See.Sense

See.Sense

Season 1 Episode 1

[00:00:00]

Phil Latz: Welcome to our first Micromobility Report podcast, where we'll discuss how we can all go further with less. I'm your host, Phil Latz, publisher of Micromobility Report

=Today, we're talking with Philip and Irene McAleese from the technology company, See.sense.

Hello, Philip and Irene. How are you?

Irene McAleese: Hello good to see good to see you and speak with you

Phil Latz: Indeed. And I should add that you're up in sunny Brisbane. Is it sunny there today?

Irene McAleese: It's actually a little overcast today which is Maybe a welcome change. We've had a very, very hot, summer and it’s starting

Phil Latz: Yeah,

Irene McAleese: to cool. We're starting to get a bit of a break and things are cooling down a [00:01:00] bit. So

Phil Latz: yeah, which is quite an adjustment from Ireland, which we'll get to in a, in a minute or more particularly Northern Ireland. But can you both, give us a brief summary of your corporate careers. You've had interesting corporate careers before founding See.sense. So perhaps a little bit of your background before we plunge into See.sense itself.

Irene McAleese: Go first, Phil.

Philip McAleese: So I'm Philip McAleese. I'm co founder and CEO here at See.sense. after studying, a degree in electronic and software engineering at university, I did a few years in air traffic control, designing simulation systems for the British government. then moved into financial software, and developed and maintained trading systems for investment banks. and as we'll come to a bit later, that was really where the story of See.sense started. Um, I was a busy executive with no time to the gym. And like many of us do, took up cycling to get some exercise back into. an otherwise busy daily routine.

Phil Latz: Awesome. And what

about [00:02:00] you, Irene? Thanks

Irene McAleese: So I, I actually studied originally psychology and, and workplace health and safety and then, um, my, my graduate career, first career was actually working for Queensland Rail here in Brisbane. but in the last 20 years, I've been overseas and, I worked for large management consulting firms like Accenture, running change. Programs, mostly workplace transformation programs for large corporates. So, lots of stakeholder management and project, management expertise, I guess, along the way.

Phil Latz: Okay, so clearly you're highly employable people, working for very big organizations. So what motivated you to ditch the safety of salaries and plunge into the, you know, risky world of a technology startup? So how did it come about?

Philip McAleese: I think there were really, two things that brought that about. first of all, whenever we went on holiday and we [00:03:00] encountered other people's businesses, we quite often thought we could do that better. and after, the arrogance of deciding that we could do that for so long, we we thought we better put our money where our mouth is and give it a go ourselves and see was it actually true and could we do it better? and the other thing was that I was working in client facing technology for an investment bank in Singapore. And so I took up cycling to get this exercise back into my daily routine. And I was traveling along a really varied of infrastructure. So everything from shared paths with pedestrians to part of the closed Formula One track, and of course the road as well. And I didn't always feel safe on that commute, much as I enjoyed it. So I wanted something that would improve, my visibility and safety, I looked around and saw that cars since 2011 all had, they had visible running lights, because in Europe they recognized that, it reduced collisions between vehicles in the daytime. Between 5 and 10%. And so I figured, well, something as big as a [00:04:00] car needs it to be seen in the daytime. Then me as a cyclist, I could do with that as well. And so, I was looking at the smartphone on my handlebars one day, and realized that a lot of the sensor technology that I'd studied at university, that was very cutting edge then. It was not very mainstream and accessible, and so I thought, well, what if we took the smart sensor technology commonly found in mobile phones into the light to give it situational awareness so it can flash brighter and faster in times of higher risk and conserve energy when it's not, then we can have something that is truly daylight visible. Bright enough to be seen, you know, at any time of day, but still be quite small and compact because it's using his energy efficiently and it's not going to need to be a big unit with, you know, an external battery and lots of wires, as would otherwise be the case. so at the bank that I was working for, there was a large global. Cycling community and I posted on the forum there and said, Hey, you know, I'm getting back into cycling having not really been on a bike since I was a teenager. what do [00:05:00] you guys think of this concept? And over the course of the next couple of days, I got more than a hundred replies that said, This is fantastic.

