Street Smarts: The Waterloo company tackling global gridlock

SPEAKERS

Kurtis McBride, John Stackhouse

John Stackhouse 00:00:06

Hi, it’s John here. If there’s one thing I bet we can all agree on, it’s this. Being stuck in traffic is a waste of time, and we Canadians seem to waste a lot of time in traffic.

Depending on your measurement, Toronto is either the worst or second-worst city in North America. Vancouver is really bad as well, and we could add to the list pretty quickly. In those big cities, the average person is wasting or spending at least a hundred hours a year sitting in traffic. That’s an extraordinary drain on our patients, but also on our economy and doing all sorts of things for the environment that we may not want either. Well, technology can help us, maybe not eliminate traffic, but certainly alleviate it at a much greater clip than we’re seeing today.

And today on Disruptors, we’re going to meet a really impressive Canadian entrepreneur, Kurtis McBride, who is doing just that, not only here in Canada, but around the world for the last 20 years. He and his team at the Waterloo company, Miovision, are tackling the problem head on, not just by installing traffic management devices at intersections, but now building a real-time operating layer for cities, intersection by intersection, right around the world. And as you’ll hear from Kurtis, they’re also using AI to help us all navigate our urban lives a lot more efficiently. You’re about to hear about a real Canadian growth story from an entrepreneur who is tackling head-on trade challenges with the United States and trade opportunities around the world. Kurtis, welcome to Disruptors.

Kurtis McBride 00:01:51

Thank you. Happy to be here.

John Stackhouse 00:01:53

I suspect there’s not a listener who won’t have an opinion, probably strong opinions about traffic. So I’m really excited to hear about the future of traffic and how technology and AI may ease some of our pain. But before we get into the future, maybe we can go into the origin story of Miovision.

Kurtis McBride 00:02:10

Yeah. So I went to the University of Waterloo and I was a co-op student, feels like yesterday, but one of my last co-op jobs was working at a transportation engineering firm in Toronto. And occasionally I would be asked along with the other students to go out and do manual traffic counts. So we’d sit at the side of the road, baking hot July summer, downtown Toronto with a clipboard and count how many cars turned left and turned right and all that kind of good stuff. And then you’d go back to the office on Monday and get involved in the projects, how the data was being used to make very expensive, important decisions about how to improve traffic flow through a city. And then you drive home and you’d experience bad traffic and you could kind of put this all together that this is why traffic is so bad.

So that was really the need. And then did my master’s in computer vision, trying to work on a better way to count cars, and that ultimately turned into the company.

John Stackhouse 00:03:03

What a great case study of why co-op placements and broader work integrated learning is so valuable for the economy.

Kurtis McBride 00:03:12

We’re a big supporter of the co-op program. We have lots of co-op students here and in some way, I guess, paying it forward.

John Stackhouse 00:03:17

That’s great. So tell us about the Better Mouse Trap that you initially developed at Miovision. How did it work? And then we’ll get into how it’s advancing, especially with AI.

Kurtis McBride 00:03:27

Yeah, that Better Mouse Trap was basically an eye in the sky. So a video sensor that goes up, call it 30 feet in the air, looking down at the intersection, single camera view, can kind of see the whole intersection. And then we build effectively a digital twin, for lack of a better term, with that information. And then we provide a whole range of software services to cities to help them better understand what’s happening in the intersection network and then help them to improve it, reducing congestion, improving safety, improving transit performance, reducing response times from emergency responders, all different kinds of things that we do once we have that base level of data.

John Stackhouse 00:04:04

How is technology, and we’re seeing technology accelerate in every sector, changing approaches to traffic management and congestion.

Kurtis McBride 00:04;13

Yeah. I’ve been through lots of hype cycles, whether it was blockchain or IoT or Smart City and all of these sort of cycles that have come and gone. But I think with AI, maybe when it first came along, specifically generative models first came along, felt like another hype cycle, but it is far from it, as you say, transformational in terms of its implications. We’ve been applying it in two different ways. One way is software development, writing code used to be the rate limiter to growth in a business like Miovision. That’s not true anymore. We’re seeing improvements in productivity and software development using generative AI. The other place where we’ve applied it is we launched a product called Mateo last year, and Mateo is a conversational interface to the traffic data that you have in your network. We take a city and we go from a largely citizen complaint-based source of information to now we’re giving you 10 times a second a full digital twin of everything happening in your city.

