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

SCC-IRG 第 1 轨道：全民出行 - 为服务不足的社区利用新兴交通解决方案

• 批准号: 1952011
• 负责人: Abhishek Dubey
• 金额: $ 213.49万
• 依托单位: Vanderbilt 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: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1952011&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Mobility; Harnessing

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.

公共交通基础设施是建设公平社区的重要组成部分。然而，公共交通机构在历史上一直在努力实现这一目标，因为它们经常在资源方面受到严重的压力，因为它们必须在将服务集中到为大量人群服务的路线和分散服务以确保各地的人们至少能够获得一些服务之间做出权衡。有一个解决方案，对改善公共交通系统有很大的希望，那就是将固定路线服务与微型交通系统相结合：多乘客交通服务，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共同的上车点或下车点。然而，由于缺乏社区参与，在整合固定交通时无法处理运营的不确定性，以及无法处理系统级优化挑战，大多数微型交通系统在过去都失败了。该项目与查塔努加地区区域交通管理局（CARTA）合作，采用社会关系方法进行社区参与，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城市展示其有效性。该项目的成果将是一个部署就绪的软件系统，可供机构用于有效设计和运营微型交通服务。算法工具链将得到优化选择参数和可持续管理算法所需数据的机制的补充。此外，该项目还将提供一组范例案例研究和经过验证的社会方法，以吸引社区并了解他们的需求，这些需求将被纳入算法。这可能会影响到美国众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，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发展和扩展提供模式。该项目的智力价值在于新颖的社区参与方法，以及结合了运筹学和数据驱动、基于学习的集成系统优化。为实现这一目标，该项目将作出四项关键贡献。首先，该项目将开发一种新的有针对性的外展方法，使用社会资本的关系网络（例如，社区中心、教会和宗教团体、学校和类似的组织结构），并建立了一个分类需求模型，以设计一个创新的微型交通系统。该项目的假设是，所提出的方法对公共交通乘客的行为影响将显着高于只关注改进的交通系统的经济或节省时间的好处的方法。其次，该项目将引入一个可持续和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。这一点至关重要，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量数据分析。此外，该数据存储为我们提供了一个机会，可以通过设计方法为项目期间收集的数据集提供隐私。具体来说，该项目将为多模态数据集开发新型集成匿名化混合器（例如，在时空-事务混合器中将不同模态的信息（例如位置跟踪和事务）混合在一起），其实现给定级别的隐私（使用差分隐私的符号量化），同时最大化交通查询的准确性，这不仅依赖于数据的隐私属性，而且依赖于查询的需要。第三，该项目将开发具有不确定性意识的车队管理和调度算法，该算法将需求聚合和由拥堵、事故及其对系统的影响（包括用户和车队运营）引起的环境不确定性结合起来。第四，该项目将利用非静态环境主动学习的最新进展，通过上下文辅助信息设计算法，帮助探索和优化微交通算法的超参数选择。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

Designing Equitable Transit Networks

设计公平的交通网络

• 发表时间: 2023
• 期刊: and Optimization (EAAMO
• 作者: Sophie Pavia;J. Carlos Martinez Mori;Aryaman Sharma;Philip Pugliese;Abhishek Dubey;Samitha Samaranayake;Ayan Mukhopadhyay
• 通讯作者: Ayan Mukhopadhyay

Scalable Pythagorean Mean based Incident Detection in Smart Transportation Systems

智能交通系统中基于可扩展毕达哥拉斯均值的事件检测

• DOI: 10.1145/3603381
• 发表时间: 2023
• 期刊: ACM Transactions on Cyber-Physical Systems
• 影响因子: 2.3
• 作者: Islam, Md. Jaminur;Talusan, Jose Paolo;Bhattacharjee, Shameek;Tiausas, Francis;Dubey, Abhishek;Yasumoto, Keiichi;Das, Sajal K.
• 通讯作者: Das, Sajal K.

On the Value of Dynamism in Transit Networks

论交通网络动态的价值

• DOI: 10.1287/trsc.2022.1193
• 发表时间: 2023
• 期刊: Transportation Science
• 影响因子: 4.6
• 作者: Martínez Mori, J. Carlos;Speranza, M. Grazia;Samaranayake, Samitha
• 通讯作者: Samaranayake, Samitha

Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes

用于预测公共交通路线客流量的神经架构和特征搜索

• DOI: 10.1109/smartcomp55677.2022.00023
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Smart Computing (SMARTCOMP
• 作者: Ayman, Afiya;Martinez, Juan;Pugliese, Philip;Dubey, Abhishek;Laszka, Aron
• 通讯作者: Laszka, Aron

Mobility-On-Demand Transportation: A System for Microtransit and Paratransit Operations

按需出行交通：微型交通和辅助交通运营系统

• DOI: 10.1145/3576841.3589625
• 发表时间: 2023
• 期刊: Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week
• 作者: Wilbur, Michael;Coursey, Maxime;Koirala, Pravesh;Al-Quran, Zakariyya;Pugliese, Philip;Dubey, Abhishek
• 通讯作者: Dubey, Abhishek