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Problems and Prospects of Integrating Artificial Intelligence into Public Transport in Ukraine: An Analytical Context
Martynovych N. O., Sundulov Y. H.

Martynovych, Nataliya O., and Sundulov, Yevhen H. (2025) “Problems and Prospects of Integrating Artificial Intelligence into Public Transport in Ukraine: An Analytical Context.” Business Inform 9:229–238.
https://doi.org/10.32983/2222-4459-2025-9-229-238

Section: Economics of Transport and Communications

Article is written in Ukrainian
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UDC 005.21:005.8:004.9

Abstract:
The current stage of transport sector development is characterized by increasing digitalization and the transition to intelligent transport systems, in which artificial intelligence (AI) technologies play a key role. For Ukraine, which is undergoing infrastructure recovery and modernization, integrating AI into public transport is an important condition for improving transport efficiency, traffic safety, and urban mobility sustainability. The aim of the article is to identify the problems and prospects of integrating artificial intelligence into public transport in Ukraine. The main objectives of the study were: analyzing the dynamics of global investments in AI in the transport sector; determining the regional share of intelligent solution implementation; establishing the proportion of AI usage in transport in 2015, 2020, and 2025; identification of countries leading digital modernization and key areas for the development of intelligent transport systems (MaaS, ADAS, Big Data, IoT); analysis of Ukrainian realities and the structuring of problems and prospects across five areas. The study applied analytical, evaluative, and synthetic approaches, which involved using official data from the European Parliament, INRIX Research, CEPS/UNIDO, AI Watch, the Ministry of Digital Transformation of Ukraine, and WINWIN AI. Methodologically, the work is based on problem-comparative analysis, systematization of statistical data, and synthesis of analytical materials to identify trends in the digital transformation of transport. The results of the study showed that global investment in AI implementation in the transport sector is growing at an average annual rate of 28–30%, with Singapore, the Netherlands, Finland, Sweden, Spain, and South Korea being the leading countries. It has been shown that the use of AI in public transport leads to a reduction in traffic congestion by 25–30%, fuel consumption by 20–35%, CO2 emissions by 18–35%, and traffic accidents by 15–30%. In the Ukrainian context, limited funding, fragmented initiatives, staff shortages, outdated infrastructure, and regulatory uncertainty have been identified, which coexist with the potential for the development of MaaS, management digitalization, transport eco-modernization, implementation of ADAS systems, and the establishment of AI legal standards. The scientific novelty of the study lies in grouping the problems and prospects of AI integration into Ukraine’s public transport into five analytical groups: economic, organizational, human resources, technical-technological, and regulatory-legal. This approach, unlike existing ones, allows for a considered determination of a set of indicators that will serve as the basis for the further development of an authors’ methodology for assessing the level of AI integration in the transport sector. The presented results have practical value for forming strategic decisions in the field of digital transformation of transport and the implementation of «smart mobility» principles. Prospects for further research involve conducting empirical studies with the participation of transport industry experts to determine the strength of connections between digitalization performance indicators and the level of infrastructure readiness, as well as developing an original methodology for assessing the level of AI integration into public transport in Ukraine.

Keywords: artificial intelligence, public transport, digital transformation, transport infrastructure, intelligent transport systems, urban mobility, sustainable development, project management.

Fig.: 4. Tabl.: 2. Bibl.: 22.

Martynovych Nataliya O. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Management and Public Administration, State Tax University (31 Universytetska Str., Irpin, Kyiv region, 08205, Ukraine)
Email: [email protected]
Sundulov Yevhen H. – Masters Student, State Tax University (31 Universytetska Str., Irpin, Kyiv region, 08205, Ukraine)
Email: [email protected]

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