Pauli Isoaho: AI in the Things, Logistics & Mobility Ecosystem: Overcoming current Challenges on the Road to Smart Mobility

All right. So I guess that now. All right. Okay. So first time background of this, about topic. So urbanization and continues. So cities around the globe are growing all the time and, and, and no one have predicted such so fast.

And, and, and, and it's expected that way. 70% of the population in 2050 bill will be living in cities. And then of course, this means that the countryside will be more empty than ever before. And this will continue all the time. And what does about the problems, of course, then this continue and get bigger all the time. So like congestions traffic jams, so they can get bigger and bigger and you can build more roads for them.

That's, that's everybody know nowadays. And, and also the CO2 for the greenhouse effect.

And this, this is now big topic now and how to get less. And then currently we are getting more and more of the traffic and more CO2 emissions and then air pull. And also that's big, big issue in, so like some statistic about like first about this in USA, they estimate that 120 billion every year to the traffic chance. So that's one 20 billion every year. And the CO2, the urban mobility accounts for 40% of all CO2 emissions of road transport.

So it's, it's almost half of the CO2 emissions. And, and, and you can understand that because so many cars and, and, and slow traffic. So that's like that.

And, and about this air pollution. So in EU, it's estimated that it causing every year, 400,000 premature deaths.

So 400,000 premature deaths. So these are really big problems.

Of course, we have this old dream, that car is like, like, you can see car advertisement like this. So it's drive there alone. And this is the reality, like in most European cities and even in, he, you even, this is not so big city, some other, then the challenges so about this first is this public transport first mile, last mile problem. So the most effective master transit are train and subway, but you have to live close this station that they are effective.

So that's, that's a problem there challenge. And, and then, then this people go same time, same direction at the same time. So most people, people go work around eight, nine. Most of people come from work for five or like that.

So, and the last one people come from same place at the same time. So like, example, if there's some sport or music event, then, then people are coming.

Same time, same time, a lot of people getting out and, and how, how to get out out from there. And, and, and, and he had the challenges that the is, there would be some, some kind of shared reha. So are they in the right or wrong place there, or that's challenge, then we continue one other slide about these challenges. So the first, the previous, they were made like a technical technical challenges, but this what I, this page, they are mainly, they are non non-technical challenges. So like we see the law law is always behind the technology.

So we, we could have done something already, but there there's some law. It says that you cannot do like this one.

And, and one example is, is what's we could have a, like automated autonomous ships sailing on the seas, but there's this regulation called solars, which says that you have to have some humans on the ships. And, and, and before that is changed, then cannot be fully autonomous automated ships on the, on the sea. And the biggest challenge is the attitude. So like this slogan, what I found that I, I don't always try, oh, wait, yes, I do.

So we, we are humans. So we have difficult to change our habits.

We, we are humans, we are lazy, we us to this easiest and like that. So, and then like I, when I looked some did a presentation about the smart mobility, one was from a year, 2009.

And, and, and they, they were saying that we should do this and that. And, and now we are at 2020 and I didn't see anything, what they proposed is done anywhere in the world.

So, so it's, it's this big challenge, this attitude, okay. Now we, the solutions then, and, okay, this is the management guru consultant. Peter drer most famous, one of the most famous GOs. If you can't measure it, you can't improve it. So same thing here in smart mobile mobile that we have to, in order to improve this problems or the make them better. So we have to first measure what is currently and then measure the progress. So here are the main tech for the solution. So it's not only the AI.

Of course, we need IOT to gather data, measure things like we account what type of we hike, what time they passed and, and maybe how many passengers there were. And, and also you can, you, the IOT to calculate how many humans are using this path or, or like that.

So, and, and of course this kind of environmental stuff, like whether, and, and, and this, this can be better using the IOT. And, and, and of course this pollution and CO2 can be a method using the IOT, and then this, we need some transportation methodology and, and here, the fight is coming and it's, it's designed for this kind of data. One of the main principles is that, that you need to have easily to transport data. And then that's, that's of course we can use the currently 40 of course here.

So it's, it's, we don't have to wait for the 5g, but it's coming already in Finland. We have your enhancing, we have 5g network here. Also this 5g enable that the cars or the, we can talk to each other. So this kind of, we have we to, we communication will be possible. And that's, of course, when the cars can like talk to each other, then they can also start, like, what is the, like, going more like coordinated way, so, so they can control each other. And then if one is breaking the other, others also will know that now's breaking.

