## 4 Ingolstadt Traffic Scenario  
The Ingolstadt traffic scenario (InTAS) is a comprehensive and highly detailed traffic simulation model, developed to accurately replicate real-world traffic conditions in Ingolstadt, Germany. This model not only includes a precise representation of the city's road network but also incorporates essential urban elements such as buildings, bus stops,parking areas, and traffic lights, derived from official data and satellite imagery [31]. The development of InTAS involved transforming real geographical and demographic data into a simulation environment through tools like OpenStreetMap and Simulation of Urban Mobility (SUMO), allowing the model to accurately reflect the current road infrastructure and traffic management systems. Furthermore, InTAS simulates vehicle behavior and traffic demand based on data collected from actual traffic measurement points, making it a powerful tool for testing advanced vehicle-to-everything (V2X) communication technologies and optimizing traffic flow.  
Originally developed for Ingolstadt, InTAS can be adapted to other cities by replicating its rigorous data collection and modeling process, thereby extending its applications in urban traffic management, public transportation, and vehicular communication systems. This adaptation requires the acquisition of local geographical and demographic data, including road networks, traffic patterns, and public transport schedules, which can be integrated into tools such as OpenStreetMap and SUMO to accurately reflect the specific urban environment.  
The selection of InTAS for evaluating the MMtQHU model is based on its ability to faithfully represent the unique traffic dynamics of Ingolstadt, which are influenced by key socioeconomic and structural factors. As an industrial hub, Ingolstadt experiences significant traffic congestion due to the high volume of vehicles related to employee commutes and logistics, with Audi contributing to 43% of the local workforce. The industrial sector's three-shift work model leads to distinct traffic peaks during shift changes, requiring urban planners to account for these variations to minimize disruptions. In addition, Ingolstadt has a higher vehicle-per-inhabitant rate than other cities, further intensifying traffic during peak hours. This is compounded by school traffic, as many parents drive their children to educational institutions. To capture these dynamics, InTAS incorporates a detailed map covering 51.54 km\({}^{2}\) with 717 km of road length, 3342 nodes, and 7968 edges, alongside 13 public parking facilities, 56 bus lines, and 404 stops.  
InTAS integrates real-world demographic and traffic data, including public transportation schedules and 98 traffic lights, 20 of which are based on actual programs while the remainder are controlled by automated systems. A key aspect of the simulation is the interaction between vehicles and the urban environment, particularly the 21,756 buildings, which impact wireless communication systems. This comprehensive simulation framework allows for an accurate representation of urban traffic, which is critical for evaluating the performance of the MMtQHU model in real-world conditions.  
This study integrates these features into the MMtQHU model to create a realistic and representative case to evaluate its applicability in urban traffic environments. The main objective is to analyze how driver behavior, influenced by institutional, organizational, and psychological factors, causes deviations from predefined routes, thus modeling human uncertainty. These deviations generate variability in the allocation of resources within the 5G network, which directly affects its performance.