- 4 days ago
Cities are being redesigned at unprecedented speed. Low-emission zones are multiplying as curb space is contested by delivery fleets, ride-hailing platforms, cyclists, and pedestrians. Every day, Europe’s largest delivery fleets generate continuous, hyper-local data on road conditions, congestion, patterns and infrastructure wear that is typically beyond the capability of most cities and mobility platforms operating independently. This session asks whether cities, mobility platforms, and logistics players can move toward genuine co-design powered by shared intelligence? What will it take to turn real-time road data into infrastructure decisions that make streets safer, cleaner, and more efficient for everyone who uses them?
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TechTranscript
00:20Good morning. I'm Jean-Philippe, partner from PDUC. I work in strategy for local
00:28authorities in France and Europe. Today, you see cities are reaching a tipping point. You know
00:37that more than half of the population live in cities, and that number is expected to rise
00:44towards 70% of the commune decades. And at the same time, city leaders are under pressure from
00:51congestion, climate change, high temperature like today, infrastructure needs and rising
00:58expectations from citizens. But what is really challenging and changing is mobility. Mobility
01:06is no longer just about moving people or goods. Mobility is becoming a powerful data stream
01:14and asset. Every day, fleets, platforms, sensors, infrastructure generate massive amounts of data.
01:23Delivery vehicles become moving sensors, platforms generate behavioral sites, and cities deploy IoT and
01:33digital twins. One of the challenges today is turning that large amount of data into shared,
01:41trusted intelligence that actually improve cities and how it functions. That's exactly the question
01:49of the panel today. Can cities, mobility platform, logistic players, co-design streets through
01:55shared intelligence, but also how to create value, monetary value, but also value for citizens?
02:04Let me introduce our panelists. Martin, CEO of Geopoist Vision, transforming one of Europe's largest delivery
02:14fleets into a real-time platform for street-level data and infrastructure intelligence.
02:21Brooks, Global Business Development Manager for Smart Cities at ESRI, helping cities turn geospatial data
02:28into actionable intelligence through GIS and digital twins. David of ESRI, director of attractiveness,
02:38economic development, innovation, and culture at Metropole du Grand Paris, orchestrating innovation across
02:44more than 130 municipalities. And finally, Clementine, representing CLEM, a mobility and energy platform
02:53operators, deploying shared mobility, and smart charging infrastructure across more than 200 territories,
03:01including, and we must mention that, rural and peri-urban areas. So we see here today in the panel,
03:10we have the great chance to have the full value chain, data generation at scale with logistics,
03:20data structure and decision intelligence with digital twins, public orchestration with a metropole,
03:27and shared mobility system with CLEM. So let's start here, the promise. Who brings what? So Martin,
03:36let me start with you. Geopoist, you operate 10,000 vehicles across Europe, but all those vehicles are
03:45not no longer just delivering parcels. They are also collectively collecting real-time street-level data
03:54across millions of kilometers. So tell us, what can logistic fleet see about cities that other actors
04:03cannot see at the same scale of frequency? Yeah, absolutely. Thank you. So maybe let me start with a
04:10quick word about Geopoist because Geopoist is today one of the largest delivery network
04:15in the world. We operate in more than 50 countries and we deliver more than 2 billion parcels a year
04:22with our commercial brands such as DPD or Chronopost in France. And thanks to that, we operate, we also
04:30operate the largest vehicle fleet in Europe. We are talking about almost 75,000 vehicles on the road
04:38every day. So what we started doing, we started equipping our vehicle fleet with connected dash cam.
04:44This is a whole new activity, which is called Geopoist Vision. And these vehicles will collect
04:50street-level imagery on a day-to-day basis on all the roads. And thanks to that, we are already
04:57implemented in more than 20 countries in Europe, and we are already the largest and freshest street-level
05:04emerging platform in Europe. So we are able to collect data at a very unique freshness on every
05:12kind of road from city center to rural areas, from residential street to highways.
05:19But what would be for you the first problem cities could solve using your fantastic data?
05:24I would say that there are three main things. The first one that maybe sounds surprising, but there
05:32is no public database on the speed limits, traffic lights, traffic signs. So being able to detect this
05:39kind of assets and update them over time is very key for cities and maybe David will be able to
05:47confirm.
