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.

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:

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.

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:

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.

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?