Transport and Tech with Brian O'Rourke

There are tech firms that get into transport, you may have heard of Uber for example, and there are plenty of transport people struggling with tech. But there aren't many tech firms that literally started in a bus garage. But today I'm talking to the founder of one, so let's hear that founding story, let's hear how they've got on. Welcome to this week's episode of the Free Wealing Podcast. Welcome to Free Wealing, the Free Thinking Podcast for Transport Changemakers. Each week of the new fresh voices, new ideas and unconventional thinking. So let's get started now with this week's edition of the Free Wealing Podcast. So my guest this week exists an intersection that is probably relatively unusual. It's where AI and bus garages meet each other. And in this case, the founding story of the business is literally the case of AI, at least in data form and bus garages meeting each other. I'm talking to Brian O'Wulk who's the CEO and co-founder of the Data Analytics and AI Platform City Swift. Brian, welcome to the Free Wealing Podcast. Thanks for having me on Thomas. It's great to be here. And as I say, when I say that AI and bus garages meet is not just because you are an AI person who spends a lot of your life I'm guessing in bus garages, tell me about the story of you and your childhood mate Alan. Yes, so myself and Alan, who's my co-founder in City Swift, we've actually been best friends since we were 12. We grew up in a rural part of Ireland. We actually grew up in the same village and I was probably the jokers that I was one side of the village plane with computers growing up. Alan was the other side of the village plane with buses. So his family actually started a bus business, fairly his coaches in the 1980s. And I grew it to be kind of one of the largest, most successful kind of independent private operators in the midlands of Ireland. So to do a lot of kind of private higher services, schedule services for the NTA for government and a lot of school contracts as well. So I think Alan literally grew up knowing probably all the out run of bus company, all the problems within the industry. And it wasn't until we kind of finished university probably back in 2015, 2016. And like every good Irish story, it started on a Friday evening in the pub, which we had finished working myself and Alan kind of got together. He had gone back after finishing university to work in his family business full time for probably a year or two at this stage. And as the evening progressed, Alan probably shared a lot of the problems that he was facing and trying to probably optimize the operations of the bus company or his family business. And I think as we progressed, we probably thought that some of these problems could be solved using data and technology. And that's probably where the very start of cities with I find this fascinating because I used to run a snap, which was a two-sided marketplace platform in the coach sector. So the operator side of my platform was entirely businesses like Farrell his coaches named after the founding family, still run by the family, family and businesses, doing a mixture of routes and private higher contracts. So I know that kind of business well. And they are generally, if I'm being completely candid with you, they're generally not particularly digital first. And they are often very traditional, operating frequently highly manually or using bus industry systems that still operate on sort of traditional these windows. So it must have been quite a remarkable thing for one of these family owned businesses to effectively spawn a modern AI based data platform. How did that, after you had that drink in the pub, how did you actually start solving the actual problems of an actual family owned bus company? Is that where you started? It exactly how we started. And we probably were closer to what snap was when we started first. So I think Alan's family business were probably not that they were digital native, but they were probably a little bit ahead in that they rolled out some kind of coach management software, probably five to ten years previous to this. And from that coach management software, I was able to go in and do kind of an export of the data. And I suppose what we've seen in the data of his family business was the utilization of the vehicles was very low. So, you know, obviously their peak period was when schools were on in the morning times and the evening times and during the day they would have a lot of schedule services, but it evenings and weekends, you know, 90 something percent of his coaches were sitting idle in the in the garage in the bus depot. And I suppose that was the first insight that we got from, you know, Alan's family business using export from that software system. And I suppose what we tried to do is how would we fill up a utilization of them coaches at evenings and weekends. And we started building kind of a B2C platform in the in the very early days, which was basically we were crowdsourcing new private higher roots. So if, you know, we could get 20 people who wanted to go from one town to a bigger town to go out to a pub or a nightclub at a weekend, but they didn't all know each other, we would then create a kind of private higher service for them to do that. So it was kind of the joining groups of people together to go to events, nightclubs, a various other kind of activities and mainly leisure weekends and evenings and then use Alan's family business to do that. We were semi successful doing us at the nightclub. It's very once off and it worked a little bit, made a bit of money. The events is where it was really successful. So the big major concerts in Dublin, Galway and these sorts of areas in Ireland and we were actually able to guess, you know, charge a decent price, particulars and, you know, rent