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

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

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Building a wim data archive for improved modeling design and rating

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



  • Data Almanac


  • WIM Archive Quality Control

  • Sample Uses of the WIM Archive

    • Sensor Health and Calibration

    • Uses for LFR and MPDEG

    • System Performance and Information

    • Planning Data

Data almanac

Describe the data source

DATA almanac

Data almanac1

Data Almanac

  • 22 reporting WIM sites

    • All upstream of weigh stations

    • All are CVISN sites

    • April 2005 - March 2008

      • 2007 - 12,054,552 trucks

    • Intermittent data outages and problems

    • Data quality and accuracy?

Data almanac2

Data Almanac

  • These WIM sites provide

    • Axle weights

    • Gross vehicle weight

    • Spacing

    • Vehicle class

    • Unique transponder numbers

Axle weight sensors

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

Wim layout

WIM Layout

Trucks Bypass

R.F. Antenna

1 mile




High Speed



WIM Sorting Site


(with Overhead AVI)





Sorting lanes

Sorting Lanes

Exit Lane

Weigh Lane

  • WIM installed on entrance ramp to determine if vehicle is close to legal weight

  • Legal trucks directed to exit facility without being weighed statically

Indiana wim site layout

Indiana WIM Site Layout

  • Loops – vehicle presence & speed measurement

  • Load Cell – weight & speed

  • Piezo – speed & axle spacing





Load Cell




Wim classification algorithm

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

Electronic screening



State operated/developed; compatible with NORPASS

Electronic screening

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

  • Three types of tags

    • Heavy Vehicle Electronic License Plate (HELP)’s PrePass program

    • North American Pre-clearance and Safety System (NORPASS)

    • Oregon Green Light Program

  • All RF tags

Portal 101

What is PORTAL PORTAL - Postgresql database


Quality control

Describe Quality Control Metrics




  • Summarize Existing Quality Control Metrics

    • Speed Accuracy

    • Weight Accuracy

  • System-Wide Monitoring vs. Site-based Monitoring

    • SQL ServerExtract Pivot Tables (.CUB)

  • Move Beyond Heuristic (sometimes visual) Rules to a Statistically Solid Decision Making Criteria

    • Learn from Manufacturing World of Statistical Process Control

Data quality assessment

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

Past quality control hurdles

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

Accuracy metrics illustrated

Accuracy Metrics Illustrated

  • Vehicle Classification

  • Axle Spacing

    • Speed

  • Axle Weight

    • Wheel weight

Accuracy Metric

Accuracy Metric

Speed accuracy metric

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 feet

    • Population average 4.30-4.36 feet

2002 sales data

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

Wim speed control chart

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 feet

    • Centerline (CL)= 4.33 feet

    • Lower Control Limit (LCL) = 4.30 feet

Speed axle spacing

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

Calibration factor

Speed accuracy importance

Speed Accuracy Importance

  • Used to calculate axle spacing

    • Vehicle classification

    • Bridge formula compliance for enforcement

  • Used to apply weight calibration factors in some WIM systems

Why unclassified

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 Spacing

30-35 feet typical

Weight accuracy metric

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

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 Spacing

    • Logarithmic relationship

    • Steer Axle Weight/Axle 1-2 Spacing

  • Steer Axle Wheel Weight

  • Gross Vehicle Weight Distribution

    • 28-32 kips unloaded

    • 70-80 kips loaded

Dahlin s gvw criteria

Dahlin’s GVW Criteria

3rd Peak?

Class 9 gvw distribution

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 bins

    • Bin width affects precision

Mixture models

Mixture Models

  • Fitting a continuous function to GVW distribution – combination of normal distributions

  • Use algorithm to fit “mixture” of normal distributions

    • Obtain mean and covariance for each component

Gvw fit 1 component

GVW Fit – 1 Component

Mean 1 = 56 kips

Covariance 1 = 310 kips

Proportion 1=100%

Gvw fit 2 components

GVW Fit – 2 Components

Mean 1 = 74 kips

Covariance 1 = 11 kips

Mean 2= 48 kips

Covariance 2=222 kips

Proportion 1=33%

Proportion 2=67%

Gvw fit 3 components

GVW Fit – 3 Components

Mean 1 = 75 kips

Covariance 1 = 9 kips

Mean 2= 52 kips

Covariance 2=193 kips

Mean 3=30 kips

Covariance 3=11 kips

Proportion 1=31%

Proportion 2=57%

Proportion 3 =12%

Wim speed axle spacing accuracy

WIM Speed/Axle Spacing Accuracy

  • Common speed accuracy metric isClass 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 health

Describe from Quality Control Metrics About Sensor Health


Lfr and mpdeg

Describe Higgins Work


System performance

Describe Sample PMs

System performance

Freight performance metrics

Freight performance metrics

  • Average travel times on key corridors

  • Ton-miles on each corridor by various temporal considerations

  • Overweight vehicles on corridors by temporal variation (measuring change)

  • Seasonal variability in loading, routes, and volumes

  • Percent trucks with tags on each corridor

  • Origin-destination matrix

Freight performance metrics1

Freight performance metrics

Freight performance metrics2

Freight performance metrics

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

Building a wim data archive for improved modeling design and rating


Emigrant Hill

Farewell Bend

Next steps

Next Steps



Dave McKane and Dave Fifer, ODOT

Kristin Tufte and Heba Alawakiel, PSU

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