Toward an Integrative Approach to Machine Learning for Traffic Management

该项目将创建并试验一种综合方法，将机器学习(ML)方法应用于交通管理，在新时代，拼车被视为移动即服务(MAAS)不可或缺的一部分，以提高所有用户的移动性。一种集成的方法定制了ML方法的原则--而不是预先打包的工具箱--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进和不断发展，运输部门目前正在经历巨大的破坏。海洋共享、电气化和自动化变革预计将改变城市和其他地区的个人和公司规划、提供和使用移动性的方式。随着共享的广泛趋势在未来十年继续下去，交通管理的重要性将会增加。长期以来，拼车一直被认为是交通管理的一颗唾手可得的果实。多亏了新兴技术(如交通网络公司和自动驾驶)，拼车的受欢迎程度近年来显著增长。推动这种新兴趣的是让拼车变得灵活、用户友好、高效和廉价的承诺。在Maas时代，拼车可能会成为一种主要的出行方式，如果不是占主导地位的话。该项目的结果将帮助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。该项目将为涉及顺序分层优化的一系列具有挑战性的运输问题创造一种新的方法。通过将交通科学、博弈论、最优化和ML结合在一起，该框架体现了一种跨学科研究的综合方法。它还为一方面利用ML在交通领域的力量开辟了新的途径，另一方面也给ML社区的注意力带来了激动人心的新挑战。该项目介绍和分析了拼车管理交通的问题，这一问题源于最近的技术进步，但尚未得到太多关注。这个问题将交通运输中的一个传统话题--分层优化与拼车--一个在几个学科(经济学、计算机科学和交通)中迅速发展的研究领域--联系起来，并有望推动这两个领域的前沿。这些发现将加深我们对拼车的理解，不仅是作为一种合作出行方式，也是作为一种交通管理工具。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.