Abstract
This study examines determinants of bicycle volume in the built environment with a five-year bicycle count dataset from Seattle, Washington. A generalized linear mixed model (GLMM) is used to capture the bicycle volume change over time while controlling for temporal autocorrelations. The GLMM assumes that bicycle count follows a Poisson distribution. The model results show that (1) the variables of non-winter seasons, peak hours, and weekends are positively associated with the increase of bicycle counts over time; (2) bicycle counts are fewer in steep areas; (3) bicycle counts are greater in zones with more mixed land use, a higher percentage of water bodies, or a greater percentage of workplaces; (4) the increment of bicycle infrastructure is positively associated with the increase of bicycle volume; and (5) bicycling is more popular in neighborhoods with a greater percentage of whites and younger adults. It concludes that areas with a smaller slope variation, a higher employment density, and a shorter distance to water bodies encourage bicycling. This conclusion suggests that to best boost bicycling, decision-makers should consider building more bicycle facilities in flat areas and integrating the facilities with employment densification and open-space creation and planning.
Highlights
Bicycling is a sustainable alternative to driving for short-distance trips because of its economical, healthy, and environmentally-friendly attributes
The results show that time-varying variables—non-winter seasons, peak hours, and weekends—are essential determinants of bicycle traffic
This study identifies and addresses significant temporal autocorrelations of bicycle counts within sites over five years, which indicates that the longitudinal correlations must be adjusted to avoid possible biased estimations when modeling the relationship between bicycle counts and the other fixed covariates
Summary
The Journal of Transport and Land Use is the official journal of the World Society for Transport and Land Use (WSTLUR) and is published and sponsored by the University of Minnesota Center for Transportation Studies. The bicycle volume data play an important role in policy making, bicycle route planning, road safety improvement, municipal investment, and maintenance priorities (Hankey et al, 2014). Bicycle crash risk is often calculated by the number of bicycle collisions divided by a denominator indicating bike volume such as the number of bicycle trips, the bicycle miles traveled, or the number of bicyclists (Vanparijs, Panis, Meeusen, & de Geus, 2015) Generating these preferred denominators, mostly unavailable will help researchers accurately measure the bicycle crash risk. Realizing the importance of measuring bicycle volume, several US cities, such as Portland, Minneapolis, and Seattle, have started manually and automated counting bicycles These new datasets provide researchers with opportunities to identify explanatory factors, especially time-varying factors, of bicycle volume to propose more pertinent policies to promote bicycling. This study is organized as follows: section two provides a review of the existing studies on bicycle volume and bicycle use; section three presents the research design including data description, statistical concerns and model specification, and variable selection; section four discusses the results of descriptive and inferential analyses; section five synthesizes findings, discusses their policy implications, and directs future research
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