Infrastructure Technology Podcast: Smart Traffic and Asset Management

• Importance of Infrastructure Maintenance – Michigan DOT’s Ancillary Structures Program, supported by HNTB software, emphasizes the need for proactive inspections and maintenance of critical structures like bridges, overpasses, and traffic signals.
• Risk Mitigation – By assessing vulnerabilities, programs like the Ancillary Structures Program helps prevent structural failures, ensuring public safety and reducing costly emergency repairs.
• AI-Driven Traffic Optimization – LYT’s system leverages AI to adjust traffic signals dynamically, reducing congestion and improving traffic flow.
• Data-Driven Decision-Making – The platform provides cities with real-time data insights, allowing for more effective transportation planning and long-term infrastructure improvements.

In this episode of Infrastructure Technology Podcast, we explore the latest innovations revolutionizing urban infrastructure and traffic management. We dive into Michigan Department of Transportation’s Ancillary Structures Program, an initiative designed to maintain the safety and integrity of bridges, overpasses and other critical structures through proactive inspections and advanced technology.

We also sit down with the founder of LYT, a leader in smart traffic solutions, to discuss how AI-driven technology is optimizing traffic flow, reducing congestion and improving emergency response times. From enhancing sustainability to making data-driven urban planning decisions, this episode sheds light on the future of intelligent city infrastructure.

Gavin Jenkins (00:01):

Welcome to the Infrastructure Technology Podcast. I'm Gavin Jenkins, Senior Management Editor of Roads and Bridges. And with me as always, we have Brandon Lewis of Mass Transit Magazine--he's an Associate Editor. And we also have Harlee Hewitt, Associate Editor of Roads and Bridges. It's Tuesday, how you guys doing?

Harlee Hewitt (00:24):

Go ahead and say it, Brandon.

Brandon Lewis (00:25):

Number seven.

Gavin Jenkins (00:33):

So we're a little bit ahead of the game in terms of recording. So we're recording this just a couple days after Saturday Night Live celebrated 50 years on the air. And I'm going to tie this into today's episode, I promise you, but just humor me for a second. Brandon, do you have a favorite sketch or cast member from Saturday Night Live?

Brandon Lewis (00:59):

I don't. My parents were big SNL bands, but I never really got into it that much, to be honest with you. By the time I sort of got older and realized it was sort of a thing, by the time I could stay up that way Saturday the night, it was sort of in its “down years”.

Gavin Jenkins (01:22):

Down years? How dare you?

Brandon Lewis (01:23):

I mean, that's what the young kid saying nowadays.

Gavin Jenkins (01:26):

What down years are you talking about? I mean, when they had Bill Hader and Andy Sandberg on?

Gavin Jenkins (01:39):

What about you Harlee?

Harlee Hewitt (01:40):

That Bill Hader skit, I don’t know what it was called. What was his name? Where he would be the nightlife like reporter or whatever. Yeah, that was my favorite one also. I mean, I used to watch that constantly with my dad, so I feel like I'm well acquainted. But I loved Seth Meyers when he did the news part, that's when I really started watching it as a kid. And then my dad used to watch the Will Ferrell skits all the time.

Gavin Jenkins (02:17):

Gavin Jenkins (2:20):

You’re talking about Stefan, that's the character

Harlee Hewitt (02:20):

Stefan. That is correct. Love him. It's hilarious because they had him read right off of a teleprompter and he had no idea what he would even be saying.

Gavin Jenkins (02:33):

Like you he's a native Oklahoman.

Harlee Hewitt (02:34):

He sure is. Yeah.

Gavin Jenkins (02:38):

So Saturday live, really important to me. Whenever I was in fifth, sixth grade, I started watching. That's when I was not alone on Saturday nights, but my brothers would had social lives, so they were older, they were going out. My mom would just be in another room doing her thing. And so I was allowed to stay up late and watch. And I started watching during a golden era where they had Michael Myers, Dana Carvey, Phil Hartman, and a man by the name of Chris Farley.

Harlee Hewitt (03:13):

My dad's fave.

Gavin Jenkins (03:14):

And Chris Farley spoke to me. It's like he got my sense of humor and I got his sense of humor. And his most famous sketch was a motivational speaker who would be down in a basement drinking coffee and then would come upstairs and start lecturing teenagers. And he always would say that he was down, he was living in a van down by the river and he just got me. And then he was always hiking up his pants and then he would fall into a table and it's like, it's just like Farley got who I was as a 12-year-old. And I say that all to say that the company, HNTB, which is on our show today, just gets roads and bridges. I have been in this position for three years and Roads and Bridges get submissions from engineering firms across the country. And we're an application based magazine and we just want to hear about how projects are done and the innovation and design and hard work and safety that goes into them. And HNTB gets that mission and they give us such great content to really just create a national dialogue about this industry. And today they're bringing us some collaboration that they've done with Michigan's DOT. And with that, I want to turn it over to you, Harlee, because you're the one that did this interview. So please tell us about HNTB'S work with Michigan's DOT.

