WIM  Data

Analyst’s Manual

Publication No. FHWA‐IF‐10‐018  
FOREWORD

This manual provides information and recommended procedures to be utilized by an agency’s 

Weigh-in-Motion (WIM) Office Data Analyst to perform validation and quality control (QC) 

checks of WIM traffic data. This manual focuses on data generated by WIM systems that have 

the capability to produce high quality data. Many of the recommended data QC procedures are 

dependent upon data containing wheel loads (in conformance with the Type I WIM system 

requirements of ASTM E 1318). However, the more basic QC procedures discussed may be of 

use to an analyst performing checks on data generated by systems generating only axle load data 

(conforming to Type II system requirements of ASTM E 1318) and/or systems relying upon 

autocalibration features deemed necessary to obtain loading data adequate for certain programs.

This document is intended to present the WIM data analysts with the necessary information and 

guidance to identify missing or invalid WIM data, to determine the cause and extent of missing 

or invalid data, and the course of action to correct problems. Basic information and 

recommendations are provided for the novice analyst, and more extensive procedures and 

guidelines are provided to develop and assist experienced analysts.

To follow the procedures recommended in this manual will take a great deal of time and effort 

by the data analyst. However, the proper installation and maintenance of high quality WIM 

systems is a costly investment. Such investment provides an agency only with the capability to 

obtain high quality traffic data. Such high quality data will not be achievable in the absence of 

following diligent data QC and system monitoring procedures.

Notice

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

in the interest of information exchange. The U.S. Government assumes no liability for the use of 

the information contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement.
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TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-IF-10-018

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

WIM Data Analyst’s Manual

5. Report Date

June 2010

6. Performing Organization Code: 

7. Author(s)

Rich Quinley

8. Performing Organization Report No. 

9. Performing Organization Name and Address

The Transtec Group

6111 Balcones Drive

Austin, TX 78731

10. Work Unit No.

11. Contract or Grant No.

DTFH61-08-D-00026

12. Sponsoring Agency Name and Address

FHWA Office of Pavement Technology

1200 New Jersey Ave. SE

Washington, DC 20590

13. Type of Report and Period Covered

Technical Manual

14. Sponsoring Agency Code

HIPT-30

15. Supplementary Notes

FHWA Contracting Officer’s Technical Representative (COTR): Mike Moravec

16. Abstract

This manual provides information and recommended procedures to be utilized by an agency’s Weigh-in-Motion 

(WIM) Office Data Analyst to perform validation and quality control (QC) checks of WIM traffic data. This 

manual focuses on data generated by WIM systems that have the capability to produce high quality data. Many 

of the recommended data QC procedures are dependent upon data containing wheel loads (in conformance with 

the Type I WIM system requirements of ASTM E 1318). This manual has been developed to ensure that high 

quality WIM data is collected and used to produce reliable and representative load spectra for input into the 

Mechanistic-Empirical Pavement Design Guide (M-E PDG) software resulting in reliable and predictable 

pavement designs. While this is the primary use, there are many other uses including performing checks on data 

generated by systems that produce only axle load data.

17. Key Words

WIM Data Analysis, WIM Data Validation, WIM

Data Quality Control, Traffic Inputs for Pavement 

Design, M-E PDG, and Load spectra

18. Distribution Statement

No restrictions. This document is available to the public 

through the Federal Highway Administration (FHWA)

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

183

22. Price

N/A

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized
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TABLE OF CONTENTS

SECTION 1. INTRODUCTION .................................................................................................... 1

1.1. OVERVIEW OF WIM DATA ............................................................................................ 1

1.2. SIGNIFICANCE OF HIGH QUALITY WIM DATA ........................................................ 2

1.3. OVERVIEW OF THE MANUAL....................................................................................... 2

SECTION 2. WIM BASICS........................................................................................................... 5

2.1. WIM SYSTEM VS. WIM SITE.......................................................................................... 5

2.2. DEFINITIONS AND TERMS............................................................................................. 9

2.2.1. WIM.............................................................................................................................. 9

2.2.2. Site Assessment ............................................................................................................ 9

2.2.3. Weight......................................................................................................................... 10

2.2.4. GVW........................................................................................................................... 10

2.2.5. Calibration and Validation (Using Test Trucks Onsite) ............................................. 10

2.2.6. LTPP ........................................................................................................................... 11

2.2.7. LTPP SPS TPF Study ................................................................................................. 11

2.2.8. Traffic ETG................................................................................................................. 11

2.3. VEHICLE CLASSIFICATION VERSUS VEHICLE TYPE............................................ 11

2.4. TASKS OF THE OFFICE DATA ANALYST ................................................................. 17

2.4.1. Causes of Missing or Invalid Data.............................................................................. 17

2.4.2. Factors Affecting WIM Data Quality Which Can Be Controlled............................... 18

2.4.3. Factors Affecting WIM Data Quality Which Can Be Somewhat Controlled (By Site 

Selection) .............................................................................................................................. 18

2.4.4. Factors Affecting WIM Data Quality Which Cannot Be Controlled ......................... 18

2.5. WHAT QUALITY OF DATA SHOULD BE EXPECTED? ............................................ 19

SECTION 3. STEPS FOR DATA VALIDATION AND SYSTEM MONITORING FROM 

OFFICE......................................................................................................................................... 21

3.1. INITIAL REAL-TIME REVIEW...................................................................................... 22

3.2. DATA REVIEW USING CANNED REPORTS .............................................................. 26

3.2.1. Class and Speed Reports............................................................................................. 26

3.2.2. Individual Vehicle Record Summary Reports............................................................ 39

3.3. AUTOMATED VALIDATION PROGRAMS ................................................................. 53

3.4. DATA QC – FOLLOW-UP PROCEDURES.................................................................... 54

3.4.1. Real-Time System Check of Parameters and Settings................................................ 54

3.4.2. Remote Real-Time Tests and Diagnostics of System Component Operation ............ 58

SECTION 4. EXTENSIVE DATA ANALYSES UTILIZING INDIVIDUAL VEHICLE 

RECORDS.................................................................................................................................... 63

SECTION 5. STEPS FOR MONITORING SYSTEM CALIBRATION FROM OFFICE ......... 75

5.1. GENERATE WEIGHT AND AXLE SPACING STATISTICS FOR SAMPLE OF 

TRUCK TRAFFIC STREAM .................................................................................................. 75

5.2. MONITORING TRUCK TRAFFIC STREAM STATISTICS OVER TIME................... 94

5.3. EFFECTS OF CALIBRATION FACTOR ADJUSTMENTS ON TRUCK TRAFFIC 

STREAM DATA ...................................................................................................................... 99

5.4. ADJUSTMENT OF CALIBRATION FACTORS BASED UPON TRUCK TRAFFIC 

STREAM DATA .................................................................................................................... 113
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5.5. RESOLVING ACCURACY PROBLEMS IDENTIFIED BY MONITORING OF 

TRUCK TRAFFIC STREAM ................................................................................................ 115

5.5.1. Gross Weight Distribution ........................................................................................ 115

5.5.2. Individual Sensor Weight Outputs............................................................................ 116

5.5.3. Axle Spacings (and thereby speed)........................................................................... 118

5.5.4. Overall Vehicle Length............................................................................................. 118

5.6. MAKING BEST USE OF AVAILABLE RESOURCES................................................ 119

SECTION 6. REFERENCES...................................................................................................... 121

APPENDIX A......................................................................................................................... 123

APPENDIX B......................................................................................................................... 155

APPENDIX C......................................................................................................................... 171
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LIST OF FIGURES

Figure 1. List. ASTM E 1318 Type I WIM Systems Requirements............................................... 2

Figure 2. Photo. Staggered weigh sensors (bending plates) and detection loops (single threshold 

system). ........................................................................................................................................... 6

Figure 3. Photo. In-line weigh sensors (single load cells), a trailing axle sensor, and detection 

loops (single threshold system)....................................................................................................... 6

Figure 4. Photo. In-line weigh sensors (quartz piezos) and detection loops (double threshold 

system). ........................................................................................................................................... 7

Figure 5. Photo. Controller cabinet, front view. ............................................................................. 8

Figure 6. Photo. Controller cabinet, rear view, with in-road sensor inputs to controller and 

lightning protection......................................................................................................................... 8

Figure 7. Photo. Two-lane WIM site, with Portland cement concrete (PCC) pavement installed 

for approach and departure. ............................................................................................................ 9

Figure 8. Photo. Class 7, single-unit truck with four of its five axles in contact with pavement. 12

Figure 9. Photo. Class 8, Type 2S1............................................................................................... 12

Figure 10. Photo. Class 8, Type 3S1............................................................................................. 13

Figure 11. Photo. Class 9, Type 3S2............................................................................................. 13

Figure 12. Photo. Class 9, Type 3S2 with “spread” rear tandem.................................................. 13

Figure 13. Photo. Class 9, Type 32............................................................................................... 13

Figure 14. Photo. Class 10, Type 3S3........................................................................................... 14

Figure 15. Photo. Class 11, Type 2S12......................................................................................... 14

Figure 16. Photo. Class 12, Type 3S12......................................................................................... 14

Figure 17. Picture. Class 13, Longer Combination Vehicles (LCVs). ......................................... 15

Figure 18. Screen shot. Site menu, time and date......................................................................... 22

Figure 19. Screen shot. Site menu, stored data files. .................................................................... 23

Figure 20. Screen shot. Site real-time “View Vehicles” mode for system’s Lane 1. ................... 24

Figure 21. Screen shots. Menu screen displaying portion of classification algorithm entries...... 25

Figure 22. Screen shot. Site real-time “View Vehicles” mode..................................................... 25

Figure 23. Screen shots. Site real-time menu screen displaying calibration factors.................... 26

Figure 24. Reports. Missing hourly data for lane or lanes............................................................ 28

Figure 25. Report. Missing data all lanes. .................................................................................... 29

Figure 26. Report. Erroneous classification for lane .................................................................... 31

Figure 27. Report. Erroneous speeds for lane............................................................................... 32

Figure 28. Report. Speed by class for lane. .................................................................................. 33

Figure 29. Report. Class and speed distributions, Lane 1 versus Lane 2. .................................... 34

Figure 30. Report. Traffic congestion; legitimate low speeds. ..................................................... 36

Figure 31. Report. Change in class count pattern and increase in system errors for lane. ........... 37

Figure 32. Report. System error identification. ............................................................................ 38

Figure 33. Report. Individual vehicle record class and weight summary data for one lane......... 41

Figure 34. Screenshots. Examples of vehicles not classified by the system................................. 43

Figure 35. Screen shots. Legitimate Class 13 vehicles................................................................ 44

Figure 36. Screen shot. Vehicle misclassified as Class 13 due to “ghost” axles.......................... 44

Figure 37. Screen shot. Two records for one combination vehicle. ............................................. 45

Figure 38. Photo. Class 9 Type 32 with long towbar.................................................................... 45

Figure 39. Photo. Class 9 logging truck........................................................................................ 45

Figure 40. Report. Class distribution, weight violation counts, and warning counts by lane....... 47
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Figure 41. Report. Error and warning vehicles by hour for lane. ................................................. 48

Figure 42. Screen shot. Vehicle with “light” wheel weights on left side. .................................... 49

Figure 43. Report. Gross weight distribution for each truck class for lane. ................................. 51

Figure 44. List. Summary of FHWA TMAS QC Checks............................................................. 53

Figure 45. Screen shot. Example of a record of a system’s setup parameters for one lane.......... 56

Figure 46. Screen shot. Example of a record of a system’s weight calibration factors for one lane.

....................................................................................................................................................... 57

Figure 47. Screen shot. Example of a system’s sensor configuration and loop delay constant for 

one lane. ........................................................................................................................................ 58

Figure 48. Screen shots. Example of a system’s menu screen for selecting system tests (“System 

test”) and three examples of the tests............................................................................................ 60

Figure 49. Screen shot. Example of a system’s loop and weigh sensor duration diagnostics...... 61

Figure 50. Screen shot. Example of system’s menu page displaying a bending plate sensor’s 

baseline value................................................................................................................................ 61

Figure 51. Screen shot. Portion of simple spreadsheet filtered for unclassified vehicles............. 64

Figure 52. Screen shot. Plot of Class 9 steer axle wheel weights by hour of day. ....................... 66

Figure 53. Screen shot. AX1LT sorted for ascending values. ...................................................... 67

Figure 54. Screen shot. AX1RT sorted for ascending values....................................................... 68

Figure 55. Photo. Class 9, Type 3S2 typical axle and wheel alignment crossing right and left 

weigh sensors................................................................................................................................ 68

Figure 56. Screen shot. Calculated fields for testing “Invalid Measurement” flags and analyzing 

axle imbalances............................................................................................................................. 69

Figure 57. Screen shot. Invalid Measurement weights by GVW range....................................... 71

Figure 58. Screen shot. Table displaying summary of axle right versus left weight imbalance 

statistics......................................................................................................................................... 72

Figure 59. Photo. This vehicle combination may conform to a Class 9 Type 2S3 under some 

classification schemes................................................................................................................... 76

Figure 60. Tabular Report. Distribution of lane counts by GVW, for site with mix of both loaded 

and empty Class 9 vehicles........................................................................................................... 78

Figure 61. Report Graph. Distribution of lane counts by GVW, for site with mix of both loaded 

and empty Class 9 vehicles........................................................................................................... 79

Figure 62. Tabular Report. Distribution of lane counts by GVW, site with very few empty Class 

9 vehicles. ..................................................................................................................................... 80

Figure 63. Report Graph. Distribution of lane counts by GVW, site with very few empty Class 9 

vehicles. ........................................................................................................................................ 81

Figure 64. Procedure. Pre calibration - right and left sensor balance. ......................................... 82

Figure 65. Report. Distribution of Class 9 weights and axle spacings by speed for one lane, flat 

roadway grade............................................................................................................................... 84

Figure 66. Report. Distribution of Class 9 weights and axle spacings by speed for one lane, uphill 

grade.............................................................................................................................................. 87

Figure 67. Report. Distribution of Class 11 weights, vehicle length, and wheelbase by speed for 

one lane. ........................................................................................................................................ 89

Figure 68. Screen shot. “Tables” Worksheet. ............................................................................... 91

Figure 69. Screen shot. Weight statistics for calibration monitoring and tracking....................... 92

Figure 70. Screen shot. Additional statistics for calibration monitoring and tracking. ................ 92

Figure 71. Screen shot. GVW distribution plot. ........................................................................... 93
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Figure 72. Screen shot. Weights versus speed statistics. .............................................................. 94

Figure 73. Graph. Traffic stream Class 9 GVW distribution plots for 12 consecutive months, 

long haul high volume. ................................................................................................................. 95

Figure 74. Graph. Traffic stream Class 9 Axle 1 weight distribution plots for 12 consecutive 

months........................................................................................................................................... 96

Figure 75. Graph. Class 9 GVW distribution plots for spring season over three-year period, local 

traffic............................................................................................................................................. 97

Figure 76. Graph. GVW distribution plots for 12 consecutive months, low volume................... 98

Figure 77. Graph. Class 9 GVW distribution plots for 12 consecutive months, weight shift. ..... 99

Figure 78. Graphs. Calibration test truck GVW WIM error versus speed plots, and truck traffic 

stream speeds versus calibration test truck speeds plots............................................................. 101

Figure 79. Graph. Class 9 traffic stream GVW distribution plots before and after calibration 

factor adjustments. ...................................................................................................................... 102

Figure 80. Graph. Class 9 traffic stream GVW distribution plots over period with no calibration 

factor changes. ............................................................................................................................ 103

Figure 81. Graph. Calibration test truck GVW WIM error versus speed plots. ......................... 104

Figure 82. Graph. Calibration test truck GVW WIM error x speed plots, before and after factor 

adjustments. ................................................................................................................................ 105

Figure 83. Graph. Effects of calibration factor adjustments on traffic stream WIM weights. ... 106

Figure 84. Screen shot. Tracking of system modifications and monthly calibration monitoring 

statistics....................................................................................................................................... 107

Figure 85. Graph. Class 9 GVW distribution plots, empty and loaded peaks too heavy. .......... 110

Figure 86. Graphs. Calibration test truck GVW WIM error x speed plots and truck traffic stream 

speeds versus calibration test truck speeds plots, ineffective calibration. .................................. 112

Figure 87. Screen shot. Weights versus speed statistics, ineffective calibration........................ 113

Figure 88. Procedure. Procedures and examples for adjusting calibration factors based upon 

traffic stream data statistics......................................................................................................... 117

Figure 89. Procedure. Procedure and example for adjusting axle spacing lengths (and thereby 

speeds)......................................................................................................................................... 118

Figure 90. Procedure. Procedure and example for adjusting overall vehicle lengths................. 119

LIST OF TABLES

Table 1. Example Class Scheme................................................................................................... 16
1

SECTION 1. INTRODUCTION

Weigh-in-Motion (WIM) systems meeting the Type I requirements of ASTM E 1318 have the 

capability of producing continuous high quality traffic data for multilane roadway locations. 

These WIM systems produce various data elements for each vehicle passing through the site, 

including:

• Time and Date

• Lane

• Speed

• Vehicle Classification

• Wheel Load

• Axle Load

• Axle Group Load

• Gross Vehicle Weight

• Individual Axle Spacings

• Overall Vehicle Length

• Violation Code

It is noted that some of the listed data elements may not actually be stored by the system as raw 

data onsite, but is instead generated during the data processing session following transfer of the 

raw data to the Office Computer (discussed below). The stored data element “Lane” is typically 

the “WIM” lane number as determined by a particular system’s sensor configuration and sensor 

inputs to the controller unit. However, for reporting purposes, the vendor’s or agency’s 

application software is programmed to display the data for a lane or lanes based upon the 

agency’s lane designation (e.g. “Northbound No. 1”).

1.1. OVERVIEW OF WIM DATA

A WIM system’s controller typically stores both summary (binned) data and vehicle record data 

for each day.

• Binned data

o All of a day’s vehicles are typically binned by count for hour of day, lane, 

classification, and speed range (see previous explanation of “lane” as stored by 

the controller).

o Contain no individual vehicle data elements.

• Individual vehicle record data

o Include data elements for individual vehicles.

o Typically the system allows the user to define parameters, such as classification 

or front axle weight threshold, which determine whether a record is stored for a 

particular vehicle or whether the vehicle is simply counted in bins.

o These individual vehicle records are sometimes referred to as “Per Vehicle 

Records” (PVRs) or “Truck Records”.
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It is the function of the WIM system’s onsite controller to process inputs from the in-road 

sensors and to create and temporarily store the binned data and the individual records, typically 

in binary format. This raw data is routinely downloaded or otherwise transferred to the analyst’s 

Office Computer (sometimes referred to as Host Computer). An application software program 

provided by the WIM system vendor is then utilized to process the raw data, including the 

generation of reports and ASCII files and the view of individual vehicle records. Some agencies 

utilize their own custom application software to process the raw data. Also, some agencies 

utilize their own or third party software to automate the raw data downloads and/or perform data 

validation checks.

1.2. SIGNIFICANCE OF HIGH QUALITY WIM DATA

Truck wheel loading data is of particular interest to determine inputs to the MechanisticEmpirical Pavement Design Guide (M-E PDG) software. However, for loading data to be 

considered of high quality, such data must meet the ASTM E 1318 Type I requirements for 

accuracy displayed in Figure 1.

 

Figure 1. List. ASTM E 1318 Type I WIM Systems Requirements.

Note that high quality WIM equipment properly installed in structurally sound and smooth 

pavement, at a site with proper roadway geometry and traffic operating characteristics, has the 

capability to produce loading data with much higher accuracy than those required by ASTM E 

1318. However, to produce high quality data, the WIM system must be properly monitored and 

maintained.

1.3. OVERVIEW OF THE MANUAL 

This Manual describes recommended procedures to be followed by an agency’s WIM Office 

Data Analyst in performing data quality checks of WIM traffic data. This section (Section 1) 

provides an overview of a WIM system's function and its data output, describes the quality of 

WIM data that are addressed in this manual, and provides an overview of the manual itself.

SECTION 2 provides information on “WIM Basics” which may be helpful to the novice analyst.

SECTION 3 provides guidance and recommendations on performing data validation and 

performing system monitoring remotely from the office, including remote real time checks of 
3

traffic, reviewing reports generated by the office computer’s WIM application software, and 

follow-up procedures to be performed when questionable data is identified.

SECTION 4 discusses procedures for performing extensive analyses of individual vehicle 

records by importing the WIM data into spreadsheet or database programs.

SECTION 5 discusses procedures for monitoring a system’s calibration over time and 

procedures that may be taken to fine-tune a system’s calibration factors in order to provide the 

most accurate size and weight data possible.
4
5

SECTION 2. WIM BASICS

2.1. WIM SYSTEM VS. WIM SITE

A WIM system, as used in this manual, refers to the following components:

• One controller, its computer, and associated electronics.

o CPU

o Sensor Interface Cards

o Communication Interface

o Data Storage Medium

o Software and/or Firmware

• All roadway sensors and their leads for all lanes for which traffic data is being processed 

by the controller (at least one lane must have weigh sensors).

• Controller support items such as lightning protection, uninterruptable power supply, etc.

A WIM site, as used in this manual, refers to a specific roadway location at which a WIM system 

has been (or will be) installed. Such a site includes:

• All WIM system components.

• The power and communication service facilities.

• All wiring, conduits, pull boxes, and cabinets necessary to make the WIM system 

functional.

• The pavement section in which the roadway components are installed and the pavement 

approach and departure from the in-road sensors.

Figure 2 through Figure 6 display WIM system components and Figure 7 displays a two-lane 

WIM site.
6

Figure 2. Photo. Staggered weigh sensors (bending plates) and detection loops (single 

threshold system).

Figure 3. Photo. In-line weigh sensors (single load cells), a trailing axle sensor, and 

detection loops (single threshold system).
7

Figure 4. Photo. In-line weigh sensors (quartz piezos) and detection loops (double threshold 

system).

For single threshold weighing, each axle’s right and left wheel or dual wheel is weighed once by 

the right and left sensors. For staggered (leading and trailing) sensors, a vehicle’s speed is 

calculated based upon the time it takes for each axle’s wheels to hit the leading and trailing 

sensors. For in-line (side-by-side) weigh sensors, speed can be calculated by one of two 

methods:

1. An axle sensor (non-weighing) may be installed downstream of the weigh sensors and

speed is calculated based upon the time it takes for each axle’s wheels to hit the weigh 

sensors and trailing axle sensor.

2. If no axle sensor is installed, the speed is calculated based upon the time between a 

vehicle’s triggering the leading and trailing loops. This is not as accurate as using sensorto-sensor time measurements.

For double threshold weighing, each axle’s right and left wheel or dual wheel is weighed twice 

by the right and left sensors. The system then reports a single left weight and a single right 

weight for each axle. A vehicle’s speed is calculated based upon the time it takes for each axle’s 

wheels to hit the leading and trailing weigh sensors.

Throughout this manual, a WIM system’s right and left weigh sensors are discussed in regard to 

weight data output analyses, diagnostics, calibration, etc. Such right and left sensors will be 

treated as single sensors even though for double threshold systems there are actually two right 

sensors and two left sensors.
8

Figure 5. Photo. Controller cabinet, front view.

Figure 6. Photo. Controller cabinet, rear view, with in-road sensor inputs to controller and 

lightning protection.
9

Figure 7. Photo. Two-lane WIM site, with Portland cement concrete (PCC) pavement 

installed for approach and departure.

2.2. DEFINITIONS AND TERMS

2.2.1. WIM

As defined in ASTM E 1318:

“...the process of estimating a moving vehicle’s gross weight and the portion of that weight that 

is carried by each wheel, axle, or axle group, or combination thereof, by measurement and 

analysis of dynamic vehicle tire forces.”

2.2.2. Site Assessment

Refers to onsite activities preceding either an onsite evaluation or calibration to verify and 

document that:

• The WIM system is operational.

• The sensors have no visible problems.

• The pavement condition shows no apparent deterioration.
10

2.2.3. Weight

Throughout this manual, the term “weight” will be used, even though it may be technically 

appropriate to use another term such as “load” or “force”. Weights will typically be expressed in 

kips (k), where 1 k equals 1000 pounds (lbs).

2.2.4. GVW

Throughout this manual, the term Gross Vehicle Weight (GVW) is used to refer to the sum of all 

of a vehicle’s wheel weights or axle weights. GVWs will typically be expressed in units of kips.

2.2.5. Calibration and Validation (Using Test Trucks Onsite)

Both calibration and validation utilize a process by which the known static axle and/or wheel 

weights and known axle spacings of one or more test vehicle(s) are compared with the 

corresponding estimates from a WIM system’s reported dynamic wheel weights and axle 

spacings for such test vehicle(s). 

The purpose of calibration is to determine and implement the WIM system settings which will 

result in the system’s generating the best possible estimate of static axle and/or wheel weights, 

axle spacing distances, and vehicle speeds for the most typical truck configurations in the traffic 

stream over the range of speeds typical of such truck configurations.

The purpose of validation is to check a system’s accuracy for conformance to an agency’s 

specified requirements. Once a system has been initially calibrated, test trucks should be run on 

a routine basis (or as otherwise deemed necessary) to check the system’s calibration. This is also 

typically referred to as a validation. If such validation indicates that the system meets accuracy 

requirements but that accuracy could be improved, then the calibration factors may be adjusted 

and additional test truck runs made to confirm that the factor adjustments produced the desired 

effect.

2.2.5.1. Calibration Factor

Refers to a user-defined value that is used by a WIM system to convert raw sensor readings into 

weights.

2.2.5.2. Calibration Factor Speed Point

Also referred to as Speed Bin, refers to a user-defined speed for which a calibration factor can be 

entered for a weigh sensor. Certain WIM systems provide for three or more calibration factor 

speed points, which allow the user to determine appropriate calibration factors over a range of 

vehicle speeds which will best compensate for the effects of speed. For speeds between the 

speed points, the system uses linear interpolation to apply calibration factors to the sensor’s 

readings.
11

2.2.5.3. WIM Error

Is the difference between a test truck’s static weights and the corresponding WIM reported 

weights as derived from the test truck’s dynamic readings.

2.2.6. LTPP

Refers to the Long-Term Pavement Performance (LTPP) program

(http://www.fhwa.dot.gov/pavement/ltpp/), a 20-year study of in-service pavements across North 

America. Its goal is to extend the life of highway pavements through various designs of new and 

rehabilitated pavement structures. The LTPP program evaluates different pavement materials 

under different traffic loading, environmental and subgrade soil conditions. Different pavement 

maintenance practices are evaluated as well. The LTPP program was established in 1987 under 

the Strategic Highway Research Program (SHRP), and is now managed by the Federal Highway 

Administration (FHWA).

