Policies and Strategies for Evolving and Managing Automated Mobility

发展和管理自动出行的政策和策略

• 批准号: 1904575
• 负责人: Yafeng Yin
• 金额: $ 52.98万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1904575&HistoricalAwards=false
• 关键词: Policies; Strategies; Evolving; Managing; Automated

The deployment of automated vehicles (AVs) is rapidly approaching with a push from governments who are relaxing laws to allow AVs to operate on highways, and industry, both manufacturers and mobility service providers, who are heavily investing in the development of the technology and its applications. AVs are expected to tremendously enhance the efficiency, safety and convenience of existing transportation systems. However, all these benefits hinge on the level of market penetration of AVs being sufficiently high. At low market shares, AVs exert little impact on enhancing transportation system efficiency. Worse yet, early deployment of AVs may even compromise the efficiency. The transition period is expected to be lengthy. If we can shorten it, the tremendous benefits promised by AVs can be realized sooner. This grant thus sets out to investigate incentivizing policies and innovative traffic management strategies to promote the development and deployment of AVs to maximize the social benefit over the entire duration of the AV deployment. Specifically, incentivizing policies will nurture the AV market and accelerate their adoption while innovative traffic management schemes aim to better utilize AVs in the traffic stream and promote high-occupancy mobility services to maximize the benefits of AVs at a given market share. The synergies between incentivizing policies and traffic management schemes may create an upward spiral for the AV deployment and particularly reduce the duration of initial deployment where AVs exert little or even negative impact on enhancing efficiency. This grant will provide timely support for government agencies to better understand the impacts and implications of AVs and provides guidance on their development and deployment. This grant will involve students at all levels and traditionally underrepresented students, and offer fresh materials and case studies for courses on emerging automated mobility. Research results will be broadly disseminated through a variety of media.The research will be conducted in two main thrusts. The first is the study of policies like tax credits, subsidies, and preferential treatments for AVs etc., which can incentivize the deployment of AVs from lower to higher penetration rates to maximize the benefits of AV deployment throughout a planning horizon. As part of the first thrust, we plan a continuous time principal-agent framework in which the government is the principal who offers an incentive policy, and the manufacturer is the agent who sets the retail prices of AVs. The optimal incentive mechanism is obtained by considering the interplay between these two entities. In the second thrust, we will develop innovative schemes to improve the social welfare of the transportation system. These schemes could involve headway-based congestion pricing for penalizing excessive headways of prototype AVs or occupancy-based pricing for promoting high occupancy mobility. In parallel, a distributed control scheme will be developed to use AVs in the traffic stream as control actuators to distribute traffic demand across the transportation network to reduce congestion. As the impacts of incentivizing policies from Thrust 1 and traffic management strategies from Thrust 2 are intertwined, an iterative application of the models developed in both thrusts can prescribe a wise course of actions to evolve and manage automated mobility. If successful, this grant makes three critical contributions: a continuous time principal-agent approach for incentive policy analysis, data-driven headway- or occupancy-based congestion pricing and distributed control of AVs for managing traffic flow. Specifically, we formulate the incentivizing policy design as a non-zero dynamic Stackelberg game under asymmetric information, a class of problems extremely difficult to solve using traditional techniques. We offer an innovative framework to decouple the decision-making processes to make the problem mathematically tractable. Our research also advances the theory of congestion pricing by providing a new framework of designing fine-grained pricing schemes based on vehicle trajectory and occupancy. It shifts the paradigm from model-based pricing to be more data-driven. The distributed control of AVs for managing network traffic flow enriches the traffic control literature and theorizes participatory traffic control that is distributed, scalable and effective.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.

