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：我们讨论显示一天内流量变化的交通量计数数据模型。 对于模型,我们采用卡托-琼斯分布的混合物,其中每个部件都是单式的,并且提供各种偏差和细度。我们考虑两种参数估计方法,即时间和最大可能性的修改方法。这些方法被认为有助于将拟议混合物与我们的数据相匹配。因此,交通量的变化被分类为日间和晚间流量,其分布形状不同,特别是偏差和细度不同。