The increasing use of bicycles requires an expansion of the bicycle infrastructure. This requires improved knowledge about the quality of the bicycle network. However, so far there are no evaluation criteria for cycling facilities based on robust data (e.g., lost and waiting times) and assessments by cyclists. A data-based quality assessment could support municipalities and practitioners in transport planning in prioritizing support measures in order to optimally develop cycling with regard to the goals of the NRVP 3.0.
In the INFRASense project, a data application is being developed to automatically determine the quality of municipal cycling facilities based on crowdsourced data using advanced methods of data science. The data will be exported to municipal geographic information systems (GIS) and made publicly available for discussion using a web application (Open Quality Monitor). Feedback from cyclists will be used to optimize the quality assessment in a user-friendly way.
In order to derive patterns from the data, project-internal calibration runs are carried out with a bicycle sensor system that transmits data in real time. On the basis of the generated and other data (e.g. material, type of cycle path), criteria for the quality evaluation of cycle paths (e.g. surface condition, time loss) are defined and weighted. After practical experience over approx. twelve months with active citizen participation to measure disturbances, waiting times, etc., the qualities of the cycle paths are determined automatically on the basis of the data. The results are then visualized in a web application and made available to cyclists for validation. The feedback from cyclists generated in this way will be used to optimize the algorithms for quality assessment. The results will be applied to a neighborhood and the bike path network in Oldenburg and implemented in planning.