And if you make one, can you make one for me as well? and so that was really the catalyst we needed having accidentally done that initial market research. we went ahead, gave up our corporate careers. we wanted to move to either Australia or Northern Ireland for personal reasons as well, because we just had our second child and wanted a bit more family support. And so everything came together. we moved to Northern Ireland and founded, See.sense.

Irene McAleese: I'd probably add to that, that Phil does have an inventive mind. There'd been a couple of other potential business ideas that he had been thinking of at the time. but this one is the one that I think really resonated with me, Phil, because I thought, this is, this is something that, if we can improve safety for cycling and therefore help get more people onto bikes, you just see already that bigger vision of getting more people on bikes that helps to reduce [00:06:00] congestion and pollution in our cities, improves people's health

and, If we're going to give up our corporate careers, it has to be something that you could be passionate about. and something that, you know, it's, actually I didn't know then how much of a hard slog and just how hard it would actually be a startup as well. But I'm really

Phil Latz: Yeah,

Irene McAleese: have that foundation of this is actually something that we, we can feel proud of. I feel, we're, we're making hopefully some kind of dent in the world a little bit where, and it keeps us motivated as we go through, because you've got that underlying kind of interest and genuine passion in it,

Phil Latz: yeah,

Irene McAleese: than, I guess, just some business idea that you might've dreamt up to, you know, see

Phil Latz: just, just to make a dollar.

Irene McAleese: Yeah.

Phil Latz: Yeah. So, you know, you've already explained that See.sense, you know, started as a taillight company if you like, but it's a taillight with a difference, isn't it? It's not really about the taillight, but the secret sauce inside. So [00:07:00] could you enlighten us as to what's actually inside and what it lets you do beyond just being a taillight?

Because obviously there's, you know, even when you started, there were already a lot of them

Philip McAleese: Yes.

Phil Latz: the market. Yeah. Right.

Philip McAleese: We used to describe it as like Formula 1 telemetry for bikes, so we've got a number of sensors on there that we run up to 800 times per second, and then we run it through, AI, which allows us to profile and understand both the environment around the cyclist, but actually what the cyclist is doing as well, and so it gives us really, really great insights, around not just where people go, but actually what their experiences are,where the infrastructure is working, where it can be improved. And so I guess if we kind of compare it to say, floating car data, so connected car data, the early generations of that you had, you know, indications of where say the anti lock brakes were being triggered or where the stability control was being triggered as time has moved on, those [00:08:00] systems have become more complicated.

Or more, I guess advanced, we're not getting things like the, the forward warning. So where the car is detected an imminent collision with, with a cyclist, a pedestrian, another car, and it will report those back as well. but they're still not quite at the level where they're individually profiling the driver. Which is where a lot of, insurance companies would really love to get to because they can't make certain that they're black boxes registering journeys that are being taken by the learner driver, not by their parents or somebody more experienced. we can actually tell a lot of that. We've been using, AI for the last 10 years, way before chat GPT made it cool. And so we can actually understand the experience of the cyclist, and their, and their, guess. competency and fluidity at being able to ride a bike. And so what that allows us to do is, if you imagine you've got a less experienced cyclist, you know, someone that's just started, they'll tend to react to everything as they come upon it.

And so their inputs into the bike will be [00:09:00] abrupt. You know, there'll be sharp spikes in acceleration and swerving and that sort of thing. Whereas an experienced cyclist will tend to look further ahead anticipate more and generally react less. And when they do react, they'll also apply the brakes less quickly. So in order to get this kind of data from the bike and make it useful, you've got to standardize it. And so our AI, because we understand the experience and level of the rider allows us to see what's normal for that rider. And so what we're actually scoring is not an abrupt acceleration, deceleration or swerve or anything like that. It's actually how far outside the normal envelope of performance that the cyclist is. And that allows us then to see. Doesn't matter whether you're just beginning on your cycling journey, or you've been cycling for 20 years and are very experienced, we can get a standard reading for every time that you're caught out by, you know, a pothole that you didn't see until the last moment, a pedestrian potentially going to step out in front of you. A car that you think is going to overshoot a junction and so on. And so we're [00:10:00] able to build up these really, really rich data sets aggregating that across, you know, more than 100, 000 cyclists, many millions of journeys and billions of sensor readings to really give never before seen data around Where our cities are working, and where perhaps they can be, looked at in more detail and improved.

