Characteristics of cyclists using fitness tracker apps and its implications for planning of bicycle transport systems

Kwayu, Keneth Morgan; Lyimo, Sia Macmillan; Kwigizile, Valerian

doi:10.1016/j.cstp.2021.06.004

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Further complicating attempts to measure representativeness is a lack of comparison data on the composition of cyclists in a city or region. Still, research to date suggests that Strava users, the most common source of bicycle user data, are more likely than the general cyclist population to be male ( 14 , 15 ), to be aged 25 to 44 ( 14 – 16 ), to ride more frequently ( 17 ), and to log a greater proportion of recreational/leisure trips (e.g., Garber et al [ 17 ], Kwayu et al [ 18 ], Musakwa et al [ 19 ], and Watkins et al [ 20 ]). For reference, recent U.S. national estimates of adult cyclist gender split range from 51% to 60% male for all cyclists and 73% male for bicycle commuters ( 21 – 24 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Potential of Crowdsourced Data to Estimate Network-Wide Bicycle Volumes

Broach

Kothuri

Miah

et al. 2023

Transportation Research Record: Journal of the Transportation R

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This research integrated and evaluated emerging user data sources (Strava Metro, StreetLight, and hybrid docked/dockless bike share) of bicycle activity data with conventional “static” demand determinants (land use, built environment, sociodemographics) and measures (permanent and short-duration counts) to estimate annual average daily bicycle traffic (AADBT). We selected six locations (Boulder, Charlotte, Dallas, Portland, Bend, and Eugene) covering varied urban and suburban contexts and specified three sets of Poisson regression models: city-specific models, an Oregon pooled model, and all cities pooled. Static variables, Strava, and StreetLight complemented one another, with each additional data source tending to improve the model performance. Sites with lower volumes were more difficult to predict, with considerable error in even the best-performing models. City-specific models in general exhibited improved fit and prediction performance. Expected prediction error increased by a factor of about 1.4 when using Strava or StreetLight alone, but without static adjustment variables, to predict AADBT. Combining Strava plus StreetLight, but without static variables, increased error slightly less; by 1.3 times. We also found that transferring the model specifications from one year to the next without re-estimating the model parameters resulted in a 10% to 50% increase in error rates across models, so such transfer is not recommended. The findings from this study indicate that rather than replacing conventional bike data sources and count programs, old “small” data sources will likely be very important for big data sources like Strava and StreetLight to achieve their potential for predicting AADBT.

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