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

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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.