Speaker 1: Pushkin, this is solvable. I'm Ronald Young Junior. We are
Speaker 1: trying to push people towards decision that are less carbon intensive,
Speaker 1: so create less climate emissions from transportation. Also better for
Speaker 1: expenditures of tax dollars being more wise, as well as
Speaker 1: improving safety outcomes in transportation. For Laura Schuel, making greened
Speaker 1: decisions isn't a matter of juggling one hundred little choices
Speaker 1: like wind, unplug appliances, eat less meat, or swap out
Speaker 1: incandescent bulbs. For LEDs, choose a car that gets fifty
Speaker 1: mpg over forty mpg, you don't have to think about
Speaker 1: it as much. So like think about the big decisions
Speaker 1: that matter and like stop freaking out if you forget
Speaker 1: your cloth grocery bags. Once. As CEO of street Light Data,
Speaker 1: Shoel has the data to back this stuff up, but
Speaker 1: you won't find her postilytizing yet. I have a fundamental
Speaker 1: belief that informed decisions will arc towards changes that I
Speaker 1: want to see in the world. But we, as data providers,
Speaker 1: we have to be neutral because we're a source of truth.
Speaker 1: Street Light Data is a transportation analytics company, and Laura
Speaker 1: Schuel thinks how we move can be changed for the better.
Speaker 1: My name is Laura Schuel. I'm the CEO of street
Speaker 1: Light Data. How to monitor and mold our transportation systems
Speaker 1: into smart and environmentally friendly systems is a solvable problem.
Speaker 1: I recently watched Bishion Impossible three and Tom Cruise, who
Speaker 1: plays Ethan Hunt, is a part of the Impossible Mission force.
Speaker 1: In this one, he's actually getting married and as a result,
Speaker 1: his cover for working for the Impossible Bishion Force is
Speaker 1: that he works for the Bureau of Transportation. So he's
Speaker 1: at a party and he makes this cobbent where he goes. Yeah,
Speaker 1: traffic patterns are so crazy. It moves, it's like an organism.
Speaker 1: It's very interesting thing. And everyone kind of looks at
Speaker 1: him like this guy's kind of boring. And so what
Speaker 1: I'm thinking about our listeners, I'm thinking about them and
Speaker 1: thinking about data could beat something that's like extremely boring
Speaker 1: to them. But give me a piece of data that's
Speaker 1: excites you the most and something that you think a listener,
Speaker 1: just a casual person would find interesting when it comes
Speaker 1: to talking about transportation data. Well, first of all, that
Speaker 1: movie sounds awesome, and my company is immediately going to
Speaker 1: have a movie night about it. Second of all, he's
Speaker 1: not boring. He sounds like the most interesting person at
Speaker 1: the party. I love that. One of the things that
Speaker 1: I hope is coming with Secretary Budajes and President Biden
Speaker 1: is a demand that we calculate our transportation greenhouse gases,
Speaker 1: which we don't really do. We kind of swag it.
Speaker 1: One of the concerns with that is that then it
Speaker 1: would be bad for rural places, because rural places, especially
Speaker 1: with highways, are places where you have a lot of
Speaker 1: miles driven that have nothing to do with that rural place.
Speaker 1: Right people are cutting through min DOT Minnesota DOOT used
Speaker 1: our data to do a version of greenhouse gas emission
Speaker 1: that attribute the miles driven to the destination of the
Speaker 1: car in the truck. And what that does is it
Speaker 1: properly demands payment, so to speak, from the cities from
Speaker 1: the fact that they cause all this driving to and
Speaker 1: from them, and doesn't put disproportionate cost in carbon on
Speaker 1: the rural areas. That sort of data driven approach could
Speaker 1: reduce some of the future pushback we're going to see
Speaker 1: about carbon accounting from transportation. One interesting thing that I
Speaker 1: heard about you was that you started off as a
Speaker 1: comparative literature major. So how did you end up going
Speaker 1: for comparative literature to data analytics. Ah, it is a
Speaker 1: classic story of a great professor. So I was in
Speaker 1: college and I was majoring in comparative literature, and I
Speaker 1: assumed I would be a literature professor. That just seems
Speaker 1: my destiny. And because of distribution requirements, you had to
Speaker 1: take one science class by the end of sophomore year.
