6/6/2012 1 Data Quality Verification & Sensor Calibration for WIM Systems Chen-Fu Liao & Gary Davis Department of Civil Engineering University of Minnesota TRB 2012 NATMEC Conference June 4-7, Dallas, Texas Acknowledgements RITA, USDOT and UMN ITS Institute MnDOT – Ben Timerson & Staff in Transportation Data & Analysis Sushanth Kumar – Graduate Research Students Minnesota Traffic Observatory, UMN
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6/6/2012
1
Data Quality Verification & Sensor Calibration for WIM Systems
Chen-Fu Liao & Gary DavisDepartment of Civil Engineering
University of Minnesota
TRB 2012 NATMEC ConferenceJune 4-7, Dallas, Texas
Acknowledgements
RITA, USDOT and UMN ITS Institute
MnDOT – Ben Timerson & Staff in Transportation Data & Analysis
Sushanth Kumar – Graduate Research Students
Minnesota Traffic Observatory, UMN
6/6/2012
2
Outline
Literature ReviewWIM Data MonitoringMixture Model – GVW9Cumulative Sum (CUSUM)
MethodologyAnalysis ResultsConcluding Remarks
Dahlin, 1992
Recommended 3 measures for WIM quality assurance
1. Class 9 steering axle weights1. Class 9 steering axle weights
< 32 kips 8.4 kips
32‐ 70 kips 9.3 kips
> 70 kips 10.4 kips
2. Class 9 GVW
2 peaks
unloaded 28 32 kipsunloaded: 28‐32 kips
fully loaded: 70‐80 kips
3. Flexible ESAL factor
compare with “properly calibrated system”
6/6/2012
3
Class-9, Speed Distribution N=2955 (Observed), 8/2/2010
Fre
qu
en
cy
02
00
50
0
Mean=65.2, Median=65.0, Sd= 4.2 (MPH)
40 50 60 70 80
Class-9, GVW Distribution N=2955 (Observed), 8/2/2010
enc
y
15
0
Peak1=30.0 (kips), Peak2=76.0 (kips)
Fre
qu
e
0 20 40 60 80 100 120
05
0
Han, Boyd, Marti, 1995
FHWA‐LTPP study
Formal use of statistical quality control methods to monitor WIM systems