CCRI: Planning-C: Planning to Build Digital Infrastructure for Real-Time, Continual, and Intelligent Transportation Analysis and Management

CCRI：Planning-C：规划构建实时、持续、智能交通分析和管理的数字基础设施

• 批准号: 2213731
• 负责人: Qi Zhang
• 金额: $ 9.37万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213731&HistoricalAwards=false
• 关键词: CCRI; Planning; Build; Digital; Infrastructure

Readily available and easy-to-use simulators have been driving the recent advances in artificial intelligence (AI) research. This project leads planning and outreach activities that envision a large-scale research infrastructure that operates in parallel with real-world transportation systems in real time, continually, and integrating features to support AI-based discovery of new and more effective traffic management strategies. The infrastructure provides access to three types of users: data providers (e.g., local communities) who offer traffic data and/or controllers, researchers who utilize the infrastructure to discover traffic management strategies, and traffic managers who aim to gain insights on effectively managing the traffic through the infrastructure’s visualizations and analyses. This Planning project includes activities to 1) develop proof-of-concept that establishes a prototype traffic simulator that supports AI-based automatic calibration and intelligent traffic control, 2) organize workshops that outreach to researchers in relevant CISE sub-disciplines to formulate, refine, and prioritize new research opportunities enabled by the envisioned infrastructure, and 3) outreach local communities and agencies to identify key community needs. These planning activities are designed to build a cross-disciplinary and community-integrated team for a Medium-New CCRI proposal, which ultimately builds and maintains the envisioned infrastructure.Success of the envisioned infrastructure entails fundamental advances and seamless integration of multiple disciplines, including mobile computing, modeling and simulation, transportation science, and artificial intelligence. Moreover, the envisioned infrastructure significantly reduces barriers to innovations in using AI for improving transportation management that can bring transformative benefits to transportation planning for increased mobility, safety, energy efficiency, etc. Finally, the planning activities are expected to engage a broad-based community, from undergraduate students that can benefit from improved curricula from the infrastructure to diverse participants that can access the workshops from anywhere in the world through virtual attendance. Project URL: https://ccri-planning-intelligent-transportation.github.io/ The project repository will hold data, code, simulators, etc., for at least two years.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.

随时可用且易于使用的模拟器一直在推动人工智能（AI）研究的最新进展。该项目领导规划和推广活动，设想一个大规模的研究基础设施，与现实世界的交通系统并行运行，在真实的时间，持续，并集成功能，以支持基于人工智能的新的和更有效的交通管理策略的发现。基础设施提供对三种类型的用户的访问：数据提供者（例如，本地社区），他们提供交通数据和/或控制器，研究人员利用基础设施来发现交通管理策略，以及交通管理人员，他们旨在通过基础设施的可视化和分析来获得有效管理交通的见解。该规划项目包括以下活动：1）开发概念验证，建立支持基于AI的自动校准和智能交通控制的原型交通模拟器，2）组织研讨会，与相关CISE子学科的研究人员进行交流，制定，完善和优先考虑由设想的基础设施实现的新研究机会，以及3）与当地社区和机构进行外联，以确定关键的社区需求。这些规划活动旨在为中新CCRI提案建立一个跨学科和社区整合的团队，最终构建和维护设想的基础设施。设想的基础设施的成功需要多个学科的根本性进步和无缝集成，包括移动的计算、建模和仿真、交通科学和人工智能。此外，设想的基础设施大大减少了使用人工智能改善交通管理的创新障碍，这些创新可以为交通规划带来变革性的好处，以提高流动性，安全性，能源效率等。从可以从基础设施的改进课程中受益的本科生到可以从世界任何地方访问研讨会的各种参与者通过虚拟出席。项目URL：https://ccri-planning-intelligent-transportation.github.io/项目存储库将保存数据，代码，模拟器等，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

Assessing the impacts of ridesharing services: An agent-based simulation approach

• DOI: 10.1016/j.jclepro.2022.133664
• 发表时间: 2022-08
• 期刊: Journal of Cleaner Production
• 影响因子: 11.1
• 作者: Ruixiao Sun;Xuanke Wu;Yuche Chen

A Dynamic Programming Model for Joint Optimization of Electric Drayage Truck Operations and Charging Stations Planning at Ports

• DOI: 10.1109/tits.2023.3285668
• 发表时间: 2023-11
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Xuanke Wu;Yunteng Zhang;Yuche Chen

Online transportation network cyber-attack detection based on stationary sensor data

• DOI: 10.1016/j.trc.2023.104058
• 发表时间: 2023-04
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Ruixiao Sun;Qi Luo;Yuche Chen