SCC-IRG Track 1: Mobility for all - Harnessing Emerging Transit Solutions for Underserved Communities

公共交通基础设施是建设公平社区的重要组成部分。然而，公共交通机构一直在努力实现这一目标，因为他们经常在资源方面受到严重压力，因为他们必须在集中服务于为大量人群服务的路线和分散服务之间做出权衡，以确保各地的人们至少可以获得一些服务。一个对改善公共交通系统大有希望的解决方案是将固定路线服务与微交通系统相结合：多乘客运输服务，使用动态生成的路线为乘客提供服务，并可能期望乘客往返于公共上下车点。然而，由于缺乏社区参与，在整合固定交通时无法处理操作的不确定性，以及无法处理系统级优化挑战，大多数微交通系统在过去都失败了。该项目与查塔努加地区交通管理局（CARTA）合作，采用社会关系方法促进社区参与，设计以社区为中心的微型交通服务，增强固定线路公共交通网络（改善交通可达性），并在查塔努加的代表性城市展示其有效性。该项目的结果将是一个部署就绪的软件系统，可以被机构用来有效地设计和运营微型运输服务。算法工具链将辅以优化选择参数和可持续管理算法所需数据的机制。此外，该项目将提供一组范例案例研究和一套经过验证的社会方法论，以吸引社区并了解他们的需求，这些需求将被输入算法。这将潜在地影响美国许多没有完善交通系统的城市，因为该项目不仅将提供可重复使用的运营系统，而且还将展示综合社会技术研究和强大的社区参与如何为可持续性和扩展提供模式。该项目的智力优势在于采用新颖的社区参与方法，将运筹学与数据驱动、基于学习的集成系统优化相结合。为了实现这一目标，该项目将做出四个关键贡献。首先，该项目将开发一种新的有针对性的外展方法，利用社会资本的关系网络（例如，外展到社区中心、教会和信仰社区、学校和类似的组织结构），并建立一个分类需求模型来设计一个创新的微交通系统。该项目的假设是，与只关注改善交通系统的经济或节省时间的好处的方法相比，拟议的方法对公共交通乘客的行为影响要大得多。其次，该项目将引入一个可持续的、有弹性的数据集成平台，该平台可以动态调整用于影响设计参数和评估交通系统性能的传感器数据的位置。这是至关重要的，因为云计算对于社区合作伙伴来说仍然非常昂贵，特别是对于实时高速和大容量的数据分析。此外，该数据存储为我们提供了对项目期间收集的数据集采用隐私设计方法的机会。具体而言，该项目将为多模式数据集开发新型集成匿名化混合器（例如，将不同模式的信息混合在一起，例如位置跟踪和交易，在时空事务混合器中），实现给定的隐私级别（使用差分隐私符号进行量化），同时最大限度地提高传输查询的准确性，不仅依赖于数据的隐私属性，还依赖于查询的需求。第三，该项目将开发不确定性感知的车队管理和调度算法，将需求聚合和由拥堵、事故及其对系统（用户和车队运营）的影响引起的环境不确定性结合起来。第四，该项目将利用具有上下文侧信息的非平稳环境主动学习的最新进展来设计算法，这将有助于探索和优化微交通算法的超参数选择。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。

Public transportation infrastructure is an essential component in cultivating equitable communities. However, public transit agencies have historically struggled to achieve this since they are often severely stressed in terms of resources as they have to make the trade-off between concentrating service into routes that serve large numbers of people and spreading service out to ensure that people everywhere have access to at least some service. A solution that holds great promise for improving public transit systems is the integration of fixed-route services with microtransit systems: multi-passenger transportation services that serve passengers using dynamically generated routes and may expect passengers to make their way to and from common pick-up or drop-off points. However, most microtransit systems have failed in the past due to the lack of community engagement, inability to handle the uncertainty of operations when integrating the fixed transit, and inability to handle the system-level optimization challenges. The project takes a socio-relational approach to community engagement in collaboration with the Chattanooga Area Regional Transportation Authority (CARTA), design a community-centric micro-transit service that augments fixed-line public transit networks (improving transit accessibility), and demonstrate its effectiveness in the representative city of Chattanooga. The outcome of the project will be a deployment-ready software system that can be used by an agency to design and operate a micro-transit service effectively. The algorithmic toolchain will be complemented by mechanisms to optimally select the parameters and sustainably manage the data required by the algorithms. In addition, this project will provide a set of exemplar case studies and a validated social methodology to engage the community and learn their requirements, which will be fed into the algorithms. This will potentially impact a wide range of cities in the U.S. that do not have well-developed transit systems as the project will not only provide a reusable operations system but also demonstrate how integrated socio-technical research and strong community engagement can provide a pattern for sustainability and expansion. The intellectual merit of this project lies in the novel community engagement approach and combined operations research and data-driven, learning-based integrated system optimization. Towards this goal, the project will make four key contributions. First, the project will develop a novel targeted outreach approach that uses the relational networks of social capital (e.g., outreach to community centers, congregations and faith communities, schools, and similar organizational structures) and builds a categorical demand model to design an innovative micro-transit system. The project's hypothesis is that the behavioral impact on public-transit ridership with the proposed method will be significantly higher than with an approach that focuses only on the economic or time-saving benefits of the improved transit system. Second, the project will introduce a sustainable and resilient data-integration platform that dynamically adjusts the location of the sensor data used to affect the design parameters and assess performance of the transit system. This is crucial because cloud computing is still very expensive for community partners, especially for real-time high-velocity and high-volume data analysis. Further, this data store provides us an opportunity to take a privacy by design approach for the datasets collected during the project. Specifically, the project will develop novel integrated anonymization mixers for multi-modal datasets (e.g., mixing information of different modalities, such as location traces and transactions, together in a spatiotemporal-transactional mixer) that achieve a given level of privacy (quantified using the notation of differential privacy) while maximizing the accuracy of transit queries, relying on not just the privacy-properties of the data but also on the needs of the queries. Third, the project will develop uncertainty-aware fleet management and dispatch algorithms that incorporate demand aggregation and environmental uncertainty caused by congestion, incidents, and their impact on the system (both for the users and fleet operations). Fourth, the project will leverage recent advances in active learning for non-stationary environments with contextual side information to design algorithms that will aid in the exploration and optimal selection of hyperparameters for microtransit algorithms.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.