1 00:00:15,076 --> 00:00:24,436 Speaker 1: Pushkin, this is solvable. I'm Ronald Young Junior. We are 2 00:00:24,436 --> 00:00:28,036 Speaker 1: trying to push people towards decision that are less carbon intensive, 3 00:00:28,196 --> 00:00:32,276 Speaker 1: so create less climate emissions from transportation. Also better for 4 00:00:32,676 --> 00:00:36,036 Speaker 1: expenditures of tax dollars being more wise, as well as 5 00:00:36,076 --> 00:00:39,836 Speaker 1: improving safety outcomes in transportation. For Laura Schuel, making greened 6 00:00:39,876 --> 00:00:42,996 Speaker 1: decisions isn't a matter of juggling one hundred little choices 7 00:00:43,036 --> 00:00:47,236 Speaker 1: like wind, unplug appliances, eat less meat, or swap out 8 00:00:47,236 --> 00:00:50,476 Speaker 1: incandescent bulbs. For LEDs, choose a car that gets fifty 9 00:00:50,556 --> 00:00:53,276 Speaker 1: mpg over forty mpg, you don't have to think about 10 00:00:53,276 --> 00:00:55,436 Speaker 1: it as much. So like think about the big decisions 11 00:00:55,476 --> 00:00:57,436 Speaker 1: that matter and like stop freaking out if you forget 12 00:00:57,476 --> 00:01:01,676 Speaker 1: your cloth grocery bags. Once. As CEO of street Light Data, 13 00:01:01,996 --> 00:01:05,236 Speaker 1: Shoel has the data to back this stuff up, but 14 00:01:06,036 --> 00:01:10,516 Speaker 1: you won't find her postilytizing yet. I have a fundamental 15 00:01:10,596 --> 00:01:15,476 Speaker 1: belief that informed decisions will arc towards changes that I 16 00:01:15,516 --> 00:01:18,676 Speaker 1: want to see in the world. But we, as data providers, 17 00:01:19,036 --> 00:01:21,356 Speaker 1: we have to be neutral because we're a source of truth. 18 00:01:21,996 --> 00:01:25,716 Speaker 1: Street Light Data is a transportation analytics company, and Laura 19 00:01:25,836 --> 00:01:29,436 Speaker 1: Schuel thinks how we move can be changed for the better. 20 00:01:29,996 --> 00:01:32,956 Speaker 1: My name is Laura Schuel. I'm the CEO of street 21 00:01:32,996 --> 00:01:36,676 Speaker 1: Light Data. How to monitor and mold our transportation systems 22 00:01:36,676 --> 00:01:47,676 Speaker 1: into smart and environmentally friendly systems is a solvable problem. 23 00:01:47,716 --> 00:01:51,036 Speaker 1: I recently watched Bishion Impossible three and Tom Cruise, who 24 00:01:51,036 --> 00:01:53,796 Speaker 1: plays Ethan Hunt, is a part of the Impossible Mission force. 25 00:01:53,876 --> 00:01:58,316 Speaker 1: In this one, he's actually getting married and as a result, 26 00:01:58,356 --> 00:02:00,996 Speaker 1: his cover for working for the Impossible Bishion Force is 27 00:02:00,996 --> 00:02:03,436 Speaker 1: that he works for the Bureau of Transportation. So he's 28 00:02:03,436 --> 00:02:05,956 Speaker 1: at a party and he makes this cobbent where he goes. Yeah, 29 00:02:05,996 --> 00:02:08,356 Speaker 1: traffic patterns are so crazy. It moves, it's like an organism. 30 00:02:08,436 --> 00:02:10,636 Speaker 1: It's very interesting thing. And everyone kind of looks at 31 00:02:10,716 --> 00:02:12,956 Speaker 1: him like this guy's kind of boring. And so what 32 00:02:13,036 --> 00:02:15,436 Speaker 1: I'm thinking about our listeners, I'm thinking about them and 33 00:02:15,516 --> 00:02:19,036 Speaker 1: thinking about data could beat something that's like extremely boring 34 00:02:19,116 --> 00:02:21,556 Speaker 1: to them. But give me a piece of data that's 35 00:02:21,636 --> 00:02:24,236 Speaker 1: excites you the most and something that you think a listener, 36 00:02:24,396 --> 00:02:26,996 Speaker 1: just a casual person would find interesting when it comes 37 00:02:26,996 --> 00:02:30,436 Speaker 1: to talking about transportation data. Well, first of all, that 38 00:02:30,516 --> 00:02:32,596 Speaker 1: movie sounds awesome, and my company is immediately going to 39 00:02:32,636 --> 00:02:35,076 Speaker 1: have a movie night about it. Second of all, he's 40 00:02:35,156 --> 00:02:37,196 Speaker 1: not boring. He sounds like the most interesting person at 41 00:02:37,196 --> 00:02:40,796 Speaker 1: the party. I love that. One of the things that 42 00:02:40,836 --> 00:02:44,316 Speaker 1: I hope is coming with Secretary Budajes and President Biden 43 00:02:44,836 --> 00:02:48,156 Speaker 1: is a demand that we calculate our transportation greenhouse gases, 44 00:02:48,156 --> 00:02:50,316 Speaker 1: which we don't really do. We kind of swag it. 45 00:02:50,796 --> 00:02:53,076 Speaker 1: One of the concerns with that is that then it 46 00:02:53,116 --> 00:02:56,876 Speaker 1: would be bad for rural places, because rural places, especially 47 00:02:56,876 --> 00:02:58,836 Speaker 1: with highways, are places where you have a lot of 48 00:02:58,876 --> 00:03:02,276 Speaker 1: miles driven that have nothing to do with that rural place. 