AI-Driven Mobility-as-a-Service: A Review
TL;DR
The findings show that successful implementation of AI-enabled MaaS depends not only on advances in AI algorithms but also on high-quality interoperable data, effective governance, regulatory support, public trust, and collaboration among stakeholders.
Abstract
Mobility-as-a-Service (MaaS) has emerged as a promising approach to urban transportation by integrating multiple mobility services into a single digital platform for trip planning, booking, and payment. More recently, Artificial Intelligence (AI) has expanded the capabilities of MaaS, enabling more efficient data processing, predictive analytics, personalized services, and intelligent decision support. This narrative review examines the current state of research on AI-enabled MaaS from both technological and socioeconomic perspectives. The analysis covers five major research areas: data integration and interoperability, predictive systems and demand forecasting, AI-enabled decision support for policy and planning, fairness and ethical AI, and cybersecurity and privacy protection. The findings show that successful implementation depends not only on advances in AI algorithms but also on high-quality interoperable data, effective governance, regulatory support, public trust, and collaboration among stakeholders. The review concludes that the main challenges facing AI-enabled MaaS are no longer primarily technical but organizational, institutional, and social. Future research should focus on trustworthy and explainable AI, privacy-preserving learning, standardized evaluation methods, fairness-aware optimization, resilient cybersecurity, and long-term assessments of MaaS impacts on sustainable urban mobility.