Harlee Hewitt (05:00):

Absolutely. This is a kind of unique program in that, well, lots of DOTS and other agencies have asset management programs where they manage and maintain, whether it's infrastructure or their vehicles that they're using. But this is kind of unique because they're dealing with ancillary structures. So they have over 70,000 of them too. I think when some people hear that word ancillary, they might think random, maybe not super important structures, but these are, they're culverts and things like retaining walls and as I assume they will, our audience knows well that culverts are very, very important, especially they help with the structure of the bridge, they help with maintaining the weight, distributing the weight, water flow, wildlife crossing, I mean lots of things. So if you have kind of broken down culverts, you have a broken down bridge and this is really unique. HNTB is bringing kind of their own technology and adapting it to what MDOT is doing with the program. So they bring these digital infrastructure solutions that you'll hear about and it's really helping them, like I said, just inspect and inventory these structures and maintain them proactively so they're not breaking down.

Gavin Jenkins (06:27):

Alright, so that was a great introduction, Harlee. And so with that, let's turn it over to your interview with Michelle O'Neill and Dani Booms with HNTB.

Harlee Hewitt (06:50):

Welcome to the first interview of this episode of the ITP. I am obviously Harlee Hewitt. I'm your co-host, I’m Associate Editor at Roads and Bridges. So far on the program, we have covered asset management in a few key ways already. We've talked about buses, we've talked about other utility vehicles that are on the roads, on the mass transit side of things, but we have also covered on the roads and bridges side how folks are using technology to update their bridges to make sure they're maintained correctly. But I think we can take it a step further today with our guests and with our topic, and that is with Michigan DOT's Ancillary Structures Program. So in a nutshell, that's an asset management system and database framework and it accounts for over 70,000 ancillary structures across the state of Michigan. So to tell me more about it, to make me an expert as well in the program we've got Michelle O'Neill, who is a professional engineer and the program manager for the Ancillary Structures program. And then we also have Dani Booms who is a professional engineer and project manager with HNTB, that is the firm who is helping as a consultant on the program and they are helping with the different digital infrastructure solutions as well. So Michelle, Dani, great having you here. If you would, I think it would be a great place if you would just start with how the program came about, what was the inception of it and the identified need for Michigan.

Michelle O'Neill (08:32):

Thanks Harlee and thank you so much for having us today and inviting us to speak on your show. I'm excited to talk about this. It's near and dear to my heart. And so if I can take you back for a little bit in a little history tour of MDOT. Prior to the official ancillary structures statewide program, MDOT had always been focused on asset management with primarily the focus on pavement and bridges being that driving force. And over time as technology became more readily available, the regions, we have regions tended to that have their own system of monitoring performance of their assets and the condition of their assets at a regional level. And once the GIS technology became more available, and we had some really well-meaning individuals across the department who were very excited to get out there and start using these tools to manage assets, we discovered that there really was a need to put together a statewide program to make sure that we were standardizing all of the information that was being collected.

(09:55):

In 2017, we really started out on a robust effort to collect all of our culvert data information at a statewide level. And then in 2019 there was a condition related issue with a retaining wall on one of our depressed freeways in the metro Detroit area that really brought to light how MDOT could benefit from a more robust statewide program that included all of the assets that had that structural component to them. And so in 2020, the MDOT executive team authorized the creation of the ancillary structure unit and the program, and that's when things really kicked off. And due to lack of resources internally, we ended up consulting out the program using kind of an innovative method of contracting that Dani's going to touch on called Program Management Consultant and HNTB was the selected vendor when we went out to selections for that. And so Dani, if you want to go ahead and highlight that program and what that looks like on your end.

Dani Booms (11:09):

Yeah, thank you. HNTB and our sub consultants are definitely thrilled to be able to support MDOT in this asset management program as the program management consultant. We began work on the program in 2020 and our scope generally includes conducting inspections, providing engineering services, maintaining the asset database, and producing some usable information from the data we gather. However, on day one in 2020, we couldn't just send inspectors out into the field to start collecting data. There was a significant startup effort that was undertaken to get the program off the ground. So to start, the team did a lot of research and held a ton of meetings. This was between HNTB MDOT, our sub consultants and many others to get into the nitty gritty of things like what does a seven mean on the rating scale as we're rating the condition of assets, what types of issues do we want flagged in the field?