2.2.7. LTPP SPS TPF Study

Refers to the LTPP Specific Pavement Study (SPS) Traffic Pooled Fund Study, TPF-5(004). 

Phase I of this study consists of assessing, evaluating, and calibrating WIM systems used to 

collect traffic data at the SPS sites across the country. Phase II consists of the installation and 

maintenance of new WIM equipment as necessary to ensure high-quality data collection.

This Manual cites a number of LTPP and LTPP SPS TPF Study documents such as the LTPP 

Field Operations WIM Guide, the WIM Model Specifications (See Appendix A) and the LTPP 

Classification Scheme. These documents are extensive and contain valuable information for 

WIM equipment and site maintenance.

2.2.8. Traffic ETG

Refers to the Transportation Research Board (TRB) Expert Task Group (ETG) on LTPP Traffic 

Data Collection and Analysis. The Traffic ETG is composed of individuals with significant 

experience and involvement in the collection and/or analysis of truck traffic data. The Traffic 

ETG provides advice and guidance to the staff of the LTPP program regarding the reliability and 

precision of traffic data, among other things.

2.3. VEHICLE CLASSIFICATION VERSUS VEHICLE TYPE

As used in this manual, vehicle classification refers to the identification of vehicles according to 

FHWA’s 13 Class Scheme as described in the Traffic Monitoring Guide 

(http://www.fhwa.dot.gov/ohim/tmguide/). However, individual classes within this scheme 

include vehicles with different axle configurations and operating characteristics that need to be 

uniquely identified by a WIM system’s classification algorithm. Additionally, the ability to 

perform analyses on vehicles with similar axle configurations and operating characteristics, 

regardless of FHWA classification, can be of great benefit in performing data analyses. Vehicle 
12

type is used in this manual to refer to vehicles with similar axle configurations and operating 

characteristics. A few examples of vehicle types follow.

Class 7 includes all trucks on a single-frame with four or more axles. For trucks with “variable 

load suspensions” or “lift axles” (as shown in Figure 8), only the axles in contact with the 

pavement are counted to determine classification.

Figure 8. Photo. Class 7, single-unit truck with four of its five axles in contact with 

pavement.

Class 8 includes several common three- and four-axle single-trailer configurations. Figure 9

displays a two-axle tractor with a single axle semi-trailer and Figure 10 displays a three-axle 

tractor with a single axle semi-trailer. For this method of defining a truck combination type, the 

first value is the number of axles on the power unit (tractor or straight truck), the “S” signifies a 

semi-trailer, and the following value is the number of axles on the trailer.

Figure 9. Photo. Class 8, Type 2S1.
13

Figure 10. Photo. Class 8, Type 3S1.

Class 9 includes five-axle single-trailer trucks. Figure 11 displays the three-axle tractor and twoaxle semi-trailer, which is by far the most predominant Class 9 type. Figure 12 displays the 

same type but with a “spread” tandem on the trailer. If this axle spread exceeds eight feet it is 

not a true tandem axle and is considered to be two individual axles. Figure 13 displays a threeaxle straight truck pulling a two-axle full trailer. As such, there is no “S” preceding the value 

defining the trailer’s number of axles.

Figure 11. Photo. Class 9, Type 3S2.

Figure 12. Photo. Class 9, Type 3S2 with “spread” rear tandem.

Figure 13. Photo. Class 9, Type 32.

Class 10 includes six-axle single trailer trucks. Figure 14 displays the most common 

configuration, the Type 3S3 which has a semi-trailer with a tridem axle.
14

Figure 14. Photo. Class 10, Type 3S3.

Class 11 includes five-axle multi-trailer trucks. Figure 15 displays the most common 

configuration, the Type 2S12. The first value defines the number of axles on the power unit, the 

“S1” defines the single axle semi-trailer, and the last value defines the second trailer as a twoaxle full trailer.

Figure 15. Photo. Class 11, Type 2S12.

Class 12 includes six-axle multi-trailer trucks. Figure 16 displays the most common 

configuration, the Type 3S12. 

Figure 16. Photo. Class 12, Type 3S12.

Class 13 includes multi-trailer trucks with seven or more axles for which there are a large 

number of possible axle configurations. Although there are exceptions, most agencies do not 

find it necessary to uniquely define these by type since they account for a very low percentage of 

the truck traffic stream. Some states allow very heavy mining or timber hauling “trains” with 

many axles, which they may find beneficial to capture by type for analyses. Some states allow 

Longer Combination Vehicles (LCVs), which do have consistent configurations as displayed in 

Figure 17.
15

Figure 17. Picture. Class 13, Longer Combination Vehicles (LCVs).

Table 1 displays the basic class scheme that was recommended for use in the LTPP study by the 

Traffic ETG. The intent of this scheme is to include the most common vehicle types found 

nationwide and to be supplemented with additional vehicle types unique to certain regions. It is 

important to note that the axle spacing and weight parameters of any desired scheme must be set 

up as an algorithm specifically formatted for use by a particular WIM system.

Note that although the “LTPP Classification Scheme for SPS WIM Sites” displayed in Table 1 is 

currently in use, it is considered a work in progress subject to revisions and enhancements. For 

more information regarding this document contact ltppinfo@dot.gov.
16

Table 1. Example Class Scheme.
17

2.4. TASKS OF THE OFFICE DATA ANALYST

The primary tasks of the Office Data Analyst are as follows:

• Identify any missing or invalid data.

• For missing or invalid data, attempt to determine:

o Cause

 Ongoing

 Intermittent

 Isolated, one time event(s)

o Extent

 What part of the day’s data should be flagged as invalid?

• All data by time frame 

• One lane only

• Classification counts valid, but not loading data

o Course of action to correct ongoing or intermittent problem

 Fix remotely from office

 Call for field visit

• Monitor each WIM system for maintenance of calibration.

2.4.1. Causes of Missing or Invalid Data 

Missing data for all lanes would most likely be due to a power outage at the system’s controller 

or the controller being otherwise shut down. The primary causes of invalid data are as follows:

• System component malfunction

o Failure vs. intermittent

• Improper system settings

o System component operational variables, such as

 Loop timeout

 Weigh sensor thresholds

o System processing variables, such as

 Classification algorithm

 Calibration factors

• Site conditions

o Rough pavement

o Roadway geometry

• Traffic conditions

o Congestion

o Lane closure or alignment shift
18

o Changing lanes when crossing sensors

• Weather

o Effects on system components, such as

 Moisture intrusion

 Bending plate scale pit filled with ice

o Effects on traffic, such as

 Crosswind

 Traffic not within lane/shoulder striping due to heavy snow on pavement

o Weather web site address

 http://www.nws.noaa.gov/

In regard to system settings, classification algorithms and calibration factor values have no 

bearing, within reason, on a system’s proper operation. Determination of classification 

algorithms and calibration factor values are more of a “fine tuning” process to generate the most 

accurate data possible as opposed to generating valid versus invalid data (unless the values are 

obviously erroneous). However, settings related to component operation, such as loop timeout 

and weigh sensor thresholds, will determine whether vehicles and vehicle combinations are 

properly detected and their wheels are properly counted and weighed.

2.4.2. Factors Affecting WIM Data Quality Which Can Be Controlled

• Site conditions

o Roadway geometry

o Pavement stability and smoothness

• Equipment quality and performance capabilities

• Equipment installation and routine maintenance

• Pavement maintenance

2.4.3. Factors Affecting WIM Data Quality Which Can Be Somewhat Controlled (By Site 

Selection)

• Traffic characteristics

o Changing lanes through site

o Wheels on or partially on shoulder

o Speed changes or stop and go 

 Merging vehicles

 Congested traffic

Power Point presentations that describe site conditions and traffic operating characteristics that 

should be considered in determining a suitable location for a WIM system installation are 

available online at www.QualityWIM.com

2.4.4. Factors Affecting WIM Data Quality Which Cannot Be Controlled

• Out of round tires
19

• Dynamically unbalanced wheels

• Tire inflation pressure

• Vehicle suspension

• Wind

• Vehicle aerodynamic features

• Type of load, particularly liquid, and/or method of loading

• Acceleration/deceleration

• Undesirable traffic conditions due to weather or work on roadway

2.5. WHAT QUALITY OF DATA SHOULD BE EXPECTED?

No WIM system can produce perfect data, even with high quality equipment and ideal site 

conditions. It is expected that any data file is going to contain some invalid data. The analyst

must consider the characteristics of the WIM site, the characteristics and features of the WIM 

system, and the traffic characteristics in determining if the system is producing the best data 

possible. Regardless of what the minimum data quality requirements are, any WIM system 

should be monitored and maintained as to produce the best possible data given the system’s 

potential. The key is to keep bad data to a minimum, giving consideration to each WIM 

system’s potential, and to quickly recognize, identify, isolate, and correct the cause of erroneous 

data.

Many data problems can be corrected from the office. Even if a problem does require a service 

call, the service technician’s time onsite can be greatly reduced if the analyst has narrowed down 

the potential causes of the problem. Neither the purging of entire daily data files nor major WIM 

system corrective actions are necessary if only a scattering of bad data is found when performing 

routine data quality control checks. If the amount of bad data starts to increase and goes from 

random to chronic, the analyst needs to take corrective action, unless the problem can be tied to 

an atypical site condition (e.g. traffic or roadwork).
20
21

SECTION 3. STEPS FOR DATA VALIDATION AND SYSTEM MONITORING FROM 

OFFICE

This section provides guidance and recommendations as to the steps to be taken by the Office 

WIM Data Analyst to validate data (Data Quality Control, or Data QC) and to monitor a WIM 

system’s operation. Such steps include:

• Access the system from the office and perform initial real-time reviews.

• Perform reviews of canned reports to determine if a system is consistently operating 

properly, and if traffic is moving through the site within the lane lines at consistent 

speeds within the operating range of the system.

• Access the system from the office to perform system diagnostics if the data indicates that 

the system is not operating correctly.

WIM system site controller access display screens and setup procedures, as well as the 

application software provided by the WIM manufacturers for the user’s Office Computer, vary 

by manufacturer. It is far beyond the scope of this manual to provide procedures applicable to 

each specific manufacturer or a manufacturer’s specific equipment type or version of application 

software. The screen shots and sample report displays utilized in this manual are mostly taken 

from systems of two different manufacturers of WIM equipment. The intent is to provide 

general guidance on what to look for, as well as procedures to follow. Documentation on 

specific equipment and software provided by the WIM equipment manufacturer should be 

thoroughly reviewed by the analyst to determine how the examples in this manual can be 

applied.

The following recommendations on performing data QC and system monitoring do not include 

all possible procedures. The intent is for the novice analyst to use these recommendations as a 

starting point, and then develop additional procedures and checks as experience is obtained in 

working with the features of specific systems and Office Computer application software. For the 

experienced analyst, perhaps the following recommend procedures will provide additional tools 

to those already developed such that more thorough data QC and system monitoring analyses can 

be performed.

In addition, it is not the intent that the data QC checks monitor the fine-tuning aspects of a 

system’s calibration. Calibration monitoring will be covered in SECTION 5. Calibration 

monitoring is only effective when using data from a system that is operating correctly and for 

which the traffic operating characteristics are normal. The intent of the data QC checks of each 

day’s data file is to ensure that invalid data, whether caused by system malfunction or atypical 

traffic operating characteristics, is identified and flagged such that only data appropriate for its 

intended use is disseminated.
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3.1. INITIAL REAL-TIME REVIEW

For agencies utilizing fully automated and unattended WIM data file download, calling up each 

system prior to the predetermined start of the download may not be feasible. In this case, if a 

system (or its communication link) is not operating properly, it will be evident following the 

download session. Otherwise, it is recommended that prior to downloading a significant number 

of data files from the system that it be accessed remotely from the office and spot-checked, as 

described below, to verify that the system is, in general, operating correctly. These checks are 

intended to identify any significant ongoing system problems without waiting for the data to be 

downloaded and reviewed, as well as to identify data files which contain significant amounts of 

missing and/or invalid data which do not need to be downloaded.

1. Does the onsite modem answer? 

If not, first ensure that the Office Computer’s communication software and modem are properly 

configured. The quickest test for this is to call another WIM system similar to the one that is not 

responding. If it is confirmed that the Office Computer is communicating with other systems 

normally, a site visit is necessary to determine if the power or phone service is out, or if the site’s 

modem is not working.

2. Does the onsite modem answer, but the system is not responding? 

If so, a site visit is necessary to check the modem configuration, and if the modem configuration 

is correct, the status of the controller.

3. Is the system’s time and date correct? See Figure 18 for examples from two different 

systems. 

If not, correct time and/or date and determine if the affected files may still be of use for the 

intended purposes. 

Figure 18. Screen shot. Site menu, time and date.
23

4. Do the stored files appear to be complete, and their file sizes appropriate? See Figure 19

for example.

For the site used in the example, it is typical for the weekday files to average about 500,000 

bytes and the weekend files to average about 270,000 bytes, so these file sizes appear to be 

reasonable. The partial current day’s file size also appears to be reasonable given it is for almost 

12 hours. If the file sizes appear to be unreasonable, determine if the affected files may still be of 

use for the intended purposes. Unreasonable file sizes sometimes result from changes in the 

number of individual vehicle records that are being captured by the system, which may indicate 

that a system component is malfunctioning or that a system setting has changed. If it is obvious 

that a number of the daily data files are not usable, it may be beneficial to download only one of 

the files initially, in order to analyze and determine the cause of the problem.

Figure 19. Screen shot. Site menu, stored data files.

5. In viewing real-time vehicles, do their data elements appear to be reasonable for each 

lane (it is recommended that each lane be checked individually)?

Check the following items:

• Classifications

• Axle Spacings

• Speeds

• Weights

• System error or warning flag codes
24

Figure 20 displays a few records from a system’s Lane 1 real-time truck traffic stream. In 

analyzing the records with “Ve.-Code: 15” (unclassified vehicles in the classification algorithm 

being used by this system), it should be obvious to the analyst that the three vehicles classified as 

“15” have spacings and weights which are typical of the Class 9’s Type 3S2. This should 

quickly prompt a check of the system’s settings for the classification algorithms. 

Figure 20. Screen shot. Site real-time “View Vehicles” mode for system’s Lane 1.

In reviewing the system’s classification scheme algorithm setup displayed in Figure 21, it is 

evident that a system malfunction has occurred. This is a classification algorithm setup 

convention in which the two rightmost numbers for the axle distances (spacings) are decimal 

places (values in feet). What is supposed to be Type 9, the FHWA Class 9 - 3S2 configuration, 

is instead a Type 75, with some random spacing definitions. Since this system’s classification 

algorithm does not include a Type 9, all vehicles conforming to the intended Type 9 axle spacing 

parameters are labeled Class 15 (unclassified). The next classification algorithm in Figure 21, 

shown as Type 14, is the Agency’s Class 14 (FHWA’s Class 9 – 32 configuration), which has 

maintained the correct axle spacing scheme. 

Figure 22 displays an example of two vehicles with “Significant Weight Difference” warning 

flags. In monitoring all Lane EB#2 vehicles for a longer time frame the analyst notes that many 

or even all are flagged with this warning. For this system, this warning flag indicates that the left 

versus right sensor weight outputs of one or more of a vehicle’s axles exceed a 40 percent 

difference. In examining the left and right sensor weight outputs, the left weights appear to be 
25

reasonable. However, the right weight outputs appear to be only about half of the left weight 

outputs. This should prompt a check of the calibration factors for obviously erroneous low 

values for this lane’s right weigh sensor.

As found As originally entered

Type 75 Type 9

Dist. axle low: 2947 Dist. axle low: 600

Dist. axle high: 8251 Dist. axle high: 2600

Dist. axle low: 1939 Dist. axle low: 300

Dist. axle high: 7445 Dist. axle high: 599

Dist. axle low: 2242 Dist. axle low: 600

Dist. axle high: 6713 Dist. axle high: 4600

Dist. axle low: 1561 Dist. axle low: 300

Dist. axle high: 1090 Dist. axle high: 1099

Total weight low: 1000 Total weight low: 1200

Total weight high: 0 Total weight high: 0

Lim. Total weight: 8000 Lim. Total weight: 8000

Type 14

Dist. axle low: 600

Dist. axle high: 2600

Dist. axle low: 300

Dist. axle high: 599

Dist. axle low: 600

Dist. axle high: 2300

Dist. axle low: 1100

Dist. axle high: 2700

Total weight low: 1200

Total weight high: 0

Lim. Total weight: 8000

Figure 21. Screen shots. Menu screen displaying portion of classification algorithm entries.

Record Number 32405 Dir/Ln East/2 GVW 31.5 kips Length 65 ft

Class 9 Speed 59 mph Max GVW 80.0 kips ESAL 0.066

Monday Feb. 24, 2003 Time 23:21:52.63

Axle Spacing Left WT. Right WT Total WT Allowable

(ft) (kips) (kips) (kips) (kips)

1 4.8 2.5 7.3 20.0

2 16.8 4.7 2.4 7.1 17.0

3 4.3 4.2 2.1 6.3 17.0

4 33.2 3.8 1.9 5.7 17.0

5 4.2 3.4 1.8 5.2 17.0

Warning: Significant Weight Difference!

Record Number 32415 Dir/Ln East/2 GVW 48.1 kips Length 75 ft

Class 9 Speed 61 mph Max GVW 80.0 kips ESAL 0.365

Monday Feb. 24, 2003 Time 23:21:18.53

Axle Spacing Left WT. Right WT Total WT Allowable

(ft) (kips) (kips) (kips) (kips)

1 5.8 1.8 7.6 20.0

2 17.2 7.5 3.0 10.4 17.0

3 4.3 8.4 3.1 11.5 17.0

4 34.0 6.1 3.8 9.9 17.0

5 4.1 5.4 3.3 8.6 17.0

Warning: Significant Weight Difference!

Figure 22. Screen shot. Site real-time “View Vehicles” mode.
26

Figure 23 displays the menu “path” for this particular system to view the current calibration 

factor settings for the weigh sensor (“WIM Sensor 3”) in question. The analyst should compare 

these factors with those on record to see if they are correct. If these factors are correct, 

additional diagnostics can be performed on this sensor as will be discussed later.

Figure 23. Screen shots. Site real-time menu screen displaying calibration factors.

This short term monitoring session of individual vehicles passing through the system in real-time 

is intended to catch only significant and ongoing problems with the system. Data QC is 

necessary to detect problems that are not so obvious or problems of an intermittent nature.

3.2. DATA REVIEW USING CANNED REPORTS

Following the downloading of a WIM system’s daily data file (or files) to the Office Data 

Analyst’s Office Computer, a data QC check is performed to determine if the data is suitable for 

its intended use.

3.2.1. Class and Speed Reports

It is recommended that the first check be made using daily class and speed reports summarizing 

the binned raw data. The purpose of this check is to take a quick look at each day’s data to 

identify:
27

• Any missing vehicles for hour(s) in:

o All lanes

o One lane only

• Any atypical class count distributions

• Any atypical speed patterns

Figure 24 displays two reports with daily vehicle counts by hour for Lane Numbers SB1 and 

SB2. For this site, Lane SB1 is the outside lane, or “driving” lane, which normally carries most 

of the traffic. In the first report, it is obvious that the counts decrease significantly for Lane SB1 

starting sometime after Hour 7 and ending sometime prior to Hour 16. However, a look at the 

counts for this time frame for the adjacent Lane SB2 (inside lane, or “passing” lane) indicates 

that the counts significantly increase. The total counts for both lanes are normal. This example 

represents a typical lane closure situation and the system is counting vehicles just fine. In the 

second report, both lanes are exhibiting a large drop in counts starting sometime after Hour 18 

and ending sometime prior to Hour 21. This indicates that, for some reason, the system was not 

counting during this period or that some major event caused the closure of both lanes.

Figure 25 displays a report for a system that covers all four lanes (two lanes in each direction) of 

the roadway. This is an obvious case of either a temporary loss of power or the system simply 

malfunctioning for several hours (Hour 9 through Hour 14).
28

Figure 24. Reports. Missing hourly data for lane or lanes.
29

Figure 25. Report. Missing data all lanes.
30

Figure 26 displays a portion of a daily report with hourly counts by classification for the 

system’s Lane 2. Starting during Hour 12 - 13 there was a large increase in counts for Class 1 

and unclassified vehicles (Class 15, for this particular system). Also, a significant decrease in 

counts for the “good” classes is evident (although this lane has very few large trucks).

Figure 27 displays a portion of a daily report with speed range counts by hour for the same 

system, day, and lane as the Classification by Hour report displayed in Figure 26. At the same 

time that there were significant changes in the classifications, there were significant changes in 

the speeds as well. Also, Figure 28 displays that a vast majority of the invalid speeds are 

attributable to Class 1s and Class 15s (unclassifieds). 

In that this site is located in an urban area with commute traffic, some of the low speeds may 

very well be legitimate. These three reports are from a system for which loop or loop-processing 

problems will typically result in Class 1 and/or Class 15 vehicles and unrealistic speeds. 

Although consideration must always be given to the possibility that a system’s erroneous data 

may be attributable to congestion, an accident, or work on the roadway causing stop and go 

traffic conditions, the fact that the erroneous data continued for the entire afternoon suggests that 

for this example the system simply is not processing properly for Lane 2. As an additional 

check, Figure 29 displays a report comparing class and speed data for Lane 2 with that of 

adjacent Lane 1. Although Lane 1 does appear to have some erroneous data (probably due to the 

traffic conditions), its reasonable class and speed distributions make it evident that the Lane 2 

problems are not due to major traffic issues.
31

Figure 26. Report. Erroneous classification for lane
32

Figure 27. Report. Erroneous speeds for lane.
33

Figure 28. Report. Speed by class for lane.
34

Figure 29. Report. Class and speed distributions, Lane 1 versus Lane 2.
35

Figure 30 displays a Speed by Hour report for Lane #1 for a WIM site that routinely experiences 

traffic congestion. For such a site, an increase in system errors and/or questionable classification 

counts should prompt the analyst to review the traffic speed distributions for the period of time in 

question. In analyzing the speed pattern and traffic volumes it is apparent that this site actually 

did experience very slow speeds for a two-hour period (probably stop and go). In this case, 

congestion appears to be the cause of invalid data, and not improper system operation.

For sites that experience routine traffic congestion, the onsite testing following initial installation 

and start-up should have confirmed whether or not the system met functional requirements in 

regard to its ability to properly generate data when traffic is travelling at the minimum speed of 

the required speed range. For example, if the specifications require that the system must 

function properly when traffic is travelling within a range of 5 mi/h to 100 mi/h, and onsite 

observation confirmed that errors start occurring only when vehicles actually stop when over the 

sensors, the system was functioning correctly. If, on the other hand, errors started occurring 

when vehicles were moving in excess of 5 mi/h, the system was not functioning correctly and the 

system should not have been accepted. If this type of testing was not performed prior to system 

acceptance, it should be performed as soon as the Office Data Analyst identifies low speeds as 

the potential cause of invalid data. Such onsite observations of the effect of low speed traffic on 

the system’s data output should be documented such that the analyst will be able to make a 

judgment as to whether invalid data is due to system error or traffic conditions.

It is important that the analyst develop rules for each WIM site in regard to what levels of routine 

data errors caused by traffic operating characteristics may be expected without raising a flag that 

a system might be malfunctioning. A site located on a wide-open rural interstate freeway should 

experience very few routine data errors due to traffic operating characteristics whereas a site 

located on an urban roadway might experience a relatively high level of routine data errors 

which are caused by traffic, not system malfunction.

Figure 31 also displays a portion of a daily report displaying hourly counts by classification for a 

system’s Lane #1. However, this system has the capability to identify specific types of system 

errors and include such error counts in a Classification bin (Class 14, for this particular system). 

For Hours 7 through 10 there was an increase in counts of system errors and unclassified 

vehicles (Class 15, for this particular system) and an apparent decrease of counts for the other 

vehicle classes.

Figure 32 displays a portion of another daily report for the system, date, and lane displayed in 

Figure 31. This report makes it evident that between Hours 7 and 10 there was an increase in 

loop errors/warnings (“Dwn Only”), as well as an increase in both unequal left sensor versus 

right sensor axle detections (“Uneq Det”), and no axle detections (“Zero Axl”). Although these 

system errors would not appear to be significant, they indicate either erratic traffic patterns or 

that the system was having some problems during this period of time. This system should be 

carefully monitored to determine if this problem was an isolated event or if it is occurring (or 

worsening) on a regular basis.
36

Figure 30. Report. Traffic congestion; legitimate low speeds.
37

Figure 31. Report. Change in class count pattern and increase in system errors for lane.
38

Figure 32. Report. System error identification. 
39

In monitoring the distribution of vehicle classification counts, certain anomalies are best checked 

by “drilling down” to individual vehicle records by means of a process that will be covered in 

detail in Section 4. A few examples follow:

• Too many Class 13s and/or Class 15s caused by:

o Improper loop delay setting, which results in two or more vehicles being 

combined into a single vehicle when traffic is dense.

o Malfunctioning weigh sensor or improper sensor sensitivity setting, which results 

in a system adding axles (“ghost” axles) to vehicles.

• Too many Class 8s in relation to Class 9s caused by a system dropping one or more axles 

from some Class 9s.

• Too many Class 6s in relation to Class 9s caused by improper loop delay setting, which 

results in some Class 9s’ tractor and trailer being split into two individual vehicles.

Note that a “drill down” to individual records can only be performed on vehicles meeting the 

user’s criteria for a system’s storing data as individual vehicle records (as opposed to a vehicle 

being only a “count” in various bins). Identification of erroneous data via review of reports 

generated from binned data is the first step in determining whether or not a system is, in general, 

functioning correctly and whether or not each day’s data is suitable for its intended use. 

The review of reports generated from individual vehicle records, as covered by the procedures 

that follow, provides more insight as to whether or not a drill down to certain individual vehicle 

records in order to determine the cause(s) of erroneous data is feasible. Subject to a system’s 

data storage capacity, it may be of benefit to program the system to capture all vehicles to 

individual vehicle records for a day, or even a partial day, so that more comprehensive analyses 

can be performed on erroneous and/or questionable data.

3.2.2. Individual Vehicle Record Summary Reports

The next step in the QC process is a check of reports that summarize data contained in the 

individual vehicle records. Typically, a system is programmed to capture trucks, either by 

classification or by steer axle weight threshold, to individual vehicle records. The primary 

purpose of this check is to:

• Identify classification problems caused by:

o Improper classification algorithms

o Improper loop settings

o Improper weigh sensor threshold setting

• Identify inaccurate weights caused by:

o A malfunctioning weigh sensor

 On-going vs. intermittent

o Improper weigh sensor threshold setting
40

• Identify obvious calibration problems.

