[MUSIC] My name is Constantinos Antoniou and I'm a Professor and the Chair of Transportation Systems Engineering at the Technical University of Munich. During the last few years, I have been working on the topic of acceptance of urban air mobility and related modes. And today I would like to share some of these experiences and some of these interesting material. As you have heard a lot in this course, there is a large number of vehicle concepts and a lot of infrastructure requirements for urban air mobility. We are going to focus a little bit more now on the human factor on the user and what the user expects from this and which are the factors that will determine whether the users adopt, accept and use these services. So what will you learn today? First of all, we will think about why we do this, why we need to worry about the users and about the potential acceptance, adoption and demand for these services. Once we have established that, we're going to look at how we can achieve this. And the first aspect is how do we estimate the demand, the future demand for these services? Because at the end of the day we have the supply, we have the services that are being offered, and then we have the demand for these services and what will determine their use is the demand supply interactions. Once we have looked at how we can estimate the demand, we look at which are the factors that will determine whether the users will accept and use this service. These are the potential users of the service. Once we have established that the next thing is to look also at the broader social or societal acceptance of these services. Because often there are a lot of issues that can arise from factors and persons not related directly to the use of the service. At the end, we're going to put all of this together and understand what, demonstrate what we can show and what we can use and do with this. So now we're going to continue by looking at why we do this. So urban air mobility is currently a service that is being developed, there is a lot of research and applications going on, but we don't have the service really happening. So in order to be able to assess how to develop it, how to evaluate it, how it will be used, we resort to simulation. And simulation has a lot of requirements, there is the supply side, there is for example, the capacity of the vehicles, the way in which the vehicles operate and their constraints. And then as we discussed, there is also the side of the users. And at the end of the day, what we observe and what will happen is the interaction of the demand and the supply of how the users use these services, the available capacity of the services. Considering that I'm working primarily with demand, I will argue that the supply side is relatively straightforward. And we can get some of this information from the vehicle manufacturers and the people designing the services. However, dealing with people is a little bit harder because we are not always rational and we don't always operate in the correct way. One example is air transportation where people often have some fear of flying, whereas statistically and objectively it is the safest mode of traveling. In order to simulate the supply side, we resort to traffic simulation models such as MATSim, and we extend this in order to incorporate urban air mobility and new modes like Hyperloop and so on. There are a lot of parameters that we need to estimate and to find, and this is where the difficulty comes. In terms of for example, the supply side, we need to know how long it will take for people to board one of these vehicles to be processed or maybe the flight time. The demand side on the other hand is a little bit tougher because we cannot resort onto the manufacturers of the vehicles or the people that design these services. But instead we have to try to understand how people behave, and therefore, we have to collect data and analyze the data from people. And the issue here is that these services do not exist, so we cannot collect revealed data. So we cannot go out and measure something but we have to ask people about that. And this of course has a lot of challenges, a lot of difficulties, because we all have a lot of biases and prejudices which come out even if we don't want to do that. The first thing that we want to do is demand forecasting. So we want to understand how many people are going to use the service and how often they're going to use it. Which features of these services are making it more attractive? For example, are people thinking more about the privacy aspects? Are people thinking more about the safety aspects? Are people thinking more about the autonomy aspects? Which are the factors that are going to influence if they're going to use one service more or less? Similarly, how does cost or speed of a service affect their choices? Understanding this is very important because not only can we plan these services accordingly, but we can also simulate the expected responses of these people for various future scenarios. And as mentioned before, because these services do not exist, we cannot let them choose from some existing services. But we have to describe to them the theoretical, the hypothetical scenarios to which they will have to respond. Finally, an important aspect is to try to understand how each segment of the population is going to respond to these services. For example, how will gender, AIDS, socio economical background, level of education, number of cars that each individual owns affect their choices. All these are aspects that need to be explicitly considered. In terms of acceptance, we have to answer questions like, what are the main factors that will affect this acceptance? Is trust to the system, will people require that there is an interaction with somebody on the ground? Similarly, in terms of societal acceptance, the question is, will people that do not use the service create barriers for the service? How important will noise or visual occlusion be in their acceptance of this service? So we're going to look a little bit at one specific topic that is of interest here, and that has to do with collecting data for unavailable modes for modes that do not exist. And this is a very, very interesting topic in general, and it's a very broad topic. So the issue is that we need to ask people to understand their behavior and what they do. When we are talking about the choice between car or public transport for example, we can monitor people, collect data and understand who is using which mode. In the case of modes that do not really exist or are emerging like urban air mobility, we do not have the option of collecting this kind of data, observational data, which we also call revealed preference data. Instead we resort to so called stated preference data, where we create a number of scenarios and we explain to the respondents this hypothetical scenarios and ask them to choose. And this is something that we're going to explore in a lot of the components that we're going to look today. Again, there are several reasons to use this kind of stated preference experiments. We are going to focus only on the fact that there are unavailable modes. There are other reasons, like for example, the revealed data do not provide us with a lot of variability, do not provide us with a lot of information, and we want to create more scenarios. By using stated preference experiments, we can create the scenarios that we need and extract the data that are suitable and required for our downstream analysis. [MUSIC]