(12:06):

Things like that. Once we were able to do that, we took on all sorts of data sets that were already being developed. As Michelle mentioned throughout the state, each region was kind of doing their own management of assets. So we pulled all that in to a standardized approach for the single source of truth. So there was probably about a year of startup time for those first few asset classes. We started with four priority assets and then from there we were able to start inspecting those assets and while we started inspecting those assets in the second year, we were able to get the remainder of the asset classes ready for inspection. And so now at this time we're able to inspect all of those assets.

Harlee Hewitt (12:51):

I see, so you've both kind of touched on a few, but I was wondering with a program that manages 70,000 assets total in 16 different structure types as I hear it, what's included in those structure types then?

Dani Booms (13:14):

And the term ancillary structures is certainly vague and kind of open for interpretation. In this case for MDOT, we're talking about generally a lot of structures other than bridges. So these are things like retaining walls, noise walls, culverts less than 10 feet. If they're greater than 10 feet, they're already part of the bridge inventory, overhead signs, light and signal poles and communication towers kind of broadly. Those are the assets we're talking about here.

Michelle O'Neill (13:41):

I do want to add too something that was really cool I thought when we built out the program, so the first thing you have to do is to figure out what you're going to collect for each asset. And we are a multimodal industry and so the state of Michigan also, we have the office of rail, so we did reach out to our other stakeholders within the state so that for example, when we have culverts, we're talking about ancillary structures on the state highway trunk line system, but we did create fields for the office of rails. So if the office of rail wanted to go and collect culverts underneath the rail system that they own, they certainly could do that and piggyback on what we've already built on. So I thought that was really fascinating and was kind of a cool thing to tie all of those different modes together.

Harlee Hewitt (14:35):

Sure. And kind of on that note and tying other facets of transportation in, how do you continually determine what assets to prioritize? Is this always shifting for you?

Michelle O'Neill (14:52):

So initially we prioritized it using a risk-based approach and we had the largest most robust inventory for culverts because we had started that as I had mentioned back in 2017. And obviously if a culvert fails, it's underneath a roadway, there's a big risk to the motoring public, we don't want the highway to collapse. And then we also prioritize retaining walls and sign structures. So cantilevers and trusses were the other two. So that's for the four that Dani mentioned that we started out initially with, we had the best inventory information available and those were the ones that were easiest for us to get started on and we felt had the highest risk factor for the motoring public if we didn't have that data available. And once we fully built out the inspection program for those four, like Dani mentioned, we were able to start phasing in the remaining assets, developing the training and the inspection procedures for those.

(16:01):

So initially when we set out, we started with culverts with the goal of inspecting 20% of the network each year over five years. So at the end of the five years we would have 100% inspected and for the retaining walls and sign structures, we did 50% of the network over a two year period. So that's how we initially set out. And then once we rolled out the remaining assets, we really got off the ground running and were able to add many more of them in immediately. So there was less need to prioritize initially because we were able to just get out there and going with the rest of them.

Harlee Hewitt (16:44):

I see. So why we're here, why are we're talking about this at all is because of the technology that's helping you along the way and helping with efficiency in the program. So HNTB has helped you develop asset management programs and also as Dani mentioned, you're using GIS based applications. Can you expand on how all these technologies you're utilizing work? I guess start with what they are and then how it's benefiting the program?

Dani Booms (17:18):

Yeah, I can start talking about the GIS based application that we use to do all of our data collection and management of assets. So going back to what I said before about the robust startup process that there was on the program, one of the biggest components of this was setting up the data collection system and there were a few different avenues that we could have gone with this, but I think we ended up in kind of the perfect place by starting with an off the shelf GIS platform. So we started with Esri’s ArcGIS, online and arc GIS, enterprise portal, and also field maps and a few other applications. With these tools, we were able to get our inspectors out in the fields a lot sooner than if we had tried to configure a more custom software to begin with. So with this, we have inspectors out in the field with iPads, they input data using field maps directly into the asset management database, and that includes with high quality photos that they take and that information gets uploaded in real time so that people in the office can see those inspections as soon as they're posted.