a private higher bus and get 50 people going up to all these events and make a bit of money. I think we realized pretty quickly that it was going to be very hard to scale that business model and, you know, it was very one-soft project-based type of work and what we really wanted to get into is, is there any reoccurring services? So, you know, is there X amount of people who need to go from a town to a business park on a Monday to Friday basis where there is kind of unmet demand or untapped demand within the kind of general public and that's when we started to kind of merge into, you know, should we actually be a B2C and start these routes ourselves or should we look to partner with the bigger operators, especially in the UK who have, you know, 6,000 buses at the length and breadth of the UK, but they're probably not as data literators we are in trying to find that demand and then optimizing the operations to actually be able to kind of react to that dynamic demand that's out there. Absolutely fascinating. I mean, it's so familiar for me from Snap, as you know, that core insight of operators having surplus vehicles weekends and evenings, going through that process of trying to find optimal routes, realizing that it was all about scale and you either need high frequency repeating routes, which is where we ended up or it doesn't work, I mean, very familiar. But, of course, what you then did is took it in a different direction and became a B2B provider of data analysis services. And tell me just in two sentences, what it is that you do for bus companies. Yeah, so look what we do is we take all the data that's coming off on board hardware. So your telemantic systems, your ticketing machine, your AVL CAD software, at which basically all the GPS things, what the bus is actually doing in real life, then we compare that to the schedule data that's coming out of the scheduling systems. So we have what was meant to happen, what actually happened in reality. And then we combine in the ticketing data on top of that, which is how many people are getting on an after-bus at the various stops and the various routes. Once we have that cleaner, richer data set in by joining up their various usually siloed data sets, we then have built a advanced analytics one, which allows operators and authorities to have a bird's eye view of the entire network, or they can drill down into a root basis, even to a stop by stop basis, and look at all the key metrics that are really important to them. And then I think our flagship module that we've built on top of the cleaner data is actually our optimizations and simulations. And what that allows operators and authorities to do is kind of run what if scenarios. So what if I want to add a bus into a route, what's the best route to put it in, where's the highest or why from doing that, what if I want to invest in my routes to get it them all up to 90%, how much is that going to cost me, where do we need to put in additional resources, and how do I adjust my timetables to able to hit them kind of at a reliability or efficiency targets. I think it's pretty revolutionary for the industry because historically an operator in entirety would have probably had to put the routes on the roads using semi available, not accessible data, wait for six months, then reevaluate, doing kind of either surveys or sentiment from drivers, and then we'll go into this iteration, which is a six to 12 month cycle time, whereas using cities with their actually able to simulate it all up front, and when they're putting new timetables, new schedules on the road, they can be kind of semi guaranteed of success that they're going to be able to hit the commercial targets that they're aiming for. You've been working now with many, many bus companies and operators around the country and beyond. How has this sector been to try to embed AI-based analytics solutions? My understanding is that the kind of what you do should make buses cheaper to run and better for customers. So it kind of feels like a bit of a no-brainer. Yeah, I first saw you present this, oh gosh, I'm trying to remember when it was, but it was well before the pandemic, and it's taken time to embed. What's it been like trying to embed this kind of solution within the transport space? Yeah, it's been an interest in journey, and I think we've had a lot of learnings. It definitely is an interest in industry that's very passionate about what they do, and you know, from our users, which is the bus scheduling experts to the kind of commercial directors and management there is very much. It has to work, it has to deliver ROI, it has to scale. And I think the industry there's a bit of a no nonsense approach to it. You know, probably other industries, there is, you know, people can win with marketing and fluff, whereas this industry, it's real. You have to deliver tangible ROI. We went on a long journey, Thomas. I think probably our first customers were probably like national express, redding buses back in kind of 2018, 2019, pre-COVID, who were early adapters, who we got data from, we experimented with it and started building kind of the initial use cases of the analytics and optimization tooling. And they were great to work with. They were smaller deals. They were kind of pilot type work. I think one thing we learned really, really quickly was it couldn't be a black box solution. If you go to a bus scheduler, they are the experts of the bus schedules in their area. They know the routes better than any AI, any data system, whatever know them. They know where the schools are, they know where the hospitals are, they know the junctions, they know the traffic lights, and they're really experts in their local area. And I remember when we first turned up, we had kind of this is press this button, magic, create this new timetable, and building