A Perspective in Cycling Big Data for Smart Cities
Brian Riordan
Customer Success Lead
Strava Metro
Strava
The social network for cyclists and runners
Community Generated Data Monitoring & Activation
What is Strava Metro?
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Big Data
Big Data in Respect to Strava
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The Challenges
Big Data & Government Challenges
Budgets in constant flux
Culture crushes the ability to take risk
Minimal or no dedicated
technical talent
Red tape and process creates gaps
between industry and government
The Cycling Data Culture
Understanding Local Culture & Needs
Big Data Solutions and Examples
Strava Metro Bringing Data Layers Together
Data designed to be merged with local datasets to create deeper insights:
traffic,crashes,proposed bike paths, etc.
Detecting Behaviour Route Choice
Metro provides key insight into how the cycling population is adapting to new cycleways,
protected lanes and surging car populations. The left image shows the GPS points pre (pink),
post (blue) after a new cycleway was opened. The Metro data on the right shows the actual
change in percent with blue losing trips and red gaining trips.
Blending Data: Strava Metro and Counters
Using counting programs with the Metro data allows the data to become
even more useful. Strava correlation with counting programs is statistically
amazing, with r-squared values typically around 0.8.
Blending Data: Strava Metro and Counters Cont’
16,297 Strava Bike Trips
X 27 Multiplier
= 440,019 year bike trips
(199,476 6- 9am)
How far can we push this? ---> Total Miles Traveled in SDOT by Bike in 2014:
63,253,198
Identifying Core Route Choice
Weekday
Weekend
Strava Metro: Data View
Big data <> Easy to Use:
Therefore we need to create tools and views into the data that allow for
quick interpretation. Deep analysis can always come after.
AMAL Region
Thank you