Tag Archives: commuting

Beyond the MTA into the World of Commuting Obsessives

Every commute, no matter how long or how short, begins with a single step venturing from the comfort of home into the unforgiving outside world. My journey into the realm of transportation studies began when I moved to West 125th Street and purchased a small blue notebook.

Broadway and West 125th Street mark the border between Harlem and Morningside Heights, an area where Vino Fino and Max Caffè bleed into Rite Check Cashing. It is, like many gentrifying areas of the city, a diverse neighborhood. But the denizens of Broadway and West 125th stand united in the face a common nemesis: the capricious and unreliable companion that is the 1 train.

The 1 train is our sole way of reaching the vibrant world that exists below 125th Street. Yes, you could walk three long blocks east to the A train but no one does that. Instead, you perch on a platform high above the traffic below, staring at an electric sign that unhelpfully reads “Next 1 Train: DELAY” and wondering why you ever moved to New York City. Usually in these moments it is raining.

The small blue notebook was my defense against the 1’s unreliability. Inside are tidy columns filled with information about the amount of time spent waiting for the train, time to destination and the ambiance of the subway car (points gained for attractive people, points deducted for vomit). I’ve lived at West 125th Street for eight months now and the notebook is almost full. I find the same tranquility in these columns of numbers that others seek in a church service or yoga class.

I’ve never shown anyone the notebook, having long ago learned that private obsessions have a tendency to seem odd to others. It wasn’t until I attended my first hackers’ meet-up that I realized that others shared my obsession. They had, in fact, built an entire culture around the bizarre pattern that occurs five days a week when millions of people leave their homes and weave past one other: walking, driving, running up stairs and down escalators to jump onto trains. These travelers arrive at another building, where they remain for several hours. At the end of the day they stand up, leave their desks and repeat the pattern in reverse. Millions of tiny dots moving in a dizzying ballet of repeated motion: this is what data scientists see when they look at commuters.

I could try to make you understand the massive feats that go into enabling this dance by citing numbers. I could say that on an average Monday, 7.4 million people ride a New York City subway or bus and that the number increases by 700,000 if you include the surrounding metropolitan area. I could tell you that the Metropolitan Transportation Authority spent $12.6 billion serving these riders last year.

But this isn’t a story about numbers. This is a story about those curious individuals who look at their fellow human beings on the subway and see data points clustering into patterns. It’s a look into the beige and grey cubicles of the bureaucrats who spend their days studying what we do when we leave our apartments. It’s a glimpse of the data geeks who battled in court for the right to improve your daily trip and the futurists who want to radically transform the subways that transport us. Most of all, this is a journey into the world of those trying to see logic where the rest of us see only chaos.


Seat: Yes. Ambiance: 9. Time in transit: 0 minutes.

My investigation of the people who shape our commute began, counterintuitively, over the phone. Rosalie Ray, an economist at the Department of Transportation, was based in Boston. Thus, I was able to avoid the 1 train on a snowy January day and interview her from the comfort of my armchair. (Such telecommuting is on the rise but I wouldn’t discover that fact until months later.)

Ray was a casual acquaintance who had once spent 30 minutes animatedly explaining the intricacies of transportation budgeting to me at a party in Virginia. She readily obliged to sketch out the public transit landscape as it related to commuting.

It is a universally acknowledged truth that any academic field will have opposing camps that are vehemently divided on a point that seems as monumental to them as it does trivial to the rest of the world. The field of public transportation studies is no exception. One point of such contention is my very own New York City.

The problem? In the world of commuters, New York City is an outlier. But it’s an incredibly important outlier. Around half of America’s transit riders reside in the New York City metropolitan area, explained Ray. In terms of scale – both of geographic area and of population – there is no other metropolitan area like New York in the entire country. For urban planners this presents a problem.

Do you discount New York and model commuting plans on other, more “American” cities? Or do you make New York the model of scale towards which all other cities should aspire? (As a Bostonian, Ray may have been biased on this point.) Current studies on commuting and public transportation either exclude New York completely or treat it as an exception.

She explained that other rifts have developed even among those urban planners who agree on prioritizing New York. In any system of commuters or transit riders, you have groups of people whose journeys are competing against one another. You feel this when you are trying to catch the walk signal at a crosswalk only to be thwarted by a car making a right turn or – less statistically likely in New York City – if you are driving and have to brake hard for the yellow light because of an overeager pedestrian.