trust with this kind of black box solution just didn't work. They wanted to really, there needed to be transparency around how the data in AI had come up with this solution, how the platform did. And what we actually built in behind every single time and point of time and point or every single kind of suggestion that we make in the platform, a bus scheduler can deep dive down on it. They can see this is the data, this is the runtime set has created this optimized timetable, both from like a really stop-to-stop basis each time of the day basis. And what that enabled them to do was kind of really build trust in, I understand these outputs, I understand why the computer has come up with these outputs. And sometimes I need to override them and we built in kind of this capability that the scheduler can override them because like a 3pm on weekday, they understand, well actually we have a school at this point of a route and we want to add in an extra two three minutes because there's going to be additional boardings needed. We want the bus on time so when the kids come out of school, the bus is waiting for them and it makes that operation streamlined and things like that are hard to pick up in a data in AI platform, no matter how clever it is. So we built in all these capabilities to users, and what had actually happened was the executives in these companies obviously wanted to improve my reliability, improve my efficiency, it's a win-win. Getting the users to buy into using this platform was actually the harder thing and I think by building transparency and the ability for them to actually override the system when they know they have more knowledge than the system does, was two big areas where we've gone kind of full circle where now I think the users of the CSW platform are the biggest champions of it and I think that was a long journey to go on. But I think the key thing by building any technology to technology is for humans, it's for people and it's to make the schedulers lives easier and we spend some, me personally, in Allen especially, we were spending three or four days a week on the road pre-COVID, especially with users with our early adopters listening to their feedback, taking everything on board, iterating on a quickly coming back to them with updates, asking for their advice along the way and I think you get real buy-in from that and I think from there we've started to see the scale, I think COVID was a funny time obviously for the industry, like we went from having a couple of pilots with national express and redding and a couple of others to 90% of their customers and their revenue basically disappeared overnight and we were very unsure at that stage, you know, the viability of to these fifth-going forward where these customers going to be able to continue to pay us, where they are going to want to scale up and roll the software out across their fleets and for the first maybe 12, 16 weeks, you know, it was definitely a tricky time. We kind of decided to let's get our heads down and just trying to deliver as much value as possible to the couple of customers we had at the time and really just being a aid to them to help them understand what's going on in their bus network and I think we got our probably our first big break, probably about three to four months into COVID where national express because we'd been kind of working with them with such a white glove treatment, they decided to fully roll out around a little splatter from across Birmingham because what they were struggling with was they probably had dropped to like Sunday only schedules across all of Birmingham because of COVID but what they were starting to see was the buses were getting too full for social distancing at certain times of the day, especially in around the route going to hospitals because shift staff was turning over and then you were having full buses or nearly full buses at them times and we were able to identify all of that through the day, they didn't fully understand the problem until we had done that and what they went down and added on additional buses at them kind of shift turn over times for hospital workers and it was obviously a big massive way for national express at that time and obviously for the passengers as well and for health and safety with everything going on and a national express besides roll out the platform to have that type of analytics for their entire network as passengers started to come back throughout the kind of various phases of COVID and I think that kind of proved to us that the platform was incredibly valuable and proved that the customers as well and national express in particular I think COVID in some ways accelerates innovation in certain areas because I'm not sure if COVID hadn't happened, would national express have seen so much value and been able to kind of adopt and use the platform for their general day today as when there was a crisis they were able to obviously then use it and see the value of it in a different way and I think that was probably a big springboard then for roses of business to kind of grow from. One of the things you said there which was interesting was around the challenge of the schedulers adopting this and of course the schedulers have been trained to do a job which is fundamentally different in a world where the kind of solution that your offering exists and the kinds of things you're able to do will only accelerate. Does that mean we need to fundamentally rethink that role? Is it a different job in future? I think it's starting to evolve historically, you know, a schedule is what have worked with pen and paper and they would have been kind of rulers drawn drawn on paper and coming up with the best possible solutions but it's obviously very hard to make iterations when it's pen and paper and make changes. I think the introduction of