Theories of Urban Traffic Dynamics and Adaptive Control for the Age of Big Data

该项目将探索如何将大数据技术应用于交通信号控制。 该项目将利用图论和数据科学的最新进展来识别不同的拥堵模式，并开发独特的治疗方法，以适应城市街道的交通拥堵。在理想化条件下进行了广泛的前期工作，这项研究将从理想化条件跨越到复杂的现实世界环境，对减少拥堵和帮助国家经济增长产生重大影响。为该项目设计的算法将是开源的，有可能在普通的交通和通信基础设施上实现。 开发一个平台来模拟在现实环境中开发的算法将是一个有价值的教学和推广工具。 该项目的变革性在于其计划：（i）检测和区分交通拥堵在城市街道上可能表现出的各种几何模式;以及（ii）针对这些不同模式进行目标处理。 几何图案的检测将使用大数据监测技术，并通过将街道网络表示为图形来进行。 为这些不同的几何图案设想的治疗需要重新定时的交通信号，既适应米封锁的社区和同步绿色阶段，以配合立即下游的队列消散。 这些将与另一种治疗方法相结合，根据需要加快公交车通过交通信号灯的速度。 针对现有的邻域交通模型以及无模型强化学习方法，设想了具体的增强功能。 增强的方法将用于预测治疗在复杂的现实环境中的表现。 该项目还将探索如何使用大数据技术测量和处理的输入来不断改进模型和无模型预测。 然后，进一步的探索将把精确的预测与优化技术结合起来，这样治疗方法就可以在一天中适应城市不断变化的拥堵模式。 一旦以上述方式推广，将使用模拟测试和改进治疗方法。 上述算法将被设计出来，而精细化的治疗方法将被编码到模拟平台中，用于教学和推广目的。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。

The project will explore how big-data technologies can be applied to traffic signal control. The project will leverage recent advances in graph theory and data science to identify distinct congestion patterns and develop unique treatments that would adaptively treat traffic congestion on urban streets. Extensive preliminary work has been conducted under idealized conditions and this research will make the leap from idealized conditions to complex, real-world settings, for significant impact in reducing congestion and aiding economic growth for the nation. Algorithms designed for this project will be open-source, with the potential to be implemented on ordinary traffic and communication infrastructures. The development of a platform to simulate the developed algorithms in realistic settings will be a valuable teaching and outreach tool. The project's transformative quality lies in its plans to: (i) detect and distinguish the various geometric patterns that traffic congestion can exhibit on city streets; and (ii) target treatments to suit those distinct patterns. The detection of geometric patterns will be pursued using big-data monitoring technologies, and by representing street networks as graphs. Treatments envisioned for those distinct geometric patterns entail the re-timing of traffic signals, both to adaptively meter cordoned neighborhoods and to synchronize green phases to coincide with the dissipation of queues immediately downstream. These will be integrated with another treatment that expedites bus movements past traffic signals on as-needed bases. Specific enhancements are envisioned for existing models of neighborhood traffic, and for model-free, Reinforcement Learning approaches. The enhanced methods will be used to predict how the treatments perform in complex, real-world settings. The project will also explore how model and model-free predictions might be continually refined using inputs measured and processed with big-data technologies. Further exploration will then go toward coupling the refined predictions with optimization techniques, so that treatments can adapt over the day to suit a city's evolving congestion patterns. Once generalized in the above ways, the treatments will be tested and refined using simulation. The aforementioned algorithms will then be designed, and the refined treatments will be coded into the simulation platform for teaching and outreach purposes.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.