49 00:03:02,356 --> 00:03:05,556 Speaker 1: Right people are cutting through min DOT Minnesota DOOT used 50 00:03:05,556 --> 00:03:09,116 Speaker 1: our data to do a version of greenhouse gas emission 51 00:03:09,316 --> 00:03:13,196 Speaker 1: that attribute the miles driven to the destination of the 52 00:03:13,236 --> 00:03:15,436 Speaker 1: car in the truck. And what that does is it 53 00:03:15,516 --> 00:03:19,876 Speaker 1: properly demands payment, so to speak, from the cities from 54 00:03:19,876 --> 00:03:21,716 Speaker 1: the fact that they cause all this driving to and 55 00:03:21,796 --> 00:03:25,796 Speaker 1: from them, and doesn't put disproportionate cost in carbon on 56 00:03:25,836 --> 00:03:29,076 Speaker 1: the rural areas. That sort of data driven approach could 57 00:03:29,116 --> 00:03:31,716 Speaker 1: reduce some of the future pushback we're going to see 58 00:03:31,956 --> 00:03:35,836 Speaker 1: about carbon accounting from transportation. One interesting thing that I 59 00:03:35,876 --> 00:03:38,756 Speaker 1: heard about you was that you started off as a 60 00:03:38,796 --> 00:03:43,516 Speaker 1: comparative literature major. So how did you end up going 61 00:03:43,596 --> 00:03:50,276 Speaker 1: for comparative literature to data analytics. Ah, it is a 62 00:03:50,356 --> 00:03:53,396 Speaker 1: classic story of a great professor. So I was in 63 00:03:53,436 --> 00:03:56,076 Speaker 1: college and I was majoring in comparative literature, and I 64 00:03:56,116 --> 00:03:59,516 Speaker 1: assumed I would be a literature professor. That just seems 65 00:03:59,556 --> 00:04:03,156 Speaker 1: my destiny. And because of distribution requirements, you had to 66 00:04:03,196 --> 00:04:05,796 Speaker 1: take one science class by the end of sophomore year. 67 00:04:06,396 --> 00:04:10,916 Speaker 1: So I took Introduction to Environmental Engineering just because it 68 00:04:10,996 --> 00:04:13,316 Speaker 1: fit my schedule. And I came out of the final 69 00:04:13,396 --> 00:04:15,796 Speaker 1: and I was like, Oh, climate change is the most 70 00:04:15,836 --> 00:04:19,156 Speaker 1: important thing of my generation, and now no, I must 71 00:04:19,516 --> 00:04:23,076 Speaker 1: spend my life working on this. So you founded a 72 00:04:23,076 --> 00:04:26,396 Speaker 1: company called Streetlight. Tell me a little bit about what 73 00:04:26,476 --> 00:04:30,996 Speaker 1: it does. Street Light is a transportation analytics company. So 74 00:04:31,236 --> 00:04:34,596 Speaker 1: from the perspective of somebody making transportation decisions, like a 75 00:04:34,676 --> 00:04:37,996 Speaker 1: government or an engineering firm, or somebody starting like a 76 00:04:38,036 --> 00:04:41,236 Speaker 1: delivery company before street Light, they're operating a world with 77 00:04:41,316 --> 00:04:44,076 Speaker 1: little data. So you have to make a decision about 78 00:04:44,116 --> 00:04:46,356 Speaker 1: like should I spend two billion dollars on this highway 79 00:04:46,356 --> 00:04:49,916 Speaker 1: extension or a billion dollars on this new transit line. 80 00:04:50,116 --> 00:04:51,796 Speaker 1: But there was very little data to guide you. So 81 00:04:51,836 --> 00:04:54,076 Speaker 1: what street Light does is we take advantage of the 82 00:04:54,116 --> 00:04:57,556 Speaker 1: fact that everything that moves now is collecting data. Smartphones, 83 00:04:57,596 --> 00:05:03,076 Speaker 1: connected cars, connected trucks, little scooters. There's data embedded in stoplights, everything, 84 00:05:03,396 --> 00:05:05,476 Speaker 1: and we license it from all different types of places 85 00:05:05,476 --> 00:05:08,516 Speaker 1: and a privacy appropriate manager and smush it together with 86 00:05:08,556 --> 00:05:11,036 Speaker 1: a lot of p terry algorithms and machine learning so 87 00:05:11,076 --> 00:05:13,676 Speaker 1: that you can look up a transportation fact as easily 88 00:05:13,716 --> 00:05:16,036 Speaker 1: as you might look up a fact in Wikipedia. So 89 00:05:16,116 --> 00:05:18,076 Speaker 1: what would you say The benefit of this data is 90 00:05:18,156 --> 00:05:22,476 Speaker 1: for whom our product is not for consumers like you 91 00:05:22,556 --> 00:05:25,956 Speaker 1: or me. It's for transportation professionals, and that's usually someone 92 00:05:25,996 --> 00:05:29,236 Speaker 1: in government, someone an engineering firm, or someone with their 93 00:05:29,276 --> 00:05:34,316 Speaker 1: own private transportation company like Uber or a private tollway 94 00:05:34,396 --> 00:05:36,596 Speaker 1: or things like that. So the benefit is, instead of 95 00:05:36,636 --> 00:05:40,036 Speaker 1: making decision based on somebody yelling at you and kind 96 00:05:40,036 --> 00:05:42,636 Speaker 1: of your gut, you make a decision that is based 97 00:05:42,676 --> 00:05:45,476 Speaker 1: on data and that is based on a real understanding 98 00:05:45,476 --> 00:05:49,756 Speaker 1: of the context. That's the fundamental benefit. Now, then there's 99 00:05:49,756 --> 00:05:51,636 Speaker 1: a bigger question, which is what is the benefit of 100 00:05:51,676 --> 