(18:27):

We do have a robust QC process that is administered through the GIS platform and then we've been able to develop multiple different types of dashboards through GIS that are kind of dependent on different audiences within MDOT. So the things that maintenance coordinators need to see from our database is different from the things that people who are doing long-term capital planning need to see. So we've been able to customize dashboards for those specific use cases to give people the information that they need from our database. And so like I said, the off the shelf approach helped us get to where we are. We also learned at ton of lessons and made a lot of changes to the database along the way as we learned what did and didn't work. And I'll let Michelle talk a little bit more about where that's headed next.

Michelle O'Neill (19:12):

Thanks, Dani. So MDOT has adopted the go forward plan to migrate our program over to AASHTOWare Bridge Management (AASHTOWare BrM). AASHTOWare BrM is in the process of rolling out version 7.0 next year, and that software has modules for ancillary assets. We did have to do some customizations for the Michigan needs, and so for that reason we haven't been able to launch yet at this time. We're hopeful that we can launch next year. The benefit of using AASHTOWare BrM is that we are storing all of the data under one system and we are able to track safety related requests for action. We have a process built into our program as well, would let us track that a little bit more closely. We can assign inspections to specific work groups or users and also assign work tasks and track the completion of work tasks through that system. And hopefully in the future we're hopeful to see some built-in features like lifecycle cost analysis that can be added on in the future. And so that's what we're looking at for the future. But right now, as Dani mentioned, we are often running using the GIS products.

Harlee Hewitt (20:51):

Okay, great. So the program's efforts are also supported by nine custom online training modules and they provide guidance as I understand, for ancillary structures and asset inspection processes. So tell me a bit about how that has benefited the program.

Dani Booms (21:08):

So when this all started out and we created the Ancillary Structures inspection manual, which we call MiASIM, the Michigan Ancillary Structures Inspection Manual, it's about 500 pages long with all 16 asset classes included. And so the training modules become very essential for onboarding new people to the program, especially our inspectors that are out in the field. We started these training modules as just, they were live team like Microsoft Teams trainings that were recorded, but we were able recently to convert seven of those modules over to a more robust learning management system. And the other two are on their way to that as well soon. And one of the biggest benefits that we've been able to see with these training modules is that it has helped us bring people into the program that maybe didn't even have a background in transportation to start with. Our inspectors can come from a variety of different backgrounds. They take these trainings and then we pair them with an experienced inspector out in the field and they stay with our inspectors always work in pairs so they stay together and all of their inspections go through that robust QC process that I talked about before. So it's been really great for us from even a workforce development standpoint of being able to bring new people into the transportation industry.

Harlee Hewitt (22:28):

Yeah, yeah, that's awesome. So I'm also wondering then how this program can serve as an improved model for asset management that can be implemented throughout the country in transportation agencies. How is this maybe adaptable for other people in their own ways and their own needs?

Michelle O'Neill (22:52):

We have had a lot of people reaching out to us and asking us questions about the program, which is fantastic. And through a series of conferences and different meetings with other folks that are involved in asset management, word kind of spreads. Every state has their own way of managing assets that tends to be varying degrees of maturity, right? One state might be really robust in their culvert inspections and asset management and another state might be more so along the lines of sign structures. And the thing that makes Michigan unique is the fact that we've brought all of this asset management under one umbrella. And that I think is where people are asking us, how are you doing this? How can you manage such a large program? And I really have to say the program management consultant concept is really what has allowed us to be successful in that arena because our team is very, very small.

(23:56):

It's myself and then I have four other people that I work with. And without having the support of a consultant and the resources to be able to send people out on a statewide level and do all these inspections and compile all this data, we wouldn't be able to do that. And it's nice because it doesn't draw from the MDOT resources in the regions. That was also a priority for us that we didn't want to be taking away from other people's normal everyday duties. So in terms of sharing this information with other state DOTS, we've had peer exchanges, for example, we had an in-person meeting with the folks from Indiana and we're just learning a lot from each other. Right now, we're willing to share our inspection manual. We've put it out there online for folks to view. And then similarly, we've drawn from the experiences of other DOTS. Ohio DOT had some really great innovations in the way that they were using RC cars with cameras to inspect culverts. We took that and they helped us get started using something like that and we've taken that and expanded upon it. I'm looking forward to additional conferences and research projects that are going on nationally in the area of asset management and sharing all of our lessons learned for sure.

Harlee Hewitt (25:22):

Awesome, awesome. So finally, to get you out of here on this question, what does the future hold in both of your eyes, maybe for asset management in the context of technological advancement and growing impact on digital data and the transportation sector? Just give me your thoughts there.

