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：
2149511
负责人：
Christos Cassandras
金额：
$ 40万
依托单位：
Trustees of Boston University
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 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149511&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，例如连接和自动化的车辆以及共享的移动性，为用户提供了更好的机会，使用户能够更好地监控运输网络条件并做出更好的决策，以提高安全性和运输效率。但是，运输网络中不同水平的车辆自动化可以显着改变运输效率指标（旅行时间，能源，环境影响）。此外，我们预计有效的运输可能会改变人类旅行行为，从而导致反弹效应，例如，通过提高效率，降低了旅行成本，因此增加了旅行的意愿。后来的车辆将增加行驶的整体车辆，这反过来又可能会否定能源和旅行时间的好处。该项目将与现实世界中的数据合并新兴的移动系统和模式，并处理的信息，从而导致具有广泛经济，环境和社会意义的公平运输系统。我们期望该项目的结果能够增强我们对新兴行动系统的反弹效应，旅行需求和能力的变化，人类接收，采用和使用的理解。这项研究的结果将提供一个在线学习框架，该框架将旨在在给定的运输网络中分发旅行需求，从而导致旅行者愿意接受的社会上最理想的流动系统。 “社会上最佳的流动系统”被定义为（（1）是有效的（就能耗和旅行时间而言），（2）不会引起反弹效应，并且（3）确保运输中的公平性。该框架将通过合并学习和控制方法来确定最佳控制网络物理系统的新方法。它包括开发新方法，以增强运输和旅行者接受的可及性，安全性和权益。在拟议的框架的背景下，当不公开知道旅行者的私人信息时，“社会规划师”面临着将旅行者的偏好汇总为集体，全系统决策的问题。机理设计理论将用于得出所有旅行者的最佳路线和运输模式的选择，以最大程度地提高运输和旅行者接受的可及性，安全性和权益。将开发针对上下文匪徒问题的在线学习算法，以确定旅行者的偏好，并确定他们将如何应对社会规划师关于路线和选择运输模式的建议。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来获得的支持。