Urban planners have to prioritize these various groups—drivers, subway riders, bus riders, cyclists, walkers, super-commuters—not only in how they build streets but also in how they allocate budgets. “Rail is a higher income bracket,” Ray said matter-of-factly. Buses cost more money and generate less revenue (at least in New York City), but serve lower income communities that have greater need, according to a New York Metropolitan Transportation Council presentation in October 2012.

Cities have to find a way to evaluate the needs of the different groups and justify their spending patterns. For years that way was something called the “four step model.”

The four step model, a relic of the 1950s, looks at a person’s journey from four different angles in an attempt to forecast the effects of transit changes. [1] It has been the dominant tool of urban planning for decades. Now, a new tool is being employed both to enhance the four step model and to radically transform how urban planners look at commuting trips. That new tool is data, but not just any data. “Big data,” said Ray, excitement palpable even over the phone. “The four step model was the best they had for a very long time,” she said. “But before you could never know where everyone actually wants to go. You just model it.”

As anyone who has relied on HopStop, Google Maps or a similar public transit app knows, the availability of data can make the daily life of a commuter easier. Big data plays another, equally important role in the life of the commuter, by transforming the way that citizens influence public transportation planning. A quick Google search revealed an upcoming MeetUp designed to bring together programmers, non-profit activists and government transportation authorities. Now it would also attract one journalist.

W.125th to 99 Madison Avenue: 30 minutes on the 1 and N trains according to Google, which was five minutes off. Apparently, Google doesn’t account for 4 -nch heels in their walking and transfer time estimations.

Seat: Yes. Ambiance: 4. Time in transit: 35 minutes.

The OpenData NYC meet-up was hosted at ThoughtWorks, one of the many Manhattan tech start-ups indistinguishable from each other with their fridges full of beers and vague mission statements. ThoughtWorks was unusual only in that its offices were in Midtown rather than the downtown corridor of the original “Silicon Alley.”

ThoughtWorks, like all New York start-ups, is hungry for developers. It was a calculated decision on their part to open their offices and beer fridge to that night’s MeetUp. Though two presenters were from government agencies, civically minded programmers were scattered throughout the audience. The buzz was about the new bike-sharing program that was going to be rolled out in the spring. Everyone seemed to know each other and to know each other’s work. An IT Director from a local transportation advocacy group and an NYU urban planning student in a hoodie traded bike route tips over Brooklyn Lagers.

This particular MeetUp was punnily entitled “All Aboard! The Reboot, a Presentation of MTA’s Bus Time & NYC DOT Data” and pizza had been promised in addition to the beer. Though we were ostensibly here to discuss the release of new bus information, the real purpose was to provide a safe space where developers and data-loving bureaucrats could meet.

Once fed, we were ushered into a small conference room with fluorescent lighting and two columns of chairs facing the speakers, who were from the Metropolitan Transportation Authority and the NYC Department of Transportation (two distinct groups, the first run by the state and the second by the city). The techies whipped out their Macs and the presentation began.

I could elaborate on the bus data shared but there’s a reason that the room was filled with programmers, not journalists. Data – even for someone who logs every trip she takes on the 1 in a small blue notebook – is dry. It does not become more scintillating when presented in PowerPoint format by a bureaucrat. So I will not go into detail except to say that busses are installing GPS trackers so that there will soon be more accurate real-time information and that this news was received with great enthusiasm by all in the room.

The harmony apparent between the MTA and the programmers was astounding when you consider the schism that existed only four years ago. The MTA historically has had a tumultuous relationship with data and particularly with programmers. For a period in the early to mid 2000s, the MTA filed a series of lawsuits against developers who wanted to use the schedule data. Metro-North, the commuter rail that connects the city to the wealthy bedroom communities of Connecticut, was especially notorious for its litigious tendencies. As recently as 2009 the MTA sued Chris Schoenfeld, creator of the StationStops mobile app, demanding he pay licensing fees for schedule data. Within a year of the lawsuit against Schoenfeld, the MTA had backed down and agreed to open up data to everyone. Or, at least, to stop suing developers if they tried to use what data was available.

