A spatial modeling approach to estimating bike share traffic volume from GPS data

GPS-equipped bike-share fleets are a source of rich data that can be used to estimate cycling volumes to assist infrastructure investment decisions aimed at increasing ridership. Using global positioning system (GPS) trajectories collected between January 1st and December 31st, 2018 by Hamilton Bike Share (HBS), the volume of bike share trips on every traveled link in the HBS service area is modeled. A map-matching toolkit is used to generate users' routes to derive the number of observed bike share trips on every traveled link. To model annual bike share traffic volumes, several variables were created at the link level including accessibility measures, distances to important locations in the city, proximity to transportation infrastructure, and bike infrastructure. A linear regression model was estimated, incorporating eigenvector spatial filtering to remove spatial autocorrelation. The results suggest that the largest positive predictors of bike share traffic volumes in terms of cycling infrastructure are those that are physically separated from automobiles by a space or barrier. Additionally, hubtrip distance accessibility, a novel measure, was significant in the model, outperforming other accessibility metrics. A demonstration of how the model can be used for planning cycling infrastructure upgrades is presented.

Automated summary

GPS-equipped bike-share fleets provide rich data that can be used to estimate cycling volumes for infrastructure investment decisions. The study found that physically separated cycling infrastructure from automobiles and hubtrip distance accessibility were significant predictors of bike share traffic volumes. This model can be useful in planning cycling infrastructure upgrades.