Irene McAleese: I guess, Phil has talked there quite a lot about the data and how the data is different, but I think the core, it's really important to remember as well that, these, these actually are bike lights that are out in the market that compete against other bike lights that are for sale. and there's lots of use cases back for the cyclists themselves.

so all those sensors that are in, inside the light Phil actually, they do react to their environment as well. So when, when the cyclist is,

filtering through traffic, going through a roundabout, approaching, headlights from a car, the light does detect that [00:11:00] and it's going to flash brighter and faster.and that's one of the things that actually has made our bike lights quite popular, this reactive, element. because, of course, when the light reacts by flashing brighter and faster in those scenarios, what it's doing is becoming, it's daylight visible, it's bright, but then it's able to conserve its battery power at other times. and so what you get is a daylight visible Bike light with a really long runtime, more than 15 hours on a, on a charge. and that's why we've become British Cycling's preferred bike light supplier, Cycling Ireland's preferred bike light supplier, which is great because it's basically, it's not just tech for tech sake. It's actually an excellent bike light that cyclists do really love to use. and also it has this connected,

Phil Latz: So,

Irene McAleese: features.

Phil Latz: what motivation is there for the cyclist to share their data? And this will be about a triple barrel question, all right? So [00:12:00] number one, what motivation is there? Related to that, do you have customers who buy the light but then don't share the data? And number three, just to confirm the mechanics of sharing that data, is they have to link it through their phone?

And Via an app and, and live upload. Is that correct? So could you answer those three questions?

Irene McAleese: Yeah. So, when, when, when people buy our bike light, they can, they can choose to share their data. So it's very, very transparent. So they opt in using the app, if they want to share their data. And it's important to remember that when we share their data, we're sharing only aggregate anonymized data with the cities.

So really they're not looking at their individual journey because. That's actually not so interesting as the patterns that emerge out of out of that, you know, where we're seeing swerving or breaking etc at

Phil Latz: Yeah,

Irene McAleese: a junction. That's that pattern is far more interesting than [00:13:00] the individual journey. So, but anyway, we're only sharing aggregated data. So it is completely optional. We actually have a really high percentage of our users who do opt in. because when they opt in, we also provide or unlock quite a lot of other features in the, in the app. so the app has a crash detection alert, so if they do have a crash it would send their location to their nominated contact. It's got a theft alert in it as well, soyour bike is moved and you're not on it, works over Bluetooth, so it's about 100 meter range, but it would send you a notification. And you get, low battery notifications, so if your battery level is low, you would get an alert. And you also get your own personal ride stats back through there as well, which, people quite enjoy seeing, so how many, calories they've burnt, or how much CO2 they've saved as well. so there's a lot of connected features in the app that they unlock as a kind of [00:14:00] reward, for sharing their aggregated, anonymized stats. data. but I will say that when we have surveyed our, our community, there's, you know, there's a lot of, you know, those things I've spoken about are the what's in it for me, the individual, you know, benefit of unlocking those features in the app, but there is, there is an element of, a large proportion of our cyclists who just genuinely want that to see that data contribute to the wider good. okay,

Irene McAleese: that this data can be used to help improve conditions for all cyclists in the city is a, is a motivating factor for, for, for many cyclists who want to share that data. And certainly on the projects we do with cities where we, we work with cities to deploy these lights, that's actually one of the main overriding considerations,

Phil Latz: okay. So just the third part of that triple barrel question about the connectivity with the smartphone. So is it a live feed or is [00:15:00] it, is it, is it stored and then uploaded at the end of a ride or how does it work?

Philip McAleese: Yeah, so there's a Bluetooth connection maintained between the light and the app as you cycle around, and so it's

Phil Latz: Yeah.

