Empirical use and Impact analysis of MaaS (EIM)

Individual private car use is inherently unsustainable when compared to shared modes. Yet, it is one of the main travel modes around the world, often due to a lack of alternatives. The introduction of novel shared travel modes and their integration with public transport (Mobility as a Service, MaaS), promises to enable seamless intermodal travel, thus facilitating a behavioral change from private car use to more sustainable, shared modes. Due to a lack of substantial behavioral data, it remains unclear whether and how MaaS actually changes travel behavior (e.g., mode choice, car ownership) or how more sustainable travel behavior can be encouraged (e.g., through optimized service bundling, real-time mobility prediction). This proposal outlines a research agenda to study these questions using the SBB City-Bundle, a new MaaS product integrating mobility services across multiple providers to be launched in Q3/2019, as a case study. We plan to develop an integrated representation of relevant mobility and context data based on person-specific graphs. This allows the application of both state-of-the-art research methods in transport behavior (hybrid choice and multivariate probit models) and geographic information science (spectral clustering, graph neural networks) on such heterogenous data. This is an unprecedented opportunity to advance our so-far limited scientific understanding of human mobility behavior under the influence of multimodal mobility packages. Results will be of value to SBB, as they offer insights into why customers value their new product and how to represent mobility and context data from a variety of sources to conduct efficient user-profiling and prediction. Last but not least, our results will inform policy-making towards more sustainable passenger transport in times where advice is urgently needed. ETH IVT and IKG combine expertise in transport behavior and geographic information science and the proposed collaboration with SBB NMD has already proven successful in the past.


Point of contact

Prof. Dr. Martin Raubal
Chair of Geoinformation Engineering

Prof. Dr. Kay Axhausen
Chair of Transport Planning and Systems


Start | duration

01.08.2019 (18 months)


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