AVL systems and the next bus arrival systems is probably the biggest innovation that happens that enables then this kind of wave of new scheduling because basically GPS receivers were put on every bus in the country, in the UK and Ireland and the main kind of purpose of doing that was actually to inform passengers where buses were on real time and then so they can give predictions of how far it is away from a bus stop but the secondary effect of doing that was meant that there's GPS receivers every 30 seconds GPS pins for every single route in the UK which is obviously now flowing through the boards and it's really really useful but we were unable to kind of hook into that data and then start using this to go well actually if we have the data on how long it takes the bus to get from A to B to C to D to E on a route we could potentially feed that in to our scheduling best practice and that was starting to happen before we came along but I think the big problem that schedulers faced was it's literally terabytes of data and it can be quite dirty to that data because if a driver puts in the wrong call into a ticket machine that kind of pollutes all the data downstream if two buses swap they're kind of tripped by these for whatever reason and what we found was like 20% of that data is actually you know dirty and needs to be enriched or cleansed and before we came along like a scheduler or the bus companies they weren't really able to handle it they weren't able to use it and you either had massive manual efforts use of microsoft excel or various sort of kind of methods to trying cleanses and actually get something useful out of us but not really fully be able to utilize it all or they just went back to driving on buses with stopwatches and taking really small sample sizes so we came along and we were able to kind of pull in every bit of that data at 99% of us and then make it really really usable and I think it's changed the role in a way that instead of schedule spending loads of time trying to manipulate data they're actually what we're starting to see now is they're they're making spending more time on kind of commercial decisions so it's should I run a Friday only schedule do I have the capabilities to do that do I have the vehicles to do that you know should I work with my local authority on bus priority measures because we're seeing consistent delays at certain parts of routes I think it's changing the role but it's changing them to be a much more commercial outcome driven and I think schedulers are once they fully understand the capabilities it's actually enabling them to do their jobs faster better and get better outcomes but it is taking a more data lens view of it but generally nearly all the schedules that we meet in the UK they're very analytical they're very kind of numbers driven it's the nature of the role and the thing they care most about is driving the performance of the bus network and generally when they get it to and get trained up and and they're starting to adapt it and understand how it can be used we're seeing them kind of embraces at an ever increasing race I do think that the wanting for schedulers especially is that when we were scaling the bus what worked really well was Alan's domain knowledge work and myself over there with them learning from them listening to them understanding their currents you know pain points either with the product or the scheduling process and really having that deep domain knowledge and I think what we've done really really well in city swift is we've hired I think it's 20 25 people currently who work for city swift come from the domain industry I don't have worked with authorities operators transport consultants and see kind of backgrounds So they actually guess. the industry, they get the challenge in its whizzes, they've been in the trenches, I suppose, and then there are the people in Cityswift who are kind of translating the power of Cityswift to our users, but on the same way, they're translating what the users' requirements back to our technology people. And I think having that two-way communication flow has been absolutely critical to getting kind of users with adopt the platform and then for users and operators to actually get the ROI out on the other side. Let's talk about the customer impact for a few minutes, because optimization can be a double-edged sword. If you look at the work I'm doing, I'm doing a project called Mini-Switzland at the moment, which is trying to bring some of the lessons from the huge success of the Swiss transport network into the UK. One of the things that in Switzerland they do is every service runs every hour, every bus runs every hour, every train runs every hour, and they meet each other at every stop. So as a user, you can make really fast connections. You get a bus to a station, the train's waiting for you to get on the train, it drops off at the next station. There's a fast train from there, take it to another station where you can pick up a bus that's waiting for you there, set you a train trying to go, they all connect. But actually, the success of that from a customer point of view, which feels highly efficient and optimised, is that a lot of the routes aren't massively optimised. Instead of running every hour, it might be more efficient to run every 48 minutes or to run a bus that's 48 minutes, and then the next one's 47 minutes later, the next one's 32 minutes later, the next one's 54 minutes later, depending on traffic conditions, etc. So how do you balance optimising the route with the customer experience? Yeah, it's a really good question. I think one of the areas we learned quite quickly was the passenger needs, at the end of the day, the passengers are the ones who are paying for the transport, they have the needs to get to and from their locations. In this day and age, they