3.2.2.1. Identification of Classification Problems

Although some of these classification checks may appear to duplicate checks made on the binned 

data, they lead the way to a drill down process to analyze individual vehicle records in order to 

determine the cause of erroneous and/or questionable data. 

Figure 33 displays a report that summarizes data from individual vehicle records for one lane of 

a system. This system is programmed to capture any vehicle with a steer axle weight of 3,500 

pounds or greater, regardless of class, as a stored vehicle record. However, this report includes 

only truck classes (4 through 14) and unclassified vehicles (Class 15 for this system). A review 

of this type of report provides the analyst with a good overview of whether or not the system is 

functioning properly, and if not, what to look for in a drill down to individual vehicle records for 

further analyses.

Classification distributions and the number of unclassified vehicles can vary significantly from 

site to site and the analyst must be knowledgeable as to what type of pattern is typical for each 

site. The report displayed in Figure 33 is for a site in Michigan that does experience a relatively 

high volume of Class 13 vehicles. Although for most states a seven percent Class 13 count 

would indicate a problem, the summary data included in this particular report does not suggest 

any significant problems with the system. However, if this type of report did indicate a 

classification problem, the analyst might want to take a quick look at a few individual records 

generated by the Office Computer application software before going to the effort of importing 

data into a spreadsheet or database program for extensive analyses (as will be discussed later).
41

Figure 33. Report. Individual vehicle record class and weight summary data for one lane.
42

Figure 34 displays three examples of unclassified vehicles (Class 15 for this system). The first 

record is actually a Class 9 vehicle that was not properly classified due to its short Axle 2-3 

spacing. The average drive tandem spacing for the Class 9’s predominant Type 3S2 vehicle is 

4.3 feet. From this record, it cannot be determined if any of the items below apply:

• This was an unordinary Class 9 with very small drive tandem wheels.

• The system did not properly process the Axle 2-3 spacing.

• All of the spacings are too short because the system is not properly processing the axle 

spacings.

It is noted that the “Speed” is reasonable, as is the “Veh.Length” (overall vehicle length). For 

systems that have been calibrated for overall vehicle length, if a vehicle record suggests that the 

system has elongated axle spacings or has added one or more trailing “ghost” axles, a 

comparison of the overall wheelbase (sum of all axle spacings) with the overall vehicle length is 

in order. With very rare exceptions, the overall length should be greater than the overall 

wheelbase.

The second record’s data indicates that the system properly processed the vehicle’s data 

elements. The system’s classification algorithm should be checked to see if one of the items 

below applies:

• A three-axle vehicle with an Axle 1-2 spacing of 24.9 is not accounted for.

• The axle spacings are covered but the subject vehicle’s gross weight is not accounted for 

in conjunction with the axle spacings.

The third record includes a “ghost” axle (Axle No. 3). This could be caused by either a 

malfunctioning weigh sensor, or an improper weigh sensor threshold setting.
43

Figure 34. Screenshots. Examples of vehicles not classified by the system.

Figure 35 displays two vehicle records which would appear to be legitimate Class 13s based 

upon the vehicles’ data elements. Figure 36 displays a vehicle labeled as Class 13 due to its axle 

spacings and gross weight meeting the classification algorithm’s parameters for Class 13. 

However, it is evident that this record contains “ghost” axles. 

If a system generates a significant number of vehicle records that contain “ghost” axles, and a 

diagnostic check indicates that the signals from the weigh sensors look ok, try adjusting the 

sensors’ threshold settings and observe the effect on the sensors’ weight reading outputs. These 

adjustments must be made with care such that the system does not start to drop axles from its 

vehicle data outputs. One check for this is a before and after comparison of the Class 9 versus 

Class 8 ratio. If the Class 8 counts increase and the Class 9 counts decrease, it is a good 

indicator that the system is dropping axles for some of the Class 9s.
44

Figure 35. Screen shots. Legitimate Class 13 vehicles.

Figure 36. Screen shot. Vehicle misclassified as Class 13 due to “ghost” axles.

Figure 37 displays two vehicle records in succession, the first a Class 6 and the second a Class 5. 

Note that these two records are in the same lane at the same time (at least to the nearest second). 

It is also noted that the second vehicle has a recorded speed of 144.3 mi/h, which is another 

indicator that the WIM record is not valid. These two records are actually for a single 

combination vehicle, probably a Type 32 truck-trailer (Class 9) with a long tow bar (something 

similar to the photo in Figure 38). Since the loops did not pick up the tow bar and “timed out” 

before detecting the trailer, the system treated this combination vehicle as two individual 

vehicles. 

Figure 39 displays a Class 9 logging truck. This vehicle is comprised of a three-axle tractor and 

a tandem trailer connected to the tractor by only the logs and a tubular steel connector. This is 

another configuration that can result in a system treating the combination vehicle as two 

individual vehicles. However, in this case, the second record would have an Axle 1-2 spacing of 

approximately 4.3 feet and, due to the tandem’s being too heavy for a Class 1 vehicle under most 

class algorithms, would probably have been labeled as an “Unclassified” by the system. 
45

Figure 37. Screen shot. Two records for one combination vehicle.

Figure 38. Photo. Class 9 Type 32 with long towbar.

Figure 39. Photo. Class 9 logging truck.

To prevent a system’s loops from dropping out and creating two individual records for certain 

combination vehicles, the typical fix is to increase the system’s loop time-out setting. However, 

for a site that at times experiences heavy traffic, an increase in the loop time-out setting may 

result in a system combining two or more tailgating vehicles into a single record. For this type 

of site the adjustment of the loop time-out setting may be a trial and error process to determine 

what setting produces the fewest errors. Consideration should also be given to the intended use 
46

of the data. If the priority is to collect the most accurate truck size and weight data as possible, it 

may not be deemed important if some tailgating autos are not counted and classified properly.

Any system is going to label some “real” vehicles as unclassified and misclassify some vehicles 

as displayed in the above examples. This is particularly the case when vehicles do not pass 

through the site within the lane lines or at normal speeds. A few random misclassified vehicles 

are not normally cause for concern. However, if a system’s percentage of unclassified or 

misclassified vehicles starts to increase in the absence of verification that traffic characteristics 

have changed, a more in-depth analysis of vehicle records should be performed.

3.2.2.2. Identification of Weight Data Problems

Reports generated by a WIM vendor’s application software should also include summary 

information on the weight data contained in the individual vehicle records. The report shown in 

Figure 33 displays information on the number of vehicles having “invalid measurements” (as per 

criteria programmed by the user) as well as information on the number of vehicles flagged as 

being in violation of weight limits, and a breakdown of the types of weight violations. This 

particular system conforms to LTPP’s “model specification” (refer to Appendix A), which states:

An "invalid measurement" code shall be assigned to any vehicle ... when:

• The left and right wheel weights of any axle have a difference of 40 percent or more; and

• Either of the wheel weights of such axle exceeds 2.0 kip. Both the 40 percent and 2 kip 

values shall be programmable by the operator.

Regardless of a system’s method(s) for flagging potentially erroneous weights, any system is 

going to generate some weights that are not valid estimates of static weights. As with erroneous 

classification, the number of erroneous weights can vary significantly among WIM sites 

depending upon truck operating characteristics, pavement conditions, type of equipment, etc. 

Based upon documentation gathered during onsite testing and observation, as well as initial 

analyses of vehicles being flagged as having potentially erroneous weights, the analyst should 

develop rules for each WIM site in regard to what levels of routine weighing errors caused by 

truck operating characteristics may be expected without raising a flag that a system might be 

malfunctioning.

Figure 40 displays a report that provides weight summary information from individual vehicle 

records for each of a system’s three WIM lanes. For this system, a warning flag is applied to a 

vehicle’s record when an “invalid measurement” (as discussed above) is detected, as well as 

other detections by the system (such as unequal detection counts by the sensors of left versus 

right wheel hits) that the vehicle’s axle weights and GVW might be erroneous. Although the 

system’s assignment of warning flags for 13 percent of the trucks in Lane 1 is certainly not 

indicative that a weigh sensor is not working, it is a relatively high percentage and should prompt 

a more detailed analysis to determine which weigh sensor is causing the problem. Upon such 

determination, real-time checks of the system’s settings for the sensor can be made, and 

depending upon the particular features of the system, diagnostics on the sensor can be performed. 
47

If all real-time checks of the sensor do not indicate a problem, more extensive analyses should be 

performed to try to determine a pattern of intermittent malfunction. This system provides 

detailed reporting information on system errors and warnings, so the next step is to generate a 

report on the errors and warnings.

Figure 40. Report. Class distribution, weight violation counts, and warning counts by lane.

Figure 41 displays an abbreviated report listing counts of vehicles having system errors or 

warning flags by hour of day for the system’s Lane EB4. This report makes it evident that the 

relatively high percentage of warnings displayed in the Figure 40 report is due to “Wt Dif” flags, 

which indicates that these vehicles met the criteria for “invalid measurement” as discussed 

previously. It is also noted that this problem is occurring on an intermittent basis. During each 

of the hours 2 to 3, 10 to 11, and 23 to 24, over 26 percent of the vehicles were flagged. It would 

appear that the problem occurs to a much lesser extent during the afternoon hours, rather than 

late night and morning hours. Any number of conditions, including the effects of temperature or 

moisture on conductor connections, can cause intermittent erroneous outputs by a weigh sensor, 

as shown in this report. 
48

Figure 41. Report. Error and warning vehicles by hour for lane.
49

For sites that experience strong crosswinds and also have a relatively high percentage of 

combination vehicles with empty or very light trailers, a review of some of the vehicle records 

with “Weight Difference” warning flags might reveal that a high percentage of these records 

look similar to the display in Figure 42. This vehicle, traveling in a bidirectional site’s eastbound 

lane, is an empty 2S12 Class 11 with very little weight on the trailer wheels and the trailers’ 

lighter wheel weights are all on the left side. If the analyst can verify that the site did experience 

windy conditions for the time frames that the “Weight Difference” warning flags were higher 

than normal and that the winds were in a direction (from the north) that would hit the trailers of 

the eastbound vehicles on their left sides, the odds are very good that the left weigh sensor is 

functioning properly. Likewise, if the right weights of the empty trailers in the site’s westbound 

lanes have lower readings, it is very likely that wind is causing the warning flags.

Figure 42. Screen shot. Vehicle with “light” wheel weights on left side.

For the example vehicle record displayed in Figure 42, the trailers’ left and right wheel weight 

differences are insignificant from both axle weight and gross weight perspectives. If vehicles 

flagged by a system as having potentially erroneous weights due to the left versus right axle 

weight imbalance percentage are automatically discarded from weight reporting by the analyst 

for a site that routinely experiences crosswinds, many legitimately weighed empty vehicles may 

be discarded. This might drastically skew data utilized for both weight violation and loading 

analyses purposes. It is strongly recommended that for a system including features allowing the 

analyst to program parameters for assignment of left versus right imbalance flags that the system 

be programmed not to flag vehicles that are obviously empty and have minor left versus right 

weight imbalances for the trailer axles. For the example displayed in Figure 42, increasing the 

minimum wheel weight threshold to 3.0 k would result in this vehicle not being flagged.

For systems utilizing some type of “off-scale” sensors, which detect and flag any vehicle with 

one or more wheels not fully hitting a weigh sensor, there is no guesswork. If a wheel hits an 

off-scale sensor, the wheel’s full weight is not being reported by the system.

For sites that do not have extremely smooth pavement profiles, the pavement may cause enough 

bouncing of truck wheels (particularly those of empties) to cause a significant left versus right 

wheel weight imbalance. If it is not feasible to fix the pavement smoothness problem, the 

analyst will need to determine what level of potential weight error flags is normal for the site and 

whether or not the system should be re-programmed to ignore imbalances for the lighter axles (if 
50

the system has this feature). This may also be the case where a weigh sensor is not perfectly 

even with the adjacent pavement, due to either a poor installation or post-installation rutting in 

the pavement.

Also, some sites experience occasional trucks that travel with their right wheels very close to, or 

even on, the shoulder stripe of the outside lane. Unless the weigh sensors at such a site extend to 

the right of the shoulder stripe, the system may report only partial weights for the right wheels of 

these trucks. Although this condition is simple to verify by onsite observation or by use of offscale sensors, to determine by data analyses alone requires extensive effort.

A system’s reporting of the types and/or times of error and warning flags is very beneficial to the 

analyst in isolating the cause and extent of erroneous weight problems. However, intermittent 

malfunction problems can be very difficult to diagnose, and the analyst may have to make 

extensive detailed analyses of individual vehicle records. This is best performed by importing 

the vehicle records into a spreadsheet or database program, as will be discussed in SECTION 4. 

In the absence of adjustments in a system’s calibration factors or a change in a site’s truck 

operating characteristics, a significant increase or decrease in a system’s weight violations 

suggests a problem with weight outputs beyond “fine tuning” of calibration factors. Weight 

violation data contained in reports such as those displayed in Figure 33 and Figure 40 should be 

monitored by the analyst for significant changes from the norm.

Figure 43 displays a report listing gross weight distributions for each truck classification for the 

system’s Lane 4. If the WIM vendor’s application software or the agency’s software provides 

for generation of a daily report displaying gross vehicle weight averages and/or distributions, it 

would be a good idea for the analyst to also monitor these for any significant changes. The 

monitoring of the 3S2 steer axle average weights for any significant increase or decrease is also a 

good check if the application software provides for such reporting.

It may be difficult to determine if a change in weight outputs during cold weather is due simply 

to a seasonal variability in truck operating characteristics, or if cold temperatures are having an 

effect on either the weigh sensors themselves or a system’s electronics. Although long-term 

monitoring, as will be discussed in SECTION 5, may provide an answer, performing an onsite 

validation with test trucks during cold weather would be of great benefit. For bending plate 

systems, consideration should always be given to the possibility of the scale pits being filled with 

ice.

If it is apparent that a weigh sensor is generating erroneous weights (or no weights) and the 

problem cannot be fixed from the office, it may be possible to access the system and, via the 

system software, remove the problem sensor and double the good sensor’s output as a temporary 

fix until an onsite fix can be performed.
51

Figure 43. Report. Gross weight distribution for each truck class for lane.
52

3.2.2.2.1. Recap of Erroneous Weight Output Checks and Analyses

A system’s generating erroneous weight outputs for a particular lane may be caused by:

• A malfunctioning weigh sensor.

• An improper system setting for a weigh sensor.

• An obviously erroneous calibration factor or factors.

• Trucks not traveling within lane lines.

• Strong crosswinds.

• Effects of very cold temperatures on weigh sensors or electronics.

• For bending plates, a scale pit filled with ice.

To identify the cause, extent, and action to be taken the analyst should use a “drill down” 

process. By reviewing canned reports the analyst can typically determine that the weights for a 

particular lane either have changed from the norm or are obviously erroneous, and whether or not 

the problem is ongoing or intermittent. Depending upon the extent of the erroneous weight 

outputs, the analyst may need to review only a sampling of individual vehicle records generated 

by the vendor’s application software to identify the cause. It is very beneficial to understand and 

utilize any error and/or warning flags applied to the vehicle records by a particular system. If the 

cause of a problem cannot be identified by a quick review of a few vehicle records, it may be 

necessary to perform extensive analyses of individual vehicle records as will be discussed in 

SECTION 4.

As a summary, to check for erroneous weight outputs, the analyst should:

1. Determine which weigh sensor is generating erroneous weights by checking for:

o “0” outputs.

o Partial weight outputs.

o Weight outputs that have significantly increased.

o Erratic weight outputs.

2. Determine if a sensor is generating erroneous weights consistently or intermittently.

o If consistent:

 Check sensor’s calibration factor settings.

 Remotely access system and perform any sensor diagnostics available by 

the system.

o If intermittent, attempt to determine if there is any type of pattern that provides a 

clue to the cause, such as:

 Time of day.

• Temperature or moisture.

 Heavy versus light traffic.

 Ongoing roadwork in the vicinity of the site.

 Crosswinds.
53

3.3. AUTOMATED VALIDATION PROGRAMS

Several agencies validate WIM data utilizing proprietary software programs, which flag any 

vehicle in the individual vehicle records that does not conform to user defined rules and generate 

summary reports on flagged vehicles.

The Travel Monitoring Analysis System (TMAS) is an example of an automated validation 

program. TMAS is currently used to submit monthly traffic volume data to FHWA and will be 

used to submit vehicle classification and truck weight data to FHWA as well, replacing the 

Vehicle Travel Information System (VTRIS). Figure 44 presents a summary of the TMAS 

quality control checks for WIM data. It is noted that the TMAS checks are performed utilizing 

TMG formatted data files which do not contain all WIM data elements (such as a warning or 

system error code) whereas some automated validation programs perform checks utilizing the 

“raw” data files.

FHWA Travel Monitoring Analysis System 

Quality Control checks

Class Quality Control checks

24 hours of data check

Consecutive zero's check

% Class by day Maximum

% Class by day based on historical value

Weight Quality Control checks

Total Weight vs. Sum of Axle Weights

Any Axle Weight Range Check

Any Axle Spacing Range Check

Sum or Axles by Vehicle Class

Minimum Number of Axles Per Class

Steering Axle Weight Average vs Historical Average

(class 9 by day by lane)

Average Tandem Spacing vs Historical Average

(class 8 and above by day by lane)

Figure 44. List. Summary of FHWA TMAS QC Checks.

Appendix B lists a multitude of possible validation rules that were developed under the

Transportation Pooled-Fund Study SPR-2 (182), titled Traffic Data Edit Procedures Pooled Fund 

Study, Traffic Data Quality "TDQ". Additional information on this study is available online at 

http://www.fhwa.dot.gov/policy/ohpi/tdep.htm.
54

3.4. DATA QC – FOLLOW-UP PROCEDURES

If routine QC checks suggest a problem with a system’s operation, the analyst should remotely 

access the system and perform a check of the system’s setup parameters and the settings for any 

component that might be causing the problem. If the setup parameters and component settings 

appear to be correct, any diagnostics of the components provided for by the system should be 

performed. The data analyst may need technical assistance in performing and analyzing certain 

component diagnostics. It may also be beneficial to perform extensive data analyses, as 

discussed in SECTION 4. In this section it is discussed how to determine which component is 

malfunctioning, and if the problem is intermittent, at what time of day or under what conditions 

the malfunction occurs.

It is extremely important to determine whether sensor and/or processing problems are caused by 

system malfunction, by improper system settings, by traffic conditions, or by environmental 

conditions!

3.4.1. Real-Time System Check of Parameters and Settings

The purpose of this check is to utilize the remotely accessible features of a system to check, and 

if necessary, modify parameters, settings, values, etc. affecting system operation and data output. 

Such modifications made from the office can often eliminate various data problems without the 

need to make a visit to a site. Checks may include (but certainly not limited to):

• System/site setup configurations.

• Time and date.

• Parameters for data file collection, vehicle record capture, etc.

• Parameters for assigning flags for potentially erroneous weights, warnings, system errors, 

etc.

• Parameters for assigning weight violation codes.

• Classification algorithm.

• Loop time out settings.

• Weigh sensor thresholds (including “zero”).

• Calibration factors for weights, speed (and thereby axle spacings), and overall vehicle 

length.

It is critical that upon acceptance of a WIM system from the contractor or vendor all system 

parameters, settings, values, etc. be documented and that any subsequent adjustments be 

documented and maintained as well.

It is far beyond the scope of this manual to go into detail on all of the various WIM system 

features available by the various system manufacturers. Several examples are displayed, but it is 

the responsibility of the analyst (perhaps with some dependency on available technical support) 

to become familiar with available features of any particular system.

Figure 45 displays an example of a record of the various parameters and values for Lane 4 of a 

particular system that is utilizing Quartz Piezo weigh sensors. The information on the lane’s 
55

sensor configuration should not change unless modifications are made to the layouts of the site’s 

lanes and/or sensors. 

• The “Axle Sensors” parameters provide the system with each weigh sensor’s input 

information and the distances from the leading edge of the lead loop to each of the weigh 

sensors.

• The “Loop” parameters provide similar type information for the loops. The loop “Width 

(cm)” values are used by the system to generate each vehicle’s overall length, and these 

values are “fine tuned” by analysis of calibration or validation test truck data.

• The “Processing” parameters include:

o A “MaxTimeout” value, which is the time allowed (in milliseconds (ms)) between 

a vehicle’s triggering the lead loop and when the vehicle has been considered to 

have completed passage through the system (thus completing the vehicle’s 

record).

o User defined values for flagging vehicles with left versus right axle weight 

imbalances.

o The distance between the leading and trailing weigh sensors (“Axl Sep”), which is 

used by the system to generate each vehicle’s speed.

o A value (in percent) for adjusting steer axle calibration factors (“Dynamic 

Comp”)

The values for the “Axl Sep” and the “Dynamic Comp” are typically fine-tuned based upon 

analysis of calibration or validation test truck data.
56

Figure 45. Screen shot. Example of a record of a system’s setup parameters for one lane.
57

Figure 46 displays a record of the same system’s calibration factors for its Lane 4. It is noted 

that this system provides for calibration factors for five speed points.

Figure 46. Screen shot. Example of a record of a system’s weight calibration factors for one 

lane.

Figure 47 displays a remotely accessible onsite menu page from another system type that also 

shows various parameters for the site, system sensor channel inputs, and the current value for 

“Loop delay constant” (which is the loop “timeout” or “drop out” previously discussed in this 

manual).
58

Figure 47. Screen shot. Example of a system’s sensor configuration and loop delay constant 

for one lane.

It is important that the analyst have available all system setup parameter records. It is also 

important that the analyst be made aware of any changes in a system’s values and/or factors 

related to calibration, and that the analyst record and maintain such values and factors for 

reference. 

3.4.2. Remote Real-Time Tests and Diagnostics of System Component Operation

The purpose of this check is to utilize the remotely accessible features of a system to check 

signal outputs and/or other operational aspects of individual components of a system.

Again, it is far beyond the scope of this manual to go into detail on all of the WIM system 

features available by the various system manufacturers. Several examples will be displayed, but 

it is the responsibility of the analyst (perhaps with some dependency on available technical 

support) to become familiar with the features of any particular system. For certain systems, very 

detailed analyses can be performed on a sensor’s raw signal output, but obtaining and analyzing 

such signal output is typically best left to engineers or technicians well versed in a system’s 

operation.

Figure 48 displays a remote accessible onsite menu page from a system as well as three of the 

tests that can be accessed from the menu.

• Menu Item “5” displays how long each passing vehicle is sensed by the leading and 

trailing loops for each of the site’s four lanes. The values are displayed in timer “ticks”, 

each tick being the number of milliseconds set up in the system’s setup menu (4.5 ms for 

this system). A value less than 20 usually indicates a misadjusted or faulty loop.

• Menu Item “6” displays the frequency, detuning, and output status for each of the four 

loop channels on the selected “DIP” loop board.
59

• Menu Item “C” displays the analog to digital conversion value for the bending plate 

assigned to the system’s Channel 1 with no traffic crossing the sensor. For this system, 

this value should be approximately 800. Although the “Measured value” will briefly 

increase when a vehicle crosses the weighpad, and the “max. value” will also increase; 

the “min. value” should not decrease. An extreme reading, such as “0” or “4096”, 

typically indicates failure of either the bending plate or its amplifier board.
60

Figure 48. Screen shots. Example of a system’s menu screen for selecting system tests 

(“System test”) and three examples of the tests.
61

Figure 49 displays Lane SB#2 real time vehicles in another system’s “Diagnostics” mode. For 

each vehicle, the lead and trail loop durations, as well as the axle count detections and their 

durations for each of the two bending plate weigh sensors are displayed. Observing the real time 

traffic for several minutes per lane in this mode provides a good idea if the loops and weigh 

sensors are functioning correctly.

Figure 49. Screen shot. Example of a system’s loop and weigh sensor duration diagnostics. 

Figure 50 displays the same system’s remotely accessible onsite menu page showing the current 

“Base Line Value” for the system’s “WIM Sensor 1”. This value should be 2048, plus or minus 

100. For this system type, if a sensor’s base line is out of range it will require an onsite visit to

make adjustments to the sensor’s interface card. An extreme value, such as 0 or 4096, typically 

indicates failure of the sensor or its interface card.

Figure 50. Screen shot. Example of system’s menu page displaying a bending plate sensor’s 

baseline value.

It is typical that at times the analyst may be able to attribute invalid data to traffic and/or 

environmental conditions instead of system malfunction. There may also be times that the 

analyst or technical support personnel can make corrections or adjustments to a system’s setup 

parameters or various component settings remotely from the office to correct data problems. 

Unfortunately, at times it is necessary either to replace a failed system component or to make an 
62

adjustment to a hardware component that cannot be accomplished remotely via software or 

firmware. However, the more information the analyst can provide to the field technician as to 

which component is possibly causing data problems, the better prepared the field technician can 

be in having the necessary tools and equipment to fix the problem.
63

SECTION 4. EXTENSIVE DATA ANALYSES UTILIZING INDIVIDUAL VEHICLE 

RECORDS

The purpose of performing extensive analyses of the individual vehicle records is to attempt to 

isolate and identify system component problems not identifiable by routine real time reviews, 

data QC, or real time checks of a system’s parameters and settings. Such analyses are typically 

necessary when system component problems are intermittent and/or subtle in nature.

The method for performing these analyses is to import the individual vehicle records into a 

spreadsheet or database program and perform search, filter, sort, or other procedures as well as to 

have the program generate tables and graphs necessary to find any pattern which might isolate 

intermittent system component malfunction. It may also well be that analyses of questionable 

data indicates such data is probably attributable to conditions other than component malfunction. 

The ability to import the individual vehicle records is, of course, somewhat dependent upon the 

data file format of the records. Appendix C provides guidance on data import using Excel and 

ASCII data files formatted in accordance with LTPP’s model specifications.

Figure 51 displays a snapshot of a portion of a simple spreadsheet that was created by importing 

data from an ASCII text file as per the procedures contained in Appendix C. This spreadsheet 

includes only the data elements from the individual vehicle record included in the ASCII text 

file, although as displayed in Figure 51, it has been filtered for records of vehicles that could not 

be classified by the system using its classification algorithm. This system assigns a “Class 15” to 

its unclassified vehicles. It is often beneficial to delineate the Axle Spacing columns as has been 

done in this example. By performing sorting and filtering schemes on unclassified vehicles, the 

analyst may be able to detect flaws in the classification scheme or possibly an error in how the 

classification scheme was entered into the system’s classification algorithm. Also, analysis of 

the records may indicate that the vehicles were not properly processed by the system (as would 

appear to be the case in this example).