Philip McAleese: Using the app to buffer the data. do then collect it up and send it intermittently just to make it more efficient from a data usage perspective. And if

Phil Latz: Right. Okay.

Philip McAleese: connectivity at that stage, it'll wait for Wi Fi and upload it later.

Phil Latz: Okay.

Philip McAleese: We also make it really obvious when we're collecting data and when we're not. although it has to ask for data. collection to be available when the app is not in focus or not on the screen. That's just because you're going to have your phone locked. whenever you're, cycling around, there's nothing, you know, more sinister than that. But we make sure that we actually only collect data whenever the light is connected to your phone. So you see a blue flashing light to indicate the Bluetooth on the lights. and

Phil Latz: Right.

Philip McAleese: when the light is flashing, so when it's turned on. So if the light's not on, it's not connected to your app, it's not connecting data. And if it is, then it is. So it's really simple. Mm

Phil Latz: Okay. Now, you've [00:16:00] mentioned cities about three times now, Irene, so we'll move on to that. And from a city's perspective, how can cities use your data to improve micromobility?

Irene McAleese: hmm. So I think cities, at the moment are getting more and more interest, so active travels definitely come up the agenda, particularly post COVID many cities, and there's more and more investment starting to happen, in building infrastructure to support that change and just, and this is really where data comes in because, up until now there's been very limited data from cycling, you would have the kind of You know, count data from loops in the road.

Maybe some cameras mounted at junctions. You've got travel surveys. There is app data,the, as Strava or or other, sort of sports related one. But these often can be quite skewed to particular groups. and really what cities are saying [00:17:00] to us, and what we're hearing through many of the conferences and events and things that we participate in, is that they're, compared to other modes, cycling data is, Is less available.

So cars, for example, just have so much more data about them. And when cities are planning how to, you know, how to model and resource for transport they've got all this data from cars, but there's very little data from cycling. And the real gaps that exist for the cycling data, are around, um, safety, understanding, um, the experience of cyclists.

on, on the different infrastructure. There's gaps around, having representative data from different, types of cyclists. So as mentioned, not, not just all skewed to the athlete end of the spectrum, trying to understand how women or different other commuters move around the cities. and so really what we're doing at See.sense is really trying to help [00:18:00] fill some of those gaps while complementing some of the other sources of data that are out there.

Phil Latz: So can we, I'm just going to interrupt because, I, I know two things I'd like to specifically asked you about that I've heard you talk about before, that your, light can detect near misses, rather than have to wait for people to be killed in crashes. And the second one is that you can even detect a pothole before a pothole is there, like the conditions that a pothole is likely to form.

So can you just very briefly, elaborate on both of those two characteristics? Hmm.

Philip McAleese: In terms of the near misses,

um,

people imagine that, there's some sort of sensor that is detecting cars in the vicinity of the bike and, reacting to that. It's actually looking at the individual rider. So, and understanding what's normal for them. So a near miss event, we're measuring the reaction of the individual to that near miss. So it can be, it could be as a car or lorry drafts too close. [00:19:00] To the to the rider. It'll cause an upset and causes a left right wobble so we can detect that. more frequently what we're actually seeing is. a brake or swerve event that's unusual. So a little bit like if you imagine you, you do an emergency stop in a car, it's not really about how hard you press the brakes. It's how suddenly it's, it's going from, you know, a normal condition to suddenly applying a reasonable amount of force. and so we, we measure exactly the same thing on the bike. And that's where our profiling of the rider really comes in to understand what constitutes an emergency brake for them. Because cyclists will break it varying differences of deceleration, and actually they can break quite hard coming down a hill towards a junction, for example. So we need to be a lot more nuanced and, understand that a lot better. and so that's where we worked actually with the Royal Society for the Prevention of Accidents in the United Kingdom to really evidence that, these indications that we were collecting around abnormal breaking and swerving. [00:20:00] correlated with stats 19, which are the UK's, police reports where somebody is injured or killed in a collision. And so what we saw is that you're two and a half times more likely to break or swerve abnormally in proximity to one of these known locations. than at other locations. and so we were able to see, you know, clusters around where we'd seen large numbers of collisions. and we also seen indications where there hadn't been collisions indicating, you know, potentially areas for further research or study. by extrapolation of that, we then looked at, some of the, the cycling infrastructure, which was put in a temporary way in COVID. So we were able to see the before and after picture and in places like Portland and London, where a lane went in, were able to see overnight, literally on the construction date of the of the infrastructure that the abnormal swerving and breaking behavior just disappeared.

