Challenges and Opportunities of Emerging Data Sources to Estimate Network-Wide Bike Counts
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 3
Abstract
Emerging sources of mobile location data such as Strava and other phone-based apps may provide useful information for assessing bicycle activity on each link of a network. Despite their potential to complement traditional bike count programs, the representativeness and suitability of these emerging sources for producing bicycle volume estimates remain unclear. This study investigates the challenges and opportunities by fusing Strava data with short-term and permanent conventional count program data to produce bicycle volume estimations using clustering and nonparametric modeling. Analysis indicates that the concentration of permanent counters at high bicycle volume locations presents a significant challenge to produce network-wide daily volume estimations even though Strava data demonstrate potential in mitigating the estimation bias at lower-volume sites. Despite the contribution of Strava to develop reliable and spatially and temporally transferable bicycle volume estimations, significant challenges remain to rely on Strava counts alone to characterize network-level activities due to sampling bias and spatial representations. This study will help planners discern and assess the challenges and opportunities of using emerging data in bicycle planning.
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Data Availability Statement
The programming code generated or used during the study are proprietary or confidential in nature and may be provided only with restrictions, including Strava data used in the modeling. Other data generated by the authors or analyzed during the study are available as follows. Weather data was derived from Weather Underground, available at https://www.wunderground.com/history (Weather History & Data Archive 2021). Sociodemographic data is available at National Historical Geographic Information System (NHGIS), Steven et al. (2020) https://www.nhgis.org/. Major generator, network, bicycle facilities, and road functional class data sets utilized to support the findings of this paper are derived from PortlandMaps Open Data, City of Portland, Oregon (2021) (https://gis-pdx.opendata.arcgis.com/) and CivicApps for greater Portland (CivicApps.org 2020).
Acknowledgments
This project was funded by the National Institute for Transportation and Communities (NITC-1269), a US DOT University Transportation Center, though a Pooled-Fund in partnership with the following contributors: Oregon DOT, Virginia DOT, Colorado DOT, Central Lane MPO, Portland Bureau of Transportation, District DOT, and Utah DOT. The authors confirm contribution to the paper as follows: study conception and design: Miah, Hyun, Mattingly; data collection: Kothuri, Broach, McNeil, Miah; analysis and interpretation of results: Miah, Hyun, Mattingly; draft manuscript preparation: all authors. All authors reviewed the results and approved the final version of the manuscript. The authors confirm sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 3 • March 2022
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© 2021 American Society of Civil Engineers.
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Received: Apr 7, 2021
Accepted: Oct 8, 2021
Published online: Dec 28, 2021
Published in print: Mar 1, 2022
Discussion open until: May 28, 2022
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