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极为重要。因此，迫切需要开发一种方法来纠正和改善城市交通的流动性和安全建模，连接这两个独立的研究领域。未能满足这一需求是一个紧迫的国家和国际问题，因为在建模方面没有进步的情况下，安全风险和移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，关键的研究目标是协同美韩合作，创建一个统一的数据驱动算法框架，通过利用人工智能（AI）自动预测高速公路修复的移动性和安全影响。为了满足这一及时的需求，研究小组提出了一项新的倡议：三个年度高级交通基础设施信息学研究所（ATI 3），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，将最佳的基础设施移动性和安全分析实践协同整合到统一的人工智能数据驱动算法框架中，该框架在“利用数据革命”和“融合研究”的NSF大思想方面具有智力价值。该IRES将从参与的美国大学派遣15名研究生;每个学生将在韩国大田的KAIST学习三年，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的卓越，丰富这一重要研究领域的国际高影响力学习和研究经验。所提出的方法是独一无二的，因为它通过考虑大数据分析和人工智能技术（ATI 3 I），混合模拟融合的驾驶员行为建模（ATI 3 II）以及高保真预测来模拟移动性和安全中断水平，该预测将驾驶员的行为作为强化AI深度学习算法（ATI 3 III）的主要影响。ATI 3的拟议研究活动将与全面的教育计划紧密交织在一起，其总体目标是通过以项目为基础的研究为中心的环境促进学生的转型学习，同时广泛吸引从业者和社区参与基于项目的研究项目。中心假设是，制定一个为期三年的年度ATI计划，学习新的方法和技术，然后利用这些技术来模拟由于公路修复而导致的安全和流动性中断的水平，将使IRES研究员能够研究新的发现，这些发现可能会纠正和改善工作区安全和流动性建模的结果。新的安全-移动集成系统将为比较分析康复方案提供严格的理论基础，以便以全新的方式评估驾驶员的不便和安全风险。它将为驾驶员的随机路径选择行为及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将导致研究界和从业人员首次看到系统的估计方法，以确定最安全，最经济的交通计划，这将是更聪明的（更好的机动性，更少的旅行时间，和更低的道路使用者成本）和更环保（减少车辆运营成本和环境成本）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。

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.