Photo by DDOTDC on Flickr.

Bicycling is among the lowest-cost ways to travel through a city, and has health and fitness advantages, too. But the most direct practical benefit of bicycling comes when it’s also the quickest way to travel. In downtown DC, it usually is.

Using the recently released Capital Bikeshare trip data and trip plans from Google Maps, I compared travel times for trips between pairs CaBi stations in downtown DC. If parking takes only 5 minutes, the median Capital Bikeshare rider traveled faster than a car more than ¾ of the time.

For all but the slowest riders, bicycling is always faster than transit and walking. For some trips, it is the fastest option of all.

25 trips around downtown DC

The analysis

I picked 25 random station pairs in downtown DC (1 mile radius around Metro Center, shown above) for this study. For each origin-destination pair, the Capital Bikeshare trip data gave me a large number of bicycle trip time measurements, but I needed to know how long the trip would take by other modes like car, transit or walking.

Since no data sets exist for those modes, I used Google Maps’ time estimates as a proxy. A comparison of Google’s bike trip time predictions with real data from Capital Bikeshare riders returned a strong correlation (r = 0.93), confirming that Google’s estimates are probably a sufficiently accurate replacement.

For Capital Bikeshare trip data, I started with the data set cleaned up by Corey Holman. The data set contains over 1.3 million trips over a period of about 14 months.

But one major issue remained. I needed to make sure the data measured the direct trip time between a pair of stations. While most Capital Bikeshare trips are frequent riders going directly from one station to another, some trips are tourists taking a long leisure ride that just happens to start and end at these stations. Since I was only interested in direct trips, I only considered trips taken by registered users.

The graph below illustrates a sample station pair. You can see that registered users (in red) have a very different pattern of trip times than casual users (in purple). Registered users take trips of slightly different durations depending on their speed and exact route, but they seem to be going fairly directly from point to point, as their trip times follow a normal distribution with a long right tail. Casual users’ trip durations are wildly different and don’t follow as clear a pattern.

Histogram showing the distribution of all trip times from 19th & Constitution to 19th & L.

In the diagram above, the black tire track shows driving time while the green line shows bike time from Google.  The stacked bars show Capital Bikeshare trip times for registered (red) and casual (purple) users. The peaked distribution between 6-12 minutes reflects direct trips, and the longer trips reflect leisure rides. The pink dot shows the trip time of the fastest 10% rider; the red dot shows the median rider.

The results

The final results are shown in the figure below. The fastest 10% of riders, traveling at 10 miles per hour, were faster than a car trip with parking time added in every trip studied — 100%.

While an average rider on a bike, traveling nearly 8 miles per hour, will rarely beat a direct car trip without traffic or parking over the same distance, the fastest riders have a decent shot. When bike trips are compared to a direct car trip (like being chauffeured), the median rider was faster in 4% of trips studied. The fastest 10% of riders were still faster in 24% of trips. But in the real world, where cars have to find parking, bicyclists win big, whether they’re fast riders or average ones.

Percentage of trips where biking is faster, depending on a Bikeshare user’s speed. The graph on the left assumes direct car trips, the graph on the right assumes 5 minutes of parking time.

To be sure, these comparisons are not perfect. Both driving and cycling times have caveats.

For driving times, Google Maps no longer considers traffic delays in their trip time calculations, but rush hour gridlock in downtown DC will add huge delays to car trips, when bikes can zip through. In the bike trip data, I could help compensate for daily variation in weather and traffic by randomly selecting only one of several trips per day between a pair of stations, but this was not possible for driving times.

Second, even assuming 5 minutes of parking time is charitable to drivers. Donald Shoup reports (PDF) an average of 8 minutes of time spent cruising for parking, in multiple studies from multiple cities. That doesn’t even include the time spent paying a meter or getting out of a garage.

These travel times only count trips from one Capital Bikeshare station to another station, not the walking times to and from Capital Bikeshare stations. Therefore, they most closely reflect the times for people bicycling on their own bikes. Trips using Capital Bikeshare take a few extra minutes. While some people are lucky enough to be located very close to a CaBi station, most of us have to walk a couple of minutes to the nearest station.

A more sophisticated study could use arbitrary trips on the downtown grid to estimate the extra walking time for Capital Bikeshare trips, and better estimate the time Washington drivers actually spend in traffic and parking.

Despite these caveats, my results are not anomalous. A related study in Lyon, France, agrees that shared bikes were faster than cars in the central city.

That study used precise trip distance information from a “counter on the bike,” which unfortunately isn’t possible with Capital Bikeshare. They also inferred that bicyclists were taking unapproved shortcuts through the city center, but our data shows that even in downtown DC, with few shortcuts, shared bikes are still highly efficient.

I would have liked to study every single pair of Capital Bikeshare stations, but was limited by the tedious task of getting trip times from Google Transit. Since I was primarily interested in testing the effectiveness of bicycling around the city’s downtown core, where it has the best potential to overcome traffic, that was where I focused.

It would not be surprising if bicycling were equally effective in other dense neighborhoods such as Dupont, Logan, Shaw, Adams Morgan, or Capitol Hill, but I have not tested this. Some trips, like Anacostia to Arlington, may not be very efficient by bicycling because routes are lacking, though bikes often perform well in commuter competitions like last year’s in Reston.

If you’re interested in another set of trips or cluster of stations, we could set up a collaborative spreadsheet with instructions for collecting the numbers needed.  Let me know in comments below, or send me an email. And if you’re interested in working with Capital Bikeshare data or software, please join us at the new developer forum.

Matt Caywood is a DC resident and co-founder and CEO of TransitScreen, which brings live transit information displays into public spaces all over the world. He co-founded Mobility Lab’s Transit Tech project and is an advocate for open transportation data.