Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 오철 | - |
dc.date.accessioned | 2018-03-22T05:38:12Z | - |
dc.date.available | 2018-03-22T05:38:12Z | - |
dc.date.issued | 2016-03 | - |
dc.identifier.citation | JOURNAL OF ADVANCED TRANSPORTATION, v. 50, No. 2, Page. 180-196 | en_US |
dc.identifier.issn | 0197-6729 | - |
dc.identifier.issn | 2042-3195 | - |
dc.identifier.uri | https://onlinelibrary.wiley.com/doi/full/10.1002/atr.1325 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11754/50510 | - |
dc.description.abstract | Because 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. Copyright (c) 2015 John Wiley Sons, Ltd. | en_US |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea grant funded by the Korea government (MEST) (NRF-2010-0029449). | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | WILEY-BLACKWELL | en_US |
dc.subject | outlier detection | en_US |
dc.subject | data correction | en_US |
dc.subject | locally weighted regression (LWR) | en_US |
dc.subject | global positioning system (GPS) | en_US |
dc.subject | MAP-MATCHING ALGORITHM | en_US |
dc.subject | DETECTOR DATA | en_US |
dc.subject | PROBE DATA | en_US |
dc.title | Application of locally weighted regression-based approach in correcting erroneous individual vehicle speed data | en_US |
dc.type | Article | en_US |
dc.relation.volume | 50 | - |
dc.identifier.doi | 10.1002/atr.1325 | - |
dc.relation.page | 180-196 | - |
dc.relation.journal | JOURNAL OF ADVANCED TRANSPORTATION | - |
dc.contributor.googleauthor | Rim, Heesub | - |
dc.contributor.googleauthor | Park, Seri | - |
dc.contributor.googleauthor | Oh, Cheol | - |
dc.contributor.googleauthor | Park, Junhyung | - |
dc.contributor.googleauthor | Lee, Gunwoo | - |
dc.relation.code | 2016008263 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DEPARTMENT OF TRANSPORTATION AND LOGISTICS ENGINEERING | - |
dc.identifier.pid | cheolo | - |
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