Invited research talk in the Physics-enhanced Machine Learning methods in Engineering practice Seminar at the Alan Turing Institute, UK.
Road traffic congestion poses serious challenges to urban infrastructures. Such dynamical loading of the vehicular arteries impacts both the social and the economic lives of people. The main goal of this work is to propose a new methodology for jointly modelling, learning, and controlling the dynamics of the arterial traffic flows for accurate phase offset calculation. The approach introduces a nonlinear coupled oscillators model of the traffic network signaling system, along with a nonlinear control mechanism that allows it to capture complex flow patterns and unpredictable variations. This ensures a robust control of the oscillator-based model towards self-organization under dynamical demand changes based on measurement of local traffic data. In order to benefit from realistic run-time performance under real traffic flows, the system is efficiently implemented in spiking neural networks, hence avoiding optimization routines and allowing it to operate in real-time. Our real-world data experiments demonstrate that the methodology excels in minimizing typical traffic key performance indices when phase offset is calculated depending on the real-time demand measurements.