Though the MTA is overseen by the Governor’s office, not the city, it is the organization that controls the New Yorker’s daily commute. It was the MTA that got the city up and running so quickly after Hurricane Sandy and it is the MTA that is responsible for the weekly delays I experience on the 1. It controls not only the subways and the buses, but also the Staten Island Ferry, Long Island Rail and Metro-North Rail. The MTA is big and bureaucratic and up until recently, they did not like app developers or data journalists at all. I couldn’t understand my commute without understanding the MTA.

W. 125th to 199 Water Street: A seemingly interminable ride down the length of Manhattan on a 1 train that creaked and paused between stations.

Seat: Yes. Ambiance: 6. Total time in transit: 53 minutes.

My source, a consultant to the New York Metropolitan Transportation Council, glanced nervously at the blinking light on my recorder. “Let’s keep everything I just said off the record. Everything,” he said. He mentioned that he had been a journalist and that he knew how these things worked. For context, he had been explaining to me the statistical method by which NYMTC discerned commuter habits. It wasn’t exactly the WikiLeaks cables.

We were at a small table at R&R Coffee, a hipster oasis with Intelligentsia Direct Trade coffee in the grubby part of the Financial District near the Fulton Street stop. My antsy source agreed to put me in touch with the Public Information Officer at NYMTC. Several weeks of emailing later I was back in the neighborhood to meet with the urban planners doing the top-secret statistical analysis.

NYMTC is pronounced “Nim-tick” by those in the know. It is most often used in referenced to research, as in “Did you see the new Nim-tick report on super-commuters?” They’re the state-sponsored data-crunching hub of New York transportation research. They’re numbers people.

This is how it works: all of the transportation organizations in the region–MTA, Port Authority, and New Jersey Transit to name a few–send their data to NYMTC. NYMTC spends months synthesizing that data and ultimately producing something called the Best Practices Model, or the BPM as it is casually called on the 22nd floor of 199 Water Street. The BPM is a gloriously complex array of predictions of future transportation habits and it occupies much of the time of Sangeeta Bhowmick, who is sitting to my right.

Bhowmick wore a black turtleneck and red lipstick with a no-nonsense air of authority. She was there to discuss the BPM. Bhowmick was in charge of an entire team that spent its waking hours building the analysis. She knew the model inside and out. Munnesh Patel, to her right, was there to explain everything relating to their data practices. Across from me was Gerry Bogacz, the Planning Director who had been at NYMTC for over 15 years. This was the dream team of transportation planners, those who were moving the four step model into the future.

The BPM came into existence after the Clean Air Act of the 1990s required cities to better predict the impact their transportation’s impact on the environment. It takes into account every type of data you could consider: demographic shifts, changes in consumer commuting habits, the rise of telecommuting and “super” commuters (those who fly in or come from several hours away). Bhowmick wrangles all of these factors into one complex model that predicts what our commute will look like in the future.

The room that creates the BPM is mostly empty, except for the computer terminals lining the walls. People, mostly youngish and mostly men on the day I visited, sit at these terminals in front of spreadsheets, numbers and graphs. In the middle of a room is a giant counter with drawers for maps. Bogacz laughs a bit sheepishly, “We never use maps anymore. This has just become a table.”

Taped to the wall is the most recent iteration of the BPM. It’s an enormous four-foot long flow-chart with yellow rectangles, orange diamonds, red ovals and turquoise squares. Inside the shapes are phrases like “Transit Skims Procedures” and “Household Auto Journey Procedures.” Lines connect the shapes into one enormous web representing my daily commute, as well as those of 7 million other people. It is a thing of beauty and utterly incomprehensible.

A copy of the BPM is shared with all of the transportation organizations that contributed data. I ask Bhowmick if those organizations are able to understand the model and she laughs, saying yes, at least a few of them can. NYMTC also gives each organization more specific recommendations and analysis, what macro trends are in the pipeline and where funding could be best applied. “Then what happens?” I asked. Bogacz shrugged. Either the organizations follow the recommendations or they don’t, he said. NYMTC has no decision-making authority.

I had enjoyed my time with the data crunchers but I needed to find the people who actually made the decisions affecting my commute. In New York, there are as many as 12 transportation organizations but I wanted to learn about the big one, the one that managed the 1 train. I still needed to learn about the MTA.

