Curb Enthusiasm
The Curb Enthusiasm podcast offers unique insight on what’s new, innovative, and exciting in the world of transportation and urban planning. Created by the New York City Department of Transportation, the podcast is focused on issues at the local, national, and international level and features guests who discuss the most consequential transportation work happening all over the globe.
Curb Enthusiasm
Episode 8 (Part 1): Eric Beaton on Why Your Street May Feel More Congested and How Data Informs DOT Projects
Eric Beaton, NYC DOT deputy commissioner for transportation planning and management joins Emily Weidenhof to discuss how NYC DOT collects data, why road users’ feelings don’t always match traffic data, how we’re using data to inform transportation planning, and more. This is part one of a two-part episode.
Curb Enthusiasm Episode 8 Transcript
00;00;01;05 - 00;00;28;29
Emily Weidenhof
Welcome to Curb Enthusiasm. I'm Emily Weidenhof. On this episode, we chat with Eric Beaton, deputy commissioner for transportation planning and management at New York City. D.O.T. Eric oversees the design of city streets, including the city's Vision Zero Safety program, development of bicycle, pedestrian and public space programs, and the implementation of Select Bus Service and other transit projects and collaboration with the MTA.
00;00;29;02 - 00;00;43;10
Emily Weidenhof
Eric has a master's degree in urban planning from Harvard University's graduate School of Design, and a bachelor's degree from Cornell University. All right. Welcome, Eric. Super excited to be speaking with you today.
00;00;43;14 - 00;00;44;15
Eric Beaton
Great to be here.
00;00;44;17 - 00;00;59;07
Emily Weidenhof
So you are deputy commissioner of transportation planning and management, T.P.M. to us insiders. Can you tell us a little bit about your role and the TPM team and what you do at D.O.T.?
00;00;59;13 - 00;01;17;11
Eric Beaton
Sure. And I love my job, because what we get to do is the stuff that really affects people on the street every day. And when my kids ask what I do, I say it's the stuff you see on the street. It's signs. It's markings, and it's all the ways we can use those things to make our streets function better for New Yorkers.
00;01;17;12 - 00;01;37;10
Eric Beaton
So it's a very real, very tangible job. Our division goes from planning through engineering all the way to operations. So we don't just get to think about what we do on the street. We get to go out and do it. And, you know, I love being able to go around the city and see things that our division, our agency, did to really make a tangible difference in people's lives.
00;01;37;13 - 00;01;50;02
Emily Weidenhof
Definitely. And we are here today to dig a little bit deeper into that work and talk about data, something that you are certainly an expert in and think a lot about.
00;01;50;05 - 00;01;55;28
Eric Beaton
I'm so happy to be here for that. No one ever wants to talk data, but it's just it's the thing behind everything we do.
00;01;56;00 - 00;01;59;08
Emily Weidenhof
Yeah, absolutely. So let's start there.
00;01;59;09 - 00;02;05;17
Emily Weidenhof
What kind of data does D.O.T care about? And how do we collect that data?
00;02;05;22 - 00;02;21;01
Eric Beaton
Sure. Yeah. And it's one of the places where the world has changed the most in the 18 years I've been working here, because used to be transportation data was either two kinds. You know, it was traffic data. You count the number of cars going past the place or you count the number of cars turning at a corner.
00;02;21;01 - 00;02;38;09
Eric Beaton
And, you know, maybe you count the pedestrians, maybe you count the bicycles. But it's just what is happening at that moment, at that time. And we still do that. We still need to know how many cars, how many pedestrians are going down the street. But we can now know so much more. We can learn about where people are going to and going from.
00;02;38;12 - 00;02;59;11
Eric Beaton
We can use big data to understand large movement patterns. We do much more talking to people and getting data about how they feel like they're using the street. We can look at, economic data to see how what we do affects what happens out there. And being able to feed all of that data into decision making allows us to really understand what we're doing, not just in terms of the movement of people.
00;02;59;14 - 00;03;10;07
Eric Beaton
That's still a big part of what we do, but also in terms of safety, in terms of the streets as public realm and all the ways that we can bring that data in to make the streets function in all those different ways.
00;03;10;08 - 00;03;17;16
Emily Weidenhof
Absolutely. And can you also share a little bit about how we collect data? Do we do it all ourselves?
00;03;17;17 - 00;03;27;02
Emily Weidenhof
You know, what are the different methods and sources and and how do we check and, you know, quality assurance, quality control of our data?
