Excellence in Research:Towards Data and Machine Learning Fairness in Smart Mobility

该项目支持研究公平意识方法的发展，以解决智能移动应用中普遍存在的数据和机器学习（ML）偏见。随着智能传感器和计算能力的进步，高保真交通数据与人工智能(AI)/ML的集成对于推进智能移动应用至关重要。该项目旨在研究如何促进公平、公正和负责任的人工智能利用，以应对各种智能移动挑战，如车辆轨迹预测、减少拥堵、提高安全性等。主要机构是摩根州立大学，这是一所R2公立历史黑人学院和大学（HBCU），该项目促进本科生和研究生的研究参与，重点关注历史上边缘化背景的个人。此外，为了让未来的劳动力为交通领域不断发展的技术格局做好准备，该项目将从K-12到高等教育的STEM学习与尖端的AI/ML方法及其在智能交通中的应用联系起来，成为一座桥梁。该项目旨在研究公平感知方法的发展，以减轻智能移动领域中通常由数据收集、处理和建模引起的常见数据和机器学习偏差。具体来说，该项目针对ML应用程序生命周期中的三个关键偏差：测量偏差、表示偏差和聚合偏差。定制的机器学习方法旨在减轻每种类型的偏差，为特定的智能移动应用量身定制，包括车辆轨迹校正和预测、交通流量和网络建模、出发地和交通需求预测等。该项目的潜在发现可以促进机器学习方法在智能移动中的公平和公平应用，并可以对其他科学和工程领域产生广泛影响，例如智能和自主系统，机器人技术以及其他依赖于负责任使用AI/ML的研究领域。来自代表性不足群体的学生，特别是摩根州立大学的非裔美国学生，被强烈鼓励参与这项研究。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 project supports research examining the development of fairness-aware methodologies to address prevalent data and machine learning (ML) biases within smart mobility applications. With the advancements in intelligent sensors and computing power, the integration of high-fidelity transportation data with Artificial Intelligence (AI)/ML has become essential for advancing smart mobility applications. This project aims to investigate ways to promote fair, equitable, and responsible AI utilization in tackling diverse smart mobility challenges, such as vehicle trajectory prediction, congestion reduction, safety improvement, and so on. With the primary institution being Morgan State University, an R2 public Historically Black College and University (HBCU), this project fosters research engagement among undergraduate and graduate students, with a focus on individuals from historically marginalized backgrounds. Furthermore, to prepare the future workforce for the evolving technological landscapes in transportation, this project serves as a bridge by connecting STEM learning from K-12 through post-secondary education with cutting-edge AI/ML methods and their applications in smart mobility.This project aims to investigate development of fairness-aware methodologies to mitigate commonly encountered data and ML biases that are often induced from data collection, processing, and modeling within the smart mobility domain. Specifically, this project targets three critical biases throughout the ML application lifecycle: measurement bias, representation bias, and aggregation bias. Customized ML methodologies are devised to mitigate each type of biases, tailored for specific smart mobility applications, including vehicle trajectory correction and prediction, traffic flow and network modeling, origin-destination and traffic demand forecasting, among others. Potential findings from this project can promote fair and equitable applications of ML methods in smart mobility and can have broad impacts on other science and engineering fields, such as smart and autonomous systems, robotics, and other research domains that depend on the responsible utilization of AI/ML. Students from underrepresented groups, particularly African-American students at Morgan State University, are strongly encouraged to participate in the research.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.