00:05:54,116 Speaker 1: making a good decision? And we are trying to push 101 00:05:54,116 --> 00:05:57,716 Speaker 1: people towards decision that are less carbon intensive, so create 102 00:05:57,836 --> 00:06:02,476 Speaker 1: less climate emissions from transportation. Also better for expenditures of 103 00:06:02,476 --> 00:06:05,956 Speaker 1: tax dollars being more wise, as well as improving safety 104 00:06:05,956 --> 00:06:11,156 Speaker 1: outcomes in transportation. This all sounds very noble, and I'd 105 00:06:11,196 --> 00:06:17,396 Speaker 1: be interested to know how you picked this specific lane. 106 00:06:17,436 --> 00:06:19,436 Speaker 1: I would say, when well, I guess lane, we're talking 107 00:06:19,476 --> 00:06:23,196 Speaker 1: about translation lane. How would you pick this specific lane 108 00:06:24,156 --> 00:06:26,316 Speaker 1: when it comes to trying to solve for climate change? 109 00:06:27,076 --> 00:06:30,076 Speaker 1: The climate community is paying less attention to transportation than 110 00:06:30,116 --> 00:06:32,636 Speaker 1: to other parts of the climate crisis. So really I 111 00:06:32,636 --> 00:06:35,636 Speaker 1: could have chosen anything, because it's all worth a lifetime, 112 00:06:35,956 --> 00:06:37,356 Speaker 1: But I was like, well, I got to pick something. 113 00:06:37,796 --> 00:06:41,876 Speaker 1: Transportation I think has less people, so it deserves my attention. 114 00:06:42,076 --> 00:06:44,836 Speaker 1: And also it's just interesting. I mean, where people go, 115 00:06:45,596 --> 00:06:48,556 Speaker 1: how they think about the new round about in their city, 116 00:06:48,876 --> 00:06:51,036 Speaker 1: how they think about their commute. I have. As soon 117 00:06:51,076 --> 00:06:53,756 Speaker 1: as you say you're in transportation, your cocktail party conversations 118 00:06:53,756 --> 00:07:02,156 Speaker 1: are covered because everybody wants to talk about it. You're 119 00:07:02,156 --> 00:07:05,596 Speaker 1: a for profit company, so how do you control or 120 00:07:05,636 --> 00:07:08,196 Speaker 1: and how can you really give guidance the folks to 121 00:07:08,276 --> 00:07:10,516 Speaker 1: make why is the cis when it comes to climate change? 122 00:07:10,796 --> 00:07:13,956 Speaker 1: Rather than just using your data to maybe monetize or 123 00:07:14,556 --> 00:07:18,396 Speaker 1: build more malls, just give more opportunities to sell things 124 00:07:18,476 --> 00:07:21,076 Speaker 1: the people rather than actually doing the things that would 125 00:07:21,076 --> 00:07:24,596 Speaker 1: improve the planet. That is a really good question. So 126 00:07:24,636 --> 00:07:26,876 Speaker 1: before I started street Light, I had never worked at 127 00:07:26,916 --> 00:07:29,916 Speaker 1: a for profit company. I had worked for nonprofits, not 128 00:07:30,036 --> 00:07:33,596 Speaker 1: for profits, and geo's. I'd worked for the government. I'd 129 00:07:33,596 --> 00:07:36,876 Speaker 1: been an academia. I'd been getting my PhD. With all 130 00:07:37,756 --> 00:07:41,436 Speaker 1: love and respect to my nonprofit friends and colleagues and 131 00:07:41,476 --> 00:07:44,156 Speaker 1: my government friends and colleagues, I had felt a little 132 00:07:44,156 --> 00:07:47,356 Speaker 1: bit frustrated by the scope you can get when you're 133 00:07:47,396 --> 00:07:50,596 Speaker 1: at a nonprofit and you're constantly scrambling for money. I 134 00:07:50,636 --> 00:07:52,516 Speaker 1: had seen a lot of green tech companies that I 135 00:07:52,556 --> 00:07:56,716 Speaker 1: thought were scaling their impact faster than I believed possible 136 00:07:56,756 --> 00:07:59,156 Speaker 1: in the nonprofit sector. But there's a trade off there 137 00:07:59,436 --> 00:08:02,236 Speaker 1: because we almost never tell our customer you should do 138 00:08:02,276 --> 00:08:04,756 Speaker 1: this because it's greener, you should do this because it's 139 00:08:05,356 --> 00:08:08,716 Speaker 1: better for asthma costing pollutants. If they ask us about that, 140 00:08:08,756 --> 00:08:12,516 Speaker 1: we'll all is given the answer. But as data providers, 141 00:08:12,836 --> 00:08:15,196 Speaker 1: we have to be neutral because we're a source of truth. 142 00:08:15,876 --> 00:08:20,796 Speaker 1: So I have a fundamental belief that informed decisions will 143 00:08:20,916 --> 00:08:23,356 Speaker 1: arc towards changes that I want to see in the world. 144 00:08:23,676 --> 00:08:26,036 Speaker 1: And one of the reason the transportation world is not 145 00:08:26,116 --> 00:08:28,236 Speaker 1: headed in the direction that I like is because we 146 00:08:28,236 --> 00:08:30,436 Speaker 1: don't use data. We base decisions on what we've done 147 00:08:30,476 --> 00:08:33,676 Speaker 1: in the past or the more powerful lobby. So there 148 00:08:33,756 --> 00:08:36,996 Speaker 1: is a faith jump in choosing to go that for 149 00:08:37,156 --> 00:08:40,836 Speaker 1: profit route. Do you think that's a little more optimistic 150 00:08:41,076 --> 00:08:44,756 Speaker 1: than is necessary to actually solve the problem of climate change? 