We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using 1 regularization and a sequence of tanh layers. The challenge of predicting traffic flows are the sharp nonlinearities due to transitions between free flow, breakdown, recovery and congestion. We show that deep learning architectures can capture these nonlinear spatio-temporal effects. The first layer identifies spatio-temporal relations among predictors and other layers model nonlinear relations. We illustrate our methodology on road sensor data from Interstate I-55 and predict traffic flows during two special events; a Chicago Bears football game and an extreme snowstorm event. Both cases have sharp traffic flow regime changes, occurring very suddenly, and we show how deep learning provides precise short term traffic flow predictions.
Polson and Sokolov REF showed that deep learning architectures could capture the nonlinear spatio-temporal characteristic of traffic flow evolution caused by accident or bad weather.
Deep Learning for Short-Term Traffic Flow Prediction
Engineering Mathematics