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

运输机动性和安全问题在国际上具有极其重要的意义。因此，迫切需要开发一种方法来修正和改进城市交通机动性和安全性模型，将这两个独立的研究领域连接起来。未能满足这一需求是一个紧迫的国家和国际问题，因为在建模方面缺乏进步的情况下，安全风险和流动性中断将继续导致浪费时间和不必要的生命损失，并仍然是经济的负担。有鉴于此，关键的研究目标是美韩协同合作，创建一个统一的数据驱动的算法框架，通过利用人工智能(AI)自主预测高速公路修复的机动性和安全影响。为了满足这一及时的需求，研究团队提出了一项新计划：每年三所先进运输基础设施信息学院(ATI3)，与韩国同行合作，培训美国学生的尖端技能。这些研究所将促进国际合作，将最佳基础设施移动性和安全分析实践协同集成到一个统一的人工智能数据驱动的算法框架中，该框架具有与NSF的“利用数据革命”和“融合研究”的大想法有关的智力优势。这个IRES将从参与的美国大学部署15名研究生；每个学生将在韩国大田的KAIST为期三年的时间里每年学习三周。韩国独特的交通挑战和KAIST的解决方案将为美国学生提供真正出类拔萃的、丰富这一重要研究领域的国际高影响力学习和研究经验。该方法的独特之处在于，它通过考虑大数据分析和人工智能技术(ATI3 I)、从混合模拟中融合的驾驶员行为建模(ATI3 II)以及使用增强型AI深度学习算法(ATI3 III)说明驾驶员行为是主要影响因素的高保真预测，对机动性和安全干扰程度进行建模。ATI3的拟议研究活动将与全面的教育计划紧密交织在一起，总体目标是通过以项目为中心的研究环境促进学生的变革性学习，同时让从业者和社区广泛参与基于项目的研究项目。中心假设是，建立一个为期三年的年度ATI计划，以学习新的方法和技术，然后利用这些技术来模拟由于高速公路修复而造成的安全和流动性中断的水平，这将使IRES研究员能够研究可能纠正和改进工作区安全和流动性建模结果的新发现。新的安全-移动一体化系统将为比较分析康复方案提供严格的理论基础，以便以一种全新的方式评估驾车者的不便和安全风险。它将为司机的随机路线选择行为与其在交通排队延误和碰撞风险中的后果之间的新的相互作用提供见解。一旦成功完成，IRES将使研究界和实践者第一次看到系统的评估方法，以确定最安全、最经济的交通计划，这些计划将比现有的计划更智能(更好的机动性、更少的旅行时间和更低的道路用户成本)和更环保(更少的车辆运营成本和环境成本)。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.