05:48The second thing, there is also road maintenance. Being able to detect the potholes
05:52as soon as possible can make huge savings for the cities. And last one, I would say,
06:00will be around smart cities or connected cities where we are able to provide also the state of the
06:08urban furniture at a very, very recent state. So that can have also a lot of value for cities,
06:16but also telco players or utilities companies. Thank you. So you capture the data. So,
06:22Brooks, maybe let me turn to you. If my team represents the opportunity for the new data sources,
06:30you, I think, represent the question of how to make sense of it. Your message often is very clear
06:35when you have some talks. The value is not in the data itself, but in turning it into shared trust
06:41and
06:42intelligence. So how do cities move from fragmented data stream to a shared operating picture?
06:49Can you tell us more about it? Yeah. So I would first respond that the data does have value. I
06:55just think it could have more value. So I lead the local government business practice for Europe on
07:01behalf of Esri. We're a geospatial information system software provider. Many of you probably know us.
07:08We've been around for a few years. And in that capacity, what we do is we help customers essentially
07:15make use of some of this new data, new capabilities without spending too much money using the existing
07:21investments that they've already made. And so that's what I do here in Europe. And when it comes to this
07:26new data, it's very exciting because the data itself has value, but it gains tremendously more value as you
07:35integrate it with other data sets and within existing systems that cities are already operating. So
07:42and all of this is done through open standards. So I would say the data has value, but only once
07:48it
07:48becomes spatial intelligence. And to give an example, the city of Vilnius, it's the capital of Lithuania.
07:55They have, and they do it in basically three steps. They have a system of record that they collect
08:01through drone imagery and street imagery, where they collect this massive amount of imagery data.
08:07And then they have a system of insight where they actually run deep learning. Now the general purpose
08:12AI is now getting to the same accuracy as the deep learning models, but they make sense of that.
08:18For example, identifying cracks, understanding the prioritization of where those cracks need to be
08:23fixed and making sure everyone, not just the rich neighborhood gets cracks fixed. And then they have a
08:28system of engagement in which they build various different interfaces on that for the mayor to look
08:35at the progress for the engineer in the field to go and actually understand where they need to go. So
08:40I would say the data has value, but in that system stack, because geospatial, a lot of people here
08:45probably just think of it as a map, but that's just looking at it narrowly as a capability. The value
08:51is
08:51really thinking of geospatial as a unifying horizontal layer across many, many different systems. It's sort of the glue
08:57between various business systems.
09:00And concretely, because we talk a lot of the digital twin in the industry, but applied to a city,
09:08can you tell us about that? Digital twin. What do you mean by digital twin for cities?
09:16Sure. So basically what a digital twin, and it's very tangible, the digital twin is a very tangible thing.
09:22It's different than smart cities, which kind of got taken over by the marketing teams.
09:26Digital twin is very concrete. It's basically a digital representation of the physical world.
09:32And you do that, let's say, by first mapping and creating an inventory of the physical world.
09:38And then based on that, you can start to derive insights from that data.
09:43And then that, in turn, either can become part of an autonomous decision-making process,
09:49meaning maybe we've kind of figured out the algorithm for where we need to fix cracks first,
09:55so we automate that. But that type of, let's say, maturity from getting the data,
10:02creating the representation, to being able to act on it, cities are doing this. And this is what we would
10:07call
10:07not just a digital twin, but multiple digital twins that are basically aligned with the different
10:13business operations, lines of business in a city. So we've been talking about cracks in the street
10:18for a while now. But there are many other things that you can do with this data, like finding the
10:23graffiti to remove or, for example, knowing if the park benches are going to need replacing soon.
10:30You can do this all from your data set, actually. And it's quite exciting because it also opens up
10:35that ability for any city in Europe who gets parcels delivered, which is almost every city in Europe,
10:41I want to say, nearly every city, where in the past they would have had to invest like Vilnius in,
10:47for example, a drone program to capture that level of information or a bespoke truck to drive down
10:53the street and scan it at the millimeter scale. But now what we're talking about is just passively
10:58understanding through the imagery coming from these trucks in a day or five days ago what that street
11:06looked like. And that's going to then fuel the digital twins that exist within the parks and
11:11rec to understand their assets within transportation, understand their street signs and different
11:16mobility aspects. And obviously, yeah, the utilities and road maintenance crews as well. So it's going
11:23to basically fuel from that data multiple business systems or twins. Thank you. Thank you,
11:29Brooke. So we have the data, we have the platform, but let me also turn to David because your role
11:36is
11:37very unique. The Metropole du Grand Paris does not directly operate mobility services, but you are really
11:44a coordinator, orchestrator, pouring up more than 130 municipalities, which is quite unique by the size.