151 00:08:44,796 --> 00:08:46,796 Speaker 1: Because I think that people, when they have more information 152 00:08:46,916 --> 00:08:50,076 Speaker 1: make better decisions. I think that's true, but it also 153 00:08:50,316 --> 00:08:52,156 Speaker 1: means that we have to trust the person making the 154 00:08:52,196 --> 00:08:55,996 Speaker 1: decision that they'll make the right one. In general, I 155 00:08:56,036 --> 00:08:59,956 Speaker 1: am skeptical about people just because they have better information 156 00:08:59,996 --> 00:09:03,556 Speaker 1: always making the right decision. But in transportation in particular, 157 00:09:04,036 --> 00:09:07,156 Speaker 1: what we are dealing with is this immense inertia of 158 00:09:07,276 --> 00:09:11,596 Speaker 1: building more highways to facilitate more cars going fast. Biking 159 00:09:11,596 --> 00:09:16,516 Speaker 1: and walking are hugely important parts of solving transportation climate emissions. So, 160 00:09:16,836 --> 00:09:20,396 Speaker 1: as one example, a lot of attention before COVID and 161 00:09:20,476 --> 00:09:22,676 Speaker 1: during COVID has been drawn to the fact that bicycle 162 00:09:22,716 --> 00:09:26,116 Speaker 1: and pedestrian deaths are on the up and if people 163 00:09:26,116 --> 00:09:28,036 Speaker 1: are dying and it's getting a lot of press. A. 164 00:09:28,276 --> 00:09:30,516 Speaker 1: I do not want people to die, and B it's 165 00:09:30,156 --> 00:09:33,716 Speaker 1: also a challenge for our bigger goal of a more 166 00:09:33,836 --> 00:09:38,476 Speaker 1: climate friendly transportation world. One of the major issues that 167 00:09:38,516 --> 00:09:42,276 Speaker 1: came up is nobody knows like bike bike deaths per 168 00:09:42,316 --> 00:09:44,836 Speaker 1: what Like nobody knows how many bike trips there are 169 00:09:45,276 --> 00:09:47,876 Speaker 1: in the US or in a neighborhood or in a city. 170 00:09:48,156 --> 00:09:50,596 Speaker 1: And we had that data available and that has become 171 00:09:50,596 --> 00:09:53,796 Speaker 1: one of our top use cases in under eighteen months. 172 00:09:54,356 --> 00:09:56,796 Speaker 1: I think many states and cities are make much more 173 00:09:57,556 --> 00:10:01,956 Speaker 1: wise and informed decisions about bicycle and pedestrian safety, which 174 00:10:02,276 --> 00:10:05,036 Speaker 1: a is good for humans and b is good for climate. 175 00:10:05,556 --> 00:10:08,276 Speaker 1: As a data driven person, you're putting a lot of faith. 176 00:10:09,676 --> 00:10:12,836 Speaker 1: Are you seeing more of people making good decisions based 177 00:10:12,836 --> 00:10:15,436 Speaker 1: on this data than you are of them making profitable 178 00:10:15,556 --> 00:10:19,636 Speaker 1: or capitalistic decisions based on this data. I don't know 179 00:10:19,676 --> 00:10:22,356 Speaker 1: if I've ever calculated the ratio, but I always say 180 00:10:22,636 --> 00:10:24,556 Speaker 1: streetlight is not a magic box that tells you what 181 00:10:24,636 --> 00:10:27,556 Speaker 1: to do. It's a tool to help smart people do 182 00:10:27,636 --> 00:10:30,756 Speaker 1: what they want to do more effectively. Now, those smart 183 00:10:30,796 --> 00:10:34,036 Speaker 1: people could want to be doing capitalistic things. But one 184 00:10:34,076 --> 00:10:37,036 Speaker 1: thing that has been huge in my industry and transportation 185 00:10:37,036 --> 00:10:40,356 Speaker 1: and urban planning is the people who are coming into 186 00:10:40,356 --> 00:10:43,356 Speaker 1: the industry do not look like and are not motivated 187 00:10:43,396 --> 00:10:45,356 Speaker 1: by the same things that the people who came in 188 00:10:45,636 --> 00:10:47,996 Speaker 1: ten years ago or twenty years ago or fifty years ago, 189 00:10:48,036 --> 00:10:51,116 Speaker 1: many of whom are still just starting to retire. And 190 00:10:51,196 --> 00:10:55,196 Speaker 1: this generation of people coming into it are very motivated 191 00:10:55,196 --> 00:10:56,836 Speaker 1: by the right decisions, and we're trying to give them 192 00:10:56,876 --> 00:10:58,316 Speaker 1: tools so they can get done what they want to 193 00:10:58,316 --> 00:11:02,036 Speaker 1: get done. And I also will say with the government clients, 194 00:11:02,916 --> 00:11:05,156 Speaker 1: most of them that I meet, even if they are 195 00:11:05,316 --> 00:11:08,316 Speaker 1: very dedicated to say highway expansion, which is something that 196 00:11:08,476 --> 00:11:10,036 Speaker 1: if I had to some arise my life goal, it 197 00:11:10,036 --> 00:11:12,476 Speaker 1: would be to stop highway expansion in the United States. 