The article proposes an algorithm for the spatial aggregation of linear urban objects (street parking zones), based on reconstructing the street centerline from a discrete set of coordinates and forming integral attraction indicators. The algorithm is designed for mathematical modeling of demand for paid street parking and ensures the stable formation of explainable factors for subsequent use in regression and gravity models (including the Huff model). A distinctive feature of the approach is the use of principal components to order the geometry of the zones and reduce computational complexity when working with geospatial data.

Keywords: paid parking, parking demand, spatial aggregation, points of attraction, tariff policy, spatial analysis, smart city, Huff model, urban infrastructure, spatial distribution of demand

The article describes the methodology for constructing a regression model of occupancy of paid parking zones taking into account the uneven distribution of sessions during the day and the behavioral characteristics of two groups of clients - the regression model consists of two equations that take into account the characteristics of each group. In addition, the process of creating a data model, collecting, processing and analyzing data, distribution of occupancy during the day is described. Also, the methodology for modeling a phenomenon whose distribution has the shape of a bell and depends on the time of day is given. The results can be used by commercial enterprises managing parking lots and city administrations, researchers when modeling similar indicators that demonstrate a normal distribution characteristic of many natural processes (customer flow in bank branches, replenishment and / or withdrawal of funds during the life of replenished deposits, etc.).

Keywords: paid parking, occupancy, regression model, customer behavior, behavioral segmentation, model robustness, model, forecast, parking management, distribution