## 3 Results  
Using the PTV Vissim tool, the results regarding the existing state were obtained to determine the impact of heavy goods vehicles on the urban road network. The first analysed variant is the current state of a section of the road network. Named in the model as variant 0. The following parameters were analysed:  
* Average travel time;
* Queues of vehicles;
* Average speed;
* Delays. Average travel time and speed measurements were measured at three locations on the road network. These places are essential due to the location of the terminals in the city. The measurement in Section 1 is 1434 m long, in Section 2 is 412 m long, and in Section 3 is 1600 m long. All sections are shown in Figure 3.  
After the simulation, the measurement results are as follows:  
Section 1 has both a moderate average speed and an average travel time over the entire measurement period. Average travel time ranges from 78 to 88 s. The average speed does not drop below 60 km/h. The first section is characterised by traffic from residential districts located in the northern part of the city to the city centre or the Tri-City ring road. Section 2 runs in the opposite direction to Section 1. Thus, the Tri-City Ring Road to the city's northern districts provides a direct connection to container terminals. According to measurements, the highest network load is in the afternoon rush hour. The average speed drops from 70 km/h to 16 km/h. The average driving time increases to 140 s from 20 s.  
Section 3 runs from the mass terminals to the Tri-City Ring Road. Like Section 1, there are no significant fluctuations in the measurement results during the entire period. The average travel time varies from 120 to 140 s, and the average speed ranges from 40 km/h to 50 km/h. The graphs below show the results of the simulation measurements. In this paper, all measured values come from the simulations, variant 0, and all scenarios. They are called variants. The variant is 0 for the actual state, and variants from 1 to 10 represent additional HGVs from 10 to 100. Figures 4-6 show the results. Figure 4: Variant 0; Section 1—average travel time [s]. Figure 3: Sections 1–3. Referring to the results obtained from the microsimulation for the first section, the most significant changes will occur with a 40% increase in the number of heavy goods vehicles from the generating sites. The analysed average travel time for Section 1 will extend during the afternoon peak, which is noticeable in Figure 9. To the analysed afternoon peak between 15:00 and 19:00, the first significant increase in travel time can be noticed for all variants already at a 30% increase in the number of heavy goods vehicles. On the other hand, with a 40% increase in the number of trucks, there is an increase to over 200 s compared to less than 80 s in the case of variant 0 for three hours of the afternoon rush. The longest average travel time of about 400 s occurs when the number of heavy goods vehicles at generator sites is increased by 80% or more. Detailed changes in trip duration in the afternoon rush hour are shown in Figure 9.  
In the case of Section 2, there are no significant changes in the average travel time through the measuring section. This section is already crowded between 15:00-17:00, so it cannot pass any more cars, and they appear in the form of queues of vehicles at further intersections in the road network. The results of all variants for the road section are shown in Figure 10.  
In the case of Section 3, similarly to Section 1, changes in the average travel time through the measuring section occur in the afternoon and evening hours from 3:00 p.m. to 8:00 p.m. Between 3 p.m. and 4 p.m., an increase in the average travel time to over 450 s should be noted, compared to 130 s in variant 0, as well as in terms of increasing the  
Figure 8: Average travel time; Section 1—all variants—PM peak. Figure 9: Average travel time; Section 2—all variants. number of heavy goods vehicles at generator points by 60%.