Quantifying cycling traffic fluency based on big mobile tracking data

Abstract

Activity tracking data collected by mobile applications opens up a new, data-driven perspective on monitoring cycling in the city. In this work, we demonstrate how a large set of trajectories can be used to measure the cyclability of an urban infrastructure. We achieve this by defining the cycling traffic fuency index that describes the smoothness of cycling traffic on segments of a street network. Bias, uncertainty, and the divergence of infrastructure popularity presents challenges to the method, but within these limits, the index could be applied in city planning or as a routing criterion.