11:55So you sit at the point where all these data and solutions meet public reality. So tell us more, how
12:03do you see
12:04your role in this ecosystem? Thank you. Just a few words about the Greater Paris. So as you said, there
12:11are 130
12:12municipalities. Actually, we are covering the dense urban area, Paris and all the cities around. And just
12:23saying that you see that it's a very fragmented organization, territorial organization. And so it's
12:31important because you have to serve the inhabitants, the people with very local municipalities, local
12:35services. But also you have to address the big challenges at a bigger scale. And of course, the
12:41data and AI must be managed at a greater scale. And in a way, the greater scale you have, the
12:50more
12:50powerful the applications, the service will be. And so our role is to tackle this fragmentation
12:59and, well, to bring a vision and to, of course, deploy this vision. And so it's a matter of
13:06governance. It's also, of course, a matter of convincing the local mayors of the value of what
13:14you bring. That's also a problem because you mentioned data has a value. But the question is,
13:19what kind of service do you deliver to the people or to the companies, to the users of the city?
13:25And that's
13:26that's important to demonstrate the value by experimentation, but we'll get back to that.
13:32And by, with successful experimentation, trying to scale or to re-experiment also with a solution.
13:42But that's the thing. It's really starting from the use, from the value you create,
13:47and then try to deploy to the larger scale. And this orchestration, it's very important.
13:53I want somebody that has a global vision at a global scale, possibly with also technical
13:59infrastructure at a global scale, but starting from the very local use. That's what we try to do.
14:07Yeah. I think the value of what you just mentioned is very critical. Cities are, I think,
14:15just like you're interested in data and AI. AI is a lot of topics today in VivaTech. But we see
14:23their fans struggle to define their needs and translate that into decision. Why is that so difficult today?
14:32You know, if I take the example of the greater press, the average municipalities like 10 or between
14:4110 and 20,000 inhabitants and people and the mayor and the whole municipal team and administration
14:47has so many things to tackle and how it's difficult for them alone to address, you know,
15:00and they don't have always the resources and the people. And that's a real challenge. So it's about,
15:08again, building an organization, orchestration, as you said, and enabling them to be part of that
15:14change, but still continuing to deliver good quality services and the maintenance of the city and all
15:21that. So that's also, it's kind of resources, but they are very positive. Some are more or less,
15:29but they are very positive and they need, but they need to be helped and to be part of a
15:33global move
15:34towards AI, for example. Yeah. And AI and data is not new to Metropole. But I think you promote
15:43experimentation a lot. So is experimentation the best way or the new way for cities to make decisions?
15:52What do you learn with pilots? Do you recommend going for pilots or going directly for a large-scale
15:59data program? I think we do a lot of experimentation. That's what that's, I don't know if it's the best
16:07way, but that's what we try to do. So we are presently running out 75 different experimentation with
16:14different technologies, some very high tech with AI and some more low tech. So that's also interesting.
16:19And it's good for us because when you do an experimentation, it's you validate or not,
16:28of course, the solution, but also the problem that you want to address and designing and
16:33understanding what is your problem, the problem we want to solve. It's an important work. And it's
16:39also that it's also a way to have the local municipalities, the people, the inhabitants also,
16:44when we do an experimentation of a system, for example, it's we also try to discuss with the
16:50population or what local local mayors and they discuss with the population. And and I think
16:55it's good to understand it helps you also understand the problem. And that's also and even sometimes the
17:00solution doesn't work or it's not so convincing, but you have understood the problem and you can find
17:05things. So this is where it's really about being very connected to the local reality and then trying
17:13to scale it up. That's the way we do it. Yeah. So talking about pilots, scaling investments and
17:20Clementine, let me bring you also to the discussion. And in the value chain, your model is quite
17:26quite different from a logistic fleet or digital platforms. You operate a shared mobility system
17:34directly in territories. So maybe can you tell us more about that? Yes, of course. So I'll bring the
17:42lights on the blind spot of the map. I'll bring you somewhere where there is no Uber, no tram, no
17:49train,
17:50maybe a bus, maybe not, certainly not after 6pm. It's not a big city center, it's a village.
