Alleviating Travel Delay Uncertainties in Traffic Assignment and Traffic Equilibrium

这笔赠款提供资金，以便在旅行成本/延误不确定的情况下解决交通平衡、交通分配和路线指导问题。主要目标是在不明朗的情况下，纾缓交通挤塞和更有效地管理交通。我们的目标是利用三个领域的想法：随机优化/随机变分不等式，稳健(和可调稳健)优化，最后，从统计学学习理论。这些方法将使我们能够以一种容易处理的方式解决数据参数中的不确定性问题。我们的研究贡献将是：(I)设计和研究关键交通流规划和管理模型的各种公式，包括随机优化、稳健优化和学习；(Ii)了解如何以一种不依赖于数据分布假设的方式有效地融入交通延误本质中固有的随机性元素；(Iii)检验这些问题的有效解决方法；以及(Iv)通过计算验证我们的结果。如果成功，该项目的结果将为关键交通规划和管理问题提供解决方案，这些问题对出行延误的不确定性具有稳健性。这项研究对各种物流网络的潜在应用和影响是非常重要的。在交通领域，这项研究将对先进的旅客管理系统(ATMS)和先进的旅客信息系统(ATIS)领域产生影响。这项研究的目的是使用来自不同领域的方法，长期目标不仅是促进知识和加深我们对交通问题以及与自动取款机和自动取款机相关的问题的理解，而且还通过开发创造性和创新的概念来为这些领域做出贡献。其目标是建立一个综合的框架、模型和求解技术，用于将随机优化和稳健优化应用于关键的交通规划和管理问题以及相关领域，以帮助交通学者和实践者。从教育的角度来看，这个项目的成果将作为麻省理工学院教学模块的组成部分。这些包括核心课程中的模块，PI已经成为关键贡献者之一。这个项目非常适合指导本科生和研究生。

This grant provides funding in order to tackle traffic equilibrium, traffic assignment and route guidance problems when travel costs/delays are uncertain. The main goals are to alleviate congestion and better manage traffic in the face of ?uncertain conditions?. Our goal is to utilize ideas from three fields: stochastic optimization/ stochastic variational inequalities, robust (and adjustable robust) optimization and finally, learning theory from Statistics. These approaches will allow us to address the aspect of uncertainty in data parameters in a tractable way. Our research contributions will be to (i) design and study a variety of formulations - involving stochastic optimization, robust optimization and learning - of key traffic flow planning and management models, (ii) understand how to incorporate effectively the inherent element of stochasticity in the nature of traffic delays in a way that does not depend on distributional assumptions on the data, (iii) examine efficient solution methods of these problems and finally, and (iv) validate our results computationally.If successful, the results of this project will provide solutions to key traffic planning and management problems that are robust to uncertainty in terms of travel delays. The potential applications and hence impact of this research to a variety of logistical networks are very significant. In the area of transportation, this research will impact both the fields of Advanced Traveler Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS). This research aims at using methodologies from a variety of fields with the long term goal to not only advance knowledge and deepen our understanding of issues in transportation and as they relate to ATMS and ATIS, but also to contribute to these fields by developing creative and innovative concepts. The goal is to build an integrated framework, models and solution techniques for the application of stochastic optimization and robust optimization to key traffic planning and management problems and related areas that are accessible to all, in order to help transportation academics and practitioners. From an educational perspective, the results of this project will serve as components in teaching modules at MIT. These include modules in core courses the PI has already been one of the key contributors. This project lends itself ideally to mentoring undergraduate and graduate students.