Speaker 1: So I took Introduction to Environmental Engineering just because it
Speaker 1: fit my schedule. And I came out of the final
Speaker 1: and I was like, Oh, climate change is the most
Speaker 1: important thing of my generation, and now no, I must
Speaker 1: spend my life working on this. So you founded a
Speaker 1: company called Streetlight. Tell me a little bit about what
Speaker 1: it does. Street Light is a transportation analytics company. So
Speaker 1: from the perspective of somebody making transportation decisions, like a
Speaker 1: government or an engineering firm, or somebody starting like a
Speaker 1: delivery company before street Light, they're operating a world with
Speaker 1: little data. So you have to make a decision about
Speaker 1: like should I spend two billion dollars on this highway
Speaker 1: extension or a billion dollars on this new transit line.
Speaker 1: But there was very little data to guide you. So
Speaker 1: what street Light does is we take advantage of the
Speaker 1: fact that everything that moves now is collecting data. Smartphones,
Speaker 1: connected cars, connected trucks, little scooters. There's data embedded in stoplights, everything,
Speaker 1: and we license it from all different types of places
Speaker 1: and a privacy appropriate manager and smush it together with
Speaker 1: a lot of p terry algorithms and machine learning so
Speaker 1: that you can look up a transportation fact as easily
Speaker 1: as you might look up a fact in Wikipedia. So
Speaker 1: what would you say The benefit of this data is
Speaker 1: for whom our product is not for consumers like you
Speaker 1: or me. It's for transportation professionals, and that's usually someone
Speaker 1: in government, someone an engineering firm, or someone with their
Speaker 1: own private transportation company like Uber or a private tollway
Speaker 1: or things like that. So the benefit is, instead of
Speaker 1: making decision based on somebody yelling at you and kind
Speaker 1: of your gut, you make a decision that is based
Speaker 1: on data and that is based on a real understanding
Speaker 1: of the context. That's the fundamental benefit. Now, then there's
Speaker 1: a bigger question, which is what is the benefit of
Speaker 1: making a good decision? And we are trying to push
Speaker 1: people towards decision that are less carbon intensive, so create
Speaker 1: less climate emissions from transportation. Also better for expenditures of
Speaker 1: tax dollars being more wise, as well as improving safety
Speaker 1: outcomes in transportation. This all sounds very noble, and I'd
Speaker 1: be interested to know how you picked this specific lane.
Speaker 1: I would say, when well, I guess lane, we're talking
Speaker 1: about translation lane. How would you pick this specific lane
Speaker 1: when it comes to trying to solve for climate change?
Speaker 1: The climate community is paying less attention to transportation than
Speaker 1: to other parts of the climate crisis. So really I
Speaker 1: could have chosen anything, because it's all worth a lifetime,
Speaker 1: But I was like, well, I got to pick something.
Speaker 1: Transportation I think has less people, so it deserves my attention.
Speaker 1: And also it's just interesting. I mean, where people go,
Speaker 1: how they think about the new round about in their city,
Speaker 1: how they think about their commute. I have. As soon
Speaker 1: as you say you're in transportation, your cocktail party conversations
Speaker 1: are covered because everybody wants to talk about it. You're
Speaker 1: a for profit company, so how do you control or
Speaker 1: and how can you really give guidance the folks to
Speaker 1: make why is the cis when it comes to climate change?
