Quantile forecasts for traffic predictive control

M. Dutreix, S. Coogan
IEEE Conference on Decision and Control, 2017


We present a quantile regression method for predicting future traffic flow at a signalized intersection by combining both historical and real-time data. The algorithm exploits nonlinear correlations in historical measurements, and efficiently solves a quantile loss optimization problem using the Alternating Direction Method of Multipliers (ADMM). The resulting parameter vectors allow us to determine a probability distribution of upcoming traffic flow. We use these predictions to establish an efficient, delay-minimizing control policy for the intersection. The approach is demonstrated on a case study with two years of high resolution flow measurements.