Mobility as a Service for Smart Cities: A Bibliometric Analysis of Research Trends, Knowledge Structure, and Emerging Technologies (2015–2025)

Mobility as a Service has emerged as a paradigm for integrating transportation services and supporting sustainable, accessible, and user-centered mobility systems. This study presents a bibliometric analysis of literature on MaaS, with attention to data-driven and Artificial Intelligence (AI)-enabled mobility research. Following the PRISMA 2020 framework, 1,554 documents were retrieved from Scopus and Web of Science. The records were merged, cleaned, normalized, and deduplicated before analysis using Bibliometrix and Biblioshiny.

The results reveal significant development of MaaS research, with scientific production reaching a peak of 231 publications around 2022. At the country level, China is the most productive contributor, with 418 publications, followed by the United Kingdom (335) and the United States (307). In terms of citation impact, the United Kingdom records the highest total citations (3,841), while Sweden and the Netherlands achieve the highest average citations per article, with 44.7 and 43.0, respectively. At the institutional level, the University of Sydney is the most productive affiliation, with 89 publications. Thematic analysis identifies three research clusters: (1) MaaS, travel demand, and user behavior, including willingness to pay, preferences, choice, and adoption; (2) MaaS, public transport, governance, policy, innovation, and optimization; and (3) sustainable, smart, and urban mobility, including shared mobility, autonomous vehicles, electric vehicles, and smart cities. Keyword analysis confirms the prominence of MaaS-related concepts, with “Mobility as a Service” recording 404 occurrences, followed by “MaaS” (304), “Mobility” (207), and “Transport” (166).

Overall, this study provides a quantitative map of the evolution, geographical and institutional distribution, citation impact, and thematic organization of MaaS research. The identified clusters highlight the convergence of MaaS with sustainable and smart mobility, public transport, and emerging transportation technologies, providing an evidence-based foundation for future research on AI and data-driven approaches toward adaptive, scalable, and personalized MaaS ecosystems.