Speaker 1: Rather than just using your data to maybe monetize or
Speaker 1: build more malls, just give more opportunities to sell things
Speaker 1: the people rather than actually doing the things that would
Speaker 1: improve the planet. That is a really good question. So
Speaker 1: before I started street Light, I had never worked at
Speaker 1: a for profit company. I had worked for nonprofits, not
Speaker 1: for profits, and geo's. I'd worked for the government. I'd
Speaker 1: been an academia. I'd been getting my PhD. With all
Speaker 1: love and respect to my nonprofit friends and colleagues and
Speaker 1: my government friends and colleagues, I had felt a little
Speaker 1: bit frustrated by the scope you can get when you're
Speaker 1: at a nonprofit and you're constantly scrambling for money. I
Speaker 1: had seen a lot of green tech companies that I
Speaker 1: thought were scaling their impact faster than I believed possible
Speaker 1: in the nonprofit sector. But there's a trade off there
Speaker 1: because we almost never tell our customer you should do
Speaker 1: this because it's greener, you should do this because it's
Speaker 1: better for asthma costing pollutants. If they ask us about that,
Speaker 1: we'll all is given the answer. But as data providers,
Speaker 1: we have to be neutral because we're a source of truth.
Speaker 1: So I have a fundamental belief that informed decisions will
Speaker 1: arc towards changes that I want to see in the world.
Speaker 1: And one of the reason the transportation world is not
Speaker 1: headed in the direction that I like is because we
Speaker 1: don't use data. We base decisions on what we've done
Speaker 1: in the past or the more powerful lobby. So there
Speaker 1: is a faith jump in choosing to go that for
Speaker 1: profit route. Do you think that's a little more optimistic
Speaker 1: than is necessary to actually solve the problem of climate change?
Speaker 1: Because I think that people, when they have more information
Speaker 1: make better decisions. I think that's true, but it also
Speaker 1: means that we have to trust the person making the
Speaker 1: decision that they'll make the right one. In general, I
Speaker 1: am skeptical about people just because they have better information
Speaker 1: always making the right decision. But in transportation in particular,
Speaker 1: what we are dealing with is this immense inertia of
Speaker 1: building more highways to facilitate more cars going fast. Biking
Speaker 1: and walking are hugely important parts of solving transportation climate emissions. So,
Speaker 1: as one example, a lot of attention before COVID and
Speaker 1: during COVID has been drawn to the fact that bicycle
Speaker 1: and pedestrian deaths are on the up and if people
Speaker 1: are dying and it's getting a lot of press. A.
Speaker 1: I do not want people to die, and B it's
Speaker 1: also a challenge for our bigger goal of a more
Speaker 1: climate friendly transportation world. One of the major issues that
Speaker 1: came up is nobody knows like bike bike deaths per
Speaker 1: what Like nobody knows how many bike trips there are
Speaker 1: in the US or in a neighborhood or in a city.
Speaker 1: And we had that data available and that has become
Speaker 1: one of our top use cases in under eighteen months.
Speaker 1: I think many states and cities are make much more
Speaker 1: wise and informed decisions about bicycle and pedestrian safety, which
Speaker 1: a is good for humans and b is good for climate.
Speaker 1: As a data driven person, you're putting a lot of faith.
Speaker 1: Are you seeing more of people making good decisions based
Speaker 1: on this data than you are of them making profitable
Speaker 1: or capitalistic decisions based on this data. I don't know
Speaker 1: if I've ever calculated the ratio, but I always say
Speaker 1: streetlight is not a magic box that tells you what
Speaker 1: to do. It's a tool to help smart people do
Speaker 1: what they want to do more effectively. Now, those smart
Speaker 1: people could want to be doing capitalistic things. But one
Speaker 1: thing that has been huge in my industry and transportation
Speaker 1: and urban planning is the people who are coming into
Speaker 1: the industry do not look like and are not motivated
Speaker 1: by the same things that the people who came in
Speaker 1: ten years ago or twenty years ago or fifty years ago,
Speaker 1: many of whom are still just starting to retire. And
Speaker 1: this generation of people coming into it are very motivated
Speaker 1: by the right decisions, and we're trying to give them
Speaker 1: tools so they can get done what they want to
Speaker 1: get done. And I also will say with the government clients,
Speaker 1: most of them that I meet, even if they are
Speaker 1: very dedicated to say highway expansion, which is something that
Speaker 1: if I had to some arise my life goal, it
Speaker 1: would be to stop highway expansion in the United States.