198 00:11:13,196 --> 00:11:15,756 Speaker 1: That would be it. Even if they are they are 199 00:11:15,836 --> 00:11:20,076 Speaker 1: motivated by something they perceive as good, like they perceive 200 00:11:20,116 --> 00:11:22,436 Speaker 1: it as good for their community, and they perceive it 201 00:11:22,476 --> 00:11:24,996 Speaker 1: as good for jobs in their community. People who go 202 00:11:24,996 --> 00:11:27,316 Speaker 1: into government don't go into government to get rich, right, 203 00:11:28,676 --> 00:11:30,756 Speaker 1: And I'm not talking about the electeds. I don't deal 204 00:11:30,756 --> 00:11:32,396 Speaker 1: with electeds all that off, and I deal with staff. 205 00:11:32,876 --> 00:11:35,076 Speaker 1: And even if I don't agree with their definition of good, 206 00:11:35,356 --> 00:11:37,476 Speaker 1: we both agree we're trying to do something for our community. 207 00:11:37,516 --> 00:11:44,556 Speaker 1: So there's a commonplace to start talking. Tell me a 208 00:11:44,596 --> 00:11:46,956 Speaker 1: little bit about where you get your data from. We 209 00:11:47,036 --> 00:11:49,236 Speaker 1: use lots of different types of data. We're like a 210 00:11:49,356 --> 00:11:51,916 Speaker 1: surfer and we're surfing the wave of data, and data 211 00:11:52,036 --> 00:11:55,396 Speaker 1: is this always changing thing, and whatever data we use today, 212 00:11:55,396 --> 00:11:57,076 Speaker 1: it'll be different six months from now, in a year 213 00:11:57,076 --> 00:11:59,836 Speaker 1: from now. But right now, the main data we use 214 00:11:59,916 --> 00:12:02,996 Speaker 1: that's the most important is from smartphones. So we have 215 00:12:03,196 --> 00:12:05,956 Speaker 1: an opt in process, which is a much more privacy 216 00:12:06,036 --> 00:12:09,636 Speaker 1: pro privacy process where people can work with one of 217 00:12:09,676 --> 00:12:12,836 Speaker 1: our four hundred and so AT partners and opt into 218 00:12:13,036 --> 00:12:16,076 Speaker 1: deidentified locational tracking in the background. And what that means 219 00:12:16,156 --> 00:12:18,156 Speaker 1: is we don't know you're Ronald. You have a hash 220 00:12:18,196 --> 00:12:20,556 Speaker 1: to identifier, and so we never get any what's called 221 00:12:20,596 --> 00:12:22,836 Speaker 1: personally identifiable information, and we don't know your name, your 222 00:12:22,836 --> 00:12:25,796 Speaker 1: phone number, anything like that. We also get data from 223 00:12:25,796 --> 00:12:28,876 Speaker 1: connected cars that have GPS tooling in them, as well 224 00:12:28,876 --> 00:12:33,196 Speaker 1: as fleet management systems, which are truck management systems. Trucks 225 00:12:33,276 --> 00:12:35,476 Speaker 1: rip up the road and cause safety impacts in a 226 00:12:35,556 --> 00:12:37,796 Speaker 1: very different way than cars, so it's important to study 227 00:12:37,836 --> 00:12:40,596 Speaker 1: them separately. But the deep benefit of the phones is 228 00:12:40,636 --> 00:12:43,716 Speaker 1: that they cover all the modes of transportation and one 229 00:12:43,756 --> 00:12:48,076 Speaker 1: of street lights core missions and sort of differentiators. One 230 00:12:48,116 --> 00:12:50,036 Speaker 1: of the things we've done that's really new is we 231 00:12:50,116 --> 00:12:54,196 Speaker 1: measure all the modes car, truck, bike, ped riding, a train, 232 00:12:54,356 --> 00:12:58,996 Speaker 1: riding a bus, riding a ferry, eventually, jetpack whatever. And 233 00:12:59,076 --> 00:13:01,516 Speaker 1: that is something that has never really been available before. 234 00:13:01,556 --> 00:13:03,676 Speaker 1: And that's one of the reasons that cars keep getting 235 00:13:03,676 --> 00:13:05,716 Speaker 1: this hegemony is because they're the only things that are 236 00:13:05,716 --> 00:13:10,956 Speaker 1: consistently measured. So we have all those breadcrumbs floating around, 237 00:13:11,476 --> 00:13:14,716 Speaker 1: and then we mix it with data from embedded sensors 238 00:13:14,716 --> 00:13:17,196 Speaker 1: in the roadways that help us calibrate. We mix it 239 00:13:17,196 --> 00:13:21,156 Speaker 1: with bite counter data, padcounter data, data from bus ridership 240 00:13:21,556 --> 00:13:24,556 Speaker 1: data that says, you know, low income people live here, 241 00:13:24,676 --> 00:13:26,316 Speaker 1: high income people live here, this is a road with 242 00:13:26,316 --> 00:13:28,236 Speaker 1: fifty mile per hour speed limit, this is the ocean, 243 00:13:28,276 --> 00:13:31,516 Speaker 1: all sorts of contextual data to turn it into actionable 244 00:13:31,556 --> 00:13:35,596 Speaker 1: and aggregate analytics. One of the drawbacks to a data 245 00:13:35,596 --> 00:13:38,316 Speaker 1: company like street Light is that you guys are selling 246 00:13:38,356 --> 00:13:40,916 Speaker 1: your data to people that could pay. It's not something 247 00:13:40,916 --> 00:13:44,516 Speaker 1: that's free to public, but it's information about public movements. 