Optimizing Future Mobility Systems

本研究计划的目的是研究在未来移动系统的建模、设计和运行中出现的优化问题，这需要几个主要与潜在问题的规模相关的挑战。在建模方面，挑战在于开发整合多种因素的模型，如天气条件和能源价格以及潜在的不确定性。在方法方面，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：目前市场渗透率很小的互联汽车、混合动力汽车和电动汽车以及自动驾驶汽车的新创新预计将在不久的将来成为主流。这些新技术将使货物和人员的运输变得更便宜、更可持续。此外，市场正在从传统的私人车辆所有权向移动即服务转变，私人车辆所有权与共享车辆服务并存。这样的系统需要几个具有挑战性的数据驱动优化模型的解决方案，以最终运行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，比如从家到公司。通过查看来自不同来源的数据，数据驱动模型可以了解每个人的旅行模式，从而创建一个系统，可以看到人们在哪里，检查他们朝哪个方向移动，然后预测他们将去哪里。此外，考虑到交通和能源价格的预测，混合动力和电动汽车的路线可以以这样一种方式来执行，以最大限度地减少旅行成本和对环境的影响。例如，在混合动力汽车的情况下，电动发动机和内燃机的使用可以按照这样的方式进行安排，以限制温室气体在人口高度集中的地方的释放。******大规模分布式优化：有效安排电能使用以减少对环境的影响，以及路线和行程安排，应该在全系统的方法上完成。因此，将要开发的优化模型本质上是大规模的优化模型，在计算上具有挑战性。拟议的研究计划将研究可扩展的大规模优化的新技术，这将需要对优化问题的底层结构有更深入的了解。将研究的优化方法包括分解、列生成和切割平面，以及分布式实现，使所提出的解决方案方法可扩展到云平台上的实际部署。

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.