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http://dx.doi.org/10.12815/kits.2021.20.6.1

A Study on the Traffic Volume Correction and Prediction Using SARIMA Algorithm  

Han, Dae-cheol (Dept. of Highway & Transportation Research, Korea Institute of Civil Eng, and Building Technology)
Lee, Dong Woo (Dept. of Urban Policy and Administration, Incheon National University)
Jung, Do-young (Dept. of Highway & Transportation Research, Korea Institute of Civil Eng, and Building Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.20, no.6, 2021 , pp. 1-13 More about this Journal
Abstract
In this study, a time series analysis technique was applied to calibrate and predict traffic data for various purposes, such as planning, design, maintenance, and research. Existing algorithms have limitations in application to data such as traffic data because they show strong periodicity and seasonality or irregular data. To overcome and supplement these limitations, we applied the SARIMA model, an analytical technique that combines the autocorrelation model, the Seasonal Auto Regressive(SAR), and the seasonal Moving Average(SMA). According to the analysis, traffic volume prediction using the SARIMA(4,1,3)(4,0,3) 12 model, which is the optimal parameter combination, showed excellent performance of 85% on average. In addition to traffic data, this study is considered to be of great value in that it can contribute significantly to traffic correction and forecast improvement in the event of missing traffic data, and is also applicable to a variety of time series data recently collected.
Keywords
Traffic volume; ARIMA; SARIMA; Traffic calibration; Time series;
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Times Cited By KSCI : 1  (Citation Analysis)
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