Prioritization of transport innovations integration in urban environment

Introduction. The article is devoted to the study of the peculiarities of introducing transport innovations into the urban environment of modern megacities. The features of urban transport in a modern megalopolis are described and the requirements for urban passenger transport services are outlined, taking into account the growth of urbanization and the need to ensure sustainable development of territories. The features and the typology of transport innovations are given. The results of investigating foreign experience on introducing innovations in urban transport in large cities are presented. The features of the state policy in the field of urban transport system development are determined based on the case of St. Petersburg. Through the study of expert opinions and strategic documents of the city’s development, the priorities and key tasks of the development of the megalopolis urban transport system are reflected. Based on the study of materials on the practical use of transport innovations in the urban environment, the characteristics of key participants and innovation market proposals for the research object are systematized by market segments. A study was conducted on evaluations and reactions of city residents regarding the quality of transport services in the urban environment and the need to introduce innovations into the city’s transport system in light of current problems. The article highlights the concept factors that are most important in assessing the quality of transport services and ways to improve them, namely, the development of digital services, infrastructure solutions, intelligent transport systems, and their integration into the digital space of a large city, as well as the rolling stock of public transport. Based on cognitive modeling, the impact of direct and indirect interrelated effects of factors on the quality management of transport services in a megalopolis has been revealed.

Materials and methods. The data systematization method was applied to aggregate materials on the use of transport innovations in the urban environment, taking into account its features. The priorities of urban transport development were identified based on the generalization method. The identification of the problem of the St. Petersburg transport system, the assessment of the population’s reactions to the quality of transport services and the description of promising tools and means of innovative transformation of urban transport were provided through urban resident questionnaires and expert surveys. The tools of cognitive modeling of the factor-to-quality relationships for transport services in the urban environment were used.

Results and conclusions. Based on the systematization of documents for the strategic development of the urban transport system in St. Petersburg, the tasks of innovative transformation of the transport system through the introduction of innovations have been prioritized. The opinion and reactions of city residents as to the expediency of introducing transport innovations in the urban environment to solve current and future problems of the megalopolis transport system have been revealed. The main concepts-factors determining the possibilities of improving the quality of transport services for residents of a megalopolis based on a cognitive process map have been identified. The further research is related to quantifying the impact of conditions (for example, the reaction of the population of different ages and social status) and the parameters of the model, a set of factors and options for innovative solutions on the possibilities of improving the quality of public transport services based on the theory of fuzzy sets.

1. Vasilyeva M.E., Volkova E.M., Romanov A.S. Intelligent Transport Systems in Russian Agglomerations: Essence, Structure, and Directions of Development. Innovative Transport Systems and Technologies. 2023. Vol. 9. No. 4. P. 117–128. (In Russ.) https://doi.org/10.17816/transsyst202394117-128

2. Savin G.V. Smart transport and logistics system of the city: theory, methodology and practice. Upravlenets. 2021. Vol. 12. No. 6. Pp. 67–86. (In Russ.) https://doi.org/10.29141/2218-5003-2021-12-6-5

3. Mavlyutova I., Atstaya D., Grasis J. et al. The concept of urban transport and sustainable mobility in smart cities: an overview. Energies. 2023. Vol. 16. No. 8. Article 3585. (In Russ.)

4. Winkler L.O.Y., Pierce D., Nelson J., and Babajan, O. “The Impact of Sustainable Mobility Policy on Urban Transport Emissions and Energy Consumption” (2023). Nature Communications (vol. 14, no. 2357). (In Russ.) https://doi.org/ 10.1038/s41467-023-37728-x)

5. Druzhinin P.V., Moroshkina M.V., Sedova K.E. The Impact of Agglomerations on the Development of Surrounding Territories: Novosibirsk, Moscow, and St. Petersburg Agglomerations. Spatial Economy. 2025; No. 1. URL: https://cyberleninka.ru/article/n/vliyanie-aglomeratsiy-na-razvitie-okruzhayuschih-territoriy-novosibirskaya-moskovskaya-i-sankt-peterburgskaya-aglomeratsii (date of access: 04/08/2026). (In Russ.)

