## 4 Results and Discussion  
The described model tracks 20 years, spanning over 2021 and 2040. Initial values of the model variables reflect statistical data of the South Bohemian Region from 2019, studies and surveys conducted on behalf of clients archived in the South Bohemian Region or the chartered city of Ceske Budejvoice, and internal materials of the transport coordinator in the South Bohemian Region or The Public Transport Company of Ceske Budejvoice [30]. Due to the demostrative nature of the model, the applied stock and flow diagrams to the transport sector did not go through any validation. The Vensim program allows a simulation of various scenarios and interactive experiments, diversifying initial values of input variables. We initiated two schemes for analyzing the system dynamics [36]:  
* Scenario A-The supply and demand trend in the selected modes will be the same as in previous years (reflecting current historical data of measured variables)
* Scenario B-The supply and demand trend in \(PT\) will be growing in the subsequent years, calling for a supply increase in means of transport and higher utilization rates. This enhancement involves new lines, purchasing more equipment, and so on, moving the value toward the average daily \(PT\) vehicle capacity. On the other side, \(PT\) usage rates will mount to 14.5% in 2040 to satisfy the predicted demand (see Table 2). * Scenario C-We are considering a growing trend in \(PT\), but with a lower value of \(GDP\) increase each year (from the current 3.5% annual growth, a drop to the growth of 2.5% on average). The rest of the variable values remain the same as in scenario B. * Scenario D-We are considering a growing trend in \(PT\), but with a lower value of \(GDP\) increase each year (from the current 3.5% annual growth, a drop to the growth of 1.5% on average). The rest of the variable values remain the same as in scenario B. The initial variable values reflect a region or municipality, constructing possible scenarios and studying system behavior in time. Table 2 summarizes the initial values used in the simulation of trends of variables in time. The simple simulation involved four scenarios, modifying values of selected variables directly linked to the supply and demand in \(PT\). Submodels suggesting the numbers of cars, local population, and regional \(GDP\) went unchanged when measuring the variables and model structure of scenarios A and B [37]. In both scenarios, demand in \(PCT\) for travels indicated a downward trend of the \(PCT\) ratio to the division of transport work to 30% in 2040, showing the same tendency in \(PCT\). Residents' movability is also the same for both schemes, suggesting the trend from the relationship (17). The variable does not involve the initial value. Rather than that, it tracks an underlying tendency indicating _GDP_ rates and population in the region. While 2019 saw the residents' movability in the South Bohemian Region at 2.75 travels per day, the estimate for 2021 amounts to 2.8 travels/day and 3.3 travels/day in 2040, considering the model parameters (scenarios A and B) [32]. Given the hypothesis that _GDP_ per capita correlates with residents' movability, scenarios are tested where _GDP_ per capita will grow in a more moderate trend in coming years (scenario C assumes an average annual growth of 2.5%, scenario D assumes an average annual growth of 1.5%). The crucial factor in the model is the value of \"stocks\" in the form of _GDP_ volume. In general, a relationship between _GDP_ growth and population mobility is proven [31]. The residents' movability in the case of scenario C will increase over the 20 years, from a value of 2.8 travels per day to a value of 3.1 travels per day. Regarding scenario D, the increase is slower to a value of 2.9 travels per day in the final year. Figure 7 depicts the trend of residents' movability over the 20 years.