It is noted that almost all of the records displayed in the Figure 51 sampling have a “VIOL” flag 

“21” which is this system’s code for an “Unequal Axle Detection” (the right and left weigh 

sensors did not detect the same number of wheel hits for the vehicle). It is noted that when this 

system experiences a vehicle for which a wheel hit is detected on one side of an axle but not the 

other, the system “invents” the missed wheel’s weight by copying the detected wheel’s weight to 

the opposite wheel. In that this may or may not provide a reasonably valid estimate of the axle’s 

static weight, the vehicle is flagged with the warning. Most of the vehicles flagged with the 

“Unequal Detection” flag would appear to have legitimate wheel and axle weights for the first 

four axles followed by weights and/or axle spacings that appear to be erroneous. This suggests a 

problem with a weigh sensor or its signal processing. 
64

Figure 51. Screen shot. Portion of simple spreadsheet filtered for unclassified vehicles.
65

In looking at some of the records in Figure 51 from a classification only standpoint:

• The vehicle in Row 9 has the axle count and spacings of a Class 8’s Type 3S1, but the 

steer axle weight (1.7 k + 2.6 k) is too light for a typical 3S1 tractor’s steer axle 

(minimum 7 k).

• The vehicle in Row 23 would appear to have a loop-processing problem based upon its 

overall length (2148 feet), speed (8 mi/h), and axle spacing readings, or else the vehicle 

was not entirely in its lane.

• The vehicle in Row 33 meets the LTPP Classification Scheme for a Class 3 vehicle 

pulling a small 3-axle trailer, except for the long Axle 2-3 spacing length. This is 

probably a legitimate vehicle.

• The vehicle in Row 39 has axle spacings meeting the LTPP Classification Scheme for 

both a Class 3 pulling a 2-axle trailer and a Class 5 pulling a 2-axle trailer. However, the 

GVW is too heavy to conform to the Class 3 and the steer axle is a bit too light to 

conform to the Class 5 for this scheme. This is probably a legitimate vehicle.

Therefore, an analysis of the unclassified vehicles in this sampling indicates the problem is in a 

weigh sensor, not the classification scheme or algorithm. This should prompt the analyst to 

perform simple diagnostics on the sensors, and if necessary, call for engineering or technical 

support to perform sensor signal analyses. The extent that this can be performed from the office 

would be subject to the features of the system.

This system’s coding of system errors and warnings by type is of great benefit to the analyst. 

However, a detailed analysis of the records displayed in Figure 51 would still indicate a sensor 

problem even in the absence of the warning flag.

Figure 52 displays a plot of a day’s Class 9 steer axle right and left wheel weight averages by 

hour of day. There is an obvious problem in that the system is generating right and left weights 

being very different, which is attributable to either improper calibration factors or one of the two 

sensors generating erroneous weight outputs. This situation will be discussed in detail in 

SECTION 5. The purpose of this analysis is to confirm that the right weigh sensor is outputting 

a consistent weight whereas the left weigh sensor is not. Between the hours of 6 AM and 6 PM 

the left sensor’s weight outputs drop dramatically, suggesting that the sensor is at times 

generating only partial weights. This problem, in itself, is not related to calibration factor values. 

Although the Class 9 volume is at its highest during this same time frame, it is doubtful that the 

drop in the sensor’s steer axle weights is due to the sensors having a “recovery time” problem, 

given that steer axle hits are relatively well separated from other axle hits. The problem would 

also not be caused by a large number of the Class 9s riding the shoulder stripe, which would only 

affect the right sensor’s weights. Consideration might be given to the daytime and nighttime 

difference in temperature or moisture. Regardless, this plot points out a sensor problem and the 

need for further investigation of the sensor’s operation. This type of check should be performed 

for additional days to see if the pattern is consistent.
66

Figure 52. Screen shot. Plot of Class 9 steer axle wheel weights by hour of day.
67

Figure 53 displays a snapshot from a portion of the spreadsheet used to generate the Figure 52

graph with the “AX1LT” field sorted for ascending values. Many of the “AX1LT” weights are 

significantly less than the “AX1RT” weights even though the following tandem’s “AX2LT” and 

“AX3LT” weights are reasonably close to their right wheel counterparts. The tandem’s left 

wheel weights are generally somewhat lower than the right wheel weights, but based upon the 

consistency this is probably due for the most part to improper calibration factors. 

Figure 53. Screen shot. AX1LT sorted for ascending values.

Figure 54 displays a similar screen shot of the spreadsheet but with the “AX1RT” field sorted for 

ascending values. As is obviously apparent, the right weigh sensor is not displaying the partial 

weight problem noted for the left sensor.

It would be a virtual impossibility for a Class 9’s left steer axle wheel to only partially hit the left 

sensor when the trailing tandem’s left wheels fully hit the same sensor, as displayed in Figure 55.
68

Figure 54. Screen shot. AX1RT sorted for ascending values.

Figure 55. Photo. Class 9, Type 3S2 typical axle and wheel alignment crossing right and left 

weigh sensors.

The contractor performing data QC and system monitoring for the LTPP Specific Pavement 

Study (SPS) Traffic Pooled Fund study developed several procedures, utilizing Excel, for 

analyzing performance of the individual weigh sensors. The spreadsheet is set up to import 
69

individual vehicle records from data files that contain specified vehicle types included in Class 9, 

as well as the vehicles in Classes 10 through 13. This import process is described in Appendix 

C. For the data files used, columns A through AN of the spreadsheet are populated with the data 

elements for each imported vehicle record. Columns A through AM include the data elements 

required by the LTPP Model Specifications and column AN provides for a “Vendor Specific 

Optional Field”. Although various analyses can be performed with this data utilizing only filter 

and sort procedures, several additional features were added to the spreadsheet’s template for 

automating certain analyses, including the following.

Figure 56 displays the spreadsheet’s calculated fields. An “X” flag is displayed in Column AO, 

labeled “IMBALANCE”, for any record for which the vehicle meets the criteria for “Invalid 

Measurement” in accordance with LTPP’s model specifications. The two conditions for this 

calculated field are:

(1) The left and right wheel weights of any axle have a difference of 40 percent or more.

(2) Either of the wheel weights of such axle exceeds 2.0 kip. 

There may be different interpretations as to how “...a difference of 40 percent or more” is 

calculated, but the intent is that the recorded weight of the lighter of an axle’s right and left 

wheels must be at least 60 percent the weight of the heavier wheel for the axle weight to be 

deemed a valid measurement.

Figure 56. Screen shot. Calculated fields for testing “Invalid Measurement” flags and 

analyzing axle imbalances.

The actual wheel weight data in each record are used to determine if the “Invalid Measurement” 

criteria are met, regardless of whether or not a flag was assigned by the system. Cell AQ1 

provides for user input of the percent difference value to utilize for condition (1) and Cell AS1 

provides for user input of the wheel weight threshold to utilize for condition (2) of the 

specification. The analyst can experiment and play “what if” with these two values to determine 
70

what works best for each site. The default values may work well for sites which have a high 

percentage of trucks with loaded trailers, but may flag far too many vehicles at sites that have a 

high percentage of trucks with empty or very light trailers. For the spreadsheet displayed in 

Figure 55 the user has changed the condition (2) default value from 2.0 to 3.0. It must be 

remembered that the intent of the Invalid Measurement flag is to identify vehicles appearing to 

have one or more wheels that did not fully hit the appropriate weigh sensor. As was discussed 

following Figure 42, an empty trailer’s right and left wheel weight difference is insignificant 

from both axle weight and gross weight perspectives. 

For a site that has a large number of trucks with empty trailers, if vehicles flagged by a system as 

having potentially erroneous weights due to the right versus left axle weight imbalance are 

automatically discarded from weight reporting by the analyst, then many legitimately weighed 

vehicles may be discarded. This might drastically skew data utilized for both weight violation 

and loading analyses purposes. It is strongly recommended that for a system that has features 

allowing the analyst to program parameters for assignment of right versus left imbalance flags 

that the system be programmed not to flag vehicles that are obviously empty and have minor 

right versus left imbalances in terms of weights, not just percentage, for the trailer axles. Simply 

increasing the minimum wheel weight threshold from 2.0 kip to 3.0 kips might significantly 

decrease the percentage of vehicles flagged as meeting the criteria for Invalid Measurement.

Columns AT through AX in Figure 56 display the ratio of the right versus left wheel weight for 

each of axles 1 through 5 for each record. This provides the analyst with a “quick look” at each 

axle’s ratio for the vehicles with flags in the “IMBALANCE” field. If all of the axles have a 

significant imbalance on the same side it is a good indication that the vehicle may not have been 

tracking well within the lane lines. If the AX1 imbalance is significant whereas the other four 

axles look normal, this is typically an indication that a sensor has not reported an accurate 

weight. If one of a tractor’s tandem axles displays a significant imbalance whereas the other 

does not, this is also an indication that a sensor has not reported an accurate weight. 

Figure 57 displays a screen shot of the spreadsheet’s table that lists the counts and percentages of 

the sample’s Class 9 vehicles flagged as having Invalid Measurement weights by GVW 

distribution. As used for this analysis, these flagged vehicles meet the criteria “Invalid 

Measurement” discussed following Figure 56. This table makes it evident that for this site 

Invalid Measurement weights are exhibited by lighter vehicles much more than by the heavier 

vehicles. The lighter the trailer, the more subject it is to effects of bouncing and crosswinds. If 

the heavier vehicles start to exhibit an increase in the percentage of Invalid Measurement 

weights, it might well be an indication that one of the sensors is starting to malfunction.

For any data analyst desiring to create spreadsheets with the enhanced analyses features, as 

displayed in Figures 56, 57, and 58, Excel ASCII Import workbooks and documentation are 

provided online at www.QualityWIM.com. It is noted that this spreadsheet also includes the 

additional calibration monitoring analyses features that will be discussed in SECTION 5, for 

Figure 68through Figure 72.
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Figure 57. Screen shot. Invalid Measurement weights by GVW range.

Figure 58 displays a snapshot of the spreadsheet’s table showing summaries of counts and 

percentages utilizing the individual Axle 1 through 5 imbalance ratios from the Figure 56

spreadsheet (Columns AT through AX). The purpose of this table is not to identify the extent of 

the vehicles meeting the criteria for Invalid Measurement but to identify any pattern that might 

suggest one of the following:

• The right or left sensor is malfunctioning on an intermittent basis.

• A significant number of the trucks’ right wheels are not fully hitting the weigh sensor.

• Crosswinds may be having an effect on the right versus left axle weights.
72

Figure 58. Screen shot. Table displaying summary of axle right versus left weight 

imbalance statistics.

The analyst can enter the desired percentage value to ascertain the threshold for determining 

what right versus left weight difference constitutes an axle imbalance. The percentage of 

imbalances for Axle 1 should always be quite low (unless the trucks at a particular site do travel 

with their right wheels on the shoulder stripe). On the other hand, at a site with a high 

percentage of empty trucks the imbalance percentage for Axles 4 and 5 might be quite high (as in 

the example, using a 25 percent threshold).As the analyst becomes familiar with the different 

axle weight imbalance patterns, he or she will be able to identify the more subtle sensor 

problems even when a significant percentage of vehicles are not flagged as meeting criteria for 

Invalid Measurement.

Is it really necessary to go through the effort of performing such extensive analyses on the right 

versus left weigh sensor outputs? For some sites or system types perhaps not, but in the absence 

of these analyses a weigh sensor may be intermittently reporting weights that are inaccurate but 

too subtle to be noticed by means of less extensive data QC procedures. These types of analyses 

can also be quite useful in determining whether sensor outputs which appear to be inaccurate 

estimates of static weights are caused by actual sensor malfunction or by conditions related to 

truck operating characteristics or crosswinds. Once a spreadsheet or database program has been 

set up to automatically produce the types of information shown in these examples, it takes very 

little effort to make quick checks to ensure a sensor’s output is not changing. Additional 

procedures for monitoring individual sensor outputs will be addressed in SECTION 5.

It is noted that for most WIM sites the Class 9 is the predominant truck class and that the steer 

axle wheel weights are much less affected by a Class 9’s loading than the wheel weights of its 

other axles. As such, many of the extensive data analyses utilizing individual truck records focus 

on the Class 9 vehicles (particularly the 3S2). In addition, the Class 9 steer axle weights are also 

a focus of analyses regarding individual sensor weight outputs.
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There are many extensive analysis procedures that an analyst can perform, either on routine or ad 

hoc bases, other than those used in these few examples. Note that such analyses are time 

consuming and require knowledge of each site’s traffic and data characteristics, knowledge of a 

spreadsheet or database program, and even some imagination. However, these analyses can be 

extremely beneficial in identifying, isolating, quantifying, and diagnosing a system’s data 

problems.
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75

SECTION 5. STEPS FOR MONITORING SYSTEM CALIBRATION FROM OFFICE

SECTION 3 and SECTION 4 focus on data QC procedures that are intended to ensure that a 

WIM system is operating to the best of its capabilities. Although such procedures are intended 

to identify significant size and weight accuracy problems due to improper system settings, 

malfunctioning components, or traffic operational anomalies, they are not designed to monitor 

the “fine tuning” of a system’s calibration.

The objectives of the calibration monitoring procedures discussed in this section include:

• Maintain system calibration throughout the life of the system.

• Verify the desired effects of calibration factor adjustments on WIM weight, axle spacing, 

and vehicle length outputs.

• Identify weigh sensors that are intermittently and/or subtly malfunctioning.

• Adjust calibration factors for a weigh sensor exhibiting calibration drift pending onsite 

recalibration using test trucks.

• Temporarily assign calibration factors for a weigh sensor replacement pending onsite 

recalibration using test trucks.

• Schedule onsite calibrations/validation for sites with most need when funding and/or 

resources for running test trucks is limited.

5.1. GENERATE WEIGHT AND AXLE SPACING STATISTICS FOR SAMPLE OF 

TRUCK TRAFFIC STREAM

The method of this monitoring is to use large traffic stream samples (at least seven consecutive 

days of validated data) of a selected truck type or types (typically the Class 9’s Type 3S2) to 

generate reports displaying statistical data on:

• Steer axle and gross vehicle weight distributions.

• Individual outputs of right and left weigh sensors.

• Effect of speed on weight.

• Axle spacings (and thereby speed).

For sites that have a significant number of the Class 11’s Type 2S12 or the Class 12’s Type 3S12 

statistical data can be generated for checking overall vehicle length calibration.
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For calibration monitoring analyses to be effective using these recommended procedures, it is 

imperative that the data used for the samples have passed all data QC checks. Also, data for days 

when the truck volumes and/or operating characteristics may not be typical, such as a major 

holiday, should not be used in the sample. If a particular month contains days with invalid data 

and/or days with atypical truck traffic such that a consecutive seven-day sample cannot be 

obtained, simply substitute the same day(s) of the week from the closest week in the same month 

to make up a composite week’s sample. It is important that the traffic stream sample, regardless 

of the vehicle class(es) or type(s) selected for analysis, include only “real” trucks. Smaller 

power units such as pickups, Class 5s pulling trailers (see Figure 59), recreational vehicles 

pulling trailers or autos can skew the statistics.

Figure 59. Photo. This vehicle combination may conform to a Class 9 Type 2S3 under some 

classification schemes.

For sites with low volumes of Class 9 vehicles, the sample should be for 14 consecutive days. It 

is up to the data analyst to determine what size sample is actually needed to perform a 

meaningful calibration monitoring analysis, but it is noted that the contractor performing the 

Phase II calibration monitoring for the LTPP Specific Pavement Study (SPS) Traffic Pooled 

Fund Study obtained 14 day samples for any site for which a seven-day sample would typically 

contain less than 1500 Class 9 Type 3S2 vehicles. 

It is also important to note that these calibration monitoring procedures are intended to 

supplement, not replace, onsite calibrations using test trucks. Based upon analyses of the traffic 

stream statistics that indicate one or more sensors are not maintaining calibration (referred to as 

“calibration drift”) or otherwise not reporting accurate weights, the analyst may deem it 

necessary to do one of the following:

• Call for an immediate onsite validation/recalibration with test trucks.

• Make calibration factor adjustments from the office deemed necessary to maintain 

calibration until test trucks can be run at the site.
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In discussing and making recommendations on calibration monitoring procedures, examples of 

reports generated by a custom software program as well as tables and graphs from an off-theshelf spreadsheet program will be displayed. Although the discussions may state something to 

the effect that “this report should be generated...”, the intent is that information and statistics 

similar to what is included in the displayed example should be generated for review by the 

analyst. It is not intended that the programs used for example purposes be considered as the only 

recommended tools to generate necessary statistics.

The reports and graphs used for the following examples were generated by the “WIMSys” 

application of “CTWIM Suite” which is available from Caltrans at 

http://www.dot.ca.gov/hq/traffops/trucks/datawim/install.htm. A Power Point presentation on 

the CTWIM’s WIMSys application can be downloaded on the same website 

(http://www.dot.ca.gov/hq/traffops/trucks/datawim/install.htm).

Figure 60 displays a report for a seven-day sample of Class 9 vehicles for a site with weigh 

sensors installed in the system’s lane numbers 1 and 2 (northbound), and 5 and 6 (southbound). 

GVW distributions are displayed in 5.0 k ranges for each lane. The dashed line following the 

“30.0 TO 34.9” row is the typical break point for empty Class 9 trucks and the dashed line 

following the “75.0 TO 79.9” row is the GVW legal limit. This particular site experiences a 

moderate volume of both empty and loaded Class 9 trucks. This report, generated for a sevenday sample immediately following a system’s being calibrated or validated using test trucks, 

provides an excellent reference for distribution comparisons with subsequent analyses. 
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Figure 60. Tabular Report. Distribution of lane counts by GVW, for site with mix of both 

loaded and empty Class 9 vehicles.
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Figure 61 displays the same gross weight distributions but in graphical format. It is apparent that 

Lane #6 weights are a bit lighter than those for Lane #1. However, it must be noted that many 

WIM sites with bidirectional lanes do not experience the same GVW distribution patterns for 

each direction. For this sample, Lane #1 has more loaded than unloaded Class 9s whereas Lane 

#6 has more unloaded than loaded Class 9s. It is not uncommon for these patterns to change by 

day of week (hence the need for a sample from seven continuous days) or by season of the year 

(hence the need for tracking over time, as will be discussed later). Regardless of the Class 9 

Type 3S2 empty versus loaded distribution mix, it is typical for the empty distributions to peak at 

“30 TO <35” k (as they do in this example) when using the five k ranges. The loaded 

distributions peak will vary a bit depending upon a particular site’s truck operating 

characteristics, but the peak will typically occur at “70 TO <75” or “75 TO <80” k. For this 

example, the Lane #6 loaded peak being at the “65 TO 70” distribution is a bit suspicious, but its 

empty peak appears to be reasonable.

Figure 61. Report Graph. Distribution of lane counts by GVW, for site with mix of both 

loaded and empty Class 9 vehicles.

Figure 62 displays the same report as that displayed in Figure 60, but this report is for a sevenday sample from a site on a long haul route in the middle of the desert. As would be expected, 

this site has a very low percentage of empty Class 9s.
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Figure 62. Tabular Report. Distribution of lane counts by GVW, site with very few empty 

Class 9 vehicles.
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Figure 63 displays the same gross weight distributions but in graphical format.

Figure 63. Report Graph. Distribution of lane counts by GVW, site with very few empty 

Class 9 vehicles.

When reviewing GVW distributions, the analyst is trying to identify the following:

• Reasonableness of empty and loaded peak distributions given site’s truck operational 

characteristics.

• Consistency of overall distribution patterns with:

o Those in previous reports.

o Those in a report for a sample taken immediately following the last onsite 

calibration or validation using test trucks.

For sites that do have seasonal variations in truck operational characteristics, it may take a couple 

years to verify that the changes in GVW distributions are due to these variations and not 

calibration drift. It is always a good idea to perform an onsite validation using test trucks the 

first time a site’s GVW distributions change.
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The next step is to check the weight outputs of each individual sensor and to monitor the effects 

of speed on the Axle 1 weight and GVW outputs for each lane.

A key element in the monitoring of a system’s calibration and weigh sensor performance is the 

assumption that for a large traffic stream sample of Class 9 vehicles the average right and left 

steer axle weights should be approximately equal. A 2004 study (Nichols and Bullock 2004) 

determined this to be a logical assumption based upon a review of vehicle geometry with several 

truck manufacturers and an accounting for the effect of roadway cross slope. Regardless of any 

argument that this assumption is not “ground truth”, the monitoring of the balance between the 

average right and left steer axle weights is an excellent tool for identifying any drift in a sensor’s 

calibration or any subtle problem in a sensor’s performance. It is recognized that some Type I 

WIM systems have double threshold weighing whereby each right and left wheel track has two 

weigh sensors instead of one. However, such a system reports, as data elements, a single right 

wheel weight and a single left wheel weight for each axle for each individual vehicle record. In 

discussions related to right and left weigh sensors, such sensors will be treated as single sensors 

even though in some cases a system may actually have two right sensors and two left sensors.

Onsite calibrations are typically based upon the test vehicles’ static axle weights (as opposed to 

individual right and left static wheel weights) as reference values for determining WIM error. 

Therefore, it is recommended that prior to running test trucks, a sampling of the traffic stream’s 

Class 9 data be obtained and the right and left sensor calibration factors be adjusted such that the 

traffic stream’s average right and left steer axle WIM weights will be approximately equal. For 

example, if a pre-calibration Class 9 traffic stream sampling for Lane #1 showed an Axle 1 Right 

Wheel average of 5.2 k and an Axle 1 Left Wheel average of 5.6k, the calibration factors for the 

system’s Lane #1 would be adjusted as per the calculations displayed in Figure 64. These right 

and left sensor factors would then be equally increased or decreased based upon the WIM error 

as determined from test truck axle weight data. This procedure would apply to each lane being 

calibrated.

Figure 64. Procedure. Pre calibration - right and left sensor balance.

Another key element in system calibration and calibration monitoring is the recognition that 

vehicle speed is a very important aspect of a system’s proper calibration. ASTM E 1318 states, 
83

under Section 7.5.5.5, “Every vehicle interacts with the road surface differently at different 

speeds, but about the same at the same speed.” Typically, the loaded Class 9 vehicles travel at 

approximately the same speeds as the unloaded Class 9 vehicles for a WIM site with all of the 

conditions listed below:

• A significant volume of Class 9s.

• Truck traffic that maintains a steady cruising speed.

• A roadway grade of less than 0.5 percent.

Figure 65 displays a report for LANE #1 for the same seven-day Class 9 sample used for the 

report and graph displayed in Figure 62 and Figure 63. 
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Figure 65. Report. Distribution of Class 9 weights and axle spacings by speed for one lane, 

flat roadway grade.
85

When reviewing a report similar to the one displayed in Figure 65, the analyst should check the 

following (refer to the numbered blocks highlighted in Figure 65):

1. Consistency of the Axle 1’s average right and left wheel weights, and maintenance of the 

balance between the two. 

For this sample the right and left weights are only 0.1 k apart, which is acceptable. Although 

some WIM sites are exceptions and a site’s variance in seasonal truck operational characteristics 

may come into play, the Class 9 average steer axle wheel weight should remain relatively 

consistent. A concurrent change in both weights suggests either calibration drift or a change in 

truck operational characteristics. Once the right and left average weights are brought into 

balance (no more than 0.2 k difference), they should remain balanced. Any shift in this balance 

suggests that a sensor may be intermittently malfunctioning.

1. Consistency of the standard deviation for Axle 1’s average right and left wheel weights. 

For this sample both average weights have a standard deviation of 0.4 k, which is acceptable. 

Given good site and traffic conditions, these standard deviations should typically not exceed 0.5 

k. If either of these standard deviations starts to increase, it is an indication that the sensor may 

be malfunctioning on an intermittent or subtle basis.

2. For sites with ideal geometry and traffic conditions, consistency of the average Steer Axle and 

Gross Vehicle Weights throughout the speed ranges for which a significant number of the Class 

9 vehicles are travelling. 

For this sample the average GVW for the “45.0 TO 49.9” speed distribution is approximately 

four percent higher than for the higher speed distributions. Given that the sample comes from a 

rural interstate roadway with high-speed traffic, it could very well be that the calibration or 

validation test trucks were not run at speeds this low in deriving data for verifying or determining 

calibration factors. Regardless of site and traffic conditions, the Figure 65 report should be 

generated for a traffic stream sample immediately following an onsite calibration or validation 

using test trucks. For a system to be properly calibrated, the system’s calibration factors should 

be based upon data from test trucks that were run throughout the entire operating range of a 

significant majority (at least 80 percent) of the truck traffic stream. 

It is recognized that at many WIM sites a majority of the truck traffic stream travels at speeds 

well above the posted speed limit. It is not in any way recommended that an agency run test 

trucks exceeding posted speed limits in the absence of jurisdictional approval. However, it 

would certainly be beneficial if an agency could obtain proper approval for running test trucks at 

speeds consistent with the truck traffic stream flow.

3. Reasonableness and consistency of the percentage of overweight vehicles in the sample.

Even though there are no weigh stations in the immediate vicinity of this WIM site, it is very 

doubtful if 25.5 percent of the Class 9 vehicles would actually be cited for being overweight if 

statically weighed. Most vehicles passing through this site are “long haul” and will at some 
86

point have to go through a weigh station. There are at least a couple reasons why a WIM system, 

even if well calibrated, might flag a relatively high percentage of its trucks as being in violation 

of weight limits (assuming the system is programmed to use the actual weight violation 

parameters in-lieu of allowing some tolerance):

• For the Class 9 Type 3S2 to achieve maximum allowable GVW (typically 80 k) both 

tandems must be loaded as closely as possible to the maximum allowable tandem weight 

(typically 34 k). As such, if the WIM reads just a slight percent high for any of a 

tandem’s wheels the vehicle will be flagged as overweight. Although a WIM system’s 

slight overestimates and underestimates of static weights may be well within accuracy 

tolerances and average out overall in terms of reported weight, weight violation flags do 

not average out, and a WIM system’s reporting of weight violation percentage based 

upon a sample’s dynamic weight readings may be somewhat higher than if the same 

sample were weighed statically.

• To get a better ride on the open road, it is quite common for a trucker to move a vehicle’s 

king pin setting back a bit to shift weight from the steer axle to the tractor’s drive tandem 

following an exit from a weigh station. This revised king pin setting could well result in 

the drive tandem’s being overweight even if statically reweighed. It is also somewhat 

common for a trucker to move the semi-trailer’s slider tandem, which shifts weight from 

one tandem to the other. Such king pin setting and trailer tandem slider settings are 

readjusted before entering the next weigh station, but at the time these vehicles pass 

through a WIM site they may very well actually be in violation of weight limits.

