## 3 Results  
In this section, the results of travel demand model are analyzed with the OD bundling technique to evaluate the generated trips based on locational, transport mode, and sociodemographic characteristics. Thereafter, model results are presented together with several distribution analyses to understand the matching results on a detailed level by utilizing frequency charts, histograms, and cumulative distribution plots, as in other MATSim demand synthesis studies [9; 19]. ArcGIS Pro program was utilized for the visualization of OD flows (Figures 9-12), while the Matplotlib package of Python is utilized for the demonstration of data distributions (Figures 13-17). Figure 11: Origin–destination (OD) pairs of the primary activity trips (**left**) and the secondary activity trips (**right**) based on main transport mode (car—blue bundles and public transport—yellow bundles). Figure 12: Origin–destination (OD) pairs of the primary activity trips based on main transport mode (car—blue bundles and public transport—orange bundles), gender (**top** images: male—**left** and female—**right**), and age groups (**bottom** images: 18–24, 25–39, \(\geq\)40). Figure 9 shows origin-destination pairs for the primary activity trips in the study area. The OD visualization demonstrates how work- and education-related trips are concentrated based on the density of destination locations and their capacity, which is presented in the last subsection. The figure also indicates how inbound and outbound trips are included by originating and ending journeys at the synthetic gates, which is one of the main contributions of this study. The synthetic gate located in the south of the study area has higher demand than other gates, which is expected considering the concentration of living areas along with the south side of Elbe River (taking the city center as base). However, utilizing [PERSON]'s algorithm, which is based on the shortest path in the framework, might estimate south trips somewhat more than in real life. In case of data availability, the extension of the algorithm considering travel speed as path weights would provide more balanced gate demands. Figure 10 presents origin-destination pairs for the secondary activity trips (shopping, leisure, errands) in the study area. As seen in the figure, secondary trips originating from home are much more homogeneously distributed within the study area than primary trips, an observation that is expected by examining the locational distribution of home and secondary activities presented above. The figure also shows how secondary trips originating from work and educational locations (in the middle and right figures, respectively) and their destination locations are more concentrated in the center. This situation is anticipated considering the visitor concentration of facilities demonstrated in the previous subsection. Figure 11 demonstrates origin-destination pairs for the primary and secondary activity trips based on main transportation mode (car and public transport). The figure indicates that car trips are taken more for the secondary activities, while public transport trips are much more dominant than car trips for the primary activities, which is reasonably accurate because half of the commuting trips are taken with public transport in Uti nad Labem city [42]. In both cases, long (outbound) trips are taken more by car than public transport, which is an expected result. Figure 12 presents origin-destination pairs for the primary activity trips based on main transportation mode (car and public transport), age groups (18-24, 25-39, 26-39, 240), and gender. As seen in the figure, the frequency of the primary activity trips increases by an increase in age, which is consistent with greater participation in economic activities expected for the elder age groups. Additionally, the figure shows that public transport usage is slightly more dominant in female population than male; a rational result considering transport literature [43]. Figure 13: Socio-demographic characteristics.