Abstract
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.
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Acknowledgments
The authors appreciate the funding support from the Federal Highway Administration (FHWA), the Pacific Northwest Transportation Consortium (PacTrans), the USDOT University Transportation Center for Federal Region 10, the China Scholarship Council (CSC), National Natural Science Foundation of China (Nos. 51579143 and 51508094).
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Journal of Transportation Engineering, Part A: Systems
Volume 144 • Issue 5 • May 2018
Copyright
©2018 American Society of Civil Engineers.
History
Received: Mar 1, 2017
Accepted: Oct 30, 2017
Published online: Mar 14, 2018
Published in print: May 1, 2018
Discussion open until: Aug 14, 2018
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