Michelle O'Neill (25:42):

Yeah, sure. I am super, super excited about the thought of what technology may exist five years from now that we don't have today. Just looking at how far we've come in the last three to five years in this space. The thing that really strikes me is how we're going to be able to use the machine learning capabilities and the artificial intelligence capabilities to further the program without having to rely on a tremendous amount of human resources because all of the DOTS are really strained in terms of being able to have enough resources to do what you need to do. And so any opportunity we can use technology to be more efficient and more cost effective I think is really going to benefit the program. We have so many great people that are coming up with wonderful ideas. The other day we had a conversation about how thermal imaging cameras are now coming down in price. They're so much cheaper. So the thought was, would that help us when we're looking for voids behind box culverts, I knew it's been utilized in bridge decks and we were just trying to think outside the box on how some tools that used to be very, very cost prohibitive to get your hands on are now a lot more inexpensive. So I'm really excited to leverage that technology in the future and help us move from being more reactive in our maintenance of assets to being proactive across the board.

Dani Booms (27:21):

On this program specifically these first four years that we've had so far have laid a really strong foundation for the program, but we are really just starting to scratch the surface of what's possible with the data that we've collected so far. We have been able to do a little bit with machine learning, with predicting where we might be, might expect to see culverts that are in poor condition based on soil type and material type and things like that. And we also can start to take that now and look at predicting deterioration rates of assets. So based on the specific site conditions specific of a specific asset, what can we expect to see as far as lifecycle for that asset and when maintenance needs to be completed. And all of those things help proactively plan for funding needs and maintenance activities that need to happen on those assets. So just really looking forward to, like Michelle said, some of the opportunities to harness the data that we've collected and turn it into really actionable information.

Harlee Hewitt (28:21):

That's great. Well, I can't wait to have you on maybe next year as you launch some other things in the program and we'll reconvene and talk again, Michelle, Dani, thank you so much for being on the program today.

Michelle O'Neill (28:36):

Thanks Harlee. Thank you.

Gavin Jenkins (28:48):

All right, and we're back from Harlee's interview and Harlee, excellent job with that, Brandon, as Associate Editor of Mass Transit, what did you think of that interview?

Dani (29:00):

Harlee, like Gavin said, you did a great job with this one. Again, my biggest takeaway was they talk a lot about about the retaining walls and the sign structure of these roads and bridges and the ability to sort of tell when this infrastructure is sort of in need to repair I thought was really fascinating. HNTB does work a lot with mass as well. They've worked with a lot of transit agencies such as Metro Link in California, SEPTA in Pennsylvania, the UTA as well in Utah. So this definitely does relate to both industries.

Gavin Jenkins (29:45):

Alright, and so let's tell us about who you got coming up next for us. His name's Tim Menard.

Brandon Lewis (29:53):

Yeah, so Tim is the CEO and the founder of White. He's been that since 2016 and before LYT he was actually in the intern intelligent computing group at Toyota and a firmware engineer at Tesla. And we talked about LYT and how they're using this sort of cloud software solution using state-of-the-art connected vehicle and machine learning technologies to prioritize the flow of vehicles. So basically this technology can tell when a light is potentially going to turn green to allow more buses to sort of go through intersections and not let them stop as much. It's really interesting technology and we talk about the role as well that AI plays in it as well.

Gavin Jenkins (30:44):

Sounds like when we come back to wrap this episode up, we're going to be talking about smart cities and smart infrastructure. Alright, well let's turn it over to Brandon's interview with Tim Menard.

Brandon Lewis (31:10):

And welcome back to the Infrastructure Technology Podcast. Here with me today is Tim Menard, the CEO and founder of LYT. Tim, welcome to the Infrastructure Technology podcast.

Tim Menard (31:24):

Thanks Brandon. It's a pleasure to be here.

Brandon Lewis (31:27):

Tim. I appreciate it. And what we are going to be doing today is we are going to be talking about LYT and it's cloud cell solution. We're always going to be on traffic ingestion, the way AI is playing a role as well as what LYT does to help transit agencies. So I want to start off by discussing the LYT software because it is a solution that you see state-of-the-art connected vehicles and machine learning technologies to prioritize the flow of vehicles in a city and across a corridor. So what kind of technologies are being used?

Tim Menard (32:06):

Happy to. So the main components are connected vehicle technology, data science, machine learning –a form of artificial intelligence, and then traffic engineering are the major categories. And so the technology there behind all of that is that we've got traffic engineering mythology being put in the cloud where we're able to analyze real-time connected vehicle data that we get from for this instance buses as we're talking about mass transit today. We get real time bus location information. We analyze that in real time and we're able to predict using machine learning where those buses are going to be on their bus routes so that we can then inform traffic lights, how to better adjust their signal timing so we can get more green lights to buses.