17:57And in this village, there is a woman, she will take, well, she will book a shared car,
18:04an electric car. It's a public service. It's, well, she goes on clam, she books it. For us,
18:11this is not just a booking. This is a data point that shows us that there's a need. Right there,
18:18right here, someone needs to move. And for us, this is not just a single point. We have 16
18:26years of similar information throughout France in various places in France. And so we can turn this
18:36data into showing where the public decisions should go. Because basically, no ride-hailing app or no
18:44delivery system, unfortunately, will go there. And this village is not an isolated case. We've identified
18:53with the data more than 12,000 of them in France only. So we can direct the decisions through data
19:04and cover France entirely, these 12,000 data points. This is only France. So you can imagine
19:10at the European level, you can imagine if you integrate small fleets, for example, from companies
19:16of all sizes and shapes. That is a big tool to bring decisions, to make the right decisions. And now
19:26we
19:26solve it, of course, with a mobility solution. We also have to address the energy solution behind the
19:33mobility, because we need to power mobility, of course. And there, again, we, well, we need data to
19:41do it at the right time, at the right space, and with the right tools. So people will tell you
19:46electric mobility is expensive, it's complicated to manage. No, it's a tool that you need to understand,
19:53that you need to power properly. And for this, we, again, bring data, we listen to the data,
19:58and the data tells us what to do. So basically, you don't need to charge the car right away when
20:06it's
20:06plugged in, you need to charge it when, well, it needs to be charged when you need to use it.
20:11So the car listens, well, the system, because it's a mobility and energy system that we have developed
20:17for 15 years, it's not a pilot. It exists, and, well, basically, the system listens to the grid
20:25stress signals, to the solar panels, to all the signals that we can get around it, of course, the
20:31bookings, and then it charges when it needs to be charged. So we've worked on, well, evaluated the
20:40impacts of this solution with EIT mobility on a project called ARIA, and we've seen that we can
20:47save up to 35 percent of energy cost with a system like that, that has been developed with AI, of
20:54course,
20:54energy cost, and up to 55 percent if you include local solar panels. So this is a tool that can
21:03be
21:03deployed at scale in France, in territories and companies. So, well, it's on the blind spot,
21:12but it's still there and it's available. Yeah, I think it's really interesting to see that you start
21:17from a mobility solution, go to energy, and then move to the data. So are you a data company, mobility
21:26companies? This is quite a change, and show the change in mobility. You told me also in the preparation
21:34that you run an EU innovation project called Chorus. Maybe it's quite interesting to tell us more about that.
21:42Yeah, so we do work on some projects, of course, because electric mobility is, of course, a challenge
21:50on territories. So that's why we work on innovative, well, innovation, and we have to make sense
21:56of mobilities on territories where we deploy it absolutely, well, in the hardest places with no
22:03density and probably very, well, energy challenges as well. So we worked on Chorus as well,
22:10on a project with 39 partners, European partners. So we're not alone working there. And now that we
22:18all develop separately our own transportation systems, we need to make them work together and be,
22:24well, we need to make them work sense together. So interoperability is a challenge. It's what we're
22:30addressing with Chorus and with the 39 partners. And it's a program in which, well, the 39 partners,
22:37we work with autonomous vehicles, autonomous buses, car sharing, bikes, parkings, and AI coordinates them
22:46together to make a system that works in a larger environment than rural area. But still, in rural
22:53area, you will have adapted local systems. And in bigger peri-urban systems, then you need something
22:59to make sense. So we work with AI to coordinate larger transports all together.
23:04Yeah, thank you very much. Very imperative. But comes the challenges, because you all produce data
23:11from different sources, and comes the question of fragmentation. And in the past, we see that
23:18smart city initiatives remain sometimes fragmented, or are stuck a bit at pilot stage. David, you often
23:27mention in your talk the risk of fragmented and disconnected tools across cities. Is, at the end,
23:35today, fragmentation the main enemy of smart cities? And then, or maybe other panelists, you can provide
23:43your insight. I think, well, it is, of course, something that doesn't help to scale, obviously. And the point
23:55with AI, as we know, the more data we have, the more efficient, the more data you have, the more
24:01you can
24:02train your models, and the more efficient are your services or your application. So the question of
24:09capitalizing, I mean, when you do an experimentation, local experimentation, of course, as I said,
24:15you have a, you design a use case, you test a technology or solution, but also you collect data
24:22at that point. You define data, you collect it, sometimes you curate your data, and that's it. And
24:30I think, for me, it's also something that it doesn't have just, you know, POC, and just
24:37expand there. But you have to capitalize in time in the data that we collected. And I think that, for
24:43example, in something we think of, and where that's a project we have is to design a platform, which would
24:50serve that. It's not compulsory for every city for municipalities to put their data there. But if
24:57they want to benefit the application, and to take profit of the power, you know, the power of AI,
25:05for example, it's good for them to share to put their data there. And, and then I would say in
25:11exchange, because they would have the service because they're not interested in the technology.
