Deep Learning with Non-parametric Regression Model for Traffic Flow Prediction

Abstract

Timely, effective, and accurate traffic movement prediction is highly important in developing and implementing the intelligent traffic prediction system. Given the overwhelming traffic in recent years, traffic data have been increasing as we enter the era of high traffic data. Existing traffic prediction methods use the traditional prediction models, which remain unsatisfactory for real-world applications. Sharp non-linearities, such as free flow, breakdown, recovery, and free congestion cause challenges in forecasting the traffic flow. This condition motivates us to reconsider the traffic forecast model based on the deep learning with the high traffic data. To predict non-linearities spatio-temporal effects, we have employed the deep learning technique with non-parametric regression. The first layer in the deep learning algorithm identifies spatio-temporal relationships between predictors and other non-linear relationships. The deep learning technique with non-parametric regression is significantly better compared with other models. Experimental results show that the proposed technique for the traffic flow forecast has a better-quality performance.