Brandon Lewis (33:00):

That's pretty fascinating to think about. I think we all sort of think about that these traffic lights are all on a timer. They really can't adjust and obviously we all know buses are bigger vehicles, so sometimes it can be more difficult for them to stop, especially if they're on in that sort of yellow light zone. So very fascinating. With that being said, let's dive into sort of how this technology has better helping with the traffic congestion?

Tim Menard (33:39):

Absolutely. So first of all, going back to the traffic light problem, everybody is affected at the traffic light. If you're a pedestrian, if you're in a car, if you're a bicycle, and we live in a world where we've hit a place where we just can't build more roads anymore, we can't put another link. Our cities are mature, they're founded, they're built, they're established, and we have infrastructure and now we have to iterate on how we can use that even more. And everybody shares some common use cases of, Hey, I waited at this traffic light, it never turned for me, it stayed red. Why doesn't it detect me? And now so much stuff is connected, our cell phones are connected, our cars are connected, police, fire, ambulance. So if we mesh these worlds together now and connect them all, then all of a sudden we take what used to be our concrete roads with wood paint and very static and now give it that touch of being, living and breathing. And so even more toward our mythology is that now you can see the big picture because we know where traffic lights are, they don't move.

(34:54):

We know what roads they affect and now because we have connectivity and data of where people are and where they're going, when you put this into one window, you have a map of the whole world like air traffic control does, where it can see every plane, where it's going and why. So if we take what we've been doing in aviation and apply it down to the road, well now you can conceptualize rather quickly that we can make significant improvements because trip data that we've always wanted really can tell us how much time we need that left turn to be on and those pedestrian crosswalks to be on because we know where people are moving to so we can help everybody move in line.

Brandon Lewis (35:35):

Yeah, I mean how times have we just as a consumer, whether it's in transit or driving or whatever the case may be, were at a light and some lights feel like they take five minutes to turn green and others do it, they take 30 seconds. But I think it doesn't really work that way,

Tim Menard (35:54):

Right. There's an envelope of time and then if you look at how much pedestrian demand or the push button across the road, they break. If they break, then the system just says, oh, we need to always serve that. So you're going to push out, especially at very large intersections, a lot of wait time where it might not be there. And if we want to live in a dynamic world where we can quickly transact and move, then we've got to have more information and more sensors. That's the part of where we play is what's not just at the intersection, but what's coming

Brandon Lewis (36:38):

For the technology that LYT is using for the transit side of things, the buses specifically, what areas are you guys focusing on? Is it more rural, is it more urban? What areas are you already in terms of this using the technology?

Tim Menard (36:58):

We're working with our urban environments, so where you're going to have large grouping of traffic lights, where they're going to control the flow. So right, our downtowns, our suburbs because we do a lot of work right now with police, fire, ambulance, buses, light rail, all the municipal vehicles. And so our platform that we call overall Lightspeed is that pun, which is if we can get vehicles traveling at light speed traveling faster, then the whole community gets better because you want an ambulance to be at your house timely. You want that firetruck to be there. And when buses feel like a train where they're going from bus stop to bus stop without stopping, well that, that's like the holy grail, right? Because now people can see that buses are getting people places. When you're on the bus you feel that you're arriving there faster and the rest of traffic that's in a car is now reconsidering, ‘hey, that's a very competitive and appealing solutions. Why am I driving?’ And most people nowadays hate driving.

Brandon Lewis (38:09):

And the most important aspect I think of it is getting people on time. I think that's the biggest misnomer about transit in general, whether it's bus or rail, is that you take transit, you're never on time and that's just not the case.

Tim Menard (38:24):

And that's the biggest heartburn is the largest amount of our daily use of any transportation is pre COVID was going to work today, it's still mostly going to work and we got to be timely. A lot of these roles require you to start on time and you can't say, oh, it's the bus driver's fault or it's this fault. You're going to be the one who's going to be told we'll find another way. So what are you going to do? You're going to ride a bike and you going to take your car, you're going to find any means necessary that's going to ensure that if your livelihood depends on your commute, then you're going to find the most reliable source and it might cost more. And that's what driving does. It costs more, but that feeling of control that you have is what's going to drive you to go that route.

