data collection through new on-the-road technologies: the need for validation
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Data Collection Through New On-The-Road Technologies: The Need for Validation. David Harkey University of North Carolina Highway Safety Research Center Traffic Records Forum July 2003 Denver, Colorado. Overview of Presentation. Roadway Inventory: Needs and Methods - PowerPoint PPT PresentationTRANSCRIPT
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Data Collection Through New On-The-Road Technologies: The Need for Validation
David HarkeyUniversity of North Carolina
Highway Safety Research Center
Traffic Records ForumJuly 2003
Denver, Colorado
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UNC Highway Safety Research Center Highway Safety Information System
Overview of Presentation
Roadway Inventory: Needs and Methods
Major Missing Element: Alignment History of Recent HSIS Efforts Results from New HSIS Study Conclusions
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Roadway Inventories
Inventory databases exists in all State DOTs
Used for planning, operations and maintenance
Range of data elementsgeometrics (roadways, intersections and
interchanges)traffic control/operations (segment speed
limits, intersection TCD, signal timings)
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UNC Highway Safety Research Center Highway Safety Information System
Roadway Inventory Collection
Manual methodsIn-field acquisition (surveys, observational
runs with DMI or GPS technology) In-office acquisition (video logs, aerial
photos, satellite images) Automated Methods
Instrumented Vans (video logs, pavement conditions, sign inventory, geometrics)
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UNC Highway Safety Research Center Highway Safety Information System
Alignment Data
Very few states have curvature and grade data
Only 2 of the 9 HSIS states have good horizontal curvature data
Known relationship between curvature and safety
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HSIS Research Focus
Improve upon the alignment data available for safety analyses
Automated methods using instrumented vehicles ARAN - Automatic Roadway
Analyzer
Roadware Group, Inc.
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History of HSIS Efforts
1996/97 - MN Data Collection and Analysis4 2-lane roads with 50+ curves over 21
milesPoor Results - Consistency and Accuracy
1999 - Re-analysis of MN Data with algorithm developed for CT DOTDramatic improvement in consistency and
accuracy
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New HSIS Effort
Evaluate the CT DOT algorithm that uses ARAN data
Repeatability (Consistency) between multiple runs on the same
segment of roadway Accuracy - Comparison to “ground
truth”As determined from ground surveys
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Data Collection Tasks
Site Selectionapproximately 50 miles distributed by
number of lanes and level of curvature ARAN Data Collection
5 passes in each direction on every routeazimuth data acquired every 4 meters
Survey AlignmentsCT DOT task from ground surveys
segments
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Data Processing
Horizontal Curve Classification and Display System (PLV-HC Software)Develop alignments from raw data
Make post-processing adjustmentsRemove unresolved (unknown) segments at
the end of a runConvert spirals to tangents and arcs (split the
difference in length)Remove “true” outliers (not be more than 1 in
the group of 5 runs)
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PLV-HC Software
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PLV-HC Software
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UNC Highway Safety Research Center Highway Safety Information System
PLV-HC Software
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Visual Confirmation
Video log produced for each run
Allowed for the removal of induced curves
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Example Analysis (Rte 190)
Initial Best-Fit AlignmentLog Analysis 1
Log Analysis 2
Log Analysis 3
Log Validation 1
Log Validation 1
Rev Analysis 1
Rev Analysis 2_QC
QC_Harkey
QC_Van Dine
Rev Validation 1
Rev Validation 2
Rev Analysis 3
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Example Analysis (Rte 190)
Adjustment 1 - removal of unknowns (unresolved
segments)Log Analysis 1
Log Analysis 2
Log Analysis 3
Log Validation 1
Log Validation 1
Rev Analysis 1
Rev Analysis 2_QC
QC_Harkey
QC_Van Dine
Rev Validation 1
Rev Validation 2
Rev Analysis 3
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Example Analysis (Rte 190)
Adjustment 2 - Conversion of Spirals to Arcs and Tangents
Log Analysis 1
Log Analysis 2
Log Analysis 3
Log Validation 1
Log Validation 1
Rev Analysis 1
Rev Analysis 2_QC
QC_Harkey
QC_Van Dine
Rev Validation 1
Rev Validation 2
Rev Analysis 3
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Example Analysis (Rte 190)
Kappa Statistics for Log Direction
0.64 0.91 0.91 3 Analyses
0.51 0.83 0.89 2 Validations
0.66 0.87 0.91 3 Analyses + 2 Validations
Kappa Statistics for Reverse Direction
0.83 0.85 0.87 3 Analyses
0.72 0.89 0.84 2 Validations
0.79 0.89 0.89 3 Analyses + 2 Validations
0.91 0.91 0.89 Quality Control Analysis (H&Y)
0.91 0.93 0.92 Quality Control Analysis (H&Y&D)
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Consistency Kappa Statistics
11 sites (22 sets of results - log and rev)
K > 0.90 9 sets 0.90 > K > 0.75 8 sets K < 0.75 5 sets
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UNC Highway Safety Research Center Highway Safety Information System
Example Analysis (Rte 190)
Accuracy AssessmentLog Analysis 1
Log Analysis 2
Log Analysis 3
Log Validation 1
Log Validation 1
Survey
K = 0.92
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UNC Highway Safety Research Center Highway Safety Information System
Accuracy Kappa Statistics
11 sites (log direction only)
K > 0.90 2 sites 0.90 > K > 0.75 4 sites K < 0.75 5 sites
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Conclusions
Are the results consistent?77 percent of the directional groups met the
threshold
Are the results accurate?55 percent of the sites met the threshold
Neither consistent nor accurate enough
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Conclusions
Need to conduct individual arc analysisvariation in length and radius across
multiple runsvariation in PCs and PTsdifferences with survey results
Critical to validate the results of any automated data acquisition technology
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For the Latest Info
Visit the HSIS web site
www.hsisinfo.org