Hi and welcome to the open online course on urban air mobility. My name is Laurie Garrow and I will be your instructor today in modeling module of this course. Here are several learning objectives for demand modeling on urban air mobility, is we want to understand why demand modeling is important. Why are we even doing this? The second is to understand two [inaudible] that had been used to model UAM demands and understand when it's appropriate to use each method. Third, we'll discuss different types of data that are used for modeling UAM demands. Finally, we're going to apply the concepts that we learned in the first half of this module to an example problem that uses cell phone data to compute UAM demand for 40 US cities. In this first subtopic, I'm going to cover demand modeling motivation, and one of the two types of common studies that have been used, namely macroeconomics studies. Why is demand modeling important for UAM? Well, first of all, it helps guide investment decisions and decisions we want to make on how we want to design the aircraft. One of the key questions I'm often asked is, should I build a more expensive aircraft that'll go faster, meaning it would attract more passengers, but it's also more expensive to build? That's an example of a question that helps guide investment where I want to put my research dollars as well as the aircraft design. A second key reason that we looked at demand modeling is that it helps us identify potential benefits and negative externalities. When we think about benefits related to urban air mobility, the first one that comes to mind is that we can go faster, we can reduce the travel time in the air, but we can also reduce the travel time on the ground for those individuals that remain in their cars because there's less cars on the road to begin with, less competition. In turn, we also have a reduction in emissions, better connectivity. Airplanes can go places that cars may not be able to go, so we are able to better connect different points. Another benefit that often comes up in urban air mobility is, there may be a potential to repurpose parking lots into vertiports for takeoff and landing slots and that could have a local economic development benefit. Another way to think about this is with the autonomous ground vehicles coming online at the same time, there's a sense that we may not need as much parking in downtown areas. This would be a way for urban air mobility to complement rather than compete with the autonomous ground vehicles going on. There are, however, some negative externalities which we've learned about in other modules. The key one is visual and noise disturbances. I'm going to see these aircraft in air [inaudible]. It also may lead to people deciding that if they can, for instance, fly to work, that they want to move to a more remote residential location. This could lead to increased travel distances or increase the missions as well if people are living further out than when they are working. We've also learned about latent demand in prior modules. Latent demand or the increase in trips that we don't currently have would also increase vehicle miles traveled and increase emissions. Finally, urban air mobility could compete more with public transit and so there could be potential decreased demand for public transit. These are some examples of benefits and negative externalities associated with urban air mobility. One of the things demand modeling helps us do is understand why these negative consequences are occurring and help us design public policies to mitigate these negative externalities. How do we model demand? There's two main types of studies that go by different names. But in general, the first kind is called macroeconomics studies or oftentimes are called global market studies in the literature. These types of studies often use regression-based methods or time series methods to relate characteristics of the population, economy, geography, current transportation system or other factors to a measure of overall aviation activity. That could be something like how many people are going to fly or how many aircraft am I going to have or how many seats am I going to offer for sale? If we look at these different types of characteristics, population measures can include things like population and population growth, income and wealth concentration or age distribution. Economic measures are usually based on gross domestic product or GDP, and this can be expressed per capita just as GDP growth. Another common economic measure is looking at the types of companies in a given area so maybe the presence of Fortune 500 companies has been used in the past. For geography, a lot of times the geographic measures are interacted with a population or an economic measure. Population density would be people per square mile or people per a square unit. City sprawl is looking at how spread out that density is. The presence of water bodies is also important for looking at urban air mobility because again, you can't take a road across the water, but you can take an aircraft across the water. That's an opportunity that may present itself in terms of better connectivity with UAM. Finally, weather tends to be important. If I'm looking at a city that's nice and sunny versus one that often has snow or fog, I'm going to have different reliability of my system or different times of the day that I'm able to really operate that reliably. Transportation measures include things like how congested the current transportation network is on the ground and also the existing vertiport infrastructure. If I want to start a UAM service, am I able to use existing helipads or existing airports for takeoff and landing? Now that we have an understanding of the factors that are used in macroeconomic models, let's think about the types of data that we would use to obtain this information. Now, the type of data is going to vary depending on the country you live in and the particular area you're looking at. But here in the US, for instance, we often use census or government data for our population characteristics. There are certain companies or again, government data sources that provide sources of demographic and economic data. Woods & Poole is a common platform here we use in the US. Oftentimes, we often have to use GIS databases to be able to look at the population area and geographic features. Finally, we often have to obtain infrastructure databases. Where are the roads and what are the speeds on the roads? Where are our airports and what are characteristics of those airports? How much traffic congestion is there on the ground? There's a lot of databases or companies that help us provide that information. Some of the key strengths and macroeconomic models or applications in which we're more likely to use them, include generating long-term forecasts of aviation activity. For UAM, we may be able to put UAM in the market in let's say five years, but it may not be profitable for 7-10 years. Being able to look to see how the population or economies are changing over a 5-15 to 20-year horizon gives us information on when UAM may turn into a profitable mode of transportation. The macroeconomic models are also helpful for comparing aviation forecasts across different countries or large geographic areas. For instance, as an investor, I may be interested in knowing whether or not it's beneficial for me to invest in the US before investing in China or vice versa. These are some of the kinds of questions that macroeconomic models are particularly helpful for trying to answer. Let's start your first quiz question. I would like you to download the open-access article linked below and look at Table 2 on Page 9. That table summarizes the results from four global market studies, their goal was to identify US cities that had a high potential for UAM service. Looking at that table, which of the following cities were included in all market studies? Your choices are Chicago, Dallas-Fort Worth, Los Angeles, and New York City. Looking at the table, I've highlighted the cities that were in all four studies. They include Dallas-Fort Worth, Houston, Miami, New York City, and Silicon Valley, which includes the San Francisco area. Clearly, on our lists that I gave you, Dallas-Fort Worth and New York City are the two that are the answers. Now, this is not to say that other cities aren't important, is just looking at the fact that four different distinct studies all identified these ones as being important.
