The existing transport infrastructure of cities faces challenges due to the extensive development of city boundaries, difficulties in changing the logistics routes of public transport, and the increased motorization of urban agglomerations. The research describes a project for a service that collects, processes, and analyzes information on the number of passengers at public transport stops. It adapts new technologies for use in new applied areas. The project’s potential lies in using artificial intelligence and machine learning in the field of route planning. It is a revolutionary approach that can transform the field of urban transport. Public transport routes, historically formed in the current geographical network of the city, often do not meet the current needs of the population. This leads to overcrowding of public transport during rush hours and insufficient loading at other times. This reduces passenger satisfaction with the quality of urban transportation and increases the operating costs of city transport authorities.