Forecasting Traffic Congestion Using ARIMA Modeling
| Autor: | Taysseer Sharaf, Khalid Elgazzar, Sumit Shah, Taghreed Alghamdi, Magdi Bayoumi |
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| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Series (mathematics) Computer science 05 social sciences 02 engineering and technology Traffic flow computer.software_genre Random walk Residual Traffic congestion 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Autoregressive integrated moving average Data mining Time series computer |
| Zdroj: | IWCMC |
| DOI: | 10.1109/iwcmc.2019.8766698 |
| Popis: | Traffic congestion is a widely recognized challenging problem that is increasingly growing around the world. This paper leverages ARIMA-based modeling to study some factors that significantly affect the rate of traffic congestion. We present a short-term time series model for non-Gaussian traffic data. The model helps decision-makers to better manage traffic congestion by capturing and predicting any abnormal status. We begin by highlighting the characteristics and structure of the dataset that negatively impact the performance of time series analysis. We use R to preprocess and prepare the dataset for the modeling phase. We use the widely adopted ARIMA model to analyze and predict the traffic flow observations, measured at an hourly-basis, in a designated area of study in California, USA. Several ARIMA models are built using ACF and PACF analysis of the traffic time series to compare with the model suggested by the auto.arima function provided by the R language that uses random walk with drift. The residual obtained from our model demonstrates high performance in predicting future traffic status. |
| Databáze: | OpenAIRE |
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