SCC: Leveraging Autonomous Shared Vehicles for Greater Community Health, Equity, Livability, and Prosperity (HELP)

SCC：利用自动共享车辆促进更大社区的健康、公平、宜居性和繁荣（HELP）

• 批准号: 1831140
• 负责人: Zhi-Li Zhang
• 金额: $ 175.09万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831140&HistoricalAwards=false
• 关键词: SCC; Leveraging; Autonomous; Shared; Vehicles

This Smart & Connected Communities (SCC) grant supports fundamental research on a critical challenge facing many cities and communities: how to leverage the emerging autonomous vehicles (AVs) to re-think and re-design future transportation services and enable smart and connected communities where everyone benefits. The research envisages an ambitious "smart cloud commuting system" (SCCS) based on giant pools of shared AVs. The envisaged SCCS has the potential to bring about far-reaching societal changes. It will provide inexpensive mobility services to all people especially people with socio-economic disadvantages, help build stronger family and community ties, and boost economic productivity and equity by mitigating or removing mobility constraints. The research will be carried out in conjunction with five community engagement pilot projects, directly contributing to US prosperity and well-being. The research involves multiple disciplines, including transportation, computer science, data science, operations research, urban design, and public policy. The multi-disciplinary approach will help broaden participation of underrepresented groups in research, and enrich students' educational experience across science, engineering, urban design, and public policy. The goal of the project is two-fold: (1) to study the feasibility, economic viability, architectural and operational designs of the envisaged SCCS; and (2) to analyze the socioeconomic challenges in realizing the envisaged SCCS to serve communities with diverse socioeconomic backgrounds. In support of these goals, the project will leverage new and emerging data on travel demand, user preferences, and activity-travel constraints to quantify system efficiency gains that can be attained from time-sharing and intelligent control of AVs as well as from ride-sharing and smart trip scheduling of users. The research will also develop optimization models and algorithms that account for essential tradeoffs, including cost, quality of service, and congestion in deciding how best to deploy AVs geographically and temporally, leading to the identification of optimal AV fleet architectures and optimal operational policies. The research will also investigate, using micro-economic/game-theoretic analysis of the incentives of both users and service providers, likely scenarios of vehicle ownership and market structures and study the impact of each scenario on traffic measures including vehicle ownership and traffic volumes as well as societal measures including community health, equity, livability, and prosperity. This research will generate fundamental knowledge on the socioeconomic opportunities and impacts of the envisaged SCCS with shared AVs, and develop guidelines for adapting the design, deployment, and operation of AVs for future smart cities.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.

这项智能互联社区 (SCC) 拨款支持针对许多城市和社区面临的一项关键挑战的基础研究：如何利用新兴的自动驾驶汽车 (AV) 重新思考和设计未来的交通服务，并实现人人受益的智能互联社区。该研究设想了一个雄心勃勃的基于共享自动驾驶汽车池的“智能云通勤系统”（SCCS）。设想的 SCCS 有潜力带来深远的社会变革。它将向所有人，特别是社会经济弱势群体提供廉价的出行服务，帮助建立更牢固的家庭和社区联系，并通过减轻或消除出行限制来提高经济生产力和公平性。该研究将与五个社区参与试点项目结合进行，直接促进美国的繁荣和福祉。研究涉及多个学科，包括交通、计算机科学、数据科学、运筹学、城市设计和公共政策。多学科方法将有助于扩大代表性不足群体对研究的参与，并丰富学生在科学、工程、城市设计和公共政策方面的教育经验。 该项目的目标有两个：(1) 研究设想的 SCCS 的可行性、经济可行性、建筑和操作设计； (2) 分析实现设想的 SCCS 服务不同社会经济背景的社区所面临的社会经济挑战。为了支持这些目标，该项目将利用有关出行需求、用户偏好和活动出行限制的新兴数据来量化系统效率增益，这些增益可以通过自动驾驶汽车的分时和智能控制以及用户的乘车共享和智能行程安排来实现。该研究还将开发优化模型和算法，在决定如何在地理和时间上最好地部署自动驾驶汽车时考虑到基本的权衡，包括成本、服务质量和拥堵，从而确定最佳的自动驾驶车队架构和最佳的运营策略。该研究还将通过对用户和服务提供商的激励进行微观经济/博弈论分析，调查车辆拥有量和市场结构的可能情景，并研究每种情景对交通指标（包括车辆拥有量和交通量）以及社会指标（包括社区健康、公平、宜居性和繁荣）的影响。 这项研究将产生有关共享自动驾驶汽车设想的 SCCS 的社会经济机会和影响的基础知识，并制定针对未来智能城市的自动驾驶汽车设计、部署和运营的指南。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。

项目成果

xGAIL: Explainable Generative Adversarial Imitation Learning for Explainable Human Decision Analysis

• DOI: 10.1145/3394486.3403186
• 发表时间: 2020-07
• 期刊: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
• 作者: Menghai Pan;Weixiao Huang;Yanhua Li;Xun Zhou;Jun Luo

Discovering the Hidden Community Structure of Public Transportation Networks

• DOI: 10.1007/s11067-019-09476-3
• 发表时间: 2020-03-01
• 期刊: NETWORKS & SPATIAL ECONOMICS
• 影响因子: 2.4
• 作者: Hajdu, Laszlo;Bota, Andras;Gardner, Lauren M.
• 通讯作者: Gardner, Lauren M.

Interactive Bike Lane Planning Using Sharing Bikes' Trajectories

使用共享自行车轨迹进行交互式自行车道规划

• DOI: 10.1109/tkde.2019.2907091
• 发表时间: 2020-08-01
• 期刊: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
• 影响因子: 8.9
• 作者: He, Tianfu;Bao, Jie;Zheng, Yu
• 通讯作者: Zheng, Yu

Transforming Policy via Reward Advancement

• DOI: 10.1109/cdc40024.2019.9029286
• 发表时间: 2019-12
• 期刊: 2019 IEEE 58th Conference on Decision and Control (CDC)
• 作者: Guojun Wu;Yanhua Li;Jun Luo

5GNN: extrapolating 5G measurements through GNNs

• DOI: 10.1145/3565473.3569186
• 发表时间: 2022-12
• 期刊: Proceedings of the 1st International Workshop on Graph Neural Networking
• 作者: Wei Ye;Xinyue Hu;Tian Liu;Ruoyu Sun;Yanhua Li;Zhi-Li Zhang