Year: 2026
Type: article
Abstract: Reliable cycling network data is crucial for bicycle research and planning. OpenStreetMap (OSM) is a globally available and open-source network dataset, increasingly used to represent bicycle infrastructure supply, mostly as a means to achieve a further goal (e.g., modelling cycling). However, infrastructure classification varies across studies and common standards are lacking, which hampers transferability and comparison. This study introduces BikeNEAT, an easy-to-use classification tool that utilises OSM data to classify bicycle infrastructure. We analyse OSM tagging patterns in the German context and investigate how bicycle infrastructure categories can be identified from OSM tags. Our contribution presents a standalone classification framework for the German context that derives bicycle infrastructure categories from complex combinations of OSM tags. The framework explicitly addresses heterogeneous OSM tagging practices, incorporates infrastructure directionality, and allows the extracted indicators and categories to be used independently of a predefined assessment workflow. The study presents the publicly available BikeNEAT code and describes how the classification framework can be applied to any OSM dataset. Using a reference dataset from Freiburg im Breisgau for validation, we show that the method performs well, with nearly 95% of categories matching the municipal dataset used as ground truth.
Institution: Technische Universität Dresden
Language: English
Subfield: Geography, Planning and Development
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,390
Funder: Technische Universität Dresden
OpenAlex ID: W7166563660