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Anomaly Detection and Cleaning of Highway Elevation Data from Google Earth Using Ensemble Empirical Mode Decomposition
| 题名： | Anomaly Detection and Cleaning of Highway Elevation Data from Google Earth Using Ensemble Empirical Mode Decomposition |
| 其他题名： | AASHTO.(2011).A policy on geometric design of highways and streets,6th Ed.,Washington,DC. |
| 正文语种： | 英文 |
| 作者： | Xinqiang Chen |
| 关键词： | Elevation;Smoothing;Denoising;Ensemble empirical mode decomposition (EEMD);Accuracy |
| 摘要： | Elevation information and its derivation, such as grade, are very important in analyses of traffic operation, safety performance, and energy consumption on highways. Google Earth (GE) is considered a reliable source of elevation information of ground surface and highway elevation. Data were extracted from GE. However, the authors found that raw GE elevation data on highways contains various anomalies and noises. The primary objective of this study was to evaluate the use of the ensemble empirical mode decomposition (EEMD) method for anomaly detection and cleaning of highway elevation data extracted from GE. Three interstate highways' segments were studied, and typical anomalies that existed in raw GE elevation data were identified. The EEMD method was then applied to decompose elevation data into different compositions with different details of original data, which were determined into those containing true information or white noise. The modeling results showed that the EEMD method was effective in excluding noises and obtaining accurate elevation data. Findings of this study can help transport authorities to create an accurate elevation data set for all highways or other road classes. |
| 出版年： | 2018 |
| 论文唯一标识： | P-72Y2018V144N05008 |
| 英文栏目名称： | TECHNICAL PAPERS |
| doi： | 10.1061/JTEPBS.0000138 |
| 期刊名称： | Journal of Transportation Engineering |
| 拼音刊名(出版物代码)： | P-72 |
| 卷： | 144 |
| 期： | 05 |
| 页码： | 58-71 |
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