Building a WIM Data Archive for Improved Modeling, Design, and Rating

Christopher Monsere Assistant Professor, Portland State University

Andrew Nichols Assistant Professor, Marshall University

NATMEC 2008, Washington, D.C. August 6, 2008

Outline Data Almanac PORTAL WIM Archive Quality Control Sample Uses of the WIM Archive

Sensor Health and CalibrationUses for LFR and MPDEGSystem Performance and InformationPlanning Data

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DATA ALMANACDescribe the data source

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Data Almanac 22 reporting WIM sites

All upstream of weigh stationsAll are CVISN sitesApril 2005 - March 2008

2007 - 12,054,552 trucks

Intermittent data outages and problemsData quality and accuracy?

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Data Almanac These WIM sites provide

Axle weightsGross vehicle weightSpacingVehicle classUnique transponder numbers

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Axle Weight Sensors Single load cells Sensors weigh vehicles

traveling at normal highway speeds

Weight measurement affected by many factors Site characteristics Environmental factors Truck dynamics

High SpeedWIM Sorting Site

(with Overhead AVI)

NotificationStation(in-cab)

TrackingSystem

RampSorterSystem

StaticWeighing

1 mileR.F. Antenna

Trucks BypassWIM Layout

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Sorting Lanes WIM installed on

entrance ramp to determine if vehicle is close to legal weight

Legal trucks directed to exit facility without being weighed statically

Exit LaneWeigh Lane

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Indiana WIM Site Layout

InductiveLoop

InductiveLoop

PiezoSensor

Load CellSensor

Loops – vehicle presence & speed measurement Load Cell – weight & speed Piezo – speed & axle spacing

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WIM Classification Algorithm Portion related to 5-axle

vehicles shown Works like a sieve Min/Max thresholds for

# of axles axle spacing axle weight gvw

Currently only use axle spacing

Vehicle Type 19 20 21 22 23 Vehicle Class 7 9 9 11 9 # of Axles 5 5 5 5 5 Min GVW 0 0 0 0 0 Max GVW 221 221 221 221 221 1 Min Weight 3 3 3 4 3 1 Max Weight 50 50 50 50 50 1 Axle Marking s s s x x 1-2 Min Spacing 0 0 0 0 0 1-2 Max Spacing 40 40 40 14.2 40 2 Min Weight 0 0 0 4 0 2 Max Weight 50 50 50 50 50 2 Axle Marking x d d x x 2-3 Min Spacing 0 0 0 0 0 2-3 Max Spacing 5.8 5.8 5.8 40 40 3 Min Weight 0 0 0 4 0 3 Max Weight 50 50 50 50 50 3 Axle Marking x d d x x 3-4 Min Spacing 0 0 0 0 0 3-4 Max Spacing 5.8 40 40 40 40 4 Min Weight 0 0 0 4 0 4 Max Weight 50 50 50 50 50 4 Axle Marking x d x x x 4-5 Min Spacing 0 0 0 0 0 4-5 Max Spacing 5.8 5.8 11.7 40 40 5 Min Weight 0 0 0 4 0 5 Max Weight 50 50 50 50 50 5 Axle Marking x d x x x

Electronic screening

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Three types of tagsHeavy Vehicle

Electronic License Plate (HELP)’s PrePass program

North American Pre-clearance and Safety System (NORPASS)

Oregon Green Light Program

All RF tags

State operated/developed; compatible with NORPASS

PrePassNORPASS

J. Lane, Briefing to American Association of State Highway and Transportation Officials (AASHTO), 22 February 2008

freight.transportation.org/doc/hwy/dc08/scoht_cvisn.ppt

PORTAL 101

What is PORTAL PORTAL - Postgresql database

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QUALITY CONTROLDescribe Quality Control Metrics

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Outline Summarize Existing Quality Control Metrics

Speed Accuracy Weight Accuracy

System-Wide Monitoring vs. Site-based Monitoring SQL ServerExtract Pivot Tables (.CUB)

Move Beyond Heuristic (sometimes visual) Rules to a Statistically Solid Decision Making Criteria Learn from Manufacturing World of Statistical Process

Control

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Data Quality Assessment Obtain data from WIM sites (nightly or weekly) Upload individual records to SQL database Macroscopic view to identify potential problems Microscopic view to investigate causes

Differentiate between problems & random noise using statistical process control (individual lanes)

Schedule maintenance/calibration accordingly

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Past Quality Control Hurdles Emerging WIM quality control metrics Enormous amounts of data Many vendors’ standard reports do not

provide level of detail necessary for effective quality control, particularly comparing multiple sites

Most DOTs do not have time or resources to perform QA/QC

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Accuracy Metrics Illustrated