25:16The mayor of a local municipality is not about technology, it's about how I can improve the
25:23service to my inhabitants, how can I manage my city in a better way, more efficient way, in a context
25:29where public money is disappearing fast. So that's, that's also something important. So I think joining
25:36forces, capitalizing in time, with, for example, a shared platform is something that could be useful.
25:43Yeah. Who wants to react? You, I see you. Yeah, no, I couldn't agree more. First of all,
25:48operating at scale can be a, a very huge challenge. That's, that's the first thing, a large investment,
25:55low return on investment. That, that, that's why on our side, we are relying on an existing asset,
26:01which is our delivery fleet. So the scale is already, I would say embedded in our solution.
26:08The second challenge will be how to deal with this huge amount of data. We are collecting million
26:14hours of video. We are able to provide, uh, hundreds, thousands of kilometers of food per day.
26:20And I think that's very, a big challenge also for the cities. And that's where, uh, I think a company
26:27like S3 Brooks comes in as well is to be able to make sense out of all this, uh, this
26:33data. And, uh,
26:35maybe, um, uh, I would say that, um, smart, smart cities won't be built by adding more cameras in the,
26:43in the streets. It would be more by using existing data sources.
26:50I can confirm. Yes. Um, and I'll echo everything you've heard so far to scale. Definitely good
26:56people. Right. And then second technology is not the issue. The third, I think it's the business case.
27:03So getting the business case, right is critical because yeah, data is the new soil. I think that
27:09was an EU, uh, uh, uh, presentation at some point. Um, this idea of data being the new soil,
27:15that everything can manifest as long as you have good data, it's true, but you can't acquire the
27:20data unless you have the business cases define what data you actually need. So I would just
27:26challenge you to also think about this yourself. When you, when you think about investing in data,
27:31you actually have to think beyond just your own use case as well. I'll give you the example of
27:37Vilnius. Again, they can't invest in an autonomous drone program like they have with this drones in a box
27:42on all the public buildings, just because they want to look at all of the streets and make sure
27:47the snow is removed on time. They have to do that. Plus make sure the, the lawns are mowed,
27:53make sure that graffiti is tracked, make sure roof conditions are also assessed in the same data set
27:59that is being collected can service all of these different use cases. Um, this goes also for the
28:06internal business systems as you bring these systems together. So the transportation, the utilities,
28:11the guys laying the fiber lines, knowing that there's going to be a construction on that same
28:15street. So they just wait a week in order to dig it up. That's also data, but it requires basically
28:21the definite definition of that one common operating picture, that use case being able to orchestrate
28:27the different digs to then derive from that, what data you actually need to have in that same picture.
28:34So I would, I would agree. And to scale, I think it, it really comes down to defining the use
28:39cases
28:39before you get to, uh, into the data. Well, for us, it's quite the opposite actually, because I'm
28:47working on the other side of the panel. So both the traffic data tells you where people go and we
28:53work
28:53where people want to go. So without this kind of data, so we need to forget about the traffic data.
