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

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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 - PowerPoint PPT Presentation

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

Page 1: 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

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

Sensor Health and CalibrationBridge and Pavement DesignSystem Performance and Planning

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DATA ALMANAC

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Data Almanac 22 reporting WIM sites

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

30,026,606 trucks

Intermittent data outages and problemsData quality and accuracy?

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Data Almanac These WIM sites provide

Axle weights Gross vehicle weight Axle spacing Vehicle class Bumper-to-bumper length Speed Unique transponder numbers

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RFID Tags - Transponders

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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

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

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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

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Primary Users ODOT Motor Carrier

Weight enforcement Workload and screening

Weight-mile tax enforcement Others want to use but

Not sure of quality / accuracyNot equipped to deal with large data sets

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PORTAL

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PORTAL -- Region’s ADUS

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What’s in the PORTAL Database?

Loop Detector DataLoop Detector Data20 s count, lane occupancy, 20 s count, lane occupancy, speed from 500 detectors speed from 500 detectors (1.2 mi spacing) (1.2 mi spacing)

Incident DataIncident Data140,000 since 1999140,000 since 1999

Weather DataWeather DataEvery day since 2004Every day since 2004

VMS DataVMS Data19 VMS since 199919 VMS since 1999

DaysDaysSince July 2004Since July 2004About 300 GBAbout 300 GB4.2 Million 4.2 Million Detector IntervalsDetector Intervals

Bus DataBus Data1 year stop level data1 year stop level data140,000,000 rows140,000,000 rows

001497

WIM DataWIM Data22 stations since 200522 stations since 200530,026,606 trucks30,026,606 trucks

Crash DataCrash DataAll state-reported crashes All state-reported crashes since 1999 - ~580,000since 1999 - ~580,000

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What’s Behind the Scenes?

Database ServerDatabase ServerPostgreSQL Relational Database PostgreSQL Relational Database Management System (RDBMS)Management System (RDBMS)

StorageStorage2 Terabyte Redundant Array 2 Terabyte Redundant Array of Independent Disks (RAID)of Independent Disks (RAID)

Web InterfaceWeb Interface

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QUALITY CONTROL

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WIM Archive Quality Control Upload all per-vehicle records to database

Only records with invalid data excluded Include “error” records

Want records with inaccurate data Plan to incorporate

Filters to exclude inaccurate dataAbility to adjust data

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Uses of the WIM Archive Sensor Health and Calibration Bridge and Pavement Design System Performance and Planning Data

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SENSOR HEALTH AND CALIBRATION

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Sensor Health and Calibration Current ODOT Practice:

Calibrate every 6 monthsor when scale operators notice “error”Use ~10 trucks (~consecutive)Not really monitoring WIM data, kept for

weight-mile tax purposes Why not use the data to monitor sensor

health and calibration?

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Class 9 Steer Axle Weight (Year)

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Class 9 Steer Axle Weight (March)

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January February March

GVW (kips) GVW (kips) GVW (kips)

Class 9 Gross Vehicle Weight

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Class 9 Axle 2-3 Spacing

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Issues How to automate “visual” assessment? WIM GVW calibration

With other WIM sites via matched tagsWith static scale via sampling

WIM axle weight/spacing calibrationWith other WIM sites via matched tags

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BRIDGE AND PAVEMENT DESIGN

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Oregon-specific Uses Bridge Design

First state-specific live-load rating factors (LFRs)

Side-by-side loading criteriaNeed ~2 weeks of CLEAN accurate dataPromised update every 2 to 5 years

Pavement DesignFacility specific factors for MEPDG

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SYSTEM PERFORMANCE AND PLANNING DATA

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System Performance and Planning

Transponder data allows unique matches Travel times on long-distance corridors

~1 million upstream-downstream pairs in 2007

Routing Planning metrics

Ton-miles on each corridor by various temporal considerations

Seasonal variability in loading, routes, and volumes

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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.

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Farewell Bend

Emigrant HillWyeth

Juniper Butte

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Travel Time, Oct 1 2007

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Conclusions Oregon’s extensive deployment useful

Transponders unique in data Building on experience with archiving

other data (i.e. freeway loops)Data improvement follows useVarious users requirements

Let the data tell the storyQuality control helps all users

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Acknowledgements Funding

UTCs: Oregon Transportation Research Education and Consortium & Rahall Transportation Institute

Oregon DOT and NCHRPNational Science Foundation (PORTAL)

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

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Thank You!

www.otrec.us

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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

Primarily configured for 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