Murmuras develops AI-supported interventions for sustainable mobility behaviour with academic partners (SMobI)

SMobI is an innovative app that uses AI to analyse mobility routines and support users in switching to sustainable modes of transport. Real-time intervention techniques help to break habits, while transport associations and local authorities receive new impetus for climate-friendly mobility strategies.

Habits as challenges for behavioural change#

Despite attractive, sustainable and often healthier alternatives such as buses, trains or bicycles, many people still use their cars out of habit. Information about environmentally friendly mobility options often reaches them too late or in inappropriate situations.

The idea: Changing AI-supported recognition and routines in mobility behaviour#

In the project "SMobI - AI-supported interventions for sustainable mobility behaviour", Murmuras, the University of Siegen and the IZT are developing an innovative app within three years that not only recognises when a mobility decision is pending, but also supports users with suitable intervention techniques to change their mobility behaviour sustainably.

Data preparation for AI-supported mobility interventions#

Murmuras GmbH analyses the existing database on smartphone and GPS usage behaviour from previous and ongoing projects. The data is checked, processed and linked with dynamic and static mobility data.

Intervention techniques and interaction paradigms for behavioural change#

Mobility decisions are influenced by environmental factors as well as individual and psychological characteristics. In order to sustainably promote mobility behaviour, the IZT, in cooperation with the University of Siegen, identifies suitable target group-specific, person-focused motivational and action-supporting intervention techniques. On this basis, the guidelines for the design of interaction concepts will be developed jointly. Together with the IZT, the University of Siegen is taking the lead in exploring the design space for suitable interaction concepts using a participatory design approach.

Linking AI engine and mobility metrics#

Building on this, methods and machine learning algorithms are integrated into a model based on historical data. The intervention techniques already identified are used as parameters for training the AI algorithms. The explored mobility metrics, i.e. measurable variables that quantify the behaviour of users, are linked to incentive systems and their effectiveness in promoting sustainable mobility decisions is analysed.

Validation and testing of the overall model#

The models are evaluated on the basis of historical behavioural data. In addition, up to 30 participants will use the research app, which maps various intervention paradigms in order to evaluate decision-making processes in real time. In addition, adaptive decision support will be tested using an active learning approach. In a field study with 100 representatively selected participants from Cologne/Bonn, the interventions will be implemented in the research app, user behaviour will be recorded and analysed, while the University of Siegen will continuously evaluate feedback and suggest optimisations.

Added value for users and mobility providers#

SMobI contributes to climate protection by helping users to consciously opt for environmentally friendly forms of mobility in their everyday lives. Transport associations, cities and mobility providers in NRW gain valuable insights into decision-making behaviour and new starting points for targeted services.

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Project Information#

Project Management
Ionut Andone & Qais Kasem

Research Field
Mobility and urbanity

Title
SMobI - AI-supported interventions for sustainable mobility behaviour

Duration
04.2025 to 03.2028

Funding Authority/Client

European Union

Ministry of Economic Affairs, Industry, Climate Protection and Energy of the State of North Rhine-Westphalia

Funding code
EFRE-20801069

Project Management Agency
NRW Innovation Promotion Agency

Project Partners
Murmuras GmbH
University of Siegen