Strava, the popular GPS-based activity tracker for athletes, has developed a strong following among cyclists and runners worldwide. The service offers competitive aspects on “segments” of road or trail, tracking best times or performance to compare against oneself or others.

Even though Strava’s team designs with athletes in mind, it’s shown a potential to serve a larger and more general audience with the global heatmap and activity playback using data aggregation in urban areas.

 

Why?

Why might Strava invest resources into such features? By embracing bike commuters and e-bikes, both the company and end-users would benefit by:

1. Growing a huge untapped market of new users

Bike commuters are the fastest growing group of cyclists in the US and a substantial percentage of bike riders around the world. E-bike users are a small but growing group of riders that will likely increase as tech advances improve price and availability of these bikes. 

2. Increasing the number of paying users

While commuters and more e-bike riders may want to start riding with Strava, the service doesn’t tailor to non-athletic uses. Providing these users a similar value proposition as for athletes could lead to higher use and willingness to pay for a membership. Many of Strava’s users commute to full time jobs and would benefit from improved ways to track active transportation.

3. Leveraging excellence from athletics into e-bikes

While electric bikes do provide motorized assist, they also provide an opportunity to analyze fitness. E-bikes measure power output contributed by the rider, a feature that a vast majority of ordinary bikes do not provide. By highlighting the fitness elements of e-bikes while downplaying the speed/time elements, Strava could also give a great user experience for e-bike riders that fits in the context of health and athletic performance.

4. Getting better data

Because Strava is athlete-focused, heatmaps and routes have a likely bias toward younger, fitter, often-male cyclists. By also catering to commuters and more casual riders, Strava could better balance its data set and provide more value to cities that may be interested in using the data for planning purposes.

 

So, how?

Without prying into Strava’s business model, it’s hard to make a case that the company should pursue these features. I’m going to focus on the ‘how’ – if their priorities aligned, what are a few cool things their team could do to meet these ends?

1. Interface enhancements

One simple change would be to add an option in the post-ride screen to mark a ride as a commute. In the right picture, you see the Desktop interface which allows a user to mark a ride as a commute (so Strava already supports the feature on the back-end), but unfortunately there’s no button in the mobile app (left) mark commutes (I’ve added one).

 

 

 

 

 

 

 

 

 

 

Other enhancements might include:

• Optimizing the mobile app startup process to allow quicker ride starts. For long recreational rides, startup time matters less since it’s a low proportion of total ride time, but matters more when a total commute ride may be 5-20 minutes.

*** Update 1/11 – Matt Laroche informed me there’s now a quick start option from mobile home screens:

• Use previously recorded user patterns (or any rides under a certain distance) to auto-populate the commute checkbox post-ride. For instance, if I ride point-to-point and previously select a route as a commute, Strava could check the starting/ending locations compared to previous commutes and pre-mark the commute check-box.

2. Highlight data that commuters care about

Once collecting reliability commute data, Strava could provide metrics more interesting to people who bike commute, such as:

1. Use average end-to-end time instead of just riding time
2. Summarize information about commutes (number, time, total distance, average distance) per week, month, year
3. Gamify The Commute – make it social, and connect people who live nearby or ride the same routes
4. Provide a “City View” option to enhance the Activity Feed: instead of a chronological feed of mostly text, display the day’s rides as a map where routes of people you follow overlay onto your city. In fact, some of this tech could be adapted from the “activity playback” feature, with a mock-up below:

As a commuter in your city, you may develop connections with friends who also use Strava to track their biking or running trips around town. A view of the city will be much more interesting for that type of user than a series of short, disjointed rides.

3. Make electric-assist bikes a feature like power meters

Here’s a screenshot from data captured at a recent test event for the Copenhagen Wheel, an add-on for regular bikes that converts them into electric-assist bikes. Newer e-bikes have sophisticated power measurement techniques that can isolate the human-powered component, which Strava could use just as if the rider had a built-in power meter.