have options as well. You have to make the bus the most attractive product on the markets for travelling in urban and inter-urban areas. One area I think we looked at it, and if you were to take a pure AI data-driven approach to it, when you're trying to optimise your run times and your schedules, you would probably look at having very different run times for different times of the day. What that would end up creating is timetables that aren't passenger-friendly. What we have to learn is you have to balance the need for optimisation with the need for what the passengers have. I think one key area is like clock face and timetables. It's a key feature that we've added into the platform from the early days is that if it's not a high frequency service, that passengers expect it to come every 15 minutes on the hour and they're looking at their clock and they're watching to check when is this bus going to come. If you're changing that, if a bus comes at 9/18/10/22 and there's these varying non-clock facing timetables, it's incredibly hard for passengers to go on the 9 o'clock, 10 o'clock, 11 o'clock because they'll end up turning up for buses and the buses aren't coming because that's not when they're scheduled to come. I think you have to balance the need for optimisation with what the passengers expect and what will actually drive a more attractive service. I think on the flip side, especially in high frequency networks, the trend is more towards your tube. You never check the timetable of the tube because it comes every 5-10 minutes and when you have that high frequency, you can be very dynamic in how you're timetabling and scheduling the service. And oftentimes, you know, that balance between clock facing and becoming more dynamic timetabling to get the efficiencies. But when we were looking at probably, if you have clock facing timetables and you're not changing the run times for peaks, what ends up happening is that the bus is sold delayed because the peak time is so much longer, your actual timetable is a work of fiction. So it's a very complex kind of problem because you need to balance for what is actually going to happen on the road, what is attractive to the passenger in the service that we can deliver and then what's the most optimised from a kind of a cost efficiency basis. And what we try and lean towards is what's the reality on the road that the passenger is going to be able to adapt to and use and be attractive for them. But they're definitely as trade-offs and I think I've seen other probably really AID-focused companies and they probably take up your efficiency view out but and don't understand probably the underlying requirements of what passengers need and expect. And I think the other party key stakeholder in this as well is drivers. It's like if you have these very dynamic, very changeable timetables from everyday of the week with changing run times for a driver to learn their timetable or learn their their road becomes incredibly difficult if you're turning up to on Monday and you're meant to drive at a certain time Tuesdays different Wednesdays different Thursdays different. So there has to be a balance between what the passenger wants and needs, what the driver is actually able to deliver and then try to account the the increases in reliability and efficiency to go along with this. And I think it is a balance but for me it's the most important thing and I think the reason why we've been successful is we've listened to our the experts within the industry, our customers and schedulers and understand from them the local requirements and try to build the configuration in depending on if it's a high frequency, a low frequency, you know, if it's the striper change over as mid-roos, if it's stopping at schools or hospitals where you want to there are certain times you have to be able to build that into the system rather than just taking up your this is what AI said kind of output from from any kind of solution. How do you deal with the risk especially in a business with AI in its capabilities of overselling what you can do because you can't build a bus lane and you can't deal with delivery vans and unpredictability on the road is a fact of life. How do you balance the capabilities that you genuinely have and can offer with the reality that it's going to be imperfect before and it's going to be imperfect afterwards? Yeah, I think it's a really good question. I think our probably mantra is to kind of under promise over deliver when we're working with customers. What we generally do when we get a new kind of large customer is we before we go and actually sign a new deal, we try and get about 12 months worth of historical data from them for kind of a to do a business case and what we'll do is we'll run that 12 months a day through the platform and go this is the the current reliability efficiency. If you were to have used the platform and put in for your time table changes, this is what potentially could have been delivered and really take a kind of a you know there's obviously kind of a lower and upper kind of case on that kind of or why in returns, but I think it's being critical that you don't over promise because it's not like a real network where you know you completely control your tracks. As you said, you know there's so many things to go on within a bus networks that make it so complex. We're probably taking a statistical view of it that you know if you were to these are the areas where if they were to have used our platform last year, this is what the result would have been and then sharing that number with them is that acceptable? Are you happy with that? And then what we try to do is track that on a quarterly basis to make sure that we're at least meeting that or exceeding that and if for any reason there is certain routes or certain parts of the network