4. Reasonableness and consistency of the Tractor Tandem Axles average spacing and its 

standard deviation.

For most locations in the U.S., the Type 3S2 vehicle’s average spacing should be 4.3 feet. This 

would also apply if the sample included the Class 9 Type 32 (although the power unit is not 

technically a “tractor”). This average (or a tight standard deviation) would not apply if the 

sample includes Class 9 Type 2S3 vehicles. For locations that have Canadian truck traffic or 

specialty truck types, consideration would need to be given to observed axle spacing 

configurations and the percentage of such atypical vehicles.

Figure 66 displays the same report as that in Figure 65 but for a seven-day Class 9 sample from 

LANE #4 of a site that has a long two percent uphill grade approach in that lane’s direction. As 

is obvious from the vehicle gross average weights column, such weights drop drastically for the 

speed ranges above 50 mi/h. This is due to the fact that the heavier the vehicle the less ability 

the vehicle has to maintain a cruising speed. For the fully loaded vehicles, with exception of 

those with the most powerful engines, their speed has dropped considerably by the time they 

reach the WIM site. 
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Figure 66. Report. Distribution of Class 9 weights and axle spacings by speed for one lane, 

uphill grade.
88

Installing WIM systems on roadways with grades greater than 0.5 percent should be avoided for 

several reasons listed below.

• Due to the lower speeds of the loaded trucks, the onsite calibration with test trucks must 

encompass a larger range of speeds to properly calibrate the system.

• One or both of the test trucks may not be able to attain the higher speeds necessary for 

proper calibration.

• When a truck is passing through the site under heavy throttle, weight is transferred from 

the steer axle to the drive axle(s). Although the WIM may accurately determine the 

dynamic wheel weights, they are not accurate estimates of the truck’s static wheel 

weights.

• The empty trucks travelling at the higher speeds may be passing the slower trucks 

through the site.

• Since the loaded and empty trucks travel at different speeds, the calibration monitoring is 

more difficult to perform.

o This report should be generated for a seven-day traffic stream sample 

immediately following a legitimate onsite calibration or validation with test trucks 

to use as a reference for subsequent comparisons.

Figure 67 displays a report for a seven-day sample for the same site, time frame, and lane as the 

Figure 66 report, but this report is for Class 11 vehicles. The only difference in the two report 

formats is that instead of providing statistics on Class 9 axle spacings, statistics are provided for 

the Class 11 overall vehicle length and wheelbase (Axles 1 through 5). For Class 11 Type 

2S12s, the overall vehicle length typically exceeds the wheelbase by approximately six feet, so, 

in comparing the sample’s average vehicle length and average wheelbase, the difference should 

be approximately six feet. This report was designed for use by California, which calibrates its 

systems for overall vehicle lengths and has a significant number of Class 11 vehicles at many of 

its WIM sites. It is recognized that many states’ WIM sites have very few Class 11 vehicles and 

as such would have no need to generate reports for Class 11 samples.
89

Figure 67. Report. Distribution of Class 11 weights, vehicle length, and wheelbase by speed 

for one lane.
90

SECTION 4 discussed the use of Excel by the LTPP contractor for performing extensive WIM 

data analyses. This Excel workbook was expanded to generate some of the statistical 

information contained in the CTWIM WIMSys reports for use in calibration monitoring. In that 

an agency’s WIM data analyst may find it easier and/or more practical to use a spreadsheet or 

database program for performing calibration monitoring than using the CTWIM WIMSys 

application, portions of the Excel workbook used for the LTPP study are described in the 

following examples. For any data analyst desiring to create spreadsheets with the calibration 

monitoring features displayed in Figure 68 through Figure 72, Excel ASCII Import workbooks 

and documentation are provided online at www.QualityWIM.com.

For most of the LTPP study sites a seven-day sample is used. For a few sites with low truck 

volumes a 14-day sample is used. As previously noted, for the calibration monitoring to be 

meaningful only data that has passed QC checks for days which have typical truck traffic should 

be included in the samples. The workbook that is used for the following examples is for one lane 

(the LTPP test section lane) and as such does not provide for user input of other lanes. The 

workbooks which are provided online at www.QualityWIM.com allow the user to enter a 

specific lane number, in addition to the vehicle class, when generating the tables and graphs.

Also, regardless of what type of traffic stream sampling is performed and what statistics are 

generated for calibration monitoring, it is imperative to perform a minimum seven-day sampling 

immediately following a system’s onsite calibration or validation using test trucks, and to 

generate the set of statistics to be used as a reference set for comparison with subsequent 

sampling statistics.

Figure 68 displays the entire worksheet, which includes calibration monitoring tables and a 

graph, as well as other tables useful for the monitoring of weigh sensor performance. The 

Classes listed in these tables are based upon a scheme whereby vehicles with five or more axles 

are classified as listed below. Note that these classes are utilized solely for the purpose of 

performing analyses using this worksheet. They are not intended to conform to schemes used to 

classify vehicles in compliance with the Traffic Monitoring Guide requirements for general data 

submission. The analyst will need to perform post-processing of the downloaded WIM data to 

generate the following classes by specific vehicle configuration type.

• CLASS 9 : Type 3S2

• CLASS 10 : Type 3S3

• CLASS 11 : Type 2S12

• CLASS 12 : Type 3S12

• CLASS 14 : Type 32
91

Figure 68. Screen shot. “Tables” Worksheet.
92

The portions of this worksheet useful for calibration monitoring include those described below.

Figure 69 displays average weights and their standard deviations for each listed vehicle class’s 

steer axle wheel weights, steer axle weight, and GVW. Analyses of these statistics have been 

discussed previously.

Figure 69. Screen shot. Weight statistics for calibration monitoring and tracking.

Figure 70 displays statistics for each listed class as discussed below.

• OVERWEIGHT- Analysis of this statistic has been discussed previously.

• INVALID WEIGHT- More pertinent to sensor performance than calibration monitoring.

• AX1 WHEEL <3.0 - The CTWIM WIMSys application filters out any record for which 

the vehicle’s right or left steer axle weight is less than 3.0 k. This spreadsheet does not 

filter out such records, but displays how many of the right and left steer axle weights are 

less than 3.0 k. To use the sample for calibration monitoring purposes, these should be a 

very low percentage. If the percentage increases for either right or left weight, it is an 

indication of either intermittent sensor malfunction or an increased number of truck 

wheels not fully hitting the sensor.

• CLASS 9 AXLE 2-3 SPACE - Analysis of this statistic has been discussed previously. 

• CLASS 11 - The correlation between the Class 11’s wheelbase and overall vehicle length 

has been discussed previously.

• CLASS 14 - For those sites with a significant number of the Type 32 truck trailer (a Class 

9 using Traffic Monitoring Guide criteria), this vehicle’s overall length is typically 

approximately six feet longer than the Axle 1 to Axle 5 wheelbase.

Figure 70. Screen shot. Additional statistics for calibration monitoring and tracking.
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Figure 71 displays the GVW distribution plot for the vehicle class entered into Cell B29 by the 

analyst. Also plotted are the average speed and the number of Invalid Measurement weights in 

conjunction with the GVW plot.

Figure 71. Screen shot. GVW distribution plot.

Figure 72 displays weights versus speeds in two different ways for the class of vehicle entered by 

the analyst into Cell B29 (see Figure 71). As discussed previously, for a site with suitable 

roadway geometry and traffic conditions, the empty and loaded trucks typically travel at 

approximately the same speeds. For “Speed Range” distributions that have a significant number 

of samples the “Avg GVW” should be reasonably consistent among those distributions, and for 

“GVW Range” distributions that have a significant number of samples the “Avg Speed” should 

be reasonably consistent among those distributions.
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Figure 72. Screen shot. Weights versus speed statistics.

5.2. MONITORING TRUCK TRAFFIC STREAM STATISTICS OVER TIME

Up to this point this Section’s examples and discussion have focused on generating and 

analyzing traffic stream truck traffic statistics for individual samples. It is recommended that 

this be performed routinely on a monthly basis, as well as any time calibration factors are revised 

for a particular system, or a system undergoes equipment or software modifications. The 

following examples and discussion will focus on monitoring and tracking these statistics over 

time to accomplish the items listed below:

• Identifying true calibration drift as opposed to seasonal variations in a site’s truck 

operational characteristics.

• Verifying the effects of calibration factor adjustments on traffic stream weights.

• Identifying degradation of a weigh sensor’s performance.
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Figure 73 displays the monthly GVW distribution plots over a one-year time frame using the 

seven-day Class 9 traffic stream sample sets used for the Figure 68 through Figure 72 statistics 

screen shots. This site is located on a long haul interstate route with high truck volumes. As is 

obvious from the plots, there are variations in the volumes but the loading characteristics are 

extremely consistent. It is noted that the GVW graph uses 2.5 k distributions, which identifies 

weight distribution variations to a much finer degree than the more typical graphs using 5.0 k 

distributions.

Figure 73. Graph. Traffic stream Class 9 GVW distribution plots for 12 consecutive 

months, long haul high volume.
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Figure 74 displays the monthly steer axle weight distributions for the same sample as that used 

for the GVW plots displayed in Figure 73. The weight of a tractor-semitrailer’s steer axle 

increases only slightly as the loading of the trailer(s) is increased. As such, monitoring of the 

steer axle is an excellent tool for identifying calibration problems or subtle system operational 

problems. Although tracking of steer axle weight distributions over time may of benefit, the 

more routine checks such as those described in Section 5.1 (e.g.: discussions regarding Figure 52

and Figure 65) are of much greater importance.

Figure 74. Graph. Traffic stream Class 9 Axle 1 weight distribution plots for 12 consecutive 

months.
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Figure 75 displays the GVW distributions for a site with low truck volumes and a high 

percentage of empty trucks for the spring season months of three consecutive years. After 

tracking traffic stream GVW plots beyond the first year, seasonal comparisons can start to be 

made. For this example, there are variations in volumes but it is evident there is little, if any, 

calibration drift taking place.

Figure 75. Graph. Class 9 GVW distribution plots for spring season over three-year period, 

local traffic.
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Figure 76 displays the GVW distribution plots over a one-year time frame using 14-day Class 9 

traffic stream sample sets. This site is located on a rural route with very low truck volumes and 

experiences extreme snow and ice conditions. Although there are definable empty and loaded 

distributions, they are not nearly as pronounced or consistent as in the long haul high truck 

volume site displayed in Figure 73. Sites such as this are more difficult to monitor for 

calibration in that the truck operating characteristics are not consistent. 

Figure 76. Graph. GVW distribution plots for 12 consecutive months, low volume.
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5.3. EFFECTS OF CALIBRATION FACTOR ADJUSTMENTS ON TRUCK TRAFFIC 

STREAM DATA

Figure 77 displays the monthly Class 9 traffic stream GVW distribution plots over a one-year 

time frame, but this system had its calibration factors decreased by four percent in late June. The 

effects are dramatic, particularly on the loaded peak distribution. It is noted that the drop in 

weights starting in July was initially attributed to calibration drift. This example emphasizes the 

importance of considering any weight calibration factor adjustments when performing calibration 

monitoring.

Figure 77. Graph. Class 9 GVW distribution plots for 12 consecutive months, weight shift.

Procedures for performing onsite calibrations and validations using test trucks are not within the 

scope of this Manual. However, it is of benefit to the data analyst to be able to analyze the test 

truck data for the purpose of comparing such data with the traffic stream data, and determining 

the effect of calibration factor adjustments on the traffic stream weights. If the analyst must 

make the assumption that the calibration was performed correctly, the best tool for use by the 

analyst is a graph displaying the test trucks’ GVW WIM error by speed plots. WIM error is 

determined by comparing a test truck’s static weight with its corresponding WIM reported 

dynamic weight. For example, if a test truck’s static GVW is 75.0 k and, for a particular run, the 

WIM reports a GVW of 76.0 k, the GVW WIM error for that truck’s run is +1.3 percent, as 

calculated from the following equation:
100

WIM Error = 100*[(GVWWIM – GVWstatic)/ GVWstatic]

In addition, it must be remembered that system calibration and its monitoring is performed on an 

individual lane basis. The Excel workbook used to generate the test trucks’ GVW WIM error by 

speed plots in the following examples is available online at www.QualityWIM.com, along with 

the corresponding detailed documentation.

Figure 78 actually displays two individual graphs that have been sized and aligned to exhibit the 

importance of considering speed when performing calibrations, or when analyzing the effect of 

calibration factor adjustments on the WIM weights for the truck traffic stream. The top graph 

displays the percent of WIM GVW error for each run for two test trucks. The solid symbols 

(“PRE VAL”) are for the WIM GVW errors using the system’s weight calibration factors in 

effect at the start of the first set of test truck runs and the non-solid symbols (“POST VAL”) are 

for the WIM GVW errors using the system’s weight calibration factors as adjusted based upon 

the PRE VAL test truck data. The amount of adjustment for each of four of the system’s five 

calibration speed points, in percent, is displayed immediately above the corresponding speed. 

As is evident from the plots, it would appear that for the higher speeds, either the desired effect 

of the adjustment was not achieved or a mistake was made in either calculating the adjustment or 

entering the revised factor for the 60 mi/h speed point. The calibration factor for the 70 mi/h 

speed point probably should have also been increased. The lower graph displays the site’s truck 

traffic stream speeds in comparison to the speeds at which WIM error data was obtained by the 

calibration test trucks. Although the posted speed limit in effect at the site probably prevented 

the test trucks from making runs at higher speeds, it is evident in comparing the two graphs that a 

majority of the runs made by the test trucks were meaningless. In effect, the calibration factor 

adjustments will probably have little noticeable effect on the WIM weights outputs for the truck 

traffic stream.
101

Figure 78. Graphs. Calibration test truck GVW WIM error versus speed plots, and truck 

traffic stream speeds versus calibration test truck speeds plots.

Figure 79 displays, for the system calibrated shown in Figure 78, the Class 9 traffic stream GVW 

distributions for samples from the two months preceding and the two months following the 

calibration factor adjustments. Although the “Oct” empty truck distribution is somewhat 

random, it is evident that the factor adjustments had no noticeable effect on the traffic stream 

WIM weights.
102

Figure 79. Graph. Class 9 traffic stream GVW distribution plots before and after 

calibration factor adjustments.

Another issue regarding calibrations utilizing test trucks that must be considered by the data 

analyst is that even when proper trucks are used and the calibration procedures are performed 

correctly, different trucks or pairs of trucks may get different results in terms of WIM error. 
103

Figure 80 displays the GVW distributions for the monthly samples over a 15-month period for a 

site during which time no calibration factor adjustments were made. As is evident from the 

plots, the loaded distribution peak and to some extent the empty trucks peak remained extremely 

consistent over the entire period indicating that no calibration drift occurred.

Figure 80. Graph. Class 9 traffic stream GVW distribution plots over period with no 

calibration factor changes.

Figure 81 displays the percent WIM GVW error plots for two different sets of test truck runs 

(two trucks each), 16 months apart, at the site displayed in Figure 80. The solid symbols (“JUN 

‘06”) are for the WIM GVW errors verifying the system’s weight calibration factors in effect at 

the time. The non-solid symbols (“OCT ‘07”) are for the WIM GVW errors using those same 

calibration factors, based upon the second set of test truck runs 16 months later. At the higher 

speeds there is a significant difference in the WIM error between the two sets of test truck runs 

even though the traffic stream data indicates that no calibration drift occurred during the time 

between the two sets of test truck runs. 
104

Figure 81. Graph. Calibration test truck GVW WIM error versus speed plots.

Figure 82 displays the percent of WIM GVW error for the initial set of runs for the “OCT ‘07” 

validation displayed in Figure 81, as well as the follow-up set of runs after calibration factor 

adjustments. The non-solid symbols (“PRE-VAL”) are for the WIM GVW errors using the 

system’s weight calibration factors that had been in effect for the preceding 16 months and the

solid symbols (“POST-VAL”) are for the WIM GVW errors using the system’s weight 

calibration factors as adjusted based upon the PRE-VAL test truck data. The percentage of 

factor adjustment for each of the system’s five calibration speed points is shown above the 

corresponding speed. As is evident from the plots, it would appear that the desired effects were 

attained, although as the speeds increase, the difference in WIM error between the two trucks 

also increases.
105

Figure 82. Graph. Calibration test truck GVW WIM error x speed plots, before and after 

factor adjustments.

From a test truck data standpoint this would be deemed a successful calibration. However, from 

the standpoint of monitoring the effects of calibration factors on the traffic stream’s WIM 

weights it is like trying to hit a moving target, as evidenced by Figure 83. 

Figure 83 displays the effects of three different sets of calibration factor adjustments, which were 

based upon test truck data, on the traffic stream WIM weights over a two-year period for the site 

displayed in Figure 80 through Figure 82. It would appear that in actuality the WIM system has 

maintained its calibration very well, whereas the WIM error based upon test truck data has been 

inconsistent for the initial calibration and three subsequent sets of validation/recalibrations. For 

the loaded trucks, it would appear that the WIM weights generated utilizing calibration factors 

based upon test truck data for the initial calibration and the October 2007 runs are too high. 

However, WIM weights generated utilizing calibration factors based upon test truck data for the 

June 2006 and April 2008 runs appear to be too low. 

To anybody not paying attention to the various calibration factor changes it would appear that 

this system is not maintaining its calibration. In fact, it is being extremely consistent and is 

simply doing what it is being programmed to do. Perhaps at some point system accuracy might 

benefit from simply splitting the differences of the test truck data sets’ WIM errors. One thing a 

graph such as Figure 83 illustrates is the excellent linearity of the system in that the traffic stream 

WIM weight outputs change in direct relationship to the changes in the calibration factors. 
106

Figure 83. Graph. Effects of calibration factor adjustments on traffic stream WIM weights.

Figure 84 displays an example of tracking the statistics from the monthly Class 9 traffic stream 

samples in conjunction with any hardware, software/firmware, or system settings (including 

calibration factors) that may have an effect on the system’s output of weights. This tracking 

sheet is for the site displayed in Figure 80 through Figure 83. As this tracking sheet is filled out 

each month, the analyst can make various determinations in regard to a system’s maintenance of 

calibration and the effects of system modifications, as described below.
107

Figure 84. Screen shot. Tracking of system modifications and monthly calibration 

monitoring statistics.

• In June 2006, factors for both right and left sensors were decreased 4.0 percent based 

upon test truck data. Was the desired effect on weights achieved?

o Yes, for the July 2006 sample, the average GVW dropped between four and five 

percent, and the average steer axle weight dropped between three and four 

percent.

• For the 16 months following the June 2006 calibration:

o Is the system exhibiting any calibration drift?

 Although the average GVW drops gradually from 50.1 k to 46.4 k (seven 

percent) before starting to increase again, the loaded distribution peaks per 

the GVW distribution plots (Figure 80) remain quite steady. This would 

indicate the calibration is not drifting. Also, the fact that by the 2007 
108

summer months these weights have returned to their 2006 summer weights 

indicates that the decrease in average weights is probably attributable to a 

seasonal change in truck operating characteristics.

o Are the right and left weigh sensors in balance and exhibiting acceptable standard 

deviations?

 With exception of the July 2007 sample, the right and left balances are ok; 

standard deviations are marginal, but there is no indication of sensor 

problems.

o In January 2007, the scale sensor interface card was replaced. Did this 

replacement affect the weight output?

 No, all weight statistics remained reasonably constant.

o Is the Axle 2-3 spacing remaining constant at 4.3?

 Yes.

o Is the overweight percentage remaining constant?

 Yes.

• In October 2007, factor adjustments were made based upon test truck data. Was the

desired effect on weights achieved?

o Yes, for the October 2007 sample, the average GVW increased between four and 

five percent, and the average steer axle weight between five and six percent.

• For the months following the October 2007 calibration, is the system exhibiting any 

calibration drift?

o The GVW is increasing. However, the increasing weight output of only the right 

weigh sensor and corresponding increase in its standard deviation, in conjunction 

with increasing “Invalid” and “Unequal Detection” flag percentages, indicate a 

sensor problem, not a calibration drift problem.

• In February 2008, balancing of right and left weight outputs was attempted by lowering 

right sensor’s calibration factors. Was the desired effect on the weights achieved?

o Yes, for the March 2008 sample, the right weight output is back to where it was 

following the October 2007 calibration.

Note that this action is only a temporary measure to make data as accurate as possible 

pending resolving the right sensor problem.

• In April 2008, firmware was upgraded. Did this upgrade affect the weight output?

o No, all weight statistics remained reasonably constant.

• In April 2008, there was onsite repair work on weigh sensors, a firmware upgrade, and 

adjustment of calibration factors from the office based upon a small traffic stream 

sample. Are the WIM weights where they should be in readiness for a planned onsite 

validation using test trucks?
109

o No, a five-day sample indicates the following:

 Although the Axle 1 weight is consistent with that following the October 

2007 calibration, the GVW is almost 10 percent higher.

 The right and left weights are slightly out of balance.

o Calibration factors were adjusted from the office again.

• In April 2008, there were factor adjustments based upon test truck data. Was the desired 

effect on weights achieved?

o In some respects, yes. Based upon the May 2008 sample, the loaded peak 

indicates lower weight readings, although it is back to where it was following the 

June 2006 calibration (refer to the GVW distribution plots displayed in Figure 

82). It is also noted that the weight statistics are now very close to those 

immediately following the June 2006 calibration.

As an example from another site, Figure 85 displays the GVW distributions for the monthly 

samples over an 11-month period. Validations with test trucks were performed in August 2007, 

with no calibration factor adjustments. In March 2008, calibration factor increases were made 

which would affect only the weights of the very low percentage of slower moving trucks. As is 

evident from both the loaded and empty truck distribution peaks, this system is reporting WIM 

weights that are too high. The empty peaks are consistently at the “35.0-37.5” k distribution 

instead of “30.0-32.5” or “32.5-35.0” as is typical. The loaded peaks, although moving around a 

bit, are at times in excess of the maximum GVW limit of 80 k. Why is this problem not being 

corrected by running test trucks? Again, the answer is speed.
110

Figure 85. Graph. Class 9 GVW distribution plots, empty and loaded peaks too heavy.
111

Figure 86, like Figure 78, displays two individual graphs that have been sized and aligned to 

exhibit the importance of considering speed when performing calibrations, or when analyzing the 

effect of calibration factor adjustments on the WIM weights for the truck traffic stream. 

However, this example portrays a system that really has not been calibrated even though time 

and resources were expended to go through the motions of performing a validation/calibration 

using test trucks. 

The top graph displays the percent of WIM GVW error for each run for the two test trucks. The 

solid symbols (“PRE-VAL”) are for the WIM GVW errors using the system’s weight calibration 

factors in effect at the start of the first set of test truck runs. The non-solid symbols (“POSTVAL”) are for the WIM GVW errors using the system’s weight calibration factors as adjusted 

based upon the PRE-VAL test truck data. The percentage of factor adjustment for each of the 

system’s five calibration speed points is displayed immediately above the corresponding speed. 

As is evident from the plots, it would appear that the desired effects were attained even though 

there was an obvious problem with the PRE-VAL Truck 2 data. The WIM error plots follow the 

“0%” error axis for the 41 mi/h to 57 mi/h speed range. The problem is that very few traffic 

stream trucks are traveling within this speed range as evidenced by the lower graph. Figure 87

exhibits additional rationale for the statement that the system “...really has not been calibrated.”

Figure 87 displays weight by speed range statistics for a seven-day Class 9 sample from this site 

using a portion of the Excel table discussed previously in regard to Figure 72. This table 

indicates that the range of speeds traveled by the calibration test trucks cover only five percent of 

the speed range traveled by the Class 9 traffic stream (which, per the lower graph in Figure 86, 

corresponds with all of the truck traffic stream speeds). This table also indicates that the average 

steer axle weights and average GVW for 77 percent of the Class 9s are considerably higher than 

that for the very small sample within the speed range covered by the calibration test truck data. 

This is probably the reason that the Class 9 traffic stream GVW distributions displayed in Figure 

85 suggest that the system’s weight readings are too high. The system has simply not been 

calibrated (or validated) for speeds above 55 mi/h.
112

Figure 86. Graphs. Calibration test truck GVW WIM error x speed plots and truck traffic 

stream speeds versus calibration test truck speeds plots, ineffective calibration.
113

Figure 87. Screen shot. Weights versus speed statistics, ineffective calibration.

5.4. ADJUSTMENT OF CALIBRATION FACTORS BASED UPON TRUCK TRAFFIC 

STREAM DATA

This section has provided recommended procedures and methods of analyses that can be 

performed by the Office Data Analyst to monitor a WIM system’s calibration. A recap of 

problems that may become apparent to the analyst in performing calibration monitoring, as well 

as options available to the analyst to improve the system’s accuracy will be provided. However, 

in that for certain situations the adjusting of calibration factors based upon analyses of traffic 

stream data instead of only test truck data will be offered as an option, the appropriateness and 

validity of such factor adjustments need to be addressed first. There are several reasons that may 

prompt the analyst to adjust calibration factors, including the following:

• Balancing weight outputs of right and left sensors.

o If the analyst uses proper procedures to modify calibration factors for the sole 

purpose of balancing the right and left sensor weight outputs, and such 

modifications do not affect any increase or decrease in axle weights, it should not 

be necessary to validate calibration by use of test truck data. However, 

verification that steer axle weights and GVW have not changed must be 

conducted by subsequent sampling and data analysis of the traffic stream.

• Maintaining accuracy pending test truck validation/recalibration.
114

o In order to continue collecting accurate data it may be beneficial to modify 

calibration factors based upon traffic stream data as an interim measure until such 

time that onsite validation and/or recalibration by use of test trucks can be 

performed to address one of the following:

 The analyst can confirm that calibration drift is occurring.

 A weigh sensor has been replaced or repaired.

 System software/firmware has been modified or an electronic component 

repaired or replaced.

If the test truck data indicates that the interim calibration factors resulted in data 

conforming to accuracy requirements such data may be disseminated. If the test 

truck data indicates that the interim factors did not result in data conforming to 

accuracy requirements, such data should be purged or its use limited.

