Optimizing Future Mobility Systems

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The aim of this research program is to investigate optimization problems arising in the modeling, design, and operation of future mobility systems, which entails several challenges mainly related to the scale of the underlying problems. On the modeling side, the challenge is in developing models that integrate a plurality of factors such as weather conditions and energy prices along with the underlying uncertainty. On the methodological side, the challenge is in developing large scale optimization techniques that can handle the scale and the uncertainty of the problems.Future Mobility Systems: New innovations in connected vehicles, hybrid and electric vehicles, and autonomous vehicles which currently have small penetration in the market are expected to become more mainstream in the near future. These new technologies will enable cheaper and more sustainable transportation of goods and people. Furthermore, the market is transforming from the traditional private vehicle ownership to mobility-as-a-service, where private vehicle ownership co-exists with shared vehicle services. Such systems entail the solution of several challenging data driven optimization models to ultimately operate an efficient and sustainable mobility system.Data Driven Models: For most people, travel patterns are the same every day, such as traveling from home to work. By looking at the data generated from different sources, data driven models can learn the travel patterns of each individual and thus create a system that sees where people are, examines in which direction they are moving, then predicts where they will go. Furthermore, by taking into account traffic and energy prices predictions, the routing of hybrid and electric vehicles can be performed in such a way to minimize the cost of travel and the impact on the environment. For instance, in the case of hybrid vehicles, the usage of the electric engine and the combustion engine can be scheduled in such a way to limit the release of green-house gas in locations with high concentration of people. Large Scale Distributed Optimization: The efficient scheduling of electric energy usage to reduce the impact on the environment, along with the routing and the scheduling of trips, should be done on a system-wide approach. The optimization models that will be developed are thus essentially large scale optimization models that are computationally challenging to solve. The proposed research program will investigate new techniques for scalable large scale optimization which would require a deeper understanding of the underlying structure of the optimization problems. The optimization approaches that will be investigated include decomposition, column generation, and cutting planes along with a distributed implementation that makes the proposed solution methods scalable for real life deployment on a cloud platform.