Collaborative Research: CPS: Medium: An Online Learning Framework for Socially Emerging Mixed Mobility

新兴的移动系统，例如，互联和自动化车辆以及共享移动性提供了最有趣的机会，使用户能够更好地监控交通网络状况，并做出更好的决策，以提高安全性和运输效率。然而，交通网络中不同级别的车辆自动化可以显著改变交通效率指标（旅行时间，能源，环境影响）。此外，我们预计，高效的交通可能会改变人类的旅行行为，导致反弹效应，例如，通过提高效率，降低了旅行成本，因此增加了旅行意愿。后者将增加车辆行驶的总里程，这反过来可能会抵消能源和旅行时间方面的好处。该项目将整合新兴的移动系统和模式与真实世界的数据和处理信息，从而建立一个具有广泛经济，环境和社会效益的公平运输系统。我们希望这个项目的成果能够加强我们对反弹效应，出行需求和能力变化，人类接待，采用和使用新兴移动系统的理解。这项研究的结果将提供一个在线学习框架，旨在在给定的交通网络中分配出行需求，从而产生一个旅行者愿意接受的社会最佳移动系统。“社会最佳的移动系统”被定义为（1）高效（在能源消耗和旅行时间方面）、（2）不引起反弹效应、（3）确保交通公平的移动系统。该框架将通过合并学习和控制方法，建立最佳控制网络物理系统的新方法。它包括制定新的方法，以提高交通的无障碍性、安全性和公平性以及旅行者的接受程度。在所提出的框架的背景下，“社会规划师”面临的问题，聚集的旅客的偏好到一个集体的，系统范围内的决定时，旅客的私人信息是不公开的。将使用机制设计理论推导出所有出行者的最佳路线和交通方式的选择，以最大限度地提高交通的可达性、安全性、公平性和出行者的接受度。在线学习算法的上下文强盗问题将被开发，以确定旅行者的喜好，并确定他们将如何回应社会规划师的建议，路线和选择的交通模式。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

Emerging mobility systems, e.g., connected and automated vehicles and shared mobility, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. However, different levels of vehicle automation in the transportation network can significantly alter transportation efficiency metrics (travel times, energy, environmental impact). Moreover, we anticipate that efficient transportation might alter human travel behavior causing rebound effects, e.g., by improving efficiency, travel cost is decreased, hence willingness-to-travel is increased. The latter would increase overall vehicle miles traveled, which in turn might negate the benefits in terms of energy and travel time. The project will consolidate emerging mobility systems and modes with real-world data and processed information leading to an equitable transportation system with broad economic, environmental, and societal benefits. We expect the outcome of this project to enhance our understanding of the rebound effects, changes in travel demand and capacity, human reception, adoption, and use of emerging mobility systems. The outcome of this research will deliver an online learning framework that will aim at distributing travel demand in a given transportation network resulting in a socially-optimal mobility system that travelers would be willing to accept. A “socially-optimal mobility system” is defined as a mobility system that (1) is efficient (in terms of energy consumption and travel time), (2) does not cause rebound effects, and (3) ensures equity in transportation. The framework will establish new approaches in optimally controlling cyber-physical systems by merging learning and control approaches. It includes the development of new methods to enhance accessibility, safety, and equity in transportation and travelers’ acceptance. In the context of the proposed framework, a “social planner” faces the problem of aggregating the preferences of the travelers into a collective, system-wide decision when the private information of the travelers is not publicly known. Mechanism design theory will be used to derive the optimal routes and the selection of a transportation mode for all travelers so as to maximize accessibility, safety, and equity in transportation and travelers’ acceptance. Online learning algorithms for contextual bandit problems will be developed to identify traveler preferences and to determine how they would respond to the social planner’s recommendations on routing and selection of a transportation mode.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.