Sensor Integration for Urban Risk Prediction

  • Project Duration: 11.2019 – 04.2022

  • Project Lead: Martin Loidl (Z_GIS)

  • Role Z_GIS: Project Partner

  • Staff: Martin Loidl, Bernhard Zagel, Petra Stutz, Robin Wendel, Christian Werner

Initial Situation

Vulnerable road users (VRUs) are threatened by a proportionally higher safety risk. In order to protect VRUs more efficiently, risk models at a high spatial and temporal resolution are required. So far, potentials and limitations of the combined use of wearable sensors and urban data ecosystems for training a predictive machine-learning model for safety risks in urban environments have not been sufficiently investigated. Insights in this context could be used in a wide variety of ICT-supported application scenarios, such as transportation management, public safety planning, crowd management, health monitoring, digital mobility services or planning.

Project Goals

  • Integration of data from heterogeneous sources.

  • Risk modelling based on machine learning algorithms.

  • Increased safety of vulnerable road users.

Expected Results

  • Concept for creating semantic interoperability of physiological, mobile sensor data with heterogeneous data sources of urban data ecosystems.

  • Transferable framework for setting up sensor networks for the integration in urban data ecosystems.

  • Predictive model of spatio-temporal occurrences and distributions of safety risks for vulnerable road users in urban road networks.

Contribution Z_GIS

  • Data acquisition, modelling and analyses.

  • Modelling traffic flows of pedestrians and cyclists.