Transforming Equilibrium Analysis Paradigm for Modeling Transportation Networks with Intelligent Traveling Agents

该项目将改变城市规划组织用于规划我国新兴互联和自动化移动系统的交通网络平衡建模范式。考虑到未来的旅行代理（联网司机或自动驾驶车辆）将具有强大的学习和计算能力，他们的旅行决策可以是算法的，战略性的和自适应的，本项目将首先研究这些智能旅行代理的网络流量动态的日常演变，并检查平衡的概念是否仍然与建模和规划未来的移动系统相关。利用连接提供的大量经验数据，该项目将开发端到端学习框架，直接从经验数据中学习相关建模组件和平衡状态。计划建模范例，如果成功，将具有广泛的市场采用的巨大潜力，并将为都市规划组织节省时间和资源，以建立和维护他们的规划模型。它可以帮助他们更好地规划和管理他们的交通网络，以减少交通拥堵和车辆排放，而不需要在扩大现有基础设施方面进行大量新投资。这个项目将包括两个重点。第一部分通过明确建模智能旅行代理的日常旅行选择来发展动力系统，然后检查动力系统的收敛性和稳定性。通过证明在网络流量动态的日常演变中仍然可以出现用户均衡，本项目打算为均衡建模范式建立一个行为基础。第二个重点是将隐式深度学习与网络均衡分析相结合，开发一个端到端框架，直接从经验数据中学习旅行代理的行为、均衡状态和其他建模组件（如果需要）。这项研究如果成功，将对推进交通网络科学做出基础性的贡献。首先，它通过明确地捕捉旅行社日常选择的决策过程，创新了建模日常交通动态的方法。其次，端到端学习和优化框架代表了交通网络建模和规划的范式转变。该框架通过将学习和决策/优化集成到单个端到端系统中，将数据决策管道融合在一起。最后，该研究丰富了博弈论的文献，提供了平均场博弈论的新应用和进一步发展，并提出了将机器学习与博弈论相结合的新途径。该奖项反映了美国国家科学基金会的法定使命，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 will transform transportation network equilibrium modeling paradigm that metropolitan planning organizations use to plan emerging connected and automated mobility systems for our nation. Considering that future traveling agents (connected drivers or automated vehicles) will possess strong learning and computation capability, and their travel decisions can be algorithmic, strategic and adaptive, this project will first investigate the day-to-day evolution of network traffic dynamics with these intelligent traveling agents, and examine whether the notion of equilibrium remains relevant for modeling and planning future mobility systems. Leveraging massive empirical data made available by connectivity, this project will then develop an end-to-end learning framework that directly learns relevant modeling components and the equilibrium state from empirical data. The planned modeling paradigm, if successful, has a great potential for widespread market adoption, and will save time and resources for metropolitan planning organizations to build and maintain their planning models. It can potentially help them better plan and manage their transportation networks to reduce traffic congestion and vehicle emissions, without requiring much new investment on expanding the existing infrastructure. This project will consist of two thrusts. The first thrust develops dynamical systems by explicitly modeling day-to-day travel choices of intelligent traveling agents and then examines the convergence and stability properties of the dynamical systems. By demonstrating that Wardropian user equilibrium can still emerge in the day-to-day evolution of network traffic dynamics, this project intends to establish a behavioral basis for the equilibrium modeling paradigm. The second thrust aims to integrate implicit deep learning with network equilibrium analysis to develop an end-to-end framework that directly learns the behaviors of traveling agents, the equilibrium state, and other modeling components, if needed, from empirical data. This research, if successful, makes fundamental contributions to advance transportation network science. First, it innovates the methodology of modeling day-to-day traffic dynamics by explicitly capturing the decision-making process of day-to-day choices of travelling agents. Second, the end-to-end learning and optimization framework represents a paradigm shift for modeling and planning transportation networks. The framework melds the data-decisions pipeline by integrating learning and decision/optimization into a single end-to-end system. Lastly, the research enriches the literature of game theory by offering novel application and further development of mean-field game theory, and presenting a new way of integrating machine learning with game theory.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.