00;03;27;02 - 00;03;39;16
Eric Beaton
Sure, we collect data so many different ways. In the most simplest form, we have a rubber tube that can go across the street and has a little reader at the end. We call it an A.T.R, automated traffic recorder.
00;03;39;19 - 00;03;59;15
Eric Beaton
And what it does is every time wheels bump over it, it sends a little puff of air down the tube and we count how many, how many wheels, how many axles have gone over that point. And you can do a little math and turn that into vehicles passing a point. That's incredibly simple, incredibly cheap. You know, just technology that hasn't changed in 75 years and still works well.
00;03;59;18 - 00;04;22;01
Eric Beaton
But then we do more than that. You know, we can work with contractors and by cell phone data to, to learn about, you know, in anonymized ways of course, but learn about origin destination patterns if someone's using a street. Are they using it to get to a local destination on the street, or are they making through trips? Are they, you know, are they really serving the neighborhood or are they just sort of passing through?
00;04;22;04 - 00;04;46;12
Eric Beaton
We have G.P.S. units in city cars and taxis, and when we can buy G.P.S data from trucks that tell us, you know, not just where they're going, but how fast. There are places where people are taking hard turns that might indicate a problem, even if the crash hasn't happened yet. Like the world of data is is advancing incredibly and we we're using data at all these different levels.
00;04;46;15 - 00;05;12;16
Emily Weidenhof
Let's talk a little bit about how we're using data to do our jobs because I think, you know, so much of this information is super critical for us to continue to learn and evolve and do our jobs better. Can you talk a little bit about some of the really interesting kind of larger scale trends that you are seeing through some, recent data collection at D.O.T.?
00;05;12;18 - 00;05;34;01
Eric Beaton
Sure. And there's some data out there that I think just isn't new necessarily, but underlies a lot of what we do, which is just, first of all, that people make mostly local trips, right? Like even for trips to work, more people work in their own boroughs than them work in Manhattan. And, you know, we always think it is like everyone's going to Manhattan, that’s the thing we have to solve for.
00;05;34;04 - 00;05;49;12
Eric Beaton
But really, that's not true. Like every single borough, more people work locally, and that's even for trips to work, for trips to stores, for trips to friends. There's a lot of very short trips that happen out there that don't get well captured if all you're doing is counting at bridges.
00;05;49;14 - 00;06;13;03
Eric Beaton
So we've done a lot of thinking about how you can do surveys and how you can look at data of different types to make sure you're capturing that local trip and think about it differently from a trip that's going a long distance. Yeah, and that's interesting. That's something that we find in some of our public space survey data that while there are lots of different types of people using plazas, there are also a lot of local users.
00;06;13;10 - 00;06;40;24
Emily Weidenhof
So sometimes when people are concerned about, you know, who is this space for? You're building this space in my neighborhood. Who is this for? It can be really helpful to look at who is using the space and find that actually, you know, in many cases there it's a lot of local neighborhood, residents that are using the street and that they're using it consistently and regularly, multiple times a month, multiple times a week.
00;06;40;26 - 00;06;57;03
Emily Weidenhof
And it becomes, you know, infrastructure that they, they rely on. And again, just really interesting finding when we're actually observing and asking people, you know, how many times they visit a space where they're from, who, who is using it.
00;06;57;05 - 00;07;03;01
Eric Beaton
Yeah. And it really adds so much, the conversation to be able to say, you know, not just I hear what you're saying, but we've looked into it, and we understand what’s really going on.
00;07;03;01 - 00;07;11;09
Emily Weidenhof
Yes.
00;07;11;09 - 00;07;23;19
Eric Beaton
A place this comes up a lot is actually around parking. People are coming in from far away because they just need to park in your neighborhood. And, you know, one of the things we've done is we send people around and we look at license plates and use the DMV to, to attach those to zip codes.
00;07;23;21 - 00;07;41;02
Eric Beaton
Increasingly now we can use the sort of big anonymized data to look at similar things. And what you find over and over again is that the vast majority of cars parked in a neighborhood belong to the people who live in that neighborhood. And, you know, that's a hard thing because it means that you can't just keep the everyone else away and everything will get better.
00;07;41;02 - 00;07;56;07
Eric Beaton
It's New Yorkers who own cars that are taking up that parking. And that's a hard truth for a lot of people. But it also means that you can start getting at like, what are the ways you can really get at car ownership? Because it's by reducing car ownership that you're really able to address things like parking.
00;07;56;13 - 00;07;57;23
Emily Weidenhof
Yeah, absolutely.
