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

职业：物理正则化机器学习理论：为智能移动系统建模随机交通流模式

• 批准号: 2234289
• 负责人: Xianfeng Yang
• 金额: $ 54.41万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234289&HistoricalAwards=false
• 关键词: CAREER; Physics; Regularized; Machine; Learning

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.

该学院的早期职业发展（CAREER）资助将支持基于经典交通模型和学习技术的融合的智能移动系统随机交通流建模的基础研究，其目标是缓解交通拥堵、提高交通安全和减少车辆。为了满足这些需求，该项目将奠定机器学习和交通流理论的基础，以更好地估计和预测交通模式。领域知识规范化机器学习的培训过程将显着提高小型和大型智能移动应用的有效性和鲁棒性。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) 利用研究成果实现交通课程现代化，(iii) 扩大 k-12 学生对年度夏季“交通训练营”的参与，以及学生中代表性不足的少数民族服务机构的人工智能俱乐部这些活动将帮助交通专业的学生更好地认识智能出行系统时代的工程知识的重要性。该项目的目标是为交通贡献基础理论和一套显着改进的算法。利用物理正则化机器学习的概念，该研究可以将连续和离散交通流模型编码到高斯过程中进行训练正则化，这种新模型可以有效解决常见的数据稀疏和噪声问题，并促进各种智能移动应用。为了适应流媒体数据方面，该项目还将开发一种新颖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，该项目可以进一步协同不同分辨率、保真度和来源的数据，以实现稀疏高斯。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、可扩展和强大的交通流模型。数据驱动的方法。这反映了 NSF 的法定使命，并且通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Discrete macroscopic traffic flow model considering lane-changing behaviors in the mixed traffic environment

混合交通环境下考虑换道行为的离散宏观交通流模型

• 发表时间: 2024-01
• 期刊: 103rd Transportation Research Board Annual Meeting
• 作者: Yi Zhang; Kaitai Yang
• 通讯作者: Kaitai Yang

Infrastructure enabled and electrified automation: Charging facility planning for cleaner smart mobility

基础设施支持和电气化自动化：充电设施规划，实现更清洁的智能出行

• DOI: 10.1016/j.trd.2021.103079
• 发表时间: 2021-12
• 期刊: Transportation Research Part D: Transport and Environment
• 作者: Azin, Bahar;Yang, Xianfeng;Marković, Nikola;Liu, Mingxi
• 通讯作者: Liu, Mingxi

An equitable signalized arterial origin-destination flow estimation by a fairness-aware artificial intelligence

通过具有公平意识的人工智能进行公平的信号化动脉起点-目的地流量估计

• 发表时间: 2024-01
• 期刊: 103rd Transportation Research Board Annual Meeting
• 作者: Yaobang Gong; Qinzheng Wang
• 通讯作者: Qinzheng Wang

A Transfer Learning-based LSTM for Traffic Flow Prediction with Missing Data

基于迁移学习的 LSTM 用于缺失数据的交通流预测

• 发表时间: 2023-01
• 期刊: 102nd Transportation Research Board Annual Meeting
• 作者: Zhao Zhang & Xianfeng Yang

Freeway Traffic Flow Forecasting Using Physics-Guided LSTM with Flawed Data

使用具有缺陷数据的物理引导 LSTM 进行高速公路交通流量预测

• 发表时间: 2022-01
• 期刊: 102nd Transportation Research Board Annual Meeting
• 作者: Zhao Zhang; Qinzheng Wang
• 通讯作者: Qinzheng Wang