LEAP-HI: On-Demand Multimodal Transit Systems

在美国，拥有汽车仍然是社会向上流动的最佳预测指标。没有车的人在获得工作、医疗保健、教育和基本服务（包括购买杂货）方面处于不利地位。公共交通有可能缓解拥堵，并提供环保和具有成本效益的机动性。然而，现有的系统经常受到“第一/最后一英里”问题的困扰，即无法将旅行者从出发地一路带到目的地。因此，能负担得起的旅行者更喜欢私家车，造成拥堵和有害气体排放。优步（Uber）和来福车（Lyft）等新型出行服务通过利用无处不在的连接来匹配乘客和潜在司机，改善了某些人群的交通状况。不幸的是，它们增加了拥堵和排放，促使一些城市限制了它们的数量。此外，这些服务对低收入市民的作用有限，甚至可能吸引富裕居民远离公交系统，从而减少收入。相比之下，“美国繁荣、健康和基础设施领先工程”（LEAP-HI）项目则探索了按需多式联运系统（ODMTS）的概念。作为多模式，ODMTS结合了按需移动服务，服务于低密度地区和沿高密度走廊行驶的高占用车辆（公共汽车或火车）。它们与微交通解决方案的不同之处在于，它们使用最先进的优化技术和机器学习，从整体上规划、运营和优化交通系统。因此，它们有可能改变不同人群的可达性，减少交通不平等，并为美国及其他城市提供可持续的交通模式。为了实现这一愿景，该奖项研究了为拥挤的大城市设计ODMTS所需的数据和决策科学。这就要求我们在规划、操作和优化ODMTS方面的能力发生重大变化，ODMTS是部署在复杂基础设施上的复杂社会技术系统。为了实现这一目标，该奖项探索了五个研究方向，解决以下高层次问题：(1)提供可扩展的优化和机器学习算法，用于设计ODMTS核心的多模式网络；(2)准确预测ODMTS的乘客需求，并将由此产生的预测模型集成到网络设计的优化模型中；(3)推广网络设计优化，以考虑和缓解拥堵；(4)ODMTS的协同设计和基础设施改进，以最大限度地提高交通系统的性能。使用完整街道和环境敏感解决方案等概念，以及(5)在自动驾驶汽车可用时将自动驾驶汽车逐步集成到ODMTS中。该奖项反映了美国国家科学基金会的法定使命，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。

In the United States, car ownership is still the best predictor of upward social mobility. Those without a car are disadvantaged in accessing jobs, health care, education, and basic services, including buying groceries. Public transportation has the potential to mitigate congestion and provide environmentally friendly and cost-effective mobility. Existing systems, however, are often plagued by the "first/last mile" problem, i.e., the inability to take travelers all the way from their origin to their destination. As a result, travelers who can afford them prefer private vehicles, creating congestion and harmful emissions. New mobility services such as Uber and Lyft have improved transportation for some population segments by exploiting ubiquitous connectivity to match riders and potential drivers. Unfortunately, they increase congestion and emissions, prompting some cities to limit their numbers. In addition, those services are of limited use to low income citizens and may even lure affluent residents away from transit systems, reducing revenue. In contrast, this Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) Program award explores the concept of On-Demand Multimodal Transit Systems (ODMTS). Being multimodal, ODMTS combine on-demand mobility services that serve low-density regions with high-occupancy vehicles (buses or trains) traveling along high-density corridors. They differ from micro-transit solutions by planning, operating, and optimizing transit systems holistically, using state-of-the-art optimization technology and machine learning. As a result, they have the potential to transform accessibility across population segments, decreasing inequalities in transportation and providing a sustainable transportation model for American cities and beyond.To realize this vision, this award researches the data and decision science needed to engineer ODMTS for large, congested cities. This requires a step change in our ability to plan, operate, and optimize ODMTS, which are complex, socio-technical systems deployed over a sophisticated infrastructure. To achieve this objective, the award explores five research threads that address the following, high-level issues: (1) the delivery of scalable optimization and machine learning algorithms for designing the multimodal networks at the core of ODMTS, (2) accurate forecasting of ridership demand in ODMTS and integration of the resulting forecasting models into optimization models for network design, (3) generalization of network design optimization to account for and mitigate congestion, (4) co-design of ODMTS and infrastructure improvements to maximize the performance of transit systems, using concepts such as complete streets and context-sensitive solutions, and (5) the incremental integration of autonomous vehicles into ODMTS as they become available.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.