On selecting an optimal wavelet for detecting singularities in traffic and vehicular data

Zheng, Zuduo and Washington, Simon (2012) On selecting an optimal wavelet for detecting singularities in traffic and vehicular data. Transportation Research Part C: Emerging Technologies, 25 18-33. doi:10.1016/j.trc.2012.03.006


Author Zheng, Zuduo
Washington, Simon
Title On selecting an optimal wavelet for detecting singularities in traffic and vehicular data
Journal name Transportation Research Part C: Emerging Technologies   Check publisher's open access policy
ISSN 0968-090X
1879-2359
Publication date 2012-12-01
Sub-type Article (original research)
DOI 10.1016/j.trc.2012.03.006
Open Access Status Not yet assessed
Volume 25
Start page 18
End page 33
Total pages 16
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Language eng
Abstract Serving as a powerful tool for extracting localized variations in non-stationary signals, applications of wavelet transforms (WTs) in traffic engineering have been introduced; however, lacking in some important theoretical fundamentals. In particular, there is little guidance provided on selecting an appropriate WT across potential transport applications. This research described in this paper contributes uniquely to the literature by first describing a numerical experiment to demonstrate the shortcomings of commonly-used data processing techniques in traffic engineering (i.e., averaging, moving averaging, second-order difference, oblique cumulative curve, and short-time Fourier transform). It then mathematically describes WT's ability to detect singularities in traffic data. Next, selecting a suitable WT for a particular research topic in traffic engineering is discussed in detail by objectively and quantitatively comparing candidate wavelets' performances using a numerical experiment. Finally, based on several case studies using both loop detector data and vehicle trajectories, it is shown that selecting a suitable wavelet largely depends on the specific research topic, and that the Mexican hat wavelet generally gives a satisfactory performance in detecting singularities in traffic and vehicular data.
Keyword Oblique cumulative curve
Short-time Fourier transform
Singularity detection
The Mexican hat wavelet
Traffic data analysis
Wavelet transform
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Civil Engineering Publications
 
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