248 00:13:44,756 --> 00:13:46,636 Speaker 1: Are you concerned to having to pay for this data 249 00:13:46,756 --> 00:13:50,236 Speaker 1: hinders its ability to be truly useful. It's a great question, 250 00:13:51,076 --> 00:13:54,756 Speaker 1: so I am, of course somewhat concerned. Some of the 251 00:13:54,756 --> 00:13:58,196 Speaker 1: mitigation steps we take are all academic researchers who are 252 00:13:58,236 --> 00:14:01,636 Speaker 1: researching something within our mission, which is climate equity safety, 253 00:14:02,396 --> 00:14:04,356 Speaker 1: get free access you just failed to form. And we 254 00:14:04,396 --> 00:14:07,876 Speaker 1: have like seventy five universities that we're working with who 255 00:14:07,916 --> 00:14:10,916 Speaker 1: are doing totally free research based on our data. And 256 00:14:10,996 --> 00:14:15,276 Speaker 1: we also have fellowships where nonprofits can apply to get 257 00:14:15,276 --> 00:14:17,836 Speaker 1: free research, and we also help promote their research. But 258 00:14:18,316 --> 00:14:21,796 Speaker 1: that is mitigation. That's not fixing the fundamental problem you've 259 00:14:21,796 --> 00:14:23,956 Speaker 1: talked about. And I don't have a great fix. And 260 00:14:24,196 --> 00:14:27,436 Speaker 1: we have a hundred staff, We spend a lot of 261 00:14:27,476 --> 00:14:29,436 Speaker 1: money on the cloud, and what we do is expensive. 262 00:14:30,276 --> 00:14:34,596 Speaker 1: We have to survive. And I think that in America 263 00:14:34,676 --> 00:14:40,236 Speaker 1: we have consistently disinvested in government driven collection of data. 264 00:14:40,316 --> 00:14:43,156 Speaker 1: But because we've made that decision that data is something 265 00:14:43,196 --> 00:14:47,356 Speaker 1: that private markets are going to develop, we can't have 266 00:14:47,436 --> 00:14:50,876 Speaker 1: everything online. What issues are you eager to see solved 267 00:14:50,876 --> 00:14:53,876 Speaker 1: in transportation in the next five years. There's a big 268 00:14:53,916 --> 00:14:58,236 Speaker 1: conversation right now in the Senate about everybody's saying in 269 00:14:58,276 --> 00:15:01,676 Speaker 1: the infrastructure bill. Oh yeah, we should measure equity in transportation, 270 00:15:01,916 --> 00:15:04,876 Speaker 1: And they're like, how, no one knows, there's no way, 271 00:15:05,196 --> 00:15:08,036 Speaker 1: Like I mean, there's forty five thousand ways, there's no agreement. 272 00:15:08,396 --> 00:15:09,796 Speaker 1: So there's going to be a lot of quick work 273 00:15:09,796 --> 00:15:11,556 Speaker 1: on that. So I'm very interested in that. Street Light 274 00:15:11,676 --> 00:15:14,916 Speaker 1: is working on a lot more direct carbon and equity 275 00:15:15,276 --> 00:15:18,036 Speaker 1: measurements now that we have a Biden administration. That opens 276 00:15:18,116 --> 00:15:20,996 Speaker 1: up the space where that could be used. So we 277 00:15:21,036 --> 00:15:23,676 Speaker 1: are going to make a lot of tooling that's more 278 00:15:24,196 --> 00:15:28,156 Speaker 1: mission direct in addition to our more neutral data collection efforts. 279 00:15:28,156 --> 00:15:31,356 Speaker 1: So that is starting now. We need to solve the 280 00:15:31,476 --> 00:15:34,356 Speaker 1: question of what does it mean to have equitable transportation 281 00:15:34,476 --> 00:15:36,276 Speaker 1: and how do you define it because there's no good 282 00:15:36,556 --> 00:15:39,836 Speaker 1: there's no good definition right now, and we're collaborating with 283 00:15:39,876 --> 00:15:43,556 Speaker 1: some nonprofits and some advocacy organizations to get explicit measurements 284 00:15:43,596 --> 00:15:45,916 Speaker 1: about that. The fact that we can measure the income 285 00:15:45,956 --> 00:15:49,236 Speaker 1: and racial distribution of where people move is a huge 286 00:15:49,316 --> 00:15:53,036 Speaker 1: leap forward and starting to measure transportation equity. I mean, 287 00:15:53,076 --> 00:15:56,076 Speaker 1: it sounds like you're saying that transportation equity begins with data. 288 00:15:56,276 --> 00:15:58,996 Speaker 1: I think everything begins with data. So take that with 289 00:15:59,036 --> 00:16:01,236 Speaker 1: a grain of salt. I mean, transportation is in a 290 00:16:01,396 --> 00:16:05,116 Speaker 1: bad way in America. It's dissequitable, it's destroying the climate. 291 00:16:05,116 --> 00:16:06,796 Speaker 1: It kills forty three thousand people a year, and like 292 00:16:06,796 --> 00:16:09,596 Speaker 1: our bridges are falling down, Like we're pretty bad. So 293 00:16:09,636 --> 00:16:11,756 Speaker 1: we have to change. And to think of a massive 294 00:16:11,796 --> 00:16:15,556 Speaker 1: systemic change without data, I just think that's insane. But 295 00:16:15,676 --> 00:16:18,276 Speaker 1: I agree it's not data alone. We are a tool 296 00:16:18,316 --> 00:16:21,276 Speaker 1: for smart people motivated by the right things to do 297 00:16:21,316 --> 00:16:24,716 Speaker 1: their job more easily. How do you motivate private companies 298 00:16:24,716 --> 00:16:32,436 Speaker 1: to care about public issues like climate change? There are 299 00:16:32,476 --> 00:16:36,516 Speaker 1: two ways you get corporations to care about climate change. 