What Mateo allows you to do is to have a plain English conversation with your traffic network. So everything from, “Where are my most unsafe intersections,” to “Where are the dirty camera lenses in my city that I have to go run a maintenance truck out to clean them,” and everything in the middle. And it’s been the most exciting product we’ve ever launched. A citizen emails in and says, “The left turn on this intersection on Tuesday afternoon is always a gong show,” essentially the city can now copy that email, paste it into Mateo and say, “Hey, Mateo, can you figure out what’s going on here and make some recommendations?”

John Stackhouse 00:05:42

And Mateo is getting to the decision making.

Kurtis McBride 00:05:45

Yeah, all the way through diagnosis and essentially recommending decisions. In theory, Mateo can deploy a change. We have so far made sure that the human is in the loop. We haven’t given Mateo the ability to just start making its own decisions, but in theory, as the technology matures and as the market gets more comfortable with what it’s capable of, in theory, it could find the problem, diagnose the problem, and fix the problem all without human intervention.

John Stackhouse 00:06:11

Yeah. I can’t imagine that’s far off. In some ways, I’ve always thought of traffic as kind of analogous to the internet. It’s just a lot of data flowing in different directions and machines generally are good at finding efficiencies for that data flow.

Kurtis McBride 00:06:25

I mean, in some ways it is. However, the complexity is if you ask a city manager, “Do you want to improve traffic?” They’re always going to say yes. But when you get into the nuances of that, what does it mean to improve traffic? Do you mean improve safety? Maybe you have an entertainment district in downtown Toronto and your focus there is on safety, like pedestrian and cyclist safety, or is it a commuter route? Is it Avenue Road or Eglinton Avenue where people are trying to get home from work? Is it transit performance? Is it making sure that the new LRT can get through the network efficiently? Improving traffic can mean lots of different things. So the nuance is really in setting the public policy, being really explicit about what is it you’re trying to solve for in this part of the city and how you measure, quote unquote, “Improvement.”

But then once you have that layer defined, then that’s where AI, both from a sensors and data collection perspective, ultimately at the generative layer, being able to turn that into an actionable response to an input signal, that’s where AI can really come to shine.

John Stackhouse 00:07:29

One of the challenges of traffic is it’s not just about signals, it’s about human behavior. How is technology evolving to accommodate and even help manage the human behavior of drivers?

Kurtis McBride 00:07:43

Today, Miovision is deployed into an intersection. We generate real-time data from the intersection. One of the things that we have data on is the signal state, red, yellow, and green. And this allows us to essentially make a prediction five, 10, 15 seconds into the future about when the light’s going to change from, let’s say from red to green or green to red. And today we stream that data into about two million passenger vehicles. So any of the Volkswagen group vehicles, so if you drive an Audi, for example, right in the dash of your Audi, it’ll show you if you drive this speed, so let’s say if you drive half the speed limit, by the time you get to the intersection, the light will turn green. So it allows the driver to be a much more active participant in the network progression, which is powerful.

So we’re two million vehicles today, plans to expand that considerably. There’s, call it 200 million vehicles in North America, for a passenger vehicle, that’s a great consumer experience. For a fully loaded transport truck, if they can better time their stopping and acceleration, it saves them significant money. It burns less fuel, there’s less emissions that result from that. So yeah, just one example, but maybe another longer range way to think about this is in order to power these AI workflows or agentic workflows, what’s important is that you have a digital truth about the context. So the way we might think about this in another market would be if you think about a stock market, the stock market is like a digital Oracle for the price of stocks.

And so agentic workflows need these digital Oracles. So those digital Oracles in some markets already exist like a stock market, but in other markets like traffic, those digital Oracles are highly fragmented and they don’t really exist. Miovision’s extremely well positioned. We take the region of Waterloo. We have a digital Oracle for the traffic network. I can tell you what the state of the traffic network was two years ago, what it is now, and I can predict what it’s going to be in the future. And I think that these agentic workflows are going to transform so many different parts of the economy. The traffic network, the digital Oracle of the traffic network is a critical part of that. It’s a critical context.

John Stackhouse 00:09:52

Yeah, that’s really interesting. I wanted to go deeper on your business model. So you initially were B2B selling to cities largely, and now you seem to be developing a platform approach. I wonder if you can take us deeper into your strategic thinking on how your technology enables or supports a platform and then how you monetize that.