W.125th to 295 Lafayette Street: 1 train to Times Square, R train to Prince Street.

Seat: No. Ambiance: 3 (very crowded, morning rush hour). Total transit time: 34 minutes.

‘I wish she still worked at the MTA,’ I thought as I sat across from Sarah Kaufman. Kaufman had been their internal advocate for open data, convincing administrators to come on board one by one. It wasn’t difficult to imagine her being convincing. She had a wry sense of humor and a matter-of-fact logic. The combination was disarming, even to a journalist trained to remain objective. I was sitting in her office at the NYU Rudin Center, laughing at her jokes–which were funny–and trying very hard not to like her. Kaufman lives in Prospect Park and takes the subway (a 22-minute ride on the F train, one of the only lines known to be more capricious than the 1).

Kaufman’s official title had been “Intelligent Transportation Systems Project Coordinator” and she was tactful about the MTA’s screw-ups. “It wasn’t a constructive move,” she said, of the decision of the organization to sue citizens attempting to make the MTA’s data more useful. Frank Hebbert, who partnered with Kaufman at the non-profit Open Plans, had been more forthright saying, “It was crazy that the MTA took people to court about this. It just doesn’t make any sense.”

The arrival of a new director who was more open to data sharing at the MTA made Kaufman’s life easier. Still, much of her time was spent going individually to all of the key people in the department, working to convince them that it was necessary to liberate schedule data. There was internal resistance. The organization was still reeling from September 11, as well as the more recent attacks on the London and Madrid train systems, which had killed hundreds of people. Letting the public know exactly when a train would pass through a busy, crowded station made people understandably uneasy. There were also more pragmatic budget concerns–due to union regulations no one at the MTA can do a new job without receiving additional compensation. Who was going to make this data usable and take on the task of sharing it with the public? Ultimately, enough momentum built up within the MTA and all of that resistance collapsed under the pressure. Now MTA holds hackathons to entice developers to use its data.

Kaufman recounted all of this to me politely but it was clear that she had explained it to journalists in the past, several times. She ultimately left the MTA precisely because she found such dealings taking a toll on her work. So much time was spent cajoling and convincing people that Kaufman had very little time to further her own projects.

When asked what those projects are Kaufman leans forward, her voice excited. “Right now, I’m doing a research project on crowd-sourcing for transit agencies to improve its management by gathering information from the public,” she explained. She added that if someone on the number 7 train tweets that something is happening on the train, the train management will likely get that before they receive feedback from the train operator who is sequestered in his own car. Or at least they would if they had some sort of formalized feedback loop. “But,” she said, “there isn’t one.”

No system has such a loop yet, though San Francisco’s BART system is the best at monitoring what riders say about it on social media. “The West Coast is so far ahead,” she said in a slightly wistful tone. “We’ll get there…eventually.”

W. 125th St. to 45 8th Avenue: 1 train to 96th St., switch to the express 3 train.

Seat: Yes. Ambiance: 9 (was given seat by lovely man with lovely expensive-looking shoes). Total transit time: 31 minutes.

The final step of my journey was to meet the people who are thinking bigger and bolder than the MTA: the futurist community. I sat at ‘Snice coffee in the West Village, staring at a sheet of paper covered in ovals and squiggly arrows. The ovals represented pods in a driverless transportation system envisioned by Garry Golden.

I came across the futurist community when I was looking for transportation conferences to attend. I had just missed the National Transportation Board conference in Washington, D.C. and wouldn’t be able to attend the upcoming Design-Build in Transportation conference in Orlando. But staring at me from the DBIT conference website, with the unself-conscious enthusiasm of an evangelist certain of his cause, was Golden.

Garry Golden

– futurist, strategist, Brooklynite – was trained at the University of Houston Future Studies Program. [2] You may have never heard of it but the program is one of the leading institutes churning out strategists who devote their time to long-term predictions. Futurists can study any number of fields but Golden specializes in transportation and urban planning.

Golden painted a picture of the New York in which I could live twenty years from now. Data-driven business models will revolutionize the taxi and bus systems. Taxis and cars will all be on demand, lessening congestion (fewer taxis circling the streets looking for passengers) and parking (fewer cars clogging up street sides). “The taxi companies are going to fight it tooth and nail,” Golden explained, his voice excited at the prospect of the impending battle.

