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.

该项目将创建和试验一种综合方法，在交通管理中应用机器学习（ML）方法，在新时代，拼车被视为移动即服务（MaaS）的组成部分，以提高所有用户的移动性。一种综合的方法定制了ML方法的原理-而不是预先包装的工具箱-并将它们与领域知识结合起来，以创建新的混合方法。 随着新技术和移动服务的引入和不断发展，交通运输部门目前正在经历巨大的混乱。 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、提供和使用移动性的方式。在未来十年，随着共享的广泛趋势继续发展，流量管理的重要性将越来越大。长期以来，拼车一直被认为是交通管理的一个低挂水果。 由于新兴技术（例如，交通网络公司和自动驾驶），近年来拼车的普及程度显著增长。推动这种新的兴趣的是让拼车变得灵活、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（如果不是主导的）出行方式。 该项目的结果将有助于公共部门评估如何利用拼车来提高我们交通系统的效率，环境可持续性和公平性。 该项目将为一系列具有挑战性的运输问题创造一种新的方法，包括顺序分层优化。通过将运输科学，博弈论，优化和ML结合在一起，该框架为跨学科研究提供了一种综合方法。它一方面为在交通领域利用ML的力量开辟了新的途径，另一方面也给ML社区带来了新的挑战。该项目介绍并分析了通过拼车管理交通的问题，这是最近技术进步的产物，但尚未受到太多关注。这个问题将分层优化（一个传统的交通问题）与拼车（一个在多个学科（经济学、计算机科学和交通）中快速发展的研究领域）联系起来，并有望推动两者的前沿。该奖项反映了NSF的法定使命，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。

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