AI-MAAS: Predictive Mobility-as-a-Service Planning

Use Case:

Development of a predictive module to anticipate user travel behavior enhance the planning of public transportation services and optimize user conversion from private to public mobility through AI.

Outcome:

AI solution positioned at TRL5, integrated into Kobla’s TRL9 multimodal platform; Clear strategy for MVP co-development with public transport operators; Focused go-to-market approach targeting user onboarding and conversion KPIs.

Ecosystem Support:

StairwAI technical and business mentoring supported the scoping of AI capabilities and the creation of an actionable business model for AI integration.

AI Relevance:

The project demonstrates how SMEs can harness AI for behavioral prediction in mobility via: I) data-driven co-design with domain operators; II) early-stage technical feasibility and MVP testing; III) integration of AI within mature TRL products; IV) customer-oriented validation loops with public and private transport actors.

Summary:

Kobla AS, a Norwegian SME specialized in sustainable urban mobility, developed the AI-MAAS module to enhance travel prediction in public transport systems. The envisioned AI component aims to forecast individual users’ next journeys, enabling mobility operators to fine-tune services and increase adoption. Supported by the StairwAI program, Kobla structured a phased strategy: starting with interviews with Public Transport Operators (PTOs), they plan to co-create a minimum viable product (MVP) and run pilot deployments with real users. The accompanying Business Model for AI Adoption focused on identifying the most critical assumptions, defining customer promises, and evaluating AI’s unique value for end-users. The technical feasibility—reached at TRL5—will complement Kobla’s existing TRL9 infrastructure, providing a mature platform for rapid scaling. The next development phase will assess how predictive analytics can improve customer conversion, particularly among current non-users of public transport. If performance meets agreed KPIs, this will position AI-MAAS for commercial deployment with a strong first customer base.