(39:11):

And until we make driving out of reach that now more people turn back to transit and bicycling. That's where we're seeing in these North American cities, this resurgence of transit really getting the priority, the investment where we're seeing in  places like New York is ‘hey, they made it harder to drive into the city. Guess what? That's going to force more people on transit’. Guess what that means? Transit has to step up. Its quality of ridership and made it compelling. So it takes time, it hurts at first, but the long-term effects just get much better. And then you wind up with other global cities that we have London, which is no one goes to London and looks at a schedule and what time bus can land you just go to the bus stop and there's like always a bus.

Brandon Lewis (40:07):

I got. So in terms of the difference in technology that you guys are working with traffic, what's the difference in the technology that's being used for buses and then for rail? Because I would assume that obviously two different forms of transportation. Are the systems somewhere or are they different? Is one harder to implement, harder to work on?

Tim Menard (40:31):

It all comes down to just the challenges of the road and then just understanding that each vehicle has got their nuances. And as you had said earlier, just when you're at a traffic light, a 30 foot bus is going to take more crossing time than a 40 foot and a 60 foot articulate. And then when you now look at the light rail equivalents, our older era trolleys are going to cross faster than our modern concepts of trolleys and light rail vehicles and those are of minutes. Well, if you look at a 60 foot articulate, that's kind of the largest bus we have on the road. But then if you go over and then you look at, well, these older multi decades old trolleys are 40 feet just like our 40 foot buses, but the average modern light rail concept is like 120, 140 feet. And then sometimes they pair those up. So if you look at most notable examples is like Dart or King County Metro where they're running two or three cons. And so you might have 900 feet of train, 900 feet of train is going to need some serious green time to cross through an intersection.

Brandon Lewis (41:48):

So it's really more about the length, the bus or the train and as we've gotten more modern technology in bus and rail those systems became longer.

Tim Menard (42:03):

Understand the vehicle dynamics so that when you then predict where they're going to be in a minute, two minute, five minutes away, if you're trying to adjust how they get through the urban sprawl of our traffic light system and impact other traffic, then the difference is if you've got 120 seconds for a 60 foot articulate, well that's at speed going to travel through each intersection in 10 or 15 seconds. So you can cascade how the traffic lights work together and what's known as traffic coordination. Whereas if you've got this big light rail vehicle that might be 900 feet long that's going to need call it 60 seconds of green time just to go through, then that's a significant retiming of the traffic lights in real time. Then that smaller bus needs. And so that's where the concept, again, of the air traffic control point of view is that if you're following a train and you're able to predict in five minutes away because the traffic light needs a whole minute of green time just to let that train through and everybody else at that light wants to go through too, that's where we can use technology now to look at the big picture of what's going to come so that we can minimize the delay to everybody else by immediately shifting to every other mode that's going to get affected and not be able to cross those tracks so we can get back to what's in parallel so that everybody gets a fair chart.

Brandon Lewis (43:39):

Are you providing this data to any agencies at all or do you work with any agencies, whether it's directly or indirectly as you're trying to implement this data?

Tim Menard (43:50):

Yes, so we are working with a lot of the transit agencies on the West coast, so agencies like Tri Met, King County Metro, the Santa Clara Valley Transportation Authority, Orange County over on the east coast working with the MBTA, working with SEPTA, working with Wego and all. That's just for the prioritization of their bosses on their corridors and it has been wildly successful.

Brandon Lewis (44:29):

Let me ask you this then. Has there been, because obviously frames it and patterns are different, east coast, west coast, have there been any different challenges that maybe you would've not foreseen on one coast or the other?

Tim Menard (44:47):

That’s the fun. Not one city's the same, right? There's more deployments in the west coast because the west Coast you could argue are newer cities. They might have newer infrastructure. The East coast, we see this a lot in New England. Look at Boston, yeah, it's an extremely established city. If you walk around there, it's very European-esque, very tight corridors. And that's where we're really living the mission of, hey, we have legacy infrastructure, how do we work with these challenges and how can we iterate? And you've got to go on a road by road or intersection by intersection basis, but just one minor improvement at a time stacks up to a whole new Boston.

Brandon Lewis (45:38):

You touched on AI, that's what we're going to sort of wrap up our conversation today. AI, I mean we've seen it at the time of this recording even the last year we've seen it now every one of the newer Google searches and chatGPT, we know it sort of gives, AI is going to be taking over this world here in the next decade or so. And with technology there's obviously benefits and disadvantages to every portion of it. One role is AI though, specifically playing in your technology at LYT?