29:02We
29:02need to look for a climb of course, but for rural areas, uh, in particular and peri urban areas,
29:08we need to look at the data that tells us where people would like to, um, to go. So the,
29:14the different,
29:15the challenge is, is quite different. And now that we have identified that the, the new challenge is
29:21bringing the infrastructure there. So the data is already there. We need to listen to it and to power
29:27decision with it. Um, of course, having very particular cases that are applicable because
29:34the scale with the uncovered area is huge and yet, um, the solutions are often fragmented. So we need
29:43to, yes, bring back some very specific solutions for specific areas and go with it, scale with the
29:49right infrastructure everywhere. So if it's, uh, the energy infrastructure, it needs to, to be developed
29:54at the right place, maybe not always where the big hubs are, but where the biggest hubs are,
30:01have like the proper infrastructure and behind it have cooperation with other means, well, other
30:07infrastructure and have a network that is coherent altogether with the different, um, well, services
30:13that don't compete for the same space, but that needs to be coordinated. The data will help coordinate,
30:19but we still need to figure out how well at the right space and the right, um, uh, the right
30:25court,
30:25like the right complementarity. Yeah. Thank you. So the doom of, uh, the, all the data, uh, together,
30:32the platforms, uh, but it raises a question is the more the data is open and the more or so
30:37you,
30:38you maybe have some risks. We see in the press and every day cyber attacks, the question of the
30:44sovereignty of the data, the risk of AI. Um, and one point is, uh, in, uh, if we picture the
30:51future,
30:51how we are going to under this question, who also should control your band data? Ultimately,
30:58public actors, private actors, the share model, uh, and the trust and governance, uh, uh, would be
31:05quite key, certainly tomorrow. So that David, uh, for you as a public actor, uh, maybe you can start and
31:13give you your view as a metropole of grand Paris, uh, about this risk from, uh, then that has real
31:20impact on the day to day. If there is a cyber attack and it shut down a number of the
31:25services you
31:26mentioned. Um, well, it's first, um, who, what we work with and, and it's really, uh, public data in
31:36terms of data about, uh, the, the, the municipalities and the territory. So, uh, this is public data.
31:44It, it's not public in terms of, it's not open data. It can be not shared and, and, et cetera,
31:51but, uh, we think it's public good. Uh, so it's good that a municipality or the metropole, the metropole
31:57du Grand Paris is, is, uh, brings trust to the inhabitants, to the people that this is the data.
32:03This is shared, you know, with this share value for everyone and we must guarantee that. And second,
32:09it's, of course they are, they are, well, you speak about cyber attacks. They also, the, the,
32:15the question of possible dependencies on, on, on softwares or on solutions, but, uh, of course it's
32:21a concern. And also it's, it's the same here. Fragmentation is a real issue because the
32:27level of protection of each local municipalities is, uh, sometimes good, sometimes not as good.
32:34And, uh, we, we deploy a program at the greater price metropolis to help them raise their level
32:40of, of preparation and protection. And I think it's a, it's a real challenge of course, and maybe
32:46some application must be centralized and more critically protected, but some things and solution
32:53and services are run just locally. But of course it's, it's, it's a real challenge. And I think
32:58acting as a, um, a third party or, or that, that can help everybody share a vision on the challenge
33:07of cyber security. Again, the, to the question of governance is very important for us. Yes.
33:12Maybe Brooks. Well, I would, I would say when it comes to being able to trust the system,
33:18we need to be able to trust our cities and just a simple answer would be citizens need to be
33:23able
33:23to see improvements and that things are working well and they tend to then trust the city. But
33:29when it comes to the, the actual data sets, um, they need to be secure and, and shareable, but
33:34not invasive. Um, I think cities do a very good job of that already when it comes to cyber security,
33:40we, as a software company find ourselves often, um, advising our own customers on this topic,
33:48because yeah, I mean, as a software company, you know, we also have to be, uh, thinking about it,
33:53but often our customers are not necessarily thinking about it. And that is the redundant systems for
33:58those critical, um, you know, 99.99% up, uh, type of systems. Um, but then understanding that not all
34:06need to operate that way. And I think that's a very critical, um, discussion that a lot of our
34:12customers need to have amongst themselves to understand what level of security should apply.
34:19Um, not only based on the data and how, um, you know, private it is, um, but really on how
34:26the system
34:27interacts with, uh, yeah, the citizens, whether or not it's a critical system or not. And I think a lot
34:33of our, our customers are, are doing that a lot of the big ones, maybe not the, the smaller, uh,
34:39customers, but you know, at least here in France, we have large, um, the, uh, the communities of cities
34:46and villages that are actually helping a lot of those smaller customers, uh, uh, get prepared, uh,
34:52similar to some of the larger ones that we've been working with.
34:56Clémentine or Martin, a few words.
