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More great maps and graphs emerge from CaBi data

It's been just over a week since Capital Bikeshare posted data files anonymously listing trip times and locations, and numerous people from DC to the UK have tried their hand at creating visualizations, analyses and interactive tools.

Photo by DDOTDC on Flickr.

Corey H posted a number of conclusions and then continued the discussion in the comments, giving readers lists of the most common trips (Eastern Market to Lincoln Park is tops), and much more.

Some commenters asked how many CaBi trips use bike lanes. There's no way to know precisely, but we can guess, and Ollie O'Brien did just that. He melded the data with a bicycle trip planning algorithm to guess what route people might have taken and plot a map:

Image by Ollie O'Brien.

O'Brien notes that this is just an estimate. For example, the model assumes that people are very likely to take a cycle track if there is one, which pushes a lot of bike traffic onto the 15th Street lane. He doesn't have data on how many of the rides actually do use 15th versus 14th or another road.

It also doesn't have the Memorial and 14th Street bridges, because the bike paths are discontinuous, so the algorithm assumes everyone going between DC and Crystal City takes the Mt. Vernon Trail and Key Bridge.

Nevertheless, this is an amazing and potentially very useful tool. For example, there are a lot of trips east-west, which the algorithm mainly guesses to take N Street since M Street is such an unfriendly road for cyclists. When DDOT builds a cycle track there, we know there will be a lot of demand and likely very significant usage.

JDLand's Jacqueline Dupree mapped the data showing rides to and from the stations in Near Southeast.

Vizualization tool for Capital Bikeshare data by Jacqueline Dupree.

I don't know how much manual work was required to prepare data for her system, but it would be great if the site could let a user view trips for any of the stations in the system in the future.

A different JD, Justin "JDAntos" Antos, dug into the data and devised some very informative graphs.

Lydia DePillis recently noticed that casual members are much more seasonal than longer-term members, leading to big spikes in ridership during the warmer and more tourist-heavy months.

Antos discovered that the percentage of trips by casual users are also far higher on weekends, but the difference is much more pronounced in spring, summer, and fall than in winter:

Percentage of Capital Bikeshare trips from casual users, by day of week and season.
Graph by JDAntos.

Confirming what Corey H found, longer-term members are much more likely to return bikes under the 30-minute no-charge threshold than casual members.

Top: Capital Bikeshare trips by duration for both user types. Bottom: casual users only.
Graphs by JDAntos.

Perhaps not surprisingly, Antos also found that trips on weekdays occur much more in rush hours while weekend trips are spread in a bell curve throughout the day.

Read his and the other posts for more great nuggets. What else would you like to know? Have you created any graphs or maps to share?

David Alpert is the founder of Greater Greater Washington and its board president. He worked as a Product Manager for Google for six years and has lived in the Boston, San Francisco, and New York metro areas in addition to Washington, DC. He now lives with his wife and two children in Dupont Circle. 


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Thanks for the link--it's not so much an issue of manual labor to expand the map to cover all docks, since it would be easier actually to not have to cull out the records for just two stations. I'm just more concerned about server load and capacity. Not ruling it out, but don't want to leap in right away.

by JD on Jan 19, 2012 3:22 pm • linkreport

Very neat stuff. The first thing that jumped out at me, though, was that there's no usage at all on the Pennsylvania Ave cycletracks in the O'Brien visualization. Which sounds like a Robert Ludlum novel, btw ("The O'Brien Visualization" staring Matt Damon).

by oboe on Jan 19, 2012 4:19 pm • linkreport

...discovered that casual members are much more seasonal than longer-term members, leading to big spikes in ridership during the warmer and more tourist-heavy months.

I don't think this data dump was needed to "discover" this. Common sense would have led one to deduce the same.

Nonetheless, this stuff is fun.

by Vicente Fox on Jan 19, 2012 4:31 pm • linkreport

This dataset would have a lot more potential if you could merge data on station full/empty status by time. I imagine with all the time stamps (say, rounding to some increment) this could be done. That would give you a handle on how many of these trips are censored observations (meaning you would have seen more if there had been more capacity) or diverted trips necessitated by dockblocks.

by Ward 1 Guy on Jan 19, 2012 4:39 pm • linkreport

I heard the bikes are equip with GPS sensors. Are these constantly collecting riding locations? Could Capital Bikeshare coordinate with DDOT to get the real statistics on bike lanes usage?

by Francis on Jan 19, 2012 4:43 pm • linkreport

IIRC, Capital Bikeshare bikes are not GPS-equipped, though some other bikeshare systems use GPS. CaBi has not said whether they plan to add GPS capability at some point in the future.

by Jacques on Jan 19, 2012 5:21 pm • linkreport

I've imported and normalized all of the data into a MySQL database. I've also brought in hourly weather observations from so that we can analyze usage in different weather conditions.