• Inconsistent test truck data.

o As displayed in Figure 81, even testing by use of proper procedures using test 

trucks that meet testing requirements may result in test truck data varying by five 

percent or more in terms of determining WIM error. Also, as displayed in Figure 

83, such differences in test truck data, particularly over a period of time, may 

make it apparent to the analyst that the data would probably be more accurate if 

the differences in the test truck data were averaged out in order to calculate 

calibration factor adjustments. 

o In the absence of evidence that test truck data is invalid, any determination of 

calibration factors based upon considerations other than the most current test 

truck data is not “truth in data”. However, analyses of test truck data to determine 

what factors will result in a system’s best estimates of static weights are much 

more of an art than a science. The extent to which the analyst is allowed to utilize 

subjective procedures in determining calibration factor adjustments is a policy 

decision. It is also noted that a site must have somewhat consistent (and thereby 

predictable) truck operating characteristics for an analyst to consider “trusting” 

traffic stream data statistics in questioning the reliability of test truck data.

• Ineffective or useless test truck data.

o Figure 86 and Figure 87 display examples of a test truck calibration that was 

ineffective due to the fact that the test truck speeds covered only a very small 

percentage of the speeds traveled by the truck traffic stream. The only way to 

obtain test truck data that would be useful in properly calibrating the system used 

for this example would be to run the test trucks at speeds up to at least 65 mi/h, 

which would be in violation of the 55 mi/h posted speed limit. This, obviously,

cannot be recommended. 

o However, it is suggested that for such a site, the owner agency discuss the 

situation with both its legal department and the appropriate enforcement agency to 

determine if there is a possible solution. For example, the use of marked pilot 

and/or shadow vehicles for the trucks or some type of signing on the trucks might 

be deemed an adequate procedure to permit the test truck to run at the same 

speeds as the truck traffic stream.
115

o In the absence of having test truck data to properly determine calibration factors, 

the agency has two choices, described below.

1. Accept the fact that the system is not calibrated and acknowledge such when 

disseminating data.

2. Subject to a site’s having somewhat consistent truck operating characteristics, 

adjust the calibration factors to provide weights consistent with predictable 

weights over the range of speeds traveled by the truck traffic stream. It is 

acknowledged that this is not “truth in data”, but neither is weight data based 

upon calibration factors that are not based upon test truck data.

5.5. RESOLVING ACCURACY PROBLEMS IDENTIFIED BY MONITORING OF 

TRUCK TRAFFIC STREAM

Typical calibration monitoring problems and options for improving a system’s accuracy include 

those described below.

5.5.1. Gross Weight Distribution

If distributions appear to be unreasonable and/or inconsistent, continue analyses to determine if it 

is potentially due to one of the items listed below.

• Change in average weight outputs.

o Either the right or left Axle 1.

o Both right and left Axle 1.

• Calibration factors changed.

• Calibration factors based upon inconsistent test truck results.

o Consider adjusting calibration factors using combination of traffic stream data and 

review of test truck data from all calibration/validation sessions.

• Calibration factors for entire range of speeds traveled by truck traffic stream not based 

upon valid test truck data.

o Consider adjusting calibration factors for each speed point based upon traffic 

stream data.

• Calibration drift.

o If confirmed to be probable, adjust calibration factors based upon traffic stream 

data as interim measure until such time calibration can be checked by use of test 

trucks.

• Seasonal change in truck operating characteristics.

o Need minimum one year of tracking distributions.
116

5.5.2. Individual Sensor Weight Outputs

• If Axle 1 weights and GVWs appear to be accurate but Axle 1 right and Axle 1 left 

average weights are different by more than 0.2 k, adjust both right and left sensor’s 

factors to bring right and left average weights into balance (see Figure 88). This should 

have no effect on either the Axle 1 weight or the GVW.

• If Axle 1 weight and GVW weight have both increased or decreased, and the entire 

increase or decrease is attributable to a weight output change in either the right or left 

sensor, adjust the factors for only the sensor for which the weights have changed (see 

Figure 88). The percentage change in GVW output should be approximately half of the 

percentage of change in the sensor’s factor. 

o Note that regardless of whether the sensor’s weight output change is attributable 

to subtle malfunction or actual calibration drift (which would be unusual for just 

one of the two sensors), calibration should be verified by test trucks as soon as 

possible. 

• If a significant change is noted in either the right or left Axle 1 average weight:

o Check calibration factor.

o If calibration factor is correct, perform real-time diagnostics and extensive data 

analyses (per SECTION 4) of sensor for potential malfunction.

• If there is more than a 0.1 k increase in either sensor’s average weight standard deviation, 

perform real-time diagnostics and extensive data analyses (per SECTION 4) of sensor for 

potential malfunction.
117

Figure 88. Procedure. Procedures and examples for adjusting calibration factors based 

upon traffic stream data statistics.
118

5.5.3. Axle Spacings (and thereby speed) 

If the average Axle 2-3 spacing for the sample of the Class 9’s Type 3S2 is not 4.3 feet, adjust 

the system’s sensor-to-sensor or loop-to-loop parameter value to bring the average spacing to 4.3 

feet (refer to Figure 89).

Note that a vast majority of the Type 3S2 vehicles in the U.S. has Axle 2-3 (drive tandem) 

spacings, which, for a large sample, average 4.3 feet. However, for locations that have Canadian 

truck traffic or “specialty” truck types, 4.3 feet may not be a valid constant. Consideration needs 

to be given to observed axle spacing configurations and the percentage of such atypical vehicles. 

The parameter values for determining axle spacing and speed should be initially determined 

based upon test truck data.

Figure 89. Procedure. Procedure and example for adjusting axle spacing lengths (and 

thereby speeds).

5.5.4. Overall Vehicle Length

If the average Overall Vehicle Length is not five to seven feet longer than the average Axle 1 to 

5 wheelbase for a sample of Class 11’s Type 2S12 vehicles (or the average Axle 1 to 6 

wheelbase for Class 12’s 3S12 vehicles), adjust the loop length parameter values (see Figure 90).

Note that the procedure described in Figure 90 assumes that the particular system calculates 

Overall Vehicle Length based upon the time of a vehicle’s inductance for either or both loops.
119

Figure 90. Procedure. Procedure and example for adjusting overall vehicle lengths.

As stated previously, the procedures for using traffic stream data to make calibration factor 

adjustments presented in this section are temporary, short-term measures and not a replacement 

for using data from on-site test truck sessions. On-site validations with test trucks should be 

performed at least on an annual basis for systems with no operational problems. Test truck 

validations should be performed as soon as possible when one or more sensors are replaced or 

other modifications made which might affect a system’s calibration or when calibration 

monitoring by use of traffic stream data indicates calibration drift. Furthermore, these 

procedures should be performed by experienced data analysts and need to be documented (why, 

how, which method). 

5.6. MAKING BEST USE OF AVAILABLE RESOURCES

One of the many benefits in performing calibration monitoring is the ability to best allocate 

available resources for performing onsite calibrations/validations with test trucks. Few agencies, 

if any, have the resources to run test trucks at every WIM site every six months on a routine 

basis, and also every time a system’s maintenance of calibration is questionable. 

If the monitoring of a particular system indicates very consistent truck traffic stream operating 

characteristics with little if any seasonal variation after a couple years of monitoring, there is 

little need to routinely validate calibration with test trucks every six months. If calibration 
120

factors are adjusted based upon truck traffic stream monitoring for more than one site, validation 

of the sites’ calibrations with test trucks should be scheduled in the order of not only the 

importance of each site’s data but also in the analyst’s confidence of the factor adjustments based 

upon monitoring. 

For sites with inconsistent truck traffic stream operating characteristics, factor adjustments based 

upon traffic stream statistics are not dependable, and any such adjustments should be validated 

with test trucks as soon as possible.
121

SECTION 6. REFERENCES

• Flinner, M and H. Horsey. Traffic Data Editing Procedures: Traffic Data Quality 

"TDQ". Final Report, Transportation Pooled Fund Study SPR-2 (182), Federal 

Highway Administration, Washington, D.C., no date. This report is available at 

http://www.fhwa.dot.gov/policy/ohpi/tdep.htm.

• LTPP Field Operations Guide for SPS WIM Sites, Version 1.0, Long Term Pavement 

Performance, Federal Highway Administration, McLean, VA, 2006. A May 2009 

revision is under review and may be obtained by contacting LTPP Customer Service 

at ltppinfo@dot.gov. 

• LTPP Weigh-in-Motion System: Model Performance Specifications and Application 

Requirements for Equipment - Hardware and Software, Long Term Pavement 

Performance, Federal Highway Administration, McLean, VA.

• Nichols, A.P. and D.M. Bullock, Quality Control Procedures for WIM Data, Final 

Report, FHWA/IN/JTRP-2004/12, Purdue University, West Lafayette, Ind., June 

2004. This report can be downloaded online at http://docs.lib.purdue.edu/jtrp/176.

• Papagiannakis, A.T., et al., NCHRP Synthesis of Highway Practice 386: High Speed 

Weigh-in-Motion System Calibration Practices, Transportation Research Board, 

Washington, D.C., 2008. This report is available at

http://www.trb.org/news/blurb_detail.asp?id=9955.

• Quinley, R., Session 6-WIM Site Selection and Session 7-WIM Site Pavement 

Preparation and Considerations, FHWA WIM/Traffic Workshop, Raleigh, NC, Feb 

24-26, 2009.

• State’s Successful Practices Weigh-in-Motion Handbook, Federal Highway 

Administration, Washington, D.C., 1997. WIM Data Analysts are referred to section 

7.3 for WIM Data QC procedures followed by Caltrans in the mid 1990s. This report 

is available from http://www.ctre.iastate.edu/research/wim_pdf/Secti7.pdf.

• Traffic Analysis Software Volume 1-User’s Guide Draft, Long Term Pavement 

Performance, Federal Highway Administration, McLean, VA, 2008. A copy of this 

guide can be obtained by contacting LTPP Customer Service at ltppinfo@dot.gov.

• Traffic Monitoring Guide, Federal Highway Administration, Washington, D.C., 2001. 

This document is available at http://www.fhwa.dot.gov/ohim/tmguide/.

• Vehicle Travel Information System (VTRIS), Federal Highway Administration, 

Website can be accessed at http://www.fhwa.dot.gov/ohim/ohimvtis.cfm. The 

FHWA W-Tables (Truck Weight Data Summaries) can be accessed at 

https://apps.fhwa.dot.gov/VTRIS/default.aspx.
122
123

APPENDIX A.

This appendix contains a copy of the draft for the model specification: 

• LTPP Weigh-in-Motion System: Model Performance Specifications and 

Application Requirements for Equipment - Hardware and Software, by the 

Long Term Pavement Performance, Federal Highway Administration, 

McLean, VA.

Note that this document is still a work in progress. Please contact ltppinfo@dot.gov for 

more information.
WIM Model Specs 124

LTPP Weigh-in-Motion System:

Model Performance Specifications and Application 

Requirements for Equipment - Hardware and 

Software

Version 2.0

NOTE: This document is still a “work in progress”.

Federal Highway Administration

Office of Infrastructure Research, Development and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike, HRDI-13

McLean, Virginia 22101-2296

ETG MODS
WIM Model Specs 125

LTPP Weigh-in-Motion System Model Performance Specifications 

and Application Requirements for Equipment- Hardware and 

Software

Introduction

It is the intent of these specifications to establish the minimum requirements for high speed 

weigh-in-motion equipment utilized the purpose of collecting traffic data at Long-Term 

Pavement Performance (LTPP) Specific Pavement Studies (SPS) sites.

High Speed Weigh-In-Motion System

Description

The Weigh-In-Motion System (hereafter WIM system) shall include equipment and software for 

collecting, processing, storing, transmitting and manipulating information related to the counting, 

classifying and speed monitoring of all vehicles and the weighing of trucks and buses at highway 

speeds.

On-site Equipment

The WIM system shall provide for single threshold weighing, and operate over a speed range of 

5 mph to 100 mph. Single threshold weighing shall consist of scales in each lane of 

measurement. The weigh sensors shall cover the entire lane width. The WIM system shall consist 

of the following components:

1. Wheel scales shall report weight data for each wheel track (right axle weight and left 

axle weight). Such wheel weight data shall be uniform across the total width of the 

scale. 

2. A WIM controller shall be installed in the controller cabinet. The WIM controller

shall include all of the equipment and software to calculate, store and transmit to a 

host computer all data specified in these specifications. Either a keyboard and 

monitor or a portable personal computer (including protective case) for the purpose of 

accessing the WIM controller shall be furnished as part of the WIM controller. The 

WIM controller shall operate on AC power with a DC battery backup system to 

provide uninterrupted power to the WIM controller during AC power outages for a 

minimum of one hour. The system shall be able to operate on solar power if AC 

power is unavailable. The modem to be installed in the controller cabinet shall be 

compatible with the host computer modems described elsewhere in these 

specifications. The user shall have the capability of entering a site designation code 

up to three characters.

3. Surge protection devices against lightning and other transient high voltage consisting 

of:
WIM Model Specs 126

A. Power Source Protection

B. Phone Line Protection

C. Loop Input Protection

D. WIM Sensor Protection

E. Grounding

1. All conduit shall be metal and bonded with #8 bare copper wire.

2. A ground rod with an impedance of 10 ohms or less shall be placed at the 

cabinet foundation.

3. The ground rod shall be connected to the electronics backplane with #6 bare 

wire.

4. If solar powered, a lightning rod shall be placed on top of the solar panel pole, 

and shall be independently grounded.

4. All necessary interconnecting cables and miscellaneous materials to make an 

operational system.

Functional Requirements

1. The WIM system shall be able to accommodate vehicles and vehicle combinations 

with up to eleven axles and shall automatically determine for each vehicle, by lane of 

travel:

A. Weight of each axle by left and right wheel weights, speed, axle spacing, and 

vehicle length. 

The WIM system shall provide for calibration features such that the accuracy 

required under LTPP standards for equipment performance verification can be 

met

B. Vehicle classification:

The WIM system shall provide for a minimum of 15 vehicle classifications. 

Class 1 through Class 13 shall be used according to the classification scheme 

shown in Section 4, Appendix A, of the Federal Highway Administration 3d 

edition of the Traffic Monitoring Guide, February 1995. Class 14 will identify 

special vehicles as determined by the user. Class 15 will identify any vehicle 

not conforming to the classification criteria for Classes 1 through 14. 

Classification criteria for Classes 1 through 14 shall be programmable by the 

user.

The WIM system shall provide sufficient flexibility in spacings and weights 

(axle and/or gross) for each of these classes so that accurate classifying is 

achievable.

C. Invalid measurements:

An “invalid measurement” code shall be assigned to any vehicle meeting the 

front axle weight threshold (discussed below) when (1) the left and right 
WIM Model Specs 127

wheel weights of any axle have a difference of 40 percent or more; and (2) 

either of the wheel weights of such axle exceeds 2.0 kip. Both the 40 percent 

and 2.0 kip values shall be programmable by the operator. Any vehicle 

assigned an “invalid measurement” code shall not be considered a “Weighed 

Vehicle” but shall be classified and counted and all vehicle data shall be 

stored in the vehicle record.

D. Determination of weight violations:

For any vehicle meeting the front axle weight threshold (discussed below), the 

WIM system shall determine which, if any, axle(s) or axle grouping(s) exceed 

the weight limits set forth in the “Weight Violation Table” contained in these 

specifications. Any vehicle with one or more weight violations will be coded 

as to such a violation or combination of violations. The weight limitations set 

forth in the “Weight Violation Table” shall be the default settings. Such 

weights shall be programmable by the user.

2. The WIM controller shall calculate and store all specified data on a storage medium. 

The on-site data storage device shall have the capacity to store a minimum of fourteen 

days of vehicle count data and individual vehicle records. The storage device shall be 

completely solid state with no mechanical components and shall be a type not 

susceptible to loss of accumulated data should electrical power be interrupted. The 

WIM controller shall continue to calculate and store data for all vehicles passing 

through the system during periods of access, both on-site by portable PC and by the 

host computer for purposes of programming, real-time view and downloading of data.

The WIM controller shall store the following data:

A. Hourly vehicle counts by lane, by class and by speed range for each 24-hour 

period (Class/Count Summary).

B. Individual vehicle records for all vehicles with a front axle weight greater 

than 3.5 kip (hereafter referred to as “truck records”). The front axle weight 

threshold for truck records shall be programmable by the operator with 3.5 

kip as default setting. Each truck record shall include, as a minimum, the 

following data:

 i. Time and Date.

ii. Lane Number.

iii. Vehicle Number.

iv. Speed.

v. Vehicle Classification.

vi Weight in kips of each wheel or dual set of wheels by left and right side 

and by axle number.

vii. Spacing in feet between each sequentially numbered axle.

viii. Overall length of each vehicle or combination of vehicles in feet.

ix. Code for weight violation(s).

x. Code for invalid measurement(s).
WIM Model Specs 128

3. Data shall be calculated and formatted such that all data can be accessed and all 

required reports can be generated by use of the WIM system application software.

4. All equipment with exception of the WIM controller’s modem shall operate properly 

within an atmospheric temperature range of -40°C to +70°C or –40°F to 158°F

without the need of an added heating or cooling device.

5. The WIM controller shall have the communication capabilities to allow off-site 

personnel to view the operation of the WIM site and to allow for data transfer through 

telemetry over a dial-up, voice-grade telephone line. The WIM controller’s modem 

shall be fully compatible with the host computer modem. The modem shall be 

specified by the WIM vendor. The WIM controller shall also allow on-site personnel 

to connect a computer to the WIM system for on-site observation and for the transfer 

of data. 

High Speed WIM System Application Software

An application program, hereafter referred to as the “system program”, which can be run on the 

host computer shall be furnished as part of the high speed WIM system. The host computer will 

be furnished by others and will consist of:

1. Personal computer using the current version of the Windows Operating System.

2. Printer

3. A 56,600 Baud modem.

The system program shall provide communications between the host computer and the on-site 

WIM controller and shall process downloaded data to generate the specified ASCII files. 

Although referred to herein as a single software program, communications functions and data 

processing functions may be provided as two separate programs as long as all functional 

requirements are met. The system program shall be “user friendly”, hierarchical menu driven and 

shall perform the following applications:

Communications

1. The communications portion of the system program shall include the following 

applications:

A. Real time view:

The real time view application shall provide for the on-line monitoring of traffic. 

The display on the host computer shall depict the axle configuration of each 

vehicle passing through the site. The contents and format for the real time display 

shall be similar to the sample display contained in these specifications. The user 

shall have the options of displaying either all traffic or only vehicle classifications 

4 through 15 as well as the option of displaying a selected individual lane or all 

lanes.
WIM Model Specs 129

Printing of the real time data on the host computer printer shall be facilitated by 

means of an on/off toggle key from the keyboard.

B. System data programming:

The system data programming application shall provide for on-line modification 

to the WIM controller’s software parameters, such as speed and weight calibration 

factors, vehicle classification parameters, weight violation table parameters, and 

front axle weight threshold.

C. Manual downloading:

The manual downloading application shall provide for the downloading of 

selected daily data files from the storage medium of the WIM controller to the 

storage medium of the host computer. The program shall provide for a listing of 

the daily data files stored in the WIM controller and shall provide for user 

selection of the file or files to be downloaded from such a listing. The program 

shall provide for the downloading of the current day’s data stored as of the time of 

downloading.

D. Automatic downloading:

The automatic downloading applications shall provide for unattended 

downloading of daily data files stored in the WIM controller’s storage medium to 

the storage medium of the host computer. The program shall provide the 

following:

i. User’s input for the date and time that unattended downloading is to 

begin.

ii. Downloading of all daily files not previously downloaded by the 

automatic downloading application.

iii. At least three attempts to make telephone connection with the WIM 

controller.

iv. At least three attempts to download files from the WIM controller

before aborting download.

v. Discontinuation of telephone connection after downloading of files 

from the WIM controller (or after an abort)) and returning the host 

computer to a standby mode.

E. History file:

The history file application shall create a daily file, which chronologically records 

events occurring during manual and automatic downloading sessions. Such events 

shall include, but not be limited to, modem result messages, start and end time of 

each file download and any pertinent messages generated by the program. The 

program shall provide for either:

i. The history file shall be in the form of an ASCII text file which can be 

viewed or sent to the printer or,
WIM Model Specs 130

ii. A menu selection which shall provide for a listing of available history 

files and user selection of a file to be sent to the printer in the form of a 

report.

2. The communications portion of the system program shall meet the following 

functional requirements:

A. Host computer’s modem configuration:

The program shall initialize the host computer’s modem so that all necessary 

operating characteristics are set.

B. Baud rate:

The program will provide for operation at a minimum rate of 19200 baud.

C. Error control:

The program shall not in any way disable the modems’ error-checking features, 

which prevent phone-line noise from corrupting data during file downloading.

D. File downloading monitoring:

The program shall display a window that allows the user to monitor the progress 

of file downloading. The program shall also provide for the abort of a file 

download.

Report Preparation

The report preparation application shall generate specified reports using the downloaded 

data. Such reports shall be sent to the host computer printer or to file. The program shall 

prepare the following reports:

1. From vehicle class/count summary file:

A. Distribution of class and speed counts by lane.

B. Distribution of vehicle counts by hour of day by lane.

C. Distribution of vehicle classifications by hour of day.

D. Distribution of vehicle classifications by day of month.

E. Distribution of vehicles by speed by hour of day.

2. From individual truck records file:

A. Distribution of truck record data by lane.

B. Distribution of weight violations and invalid measurements of vehicle 

classifications 4 through 15.

C. Distribution of weight violations by hour of day for vehicle classifications 4 

through 14.

D. Distribution of overweight vehicles by hour of day for vehicle classifications 

4 through 14.

E. Distribution of gross weights for vehicle classifications 4 through 14.
WIM Model Specs 131

 F. Distribution of 18 kip equivalent single axle loadings (ESALS) by hour of 

 day for vehicle classifications 4 through 14. Program provides for user input 

 of:

i. Pavement type:

(1) flexible pavement and structural number; or,

(2) rigid pavement and slab thickness.

ii. Vehicle status:

(1) “all “ weighed vehicles (default); or,

(2) “legal only” weighed vehicles; or,

(3) “overweight only” weighed vehicles.

 G. Distribution of axles by groups (single, tandem, tridem) by hour of day for 

 vehicle classifications 4 through 14.

H. Distribution of trucks by day of month for classifications 4 through 15.

The reports shall include all information contained in and formatted similarly to the 

sample reports contained in these specifications (See Appendix A). The reports shall be 

printed in condensed print when necessary to fit on 8-1⁄2 inch x 11-inch sheets.

Determination of 18 kip equivalent single axle loads shall be in accordance with the 

methodology of the 1993 AASHTO Pavement Design Guide.

The program shall provide for the generation of reports in the following two modes:

1. Manual Mode:

For daily reports the program shall provide for user selection of the date and the specific 

report. For monthly reports, the program shall provide for user selection of the 

month/year and the specific report. The selected month report shall include the data from 

all downloaded daily data files resident with the system program on a directory or 

subdirectory of the host computer’s storage medium. The program shall also provide for 

user selection of the lane or lanes to be covered by the specific report (not applicable to 

the “Distribution of Class and Speed Counts by Lane”, the:”Distribution of Vehicle 

Counts by Hour of Day by Lane” and the “Distribution of truck Record Data by Lane” 

reports”).

The default shall be “all lanes.” The printed report shall note which lanes are 

represented.

2. Automatic Mode:

The program shall provide for user designation of one or a combination of the specific 

daily reports for automatic processing. User selection of lane or lanes is not required (the 

“all lanes” default may be used). User selection of vehicle status for the 18 kip ESAL 

report is not required (the “all” weighed vehicles default may be used). Such designations 

shall be effected by means of either:

A. An ASCII text file, which can be revised with text editor or word processor, 

supplied with a “Sample” designation; or,
WIM Model Specs 132

B. A menu selection, which shall provide for user input designation.

Upon selection of automatic mode of report preparation by the user, the program shall 

send to the printer all pre-designated reports for all downloaded daily data files resident 

with the system program on a directory or subdirectory of the host computer’s storage 

medium.

The designated reports shall remain in effect for subsequent automatic mode sessions 

unless report designation, is revised by the user.

Truck Record Batch Print

The truck record batch print application shall provide for the display of, all on/off printertoggle of, individual truck records. The program shall provide for a listing of the daily 

truck records files available on the storage medium of the host computer and the user’s 

selection of one of those files. The program shall also provide for the user’s selection of 

the vehicle class or classes for which individual truck records will be displayed or printed 

as well as the starting hour of day.

The user shall have the following options in viewing and printing the individual truck 

records.

1. Scroll and print continuously all records for the selection of class(es); user has 

capability to stop/resume scrolling or terminates program.

2. Scroll each record one at a time; user has capability to:

1. Print displayed record and display next record.

2. Display next record.

3. Terminate program.

An example of the truck record batch print is included in these specifications. (See 

Appendix A.)

ASCII Export Utility

The ASCII export utility application shall allow the user to generate specified ASCII files 

using downloaded files. The user will have the choice of:

1. From vehicle class/count summary file:

A. ASCII classification file.

B. ASCII speed file.

2. From individual truck record file:

A. ASCII truck record file

The file formats for these files are contained in Appendix A.

TRAFFIC MONITORING GUIDE Files Utility
WIM Model Specs 133

The TMG files utility shall allow the user to generate ASCII files conforming to the 

instructions contained in Section 6 of the FHWA Traffic Monitoring Guide 3rd edition 

using downloaded files.

Data Files

Notwithstanding the method of data manipulation and formatting used by the WIM 

controller, data files shall conform to the following:

1. Individual daily data files shall be created and stored in the storage medium of the 

WIM controller. Each daily data file shall include data for each 00:00 hour 

through a 23:59 hour period and shall have a file name which uniquely identifies 

the file as to site designation, date, and file contents (i.e., class/count summary 

data, individual truck record data, or both).

2. The daily data files shall be created at the start of each day. Data for each vehicle 

shall be filed within one hour of the vehicle’s passing through the site, and the 

current day’s files shall be accommodative to efficient use of storage medium 

space and rapid downloading via modem to the host computers.

3. Daily files containing class/count summary data and individual truck records data 

may be created in the storage medium of the WIM controller as two separate daily 

files or as one daily file. However, if one daily file is created and downloaded as 

such, the system program shall create two separate daily files, each with a file 

name which uniquely identifies it as to site, date and whether it is a vehicle 

class/count summary file or an individual truck records file.

Acceptance Test 

The WIM Vendor shall demonstrate that the WIM system is available for use by the owner by 

successfully completing the acceptance test for each lane of data collection.