Speaker 1: That would be it. Even if they are they are
Speaker 1: motivated by something they perceive as good, like they perceive
Speaker 1: it as good for their community, and they perceive it
Speaker 1: as good for jobs in their community. People who go
Speaker 1: into government don't go into government to get rich, right,
Speaker 1: And I'm not talking about the electeds. I don't deal
Speaker 1: with electeds all that off, and I deal with staff.
Speaker 1: And even if I don't agree with their definition of good,
Speaker 1: we both agree we're trying to do something for our community.
Speaker 1: So there's a commonplace to start talking. Tell me a
Speaker 1: little bit about where you get your data from. We
Speaker 1: use lots of different types of data. We're like a
Speaker 1: surfer and we're surfing the wave of data, and data
Speaker 1: is this always changing thing, and whatever data we use today,
Speaker 1: it'll be different six months from now, in a year
Speaker 1: from now. But right now, the main data we use
Speaker 1: that's the most important is from smartphones. So we have
Speaker 1: an opt in process, which is a much more privacy
Speaker 1: pro privacy process where people can work with one of
Speaker 1: our four hundred and so AT partners and opt into
Speaker 1: deidentified locational tracking in the background. And what that means
Speaker 1: is we don't know you're Ronald. You have a hash
Speaker 1: to identifier, and so we never get any what's called
Speaker 1: personally identifiable information, and we don't know your name, your
Speaker 1: phone number, anything like that. We also get data from
Speaker 1: connected cars that have GPS tooling in them, as well
Speaker 1: as fleet management systems, which are truck management systems. Trucks
Speaker 1: rip up the road and cause safety impacts in a
Speaker 1: very different way than cars, so it's important to study
Speaker 1: them separately. But the deep benefit of the phones is
Speaker 1: that they cover all the modes of transportation and one
Speaker 1: of street lights core missions and sort of differentiators. One
Speaker 1: of the things we've done that's really new is we
Speaker 1: measure all the modes car, truck, bike, ped riding, a train,
Speaker 1: riding a bus, riding a ferry, eventually, jetpack whatever. And
Speaker 1: that is something that has never really been available before.
Speaker 1: And that's one of the reasons that cars keep getting
Speaker 1: this hegemony is because they're the only things that are
Speaker 1: consistently measured. So we have all those breadcrumbs floating around,
Speaker 1: and then we mix it with data from embedded sensors
Speaker 1: in the roadways that help us calibrate. We mix it
Speaker 1: with bite counter data, padcounter data, data from bus ridership
Speaker 1: data that says, you know, low income people live here,
Speaker 1: high income people live here, this is a road with
Speaker 1: fifty mile per hour speed limit, this is the ocean,
Speaker 1: all sorts of contextual data to turn it into actionable
Speaker 1: and aggregate analytics. One of the drawbacks to a data
Speaker 1: company like street Light is that you guys are selling
Speaker 1: your data to people that could pay. It's not something
Speaker 1: that's free to public, but it's information about public movements.
Speaker 1: Are you concerned to having to pay for this data
Speaker 1: hinders its ability to be truly useful. It's a great question,
Speaker 1: so I am, of course somewhat concerned. Some of the
Speaker 1: mitigation steps we take are all academic researchers who are
Speaker 1: researching something within our mission, which is climate equity safety,
Speaker 1: get free access you just failed to form. And we
Speaker 1: have like seventy five universities that we're working with who
Speaker 1: are doing totally free research based on our data. And
Speaker 1: we also have fellowships where nonprofits can apply to get
Speaker 1: free research, and we also help promote their research. But
Speaker 1: that is mitigation. That's not fixing the fundamental problem you've
Speaker 1: talked about. And I don't have a great fix. And
Speaker 1: we have a hundred staff, We spend a lot of
Speaker 1: money on the cloud, and what we do is expensive.
Speaker 1: We have to survive. And I think that in America
Speaker 1: we have consistently disinvested in government driven collection of data.
