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 Calibration  Uses for LFR and MPDEG  System Performance and Information  Planning Data 2

DATA ALMANAC Describe the data source 3

Data Almanac 22 reporting WIM sites  All upstream of weigh stations  All are CVISN sites  April March ,054,552 trucks  Intermittent data outages and problems  Data quality and accuracy? 4

Data Almanac These WIM sites provide  Axle weights  Gross vehicle weight  Spacing  Vehicle class  Unique transponder numbers 5

7 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 Speed WIM Sorting Site (with Overhead AVI) Notification Station (in-cab) Tracking System Ramp Sorter System Static Weighing 1 mileR.F. Antenna Trucks Bypass WIM Layout

9 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 Lane Weigh Lane

10 Indiana WIM Site Layout Inductive Loop Inductive Loop Piezo Sensor Load Cell Sensor Loops – vehicle presence & speed measurement Load Cell – weight & speed Piezo – speed & axle spacing

11 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 12 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 State operated/deve loped; compatible with NORPASS PrePass NORPASS 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 13

QUALITY CONTROL Describe Quality Control Metrics 14

15 Outline 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

16 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

17 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

18 ASTM Accuracy Requirements Specification tolerances (95% confidence)  Speed± 1 mph  Axle Spacing± 0.5 ft  Wheel weight± 20%  Axle weight ± 15%  Gross Vehicle Weight ± 6%

19 Accuracy Metrics Illustrated Vehicle Classification Axle Spacing  Speed Axle Weight  Wheel weight Accuracy Metric

20 Speed Accuracy Metric Class 9 drive tandem axle spacing Currently used by many agencies Target values based on manufacturer sales data  Individual trucks feet  Population average feet

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

22 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

23 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

24 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

25 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 feet typical

26 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 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  kips unloaded  kips loaded

Dahlin’s GVW Criteria 3 rd 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 bins  Bin width affects precision

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 Mean 1 = 56 kips Covariance 1 = 310 kips Proportion 1=100%

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 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%

34 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 feet  Population average feet

SENSOR HEALTH Describe from Quality Control Metrics About Sensor Health 35

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LFR AND MPDEG Describe Higgins Work 39

SYSTEM PERFORMANCE Describe Sample PMs 40

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 41

Freight performance metrics 42

Freight performance metrics 43 Using Federal Highway Administration (FHWA) / American Transportation Research Institute (ATRI) proprietary truck satellite data.

Farewell Bend Emigrant Hill Wyeth

Next Steps 49

Acknowledgements Dave McKane and Dave Fifer, ODOT Kristin Tufte and Heba Alawakiel, PSU 50