Traffic count data analysis using mixtures of Kato–Jones distributions

Abstract We discuss the modelling of traffic count data that show the variation of traffic volume within a day. For the modelling, we apply mixtures of Kato–Jones distributions in which each component is unimodal and affords a wide range of skewness and kurtosis. We consider two methods for parameter estimation, namely, a modified method of moments and the maximum-likelihood method. These methods were seen to be useful for fitting the proposed mixtures to our data. As a result, the variation in traffic volume was classified into the morning and evening traffic whose distributions have different shapes, particularly different degrees of skewness and kurtosis.