that aren't achieving the expected reliability or efficiency gains, then it goes down into why and that's where the analytics platform can be kind of deep dive into what's going on generally and then instance it's usually something to do with kind of long-term broad wrecks works or traffic management or there's been kind of some operational issues where there's like a number of vehicles available, a lot of drivers available and there's underlying issues that's causing us and then it's up to the kind of the operators and you know operations and management to decide well how do they go about fixing that but what we're trying to do is give them the root cause of what the underlying issues are but generally the over-underpromise over-deliver is the kind of mantra from a from our kind of team and I think it's in this industry especially you know you have to deliver I think it's very especially what we're doing it's very days as numbers driven and there is really no hiding if you don't deliver so it is under-promise over-deliver and then track but I find everyone within the industry as well to be to be really reasonable and like they're working in an imperfect world and and they fully understand the issues that do go on within the bus network and increasing congestion, road works and all of those other issues so you know it is a joint effort then to to try and fix any kind of issues or roots that are under performing as well. So Alan was the person with the family owned bus business and you were the tech data guy you've now been working in the transport sector for what 10 years so what have you learned about the transport sector yeah as a sort of outsider now deeply embedded in it and what have you learned about the differences between different types of businesses between public sector and private sector between big groups and public authorities. I've learned lots about the industry I think you know even before as myself and Alan we're getting started and especially when we kind of started working more with the the larger private operators like the education there is a significant kind of education to be able to gain enough knowledge to actually have conversations with the various domain experts whether that's the kind of you know managing director commercial director operations director or the network planners, schedules and an operations seems that are running the day-to-day the bus so I think there is a massive learning curve if you're to look from the the outside in. And you know, buses probably, if you're a passenger perspective, they look quite simple, drive round, collect people, drop people off, and you know, people can't understand why they don't come on time all the time. I think even new hires who joined City Swift, once they're kind of onboarded, we kind of, we put them through a kind of a training course around domain knowledge, and they're described as like doing a PhD in buses. And they can't fathom, I think, in the early stages, the complexity of the underlying operations of, you know, how about what bus company needs to do to actually operate, and the various kind of trade-offs that they're consistently making on a day-to-day basis, they actually be able to deliver the services that they constraints they're in. And the example I give is Ryanair is the Europe's, I think, the world's second largest airline and Europe's largest airline. They operate about 500 or 600 planes, whereas if you look at the UK's largest bus operator stage coach, they have about 6,000 buses. And the complexity goes with running the bus network, even in comparison to an airline, and an airline just goes from a two-one place, a bus might stop in 40 to 50 stops along the way. So there's massive external complexity that's brought into the bus industry. I think that things I've learned most is there's been massive challenges over the last 10 years. Covid-been, probably the biggest one, the knock-on effects from that driver shortages was probably the biggest challenge we seen the industry come through. And then it was passenger kind of recovery growth and kind of balancing that desire from operators and authorities to provide supply and get back up to delivering really strong performance bus networks while passenger demand kind of slowly crept back up, took close to pre-covet levels. And there was a really tricky period there where demand was only back up to 50, 60, 70%, and bus companies were trying to run 90% of their previous network. I think the probably the one underline learning is the industry is very resilient, both to kind of shocks, changes. Like, I don't think there could be a bigger starting to understand what happened during Covid. And I think the industry, especially the last three years, has really been, you know, started to grow again. And I've started to see kind of operators and authorities get way more focused again on how do we really kind of deliver the highest quality possible service to our passengers, which routes are working really well, which routes that we need to improve, how do we embrace technology to do that, whether it's a system like Zilli Swift or, you know, predictive maintenance or real-time systems. And I think the industry is, you know, really starting to adapt and grow and become even, even more resilient. I think the other kind of key learning is the industry I'm working with, the industry is supplier. They value domain knowledge. They value that you understand, you know, the problem, the industry. We've had certain people working for us who came from, you know, other industries or other backgrounds who either codenters didn't want to learn as much as, as most of us, about the industry. And sometimes it's just, it's, it's hard to translate that message. And I think the industry really cares, especially in the UK and Ireland, that you kind of understand