00;07;57;25 - 00;08;16;26
Emily Weidenhof
And also understanding usage patterns to help better shape design policy, smarter valuing of the curb. Can you talk a little bit about where some of this data that we're getting has actually led to design or policy management innovation?
00;08;16;29 - 00;08;22;23
Eric Beaton
Yeah. And curb management is the place where this is taking off more than anything else, because it used to be incredibly hard.
00;08;22;23 - 00;08;41;13
Eric Beaton
You'd have to go send someone walking down a street in circles over and over again all day, writing down license plates and then going back to the office and doing data transfer. Any work on the curb was incredibly hard. And now all of a sudden you have license plate readers. You have sensors that can capture when people are parking or leaving a space.
00;08;41;14 - 00;08;59;28
Eric Beaton
You have this big data. They can tell you how many people are driving into an area versus taking transit or walking. And it's all estimates at that level, but it's but it's real at the same time. And we can use that to start to look at like how are people really using the curb? We have about 3 million parking spaces in New York City.
00;09;00;02 - 00;09;28;06
Eric Beaton
The vast majority of them are for free, long term storage of personal automobiles, and in a lot of neighborhoods in the city., that's right. Like, you know, that's the best use of the of the curb. But there's a lot of places around the city, even in busy areas where that's not right. And we've prioritized long term parking over short term parking for people visiting stores or truck deliveries, or using it for public space and all kinds of different ways, and being able to understand, like what is really happening in the space.
00;09;28;06 - 00;09;43;10
Eric Beaton
What happens if we put a street seat there, you know, is there truck loading that we need to relocate? Is it, is it short term parking? Is it, you know, someone who just lives in the neighborhood and might park somewhere else? Helps us think about how much of this we can do and how we balance all those uses.
00;09;43;12 - 00;10;02;27
Emily Weidenhof
Absolutely. And I think sometimes the data is invisible, just like some of the tools are invisible. And a huge part of our job is being able to communicate the data behind decision making as well as, elaborating on all of the tools that we have to solve problems on the street.
00;10;03;02 - 00;10;08;23
Eric Beaton
That's right. And we're in a world where there is so much data and, you know, as professional planners, it's our in our nature.
00;10;08;23 - 00;10;31;21
Eric Beaton
We want to go out and say, here's a thousand different data points that we figured out. And we put them all together. And we came up with this profile of your neighborhood and anyone who's not us like that all just turns into noise. And so a part of the job is not just how do you collect all this data, but how do you turn it into something that's real and meaningful to take all of it and say, you know, 52% of the traffic on this street is not going to your neighborhood.
00;10;31;21 - 00;10;49;05
Eric Beaton
It's not coming from your neighborhood. It is just people driving through your neighborhood. And all of a sudden people think about the traffic on that street differently. It's not just me and my neighbors, it's these people who are adding a burden without actually benefiting anyone locally. And we have to balance that, right. People do need to make longer distance trips, too.
00;10;49;05 - 00;10;58;03
Eric Beaton
It's not that that's without value. But as we think about the use of any particular street, being able to talk to a community about what's really happening there can be really helpful.
00;10;58;06 - 00;11;14;28
Emily Weidenhof
Definitely. And I think something we hear a lot as we're engaging with communities is, tension between maybe some of the data points that we're communicating and what folks are feeling and what they're kind of observing in their daily lives.
00;11;15;00 - 00;11;26;00
Emily Weidenhof
So could you talk a little bit about the difference between the data and what we're kind of technically, how we technically understand a space versus how we may feel or experience that space?
00;11;26;06 - 00;11;32;27
Eric Beaton
Sure. And people's feelings are important to and that's part of what we have to work with, is not just, you know, what does the data say?
00;11;32;27 - 00;11;54;05
Eric Beaton
And you're wrong if you feel differently, but like, why do you feel differently? And a really good example that stuck home with me was so about, 10, 12 years ago, we were doing a bus priority project on Hylan Boulevard in Staten Island. And for anyone who's not familiar, but Hylan Boulevard is a it's a more suburban part of New York City.
00;11;54;07 - 00;12;11;12
Eric Beaton
It's, you know, dense and transit oriented compared to most parts of the country. But for New York City, it's relatively car oriented. But it has a lot of express busses that take people from Staten Island to Manhattan in areas that are far from the ferry. And we put bus lanes on the street, and we did a lot of traffic engineering work.
