Statistical processing of traffic flow characteristics data

In the course of statistical processing of traffic flows characteristics data, the check for the presence of anomalous measurements in the sampling should be done at the very start of processing. If anomalous measurements are detected, they should be excluded from the sampling at an early stage of t...

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Main Authors: Norin Veniamin, Pukharenko Yurii
Format: Article
Language:English
Published: EDP Sciences 2023-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/08/e3sconf_afe2023_04031.pdf
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author Norin Veniamin
Pukharenko Yurii
author_facet Norin Veniamin
Pukharenko Yurii
author_sort Norin Veniamin
collection DOAJ
description In the course of statistical processing of traffic flows characteristics data, the check for the presence of anomalous measurements in the sampling should be done at the very start of processing. If anomalous measurements are detected, they should be excluded from the sampling at an early stage of the processing and not taken into considerations in further calculations. Numerous criteria have been developed to detect outliers, their effectiveness depends on the sample size. In practice, for technical and economic reasons, it is impractical to obtain a large number of measurements, as a rule the sampling should be processed on the basis of limited number of observations. In this regard, methods for detection of outliers with a small number of measurements, which include the method based on the use of the Romanovsky criterion, are of great importance. However, the analysis of literary references showed that in some recently published studies it is not recommended to use the Romanovsky criterion with the number of measurements less than 20. Therefore, the purpose of this study is to test the power of the Romanovsky criterion (test) for a small number of measurements and the possibility of its application in samplings of small size (n ≤ 20). The conducted studies have shown that the power of the Romanovsky criterion is quite high and it has high reliability with a small number of measurements, which makes it possible to use it in small samples to detect anomalous measurements.
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spelling doaj.art-b38d7d7d18124648ab6264ef612b289f2023-03-09T11:17:21ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013710403110.1051/e3sconf/202337104031e3sconf_afe2023_04031Statistical processing of traffic flow characteristics dataNorin Veniamin0Pukharenko Yurii1Saint Petersburg State University of Architecture and Civil EngineeringSaint Petersburg State University of Architecture and Civil EngineeringIn the course of statistical processing of traffic flows characteristics data, the check for the presence of anomalous measurements in the sampling should be done at the very start of processing. If anomalous measurements are detected, they should be excluded from the sampling at an early stage of the processing and not taken into considerations in further calculations. Numerous criteria have been developed to detect outliers, their effectiveness depends on the sample size. In practice, for technical and economic reasons, it is impractical to obtain a large number of measurements, as a rule the sampling should be processed on the basis of limited number of observations. In this regard, methods for detection of outliers with a small number of measurements, which include the method based on the use of the Romanovsky criterion, are of great importance. However, the analysis of literary references showed that in some recently published studies it is not recommended to use the Romanovsky criterion with the number of measurements less than 20. Therefore, the purpose of this study is to test the power of the Romanovsky criterion (test) for a small number of measurements and the possibility of its application in samplings of small size (n ≤ 20). The conducted studies have shown that the power of the Romanovsky criterion is quite high and it has high reliability with a small number of measurements, which makes it possible to use it in small samples to detect anomalous measurements.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/08/e3sconf_afe2023_04031.pdf
spellingShingle Norin Veniamin
Pukharenko Yurii
Statistical processing of traffic flow characteristics data
E3S Web of Conferences
title Statistical processing of traffic flow characteristics data
title_full Statistical processing of traffic flow characteristics data
title_fullStr Statistical processing of traffic flow characteristics data
title_full_unstemmed Statistical processing of traffic flow characteristics data
title_short Statistical processing of traffic flow characteristics data
title_sort statistical processing of traffic flow characteristics data
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/08/e3sconf_afe2023_04031.pdf
work_keys_str_mv AT norinveniamin statisticalprocessingoftrafficflowcharacteristicsdata
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