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<p>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.</p>

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BikeNEAT: A Framework for Classifying Bicycle Infrastructure in OpenStreetMap

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

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  • Mirosława Łukawska,
  • Emely Richter,
  • Iwan Porojkow,
  • Stefan Huber

摘要

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.