Hi,
Thanks to those who gave me feedback, it was useful. In the meantime, I was
able to find the following references that address this issue in case
anyone has the same question in future:
Broach, J., Gliebe, J., & Dill, J. (2011). Bicycle route choice model
developed using revealed preference GPS data. *TRB 2011 Annual Meeting*,
*5464*. Retrieved from
ftp://ftp.hsrc.unc.edu/pub/TRB2011/data/papers/11-3901.pdf
Asakura, Y., & Iryo, T. (2007). Analysis of tourist behaviour based on the
tracking data collected using a mobile communication instrument.
*Transportation
Research Part A: Policy and Practice*, *41*(7), 684–690.
http://doi.org/10.1016/j.tra.2006.07.003
Hood, J., Sall, E., & Charlton, B. (2011). A GPS-based bicycle route choice
model for San Francisco, California. *Transportation Letters: The
International Journal of Transportation Research*, *3*(1), 63–75.
http://doi.org/10.3328/TL.2011.03.01.63-75
Strauss, J., Miranda-Moreno, L. F., & Morency, P. (2015). Mapping cyclist
activity and injury risk in a network combining smartphone GPS data and
bicycle counts. *Accident Analysis and Prevention*, *83*, 132–142.
http://doi.org/10.1016/j.aap.2015.07.014
Vij, A., & Shankari, K. (2015). When is big data big enough? Implications
of using GPS-based surveys for travel demand analysis. *Transportation
Research Part C: Emerging Technologies*, *56*, 446–462.
http://doi.org/10.1016/j.trc.2015.04.025
Bohte, W., & Maat, K. (2009). Deriving and validating trip purposes and
travel modes for multi-day GPS-based travel surveys: A large-scale
application in the Netherlands. *Transportation Research Part C: Emerging
Technologies*, *17*(3), 285–297. http://doi.org/10.1016/j.trc.2008.11.004
Asakura, Y., & Hato, E. (2004). Tracking survey for individual travel
behaviour using mobile communication instruments. *Transportation Research
Part C: Emerging Technologies*, *12*(3–4 SPEC.ISS.), 273–291.
http://doi.org/10.1016/j.trc.2004.07.010
On 24 April 2017 at 18:20, Carlosfelipe Pardo <[log in to unmask]>
wrote:
> Hi everyone,
>
> I would be most thankful if anyone can point me to any references (or
> insights) related to use of big data tools (specifically, using an app to
> measure bicycle use) and methods to arrive at representative sampling (or
> any discussion related to this in general. The context: We are producing
> some analysis of trips using a cycling app (www.bikoapp.com) where we
> have people earn a point for every km ridden, and at the end of one month
> we collect all data and analyze it - the app doesn't need to have its
> cellular data turned on during the ride, you just need to record your trip
> and upload it whenever you get wifi. We estimated that we'd be ok if 400
> people would use the app (population 1 million). However, some people have
> told me we shouldn't think of this as an accurate sampling because we are
> only using people with cellphones (Colombian semi-smart cellphone
> penetration is 60%). We haven't been able to find any research related to
> this specific issue, which is why I am writing.
>
> Insights are most useful on this. The entire project is described in the
> link below (webinar also) though the whole thing in Spanish (English to
> come soon, I'll also present some of this in VeloCity in case anyone would
> like to join):
>
> http://despacio.org/2017/04/07/cartagena-pedalea/
>
> Best reards,
>
> Carlos
>
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