the domain, you understand the problems that they're trying to solve for. And I think once you get to that point, you have a very collaborative working relationship with Indian industry. I would say every single customer we have, probably an in public, they're collaborating as in withles to build a better solution to build a product to solve their problems, kind of what new ideas, you know, trying to improve the existing offerings that we have. And there's a very collaborative environment. We're trying to help them be successful. They're trying to help us be successful. And I think it works really, really well in the industries as well. I think probably the other change that we've seen is franchising has probably been the newest kind of change or market dynamic to look into. And look, I think we could probably have long conversations of, you know, what's the best possible way to run bus services? Obviously that the TFL model, which has been, it's been ran and it is doing well in London. And obviously Manchester Liverpool and a lot of other authorities are utter, confirmed to be going that way or thinking about going that way. I think it's going to be an interesting time to see and in five, six years time, what's the best possible, you know, our disturbances getting better. For me, you know, even in the early days, I think that collaboration between operator and authority, whether it's in a franchised environment or a non-franchised environment, I think it's critical for the success of the bus industry. Like we used to come along and, you know, I think it's so much more important that operators and parties are working really, really closely together to deliver infrastructure improvements to, you know, bus lane, lanes, traffic like priority. And if we can get more and more of that, I think the industry is going to win and, you know, try and circumvent the issues that increase in congestion is causing within cities. So it's franchising the best way to do is it's enhanced partnerships the best way to do it. I think it's a case by case basis. And I actually think it's how well, you know, you can take an approach and it's how well you actually execute on that approach is the most important thing. If certain authorities go enhanced partnerships and they do it really, really well, that can I think deliver massive results. If certain authorities decide to go franchising and deliver it next year, you don't really, really well, then I think that can also deliver exit results. But I think it was one thing within the industry, pre-COVID and true COVID is I probably wish for as a supplier that operators and authorities were more engaged with each other and collaborative with each other because I think if you join the two of them, they can solve all of the industries, challenges in cities. Whereas if the two of them aren't working closely together, then I think it's much, much harder to solve the more real world infrastructure issues that you've mentioned previously. And I realise it's very early days, but are you starting to see that happening? Are you in the places where you are working? Are you starting to see authorities, highways owners and bus companies working more collaboratively? Or is that still something you're hoping to see happen? I think we are starting to see this. I think, you know, case by case, region by region, you know, there would have been certain areas where this was happening already. I think like one of our first introductions to this was working with Oxford. We were working with some of the private bus operators in Oxford. And an opportunity came back with Oxford City Council where they had some funding. Basically, the trade-off that was to be made was Oxford City Council work on tip of bus priority measures in place to speed up the buses, which obviously reduced costs and hopefully increased revenue, increased in passengers for the private operators and in return the private operators purchased electric buses to obviously reduce emissions. And that was kind of the trade-off and they brought us in as the kind of analytics platform to monitor our double speeds improving because of the bus priority measures as agreed between the operators and authorities. And I just thought it was a, we were in for relatively low cost. It was very small. But I just thought it was a great concept. You know, there's a win-win scenario here for both operators and authorities. The authority, you know, once electric vehicles within the city, they want less emissions. They want more passengers using it. The operator, you know, it would have been a big cap ex expenditure for the operator to go and do that, but because they were incentivized with this long-term infrastructure development within Oxford, they were able to make that investment. And I think more and more areas like that we are starting to see. And then obviously within the franchise and areas in Manchester, you know, we are starting to see where, because the authority owns the roads as well as now the bus network, you're starting to see it's much easier to make trade-offs like that because they're the overall owner. And then I think the final piece is making sure that they're working with the operator to deliver the best possible service in all these franchise environments. And I think data can have a big part to playing that as well and making sure that they're getting the, you know, performance resumes in the right place and using data to make changes to contracts where applicable. We're seeing, I think we're seeing that sound to come true as the process to go to franchising it and the actual franchising environment becomes a little bit more mature. Two final questions from me. If you look back 10 years, optimisation, simulation tools like Citruswift didn't really exist. So let's imagine we're looking forward 10 years in the