Hello everybody. My name is Elizabeth signal scrambles. I'm a PhD candidate at the Department of Mechanical Engineering and the supervision of Professor and Jasmine copulas. And here to talk about shows the social impact of the Bayesian game, mobility. Greedily, we think of self-driving cars when we talk about automation and mobility. Some other terminology that you might come across is autonomous. Vehicles are cars. Now, what I mean when I say what I mean by saying self-driving cars. If the car that doesn't require any human supervision while driving. And you can, as the picture shows, you can read a book, you can take a nap. You can do whatever you want while the car takes you to your desired desired destination. Now, self-driving cars promises to revolutionize mobility and give us a very efficient mobility system where everybody has access to any part in ethnicity or anywhere in the country. Yet there is a cut. The country is essentially that because of this kind of efficiency improvement, our social behavior and our economy will react and might create an additional demand to how much we are going to use self-driving cars. And this situation gives rise to many different questions about the facts of the self-driving cars. Efficiency improvement that might cause to the mobility system itself, but also to our society. So we want to study this problem, problem. In this talk, I will talk about what the, where the literature stands. If there's consensus how to approach this problem and so on, then I will present the modelling framework that I've been working on the last couple of years. And the solution that has, that has been able to derive. And then I will offer some conclusions and talk about future work. We have two categories when we talk about the effects of sovereign costs. One is from the technological perspective and the other one is from the societal perspective. Essentially what we'll talk about a technology to technological perspective. We'd have to talk about the benefits of self-driving cars to leading to a decrease of fuel consumption, total elimination of congestion, and increase in safety on the roads. I, but at the same time, from a societal perspective, self-driving cars are expected that are expected to lead effluents. More people to use cars more frequently. More often. And they might also move, drive people away from public transit. So essentially, we have to find a solution that balances the benefits, that technological benefits of the cars and their expected societal effects that the cars might give rise to. How can we do that? Very interesting problem. And we can think of mobility, especially in a smart I'm above the system as a service where everybody using the phone probably or the smart card that can have access to all the public transit and bald cars. While one to two, to reach the destination, to commute to travel. Using this access, bility gives rise to the, to the following phenomenon. Travelers will be, will have a more direct, explicit manner where they can influence the mobility system. So we have to take into consideration how people react, behave in a smart mobility system. Now, our solution approach is a, in this problem is to have four stages where we talk about the travelers, the agents, and what kind of information they may hold that is important to the system. This information is the own Traveler Travel preferences and needs. What do they require? They required to reach the destination safely? By that time. I prefer travel time and maybe they are in a hurry, so they need to go very fast. Using this information. And then quinine from the traverse, we have two on the thirst things come up with a mechanism. And by mechanism, I mean set of rules, set of incentives or policies that at the very end, we'll assign. It, travels the right car, the right train, the right boss, made at the same time where you have efficiency and safety. So how can we do that? One way to do that is by looking at a C. And instead of considering the individual position of each traveller, what we can do is we can focus on different neighborhoods and the connections between them. So for example, if we look at neighborhood a, neighborhood B, we can see that maybe that we have a solid line representing that the trains and a dashed line representing the different Roach. Right? Now, by looking at such an abstract, high level, absurd manner, a city. And it's different connections, mode of transportation connections. We have the ability to focus on the travelers needs and requirements. And at the same time are the capabilities and constraints of the mobility system. Taking into consideration these two things can allow us to form an optimization problem where essentially minimizing the travel inconveniences of all the travelers plus the operating cost of all the modes of transportation. By minimizing this this objective function. The summation of these two things, will allow us to find solutions that are obviously social optimal. Also, subject to a few constraints. We need to make sure that the solution that we're going to find that minimizes traveling the winds will be efficient. All travelers will voluntarily participate. No one is going to be forced to participate in this. In this new system. We don't have, we only have limited resources, limited and limited budget. So we have to also take into consideration that and also the new set of rules and policies that this whole mechanism that we have so far talk about has to be implementable real time, real life. So an implementation constrain the solution of this problem is really difficult to get unless we make some key observations. One of the key observations that can be made if by looking at the tragus mobility externality. So why do they need to do that? We need to do that so that we can come up with the right incentives to the travelers right? Now. Here, what essentially has been done is we've looked at what happens when a problem doesn't participate. What happens when a, when a traveler participates, but only to the other travelers. So we're looking at the welfare changes, take that into consideration and we can form new, very new incentives, new policies based on that, our solution essentially ensures for voluntary, voluntary participation. No one wants to manipulate the system by saying sending lies on misreporting. Also, there's no need to just invest unlimited money to get a socially optimal or socially acceptable solution, we can generate revenue. To sum up, self-driving cars are right around the corner. They promised to revolutionize mobility. And but yet, we need to consider ribbon effects. Our answer is to have a traveler sending a model that ensures efficient mobility. Future work will extend and enhance the behavioral model of travelers and explore real time and adaptable movement the solutions. Thank you very much for your attention. I'll be happy to answer any questions.
1A: Social Impact of Connectivity and Automation in Mobility, Ioannis Chremos
From Caitlin Hutchison April 13, 2021
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Connectivity and automation are two technologies that promise to make self-driving cars possible, transforming today's urban transportation system and revolutionizing mobility. Although there have been considerable research developments mainly focused on the technological impact of self-driving cars, e.g., managing congestion, reducing emissions and energy consumption, and improving safety, it is apparent that the social consequences and implications of self-driving cars have not been fully investigated and understood. A question we ask is, "How may human travelers, passengers, and drivers interact with self-driving cars, and what may be the effects on tendency-to-travel and travel demand?"
This research proposal aims to remedy our lack of understanding between human interaction and connectivity and automation in emerging mobility systems. Specifically, the vision of this proposal is to press forward with an implementable solution framework that will accelerate the safe deployment of self-driving cars and smooth the transition to fully automated transportation systems while ensuring that any emerging rebound effects are well-understood and alleviated, even prevented. The tools that we plan to develop will provide an integrative framework through which the broader social impacts of connectivity and automation will be identified, evaluated, and controlled. We aspire to develop mathematical tools that prevent the emergence of social inefficiencies, i.e., rebound effects, in mobility systems characterized by a technological and social dimension.
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