With this data we actually know — where are people picking up bikes, where they’re riding and what times they’re using them (and also where they are being dumped). We can analyze and figure out if it’s work for or recreation, or if it’s to get to or from a DART station. In The Ghost Map, the story of how John Snow (no, not that one) used data and mapping helped solve on of the world’s most horrific outbreaks of Cholera in London in 1854, author Steven Johnson talks about how in the future, we’ll use use similar techniques with more modern data collection:

We’ll use a technology that Snow would have recognized instantly. We’ll use a map. Only, this map won’t be hand-illustrated from data collected via door-to-door surveys. It will draw on the elaborate network of sensors sniffing the air for potential threats in urban centers, or hospital first-responders reporting unusual symptoms in their patients, or public water facilities scanning for signs of contamination.

We have arrived at that future…our sensors are the GPS signals coming off the bikes. We can figure out what routes people are using and know where bike crashes are happening and use it to improve our infrastructure to make our city safer and more bike friendly, which will be helpful as our city gets more dense.

Large Numbers

Unfortunately, the only way to truly know this is just to scatter bikes all over the city. Tons of them.

This isn’t an engineering problem like building a working plane, where both cost and human lives are at stake (much greater than just unused or fallen bikes). With that type of problem, you create a small prototype, see how it goes and scale up as errors are made and problems are solved. Bikeshare is a data problem, where you do the exact opposite. Create as much data as you can, look for insights and correlations and build strategies from there. This is how Artificial Intelligence works. You feed it as much data as possible and it learns — it learned to beat us at chess by playing millions and millions of games and implementing the best strategies. If you want an app to be able to recognize a hot dog, you feed it millions of pictures of hot dogs and it ‘learns’ what it is. There’s no other way.

Look at how Uber has improved their service with new ‘dedicated pick up spots.’ No longer do drivers just drive near your latitude and longitude point and then call and try and figure out where the hell you are, they now have a specific spot near there where they’ll pick you up. Uber didn’t guess where those should be. They used billions of rides of data to figure out where picking you up will save them money, us money and not clog streets making the service more efficient for everyone.

The more data the better. Johnson explains further in The Ghost Map about the importance of large numbers in dealing with solving urban issues (in this context the spread of disease):

The terrifying visibility of the outbreak did in fact sow the seeds of a cure. But it was not divine providence that drove the process. It was density. Crowd a thousand people into three city blocks and you create an environment where epidemic disease will flourish; but in flourishing, the disease reveals the telltale characteristics of its true nature. Its efflorescence points the way to its ultimate defeat. The Broad Street pump was a kind of urban antenna, sending out a signal signal through the surrounding neighborhood, a signal with a detectible pattern that allowed humans to “see” V. cholerae without the aid of microscopes. But without those thousand bodies crowded around the pump, the signal would have been lost, like a sound wave dissipating into silence in the vacuum of space. — The Ghost Map, Steven Johnson

Crowding as many bikes around the city will reveal the telltale characteristics of our systems true nature. In the same way, we lose signals when we regulate too early…

Phantom rides

If we limit to say 3 bikes at a corner, what if 5 people want to use them? We have 2 phantom bike rides — a trip someone wants to take but can’t doesn’t make it into the data set. On the other hand, any trip that no one wants to take is in the data set. It’s all those unused bikes bothering everyone. We have a more complete picture.

A large amount of phantom rides is much more damaging than dealing with urban clutter for a year.

The garbage look of the city looks bad and its annoying and pretty ridiculous, but the long term data that comes out of it is FAR more valuable than the short term ugliness.

The system will settle. These are private companies that aren’t going to continue throwing out as many bikes for market share. The City will find the appropriate number of bikes that we need and create regulation to limit to that amount. And the private companies will continue putting the bikes in the most used places.

Path Forward

We get the data from the operators and figure out where people are riding, how many riders there are and use that data to figure out the sweet spot of how many bikes there should be. We build infrastructure based on where the rides are happening (tells us so much more than a ‘survey’) — watching what people are actually doing instead of what they say. Not to mention, bikeshare is just the beginning. We are eventually going to get car share (like Car2Go) and soon fewer and fewer people will own cars and we’ll have to understand the best way to regulate these companies as well.

I will create an interactive, animated map of the routes people are taking and help do analysis in order to create an efficient city.

People will learn how to use the bikes, drivers will learn how to deal with more bikers and we can better plan new bike infrastructure. Because we’re going to need bike infrastructure. The city is getting more dense. Apartment buildings are popping up in Dallas like an abandoned whack a mole game and if all those people drive to work, it’s going to be a nightmare.

Yes, there are too many bikes. Yes, there should be regulation. But we need to find that zone between too much order and too much chaos using data, not anecdote.