Collaborative Research:CPS Medium: Population Games for Cyber-Physical Systems: New Theory with Tools for Transportation Management under Extreme Demand

交通网络需求的突然激增破坏了这些网络规划和运营所依据的平衡条件。对极端需求下人们的战略选择缺乏理解可能会导致自相矛盾的结果，例如旨在拯救生命的疏散反而导致道路上的大规模伤亡，或者开辟新道路增加而不是减少旅行时间。该项目将制定系统和程序，以管理民众的战略选择(例如，是否撤离或避难所就位，采取哪条逃生路线)和当局的行动(例如，疏散哪些地区和按什么顺序疏散，在哪里安排交通路线，是否关闭一些道路或在给定方向上开辟额外的车道)。该项目产生的工具将使更好的响应系统能够帮助地方当局管理极端需求，例如当必须疏散整个县以保护居民免受野火侵袭时。该项目将开发一个建模和仿真工具链，以预测交通瓶颈位置及其严重程度，以及预期的出行时间和延误，从而确定结果的频谱，识别最糟糕的情况，并使当局能够做出明智的决策。该技术方法植根于种群博弈，对争夺资源的大量主体之间的战略性非合作互动的动态进行建模。然而，该项目将偏离现有理论的均衡重点，并将提供暂态分析工具，不仅考虑到智能体的策略修订，而且还考虑到一系列网络和物理动力学，如交通流中的排队动力学、十字路口的响应信号控制、向智能体的信息传播以及火灾传播等危险的演变。研究任务包括用系统论方法描述瞬时行为，解释策略修订模型中的不确定性，将理论扩展到用户偏好的连续体，重新思考动态模型背后的随机过程，为时间关键型操作的短期范围修改理论，从数据中学习动态模型，以及根据人口对疏散命令的反应来制定人口与单一主体之间的广泛形式的博弈。此外，该项目将确定控制行动(如响应信号政策、道路封闭、禁用某些转弯)，以关闭数据-决策-行动循环，并引导动态朝着理想的结果和避免不安全的结果发展。该奖项反映了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。

A sudden surge in demand in traffic networks disrupts the equilibrium conditions upon which these networks are planned and operated. Lack of understanding of the population's strategic choices under extreme demand may result in paradoxical outcomes, such as evacuations aiming to save lives instead resulting in mass casualties on the road or opening up of new roads increasing rather than decreasing travel time. This project will devise systems and procedures for managing the strategic choices of populations (e.g., whether to evacuate or shelter in place, which escape routes to take) and the actions of the authorities (e.g., which zones to evacuate and in which sequence, where to route the traffic, whether to close some roads or open extra lanes in a given direction). The tools resulting from this project will enable better response systems to assist local authorities in managing extreme demand, such as when entire counties have to be evacuated to protect the residents from a wildfire. The project will develop a modeling and simulation tool chain to predict traffic bottleneck locations and their severity together with expected travel times and delays, thus determining the spectrum of outcomes, identifying worst cases, and enabling the authorities to make informed decisions.The technical approach is rooted in population games, which model the dynamics of strategic noncooperative interactions among large populations of agents competing for resources. The project, however, will depart from the equilibrium focus of the existing theory and will offer transient analysis tools that account for not only the strategy revisions of the agents, but also a host of cyber and physical dynamics, such as queueing dynamics in traffic flow, responsive signal control at intersections, information dissemination to agents, and evolution of hazards, such as fire propagation. The research tasks to enable the project's vision of a "cyber-physical population game theory" include characterizing transient behavior with system-theoretic methods, accounting for uncertainty in strategy revision models, extending the theory to a continuum of user preferences, rethinking the stochastic processes underlying the dynamical models, modifying the theory for short-term horizons for time-critical operations, learning dynamical models from data, and formulating extensive form games between a population and a single agent, motivated by the population response to evacuation orders. In addition, the project will identify control actions (such as responsive signal policies, road closures, disabling certain turns) to close the data-decision-action loop and steer the dynamics towards desirable outcomes and avoiding unsafe ones.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.