Data, AI and governance in MaaS – Leading to sustainable mobility?

•Data collection and processing in MaaS might reproduce socio-political inequalities.•AI customisation of user demand and integration of mobility supply might ignore rebound effects.•Rebound effects can be avoided if sustainability objectives are central in governance processes.•Mobility optimisation with AI in MaaS can lead to hybrid governance between humans and algorithms.•Sustainable hybrid governance needs transparency among stakeholders, citizens, and algorithms. Mobility-as-a-Service (MaaS) is regarded as key innovation for sustainable mobility, with data and AI playing a central role. This paper explores the nexus of data-AI-governance in MaaS to understand in how far sustainability is addressed. While the role of data and AI is covered by technical literature, and governance by social science literature, these discussions remain largely separate in MaaS. This paper aims to redress this issue through an interdisciplinary narrative literature review that brings together these literature sets. The research question is: How does the data-AI-governance nexus in MaaS give rise to hybrid forms of governance between humans and algorithms and what are the implications for sustainable mobility? Results show that: (1) The data collection and processing that is crucial to MaaS, might reproduce socio-political inequalities. (2) AI-driven customisation and nudging of end-user demand ignores rebound effects, that can only be avoided if sustainability objectives are central. (3) Inadequate integration of mobility service supply might exacerbate mobility challenges. (4) When mobility system optimisation through AI becomes more widespread, MaaS platforms might become a form of algorithmic governance. (5) Whether sustainability can be reached, depends on how and by whom (sustainability) objectives of algorithms will be decided. The paper concludes that hybrid governance for sustainability requires close collaboration between policymakers and industry players and acknowledging AI algorithms as important non-human actors. The paper contributes to conceptual debates on sustainability and data/AI, governance and data/AI in MaaS and beyond, and to policymaking on aligning platform systems with sustainability.