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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2206.01355 |
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| _version_ | 1866913422029881344 |
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| author | Nagasaki, Kota Kato, Shogo Nakanishi, Wataru Jones, M. C. |
| author_facet | Nagasaki, Kota Kato, Shogo Nakanishi, Wataru Jones, M. C. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_01355 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Traffic Count Data Analysis Using Mixtures of Kato--Jones Distributions Nagasaki, Kota Kato, Shogo Nakanishi, Wataru Jones, M. C. Applications 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. |
| title | Traffic Count Data Analysis Using Mixtures of Kato--Jones Distributions |
| topic | Applications |
| url | https://arxiv.org/abs/2206.01355 |