Extracting spatial patterns in bicycle routes from crowdsourced data

Abstract

Much is done nowadays to provide cyclists with safe and sustainable road infrastructure. Its development requires the investigation of road usage and interactions between traffic commuters. This article is focused on exploiting crowdsourced user-generated data, namely GPS trajectories collected by cyclists and road network infrastructure generated by citizens, to extract and analyze spatial patterns and road-type use of cyclists in urban environments. Since user-generated data shows data-deficiencies, we introduce tailored spatial data-handling processes for which several algorithms are developed and implemented. These include data filtering and segmentation, map-matching and spatial arrangement of GPS trajectories with the road network. A spatial analysis and a characterization of road-type use are then carried out to investigate and identify specific spatial patterns of cycle routes. The proposed analysis was applied to the cities of Amsterdam (The Netherlands) and Osnabruck (Germany), proving its feasibility and reliability in mining road-type use and extracting pattern information and preferences. This information can help users who wish to explore friendlier and more interesting cycle patterns, based on collective usage, as well as city planners and transportation experts wishing to pinpoint areas most in need of further development and planning.