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This study proposes a method that can help in identifying potential locations for improvements of cycling infrastructures. It addresses the need for simple and effective methods to support decision-making in bicycle planning. The city of Paris is used as a case study area because it has made considerable efforts to improve cycling infrastructures and to become more bicycle-friendly in recent years. The method (1) identifies potential locations for improvements of bicycle infrastructures on a street level and (2) on a city level considering accessibility to important destinations. The main data used in this project is street data from OpenStreetMap (OSM) and cycling infrastructure data from the Atelier parisien d’urbanisme (Apur). The proposed method can be applied with commonly available data, has clear outcomes, is reproducible, and can be applied to different case study areas. We produced a map of bicycle suitability across all of Paris, and validated it for the 30 longest segments in the city with lower bike suitability. Our validation showed that combining OSM and Apur data led to a reliable dataset, with which we modelled bikeability using the underlying network overlain on a 250 m resolution grid and destinations representing leisure activities, education, shopping, city functions and public transport. The resulting map identifies regions of the city with poor bikeability, where improvements to cycling infrastructure should be investigated.
Wysling et al. (Tue,) studied this question.