The acceptance test shall consist of the following:

1. Verification of WIM system accuracy: 

Step One

Obtain at least 2 trucks to use for testing the WIM system accuracy. Select truck types 

that are most representative of the trucks that frequent the WIM location. One of the 

test trucks shall be a class 9 truck that has air ride suspension for both tractor and 

trailer, a non-liquid load, and loaded to a minimum of 90 percent of the truck’s legal 

operating weight. The other truck will be of the 2nd most commonly occurring type of 

truck, and loaded to 80 – 90% of the truck’s legal operating weight. If the class 9 truck 

is the most common type of truck at the WIM location, it is OK to use two class 9 

trucks for testing the WIM system’s accuracy. No unloaded trucks will be used for 

testing the WIM system’s accuracy. The procedure for weighing and measuring the 

test vehicle(s) to obtain reference values is found in sections 7.1.3 to 7.1.3.7 of ASTM 

E 1318-02:
WIM Model Specs 134

7.1.3.1 “Measure the center-to-center spacing between successive axles on each 

test vehicle and record this data to the nearest 0.1 ft (0.03m) as axle-spacing 

reference values.”

7.1.3.2 “ Weigh each test vehicle a minimum of three times, with brakes released, 

as described in 7.1.1 and 7.1.2 to measure tire loads for the wheel(s) on each 

end of every axle on the static vehicle. Move the vehicle completely away 

from the scale or weigher before beginning a new set of tire-load 

measurements, and always approach the weighing devices from the same 

direction for weighing. Sum the applicable tire loads to determine wheel ,axle, 

and tandem-axle loads as well as gross-vehicle weight each time the vehicle is 

weighed.” ( A scale which weighs individual axle and tandem loads is 

acceptable).

7.1.3.3 “ Calculate the arithmetic mean for all wheel load, axle-load, tandem-axleload, and gross-vehicle-weight values that result from weighing each test 

vehicle three or more times; ...”

Average the three “static weight values” of the test vehicle(s) for the drive axle-load(s), 

1st tandem-axle load(s), 2nd tandem-axle load(s), and gross-vehicle weight(s) to derive 

the static weights used in the accuracy verification.

Some type of communication, (cellular phone, CB radio, etc.), with the driver(s) of the 

test vehicle(s) will need to be established before the initial calibration begins.

Step Two

The communications software shall have a history file, (log file), applications which 

will create a daily file, in an ASCII type format, which chronologically records events 

occurring during initial calibration runs (and the final verification runs). Such events 

shall include, but not be limited to, recording the initial calibration factors of the WIM 

system, the calibration runs, final calibration factors, and any changes made to the 

calibration factors during the initial calibration runs, (and the final verification runs). 

Step Three

The test truck(s) is driven over the WIM sensors in each lane a minimum of three times 

at each set speed point, and three times at each 8kph (5mph) increment between the 

first and third speed points. 

Due to the temperature variations usually occurring during the course of the day, the 

truck will start at the lowest speed point and continue in sequence to the highest speed 

point. If the three speed points are set at 40 mph, 55 mph, and 70 mph, then the test 

truck(s) will start at 40mph and then go in sequence to 45 mph, then to 50 mph, etc., 

until the 70 mph point is reached. The truck(s) will then start all over again and repeat 

the same sequence two more times until there are a total of 21 runs for each test truck 

used in the validation.

The gross weight percent error is calculated for each run and plotted on a “Gross 

Weight Percent Error By Vehicle Speed” graph for each WIM lane. These graphs are 
WIM Model Specs 135

analyzed to make the final adjustments to the WIM weight factors if necessary. They 

are also used to record pavement effects on vehicle dynamics for the site history.

If for any reason an adjustment needs to be made to the WIM Weight or Spacing 

factors, before all runs are completed, the validation runs will have to start all over 

again.

Step Four

Down load the data file and close and save the history (log) file.

For the site calibration to be accepted, the gross weight percent error of the validation 

data will have to be evenly distributed around the zero axis of the “Gross Weight 

Percent Error By Vehicle Speed” graph for each speed point in each WIM lane.

For a Type I WIM System the validation data will meet (or exceed) the functional 

performance requirements as found in table 2 under Section 5 of the ASTM E 1318-02 

of the Standard Specifications for Highway WIM with a tolerance for 95% probability 

of conformity:

1. Gross-Vehicle Weight: +/- 10%

2. Axle-Group Load: +/- 15%

3. Axle Load: +/- 20%

2. Continuous operation of WIM system on-site equipment for 15 consecutive days 

following completion of the WIM system accuracy validation testing. Failure of the 

system to record and store data meeting the requirements set forth in these 

specifications for an accumulated time exceeding 3 hours during the 15 day-period 

shall be cause for the acceptance test to be repeated.

3. Testing of the WIM system application software during the above noted 15 dayperiod and the full working day following the 15 day-period. Failure of the software 

to perform any application meeting the requirements set forth in these specifications 

shall be cause for the acceptance test to be repeated.

Failure of the host computer or its peripheral equipment or of a communication line not 

furnished by the WIM vendor to transmit data may not be considered unacceptable performance, 

provided the WIM vendor demonstrates to the satisfaction of the owner that the failure is not 

caused by any of the WIM vendor furnished equipment.

Maintenance and Operations Manuals

The WIM vendor shall furnish a maintenance manual for the WIM controller, including vehicle 

detector sensor units and an operation manual for the system. The maintenance manual and 

operation manual may be combined into one manual. The manual(s) shall include, but need not 

be limited to, the following items:

1. Specifications.
WIM Model Specs 136

2. Design characteristics.

3. General operation theory.

4. Function of all controls.

5. Trouble shooting procedure (diagnostic routine).

6. Block circuit diagram.

7. Geographical layout of components.

8. Schematic diagrams, signal responses and acceptable thresholds.

9. List of component parts with stock numbers.

10. Documentation for application software.
WIM Model Specs 137

Appendix A.1

Sample Reports 

PENDING: DESIGN OF ”THE “DISTRIBUTION OF AXLES BY GROUPS 

(SINGLE, TANDEM, TRIDEM) BY HOUR OF DAY FOR VEHICLE 

CLASSIFICATIONS 4 THROUGH 14” REPORT(S) AS REQUIRED BY High 

Speed WIM System Application Software, Report Preparation, (2) (G)
138

DISTRIBUTION OF CLASS AND SPEED COUNTS BY LANE 

===========================================================================================================================

SITE DESIGNATION: 

DATE: ===========================================================================================================================

 

 LANE NUMBER <number of lanes varies with contract requirements> ----------- 

 1 2 3 4 5 6 ALL LANES

 COUNT % COUNT % COUNT % COUNT % COUNT % COUNT % COUNT %

 ------------ ------------ ------------ ------------ ------------ ------------ ------------

CLASS

 1 0 0.0 0 0.0 0 0.0 6 0.0 6 0.0 

 2 22521 82.4 24464 82.7 28540 90.6 23974 87.6 99499 85.9

 3 2687 9.8 2395 8.1 --------------------------- 2324 7.4 1919 7.0 9325 8.1 4 14 0.1 21 0.1 | | 16 0.1 13 0.0 64 0.1 

 5 1152 4.2 1297 4.4 | "COUNT" entries for | 486 1.5 56 0.2 2991 2.6 

 6 82 0.3 101 0.3 | example only | 9 0.0 917 3.3 1109 1.0

 7 1 0.0 3 0.0 | | 0 0.0 24 0.1 28 0.0

 8 280 1.0 402 1.4 | | 3 0.0 32 0.1 717 0.6

 9 340 1.2 544 1.8 --------------------------- 4 0.0 0 0.0 888 0.8 10 10 0.0 1 0.0 0 0.0 1 0.0 4 0.0

 11 84 0.3 104 0.4 0 0.0 0 0.0 188 0.2

 12 3 0.0 5 0.0 0 0.0 0 0.0 8 0.0 13 1 0.0 0 0.0 0 0.0 0 0.0 1 0.0

 14 28 0.1 46 0.2 0 0.0 0 0.0 74 0.1

 15 142 0.5 206 0.7 127 0.4 434 1.6 909 0.8

 

------ ------------ ------------ ------------ ------------ ------------ ------------ -------------

TOTAL 27337 100.0 29589 100.0 31509 100.0 27376 100.0 115811 100.0

===========================================================================================================================

SPEED

(MPH)

1- 5 2 0.0 6 0.0 2 0.0 4 0.0 14 0.0

6-10 0 0.0 0 0.0 4 0.0 10 0.0 14 0.0

11-15 10 0.0 5 0.0 32 0.1 28 0.1 75 0.1

16-20 48 0.2 33 0.1 75 0.2 88 0.3 244 0.2

21-25 271 1.0 280 0.9 269 0.9 179 0.7 999 0.9

26-30 641 2.3 615 2.1 480 1.5 349 1.3 2085 1.8

31-35 1047 3.8 838 2.8 731 2.3 606 2.2 3222 2.8

36-40 1165 4.3 1073 3.6 1077 3.4 891 3.3 4206 3.6

41-45 1645 6.0 913 3.1 927 2.9 997 3.6 4482 3.9

46-50 5140 18.8 2063 7.0 1027 3.3 893 3.3 9123 7.9

51-55 9487 34.7 5641 19.1 2508 8.0 1147 4.2 18783 16.2

56-60 5613 20.5 13537 45.8 14134 44.9 3243 11.8 36527 31.5

61-65 1872 6.8 3284 11.1 7211 22.9 9701 35.4 22068 19.1

66-70 277 1.0 1170 4.0 2749 8.7 6614 24.2 10810 9.3

71-75 79 0.3 90 0.3 234 0.7 2240 8.2 2643 2.3

76-80 24 0.1 34 0.1 45 0.1 327 1.2 430 0.4

81-85 13 0.0 7 0.0 4 0.0 51 0.2 75 0.1

86-90 3 0.0 0 0.0 0 0.0 8 0.0 11 0.0

91-95 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

96-100 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

> 100 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

 

------ ------------ ------------ ------------ ------------ ------------ ------------ -------------

TOTAL 27337 100.0 29589 100.0 31509 100.0 27376 100.0 115811 100.0

AVG.SPEED 51 55 57 61 56

===========================================================================================================================
139

DISTRIBUTION OF VEHICLE COUNTS BY HOUR OF DAY BY LANE 

==============================================================================================================================

SITE DESIGNATION: 

DATE: 

============================================================================================================================== 

HOURLY SUMMARY LANE NUMBER <number of lanes varies with contract requirements> 

-------------- ----------- 

 HOURLY 

HOUR 1 2 3 4 5 6 TOTALS 

------ ----- ----- ----- ----- ----- ----- -------

00-01 

01-02 

02-03 

03-04 

04-05 

05-06 

------ ----- ----- ----- ----- ----- ----- -------

QTR TOTALS 

==============================================================================================================================

06-07 

07-08 

08-09 

09-10 

10-11 

11-12 

------ ----- ----- ----- ----- ----- ----- ------- 

QTR TOTALS ==============================================================================================================================

12-13 

13-14 14-15 

15-16 

16-17 

17-18 

------ ----- ----- ----- ----- ----- ----- -------

QTR TOTALS 

==============================================================================================================================

18-19 19-20 

20-21 

21-22 

22-23 

23-24 

------ ----- ----- ----- ----- ----- ----- -------

QTR TOTALS 

 

==============================================================================================================================

==============================================================================================================================

 

DAILY SUMMARY DAILY COUNTS BY LANE 

------------- -------------------- 

 DAILY 

 1 2 3 4 5 6 TOTALS

 ----- ----- ----- ----- ----- ----- ------

 

 

==============================================================================================================================

==============================================================================================================================
140

DISTRIBUTION OF VEHICLE CLASSIFICATIONS BY HOUR OF DAY 

================================================================================================================================

SITE DESIGNATION: LANE NO's <display user's entry as to selected lane(s)>

DATE: 

================================================================================================================================ 

HOURLY SUMMARY VEHICLE COUNTS 

-------------- -------------- 

 HOURLY 

HOUR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 TOTALS 

------ ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- -------- 00-01 

01-02 

02-03 

03-04 

04-05 

05-06 

------ ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- --------

QTR TOTALS 

================================================================================================================================

06-07 

07-08 

08-09 

09-10 

10-11 11-12 

------ ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- --------

QTR TOTALS ================================================================================================================================

12-13 

13-14 

14-15 

15-16 

16-17 

17-18 

------ ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- --------

QTR TOTALS ================================================================================================================================

18-19 

19-20 

20-21 

21-22 

22-23 

23-24 

------ ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- -------- QTR TOTALS 

 

================================================================================================================================

================================================================================================================================

 

DAILY SUMMARY VEHICLE COUNTS ------------- -------------- 

 TOTAL 

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 VEHICLES

 ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- --------

 

COUNT

PERCENT

 

================================================================================================================================

================================================================================================================================
141

DISTRIBUTION OF VEHICLE CLASSIFICATIONS BY DAY OF MONTH 

================================================================================================================================

SITE DESIGNATION: LANE NO's <display user's entry as to selected lane(s)>

DATE: 01/92 

================================================================================================================================ DAILY VEHICLE COUNTS

SUMMARY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 TOTALS 

------ --- ------- ------ ---- ------ ----- ---- ----- ------ ----- ----- ----- ---- ----- ----- --------

1 WED

2 THU

3 FRI

4 SAT

------ --- ------- ------ ---- ------ ----- ---- ----- ------ ----- ----- ----- ---- ----- ----- --------

DAILY AVG 

================================================================================================================================

5 SUN

6 MON

7 TUE

8 WED

9 THU

10 FRI

11 SAT

------ --- ------- ------ ---- ------ ----- ---- ----- ------ ----- ----- ----- ---- ----- ----- --------

DAILY AVG 

================================================================================================================================ 12 SUN

13 MON

14 TUE

15 WED

16 THU

17 FRI

18 SAT

------ --- ------- ------ ---- ------ ----- ---- ----- ------ ----- ----- ----- ---- ----- ----- --------

DAILY AVG 

================================================================================================================================

19 SUN

20 MON

21 TUE

22 WED

23 THU

24 FRI

25 SAT

------ --- ------- ------ ---- ------ ----- ---- ----- ------ ----- ----- ----- ---- ----- ----- --------

DAILY AVG 

================================================================================================================================ 26 SUN

27 MON

28 TUE

29 WED

30 THU

31 FRI

------ --- ------- ------ ---- ------ ----- ---- ----- ------ ----- ----- ----- ---- ----- ----- --------

DAILY AVG 

================================================================================================================================

MONTHLY VEHICLE COUNTS 

SUMMARY 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 TOTALS

------ --- ------- ------ ---- ------ ----- ---- ----- ------ ----- ----- ----- ---- ----- ----- --------

TOTALS 

PERCENT 

DAILY AVG 
142

DISTRIBUTION OF VEHICLES BY SPEED BY HOUR 

=========================================================================================================================

SITE DESIGNATION: LANE NO's <display user's entry as to selected lane(s)> 

DATE: =========================================================================================================================

 SPEED RANGE, MPH ---------------- 

HOUR 

RANGE 00-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76-80 > 80 -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

00 - 01 

01 - 02 

02 - 03 

03 - 04 

04 - 05 

05 - 06 

-------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS 

========== 

06 - 07 

07 - 08 

08 - 09 

09 - 10 

10 - 11 

11 - 12 -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS 

========== 

12 - 13 

13 - 14 

14 - 15 

15 - 16 

16 - 17 

17 - 18 

-------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS 

========== 

18 - 19 

19 - 20 

20 - 21 

21 - 22 

22 - 23 

23 - 24 

-------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS 

========== 

DAILY SPEED SUMMARY 

------------------- 

Total Vehicles : Total Vehicles > 55 MPH : Percentage Vehicles > 55 MPH : 

Average Speed : Total Vehicles > 60 MPH : Percentage Vehicles > 60 MPH : 

Median Speed : Total Vehicles > 65 MPH : Percentage Vehicles > 65 MPH : 

85th Percentile : Total Vehicles > 70 MPH : Percentage Vehicles > 70 MPH : 

=========================================================================================================================

=========================================================================================================================
143

DISTRIBUTION OF TRUCK RECORD DATA BY LANE <report to cover all records contained in truck records file> 

===========================================================================================================================

SITE DESIGNATION: 

DATE: 

===========================================================================================================================

 

 LANE NUMBER <number of lanes varies with contract requirements> 

----------- 

 

CLASS 1 2 3 4 5 6 ALL LANES

------ ------------ ------------ ------------ ------------ ------------ ------------ ------------

 COUNT % COUNT % COUNT % COUNT % COUNT % COUNT % COUNT %

 1 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 

 2 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 

 3 152 3.7 342 14.7 --------------------------- 87 4.0 74 1.9 655 5.2 4 18 0.4 13 0.6 | | 3 0.1 5 0.1 39 0.3 

 5 560 13.6 354 15.2 | "COUNT" entries for | 306 14.2 574 14.7 1794 14.3 

 6 129 3.1 67 2.9 | example only | 66 3.1 104 2.7 366 2.9 7 3 0.1 0 0.0 | | 0 0.0 27 0.7 30 0.2

 8 350 8.5 134 8.8 | | 278 12.9 357 9.1 1119 8.9

 9 1775 43.1 918 39.4 --------------------------- 961 44.4 1698 43.5 5352 42.7

 10 3 0.1 1 0.0 4 0.2 4 0.1 12 0.1

 11 783 19.0 332 14.2 302 14.0 754 19.3 2171 17.3

 12 56 1.4 30 1.3 32 1.5 68 1.7 186 1.5

 13 5 0.1 2 0.1 0 0.0 7 0.2 14 0.1

 14 122 3.0 34 1.5 37 1.7 104 2.7 297 2.4

 15 158 3.8 66 2.8 78 3.6 128 3.3 430 3.4

 

------ ------------ ------------ ------------ ------------ ------------ ------------ -------------

TOTAL 4121 100.0 2330 100.0 2161 100.0 3907 100.0 12520 100.0

===========================================================================================================================

 

 LANE NUMBER 

 ----------- 

 

STATUS 1 2 3 4 5 6 ALL LANES

------ ------------ ------------ ------------ ------------ ------------ ------------ ------------

 COUNT % COUNT % COUNT % COUNT % COUNT % COUNT % COUNT %

LEGAL 3353 81.4 1866 80.1 1976 91.4 3076 78.7 10271 82.0

OVR'WT 662 16.0 384 16.5 127 5.9 715 18.3 2249 18.0

INVALID 106 2.6 80 3.4 59 2.7 116 3.0 361 2.9

 

 ---------------------------------------------------------------------------------------------------------

 | | 

 | Note: |

 | The line items under "STATUS" are to be based upon the Contractor's coding scheme for weight |

 | violation and invalid measurements. If the coding system identifies invalid measurements other |

 | than imbalance (such as "out-of-range" values, system errors, etc.), each unique type of invalid |

 | measurement should be broken down as a "STATUS" line item. |

 | |

 --------------------------------------------------------------------------------------------------------- 

 

------ ------------ ------------ ------------ ------------ ------------ ------------ ------------

TOTAL 4121 100.0 2330 100.0 2162 100.0 3907 100.0 12520 100.0

=========================================================================================================================
144

DISTRIBUTION OF WEIGHT VIOLATIONS AND INVALID MEASUREMENTS FOR VEHICLE CLASSIFICATIONS 4 THROUGH 15 

=============================================================================================================================

SITE DESIGNATION: LANE NO's <display user's entry as to selected lane(s)> 

DATE: 

=============================================================================================================================

 

 VEHICLES ********* NUMBER OF ********* 

 TOTAL WITH TOTAL TOTAL PERCENT ********* WEIGHT VIOLATIONS ********* 

 VEHICLES INVALID VEHICLES VEHICLES VEHICLES 

CLASS COUNTED MEASUREMENTS WEIGHED OVERWEIGHT OVERWEIGHT AXLE TANDEM GROSS BRIDGE 

------ ------- ------------ ------- ---------- ---------- ---- ------ ----- ------ 

 4 

 5 

 6 

 7 

 8 

 9 

 10 11 

 12 

 13 

 14 

 15 

 

------ ------- ------------ ------- ---------- ---------- ---- ------ ----- ------ 

TOTALS 

 PERCENT VEHICLES NOT CLASSIFIED (CLASS 15) : _____

 PERCENT VEHICLES WITH INVALID MEASUREMENTS : _____

------------------------------------------------------------------------------------------------------------------------

| |

| Notes: |

| |

| "Percent Vehicles Not Classified" = Class 15 Total Vehicle Count / Total Vehicles Counted |

| "Percent Vehicles With Invalid Measurements" = Total Vehicles With Invalid Measurements / Total Vehicles Counted |

| "Vehicles Counted" - "Vehicles With Invalid Measurements" = "Vehicles Weighed" |

| |

| All weight and weight violation reporting and calculations based on data for "weighed vehicles" |

| |

------------------------------------------------------------------------------------------------------------------------
145

DISTRIBUTION OF WEIGHT VIOLATIONS BY HOUR OF DAY FOR VEHICLE CLASSIFICATIONS 4 THROUGH 14 

=======================================================================================================================

SITE DESIGNATION: LANE NO's <display user's entry as to selected lane(s)> 

DATE: 

=======================================================================================================================

 

HOURLY SUMMARY 

-------------- ************* NUMBER OF ************** 

 TOTAL TOTAL PERCENT ************* WEIGHT VIOLATIONS **************

 VEHICLES VEHICLES VEHICLES 

HOUR WEIGHED OVERWEIGHT OVERWEIGHT AXLE TANDEM GROSS BRIDGE

------- ------- ---------- ---------- ---- ------ ----- ------

00-01 

01-02 

02-03 

03-04 

04-05 

05-06 

------ ----- ----- ----- ----- ----- ----- -----

QTR TOTALS 

=======================================================================================================================

06-07 

07-08 

08-09 

09-10 

10-11 

11-12 

------ ----- ----- ----- ----- ----- ----- -----

QTR TOTALS 

=======================================================================================================================

12-13 13-14 

14-15 

15-16 

16-17 

17-18 

------ ----- ----- ----- ----- ----- ----- -----

QTR TOTALS 

======================================================================================================================= 18-19 

19-20 

20-21 

21-22 

22-23 

23-24 ------ ----- ----- ----- ----- ----- ----- -----

QTR TOTALS 

 

=======================================================================================================================

=======================================================================================================================

 

DAILY SUMMARY 

------------- ************* NUMBER OF ************** 

 TOTAL TOTAL PERCENT ************* WEIGHT VIOLATIONS ************** VEHICLES VEHICLES VEHICLES 

 WEIGHED OVERWEIGHT OVERWEIGHT AXLE TANDEM GROSS BRIDGE

 ------- ---------- ---------- ---- ------ ----- ------

 

 

======================================================================================================================= =======================================================================================================================
146

DISTRIBUTION OF OVERWEIGHT VEHICLES BY HOUR OF DAY FOR VEHICLE CLASSIFICATIONS 4 THROUGH 14 

======================================================================================================================

SITE DESIGNATION: LANE NO's <display user's entry as to selected lane(s)> 

DATE: 

======================================================================================================================

HOURLY SUMMARY NUMBER OVERWEIGHT VEHICLES 

-------------- -------------------------- 

 TOTAL TOTAL PERCENT 

 VEH'S VEH'S VEH'S 

HOUR WEIGHED OVERWT OVERWT 4 5 6 7 8 9 10 11 12 13 14 

----- -------- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

 

00-01 

01-02 

02-03 

03-04 04-05 

05-06 

----- -------- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- QTR TOTALS 

======================================================================================================================

06-07 07-08 

08-09 

09-10 

10-11 

11-12 

----- -------- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS 

======================================================================================================================

12-13 

13-14 

14-15 

15-16 16-17 

17-18 

----- -------- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTRTOTALS 

======================================================================================================================

18-19 

19-20 

20-21 

21-22 

22-23 

23-24 

----- -------- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS 

====================================================================================================================== ======================================================================================================================

DAILY SUMMARY NUMBER OVERWEIGHT VEHICLES 

------------- -------------------------- TOTAL TOTAL PERCENT 

 VEH'S VEH'S VEH'S 

 WEIGHED OVERWT OVERWT 4 5 6 7 8 9 10 11 12 13 14 

 -------- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

 

 ======================================================================================================================

======================================================================================================================
147

DISTRIBUTION OF GROSS WEIGHTS FOR VEHICLE CLASSIFICATIONS 4 THROUGH 14 

============================================================================================================================

SITE DESIGNATION: LANE NO's <display user's entry as to selected lane(s)> 

DATE: 

============================================================================================================================

 VEHICLE COUNTS

--------------

GROSS WT 

 KIPS 4 5 6 7 8 9 10 11 12 13 14 TOTALS

------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ------

 0- 5 

 5- 10 

10- 15 

15- 20 

20- 25 

25- 30 

30- 35 

35- 40 40- 45 

45- 50 

50- 55 

55- 60 

60- 65 

65- 70 

70- 75 

75- 80 

80- 85 85- 90 

90- 95 

95-100 100-105 

105-110 

110-115 

115-120 

 > 120 

 ------ ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ------ TOTALS 

 

============================================================================================================================

============================================================================================================================
148

DISTRIBUTION OF 18 KIP ESALS BY HOUR OF DAY FOR VEHICLE CLASSIFICATIONS 4 THROUGH 14 

FOR ___________________________________ <display user's entries as to pavement type and str. no. or slab thickness> 

===================================================================================================================

SITE DESIGNATION: LANE NO's <display user's entry as to selected lane(s)> 

DATE: VEH STATUS <display user's entry as to "LEGAL ONLY", "OVWT ONLY" or "ALL" (default) ===================================================================================================================

HOURLY SUMMARY 

-------------- 

 ESALS BY HOUR BY CLASS

 TOTAL ---------------------- 

VEH'S TOTAL 

HOUR WEIGHED ESALS 4 5 6 7 8 9 10 11 12 13 14 

----- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

00-01 

01-02 

02-03 

03-04 

04-05 

05-06 

----- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS 

===================================================================================================================

06-07 

07-08 

08-09 

09-10 

10-11 

11-12 ----- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS 

===================================================================================================================

12-13 

13-14 

14-15 

15-16 

16-17 

17-18 

----- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS ===================================================================================================================

18-19 

19-20 

20-21 

21-22 

22-23 

23-24 

----- -------- -------- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

QTR TOTALS 

 

===================================================================================================================

===================================================================================================================

DAILY SUMMARY 

------------- 

 TOTALS 4 5 6 7 8 9 10 11 12 13 14 ------ ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 

VEH'S WEIGHED : 

18 KIP ESALS : 

AVERAGE ESAL : 

 

=================================================================================================================== ===================================================================================================================
149

DISTRIBUTION OF TRUCKS BY DAY OF MONTH FOR CLASSIFICATIONS 4 THROUGH 15

=============================================================================================================================

SITE DESIGNATION: LANE NO's <display user's entry as to selected lane(s)>

DATE:

=============================================================================================================================

DAILY SUMMARY

-------------

 TOTAL TOTAL TOTAL PCT ------------------------------- COUNTED VEHICLES --------------------------------

 VEHS VEHS VEHS VEHS 

DAY CNTD WGHD OVWT OVWT 4 5 6 7 8 9 10 11 12 13 14 15 

------ ------ ------ ----- ---- ---- ------ ----- ---- ----- ------ ----- ----- ----- ---- ----- -----

1 WED

2 THU

3 FRI

4 SAT

=============================================================================================================================

5 SUN

6 MON

7 TUE

8 WED

9 THU

10 FRI

11 SAT

============================================================================================================================= 12 SUN

13 MON

14 TUE

15 WED

16 THU

17 FRI

18 SAT

=============================================================================================================================

19 SUN

20 MON

21 TUE

22 WED

23 THU

24 FRI

25 SAT

=============================================================================================================================

26 SUN

27 MON

28 TUE

29 WED

30 THU

31 FRI

=============================================================================================================================

MONTHLY SUMMARY

---------------

 TOTAL TOTAL TOTAL PCT ------------------------------- COUNTED VEHICLES --------------------------------

 VEHS VEHS VEHS VEHS 

 CNTD WGHD OVWT OVWT 4 5 6 7 8 9 10 11 12 13 14 15 

 ------ ------ ------ ---- ---- ------ ----- ---- ----- ------ ----- ----- ----- ---- ----- -----

TOTALS

PERCENT
150

 ASCII SPEED FILE FORMAT

FIELD LENGTH STARTS IN COLUMN

Lane 2 1

Hour 2 4

Count, 0-35 MPH 4 7

Count, 36-40 MPH 4 12

Count, 41-45 MPH 4 17

Count, 46-50 MPH 4 22

Count, 51-55 MPH 4 27

Count, 56-60 MPH 4 32

Count, 61-65 MPH 4 37

Count, 66-70 MPH 4 42

Count, 71-75 MPH 4 47

Count, 76-80 MPH 4 52

Count, 81-85 MPH 4 57

Count, >85 MPH 4 62

 ASCII CLASSIFICATION FILE FORMAT

FIELD LENGTH STARTS IN COLUMN

Lane 2 1

Hour 2 4

Count, Class 1 4 7

Count, Class 2 4 12

Count, Class 3 4 17

Count, Class 4 4 22

Count, Class 5 4 27

Count, Class 6 4 32

Count, Class 7 4 37

Count, Class 8 4 42

Count, Class 9 4 47

Count, Class 10 4 52

Count, Class 11 4 57

Count, Class 12 4 62

Count, Class 13 4 67

Count, Class 14 4 72

Count, Class 15 4 77

For the above two files :

 Each field shall be comma delimited.