00;12;11;12 - 00;12;33;25
Eric Beaton
We looked at the signal timing, we looked at turn lanes and just all the tools in our toolbox, and we put this out there and we thought it was going great. The busses were going 10, 15, 20% faster and traffic was the same from our perspective. You know, the evening traffic had gone from, you know, 15 minutes and 55 seconds to 15 minutes and 23 seconds.
00;12;33;25 - 00;12;54;24
Eric Beaton
Really traffic's even moving better. This is gold. And what we're hearing from everyone out there was what did you do? I feel like this is so congested now. And we're sitting, you know, in Manhattan and looking at the data and saying that these people are crazy, what's going on? And then we went out and we did a field visit, and we stood with some people who lived and worked on the corridor.
00;12;54;26 - 00;13;16;20
Eric Beaton
And we both learned something by standing out there, because what you saw was the traffic was going down the street and in a very concentrated group, the signal timing was such that it was causing everyone to travel at 25 miles an hour. And that's what we wanted to see, right? That's not different from plan, but it was different from before when the traffic was more spread out.
00;13;16;20 - 00;13;36;12
Eric Beaton
And you might get some people zipping around and then coming in and out of traffic. And whether you're that person and you're actually going faster or you're getting passed, but you see traffic moving faster, it sticks in your head as something changed. This traffic is slower and not just slower, but feeling more congested because the cars were all together in a group.
00;13;36;14 - 00;14;01;26
Eric Beaton
And so on the one hand, we could see by standing there like why people felt that way, like it probably was a little bit more stressful to drive. And we felt like even when you're lead, you couldn't, you know, do a little extra work and get there 10 seconds faster. And the people who stuck with us said, oh, I see now the cars are all in a group, but then there's plenty of time when there's no cars on the street because everyone is just moving at a constant pace.
00;14;01;29 - 00;14;22;14
Eric Beaton
And so we all learned something together, but in a way that we wouldn't have happened if we didn't stand out there and really have that conversation. So it needed to be the data coming together with understanding how people feel and then knowing that that's what's happening, we can start to preview that for people in advance. When we do it on another project, we say it may feel more congested.
00;14;22;18 - 00;14;43;00
Eric Beaton
Here's what you're going to see, we're going to pack everyone together so that everyone is going 25 miles an hour, and that will be safer, and you know, you start to play on what people don't like it. That will get rid of all those cheaters, that keep cutting you off to go faster. I think that's something that people care about as much as maybe they care about speed.
00;14;43;02 - 00;14;49;11
Eric Beaton
And by understanding that, that's what the experience is going to be, you can start building that into the narrative of a project.
00;14;49;13 - 00;14;50;10
Emily Weidenhof
Definitely.
00;14;50;13 - 00;15;10;10
Emily Weidenhof
Hey listeners, we hope you're enjoying this episode of our podcast. For those of you who are as enthusiastic about transportation and planning as we are, we'd like to hear from you. You can submit topics and questions that you'd like us to cover at NYC gov forward slash curb enthusiasm, and now back to our conversation.
00;15;10;10 - 00;15;11;13
Emily Weidenhof
00;15;11;16 - 00;15;48;04
Emily Weidenhof
Something else that we've experienced about feelings is that there are a lot of people, especially commuters, who know a place incredibly well, but they know it incredibly well during one hour in the morning and one hour in the evening. And as we start to look at data and also look comprehensively at users on the street, oftentimes we can find that maybe there are better decisions we can be making about safety or other uses of the roadway that are actually benefiting the majority of users.
00;15;48;06 - 00;15;59;04
Emily Weidenhof
But that can be hard to communicate when somebody knows a character and a very specific set of users at a very specific set of times, right?
00;15;59;04 - 00;16;03;20
Eric Beaton
You see something only at it at a time of day and you say, like, how can it ever be anything else?
00;16;03;21 - 00;16;04;06
Emily Weidenhof
Yeah.
00;16;04;08 - 00;16;15;19
Eric Beaton
And they're not wrong. Like that is 100% of their experience of that street and to a lot of people like rush hour is when they see something, especially if it's not in their own neighborhood.
00;16;15;21 - 00;16;43;04
Eric Beaton
And to some extent, we can use data and say like, look, you should believe us. And here's why. And to some extent, we have to be able to say either we're going to manage the street differently at different times of day. And I think something the agency has done really well is instead of saying yes or no, this is open or this is closed, you know that we can manage things differently at different times, times of day, or allow certain vehicles through, but not others, just a toolbox that's grown.
00;16;43;06 - 00;17;01;04
Eric Beaton
And other times we are able to say to a local community that, yes, this is going to be more congested. 8 to 9 a.m. right? We are not going to hide that from you. Like that's part of being honest with you about what this project is doing. But the other 23 hours a day, it's going to be fine.
