A comparison of outlier detection algorithms for ITS data

Chen, Shuyan; Wang, Wei; Zuylen, Henk J van

doi:10.1016/j.eswa.2009.06.008

Cited by 71 publications

(43 citation statements)

References 11 publications

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“…While an overview of the outlier detection within a spatial data setting is well presented by Kou [9], the summary of outlier detection algorithms for ITS data is provided by Chen et al [10]. In the Corey et al study, Gaussian mixture models were applied to detect and correct inductive loop detector sensitivity errors [11].…”

Section: Literature Review and Research Opportunitiesmentioning

confidence: 99%

Application of locally weighted regression‐based approach in correcting erroneous individual vehicle speed data

Rim

Park

et al. 2015

J of Advced Transportation

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SUMMARYBecause of the quality of raw data being an essential feature in determining the reliability of traffic information, an effective detection and correction of outliers in raw field-collected traffic data has been an interest for many researchers. Global positioning systems (GPS)-based traffic surveillance systems are capable of producing individual vehicle speeds that are vital for transportation researchers and practitioners in traffic management and information strategies. This study proposes a locally weighted regression (LWR)-based filtering method for individual vehicle speed data. To fully and systematically evaluate this proposed method, a technique to generate synthetic outliers and two approaches to inject synthetic outliers are presented. Parameters that affect the smoothing performance associated with LWR are devised and applied to obtain a more robust and reliable data correction method. For a comprehensive performance evaluation of the developed LWR method, comparisons to exponential smoothing (ES) and autoregressive integrated moving average (ARIMA) methods were conducted. Because the LWR-based filtering method outperformed both the ES and ARIMA methods, this study showed its useful benefits in filtering individual vehicle speed data.

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Section: Literature Review and Research Opportunitiesmentioning

confidence: 99%

Application of locally weighted regression‐based approach in correcting erroneous individual vehicle speed data

Rim

Park

et al. 2015

J of Advced Transportation

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“…The detection of outlier is a procedure that selects k samples that are considerably dissimilar, exceptional, or inconsistent with respect to the remaining data [5].…”

Section: Related Workmentioning

confidence: 99%

Deleting or keeping outliers for classifier training?

Tallón‐Ballesteros

Riquelme

2014

2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)

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“…In the field of intelligent transportation, knowledge discovery and data mining (KDD) methods have been widely utilized to analyze various kinds of traffic data to construct decision support systems for traffic management [7,20,41,42]. For these data-driven traffic management systems, one of the most important tasks is analyzing the causes of traffic bottlenecks and taking action to alleviate congestion [2,17,24].…”

Section: Introductionmentioning

confidence: 99%