6. Abdrakhmanova D.R., Saubanov K.R., Yurkov D.R., Assessment of the digital competitiveness of the region. Economic Sciences. 2025; (12): 367-378. (In Russ.)

7. Prikhodko E.S. The role of artificial intelligence in logistics. Transport business of Russia. 2026; (2):35-39. (In Russ.)

8. Solodky A.I., Yevtyukov S.S., Selezneva N.V. Intelligent transport systems – the basis of the digital transformation of the automobile and road complex. Transport business of Russia. 2026; (2): 68-72. (In Russ.)

9. Arkhipov A.E., Lyashenko S.V. Technological aspects of the implementation of the innovative paradigm of organization in multimodal transportation. Transport business of Russia. 2026; 2(88-73). (In Russ.)

10. Mustakaeva E.A. Digital transformation of water transport: from business transition to virtual-physical systems. Transport business of Russia. 2026; (2): 95-98. (In Russ.)

11. Pivovarova O.V., Makarov I.N. Innovative approaches to urban transport development: world practice and domestic prospects. Economics, Entrepreneurship and Law. 2026; Vol. 16(4). (In Russ.) https://doi.org/10.18334/epp.16.4.125933. EDN EJKJTC.

12. Azemsha S.A. The multi–route method of organizing the work of vehicles. Bulletin of SibADI, 2025;T 22(1): 38-53. (In Russ.) https://doi.org/10.26518/2071-7296-2025-22-1-38-53

13. Keyan E.G., Melnikov A.N., Tamoshina O.A. Formation of an electric vehicle efficiency parameter // SibADI Bulletin, 2025; 22(1):78-89. (In Russ.) https://doi.org/10.26518/2071-7296-2025-22-1-38-53

14. Tishkova A.O., Yakunin N.N., Yakunina N.V. Methodology for assessing the level of organization of urban passenger automobile transportation, taking into account sustainability. SibADI Bulletin, 2026; vol. 23(1): 62-75. (In Russ.) https://doi.org/10.26518/2071-7296-2026-23-1-62-75

15. Kapskij D.V., Lyapin S.A., Bogdanovich S.V. Vliyanie bespilotny`x transportny`x sredstv i smezhny`x texnologij na socioprirodoe`konomicheskuyu sredu goroda. Mir transporta i texnologicheskix mashin. 2024. № 3-1(86): 101-107. (In Russ.) https://doi.org/10.33979/2073-7432-2024-3-1(86)-101-107

16. Trofimova L.S., Gavrilin D.V., Kabzhamitova A.S. Organizaciya regulyarny`x perevozok passazhirov s uchetom obrashhenij grazhdan i dispetcherskogo kontrolya. Vestnik Sibirskogo gosudarstvennogo avtomobil`no-dorozhnogo universiteta. 2024; T. 21, № 4(98):580-593. (In Russ.) https://doi.org/10.26518/2071-7296-2024-21-4-580-593

17. Ismikhanov Z.N., Gadzhiev Sh.D., Yahyae- va A.A. Cognitive modeling of the socio-economic system of the region. Modern high-tech technologies. 2016; No. 2-2:225-228. (In Russ.)

18. Volkov V.Yu., Volkova V.V. Instrumental means of cognitive modeling in decision support systems. Bulletin of the International Academy of Systems Research. Computer science, ecology, economics. 2016; Vol. 18(1):104-108. (In Russ.)

19. Voronina E.V., Yarosh O.B., Zakieva N.I. Voronina E.V. et al., “Mathematical Model of Forecasting the Residential Real Estate Market Prices Level”, Materials Science Forum,2018; Vol. 931, pp.1101-1106, https://doi.org/10.4028/www.scientific.net/MSF.931.1101

20. Knight CR.J.K., Lloyd D.J.B., Penn A.S. Linear and Sigmoidal Fuzzy Cognitive Maps: An Analysis of Fixed Points. 2014; 15: 193-202 https://doi.org/10.1016/j.asoc.2013.10.030

21. Tsvetkov V.Ya., Solovyov I.V. Principles of cognitive management of a complex organizational and technical system. State Councilor. 2016; 1 (13): 27-32. (In Russ.)