Sensor Integration for Urban Risk Prediction

The project ‘SINUS’ (Sensor Integration for Urban Risk Prediction) explores the feasibility of matching wearable physiological sensors with data interfaces of urban data ecosystems for improving the current state of the art of predicting risk patterns for vulnerable road users in urban road networks. Different sensor equipment and setups are used and evaluated in a test field in the city of Salzburg. Between the recorded sensor data and other, formerly isolated data sources, semantic interoperability is established. Based on the resulting data set, machine learning algorithms are applied and refined for a prediction model. To assess effects, viability and transferability of the developed approach, different ICT-supported information applications are developed and tested in a field study.