Kurtis McBride 00:10:15

Yeah. So true that we sell to governments, but if you click into that, even today, we already have, I would say, somewhere on the order of eight or 10 buying segments that are buying capabilities, data, insights, outcomes from our platform, and we think that will only grow over time. So if you get to the place where Miovision’s platform is the trusted source of truth about what’s going on in the traffic network, now a whole bunch of different actors, as an example, let’s say I’m an insurance company and I want to price risk. Well, if I knew that City A has a higher frequency of conflicts, which is like basically the statistical indicator there will be a crash, there’s lots of almost crashes happening, so eventually there’s going to be a crash.

So if I had a knowledge that this city had a statistically higher probability of crashes than this city had, I could get a lot smarter about how I priced my risk, how I priced my insurance product. With this platform, this digital Oracle layer that provides essentially a digital representation of all things going on in the traffic network, the BlueJays, when they get to the World Series again this year, fan experience departments and professional sports teams want to understand how do I get people in and out of my major sporting event safely, efficiently in a way that they’re going to want to come back to the game.

So in that moment, being able to access traffic data about how people and vehicles are moving around inside of a network becomes extremely important to them. They’re never going to buy my sensor, but if they could buy access to my Oracle for seven games in the World Series, then that helps them do their job better. So we think there’s literally hundreds, maybe thousands of other adjacent markets that have a long tail need for the information we provide, and we’re starting to find ways to monetize that true layer.

John Stackhouse 00:12:06

Does this eventually become a consumer product or do you see it largely going to businesses?

Kurtis McBride 00:12:12

Yeah, I mean, I think all of the above. Now, whether or not Miovision provides just the data layer and other people provide the consumer interface. For example, we all use Google Maps. It might be Google Maps that’s delivering you the consumer experience, whether it’s Uber or your Nav system in your car. It might not be Miovision that provides the consumer experience, but we might be an enabling layer in the experience that it’s being provided to the consumer. I suspect that’s probably more likely where we’re going to land. It’ll be sort of B2B2C as opposed to directly B2C, but never say never.

John Stackhouse 00:12:45

And how do you think about the potential disruption from those platforms? I mean, what’s to stop Google Maps from eventually doing what you do or maybe an active user like an Uber doing something similar?

Kurtis McBride 00:12:58

It’s a great question. I think Google Maps like Android and Uber and things like that, they have one form of data, which is called probe data. So they basically know that there’s something moving. There’s a GPS signal moving around inside the network. My phone is moving around inside the network. They don’t necessarily know if that’s a car, a truck, a bus, a person. With that probe data, they don’t have signal state, so they’re not directly connected to the intersection like Miovision is. So they can’t give you real-time indications of red, yellow, green at the intersection. Because they don’t have ground truth on the camera, while they can give you the probe data, gives them all of the signals they’re getting, they don’t know whether that’s 90% of the traffic or 10% of the traffic. Whereas with our eye in the sky, we see 100% of the vehicle volumes.

So we just have a much better data source. We have a much higher fidelity layer of information, and it just enables us to do more. And then the other thing is we can close the loop. So even if you could get to the point with your probe data to know you had a problem, you already knew you had a problem. Citizens are calling and yelling at you. With us, you can actually come up with a mitigation, deploy the mitigation, and actually fix the problem, which you can’t do with a bunch of cell phones driving around the city.

John Stackhouse 00:14:09

I’m curious how you’re thinking about privacy. You accumulate a lot of data, you’ve mentioned the eyes in the sky, as that is increasingly processed by language models or other generative AI tools. How are you thinking through the privacy challenge or maybe it’s an opportunity for what you’re building as well?

Kurtis McBride 00:14:28

So we treat PII specifically, personally identifiable information. We treat it like plutonium. We do not want it, so we don’t generate it in the first place. We don’t store video images. We store metadata. So for example, car, truck and bus, cyclist, pedestrian. We’re not saying it was a pedestrian of this age, of this gender, of this hair color. We don’t need personally identifiable information to do the things we do for the cities that we serve, so we don’t capture it. In engineering, we talk about something called DFX, design for, and the X is insert anything. It could be quality, could be cost, could be manufacturability, could be privacy. So as part of our DFX, we always include a design for privacy, and we just make sure that we don’t capture any of it in the first place. And then downstream, that makes everything else we do easier.

