Using crowd source data in bicycle route choice modeling

Abstract

We present a bicycle route choice model modelled in Value of Distance space based on revealed GPS data and an improved network with a very detailed representation of the bicycle infrastructure and detailed calculations of the related attributes. Beside common attributes, such as trip-length, bicycle infrastructure, elevation gain and land-use, we utilize crowd sourced data to analyse the influence on route choice of intersections where a large number of individuals have indicated large congestion. The results show that the individual’s perception of high congestion in an intersection has an effect on the route choice behaviour, and that passing such intersections perceived as congested is connected to a lower utility for a given route.