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

协作研究：CPS：媒介：社会新兴混合出行的在线学习框架

基本信息

批准号：
2149520
负责人：
Andreas Malikopoulos
金额：
$ 37.96万
依托单位：
University of Delaware
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2023-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149520&HistoricalAwards=false
关键词：
Collaborative Research CPS Medium Online

项目摘要

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.

新兴的移动系统，例如互联和自动化车辆以及共享移动，提供了最耐人寻味的机会，使用户能够更好地监控交通网络状况，并为提高安全性和运输效率做出更好的决策。然而，交通网络中不同级别的车辆自动化会显著改变运输效率指标(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出行行为，导致反弹效应，例如，通过提高效率，降低出行成本，从而提高出行意愿。后者将增加车辆的总行驶里程，这反过来可能会抵消能源和旅行时间方面的好处。该项目将把新兴的移动系统和模式与真实世界的数据和经过处理的信息结合起来，从而形成一个具有广泛经济、环境和社会效益的公平的交通系统。我们期待这一项目的成果将加强我们对反弹效应、旅行需求和能力的变化、人类接受、采用和使用新兴移动系统的了解。这项研究的结果将提供一个在线学习框架，旨在分配特定交通网络中的出行需求，从而产生一个旅行者愿意接受的社会最佳出行系统。“社会最优移动系统”的定义是：(1)高效(就能源消耗和出行时间而言)，(2)不会造成反弹效应，(3)确保交通公平。该框架将通过合并学习和控制方法，在优化控制网络物理系统方面建立新的方法。它包括开发新的方法，以提高交通运输和旅行者接受度的可达性、安全性和公平性。在拟议的框架中，当旅行者的私人信息不为公众所知时，“社会规划者”面临着将旅行者的偏好汇总为一个集体的、全系统的决定的问题。运用机制设计理论推导出所有出行者的最优路线和交通方式的选择，以最大限度地提高交通的可达性、安全性和公平性，并使出行者接受。将开发针对背景土匪问题的在线学习算法，以确定旅行者的偏好，并确定他们将如何回应社会规划者关于路线和交通方式选择的建议。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。