Traffic Count Data Analysis Using Mixtures of Kato–Jones Distributions on the Circle

06/03/2022
by   Kota Nagasaki, et al.
0

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.

READ FULL TEXT
research
01/04/2023

A new over-dispersed count model

A new two-parameter discrete distribution, namely the PoiG distribution ...
research
02/07/2018

Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions

Mixtures of factor analyzers (MFA) provide a powerful tool for modelling...
research
09/09/2016

Singularity structures and impacts on parameter estimation in finite mixtures of distributions

Singularities of a statistical model are the elements of the model's par...
research
08/31/2020

Likelihood-based inference for modelling packet transit from thinned flow summaries

The substantial growth of network traffic speed and volume presents prac...
research
03/24/2023

Tackling the infinite likelihood problem when fitting mixtures of shifted asymmetric Laplace distributions

Mixtures of shifted asymmetric Laplace distributions were introduced as ...
research
08/07/2023

A Causal Inference Approach to Eliminate the Impacts of Interfering Factors on Traffic Performance Evaluation

Before and after study frameworks are widely adopted to evaluate the eff...
research
09/26/2018

Moment ideals of local Dirac mixtures

In this paper we study ideals arising from moments of local Dirac measur...

Please sign up or login with your details

Forgot password? Click here to reset