Estimating bicycle trip volume for Miami-Dade County from Strava tracking data

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.

• Record URL:
  • https://doi.org/10.1016/j.jtrangeo.2019.01.013
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0966692318308639
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09666923
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Hochmair, Hartwig H
  • Bardin, Eric
  • Ahmouda, Ahmed
  • 0000-0002-7897-0192
• Publication Date: 2019-2

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 58-69
• Serial:
  • Journal of Transport Geography
  • Volume: 75
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0966-6923
  • Serial URL: http://www.elsevier.com/locate/jtrangeo

Subject/Index Terms

• TRT Terms: Bicycling; Data analysis; Estimating; Global Positioning System; Mobile applications; Network analysis (Planning); Traffic volume
• Identifier Terms: Strava (Computer software)
• Uncontrolled Terms: Mobile device tracking data
• Geographic Terms: Miami-Dade County (Florida)
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting;

Filing Info

• Accession Number: 01696673
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 28 2019 9:40AM