And then, and then, and that, that will like make faster the traffic. And of course, if there will be use drones for some, I will later cover what we can use the drones for this, this smart mobility. So the 5g can connect those drones as, as a, like that they, they can be coordinated easily. They flight where they are needed.

Of course, we can use this kind of IOT radio, like I Laura, or even this wifi in cities, but 5g is mainly what is like coming, coming. And of course, then the artificial intelligence that's its role here is to analyze the data, make decisions.

And, and for example, what kind of task it to, to fleet management, if there's like a, some shared we high calls. So it would allocate them where they are needed, what time, and, and, and also to learn from the past that when they are needed and, and taking account, like where therefore costing and, and, and all this kind of parameters, and, and, and, and do the, put the shared hike calls to the right place where, where those are needed. And then there's this route planning, of course, AI is very capable of doing that and, and, and fully dynamic.

So if, if any changes there, it can easily change. The roots. And third example has, is dynamic dynamical pricing. Meaning that if the okay example here is that in, he, we have this city PIs, city bikes. And the problem is that they are, some stations are full of them and some are empty.

So in, in this dynamical pricing, they could be for example, three, right? If you drive the bicycle that, to that empty station.

So, so the S incentive that actually people are like a step driving them to the right places. And, and these are the main tech. Then we look some hardware or tech wear, I put here. So different kind of electrical calls can be bikes and, and studies, so that if you, you use electrical bike bike, then you try two, three times longer than without the electricity.

So that's, that's what they're seen. And, and then they are like on, on a picture, we can see they're coming this kind of one person, two person electrical rehas, which it's are covered for the rain. So that's needed at least here in newy countries. And then, and, and for snow snow, of course, in winter. So maybe also some heating. So these kind of vehicles can be used also.

And, and of course, you know, the electrical cars are coming and, and, and also electrical buses are already in, in the market. There's not so much AI on those electrical, but then the next one autonomous vehicle that's where AI is of course needed.

And, and, and we have this autonomous cars on, or level three autonomous cars on the market. And, and then in, he, they are trying this autonomous buses.

I, at the end, I have some pictures of video. So if, and then there can be used drones, like example, if they're flying, flying drones. So then like a surveillance of different things measurement of again, this different kind of measurement stuff.

And, and, and, and like that, and, and here is the, the last first mile example, what is challenge and what is the solution? So if the blue.in the middle is, is that train or, or subway station. So the walking distance it's people prefer the walk is like two, 300 meters max.

So it's, it's something, but with this kind of automate autonomous bus, we could easily then double or triple that distance. And then you can see also then the covered area is, is so how many people it can serve is, is much bigger. So the area is much bigger than the diameter chain.

So this, this is the idea that is piloted in healthy. Then on system level solutions, we have this different sharing stuff.

And, and, and so we can have like between users sharing or by public owner, like on the, that fixer, that those are those city bikes, which are now in whole healthy region. Those, and, or of course, we know this private owned sharing, like Uber and those, those which so they, they use of course, AI to, again, through this fleet management and, and, and or this resource allocation and that kind of, and then we have this MAs, so this mobility as a service, again, a system, which is the AI.

So like in he and gear, we have operator called win, which, which with fixed fee, you can use the, all the buses, all the subways trans car, the city bikes, and also the car is available. So this kind of operators that they are now getting many places.

And, and then this digital twin, so a digital model of physical assets. So here, here, the, the idea is that with the, like a digital copy of the city, you can make better planning and use the machine learning and AI to, to get it.

Like, if, if you do this, what would be the like, consequences of that one? And the last one is, is this corridor a service, or this is mainly for logistics when you, you want to have like every digital. So that's not so about the smart mobility, then some examples here is the autonomous ferry in, in Finland. So it's full autonomous.

And, and, and then the last one is the autonomous last mile bus in Helsinki. And this is now in the winter time. So it also have to work in a, in a winter time. So that's very challenge for the autonomous cost, these kind of conditions. And last one. So you can see that all this hardware and, and AI, they can enable, but the end of the day, it's the humans who can change the behavior.

So it's, it's more about what we decide to do. It's not the technology, which is like prohibiting us to do anything. So that was the last slide. So is there any questions.