I haven't had time to conflate or analyze this weather data. If anyone's interested (or would just like the data in tidy SQL), I've shared it here:


by jmc on Jan 19, 2012 6:14 pm • linkreport

How often is a bike checked in to a reasonably full CaBi station and then immediately checked out again?

What I'm trying to get at is, how frequently do people "synthesize" longer trips by chaining together short trips, to avoid the overage fee. You may not be able to tell from the available data set if it's actually the same user immediately re-checking-out the same bike, but you could get some idea how common the behavior is.

If it's very common, the operators might want to modify their fee structure -- if there's a need for this, they might want to serve it by increasing the free interval.

I, um, know people who do that. Sometimes. I've heard.

by Urban Garlic on Jan 19, 2012 8:11 pm • linkreport

@jmc: which station did you use for the weather observations?

by Froggie on Jan 19, 2012 10:27 pm • linkreport

@Froggie: the weather observations are from DCA.

by jmc on Jan 19, 2012 10:48 pm • linkreport

OObrien's visualization also leaves out the Roosevelt bridge crossing, which I use at least twice a week. That being said, I am usually the only biker on it, so perhaps it is not used much.

I imagine you could strap on a GPS unit to a subset of bikes and try to build a model of use. I don't know if the power law would work here --people do the darndest things with their bikes and their routes, so it would be hard to model.

by charlie on Jan 20, 2012 9:02 am • linkreport

So you've got the data normalized, now what? What applications would do you envision coming from this?

by TGEOA on Jan 20, 2012 9:03 am • linkreport


The Roosevelt Bridge link kinda stinks. The sidewalk is incredibly narrow, the connections on both sides of the river aren't too good. If you're on the north side of the bridge, you get dumped into the Kennedy Center lot, and it's just about impossible to go south or due east from there.

The south side sidewalk is worse, since there essentially is no connection on the VA side.

The bike/ped connections in that area of DC and the Mall are horrific. It's a terribly disjointed area that could and should be a tremendous biking asset.

The map also leaves out the 14th Street bridge trail, which I think is a much more rational connection, but there's a large park on either side you have to bike through.

by Alex B. on Jan 20, 2012 9:12 am • linkreport

@AlexB; I strongly disagree on the the Bridge. It isn't bad at all, and for my purposes (west end/Rosslyn) it works very well as a connector.

I agree with your other points, and I stated, I usually am the only biker I see.

I more often see some tourist on CABI on the south side trail. I have no idea how they got there.

by charlie on Jan 20, 2012 9:16 am • linkreport


If I have to stop when I encounter another person traveling in the opposite direction so that we don't collide with each other, then the path is too narrow.

Even if the bridge were better, the point that it works well for your purposes is kinda irrelevant, since your regular trip is the only trip pairing that really works. A major piece of infrastructure should be more versatile than that.

by Alex B. on Jan 20, 2012 9:33 am • linkreport

@TGEOA: Well I guess that's up to the community what to do with it. I just wanted to pitch in by providing a clean data set for people to work from.

I think the weather info could be fun to analyze: how temperature and weather conditions affect usage.

I've also included station GIS coordinates, so this DB can be used for map visualizations.

Finally, a dynamic tool could (fairly) easily be built on top of this DB to provide interactive historical stats.

I just don't have much time to do any of that at the moment, so thought I'd toss it out to the community.

by jmc on Jan 20, 2012 10:07 am • linkreport

I think the weather info could be fun to analyze: how temperature and weather conditions affect usage.

I can tell you that without any data: More people use CaBi when it's warm and sunny than use it when it's cold and rainy.

by Marian Berry on Jan 20, 2012 11:49 am • linkreport

@Marian Berry: Probably, but it would be interesting to see the extent to which weather affects usage. Since we know the Member Type, we can also see if casual users (who may be more likely to be tourists) are more easily dissuaded by weather than are annual members who may be more dependent on Bikeshare for transport, and might use it despite the weather.

by jmc on Jan 20, 2012 11:54 am • linkreport


I'm not discounting the data. I just can't see any uses and thought others might have some suggestions, other than something to put on the walls.

by TGEOA on Jan 20, 2012 1:17 pm • linkreport

I don't think this data dump was needed to "discover" this. Common sense would have led one to deduce the same.

Fair enough, but I prefer hard data to back up my gut instinct. Proving or disproving a hypothesis is important, even if the finding isn't surprising or interesting. Anyway, I agree - it is fun stuff! :)

by JDAntos on Jan 25, 2012 8:24 pm • linkreport

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