Mixture-Model-Based Clustering for Daily Traffic Volumes

Daily traffic volume data are collected and stored as historical data. By learning from the historical data, we can predict traffic volumes. In this paper, we propose a clustering method based on the mixture model estimation approach that was introduced in previous papers. This method is compared with the whole-curve-based clustering method. From the method we propose, we derive a partial clustering approach based on the components of the mixture model which was introduced before. The partial clustering method based on components is interesting for research that only focuses on single component. The comparison between methods shows that the mixture-model-based method can reach the results of 7.38% to 14.57% of relative errors compared with the whole-curve-based method.