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.

在新时代，该项目将通过应用机器学习（ML）方法来创建和实验，在新时代，乘车共享被视为移动性 - AS-A-Service（MAAS）的组成部分，以提高所有用户的移动性。一种综合方法量身定制了ML方法的原理 - 而不是预包装工具箱 - 并将其沉浸在域知识中以创建新的混合方法。运输部门目前正在通过新技术和机动性服务的引入和不断发展而遭受巨大的破坏。海上分享，电气化和自动化 - 有望改变城市及其他地区的人们和公司的计划，提供和使用的方式。随着未来十年中的广泛趋势的持续，交通管理的重要性将不断增长。长期以来，乘车共享一直被认为是流量管理的低悬垂水果。感谢新兴技术（例如，运输网络公司和自动驾驶），近年来，乘车共享的普及显着增长。加剧这种新的兴趣是有望使乘车共享灵活，用户友好，高效且便宜的。在Maas时代，乘车共享可能会成为主要的旅行方式，即使不是主导的旅行方式。该项目的结果将有助于公共部门评估如何利用乘车共享来提高运输系统的效率，环境可持续性和平等。该项目将为涉及顺序分层优化的一系列挑战运输问题创建一种新颖的方法。通过将运输科学，游戏理论，优化和ML汇总在一起，该框架展示了一种跨学科研究的综合方法。它还为一方面利用ML在运输领域中的力量开辟了新的途径，并为ML社区的注意力带来了刺激的新挑战。该项目介绍并分析了通过乘车共享管理流量的问题，这是由于最近的技术进步而产生的，但尚未受到很多关注。这个问题将层次优化（传统的运输中的话题，乘车共享）联系起来，在几个学科（经济学，计算机科学和运输）中迅速发展的研究领域，并有望推动两者的前沿。这些发现将使我们对乘车共享的理解不仅是一种合作旅行方式，而且还可以作为交通管理的工具。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估而被视为珍贵的支持。

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