The fundamentals of athletic training carry over to e-bikes, and could become more prevalent among bike riders than power meters (the latter being a $600+ investment). This is a basis for some of James Peterman’s work at CU-Boulder who’s analyzing the fitness benefits of sedentary individuals who begin using e-bikes. (Contact Jim if you’re interested in this study)

The same concepts of power-based training still apply: duration (time), functional threshold (sustainable power),  intensity factor (power output relative to sustainable power). All can be used to improve athletic performance for e-bike riders in the same manner used for elite athletes.

 

Wrapping up

To finish where we started: Strava aims to be the best GPS-based tracking software in athletics—but I hope to have shown some compelling reasons to see the service more deeply. Strava could become a nexus for all active transportation, not only the athletically-based kind. Athletes often commute, and people who start to use the service as commuters may transition into using it as athletes.

I put together an analysis of the 2013 Boulder B-cycle data and wanted to share a few interesting tidbits! Here’s a link to a PDF of the full presentation for anyone who wants dive right in: Boulder B-cycle 2013 Analysis

Objective

as a person in a city

I want to understand the layout/flows of bikeshare systems

so I can move around efficiently

System Layout

In 2013, the Boulder B-cycle system had:

• 22 Stations
• 276 Docks
• 138 Bikes

The largest station had seventeen docks while the smallest had nine. Note the large grey dot which marks the geographic center of the system, which did not have a station in 2013 (but does now!)

Elevation and distance from the center:

System Usage

The basic 2013 Boulder B-cycle usage statistics:

• Total 2013 rides: 28,256
• Median duration: 14 min
• Median distance*: .77 mi

Note that distance is calculated “as the crow flies” and understates actual distance traveled, but can be useful as a proxy for distance.

Here’s a map showing each station, with the largest bubbles having the most in/out trips while the smallest have the least:

 

Right away, we notice that the two most likely indicators of trip count are both proximity to downtown Boulder and proximity to the geographic center of the bike-sharing system.

The majority of trips are under 60 minutes and travel less than 1.5 miles.

 

On an hourly basis, total bicycles checked out peaks at 11AM and has a second, lesser peak at 5PM. Casual users peak gradually in the mid-afternoon while annual users patterns are less uniform. Also note that Boulder B-cycle was only available between 5AM and midnight in 2013.

 

When accounting for day of week, we see more and later-in-the-day usage toward the end of the workweek. And total usage on weekends for all users more closely mimics that of casual users in general.

But another interesting question – how does this usage change in different seasons, with sunlight and temperature likely being big factors?

In the colder/darker months, September through April, we see a much sharper usage pattern that focuses on warm daylight hours:

In the summer, we see a much wider usage pattern, extending more heavily into the evening, with many more total riders:

We see total ridership reflected in the next graph, with significant increases in the summer months (much of the increase reflecting many tourists in Boulder):

 

Station Usage Factors

Since we’ve looked at total rides, now let’s look at where people ride to/from the most. Bike-sharing is point-to-point instead of out-and-back, the system allows for net increases or decreases in the number of bikes available at any given station (which requires system rebalancing).

Stations with large bubbles marked in green are net positive in bikes, and small bubbles in yellow/orange are net negative in bikes:

 

Let’s look at some of the factors that may be in play to explain why stations are net positive or net negative.

We see there’s a correlation between distance from center and a net decrease in bikes at a station.

 

Another attribute we’d like to test is station elevation. This chart shows an inverse correlation between station elevation and net change in bikes. There may be several factors at play, but generally people have an easier time riding downhill than up.

Other Usage Factors

How sunlight trends with ridership.

How high temperature affects ridership.

How precipitation affects ridership. Yes, Boulder did get 9 inches of rain on Thursday, September 12th, 2013, during the history flooding event.

A closer look at ridership during the 2013 flooding:

Hopefully this helps understand a bit more about the bikesharing patterns in Boulder. The system has since added many more stations so I’ll hope to update some of the graphs in the future to see how things change. Please send me a comment or note if you have any questions!

@ericmbudd on Twitter.