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

虽然道路基础设施提供的通行能力有限，但在以乘用车为主的道路上运行的车辆在高峰时段很容易超过道路通行能力，导致交通拥堵、能源消耗过高和安全风险增加。在观察到乘用车由于与前一辆车的间隔相对较长而在道路上占据了很大空间的情况下，这个早期概念探索研究拨款(AGER)项目探索了新兴的模块化自动驾驶车辆(MAV)技术，该技术可以动态调整车辆之间的间隙。有了MAV技术，由多个模块化吊舱组成的车辆可以在作战过程中动态对接和分离。例如，在高峰时段，模块化吊舱将被对接到更长的MAV中，导致一起对接的模块化吊舱之间没有间隙，这明显提高了高速公路的吞吐量，减少了拥堵。而在非高峰时段，一辆较长的MAV可能会分成较短的MAV，以确保灵活的系统通达性和降低车辆运营成本。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。该项目是一种新的多式联运MAV系统范式，以实现道路运输系统的这种弹性能力。为了实现这一愿景，我们将采用多学科的理论方法(如时间地理学、排队论、交通流理论和齐次分析)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。主要的挑战是处理连续的时间和空间，而不是传统的离散时空状态的机队管理问题。这一挑战将通过将微观轨迹控制整合到宏观船队管理中来克服。如果成功，该项目将提供多式联运概念，以改善目前被分成不同模式的运输和其他相关系统。这将有助于将MAV服务从初创阶段提升为可持续发展的行业。结果将帮助交通利益相关者了解MAV服务的可行性和好处，并制定措施将其纳入他们未来的规划，这可能会对包括运输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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.