He elaborated on how public infrastructure itself would change: there would be a subway of pods rather than trains. For example, if I were returning home from 14th Street, I would go to a specific spot on the platform for those of us going to 125th. Then, a train car would come and zip us directly there without the tedious stops along the way (this is the same system already used by elevators in Manhattan’s more efficient high rises and was also the diagram that resulted in cryptic scribbles on my napkin).

While the subways made underground travel more efficient, driverless cars would revolutionize traffic flow in and out of the city. “Do you know how many driverless cars it would take to control the traffic flow?” He asked. I didn’t. “One in six, Madeline. One in six!” He pushed his glasses back up the bridge of his nose. They had slipped down in the heat of the moment. He explained further: if one out of every six vehicles was a smart, driverless car connected to a central grid, those smart cars could effectively communicate with each other and adjust speed to affect the entire traffic pattern and prevent gridlock.

I left my meeting with Golden with a paper covered in scribbles and a sense of hopefulness. I could see a future without traffic jams, where trains would appear at the moment I needed them and take me to my exact destination. Then I descended into the West 14th station and was plunged back into the real world, entropy and all. There it was, the ominous “DELAY” displayed next to the 1 train.

A garbled voice came over the loudspeakers and I exchanged questioning looks with the other bewildered citizens on the platform. No one had understood the words but we intuitively knew the prognosis was bad. I trudged out of the station and walked over to the A/B/C to head uptown.

I switched trains at 42nd, walking the long tunnel that connected the two stations. Along the passageway, enameled tiles appended to the roof shouted my feelings in all capitals: “SO TIRED.” It seemed a strange choice for public art. “The Commuter’s Lament” is eight short lines in its entirety, together creating an ode to the arduous pattern that was the point of my obsession.









What was I doing? What were we all doing? There was a reason that the commute–and the people studying it–held such a fascination for me. All of these people, all of this data and millions of dollars spent trying to make it run smoothly. And it didn’t, wouldn’t, couldn’t. I knew then that I didn’t believe in a future where the systems ran efficiently and seamless, a future where commuting was less miserable. Driverless cars may take over the streets of Manhattan while subway pods run through its tunnels, but there will always be delays. The agony of the commute will persist.

Commuting is the ultimate surrendering of control. We’ve given our tax dollars and in return the government has pledged to help us make that journey from home to work and back again. They largely succeed. But it’s never as efficiently or comfortably as we would like. For that commute, you are trapped in transit with the rest of New York.

Shortly after I moved to New York, the city suffered a bizarre mini-epidemic of violent deaths on the tracks. A man pushed a father of two in front of the Q train in Midtown. A woman shoved another man onto the tracks of the 7 in Queens. You found out about it in the New York Post the next day, or on Twitter more quickly, but at the moment all you could see was that ominous “Delay.” Seeking to find some order in the misery was what drove me to obsessively track the 1 train and the programmers to develop apps and NYMTC to build complex models. We all want to understand what lurks behind that delay sign. But you can’t.

So many people were struggling to convert commuters into data points and pin down our transportation patterns–NYMTC, the MTA, Open Plans, the data geeks, the futurists. They all hoped to root out, or at least contain, the chaos. But the poem lining the subway walls spoke to an equal truth—no matter what the commute will always have chaos and will always be agony.

The poet who wrote “The Commuter’s Lament” was Norman B. Colp, a New Yorker who died in 2007. Until his death he lived on the Upper West Side, just minutes from the 1 train. At this moment, that was the truth that made me feel less alone.

1. The “four steps” of the four step model are, in order: trip generation (how many people are traveling from a specific place), trip distribution (where are these people going), mode choice (what mode of transportation are they using to get there), and route assignment (what route will they take to get there).Back to story.

2. In one of many interesting tangents during our interview, Golden adamantly argued in favor of Houston, which he claims is unfairly maligned as a city. A glib comment on my part led to a six-minute exposition of the metropolis’s merits, from its diverse demographic profile to the clusters of vibrant neighborhoods. Apparently, Houston isn’t the next big thing. It’s the current big thing and New Yorkers have been too myopic to see it.Back to story.