Vehicle Classification Axle Spacing

Speed

Axle WeightWheel weight

Accuracy Metric

Accuracy Metric

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Speed Accuracy Metric Class 9 drive tandem axle spacing Currently used by many agencies Target values based on manufacturer

sales data Individual trucks

4.25-4.58 feetPopulation average

4.30-4.36 feet

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2002 Sales Data Data obtained from 3 of top 6 truck

manufacturers Verified at Indiana WIM sites with accurate

speed calibration

Weighted Average = 4.33 feet

Modal Value (0.01)’ would be even better than average

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WIM Speed Control Chart Class 9 Drive Tandem Axle Spacing Limits based on sales data and data from

sites with good calibration Limits same for all sites

Upper Control Limit (UCL) = 4.36 feetCenterline (CL) = 4.33 feetLower Control Limit (LCL) = 4.30 feet

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Speed & Axle Spacing WIM calculates speed by measuring

vehicle time between two sensors

WIM speed used to calculate axle spacing based on time between consecutive axles

sensor

sensorwim time

distancespeed

wimaxleaxle speed * time distance

Calibration factor

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Speed Accuracy Importance Used to calculate axle spacing

Vehicle classificationBridge formula compliance for enforcement

Used to apply weight calibration factors in some WIM systems

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Why Unclassified? All axle spacings in this lane were

overestimated by ~15% The axle spacing 3-4 is exceeding the

defined thresholds in the vehicle classification file (40’ max)

Axle 3-4 Spacing30-35 feet typical

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Weight Accuracy Metric Class 9 front axle weight

Individual trucks 8,000-12,000 lbs Population average 9,000-11,000 lbs

Currently used by many agencies This range is too big for detecting subtle drifts

Class 9 WIM Accuracy Metrics Steer Axle Weight

8.5 kips (GVW < 32 kips)9.3 kips (GVW 32-70 kips)10.4 kips (GVW > 70 kips)

Steer Axle Weight and Axle 1-2 SpacingLogarithmic relationshipSteer Axle Weight/Axle 1-2 Spacing

Steer Axle Wheel Weight Gross Vehicle Weight Distribution

28-32 kips unloaded70-80 kips loaded

Dahlin’s GVW Criteria

0%

2%

4%

6%

8%

10%

20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88

GVW Bin (kips)

Fre

quen

cy

Site 1 Site 2

Unloaded Peak 28-32 kips

Loaded Peak 70-80 kips

3rd Peak?

Class 9 GVW Distribution Modal distributions Look for peak mode shifts Trend identification difficult Very difficult to automate (QC)

Step function to find max GVW binsBin width affects precision

Mixture Models Fitting a continuous function to GVW

distribution – combination of normal distributions

Use algorithm to fit “mixture” of normal distributionsObtain mean and covariance for each

component

)(...)()()()( 22111

yfyfyfyfyf gg

g

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GVW Fit – 1 Component

0%

1%

2%

3%

4%

5%

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

GVW Bin (kips)

Fre

qu

ency

0%

1%

2%

3%

4%

5%

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

GVW Bin (kips)

Fre

qu

ency

1-Component

Mean 1 = 56 kipsCovariance 1 = 310 kipsProportion 1 = 100%

GVW Fit – 2 Components

0%

1%

2%

3%

4%

5%

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

GVW Bin (kips)

Fre

qu

ency

0%

1%

2%

3%

4%

5%

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

GVW Bin (kips)

Fre

qu

ency

2-Component

Mean 1 = 74 kipsCovariance 1 = 11 kipsMean 2 = 48 kipsCovariance 2 = 222 kipsProportion 1 = 33%Proportion 2 = 67%

GVW Fit – 3 Components

0%

1%

2%

3%

4%

5%

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

GVW Bin (kips)

Fre

qu

ency

0%

1%

2%

3%

4%

5%

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

GVW Bin (kips)

Fre

qu

ency

3-Component

Mean 1 = 75 kipsCovariance 1 = 9 kipsMean 2 = 52 kipsCovariance 2 = 193 kipsMean 3 = 30 kipsCovariance 3 = 11 kipsProportion 1 = 31%Proportion 2 = 57%Proportion 3 = 12%

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WIM Speed/Axle Spacing Accuracy Common speed accuracy metric is

Class 9 drive tandem axle spacing No consensus on proper values Verified with manufacturer sales numbers

Individual trucks 4.25-4.58 feet

Population average 4.30-4.36 feet

SENSOR HEALTHDescribe from Quality Control Metrics About Sensor Health

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LFR AND MPDEGDescribe Higgins Work

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SYSTEM PERFORMANCEDescribe Sample PMs

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Freight performance metricsAverage travel times on key corridorsTon-miles on each corridor by various

temporal considerationsOverweight vehicles on corridors by temporal

variation (measuring change)Seasonal variability in loading, routes, and

volumesPercent trucks with tags on each corridorOrigin-destination matrix

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Freight performance metrics

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Freight performance metrics

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Using Federal Highway Administration (FHWA) / American Transportation Research Institute (ATRI) proprietary truck satellite data.

Farewell Bend

Emigrant HillWyeth

Next Steps

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Acknowledgements Dave McKane and Dave Fifer, ODOT Kristin Tufte and Heba Alawakiel, PSU

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