Citation

Prabhakar R, Rixey RA. Transp. Res. Rec. 2017; 2662: 168-178.

Copyright

(Copyright © 2017, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.3141/2662-19

PMID

unavailable

Abstract

This research explored the relationship between level of traffic stress (LTS) and bikeshare ridership within the Capital Bikeshare (CB) network in Montgomery County, Maryland. Linear regression was used to build a model that could predict the relationship between bikeshare ridership and low-stress bicycle connections between stations, enhanced by various demographic and built environment variables that helped to define the regional context. Linear regression models were used to estimate total trips per year and to inform further studies that sought to increase ridership through improved bicycle facilities and environments. On the basis of the varying land uses within Montgomery County and the geography of CB stations in the county, two separate data sets were created to measure the ridership propensity within the northern part of the county around Rockville and Shady Grove and the southern part of the county around Bethesda and Silver Spring. The models illustrated the utility of LTS measures in identifying the effect of low-stress bicycle connections on bikeshare ridership. The models indicated that bikeshare station pairs that were connected by links with a higher percentage of low-stress facilities were correlated with higher bikeshare ridership. Station pairs that required a longer detour to achieve a low-stress route relative to a shortest-path route were correlated with lower bikeshare ridership. Application of the linear regression tool to estimate ridership on the basis of LTS and other context variables is transferable to other similar networks to draw conclusions about the relationship between quality bicycle connections and the level of bikeshare ridership.

Language: en