It just wasn't there anymore. Indicating what we think is a very successful deployment of infrastructure. And [00:21:00] so, because these abnormal events are much more common than the collisions, it's obviously much easier then to put in some infrastructure, do a before and after, and really understand what sort of impact of any you've had. And we have seen where infrastructure has gone in, and it's gone the other way. It's created a smaller issue or a problem in a particular location or area. so generally the overall effect is good, but sometimes we can highlight some little more tweaks that are needed. within infrastructure.

Irene McAleese: I think,

Phil Latz: Okay. Yeah. I'll show. Go on.

Irene McAleese: was just going to say, you know, a lot of, people be familiar with the pyramid where you have, you know, the, the deaths and serious injuries at the top of the pyramid.

And underneath this, you've got sort of the near misses, which essentially massively under reported, right? so what Phil's explaining there is essential. Essentially, that our data on those swerving and breaking extreme events can be used as like a surrogate safety measure to start to understand what's happening underneath there. and [00:22:00] this is really critical for, you know, initiatives like Vision Zero, which, Australia has signed up to, which, Which is about creating zero road deaths by 2050. Here in Australia and a lot of other cities around the world have signed up to this. And what you find, Phil, is that, you know, there's only so much low hanging fruit that you can get by reducing speed and doing these other things. seatbelts, etc. What, what you really have to do to then make that transition down to zero is becoming a lot more, proactively focused on preventing crashes. and this is where the more understanding you can have about the contributory factors,

Phil Latz: Hmm.

Irene McAleese: the more data that you have to baseline before and after any intervention is starting to become critical in getting those, those percentage gains, that are needed towards, [00:23:00] towards that outcome for Vision Zero.

So safety is, is a huge area of interest for, for cities now going forward. And we're definitely seeing a lot of appetite for our data for that, in that regard

Phil Latz: Okay, so we've been talking a lot at the higher level overview of what your equipment can do, your software, your programming, etc. Let's take a look. Do at least say two, perhaps even three specific examples. So you found it in 2013, so you're 11 years in now. So just to put you on the spot, if you would like to think over those past 11 years of maybe your best one, two or three case studies specific where you can name a specific city and a project and the outcome just within a couple of minutes, not, not in huge detail.

what would you pick? You can fight over it if you like, or, you know,

Irene McAleese: Well, I mean, I, I think that, I mean, I can, [00:24:00] think that there's definitely two standouts. I mean, Phil already mentioned the work with ROSPA, in, in Birmingham. But I think what a really interesting project is, is the follow on project for that with Transport for London. So Transport for London actually wanted to replicate that work that we did with ROSPA. And we did this at scale, working in partnership with Transport for London, using See.sense data across, across London to help, them understand risk, so they can look at risk. They've, they've signed up for Vision Zero, so they're, they're using car data on swerving and braking from, some car manufacturers, as one of the data feeds they would look at. they've got quite a sophisticated team. They'll look at things like all sorts of open data, the width of the road, the speed of cars, et cetera, and literally build up a risk model street by street across London. And they actually said to us that the missing piece of the puzzle for [00:25:00] them was having this kind of data from micromobility. and this is where we completed a really successful proof of concept. to demonstrate that our data could fill that gap. and we're really proud that this project went on to, um, only receive accommodation from, from CIHT, but also just in December got the Prince Michael International Road Safety Award, which is one of the most prestigious awards you can, you can win, in road safety.and so, this has really laid a fantastic platform for us to continue to collaborate with, data aggregators, in the field that, that work with large cities to ingest data. And now we've got a proven data feed that, that helps to elevate what they're doing in that regard. So that would be one project.

