Toward an Integrative Approach to Machine Learning for Traffic Management

交通管理机器学习的综合方法

• 批准号: 2225087
• 负责人: Yu Nie
• 金额: $ 54.74万
• 依托单位: Northwestern University
• 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: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225087&HistoricalAwards=false
• 关键词: Toward; Integrative; Approach; Machine; Learning

This project will create and experiment with an integrative approach to applying machine learning (ML) method in traffic management, in the new era where ridesharing is seen as integral to Mobility-as-a-Service (MaaS), in order to improve mobility for all users. An integrative approach tailors the principles of ML methods––rather than prepacked toolboxes––and immerse them with the domain knowledge to create new, hybrid methods. The transportation sector is currently experiencing monumental disruptions with the introduction and constant evolution of new technologies and mobility services. The SEA—Sharing, Electrification, and Automation—change is expected to transform how mobility is planned, provided, and used by people and companies in cities and beyond. The importance of traffic management will grow as the broad trends toward sharing continue in the next decade. Ridesharing has long been considered a low-hanging fruit for traffic management. Thanks to emerging technologies (e.g., transportation network companies and autonomous driving), the popularity of ridesharing has grown markedly in recent years. Fueling this renewed interest is the promise to make ridesharing flexible, user friendly, efficient, and cheap. In the era of MaaS, ridesharing would likely become a major, if not dominating, mode of travel. The results from this project will help the public sector evaluate how ridesharing can be leveraged to improve the efficiency, environmental sustainability, and equity of our transportation systems. This project will create a novel methodology for a range of challenging transportation problems involving sequential hierarchical optimization. By bringing together transportation science, game theory, optimization, and ML, the framework exemplifies an integrative approach to cross-disciplinary research. It also opens new pathways toward harnessing the power of ML in the transportation domain on the one hand and brings stimulating new challenges to the attention of the ML community on the other. The project introduces and analyzes the problem of managing traffic by ridesharing, which is born of recent technology advances but has yet to receive much attention. This problem connects hierarchical optimization, a traditional topic in transportation, to ridesharing, a rapidly growing field of study in several disciplines (economics, computer science, and transportation), and promises to push forward the frontiers of both. The findings will deepen our understanding of ridesharing not only as a mode of cooperative travel but also as a tool for traffic management.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.

该项目将创建并试验一种在交通管理中应用机器学习（ML）方法的综合方法，在乘车共享被视为移动即服务（MaaS）不可或缺的新时代，以改善交通管理综合方法可以定制机器学习方法的原理（而不是预先打包的工具箱），并将其融入领域知识中以创建新的混合方法。新技术和移动服务的不断发展。SEA（共享、电气化和自动化）变革预计将改变城市及其他地区的人们和公司规划、提供和使用移动的方式。交通管理的重要性将随之增长。共享出行的大趋势将在未来十年继续发展。长期以来，共享出行一直被认为是交通管理中唾手可得的成果，得益于新兴技术（例如交通网络公司和自动驾驶），共享出行的普及度近年来显着增长。 .加油这种兴趣是让拼车变得灵活、用户友好、高效和便宜的承诺。在 MaaS 时代，拼车可能会成为一种主要的（即使不是主导的）出行方式。该项目的结果将有助于公共部门进行评估。如何利用拼车来提高交通系统的效率、环境可持续性和公平性。该项目将涉及通过将交通科学、博弈论、优化、和 ML，该框架举例说明了一方面，它还为在交通领域利用机器学习的力量开辟了新的途径，另一方面也给机器学习社区带来了令人兴奋的新挑战，即通过乘车共享来管理交通问题。 ，它诞生于最新的技术进步，但尚未受到太多关注，这个问题将层次优化（交通运输中的一个传统主题）与乘车共享（多个学科（经济学、计算机科学和交通运输）中快速发展的研究领域）联系起来。这些发现将加深我们对拼车的理解，不仅作为一种合作出行方式，而且作为一种交通管理工具。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持。利用基金会的智力优势和更广泛的影响审查标准。

项目成果

Achieving Hierarchy-Free Approximation for Bilevel Programs With Equilibrium Constraints

• DOI: 10.48550/arxiv.2302.09734
• 发表时间: 2023-02
• 期刊: ArXiv
• 作者: Jiayang Li;J. Yu;Boyi Liu;Zhaoran Wang;Y. Nie