Bicycle Ridership Using Crowdsourced Data: Ordered Probit Model Approach

Abstract

Cycling is a healthier and greener travel mode that city planners and policymakers have encouraged for short-distance trips. Because cycling provides an efficient way to improve public health and reduce energy consumption, analyzing the contributing factors to bicycle usage on roadway segments is essential to quantify the impact of certain attributes on bicycle volume and to further provide a better cycling environment for cyclists to encourage nonmotorized travel. To gain a better understanding of the attributes that have a significant impact on cycling, this study collects crowdsourced bicycle data from Strava and combines the data with other supporting data, such as road characteristics, demographic information, temporal factors, geometry features, and bike facilities. An ordered probit model is then developed to analyze Strava users’ bicycle usage on each road segment in the city of Charlotte, North Carolina. The results reveal that road segment length, number of through lanes, median household income, total households in a census block, cycling on a suggested bike route, greenway, US route, and one-way road all have a positive impact on Strava user counts on a road segment from 6 a.m. to 6 p.m. Conversely, the variables for cycling on weekdays, total families in a census block, slope, signed bike routes, and suggested bike routes with low comfort have a negative impact on the Strava user counts on a road segment. Based on the modeling results, recommendations are also made to assist in improving the cycling environment and increasing future bicycle volume.

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