IRES Track II: US-Korea Advanced Transportation Infrastructure Informatics Institutes (ATI3)

IRES Track II：美韩先进交通基础设施信息学研究所 (ATI3)

• 批准号: 1953414
• 负责人: Kunhee Choi
• 金额: $ 22.46万
• 依托单位: Texas A&M 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: 2020-07-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953414&HistoricalAwards=false
• 关键词: IRES; Track; II; US; Korea

Transportation mobility and safety problems are of extreme importance internationally. Accordingly, there is an urgent need to develop a means of correcting and improving urban transportation mobility and safety modeling that connects these two independent research domains. The failure to meet this need is a pressing national and international problem because in the absence of advancements in modeling, safety risk and mobility disruption will continue to result in wasted time and unnecessary loss of life and remain a burden on the economy. In light of these, the key research objective is a synergistic US-Korea collaboration to create a unified data-driven algorithmic framework for autonomously predicting mobility and safety impacts of highway rehabilitation by harnessing artificial intelligence (AI). To meet this timely need, the research team proposes a new initiative: three annual Advanced Transportation Infrastructure Informatics Institutes (ATI3) to train U.S. students in cutting edge skills in collaboration with counterparts in Korea. The institutes will catalyze an international collaboration where the best infrastructure mobility and safety analysis practices are synergistically integrated into a unified AI data-driven algorithmic framework that has intellectual merit with regards to the NSF Big Ideas of ‘Harnessing the Data Revolution’ and ‘Convergence Research’. This IRES will deploy 15 graduate students from participating U.S. universities; each student will spend three weeks annually over the three-year duration at KAIST in Daejeon, Korea. Korea’s unique transportation challenges and KAIST’s solutions will offer U.S. students truly exceptional, enriching international high-impact learning and research experiences in this important research area. The proposed approach is unique because it models the level of mobility and safety disruption by accounting for big data analytics and AI techniques (ATI3 I), drivers’ behavior modeling fused from hybrid simulations (ATI3 II), and high-fidelity prediction that accounts for drivers’ behavior as a major influence with a reinforcement AI deep-learning algorithm (ATI3 III). The proposed research activities at ATI3 will be tightly interwoven with a comprehensive education plan, with the overall goal of promoting students’ transformational learning through a project-based research-centric environment while widely engaging practitioners and communities in the project-based research projects. The central hypothesis is that instituting a three-year annual ATI program for learning new methods and techniques, then leveraging the techniques to model the level of safety and mobility disruption due to highway rehabilitation, will allow the IRES fellows to research new discoveries that may correct and improve the results of work zone safety and mobility modeling. The new safety-mobility integration system will provide a rigorous theoretical basis for comparatively analyzing rehabilitation alternatives so that motorist inconvenience and safety risk can be assessed in a fundamentally new way. It will provide insights into new interactions between drivers’ stochastic route choice behaviors and their consequences in traffic queue delays and crash risks. Once successfully completed, the IRES will result in the research community and practitioners with the first view of a systematic estimation method to determine the safest and most economical transportation plans that would be smarter (better mobility, less travel time, and lower road user cost) and greener (reduced vehicle operating costs and environmental costs) than those in existence today.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.

运输流动性和安全性问题是极端的，并改善了连接两个独立研究领域的Urbansportation Mobility建模。鉴于这些目标，不必要的生命损失是对经济的负担。智能（AI）。关于NSF的“融合研究”的重要思想提出的方法是独特的，因为大数据分析和AI技术的安全性和安全性（ATI3 I），从混合模拟（ATI3 II）中进行的行为模型是驱动程序的行为学习算法（ATI3 III））通过基于项目的研究项目，将与全面的教育计划的转型学习与全面的教育计划相互交织。技术，然后利用高速公路康复的安全性和移动性纪律水平，将使IRES研究员能够研究新发现，以纠正和改善安全性和移动性建模。相对分析的替代方案的严格基础，因此最不便的和安全风险可以新的方式。费用）比今天存在的成本。该奖项反映了NSF的任务，并以该基金会的知识分子优点和更广泛的影响审查标准评估，被认为值得支持。