Citation

Hochmair HH, Bardin E, Ahmouda A. J. Transp. Geogr. 2019; 75: 58-69.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.jtrangeo.2019.01.013

PMID

unavailable

Abstract

Sports and fitness apps on GPS enabled cell phones and smart watches have become a rich source of GPS tracking data for nonmotorized traffic, including walking, running, and cycling. These crowd-sourced data can be analyzed to better understand the cycling behavior of a large user community. Using Strava tracking data from the Miami-Dade County area, this study identifies which transport network measures, characteristics of the built environment, and sociodemographic factors are associated with increased or decreased bicycle ridership in census block groups. For this purpose, a set of linear regression models are estimated to predict non-commute and commute bicycle kilometers travelled per block group, as well as bicycle kilometers travelled on weekends and weekdays. Eigenvector spatial filtering is applied to explicitly model spatial autocorrelation and to avoid parameter estimation bias.

RESULTS suggest that Strava data, due to its high spatial resolution and coverage, can identify in detail how the influence of explanatory variables on estimated bicycle trip volume varies between different trip purposes and days of the week. Based on the regression results, the paper presents a set of guidelines for practical design detailing which groups of cyclists would benefit most from specific bicycle infrastructure improvements.

Language: en

Keywords

Bicycle volume; Eigenvector spatial filtering; Network analysis; Strava; Tracking data