CAREER: Physics Regularized Machine Learning Theory: Modeling Stochastic Traffic Flow Patterns for Smart Mobility Systems

这项教师早期职业发展（Career）资助将支持基于经典交通模型和学习技术融合的智能交通系统随机交通流建模的基础研究。为了缓解交通拥堵、提高交通安全和减少车辆排放，许多智能移动应用需要准确、可靠和及时的交通信息作为输入。为了满足这些需求，该项目将为机器学习和交通流理论奠定基础，从而更好地估计和预测出行模式。该方法利用交通领域知识对机器学习的训练过程进行规范化。研究结果将显著提高这些智能移动应用在小尺度和大尺度上的有效性和鲁棒性。研究活动可以与一系列教育和推广活动紧密结合，包括(i)开发一个虚拟计算实验室，以促进学生教育、研究人员参与、政府雇员培训和行业合作；（ii）利用研究成果使交通课程现代化；（iii）扩大k-12学生每年夏季“交通夏令营”的参与范围，扩大少数族裔服务机构人工智能俱乐部中代表性不足的学生的参与范围。这些活动将帮助交通专业的学生更好地认识到工程知识在智能交通系统时代的重要性。该项目的目标是为交通流建模提供基础理论和一套显著改进的算法。利用物理正则化机器学习的概念，将连续和离散的交通流模型编码为高斯过程进行训练正则化。该模型可以有效地解决常见的数据稀疏和噪声问题，促进各种智能移动应用。为了适应流数据，该项目还将开发一种新的物理正则化流学习框架，可以有效地实时提高模型性能。在处理大数据时，本项目可以进一步协同不同分辨率、保真度、来源的数据，实现稀疏高斯过程和贝叶斯委员会机的快速学习。这项基础研究可以极大地促进机器学习在智能移动系统中的应用，并有助于制定可持续、可扩展和健壮的交通流模型。该项目将弥合传统交通方式与数据驱动方式之间的差距。该奖项反映了美国国家科学基金会的法定使命，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。

This Faculty Early Career Development (CAREER) grant will support fundamental research in modeling stochastic traffic flows for smart mobility systems, based on the fusion of classical transportation models and learning techniques. With the goals of mitigating traffic congestions, improving transportation safety, and reducing vehicle emissions, many smart mobility applications require accurate, reliable, and timely traffic information as input. To meet such needs, this project will lay the foundation of machine learning and traffic flow theory to yield better estimations and predictions of mobility patterns. The method uses transportation domain knowledge to regularize the training process of machine learning. The results will significantly enhance the effectiveness and robustness of those smart mobility applications at both small and large scales. The research activities can be closely integrated with a set of education and outreach activities that include (i) developing a virtual computing lab to facilitate student educations, researcher engagement, government employee training, and industry collaboration, (ii) modernizing the transportation curriculum with research outcomes, (iii) broadening the participation of k-12 students in the annual summer “Transportation Camps” and underrepresented students in the Artificial Intelligence club of a minority-serving institution. Those activities will help transportation students better recognize the importance of engineering knowledge in the era of smart mobility system.The goal of this project is to contribute fundamental theories and a set of markedly improved algorithms to traffic flow modeling. Leveraging the concept of physics regularized machine learning, the research could encode both continuous and discretized traffic flow models into Gaussian process for training regularization. This new model can efficiently resolve the common data sparsity and noise issues and facilitate various smart mobility applications. To accommodate streaming data, this project will also develop a novel physics regularized streaming learning framework that can efficiently improve the model performances in real-time. When dealing with big data, this project can further synergize data of different resolutions, fidelities, and sources to enable sparse Gaussian process and Bayesian committee machine for fast learning. This foundational research can enormously promote machine learning applications in smart mobility systems and contribute to formulating sustainable, scalable, and robust traffic flow models. This project will bridge the gap between classical transportation methods and data-driven approaches.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.