Speaker 1: But because we've made that decision that data is something
Speaker 1: that private markets are going to develop, we can't have
Speaker 1: everything online. What issues are you eager to see solved
Speaker 1: in transportation in the next five years. There's a big
Speaker 1: conversation right now in the Senate about everybody's saying in
Speaker 1: the infrastructure bill. Oh yeah, we should measure equity in transportation,
Speaker 1: And they're like, how, no one knows, there's no way,
Speaker 1: Like I mean, there's forty five thousand ways, there's no agreement.
Speaker 1: So there's going to be a lot of quick work
Speaker 1: on that. So I'm very interested in that. Street Light
Speaker 1: is working on a lot more direct carbon and equity
Speaker 1: measurements now that we have a Biden administration. That opens
Speaker 1: up the space where that could be used. So we
Speaker 1: are going to make a lot of tooling that's more
Speaker 1: mission direct in addition to our more neutral data collection efforts.
Speaker 1: So that is starting now. We need to solve the
Speaker 1: question of what does it mean to have equitable transportation
Speaker 1: and how do you define it because there's no good
Speaker 1: there's no good definition right now, and we're collaborating with
Speaker 1: some nonprofits and some advocacy organizations to get explicit measurements
Speaker 1: about that. The fact that we can measure the income
Speaker 1: and racial distribution of where people move is a huge
Speaker 1: leap forward and starting to measure transportation equity. I mean,
Speaker 1: it sounds like you're saying that transportation equity begins with data.
Speaker 1: I think everything begins with data. So take that with
Speaker 1: a grain of salt. I mean, transportation is in a
Speaker 1: bad way in America. It's dissequitable, it's destroying the climate.
Speaker 1: It kills forty three thousand people a year, and like
Speaker 1: our bridges are falling down, Like we're pretty bad. So
Speaker 1: we have to change. And to think of a massive
Speaker 1: systemic change without data, I just think that's insane. But
Speaker 1: I agree it's not data alone. We are a tool
Speaker 1: for smart people motivated by the right things to do
Speaker 1: their job more easily. How do you motivate private companies
Speaker 1: to care about public issues like climate change? There are
Speaker 1: two ways you get corporations to care about climate change.
Speaker 1: One is you point out to them that it will
Speaker 1: have a huge impact on their bottom line either today
Speaker 1: or in ten years. And a lot of corporations are
Speaker 1: there right, they get that. And the second way is
Speaker 1: their staff starts to throw a fit, so we help.
Speaker 1: We have helped some staff throw fits quietly. We don't
Speaker 1: do it that directly. That's what we do. Do you
Speaker 1: do you want us to include that? The main thing
Speaker 1: we point out is that staff. If all your staff
Speaker 1: have to drive their own cars fifty miles each way,
Speaker 1: like that will dwarf the climate impact of your office
Speaker 1: building within a couple of years. Gotcha, So we've we've
Speaker 1: worked on that. Okay, I think that's a good strategy.
Speaker 1: How can our listeners help, Like, what could someone who
Speaker 1: wants to be like a more responsible and better city resident?
Speaker 1: How can they help right now? And what about people
Speaker 1: that don't live in like bustling cities, people that live
Speaker 1: in more rural areas. How can we all help make
Speaker 1: it make transportation better for everyone? Well, one thing that
Speaker 1: I think is fun is to track your own data
Speaker 1: for a few days. One thing that I think sounds
Speaker 1: simple but nobody gets is that your short trips are
Speaker 1: less carbon emitting than your long trips. So I've had
Speaker 1: some friends, you know, lovely eco hippie friends who say
Speaker 1: do things to me like, well, you know, I take
Speaker 1: a bus every day to work. The only reason I
Speaker 1: have a car is for like, you know, we can
Speaker 1: advent to go hiking. And I'm like, well that is
Speaker 1: a hundred and ten mile drive. Like, I'd rather you
Speaker 1: drive to work every day and maybe car pool or
Speaker 1: take the train for you to adventures. So really think
Speaker 1: it's the length of the drip that matters, So track
Speaker 1: your own data. I think it will surprise you, it
Speaker 1: might make you more open to an electric car, and
Speaker 1: it might help you think about which trips really matter.
Speaker 1: I also think that as a citizen, as a private individual,
Speaker 1: there are three personal infrastructure decisions you make in transportation.
