Traffic Count Data Analysis Using Mixtures of Kato--Jones Distributions
Applications
2024-07-10 v2
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.
Keywords
Cite
@article{arxiv.2206.01355,
title = {Traffic Count Data Analysis Using Mixtures of Kato--Jones Distributions},
author = {Kota Nagasaki and Shogo Kato and Wataru Nakanishi and M. C. Jones},
journal= {arXiv preprint arXiv:2206.01355},
year = {2024}
}