自动驾驶汽车（AV）的部署正在迅速接近，政府正在放松法律，允许AV在高速公路上运行，而工业，制造商和移动服务提供商，正在大力投资于技术及其应用的开发。预计自动驾驶汽车将大大提高现有交通系统的效率、安全性和便利性。然而，所有这些好处都取决于自动驾驶汽车的市场渗透率足够高。在市场份额较低的情况下，自动驾驶汽车对提高交通系统效率的作用不大。更糟糕的是，自动驾驶汽车的早期部署甚至可能会影响效率。预计过渡期将很长。如果我们能缩短它，自动驾驶汽车所承诺的巨大好处就能更快地实现。因此，这笔拨款旨在研究激励政策和创新的交通管理策略，以促进自动驾驶汽车的开发和部署，从而在整个自动驾驶汽车部署期间最大限度地提高社会效益。具体而言，激励政策将培育自动驾驶汽车市场并加速其采用，而创新的交通管理计划旨在更好地利用自动驾驶汽车在交通流中的作用，并促进高占用率的移动服务，以最大限度地发挥自动驾驶汽车在给定市场份额中的优势。激励政策和交通管理方案之间的协同作用可以为AV部署创造一个上升的螺旋，特别是减少初始部署的持续时间，其中AV对提高效率几乎没有甚至是负面影响。这笔赠款将为政府机构提供及时的支持，以更好地了解自动驾驶汽车的影响和意义，并为其开发和部署提供指导。这笔赠款将涉及各级学生和传统上代表性不足的学生，并为新兴的自动化移动性课程提供新的材料和案例研究。研究结果将通过各种媒体广泛传播。研究将在两个主要方面进行。一是研究对无人驾驶汽车的税收抵免、补贴、优惠等政策，这可以激励AV的部署从较低到较高的渗透率，以在整个规划范围内最大化AV部署的益处。作为第一个推力的一部分，我们计划一个连续时间的委托代理框架，其中政府是提供激励政策的委托人，制造商是制定自动驾驶汽车零售价格的代理人。最优激励机制是通过考虑这两个实体之间的相互作用。在第二个重点方面，我们会制订创新计划，以改善运输系统的社会福利。这些方案可能涉及基于车头时距的拥堵定价，以惩罚原型AV的过度车头时距，或基于占用率的定价，以促进高占用率的移动性。同时，将开发分布式控制方案，在交通流中使用自动驾驶汽车作为控制执行器，在整个交通网络中分配交通需求，以减少拥堵。由于Thrust 1的激励政策和Thrust 2的交通管理策略的影响交织在一起，因此在两个目标中开发的模型的迭代应用可以制定明智的行动方案来发展和管理自动化移动性。如果成功的话，这笔赠款将做出三个关键贡献：用于激励政策分析的连续时间委托代理方法，基于数据驱动的车头时距或占用率的拥堵定价以及用于管理交通流量的自动驾驶汽车分布式控制。具体而言，我们制定的激励政策设计为非零动态Stackelberg博弈下的信息不对称，一类问题，非常难以解决，使用传统技术。我们提供了一个创新的框架来解耦决策过程，使问题在数学上易于处理。本文的研究为基于车辆轨迹和占有率的细粒度定价方案的设计提供了一个新的框架，从而推进了拥挤定价理论。它将基于模型的定价模式转变为更多的数据驱动。用于管理网络交通流的分布式自动驾驶汽车控制丰富了交通控制文献，并将分布式、可扩展和有效的参与式交通控制理论化。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Optimal investment in driving automation: Individual vs. cooperative sensing

驾驶自动化的最佳投资：个体传感与协作传感

• DOI: 10.1016/j.trb.2023.06.001
• 发表时间: 2023
• 期刊: Transportation Research Part B: Methodological
• 作者: Nourinejad, Mehdi;Bahrami, Sina;Yin, Yafeng
• 通讯作者: Yin, Yafeng

Economic analysis of vehicle infrastructure cooperation for driving automation

驾驶自动化车辆基础设施合作经济分析

• DOI: 10.1016/j.trc.2022.103757
• 发表时间: 2022
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Vignon, Daniel A.;Yin, Yafeng;Bahrami, Sina;Laberteaux, Ken
• 通讯作者: Laberteaux, Ken

Truck routing and platooning optimization considering drivers’ mandatory breaks

• DOI: 10.1016/j.trc.2022.103809
• 发表时间: 2022-10
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Min Xu;Xiaoyuan Yan;Yafeng Yin

Curbing cruising-as-substitution-for-parking in automated mobility

遏制自动驾驶中以巡航代替停车的行为

• DOI: 10.1016/j.trc.2022.103853
• 发表时间: 2022
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Radvand, Tara;Bahrami, Sina;Yin, Yafeng;Laberteaux, Ken
• 通讯作者: Laberteaux, Ken

Path controlling of automated vehicles for system optimum on transportation networks with heterogeneous traffic stream

• DOI: 10.1016/j.trc.2019.11.017
• 发表时间: 2020
• 期刊: Transportation Research Part C-emerging Technologies
• 影响因子: 8.3
• 作者: Zhibin Chen;Xi Lin;Yafeng Yin;Meng Li