,36]. Similarly, the total equivalent flow rate \(q_{e,tot,ALL}\) and the proportion of heavy vehicles \(P_{thr}\) determine the criterion for traffic management in the case of traffic instability at a large share of heavy vehicles. Considering that free-flow speed varies depending on weather conditions, in the case where the traffic flow rate is a criterion for traffic management, different threshold values of the flow rate threshold should be taken into account, depending on weather and traffic conditions. Therefore, this article demonstrates that it is reasonable to further investigate the effectiveness of the current AHTMS criteria and possibly establish new criteria for traffic management in the case of instability by using other traffic flow characteristics, such as the Coefficient of Variation of Speed CVS [30,46], the traffic flow density \(k_{c}\), or the possibility of changing lanes depending on environmental factors \(LFR_{i,c}(q_{e,tot,c})\), \(LDR_{i,c}(q_{e,tot,c})\)[19]. For comparison, on the basis of the simulation, we calculated the total delays of all vehicles \(p_{D,Sc}\) on the simulation model road network, varying the input data and calculating the equivalent traffic flow rate for the case of a highway level terrain according to the HCM methodology [17]. The graph in Figure 7(a) shows total delays \(p_{D,Sc}\) as a function of the total equivalent traffic flow rate \(q_{e,tot,Sc}\) for the scenario without special traffic management (_NB_c_), and that in Figure 7(b) shows the same scenario with the simulated measure \"overtaking ban for HGVs\" (_WB_c_). In both cases, the results of the _NB_ALL_ scenario are also shown as a reference red polyline. In addition to the results, Table 3 shows the relative differences, expressed by the index ratio \(\frac{p_{D,NB\_c}}{p_{D,NB\_ALL}}\), where \(p_{D,NB\_ALL}\) represents the total delays of the vehicles in the reference simulation. In the range of flow rates from 1500 pcu/h to about 2300 pcu/h, delays under bad weather conditions were lower than under reference conditions, except for dry weather  
Figure 8: Simulated total delays of all vehicles as a function of total equivalent traffic flow rate: (**a**) for _NB_ALL_, _NB_c_ scenarios; (**b**) for _NB_ALL_, _WB_c_ scenarios. conditions. This difference may be a result of adverse weather conditions that reduced the speed of vehicles and changed the lane distribution in some way [19]. The ratio value changed with the next step of the traffic flow rate increase. In the \(q_{e,tot,Sc}\) interval between 2300 pcu/h and 3500 pcu/h, delays were again shorter. With better weather conditions, the differences over the flow rate value of around 2900 pcu/h decreased. At a flow rate of 3500 pcu/h, the delay differences only increased and no longer fluctuated. At the end of the simulation, total delays increased by 16% in winter conditions, 15% in heavy rain, 8% in moderate rain, and up to 3% in light rain. In dry conditions, a 1% improvement over the reference value was observed. The line graph in Figure 8b presents \(p_{D,Sc}\) as a function of \(q_{e,tot,Sc}\) for the scenario with the simulated measure \"overtaking ban for HGVs\" (_WB_c_ scenarios). In all cases, delays were lower with the traffic control; however, in the \(q_{e,tot,Sc}\) interval from 1500 pcu/h to 2900 pcu/h, this difference was not as pronounced as at higher flow rates.