Traffic Flow Estimation Based on Three-layer Stacking Model

Abstract

This paper proposes a estimation model based on three-layer stacking model to estimate traffic flow. Compared with estimation models of single machine learning and a two-layer stacking model, the three-layer stacking model can achieve higher estimation performance. The traffic flow data set we apply are accumulated from 2010 to 2018 in Wales, UK. The three-layer stacking model has a three-layer structure. The first layer is made up of two estimation models and trained by 5-fold cross-validation from the original traffic flow data set to generate a new data set. The second layer takes the new data set as the input data to train the new estimation model. In the third layer, the original traffic flow data set are trained as the input of a new estimation model. Then the weights of the two generalizers are measured according to the accurate estimation results. Finally, the weighted average is done to get the final estimation results. Experimental results show that in comparison to some single machine learning model and a model using only two-layer of stacking model, the three-layer stacking model can achieve higher traffic flow estimation accuracy and reduce the variance of the model.