300 00:16:37,116 --> 00:16:40,196 Speaker 1: One is you point out to them that it will 301 00:16:40,236 --> 00:16:42,716 Speaker 1: have a huge impact on their bottom line either today 302 00:16:42,796 --> 00:16:44,716 Speaker 1: or in ten years. And a lot of corporations are 303 00:16:44,756 --> 00:16:47,316 Speaker 1: there right, they get that. And the second way is 304 00:16:47,316 --> 00:16:51,916 Speaker 1: their staff starts to throw a fit, so we help. 305 00:16:52,396 --> 00:16:56,236 Speaker 1: We have helped some staff throw fits quietly. We don't 306 00:16:56,236 --> 00:17:00,196 Speaker 1: do it that directly. That's what we do. Do you 307 00:17:00,396 --> 00:17:04,876 Speaker 1: do you want us to include that? The main thing 308 00:17:04,916 --> 00:17:07,796 Speaker 1: we point out is that staff. If all your staff 309 00:17:07,836 --> 00:17:10,356 Speaker 1: have to drive their own cars fifty miles each way, 310 00:17:10,396 --> 00:17:13,636 Speaker 1: like that will dwarf the climate impact of your office 311 00:17:13,676 --> 00:17:16,596 Speaker 1: building within a couple of years. Gotcha, So we've we've 312 00:17:16,636 --> 00:17:22,956 Speaker 1: worked on that. Okay, I think that's a good strategy. 313 00:17:25,676 --> 00:17:28,836 Speaker 1: How can our listeners help, Like, what could someone who 314 00:17:28,916 --> 00:17:32,396 Speaker 1: wants to be like a more responsible and better city resident? 315 00:17:32,716 --> 00:17:35,716 Speaker 1: How can they help right now? And what about people 316 00:17:35,756 --> 00:17:37,796 Speaker 1: that don't live in like bustling cities, people that live 317 00:17:37,796 --> 00:17:39,996 Speaker 1: in more rural areas. How can we all help make 318 00:17:39,996 --> 00:17:45,556 Speaker 1: it make transportation better for everyone? Well, one thing that 319 00:17:45,676 --> 00:17:48,436 Speaker 1: I think is fun is to track your own data 320 00:17:48,476 --> 00:17:51,716 Speaker 1: for a few days. One thing that I think sounds 321 00:17:51,716 --> 00:17:54,956 Speaker 1: simple but nobody gets is that your short trips are 322 00:17:55,036 --> 00:17:59,476 Speaker 1: less carbon emitting than your long trips. So I've had 323 00:17:59,556 --> 00:18:02,276 Speaker 1: some friends, you know, lovely eco hippie friends who say 324 00:18:02,276 --> 00:18:05,236 Speaker 1: do things to me like, well, you know, I take 325 00:18:05,236 --> 00:18:06,956 Speaker 1: a bus every day to work. The only reason I 326 00:18:06,956 --> 00:18:08,796 Speaker 1: have a car is for like, you know, we can 327 00:18:08,796 --> 00:18:11,196 Speaker 1: advent to go hiking. And I'm like, well that is 328 00:18:11,236 --> 00:18:13,676 Speaker 1: a hundred and ten mile drive. Like, I'd rather you 329 00:18:13,756 --> 00:18:16,836 Speaker 1: drive to work every day and maybe car pool or 330 00:18:16,876 --> 00:18:19,196 Speaker 1: take the train for you to adventures. So really think 331 00:18:19,236 --> 00:18:22,036 Speaker 1: it's the length of the drip that matters, So track 332 00:18:22,076 --> 00:18:23,796 Speaker 1: your own data. I think it will surprise you, it 333 00:18:23,876 --> 00:18:26,316 Speaker 1: might make you more open to an electric car, and 334 00:18:26,476 --> 00:18:28,476 Speaker 1: it might help you think about which trips really matter. 335 00:18:29,196 --> 00:18:32,156 Speaker 1: I also think that as a citizen, as a private individual, 336 00:18:32,196 --> 00:18:35,876 Speaker 1: there are three personal infrastructure decisions you make in transportation. 337 00:18:36,676 --> 00:18:39,236 Speaker 1: Where you live, which car you buy, if you buy 338 00:18:39,236 --> 00:18:43,196 Speaker 1: a car, and where you spend most of your days, 339 00:18:43,236 --> 00:18:46,436 Speaker 1: which is usually where you work. And if you optimize 340 00:18:46,436 --> 00:18:49,156 Speaker 1: those decisions a little, like if you move to an 341 00:18:49,156 --> 00:18:51,316 Speaker 1: apartment that's closer to work, or choose a job that's 342 00:18:51,316 --> 00:18:54,276 Speaker 1: a little closer, you've optimized your transportation footprint and you 343 00:18:54,316 --> 00:18:57,076 Speaker 1: don't have to like agonize about it every day. So 344 00:18:57,236 --> 00:18:59,236 Speaker 1: optimize those big You know, if you choose a car 345 00:18:59,276 --> 00:19:02,036 Speaker 1: that gets fifty mpg over forty mpg, you don't have 346 00:19:02,116 --> 00:19:04,036 Speaker 1: to think about it as much. So like think about 347 00:19:04,076 --> 00:19:06,196 Speaker 1: the big decisions that matter, and like stop freaking out 348 00:19:06,236 --> 00:19:09,676 Speaker 1: if you forget your cloth grocery bags once. Those infrastructure 349 00:19:09,676 --> 00:19:11,876 Speaker 1: decisions matter more where you live, where you work, the 350 00:19:11,876 --> 00:19:14,036 Speaker 1: relationship between them, and what car you drive if you 351 00:19:14,116 --> 