About the experience of public passenger transport reform, using the city of Khabarovsk as a case study

The current urbanization and development of Khabarovsk is characterized by population growth and car ownership, intensive construction of housing and social infrastructure, cluster development of certain industrial sectors, and transport and logistics infrastructure. This has led to a problematic state of the city’s transportation system, associated with traffic congestion, reconstruction of the street and road network (SDN), parking and storage of vehicles, accident rates, and traffic safety. The monetization of benefits and the increase in fares have led to a crisis of unprofitability in the provision of regular urban public passenger transport (UPPT) services across existing modes of transport (bus, tram, and trolleybus). A distinctive feature of the study is its integrated approach to solving optimization problems, taking into account the interests of passengers, carriers, and the city administration. The comprehensive surveys were multi-purpose, as they yielded diverse initial information necessary for addressing a wide range of urban development issues (functional zoning, settlement patterns, the placement of focal points, correspondence and their transport services, passenger travel time, road network development prospects, etc.). The need for comprehensive surveys with not only transport but also social and urban development implications was also justified by the fact that this work resulted in a comprehensive plan for the development of urban public passenger transport. This article examines the results of public transportation modeling in Khabarovsk using the modern PTV VISUM software product and a modified passenger service technology. Mathematical modeling of the route network based on optimality criteria, taking into account social norms for pedestrian accessibility of transport stops, permissible vehicle frequency and passenger travel time, reducing route duplication and the number of transfers, and considering a correspondence matrix superimposed on the city’s transportation network. This allowed for a redesign of the city’s route network, identifying transport microdistricts and new optimal routes that reduce vehicle demand and improve the user experience and convenience of using transportation services, provided that transport cards are implemented on all regular city routes.