John Stackhouse 00:15:19

What a turn to your global ambitions, Kurtis. You started in KW, Kitchener-Waterloo, but you’re now active in a really impressive range of countries. What have you learned from taking your technology global and what’s ahead for you?

Kurtis McBride 00:15:34

Everywhere I go, people would agree that traffic is not fun and that they want it to be better. So there’s a truly global market for what we do. As the world is continuing to urbanize and more and more people are moving into cities, that market’s only getting bigger. We have folks in the Middle East, we have folks in Singapore, we have folks in obviously in the US and Mexico, Europe, and expanding all the time. So I would say anywhere humans, our traffic is a problem and that’s our market.

John Stackhouse 00:16:01

But you’re also a B2B or B2B2C possibly company, and that requires teams on the ground, usually sales teams, relationship management, leads. How does that affect your business model, having to hire or acquire teams to do that sales work, but also the implementation and value add that you offer, especially to cities?

Kurtis McBride 00:16:21

What we typically do is we start small. The Middle East was one person, just recently added a second. Singapore is still one person. So we start small, build relationships, turn those relationships into pilot deployments. As those pilots start to scale up, we’ll grow the team in that area. Germany probably has, I don’t know, 20 people now. We do most of our R&D in Canada, although through acquisition, we do some of that in the US and even actually a little bit in Europe as well. But yeah, to your point, like the go to-market teams, like the sales teams, the support teams, sales engineering, all that kind of stuff, they have to be localized, but we try to invest on a success basis. The more interest we have in that market, the more skill we find in that market, the more we can invest.

John Stackhouse 00:17:01

You mentioned the United States. As we all know, it’s in some ways a more challenging market. How are you navigating the trade frictions with the US or do you encounter them at all?

Kurtis McBride 00:17:38

I mean, we do. I would say on the one hand, the US is a great market for us. They’re very technology forward when it comes to infrastructure, probably the most technology forward country in the world. We’re grateful for the opportunity to operate out of there. But the flip side is we have Build America by America, the Buy American provisions, and specifically BABA says that by October of this year, 55% of all of the input costs of any manufactured goods sold to the United States DOT, the Department of Transportation has to be manufactured in the United States. We do all of our manufacturing in Kitchener today, and so this is a big shift operationally for us. So we’re having to move production to the US to comply with BABA. So that’s a challenge. It’s a big change operationally, big change to our supply chains. There’s lots of talk about making sure that we’re leaning in to domestic manufacturers and domestic innovators to create opportunities at home.

And in the meantime, we’re trying to grow internationally. We tripled our international business outside of Canada, the US last year, and we’re hoping to triple it again this year. So really want to make sure that whatever demand we’re having to shift the US as we comply with BABA, that we’re growing our domestic demand and we’re growing our international demand so that we can maintain what has always been a very proudly Canadian manufacturing facility here.

John Stackhouse 00:18:32

What exactly would you be making that has to be made in the US and is there the capacity in the US to actually make it?

Kurtis McBride 00:18:39

I mean, it’s all of our devices that go into the intersection. So it’s sort of a smart camera. There’s a device that goes into the traffic cabinet that connects it to the internet and all that kind of stuff. And yeah, I mean, there’s capacity in the US. Inevitably, everyone’s going through this. Everyone’s trying to move manufacturing to the US. And so the cost of manufacturing in the US is certainly higher than Canada. So there’s some pricing questions that we’re having to consider. If we’re kind of being forced to move manufacturing to a higher cost market, then who’s going to bear the cost of that? Is it me or is the customer? But I would love to continue to export globally out of Kitchener. My number one goal for this year is to make sure that we’ve repopulate the demand that we’re having to ship the US so we can continue to not just serve Canada, but serve the whole global market minus the US out of Kitchener.

John Stackhouse 00:19:30

This has been a fascinating conversation and it’s so impressive what you’re building. Before we close, I want to spend a bit of time on autonomous vehicles and what AVs will do to, in your view, the future of traffic. How far off do you think we are from seeing AVs at scale on our streets?

Kurtis McBride 00:19:49

If you drive a Tesla, you’re already experiencing something that gets pretty darn close to autonomous. So the challenge I believe with the architecture today is that Tesla is a very expensive vehicle because you have to bring all of the sensors, all the computers and all the batteries with you to run the sensors and compute. Those things cost a lot. Now, over time, Moore’s Law will drive the cost of that down. Maybe in 10 or 15 years, the price performance will be such that you can buy a $ 25,000 vehicle and have it be fully like level five, fully autonomous. But the other way to think about this would be the hardest part of level five is what happens at an intersection. If the infrastructure, which it can’t today, but if the infrastructure could provide highly reliable, functionally safe data sets to the vehicles, then you wouldn’t need to bring all the sensors and compute and batteries with you to power the sensors and compute. You could cost share it.