Speaker 1: Where you live, which car you buy, if you buy
Speaker 1: a car, and where you spend most of your days,
Speaker 1: which is usually where you work. And if you optimize
Speaker 1: those decisions a little, like if you move to an
Speaker 1: apartment that's closer to work, or choose a job that's
Speaker 1: a little closer, you've optimized your transportation footprint and you
Speaker 1: don't have to like agonize about it every day. So
Speaker 1: optimize those big You know, if you choose a car
Speaker 1: that gets fifty mpg over forty mpg, you don't have
Speaker 1: to think about it as much. So like think about
Speaker 1: the big decisions that matter, and like stop freaking out
Speaker 1: if you forget your cloth grocery bags once. Those infrastructure
Speaker 1: decisions matter more where you live, where you work, the
Speaker 1: relationship between them, and what car you drive if you
Speaker 1: drive a car. As citizens, citizens have a lot of
Speaker 1: power about city level urban design decisions. If you show up,
Speaker 1: there's always feedback meetings, and if you show up, you
Speaker 1: will make a difference. And I think the other thing
Speaker 1: to remember is a lot of the people show up
Speaker 1: are people who have a very vested interest in things
Speaker 1: being the same, which may be good. Sometimes it's not,
Speaker 1: or people who just assume the worst. And usually the
Speaker 1: staff at these meetings, again it's not elected, it's staff
Speaker 1: who've chosen this career. Usually they are trying really hard.
Speaker 1: And if someone calmly showed up and said, can you
Speaker 1: show me the data? Can you show me the alternatives,
Speaker 1: they would be so excited and that person would be
Speaker 1: so impactful. So showing up and showing up trying to
Speaker 1: work with the staff instead of assuming the worst of them,
Speaker 1: I think is really powerful. You have any books or
Speaker 1: movies that you think you would recommend for people to
Speaker 1: learn more about transportation and transportation equity, Well, now I
Speaker 1: want everybody to watch Mission Impossible, Three Three Angels Protocol
Speaker 1: are the best ones. So okay, now I know I'll
Speaker 1: watch them. I'll watch them tonight. My favorite book about
Speaker 1: transportation is by John McPhee. It's called Uncommon Carriers, and
Speaker 1: it's a book about the people who do the work
Speaker 1: of freight hauling. He just writes so beautifully and with
Speaker 1: such dignity about the people who do this work and
Speaker 1: the incredibleness of the machines that get our T shirts
Speaker 1: and our popcorn to our houses, like whether it's the
Speaker 1: freight boats with the giant containers or the long contructs.
Speaker 1: That book. It gave me a sense of awe and
Speaker 1: respect for the sort of societal achievement that is our
Speaker 1: transportation system. And I think that's healthy if you're thinking
Speaker 1: about changing something. The Infrastructure Bill is not called the
Speaker 1: Infrastructure Bill, It's called the American Jobs Act. And I
Speaker 1: think it is very important, especially for technologists like me,
Speaker 1: that we don't just assume that our efficient approach and
Speaker 1: it is more efficient, is neutral like to society, and
Speaker 1: to think about these industries we're disrupting. Thank you so
Speaker 1: much for being with us, Laura, My pleasure. Laura Sewell
Speaker 1: is the CEO of street Light Data. Will include linkster
Speaker 1: suggestions on ways to learn more about transportation and data
Speaker 1: analytics in our show notes. Next time on Solvable, we're
Speaker 1: talking about sugar, salt, fat, all the good stuff and
Speaker 1: how to solve food addiction. But before you turn away
Speaker 1: feeling annoyed and clinging to a bag of delicious cheese puffs.
Speaker 1: Here's a little preview. The solution is not all on you.
Speaker 1: I hope you'll join us for that conversation. Solvable is
Speaker 1: produced by Joscelyn Frank, research by David Jack, booking by
Speaker 1: Lisa Dunn. Our managing producer is Sasha Matthias, and our
Speaker 1: executive producer is Mio Lobel. I'm Ronald Young Jr. Thanks
Speaker 1: for listening.