Optimizing Information Value in Heterogeneous Multi-agent Transportation Systems (OPTIMA)

该项目旨在利用先进的传感、通信和计算技术，研究由异构交通（汽车、自动驾驶和联网车辆、公共汽车、自行车等）组成的交通系统中的信息价值。 这些系统提供的丰富数据为信息提供提供了新的方法，这些方法有可能提高运输系统的效率、可靠性和弹性。 该项目开发了一个统一的建模框架，考虑异构非合作的利益相关者的信息提供，并解决了三个关键问题：（1）我们如何建模的自适应行为的不同的流量组件，以响应不断变化的信息更新？(2)关于系统效率、可靠性和弹性的正面和负面信息的限制是什么？ (3)在资源有限的情况下，应该在何时何地与哪些利益相关者群体沟通哪些信息，以优化系统性能？该项目为研究异构多智能体交通网络上的信息提供奠定了理论基础，并为智能移动性的信息感知、共享和保护提供了重要见解。 该方法集成了随机多智能体优化，交通网络平衡建模，变分分析，研究现代多式联运系统的信息和信息设计的价值。该项目将利用凸化，分解和近似理论的最新理论进展，以科普多智能体交互，多阶段决策和多维场景带来的计算挑战。 该项目利用了与中央佛罗里达大学（UCF）交通系统，奥兰多市和阿贡国家实验室的合作。 该项目还将为广大受众开发教育材料，并支持新一代交通工程师的发展。该奖项反映了NSF的法定使命，并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。

This project addresses the use of advanced sensing, communications, and computing technologies in studying value of information in transportation systems made up of heterogeneous traffic (cars, autonomous and connected vehicles, buses, bicycles, etc.) The wealth of data available on these systems enables new approaches to information provisioning that have the potential to improve transportation system efficiency, reliability, and resilience. The project develops a unified modeling framework for information provision considering heterogeneous non-cooperative stakeholders and addresses three critical questions: (1) How do we model adaptive behavior of different traffic components in response to evolving information updates? (2) What are the limits of positive and negative information on systems efficiency, reliability, and resilience? (3) Given limited resources, what, when, and where should the information be communicated with which groups of stakeholders to optimize system performance? The project develops the theoretical foundation to study information provisioning over heterogeneous multi-agent transportation networks and provides critical insights on information sensing, sharing, and protection for smart mobility. The approach integrates stochastic multi-agent optimization, traffic network equilibrium modeling, and variational analysis to study value of information and information design for modern multi-modal transportation systems. The project will leverage recent theoretical advancements on convexification, decomposition, and approximation theories to cope with the computational challenges brought by multi-agent interaction, multi-stage decision making, and multi-dimensional scenarios. The project leverages collaborations with the University of Central Florida (UCF) transportation system, City of Orlando, and Argonne National Laboratory. The project will also develop educational materials for a broad audience and support the development of a new generation of transportation engineers.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.