So over the long term, my view is that, and we’re talking over a 10-year period, but if the infrastructure got smarter, got more instrumented and could communicate in a highly reliable way to the vehicles, that’s one of the areas where I think level five autonomy gets closer and easier to do. Without giving away all my secrets, we are doing some thinking on, call it a 21st century generation of an intersection. Most of the intersections today are electrical cabinets. So even though we add a lot of intelligence to it, we’re adding intelligence to an electrical cabinet. And so the question is, if you were designing an intersection today, would you start with a electrical cabinet or would you start with a digital system? And if the answer is you would start with a digital system, then at some point, a hundred years from now, intersections are probably not going to be electrical cabinets.

John Stackhouse 00:21:33

How does that transformation play for the driver or the passenger? How do intersections look differently five or 50 years from now?

Kurtis McBride 00:21:43

The key difference will be that the intersections will be able to provide much richer data. So for example, as I approach an intersection at 11 o’clock at night and there’s a cyclist in the intersection and the intersection’s not well lit, the intersection will tell my car, “Be alert. There’s a cyclist present.” That’s not true today. The intersection has no idea. It can’t communicate. The other one I’ll give you sort of a metaphor. We used to buy our Sony Walkman and we used to buy cassettes and this market functioned because there was an agreed hardware interoperability standard. So you could buy your cassette, you could buy your Sony Walkman and you knew that those things would work together when you plugged them in and therefore people who’d made music were happy to spend lots of money making cassettes because they knew you could buy them and listen.

The thing that underpins markets that are based on interoperable hardware standards is supply chains, logistics, distribution, huge working capital outlays, but it works. And the intersection is very much like this today. It’s built on interoperable hardware standards. If you fast-forward to today in the music industry, we don’t buy cassettes anymore. We use Spotify and Spotify is built around interoperable software standards. And the thing that underpins markets based on interoperable software standards is you get rid of all of the manufacturing, logistics, distribution, working capital. If you want to produce and distribute a hundred million songs, you click a button and everyone has it.

Getting the intersection from an analog electrical world built on this interoperable hardware standard where if you want to upgrade 400,000 intersections in North America, that’s 400,000 truck rolls, 400, 000 manufacturing processes. In a world where you get the intersection to be fully digital and software based, if you want to upgrade 400,000 intersections, you click a button and 400, 000 intersections get upgraded.

We’re a long way from that, but once we get through this sort of one-time switching costs, get to a software enabled intersection network, the speed at which we can improve traffic flow, improve safety, reduce response times, it goes up by orders of magnitude.

John Stackhouse 00:23:47

What would you like to see the country do in quick order to seize this new moment?

Kurtis McBride 00:23:52

We still are having a tendency to try to operate within the world as it is. And as an entrepreneur, I’ve made a career out of refusing to accept the world for how it is and changing it into the thing that it needs to be in order to be successful. And I think we need more of that thinking in the public sector. And I am heartened to see that more and more those voices inside the public sector are being elevated, given more responsibility. And I’m cautiously optimistic that in the next six to 12 months, we’re going to see some real change starting to come out of the government.

John Stackhouse 00:24:22

Kurtis, you’re a real builder. That’s so well said. We need to change what is in the way rather than accept it. Thank you for being on Disruptors.

Kurtis McBride 00:24:31

Thank you for the opportunity.

John Stackhouse 00:24:35

What Kurtis and his team at Miovision have done is really a case study for our dreamers and builders. And it takes me back to his days as a co-op student when he was able to see what more experienced and maybe more sophisticated people were missing right before our eyes. So as we all look at a world that is increasingly disrupted and in some ways increasingly scary, how do we see those opportunities and how do we seize on them? That’s what this Canadian moment is really about, finding our disruptors and helping them take on the world.

If you’re looking for more ideas and insights, visit rbc.com/thoughtleadership. Our team delivers critical insights to help businesses, policymakers, and communities make informed decisions about this rapidly changing and yes, disrupted world.

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I’m John Stackhouse. Thanks for listening.