Traffic Flow Series Outlier Detection Based on Time Series Pattern Extraction and Confidence Interval Estimation
Weihua Zhang, Cheng Liang, Qinghui Nie, Bin Yang, Bing Han · CICTP 2020 · 2020
Raw data quality control is a research content of data preprocessing in Intelligent Transportation Systems (ITS). In this paper, a time series pattern extraction and confidence interval estimation based outlier detection strategy is proposed for traffic flow outlier detection. Analyzed traffic flow data includes sectional traffic volume and average speed series with 5 min time interval was collected from urban arterials in Kunshan City, China. Fluctuation characteristics of traffic flow time series are analyzed, then an STL model proposed by Chatfield is used for traffic flow time series decomposition and wave pattern extraction. Taking the extracted series pattern as modeling objects, an ARIMA model is introduced to estimate the dynamic confidence interval of the pattern series. Finally, the outliers of traffic flow time series can be identified and kicked off from the estimated dynamic confidence interval. Results show that the proposed strategy can detect the outliers of traffic flow time series.