35:00Clémentine or Martin- I would say that, um, it has to be a joint effort and this panel is
35:06a very
35:06good example and, uh, trust will be, uh, brought by transparency. So, uh, that, that's why just to
35:14give you an example, that's why our solution has been built privacy by design, which means that,
35:19uh, we tell what we say, we tell that we record images with our vehicles, people can access our website,
35:27get access to our privacy policy, get access to their data. And I think that, uh, that's very,
35:32very key, uh, uh, in this, uh, in this environment.
35:36Clémentine- Uh, for us in public services, the data has to be more shared. So for, for now,
35:41we're not talking about, um, well, security. We're talking about trying to work together at first,
35:48with the first amount of, of data and cooperation. Uh, we've worked with MGP actually, um, on a project
35:55to coordinate micro logistics. I think these kinds of projects help us coordinate and now we need to
36:01do, well, to go from local to global with data. So that's the challenge for us. We're still in car
36:08sharing at this type of, uh, of timing and in terms of energy, uh, of course, the energy, while we're
36:16working with energies, uh, to, uh, well, on V2G. So we're V2G ready. Now we need the infrastructure.
36:22We need the car to be ready. It's ready on the news. It's not ready in real life. Uh, so
36:27we need
36:27more cooperation there. We need the data of public transports to actually be public. Uh, of course,
36:34while we integrate, while we, we integrate mobility to the mass with Clément, we're the first platform
36:40to integrate our solution to the mass. So I think we need more of that, more cooperation and more
36:47data sharing both in energy and in mobility. Yeah. Thank you. So we, we see, uh, many pilots,
36:54uh, we see innovation, we see, uh, also startups, we see, uh, large, uh, large projects, but then
37:02comes a question to the systemic transformation as a whole in the city, um, and about the main blocker
37:09today. And maybe, uh, just one word, not a long sentence is how you, what is a key word, key
37:16point
37:17for you, a blocker technology, governance, trust, anything. Maybe, uh, you can start, uh, Clementine
37:23on, uh, your main blocker for you today. Uh, our main point is that we need to power, uh,
37:33an operating model that is both easy to operate. So low tech for people everywhere, but behind for
37:39scale for, um, network excellence and for operability on a global level. We need data,
37:46um, to be shared, to be coordinated and to be global.
37:53Martin? No, uh, maybe just as I was saying before, the, the main, the main challenge is to
37:59make all of this data available, uh, to, uh, to the cities. So that, that's, that's very key. And
38:06that's where also AI is, uh, is a great, great enabler right now for, uh, to, to better use this
38:13data.
38:16You asked for one word, it would be accountable. So I think accountability is, is key data should be
38:23shared, but with data security, uh, transparent, but not invasive. And when it comes to decisions,
38:28that are being made, they need to be explainable. They need to not be in a black box.
38:33So transparency in a different sense. Um,
38:39if I had to choose one word, I would say, uh, the ecosystem, because I think the key,
38:45if you want to bring innovation to a city, which is a very complex body, it needs a lot of
38:51cooperation
38:51and it needs, and we need to have a shared vision. So we have, you have to have public bodies,
38:57local municipalities, the metropolis, you have to have startups, you have to have very innovative
39:02companies, bigger, uh, urban operator. It has to move towards in the same direction. We need all
39:08the components. I mean, of course we need the technical infrastructure and we know innovation
39:13comes from the startups, from the company. We know that. And of course we want to encourage also
39:19all the companies in France and Europe, we're working with them. It's also a matter of course
39:23of, of, of economic development and sovereignty in a way, but I think working together and bringing
39:29a shared vision and working in into an ecosystem, that's really what we try to do. And this kind of
39:35cooperation and through trust is key. I would say even beyond the technical aspects.
39:43Yeah. And in a time of, uh, maybe scarcity of resources and investment for you, is it a blocker or
39:51is it, uh, still really available? Because you didn't mention really the investment capacity for
39:58these huge investments. I can, I can start by saying we often find the biggest challenges to make sure that
40:06cities, our customers are utilizing to the full potential what they already have. Um, and it was
40:13said earlier that they're not installing more cameras, you know, to look at each intersection
40:18direction, just do it with one because you can now. Um, so I think there is this, uh, there's a
40:24term
40:24called sweating your assets or being able to make use of what you already have, but in new ways.
40:30Uh, and this is absolutely critical with, with a short supply of resources, but I think, um, yeah,
40:37we're not, uh, concerned. It's more or less a journey that, uh, we have to take with, uh, with these
40:42cities, uh, that we work with. So looking forward to it, actually.
