CAREER: Integrated Multiresolution Transportation Network Modeling

这个教师早期职业发展（CAREER）计划奖的目的是调查不同规模/大小的交通网络之间的关系，学习如何量化这些关系，并发现对交通规划的影响。该研究集中在规模和规模是重要问题的三个例子：货运模型，其中除了地方规模外还考虑了多州或国家规模，区域内运输资金的分配，以及随时间迅速变化的运输网络（例如，由于事故或其他原因）。当前的多尺度模型通常是以特定的方式构建的，而不理解模型之间的基本联系。本研究的指导思想是，“混合”模型将一个模型的简化版本合并到另一个模型中，而不是将它们完全分离，这是有效找到一致解决方案的关键。如果成功，这项工作将允许网络模型代表更大的区域，特别是支持改进的交通规划，但也有助于其他领域的多尺度网络模型。研究计划的补充教育和推广计划有四个主要组成部分。首先，将在最近的教学研究基础上，为运输工程和网络分析课程开发主动学习课程。第二，将公开一个最先进的交通算法代码库，允许学生在课程项目中使用大规模的实际数据，并允许从业者使用最先进的模型进行实验，促进技术转让。守则和课程材料都将以开放获取的形式免费发布。第三，PI将积极招募和指导本科生和研究生研究人员，与大学项目对接，从代表性不足的群体中发现有才华的学生。最后，将根据研究结果开发多尺度建模新课程。

The objective of this Faculty Early Career Development (CAREER) program award is to investigate the relationships between transportation networks of different scales/sizes, learn how to quantify these relationships, and discover the implications for transportation planning. The research focuses on three examples where scale and size are important issues: freight models where a multistate or national scale is considered in addition to a local scale, allocation of transportation funds within a region, and transportation networks that change rapidly with time (e.g., due to accidents or other disruptions). Current multi-scale models are often constructed in an ad hoc manner, without understanding the fundamental connections between the models. The research investigation in this grant will be guided by the conjecture that "mixing" models incorporating simplified versions of one model into another, rather than separating them completely is the key to efficiently finding consistent solutions.If successful, this work will allow network models to represent much larger regions, specifically supporting improved transportation planning, but also contributing to multi-scale network models in other domains. A complementary education and outreach plan to the research plan has four major components. First, active-learning curricula will be developed for transportation engineering and network analysis courses, building on recent pedagogical research. Second, a library of state-of-the-art transportation algorithm codes will be made public, allowing students to work with large-scale practical data in course projects and allowing practitioners to experiment with state-of-the-art models, facilitating technology transfer. Both the code and curriculum materials will be released in open-access form, free of charge. Third, the PI will actively recruit and mentor undergraduate and graduate researchers, interfacing with university programs to identify talented students from underrepresented groups. Finally, a new course in multi-scale modeling will be developed, based on the research results.