For most New Yorkers, the daily subway commute is onerous enough without having to remember to log every single train they take each day for an entire year. But Daniel Orbach, a 24-year-old UX designer who lived in New York for two years before recently moving to Chicago, did exactly that: between December 7, 2014 and December 6, 2015, he recorded every time he took the subway, noting the line, date, and time of day. The results? He took 1,247 trains in that year, averaging 3.4 rides per day and 103.9 per month. And he learned some things about himself.

Orbach was inspired to embark on this self-quantification project hearing several graphic designers and data aficionados, including Facebook timeline designer Nicholas Felton, discuss their own self-documentary projects at a talk in Williamsburg last year.

"I left thinking, what's something I haven't seen that I could record about myself for an entire year?" Orbach said. "Then I thought 'Oh, I could record subway rides!' So I just downloaded this app and jury-rigged it to record the way I wanted, and started doing it."

The app he used is called Loggr, and it's mainly designed to track fitness, weight, or work habits. But it's unique from lots of other personal data trackers in that it allows users to add any metric they'd like to track—so Orbach added every one of the New York City subway lines as metrics and then dumped all the data he was collecting into Excel. He says he's confident he logged every single ride, minus maybe two or three, in the entire year he tracked his subway rides.



(Daniel Orbach)

For most of the year that he was tracking his rides, Orbach lived in Prospect-Lefferts Gardens, near the Q train. On a typical workday, he'd take the Q to get to his office on Crosby Street in SoHo, though he'd occasionally take the B and walk the rest of the way. In November, he moved in with his girlfriend in Sunset Park, from where he could either take the R and switch to the N, or take the D all the way and walk. That explains the abundance of yellow and orange on his charts—though the top graphic doesn't distinguish between trains within a trunk line, the second graphic shows the breakdown between the various trains.

"I don't know that tracking my rides necessarily changed my behavior, but there was a change in awareness," he said. "Something I didn't realize was how large a quantity of my total rides my commute to work was. That sort of blew me away. The fact that I rode the Q, which was not even my main train the whole year, 500 times? That is kind of crazy."

He also noticed that he took the train significantly more on the weekend, in proportion to weekdays: his weekend rides comprised 36 percent of rides, but the weekend makes up just over 28 percent of the days in a week.



(Daniel Orbach)

Orbach estimates that he saved upwards of $1,900 by using a monthly unlimited MetroCard, rather than paying $2.75 per individual swipe. However, it's worth noting that his data doesn't account for transfers, rather listing each ride on a new train as separate, so that amount saved might be slightly less if you account for transfers within stations. Orbach averaged 103 rides a month, but it only takes 43 rides to break even on a monthly unlimited pass, so even if you assume that half of those rides were transfers, that's still something like $23 dollars saved per month, or $276 per year.

"There was a person I knew who never bought a monthly card, and she would say 'Oh, I'll get around to it,' but not getting around to it might be costing you thousands of dollars," Orbach said.

There's been lots of discussion recently about whether tracking personal data—be that calorie intake, sleep, or subway rides—is actually beneficial. Orbach wrestled with that same debate as he parsed his year's worth of data: though the numbers were interesting and the visualizations helped him see his year of commuting in a different way, they didn't tell him anything about himself that he didn't already know.

"That's the thing with self quantification that I think is interesting: you're not really going to learn anything about yourself you didn't already know or couldn't guess, because you lived every single data point," he said. "It's your life; you lived it. So, going through this data, a lot of it is like, 'Oh, of course'—but I wanted to put a number to the 'of course.'"