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EAGER/Collaborative Research: Enable Elastic Capacity for Transportation Infrastructure through a Transmodal Modular Autonomous Vehicle System

EAGER/协作研究：通过跨模式模块化自动驾驶车辆系统实现交通基础设施的弹性能力

基本信息

批准号：
2022967
负责人：
Joseph Chow
金额：
$ 11.19万
依托单位：
New York University
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 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2022967&HistoricalAwards=false
关键词：
EAGER Collaborative Research Enable Elastic

项目摘要

While roadway infrastructure provides limited capacity, vehicles operating on roads dominated by passenger cars may easily exceed road capacity over peak hours, causing traffic congestion, excessive energy consumption and increased safety risks. In observing that a passenger car takes much space on a roadway due to the relatively long gap following a preceding vehicle, this EArly-concept Grant for Exploratory Research (EAGER) project explores emerging modular autonomous vehicle (MAV) technology that can dynamically adjust gaps between vehicles. With the MAV technology, vehicles composed of multiple modular pods can be dynamically docked and separated during operations. For example, during peak hours, modular pods will be docked into longer MAVs, resulting in zero gaps between the modular pods docked together, which obviously improves highway throughput and reduces congestion. Whereas during off-peak hours, a long MAV may separate into shorter MAVs to ensure flexible system accessibility and reduce vehicle operation costs. This way, the MAV service equivalently creates “elastic” capacity for fixed transportation infrastructure to adapt spatiotemporally-varying travel demand. This project is for a new transmodal MAV system paradigm to realize such elastic capacity of a road transportation system. To realize this vision, we will adapt multidisciplinary theoretical methods (e.g., time-geography, queuing theory, traffic flow theory, and homogeneous analysis) to understand and formulate operations of an MAV system. Then we will build mathematical models for the optimal design and operations of an MAV system at various scales by synchronizing demands and modular pods over time and space. The major challenge is to deal with continuous time and space as opposed to traditional fleet management problems with discrete time-space states. This challenge will be overcome by integrating microscopic trajectory control into macroscopic fleet management. If successful, this project will provide transmodal concepts to improve transportation and other related systems that are currently segregated into different modes. It will help boost the MAV service from a startup stage to a sustainable industry. The results will help transportation stakeholders understand feasibility and benefits of the MAV service and devise measures to incorporate it in their future planning, which may result in profound positive impacts on surface transportation including transit and freight operations.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.

虽然道路基础设施通行能力有限，但在以客车为主的道路上行驶的车辆在高峰时段很容易超出道路通行能力，造成交通拥堵、能源消耗过多、安全风险增加。观察到客车由于与前车之间的间隙相对较长而占用了道路上的大量空间，这个早期概念探索性研究资助 (EAGER) 项目探索了新兴的模块化自动驾驶汽车 (MAV) 技术，该技术可以动态调整车辆之间的间隙。借助MAV技术，由多个模块化吊舱组成的车辆可以在运行过程中动态对接和分离。例如，在高峰时段，模块化吊舱将对接到更长的MAV中，从而使对接在一起的模块化吊舱之间的间隙为零，这明显提高了高速公路的吞吐量并减少了拥堵。而在非高峰时段，较长的 MAV 可能会分成较短的 MAV，以确保灵活的系统访问并降低车辆运营成本。这样，MAV服务相当于为固定交通基础设施创造了“弹性”能力，以适应时空变化的出行需求。该项目旨在建立一种新的多式联运 MAV 系统范例，以实现道路运输系统的这种弹性容量。为了实现这一愿景，我们将采用多学科理论方法（例如时间地理学、排队论、交通流理论和同质分析）来理解和制定 MAV 系统的操作。然后，我们将通过在时间和空间上同步需求和模块化吊舱，为不同规模的 MAV 系统的优化设计和操作建立数学模型。主要挑战是处理连续的时间和空间，而不是处理离散时空状态的传统车队管理问题。通过将微观轨迹控制集成到宏观车队管理中可以克服这一挑战。如果成功，该项目将提供跨模式概念，以改善目前分为不同模式的交通和其他相关系统。它将有助于推动MAV服务从初创阶段发展成为可持续发展的行业。研究结果将帮助运输利益相关者了解 MAV 服务的可行性和好处，并制定措施将其纳入未来规划，这可能会对包括过境和货运业务在内的地面运输产生深远的积极影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。