Phil, you maybe want to talk about, our great project. here in Melbourne with the TAC.

Philip McAleese: Very much. So, yeah, I mean, I'm super proud and I'm delighted to be working with the transport accident [00:26:00] commission, in Melbourne, were in conjunction with I move and, one of the local universities. Deployed more than a thousand of our lights, out to cyclists in the greater Melbourne region, that's led to lots of really interesting outcomes.

And we're now working with a number of LGAs, local government authorities, and we developed this idea of a core insight report. So, one of the challenges that the LGAs have is that they don't always have enough people with, experience to be able to make use of this data and really understand it. and so in collaboration with the TAC, we've been able to, to develop this tool that allows them to, to use it in a much more accessible and convenient way. So, there's, there's a number of pieces of infrastructure that are being, built, designed to build at the moment. and we're helping them to do that before and after analysis, to understand.

where it should go, a little bit about how it should be designed, and then to evidence once it's been built, how it's actually performing. So, really delighted about that.

Irene McAleese: Last one,

Phil Latz: Okay.

Irene McAleese: I could probably slip in another two, [00:27:00] but I'll go for one. Last one, I think,

Phil Latz: No, you're only allowed to have one. I said, I said up to three. I've got to be very strict.

Irene McAleese: so I think the last one, what I'll, an example I'll use, is our project in Essex, working with Essex County Council, and I'm pulling out this example because it shows the evolution of our technology really, because we started out with bioclides. But now we work with integration of our technology into bike fleets, and e scooters as well. And this is a really interesting project in Essex where they, they got funding to actually, provide bikes to people living in deprived areas in Essex. and then they wanted to understand if those, Those bikes were actually being used, to access transport, education, shopping, these kinds of things. and then also to, to build out, infrastructure to support that. They had no data there at all. I mean, there are no Strava riders in this particularly deprived area of Essex. We [00:28:00] started the project with, a hundred bikes. It's now scaled up to 2000 bikes. and we've worked in conjunction with the project to, design data dashboards because one of the interesting things Phil is cities need to be able to use the data that's collected. You know we can have this really fantastic data but at the end of the day if it sits on the shelf and isn't used that's a problem and I'm really proud of this project because we worked with them to co design data dashboards they could pull down reports, they can share the reports with the funders to evidence usage of the bikes from behavioral change, but they can, they're also using the data to, um, to literally build out infrastructure to support.

So they've built parking, for example, bike parking from analysis of the origin destination of journeys, plus the dwell times and where people are locating their bikes. They've also used the data [00:29:00] to do before and after analysis of, of a piece of cycle infrastructure that went in and, and show the impact that it's having. so yeah, I, I, I think it's a good example because it shows that our technology can be used not just the bike lights but to integrate onto bikes and also then real, really practical, support we're providing with local authorities to actually use the data and not sit on the shelf.

Phil Latz: Yeah. It would be really satisfying when you know that, Hey, something I've done has resulted in physical, literal, sometimes concrete changes

Irene McAleese: like that

Phil Latz: on the ground.

Irene McAleese: When they built the parking, that was serious high five from everybody

Phil Latz: Yeah.

Irene McAleese: because it was like,

Phil Latz: Yeah.

Irene McAleese: oh, they built bike parking, but, you know, to go from. You know, data, data to insight and then from insight to action

Phil Latz: Yeah.

Irene McAleese: which is a

Phil Latz: Yeah.

Irene McAleese: of, you know, actionable insights. yeah, they actually put a spade in the ground and [00:30:00] dug the parking. So that is the happiest day for us to see that happening

Phil Latz: Yeah, yeah. We're coming to the end. I've only got two more questions or two more topics to touch on. firstly, I believe you've got an exciting new product that you've been working on that you might be ready to speak about publicly for the first time in terms of going beyond just the tail light for the, reason to collect the, or the method of collecting the data.

