Optimizing Future Mobility Systems

该研究计划的目的是研究未来移动系统的建模，设计和操作中出现的优化问题，这需要主要与潜在问题的规模有关的几个挑战。在建模方面，挑战在于开发将天气条件和能源价格等多种因素与潜在的不确定性沿着整合的模型。在方法论方面，挑战在于开发能够处理问题的规模和不确定性的大规模优化技术。未来移动系统：联网汽车、混合动力和电动汽车以及自动驾驶汽车的新创新目前在市场上的渗透率很小，预计在不久的将来将成为主流。这些新技术将使货物和人员的运输更便宜，更可持续。此外，市场正在从传统的私人汽车所有权转变为移动即服务，其中私人汽车所有权与共享汽车服务共存。这些系统需要解决几个具有挑战性的数据驱动优化模型，以最终运营一个高效和可持续的移动系统。数据驱动模型：对于大多数人来说，每天的出行模式都是一样的，比如从家到工作地点。通过查看从不同来源生成的数据，数据驱动模型可以学习每个人的旅行模式，从而创建一个系统，可以看到人们在哪里，检查他们向哪个方向移动，然后预测他们将去哪里。此外，通过考虑交通和能源价格预测，混合动力和电动汽车的路线可以以最小化旅行成本和对环境的影响的方式执行。例如，在混合动力车辆的情况下，电动发动机和内燃机的使用可以以这样的方式被安排，以限制在具有高浓度的人的位置中的温室气体的释放。** 大规模分布式优化：为了减少对环境的影响，电力使用的有效调度，沿着行程的路由和调度，应该在系统范围内进行。因此，将开发的优化模型本质上是大规模优化模型，其在计算上具有求解挑战性。拟议的研究计划将调查可扩展的大规模优化，这将需要更深入地了解优化问题的底层结构的新技术。将研究的优化方法包括分解、列生成和切割平面沿着分布式实现，使得所提出的解决方案方法可扩展到云平台上的真实的部署。

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.