00:19:18,156 Speaker 1: drive a car. As citizens, citizens have a lot of 352 00:19:18,196 --> 00:19:22,276 Speaker 1: power about city level urban design decisions. If you show up, 353 00:19:22,476 --> 00:19:26,116 Speaker 1: there's always feedback meetings, and if you show up, you 354 00:19:26,156 --> 00:19:28,716 Speaker 1: will make a difference. And I think the other thing 355 00:19:28,716 --> 00:19:30,316 Speaker 1: to remember is a lot of the people show up 356 00:19:30,356 --> 00:19:33,316 Speaker 1: are people who have a very vested interest in things 357 00:19:33,356 --> 00:19:36,796 Speaker 1: being the same, which may be good. Sometimes it's not, 358 00:19:37,276 --> 00:19:41,636 Speaker 1: or people who just assume the worst. And usually the 359 00:19:41,716 --> 00:19:44,876 Speaker 1: staff at these meetings, again it's not elected, it's staff 360 00:19:44,876 --> 00:19:48,476 Speaker 1: who've chosen this career. Usually they are trying really hard. 361 00:19:49,036 --> 00:19:51,276 Speaker 1: And if someone calmly showed up and said, can you 362 00:19:51,316 --> 00:19:53,356 Speaker 1: show me the data? Can you show me the alternatives, 363 00:19:53,716 --> 00:19:55,476 Speaker 1: they would be so excited and that person would be 364 00:19:55,476 --> 00:19:59,276 Speaker 1: so impactful. So showing up and showing up trying to 365 00:19:59,996 --> 00:20:03,996 Speaker 1: work with the staff instead of assuming the worst of them, 366 00:20:04,076 --> 00:20:06,876 Speaker 1: I think is really powerful. You have any books or 367 00:20:06,916 --> 00:20:10,156 Speaker 1: movies that you think you would recommend for people to 368 00:20:10,196 --> 00:20:13,156 Speaker 1: learn more about transportation and transportation equity, Well, now I 369 00:20:13,196 --> 00:20:17,316 Speaker 1: want everybody to watch Mission Impossible, Three Three Angels Protocol 370 00:20:17,356 --> 00:20:19,756 Speaker 1: are the best ones. So okay, now I know I'll 371 00:20:19,756 --> 00:20:23,436 Speaker 1: watch them. I'll watch them tonight. My favorite book about 372 00:20:23,436 --> 00:20:28,956 Speaker 1: transportation is by John McPhee. It's called Uncommon Carriers, and 373 00:20:29,076 --> 00:20:30,956 Speaker 1: it's a book about the people who do the work 374 00:20:31,036 --> 00:20:34,636 Speaker 1: of freight hauling. He just writes so beautifully and with 375 00:20:34,676 --> 00:20:38,036 Speaker 1: such dignity about the people who do this work and 376 00:20:38,116 --> 00:20:41,916 Speaker 1: the incredibleness of the machines that get our T shirts 377 00:20:41,996 --> 00:20:45,436 Speaker 1: and our popcorn to our houses, like whether it's the 378 00:20:45,516 --> 00:20:47,916 Speaker 1: freight boats with the giant containers or the long contructs. 379 00:20:47,956 --> 00:20:50,636 Speaker 1: That book. It gave me a sense of awe and 380 00:20:50,796 --> 00:20:54,076 Speaker 1: respect for the sort of societal achievement that is our 381 00:20:54,116 --> 00:20:57,636 Speaker 1: transportation system. And I think that's healthy if you're thinking 382 00:20:57,636 --> 00:21:01,276 Speaker 1: about changing something. The Infrastructure Bill is not called the 383 00:21:01,316 --> 00:21:04,196 Speaker 1: Infrastructure Bill, It's called the American Jobs Act. And I 384 00:21:04,236 --> 00:21:07,436 Speaker 1: think it is very important, especially for technologists like me, 385 00:21:07,996 --> 00:21:10,436 Speaker 1: that we don't just assume that our efficient approach and 386 00:21:10,476 --> 00:21:14,876 Speaker 1: it is more efficient, is neutral like to society, and 387 00:21:14,916 --> 00:21:19,596 Speaker 1: to think about these industries we're disrupting. Thank you so 388 00:21:19,676 --> 00:21:26,596 Speaker 1: much for being with us, Laura, My pleasure. Laura Sewell 389 00:21:26,716 --> 00:21:29,756 Speaker 1: is the CEO of street Light Data. Will include linkster 390 00:21:29,876 --> 00:21:32,876 Speaker 1: suggestions on ways to learn more about transportation and data 391 00:21:32,876 --> 00:21:36,516 Speaker 1: analytics in our show notes. Next time on Solvable, we're 392 00:21:36,516 --> 00:21:41,756 Speaker 1: talking about sugar, salt, fat, all the good stuff and 393 00:21:42,116 --> 00:21:44,956 Speaker 1: how to solve food addiction. But before you turn away 394 00:21:44,996 --> 00:21:47,916 Speaker 1: feeling annoyed and clinging to a bag of delicious cheese puffs. 395 00:21:48,356 --> 00:21:52,396 Speaker 1: Here's a little preview. The solution is not all on you. 396 00:21:53,196 --> 00:21:56,396 Speaker 1: I hope you'll join us for that conversation. Solvable is 397 00:21:56,436 --> 00:22:00,516 Speaker 1: produced by Joscelyn Frank, research by David Jack, booking by 398 00:22:00,556 --> 00:22:04,556 Speaker 1: Lisa Dunn. Our managing producer is Sasha Matthias, and our 399 00:22:04,596 --> 00:22:09,436 Speaker 1: executive producer is Mio Lobel. I'm Ronald Young Jr. Thanks 400 00:22:09,476 --> 00:22:09,956 Speaker 1: for listening.