40:46We're just going to mention or add on that or
40:51Sorry. Anyone else want to add on that or
40:54No, no, fully agree. I mean, we, we, we, we need to make data more responsible altogether.
41:00And, and also, and that, that, that's, uh, we, we have to make better use of existing data.
41:06I think it's great to see the question of investment is not the critical issue in the
41:12time today, but the use of the existing assets and you can use a lot more data on what you
41:18mentioned at the beginning leverage much more what you have today. If we take a step back and look a
41:24bit forward, uh, the future. So we could say that cities are becoming, or maybe, uh, in two years,
41:3210, 10 years, uh, they would be algorithmic. Uh, maybe in, uh, in the four minutes, we, we still have
41:38in
41:39a, in a, in a few sentence, um, what is the one condition for a city to become smarter, uh,
41:46tomorrow
41:47with, without becoming less trusted. And that is a key point is, uh, is an algorithmic city is the
41:56end goal or is it, uh, to, I mean, uh, to science fiction for you?
42:04Uh, I think, and is it desirable? I mean, the, the question for the, for the people, the, I think
42:13the
42:13question, the, the definition of intelligence is really understanding and establish links between
42:18people. And they say a smart city is a city that understand when they need technology and when they
42:24do not. And that's what we see for when we experiment things, we explain very low tech things.
42:30And sometimes, especially for example, to, to fight against climate change and to adapt to climate
42:35change. And, uh, we, we know, we know, we see, we feel the heat today and it's really, you are
42:41very
42:41low tech and very basic solutions that require investment though. And, uh, and I think smartness
42:48is about that. It's about for, for some things, you'll get a lot of value by investing into technology
42:54and for other things. So being smart is really understanding, as you said, the business case
42:59and try to find the best solution. And that's also what we're trying to do. Yeah.
43:05I'll jump on that because basically when you go behind the cities, um, it's funny how
43:11we don't really have a choice. The, if we implement a solution, it has to be smart.
43:16So you'll find villages of all sizes. Our average is 1000, uh, 1500 inhabitants per, per city. So
43:26they're not micro village. Well, they could be micro villages. They could be much bigger cities or
43:30more in peri urban areas. They have to be smart because when you implement a new solution,
43:36it needs to be low tech, uh, for the people, it needs to be high tech behind because you need
43:40to
43:41micro, you need to manage, um, a true system that is adapted to everyone that is adapted to everyone
43:48everywhere. And sometimes from a distance at any time of the day, you don't, you can't just stop
43:54buses at 6 PM because you can't operate a train, a bus or a train at 6 PM. People still
44:00need to move.
44:00So you'll find yourself with peri urban areas that are smart at the end of the day, much faster than,
44:07uh,
44:08some cities.
44:10Yeah. And we've talked about trust. So I think we, we need to say what we do and we need
44:14to do what we
44:15say. And, uh, on our side, our unique objective is to bring back power to the cities to deliver more
44:21value
44:21for their citizens.
44:23Yeah.
44:25Well, I agree with everything that's been said so far, but I will say I, I have the unique privilege
44:30of talking to a lot of the big cities in Europe and I will say what's happening now is the
44:36implementation of autonomous, uh, agents, uh, to take over a lot of tasks. And I would say the trust in,
44:45in that, that maturity, let's call it, that's happening in our cities is going to be having that
44:50human oversight factor that, uh, is critical. Um, maybe not all the time, but at least you have
44:57the ability to, uh, audit the system. And, uh, as I see that happening, I know I'm speaking just mainly
45:04from major cities in across Europe. Um, this is a very exciting, uh, technology when you have the
45:10specialization of the AI, um, to, you know, summarize the traffic incidents, you know, uh, you can maybe
45:16trust it, but you still want the police officer to look at the, uh, the results and make sure that
45:20they summarized it correctly. Yeah. Um, so that's where I see everything going and being transformed.
45:27Thank you, Brooks. Yeah. Yeah. Thank you all. Um, I think, uh, what we said is it's not all about
45:33collecting data. It's sharing it, governing, turning it into services that people trust. And with that,
45:41I would like to thank you all for your, for, for your, for your talk. And, uh, you wanted to
45:47say
45:47a last word or no, just thank you. Thank you. Thank you. Thank you all. Thank you. Thank you.
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