Smart Mobility Optimization Using Explainable Anonymization and an Incentive System for Commuting Data Donation

Abstract Motorized private transport including e‑bikes and e‑scooters in mid-sized German cities, which are Osnabrück and Münster, serving as a case study, has steadily increased. The BMFTR funded IIP project carried out a Germany-wide survey to determine the personal and common good factors of a smart mobility. The results were used to improve the congestion situation by acquiring mobility data from several sensors, processing it in the IIP system and making findings available for individual commuters and municipal traffic planers. Data sources are sensors existing in the road infrastructure, innovative traffic counting cameras and Crowd sensing of anonymized trajectories of commuters. Privacy issues are avoided by applying anonymization complying to existing data protection legislation. The city directly uses the data to adapt traffic light phases. The data is used for a digital urban twin, which can predict traffic jams up to three days in advance. The IIP commuting app notifies in advance of upcoming traffic problems. The system has been running since September 2025 and first experimental results are being presented.