00;17;01;06 - 00;17;21;28
Eric Beaton
And I think, again, people tend to think of the traffic works or the traffic doesn't work, and instead we can be honest with the community and say, here's what we think will really happen. You will see some congestion. You'll see it only at the busiest times of day. But the rest of the time you'll have this safety improvement that will work 24 hours a day.
00;17;22;00 - 00;17;51;21
Emily Weidenhof
Yeah. And feelings are also so important in traffic engineering because it's not just data and engineering, but also human habits and human behavior and the kind of confluence of some of, the more, emotional versus rational. And I think some really interesting things that that D.O.T has done is around observing how environment and human behavior impacts safety.
00;17;51;24 - 00;18;12;15
Emily Weidenhof
And so like specifically dusk to darkness campaigns, like there's a real way to, look at habits and environmental changes and be able to really engage with the public and look to change culture, change habits. Can you talk a little bit more about your work in that?
00;18;12;18 - 00;18;22;08
Eric Beaton
Sure. And I've, I have a couple examples, but Dusk and Darkness is a good place to start because it's a place where we dove deep in the data to say, is there something we should be doing differently here?
00;18;22;10 - 00;18;56;22
Eric Beaton
And it's something that came out of the data that we weren't necessarily looking for, right? Like we tend to think of safety data as like which street is most dangerous, which corner is most dangerous. And that's, of course, a very important part of, of what we do. But when we looked at time of day and just how that changes over the course of the year, and there was this very clear change as the as the clocks change, that certain hours of the day became more dangerous, irrespective of where in the city you looked.. So that says, okay, this is not a traffic engineering problem alone, right?
00;18;56;29 - 00;19;16;11
Eric Beaton
This is something that is happening at intersections that are otherwise safer and in different boroughs. And however you slice it, it's something that has to do with people are out and about when it's darker all of a sudden and not a gradual thing, but over the course of a weekend it shifts by an hour. And that said to us, okay, like we need to do education and make sure people are aware.
00;19;16;18 - 00;19;37;29
Eric Beaton
We need to work with the police department to make sure that our enforcement is very targeted at that time of day, which may not otherwise be the time of day that police would be out there doing enforcement and being very visible. And that had a real effect on safety. And, you know, there's always more to do. But it was a place where it really let us do something that we were not otherwise doing.
00;19;38;01 - 00;19;57;00
Eric Beaton
I think one of the other interesting effects, though, is it goes the other way too, that we, you know, we love to trust the data and you take all the numbers and you put them into the traffic analysis model and it says, okay, if you if you take out a lane here, yeah, there's going to be traffic from here to Albany.
00;19;57;02 - 00;20;19;03
Eric Beaton
And we have to take a second and say, wait a second. That's not how people actually work. They do not sit in traffic forever just because that's what they did the day before. People do change their minds. They do do things differently. And we have to be intelligent about, you know, not just what does it say will happen if you take out a lane or change signal timing or do any of these things.
00;20;19;05 - 00;20;44;06
Eric Beaton
But how will people really react? And sometimes that's react in a way that we like, which is maybe they should stay on the highway instead of cutting through this community. Sometimes it might go the other way where we say, well, this might actually be a problem because people will go to this other parallel street that's more residential and just isn't appropriate for that kind of traffic, but it's why you can't just take the data and take that first level of analysis and then just go with it.
00;20;44;06 - 00;21;00;13
Emily Weidenhof
You have to apply some real planning and thought to it, and that's something that's sometimes hard for people, here because we learn to trust the data, and we learn that the data is always good. And being able to take that step of, “okay, but the data isn't everything” is sometimes a hard leap for people inside too.
00;21;00;20 - 00;21;03;23
Emily Weidenhof
Right, right.
00;21;03;26 - 00;21;32;15
NYC DOT Commissioner Ydanis Rodriguez
Hi. My name is Ydanis Rodriguez, commissioner of the New York City Department of
Transportation. Thank you for listening to Curb Enthusiasm by New York City DOT. This
episode was produced by Michael Santos with video support from Sigurjon Gudjonsson, Juan Vega, and Nazareth Battice. Theme music by Michael Santos. Curb Enthusiasm is available on Spotify, Apple Podcasts, and other major streaming platforms.
00;21;32;17 - 00;21;38;13
NYC DOT Commissioner Ydanis Rodriguez
To learn more, visit nyc.gov/CurbEnthusiasm.