Tim Menard (46:10):

That's a great question because AI has been used to, is used by media as it is the one and only solution. AI is, it's like paint, slap it on anything and you're good to go. But the reality is AI is a part of our larger system. It's like a piece in a puzzle and its output for us is it's taking what used to be hard to do, what we'd say in real time. So what AI for us, what machine learning is doing for us is it's looking at what would've been a traditional traffic signal timing research project where you collect two months of data and then you have these two months of data and you hope that it's representative of the whole year or maybe the next five years and then you time traffic light on that. But where the machine learning is today is that it can do that type of exercise every week on the last two months.

(47:17):

So all of a sudden you're getting weekly updates on what the last two month trends are, and then you can add what's happening that day to look at, hey, are we within trends? Or hey, there's construction on the roads, been a change. So it's helping be a brain, but it's a component. It's not also manipulating the traffic light directly or manipulating the bus directly. The driver's still driving the bus, the information's coming up, it's analyzing and it's informing, and then the rest of the system is now taking that data and making a decision from it.

Brandon Lewis (47:53):

You just essentially able to get that data faster and able to make a decision almost in real time.

Tim Menard (48:02):

Exactly. So it's augmenting and it's finding the nuances that were really hard to do in software algorithms in prior years.

Brandon Lewis (48:19):

Tim Menard, he's the CEO and founder of LYT in 2016. Before that, he was the intern Intelligent Computing group at Toyota and firmware engineer at Tesla. Tim, this was a wonderful conversation and thank you for joining us today on the Infrastructure Technology Podcast.

Gavin Jenkins (48:48):

All right. And we are back from Brandon’s interview with Tim Menard. So I guess we're talking about smart infrastructure and how our technology today can really help with the flow of traffic. And what are your key takeaways, Harlee, from that interview?

Harlee Hewitt (49:09):

I would say my first key takeaway is that of all the connected vehicle tech that we've talked about so far on the pod, I love this because it will, like you said, it'll definitely help with traffic signaling and that means we won't be waiting at lights forever anymore once this finally comes down the pike. And also, I mean, another key thing that I thought was cool was that embedded in that is emergency responders also will be able to respond faster, but yeah, taking the connective vehicle tech, data science, machine learning, traffic engineering, combining it all into one, I mean, you can just see how that will be hopefully a more streamlined process.

Brandon Lewis (50:01):

Yeah. There's a moment in that interview where, and I think that sort of relates to everybody who's on the road. We all think that we've all been sitting at one of these lights forever, that this light never changes. Oh my God, why is this light so slow? When in reality different lights are not not designed to be longer or whatever the case might be.

Harlee Hewitt (50:28):

Or adapt to the traffic. Sometimes they're just timed a certain way, right?

Gavin Jenkins (50:35):

There also is like an existential dread when it comes to sitting of the light.

Harlee Hewitt (50:39):

Very much.

Gavin Jenkins (50:41):

Years ago, back in the mid two thousands, I was a sports writer and I was living in Martinsville, Virginia. I was writing for this Martinsville bulletin, and I was in a truck with the photographer, his name was Mike. And Mike had been a photographer at that newspaper for like 30 years.

(51:01):

And we were at this light in this small town, Martinsville in Henry County, Virginia, right near the North Carolina border. And we're sitting at this light, and he takes this, this is burned into my memory for some reason, he takes this long sigh, and he goes, ‘I think I've spent years of my life sitting at this traffic light’. And the thing is, there's no one coming. And because we live in a society that is governed by rules, you sit at the light, there's no one coming left, right? Nothing, but you sit at the light and wait and it doesn't need to be like that,

Harlee Hewitt (51:55):

Right? So imagine AI reading and adapting it and then being like, there's no one coming, there hasn't been for 20 minutes. This can actually be super short. It's amazing.

Gavin Jenkins (52:08):

Yeah, yeah, yeah. Well, wherever you are, Michael Ray, I hope you, I enjoy retirement, sir. Okay. Alright. Well that was our episode for today. We have some people to thank. But before we get into our thank yous, we forgot to do something at the beginning of this episode, and that is to remind you, our listener, that we have an email and we want to hear from you. And it is our email address is [email protected]. That is [email protected]. That is the number two in the middle of that b2b.com. Send us an email, tell us what you think. Tell us about the light in your town that you hate the most and you've spent years of your life sitting at. And also tell us about your favorite SNL skit or comedian. I'm going with Farley. Harlee's going with Hader, and I'll go with whatever the audience writes in. Okay. Alright. Alright. Well, thank you very much to Endeavor Business Media. That is our parent company and they're the ones that allow us to record this podcast. It's a great company with lots of great trade magazines and we want to thank you the listener for making it all the way to the end of this podcast. For Brandon Lewis and Harlee Hewitt, I am Gavin Jenkins. We'll see you next time on the ITP.