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.

翻译：本文探讨了交通流量数据的建模问题，该数据反映了日内交通流量的变化规律。在建模过程中，我们采用Kato-Jones混合分布，其中每个分量均为单峰分布，且能涵盖广泛的偏度和峰度范围。我们考虑了两种参数估计方法：修正矩估计法和最大似然估计法。这些方法被证明能有效将所提出的混合分布拟合至我们的数据。结果表明，交通流量变化可分为早高峰和晚高峰两类，其分布具有不同形态，特别是在偏度和峰度方面存在显著差异。