Urban geography plays a pivotal role in mobility management and the optimization of resources related to parking and traffic. The study zone for this research is a medium-sized city with a complex road network, comprising a dense historical center with mixed residential and commercial use, low-intensity peripheral districts, and major road connections intersecting the urban fabric.

This tab enables users to select specific zones of interest within the city of Caserta for targeted analysis and subsequent platform outputs. The zones displayed on the map are defined by the company managing smart mobility, representing patrol areas for agents. These zones often correspond to different time-based parking fee schedules and tariff rates. For this reason, the zones have been retained in their original format to allow the model to effectively simulate, predict, and generate the desired data. Each zone is further enriched with plotted data for relevant parking meters, parking spaces, and road segments. The map is interactive: clicking on any element reveals its corresponding ID for detailed inspection.

Additionally, a detailed table provides real-time information about the selected zones, including key environmental metrics such as Temperature, Humidity, Wind Speed, Precipitation, and Air Quality Index (AQI), all retrieved dynamically through API integration. This dual-layered approach offers a rich spatial and contextual understanding, crucial for effective urban mobility management and resource optimization.