transport sector. What kinds of solutions and services can you envisage as a data tech person existing that simply don't exist at the moment? Yeah. I think it's interesting. I think we look forward to probably on a kind of five-year roadmap. I think there's a couple of kind of themes that we see. The battery data, the telematics data, and I think this is going to present a big opportunity for both operators and authorities to harness that data to make even more improvements, you know, not just to the time table, but to the schedule, but to the wider kind of city context as well, and how people are moving and when and why. I think, you know, we should see there 100% probably electric bus rollout. I think it'd be interesting to see how quickly that happens and putting in the infrastructure for everything else. The climate dynamic is an area where we're looking at closely. And the way I would describe it probably to the laypersonist, you know, obviously if you're running airplanes, you have air traffic controllers. It's quite advanced technology that's going on there for various safety reasons and everything else. But as we look to probably be buses, the control systems, especially outside London, that the buses are used and are probably not as advanced as they could be or should be. And I think there's massive passenger benefits to actually trying to make better decisions in real time. So just, you know, services can be if services are delayed, there's not knock on effects on further services and trying to kind of give the most accurate information to passengers as well throughout that. I think autonomous is a, you know, a really interesting concept. A funny story I have is when we were starting first this business by South and Alan, probably like 2017, 2018, we were looking for investments and in Dublin and we went to a couple of kind of local venture capital investment firms and I think one we went to and we kind of pitched the idea of, you know, making buses more efficient and the venture capital has turned around to us and says, you know, that they couldn't invest in the bus industry because autonomous cars were going to come by 2020 and that nobody would need buses anymore. So I think with autonomous cars and like the timelines for that, I definitely think that hasn't come true and I think the bus industry is actually going to try and, you know, potentially having more safety features and onboard vehicles and vehicles becoming semi-autonomous could be really beneficial to the industry, but I actually see it as a massive opportunity to reduce congestion and get more people to use public transport rather than the other way around. So I think the industry is going to go from strength to strength, you know, of true to the next stages. And final question from me, if we go back to your teenage self who was friends with Alan, you were tech, data, computer, person, Google's, the largest employers in Ireland, yeah, I cannot believe you would have expected to end up as a bus industry expert. What would that teenage brands say to present day brand, do you expect? Yeah, I think it's an interesting one. I was probably, you know, Alan obviously came through a very entrepreneurial background where both his parents were, you know, live and working and breeding a family business and growing it very successfully. I was also quite an entrepreneurial albeost, you know, with computers and I worked with a family firm growing up as well. You know, it was, we were both myself and Alan were quite entrepreneurial, I suppose, when I look back to the teenage years, I think we were looking forward, as I've said, to multiple other people, it's like we started this business probably myself and Alan, we were in our middle eight, twenties, a couple years after university and, you know, 10 years later, we've been managed to scale it up. I think we're 50% of the UK and Ireland's buses on the platform currently and we're starting to internationalize the business. I don't think in my probably wildest dreams of my early days, would I have said this, you know, this is something that we thought we could do. We probably, myself and Alan started it quite small of, let's just try and help his family business to make a bit more of an margin and let's see and grow from there. And I think then, you know, truly iterations after the last 10 years we've grown and scaled. So look, I think probably, you know, wouldn't have been in my wildest dreams that we would actually be able to do on it, but I think we're really excited and I'm grateful as well. It's like every day I get to come in to work and work with my best friend from school and the two of us are growing the business together and we're working with great people, but within City Swift, but also within the industry who we enjoy spending time with and yeah, it's a great industry to be in. Many congratulations on everything you've achieved so far. We'll be watching with interest Rhino Walk for Fander and co-founder and CEO of City Swift Many. Thanks for joining me on the free wheeling podcast. Thanks for having me, Thomas. Well that's the end of this week's episode of the free wheeling podcast. I would like to thank you, my listeners for listening and I would like to thank Rhino Walk, founder of City Swift for joining me. If you have a spare 10 seconds and you're holding your smartphone in your hand and you don't already subscribed to the free wheeling podcast, then just click that subscribe button on Spotify or Apple podcasts and then it will magically appear in your inbox every single week. If you do and you've heard previous episodes, you've enjoyed them as much as this one and you feel like giving it a five star review, then be my guest. You're most welcome. Either way, but if you do or don't, I will see you next week. Have a wonderful week. Bye bye. (upbeat music)