Philip McAleese: Yeah, so the, the project in, Essex, we've been using a product called Summit, which, allows us, it's got its own cellular connection, so it doesn't require, the phone to be, connected via an app, which means that bike fleets can much more easily be, connected. So we used it with, You know, Dottie scooters in London and bike fleets in Essex and other places. we actually have a super efficient battery variant of that called Summit B, that is in final development at the moment. and this is, both [00:31:00] a consumer product, which will be marketed under a different name, but also a fleet management product. really the benefit of that is that it, it's very, very easy.

You just fit it to the bike and go, and unlike most bike trackers, which require. You know, recharging or battery replacement every few weeks. we, I've been successful in being able to allow this to track for thousands of miles over many months. so it's a, it's really quite game changing technology and it's, yeah, not too far away from, from being launched.

Phil Latz: Excellent. Excellent. And the final question I've got for you is, What other developments do you hope to see in the future? Both for looking at the bigger picture of micromobility and cities in general, and for See.sense in particular.

Irene McAleese: well, I think that, you know, there's, there's a very exciting, opportunity here for micromobility, going forward because, You know, the global micromobility market, I think the report from [00:32:00] McKinsey that I highlighted at the recent APCC conference is talking about the micromobility market reaching 360 billion by 2030, that's nearly double.

what it is now. a lot of this is coming from e bike sales in Europe. But what's really interesting is, is the appetite for, for people to, to want to use, micro mobility, that's bikes and e scooters alongside or with, you know, private vehicles. I think that the big challenge for cities going forward is reallocating space to make that happen in cities make it happen safely so that you can get that that shift and that you can attract a wide range of people. of users. it's not just the brave and fearless who, are feeling that they can navigate their way amongst the traffic. It's really thinking about how you allocate the space and how [00:33:00] you, um, to, you know, incorporate these modes. And this is, where we're working on. Data is, has to play a huge role, in this going forward for, for modeling and understanding, the movements and the experience of, users in this space and, and really stepping into this huge void in the data gap that exists in that area. So I, I really think that, there's a large opportunity there for SMEs, innovative SMEs like ourselves to, play a part, in helping to shape our cities going forward. we can move quickly. , We can apply innovation and we can work very collaboratively with lots of different partners, to co create,outcomes as well. and I think that's, part of You know, part of the excitement is that knowing , there's no, there's no silver bullet. There's no one data source that solves everything. There's no [00:34:00] player that knows everything. I think that the, the going forward partnerships, collaboration, innovation, are critical , to achieving, overcoming what needs to be achieved. And, Thank you. Yeah, this is something that I think builds on a lot of our strengths in See.sense. And so I definitely see that we should have a good role in that going forward. And just,

Phil Latz: Excellent. Any thoughts from you, Philip? Any final thoughts?

Philip McAleese: absolutely. Just add to that. I mean, I obviously have a very much technology focus and I think what is apparent to me is that the market has really matured in terms of. you know, 10 years ago when we were 2015, we started connecting this type of data, and cities just weren't ready to consume it. You know, they'd never, they haven't really used connected car data. It was very new, certainly nobody had heard of AI and nobody believed what its capabilities might be. and so I think now, with a lot more of the prevalence of connected car data and AI, and city's willingness to sort of consume and use that data and making decisions. I think it's [00:35:00] a very exciting time for companies like ourselves, and others to, to engage, to bring lots of different data sources and insights together. and largely what we're doing is providing that missing piece for micromobility. It is very difficult to get over and above just where people are going, but actually what their experience is and how we can leverage that, look at all modes and create a holistic picture, across our cities.

Phil Latz: Well, Irene and Philip, I think you are to be congratulated for the unbelievable amount of work you've done in your first decade of business, and it's certainly very exciting everything that you're doing and talking about potentially doing in the future. So thank you very much for being on the Micromobility Podcast.

Irene McAleese: Thank you, Phil. Pleasure. again.

Phil Latz: So thanks everyone for listening. We're planning to do one Micromobility Report podcast per month. If you want to find out more, visit the podcast page at [00:36:00] micromobilityreport. com. au and until next time, keep riding.