 For each day's file, there is one record

 for each lane for each hourly period.

 ASCII TRUCK RECORD FILE FORMAT

 DECIMAL STARTS

FIELD LENGTH PLACES IN COLUMN

----- ------ ------- ---------

LANE 1 1

MONTH 2 3

DAY 2 6

YEAR 2 9

HOUR 2 12

MINUTE 2 15

SECOND 2 18

VEHICLE NO. 5 21

CLASS 2 27

GROSS WEIGHT 6 1 30

LENGTH 6 1 37

SPEED 5 1 44

VIOLATION CODE 3 50

AXLE 1 RT. WEIGHT 4 1 54

AXLE 1 LT. WEIGHT 4 1 59

AXLE 2 RT. WEIGHT 4 1 64

AXLE 2 LT. WEIGHT 4 1 69

AXLE 1-2 SPACING 4 1 74

AXLE 3 RT. WEIGHT 4 1 79

AXLE 3 LT. WEIGHT 4 1 84

AXLE 2-3 SPACING 4 1 89

AXLE 4 RT. WEIGHT 4 1 94

AXLE 4 LT. WEIGHT 4 1 99

AXLE 3-4 SPACING 4 1 104

AXLE 5 RT. WEIGHT 4 1 109

AXLE 5 LT. WEIGHT 4 1 114

AXLE 4-5 SPACING 4 1 119

AXLE 6 RT. WEIGHT 4 1 124

AXLE 6 LT. WEIGHT 4 1 129

AXLE 5-6 SPACING 4 1 134

AXLE 7 RT. WEIGHT 4 1 139

AXLE 7 LT. WEIGHT 4 1 144

AXLE 6-7 SPACING 4 1 149

AXLE 8 RT. WEIGHT 4 1 154

AXLE 8 LT. WEIGHT 4 1 159

AXLE 7-8 SPACING 4 1 164

AXLE 9 RT. WEIGHT 4 1 169

AXLE 9 LT. WEIGHT 4 1 174

AXLE 8-9 SPACING 4 1 179

VENDOR SPECIFIC OPTIONAL FIELDS 184

This file shall include every "truck record" contained in the 

daily data file. Each field shall be comma delimited and padded

with blanks to complete the fixed logical record length.

For axle weight only weighing (in lieu of right and left wheel

weighing), either the "AXLE n RT. WEIGHT" or the "AXLE n LT.

WEIGHT" field may be used for the "AXLE n WEIGHT".
151

 REAL TIME VIEW ================

 

*********************************************************************************************

Veh No.: ______ Class: ______ Lane: ______ Speed: ______

 

Time: ______ Date: ______ GVW: _______ Wheelbase: _______ Vehicle Length: ______

Invalid Measurement Code:_________ Wt. Violation(s):______________

 AXLE NO.

 1 2 3 4 5 6 7 8 9 

Rt. Wheel Wt. (kips) 5.5 8.0 8.5 8.2 7.8 

Lt. Wheel Wt. (kips) 5.4 7.0 7.2 7.8 8.8

Axle Space (ft.) 11.8 4.5 36.4 4.2 

***********************************************************************************************

Note: Entries following Wheel Wt. and Axle Space are for example purposes only. 
152

 

 

 

 

 TRUCK RECORD BATCH PRINT 

 ==========================

 

 ***************************************************************************************************************************

 

 Site Designation: _________________ Lane: ________ Time: ______ Date: ______ Speed: _______ 

 Vehicle No.: ______ Class: ______ Invalid Code: ______ Veh. Wheelbase: ________ Veh. Length: _________

 Gross Wt.(kips): ___________ Weight Violation(s): __________

 

 Axle No. 1 2 3 4 5 6 7 8 9 

 

 Rt. Wheel Wt.(kip) 5.4 7.3 8.0 8.5 8.3

 Lt. Wheel Wt.(kip) 5.5 7.7 8.2 8.7 8.5

 ---------------------------------------------------------------------------------------------

 Axle Wt.(kips) 10.9 15.0 16.2 17.2 16.8 

 Axle Space (feet) 11.8 4.5 36.4 4.2 

 

 *************************************************************************************************************************** 

 

 Note: Entries following Axle Wt. and Axle Space are for example purposes only. 
153

 WEIGHT VIOLATION TABLE

 =======================================================

 (All weights in pounds)

 AXLE WEIGHT

 Axle No. 1 ------------------------ 12500

 

 All other axles --------------------- 20000

 TANDEM AXLE WEIGHT

 Two consecutive axles with an axle

spacing not exceeding 8.4 feet ------ 34000

 GROSS VEHICLE WEIGHT

 All vehicles ------------------------ 80000

 BRIDGE WEIGHT

 See page following ----
154

 BRIDGE WEIGHT 

 

 Distance in nearest whole 

foot between the extremes 2 3 4 5 6 7 8 9 

of any group of 2 or more Axles Axles Axles Axles Axles Axles Axles Axles

consecutive axles 

 < 8 ---------------------- 34000 34000 34000 34000 34000 ----- ----- -----

 8 ---------------------- 34000 34000 34000 34000 34000 ----- ----- -----

 9 ---------------------- 39000 42500 42500 42500 42500 ----- ----- ----- 10 ---------------------- 40000 43500 43500 43500 43500 ----- ----- -----

 11 ---------------------- 40000 44000 44000 44000 44000 ----- ----- -----

 12 ---------------------- 40000 45000 50000 50000 50000 ----- ----- -----

 13 ---------------------- 40000 45500 50500 50500 50500 ----- ----- -----

 14 ---------------------- 40000 46500 51500 51500 51500 ----- ----- -----

 15 ---------------------- 40000 47000 52000 52000 52000 ----- ----- -----

 16 ---------------------- 40000 48000 52500 52500 52500 ----- ----- -----

 17 ---------------------- 40000 48500 53500 53500 53500 ----- ----- -----

 18 ---------------------- 40000 49500 54000 54000 54000 ----- ----- -----

 19 ---------------------- 40000 50000 54500 54500 54500 ----- ----- -----

 20 ---------------------- 40000 51000 55500 55500 55500 ----- ----- -----

 21 ---------------------- 40000 51500 56000 56000 56000 ----- ----- -----

 22 ---------------------- 40000 52500 56500 56500 56500 ----- ----- -----

 23 ---------------------- 40000 53000 57500 57500 57500 ----- ----- ----- 24 ---------------------- 40000 54000 58000 58000 58000 74000 ----- -----

 25 ---------------------- 40000 54500 58500 58500 58500 74500 80000 -----

 26 ---------------------- 40000 55500 59500 59500 59500 75000 80000 ----- 27 ---------------------- 40000 56000 60000 60000 60000 76000 80000 80000 

 28 ---------------------- 40000 57000 60500 60500 60500 76500 80000 80000 

 29 ---------------------- 40000 57500 61500 61500 61500 77000 80000 80000 

 30 ---------------------- 40000 58500 62000 62000 62000 77500 80000 80000 

 31 ---------------------- 40000 59000 62500 62500 62500 78000 80000 80000 

 32 ---------------------- 40000 60000 63500 63500 63500 78500 80000 80000 

 33 ---------------------- 40000 60000 64000 64000 64000 79500 80000 80000 

 34 ---------------------- 40000 60000 64500 64500 64500 80000 80000 80000 

 35 ---------------------- 40000 60000 65500 65500 65500 80000 80000 80000 36 ---------------------- 40000 60000 68000 66000 66000 80000 80000 80000 

 37 ---------------------- 40000 60000 68000 66500 66500 80000 80000 80000 

 38 ---------------------- 40000 60000 68000 67500 67500 80000 80000 80000 

 39 ---------------------- 40000 60000 68000 68000 68000 80000 80000 80000 

 40 ---------------------- 40000 60000 68500 70000 70000 80000 80000 80000 

 41 ---------------------- 40000 60000 69500 72000 72000 80000 80000 80000 

 42 ---------------------- 40000 60000 70000 73280 73280 80000 80000 80000 

 43 ---------------------- 40000 60000 70500 73280 73280 80000 80000 80000 44 ---------------------- 40000 60000 71500 73280 73280 80000 80000 80000 

 45 ---------------------- 40000 60000 72000 76000 80000 80000 80000 80000 

 46 ---------------------- 40000 60000 72500 76500 80000 80000 80000 80000 

 47 ---------------------- 40000 60000 73500 77500 80000 80000 80000 80000 

 48 ---------------------- 40000 60000 74000 78000 80000 80000 80000 80000 

 49 ---------------------- 40000 60000 74500 78500 80000 80000 80000 80000 

 50 ---------------------- 40000 60000 75500 79000 80000 80000 80000 80000 

 51 ---------------------- 40000 60000 76000 80000 80000 80000 80000 80000 

 52 ---------------------- 40000 60000 76500 80000 80000 80000 80000 80000 53 ---------------------- 40000 60000 77500 80000 80000 80000 80000 80000 

 54 ---------------------- 40000 60000 78000 80000 80000 80000 80000 80000 

 55 ---------------------- 40000 60000 78500 80000 80000 80000 80000 80000 56 ---------------------- 40000 60000 79500 80000 80000 80000 80000 80000 

 57 ---------------------- 40000 60000 80000 80000 80000 80000 80000 80000 

 58 ---------------------- 40000 60000 80000 80000 80000 80000 80000 80000 >58 ---------------------- 40000 60000 80000 80000 80000 80000 80000 80000 
155

APPENDIX B.

This appendix contains excerpts from the following report: 

• Flinner, M and H. Horsey. Traffic Data Editing Procedures: Traffic Data 

Quality "TDQ". Final Report, Transportation Pooled Fund Study SPR-2 (182), 

Federal Highway Administration, Washington, D.C., no date. This report is 

available at http://www.fhwa.dot.gov/policy/ohpi/tdep.htm.
156
Traffic Data Edit Procedures - TDQ prototype software Rule Lis

Rule_ID A.3 Rule # Rule Name Rule Description

0 V42 Date is Correct and Unique If the date of the input data is not correct or unique, t

loaded into the database. An input error message w

1 V43 Lane and Direction are Correct If the lane or direction fields in the input data do not 

record, the input data will not be loaded into the datamessage will be reported. 

2 C49 Number of Axles = Number of Axle Spaces + 1 Any vehicle record where the number of axles does 

 axle spaces plus one will be flagged. 

3 W70 Number of Axles = Number of Axle Weights Any vehicle record where the number of axles does 

 axle weights will be flagged. 

4 W35 Sum of Axle Weights Does Not = GVW Any vehicle record where the sum of the axle weight

recorded GVW will be flagged. 

5 V1 Completeness of Data If the input data is insufficient or invalid in any way, a

be reported. 

6 V2 Zero Volume for an Hour Any hourly volume of zero in any lane will be flagged7 V4 Extreme Hourly Volume per Lane The hourly volume in any lane will be reported as an

this global extreme maximum: 

Thursday, September 07, 2000 

157
Rule_ID A.3 Rule # Rule Name Rule Description

8 V32 1:00 AM to 2:00 AM Volume vs. 1:00 PM to If the 1:00 AM to 2:00 AM volume is greater than the 

2:00 PM Volume volume of the same day, a warning will be reported. 

9 C1 No Classification Data If no volumes for any vehicle classes are present in t

 message will be reported. 

10 W51 Record Contains Valid Date Any vehicle record containing an invalid or unexpect

flagged. 

11 W52 Record Contains Valid Lane Number Any vehicle record containing a lane that does not m

record will be flagged. 

12 W53 Record Contains Valid Class Number Any vehicle record containing an invalid class numbe13 C24 Number of Axles Min/Max Any vehicle having more or less than the number of 

 be flagged: 

14 W36 Wheelbase Exceeds Value for Class Any vehicle of this class having a recorded wheelbas

maximum will be flagged: 

15 W39 GVW Exceeds Value for Class Any vehicle of this class having a recorded GVW gre

maximum will be flagged: 

16 W28 Front Overhang Out of Range Any vehicle with a front overhang outside of this rang

Thursday, September 07, 2000 

158
Rule_ID A.3 Rule # Rule Name Rule Description

17 W26 Rear Overhang Out of Range Any vehicle with a rear overhang outside of this rang18 W30 Sum of Axle Spaces > or = Recorded Vehicle Any vehicle where the sum of the axle spaces is gre

Length vehicle length will be flagged. 

19 W24 Record Contains Off-Scale Warning Any vehicle record containing a vendor's off-scale wa

flagged. 

20 W46 Wheelpath Imbalance Exceeds Threshold Any vehicle with the total weight on one side exceed

on the other side by more than this maximum will be 

21 C35 Vehicle Exceeding Speed Min/Max Any vehicle with a recorded speed outside of this ran22 W25 Extreme Speed Any vehicle with a recorded speed greater than this 

maximum will be flagged: 

23 W43 Heavy Class 6 Vehicle With Close Follower Any class 6 vehicle with an excessive GVW and follo

by another vehicle will be flagged. 

24 C26 Extreme Axle Spacing Any vehicle with any axle space greater than this ma

flagged: 

25 C27 Minimum First Axle Space Any vehicle with a first axle space (following the stee

this minimum will be flagged: 

Thursday, September 07, 2000 

159
Rule_ID A.3 Rule # Rule Name Rule Description

26 C28 Minimum Subsequent Axle Space Any vehicle with any axle space less than this minim27 C29 Minimum Spacing Between Axle Groups Any vehicle with a tandem or tridem axle space less 

will be flagged: 

28 W37 Axle Spacings vs. Min/Max Default Values for Any vehicle of this class will be flagged if this particu

Class greater or less than this range : 

29 W40 Axle Weights vs. Min/Max Default Values for Any vehicle of this class will be flagged if this particu

Class greater or less than this range : 

30 C30 3S-2 Drive Tandem Spacing Any 3S-2 tractor with a drive tandem spacing outside 

flagged: 

31 W50 Class 9 Front Axle Weight vs. Default Min/Max This rule is implemented by rule W40 in the TDQ Pro32 W50 Class 11 Front Axle Weight vs. Default This rule is implemented by rule W40 in the TDQ Pro

Min/Max 

33 V3 Consecutive Hourly Zero Volumes The number of consecutive zero-volume hours in an

reported as anomalous if it exceeds this daily maxim

34 V7 Consecutive Hours with Same Non-Zero The number of consecutive hours with the same non

Volume same lane will be reported as anomalous if it exceed

Thursday, September 07, 2000 

160
Rule_ID A.3 Rule # Rule Name Rule Description

35 V28 Sunday Hourly Directional Split Sunday's hourly directional split will be reported as a

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

36 V28 Monday Hourly Directional Split Monday's hourly directional split will be reported as a

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

37 V28 Tuesday Hourly Directional Split Tuesday's hourly directional split will be reported as 

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

38 V28 Wednesday Hourly Directional Split Wednesday's hourly directional split will be reported 

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

39 V28 Thursday Hourly Directional Split Thursday's hourly directional split will be reported as 

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

40 V28 Friday Hourly Directional Split Friday's hourly directional split will be reported as an

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

41 V28 Saturday Hourly Directional Split Saturday's hourly directional split will be reported as 

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

42 V9 Hourly Volume vs. Next/Prior Day The total hourly volume will be reported as anomalou

or less than the total volume for that hour of the prevby these tolerances: 

43 V17a Daily Directional Volume vs. AADT The daily directional volume will be reported as anom

or less than the previous year's adjusted directional tolerance: 

Thursday, September 07, 2000 

161
Rule_ID A.3 Rule # Rule Name Rule Description

44 V33 Daily Combined Volume vs. AADT The daily combined volume will be reported as anom

or less than the previous year's adjusted AADT by th

45 V5 Sunday Daily Directional Split Sunday's daily directional split will be reported as an

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

46 V5 Monday Daily Directional Split Monday's daily directional split will be reported as an

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

47 V5 Tuesday Daily Directional Split Tuesday's daily directional split will be reported as a

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

48 V5 Wednesday Daily Directional Split Wednesday's daily directional split will be reported a

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

49 V5 Thursday Daily Directional Split Thursday's daily directional split will be reported as a

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

50 V5 Friday Daily Directional Split Friday's daily directional split will be reported as ano

direction's percentage varies from its historical minimmore than these tolerances: 

51 V5 Saturday Daily Directional Split Saturday's daily directional split will be reported as a

leading direction's percentage varies from its historicmaximum by more than these tolerances: 

52 C48 Full Day of Data Exists If less than 24hours of data is present, a warning wil

anomalous. 

Thursday, September 07, 2000 

162
Rule_ID A.3 Rule # Rule Name Rule Description

53 C4 Extreme Daily Percent in Any Class Except 2 The daily percent of vehicles binned to any class exc

reported as anomalous if it exceeds this maximum: 

54 C37 Excessive Daily Percent by Class The daily percent of vehicles binned to any class exc

reported as anomalous if it exceeds this maximum: 

55 C38 Excessive Daily Volume by Class The daily volume of vehicles binned to any class exc

reported as anomalous if it exceeds this maximum: 

56 W16 Unloaded Class 9 GVW Distribution Peak The majority of unloaded class 9 GVWs are expecte

weight range: 

57 W16 Unloaded Class 11 GVW Distribution Peak The majority of unloaded class 11 GVWs are expect

weight range: 

58 W17 Loaded Class 9 GVW Distribution Peak The majority of loaded class 9 GVWs are expected t

weight range: 

59 W17 Loaded Class 11 GVW Distribution Peak The majority of loaded class 11 GVWs are expected 

weight range: 

60 W68 Percent of Vehicles With GVW Out of Range The daily percent of vehicles flagged for excessive G

for Class as anomalous if it exceeds this maximum: 

61 W67 Percent of Vehicles With Invalid Class The daily percent of vehicles flagged for an invalid c

be reported as anomalous if it exceeds this maximum

Thursday, September 07, 2000 

163
Rule_ID A.3 Rule # Rule Name Rule Description

62 W21 Average Class 9 Steering Axle Weight The daily average class 9 front axle weight will be re

if it falls outside of this range: 

63 W21 Average Class 11 Steering Axle Weight The daily average class 11 front axle weight will be r

anomalous if it falls outside of this range: 

64 W65 Percent of RecordsWith Invalid Dates The daily percent of vehicle records flagged for an in

reported as anomalous if it exceeds this maximum: 

65 W66 Percent of Records With Invalid Lane The daily percent of vehicle records flagged for an in

reported as anomalous if it exceeds this maximum: 

66 W56 Average Steering Axle Weight for Light-GVW The average steering axle weight of all class 9 vehic

Class 9s than 32,000 lbs. will be reported as anomalous if it f

range: 

67 W56 Average Steering Axle Weight for Mid-GVW The average steering axle weight of all class 9 vehic

Class 9s between 32,000 lbs. And 70,000 lbs. will be reported 

falls outside of this range: 

68 W56 Average Steering Axle Weight for Heavy-GVW The average steering axle weight of all class 9 vehic

Class 9s more than 70,000 lbs. will be reported as anomalous 

this range: 

69 W56 Average Steering Axle Weight for Light-GVW The average steering axle weight of all class 11 veh

Class 11s less than 32,000 lbs. will be reported as anomalous 

 range: 

70 W56 Average Steering Axle Weight for Mid-GVW The average steering axle weight of all class 11 veh

Class 11s between 32,000 lbs. And 70,000 lbs. will be reported 

falls outside of this range: 

Thursday, September 07, 2000 

164
Rule_ID A.3 Rule # Rule Name Rule Description

71 W56 Average Steering Axle Weight for Heavy-GVW The average steering axle weight of all class 11 veh

Class 11s more than 70,000 lbs. will be reported as anomalous 

this range: 

72 W58 Percent of Class 9s With Front Axle Weight The daily percent of class 9 vehicles flagged for an o

Flags weight will be reported as anomalous if it exceeds th

73 W58 Percent of Class 11s With Front Axle Weight The daily percent of class 11 vehicles flagged for an 

Flags axle weight will be reported as anomalous if it excee

74 C2 Percent of Records With Vendor Warning The daily percent of vehicle records containing a ven

Codes will be reported as anomalous if it exceeds this maxi

75 W62 Percent of Vehicles Where GVW Is Not = Sum The daily percent of vehicle records where the GVW 

of Axle Weights rounding error) to the sum of the axle weights will be 

anomalous if it exceeds this maximum: 

76 W60 Percent of Vehicles With Overhang Flags The daily percent of vehicles with overhang flags wil

anomalous if it exceeds this maximum: 

77 W8 Percent of Vehicles Where Length < The daily percentage of vehicles where the sum of th

Wheelbase greater than the recorded vehicle length will be repo

it exceeds this maximum: 

78 W10 Class 9 Average Length Within Range + The average class 9 vehicle length and wheelbase r

Average Wheelbase reported as anomalous if the average length is not w

average wheelbase and this range: 

79 W10 Class 11 Average Length Within Range + The average class 11 vehicle length and wheelbase 

Average Wheelbase reported as anomalous if the average length is not w

average wheelbase and this range: 

Thursday, September 07, 2000 

165
Rule_ID A.3 Rule # Rule Name Rule Description

80 W45 Percent of Records With Off-Scale Warnings The daily percent of vehicle records containing a ven

warning will be reported as anomalous if it exceeds t

81 W47 Pattern of Vehicles With Wheelpath Imbalance An otherwise anomalous percent of wheelpath imba

reported as anomalous if opposite wheelpath imbalaopposite directions (likely due to crosswinds). 

82 W54 Percent of Vehicles With Wheelpath The daily percent of vehicles with wheelpath imbalan

Imbalance reported as anomalous if it exceeds this maximum: 

83 W59 Percent of Vehicles that Exceed Extreme Max The daily percent of vehicles with globally extreme s

Speed reported as anomalous if it exceeds this maximum: 

84 C40 Percent of Vehicles Slower Than Speed Min The daily percent of vehicles with speeds less than t

will be reported as anomalous if it exceeds this maxi

85 C40 Percent of Vehicles Faster Than Speed Max The daily percent of vehicles with speeds greater tha

maximum will be reported as anomalous if it exceeds 

86 W61 Percent of Heavy Class 6 Vehicles With Close The percent of class 6 vehicles flagged for excessive 

Follower following vehicle will be reported as anomalous if it e

maximum: 

87 C15 Average 3S-2 Drive Tandem Spacing The daily average drive tandem spacing for 3S-2 veh

as anomalous if it falls outside of this range: 

88 W63 Percent of Vehicles With Wheelbase or Axle The daily percent of vehicles with wheelbase or axle 

Spacing Flags the default values for their class will be reported as a

exceeds this maximum: 

Thursday, September 07, 2000 

166
Rule_ID A.3 Rule # Rule Name Rule Description

89 W64 Percent of Vehicles With an Axle Weight Flag The daily percent of vehicles with an axle weight flag 

values for their class will be reported as anomalous maximum: 

90 W55 Average Left Axle Weight vs. Average Right The average left and right axle weights for all vehicle

Axle Weight anomalous if they differ by more than this maximum 

91 V19 Hourly Directional Volume vs. History An hourly directional volume will be reported as anom

from its historical minimum or maximum for that houtolerances: 

92 V40 Hourly Combined Volume vs. Recent History An hourly combined volume will be reported as anom

 its historical minimum or maximum for that hour by mtolerances: 

93 V39 Daily Combined Volume vs. Recent History A daily combined volume will be reported as anomal

its historical minimum or maximum by more than the

94 V17b Daily Directional Volume vs. History A daily directional volume will be reported as anoma

its historical minimum or maximum by more than the

95 V29 Daily Percent Distribution by Lane vs. History The daily lanal distribution will be reported as anoma

differs from its historical average percent by more th

96 C12 Daily Volume Binned to One Class vs. History The daily volume binned to a single vehicle class exc

reported as anomalous if it differs from its historical m volume by more than these tolerances: 

97 C11 Daily Percent Binned to One Class vs. History The daily percent binned to a single vehicle class wi

anomalous if it differs from the historical average permore than these tolerances: 

Thursday, September 07, 2000 

167
Rule_ID A.3 Rule # Rule Name Rule Description

98 C23 Daily Volume of Both Class 6 and 1 Exceed The daily volumes of class 1 and class 6 vehicles wi

History anomalous if both are greater than their average hist

99 C22 Daily Ratio of Class 2 to 3 vs. History The daily ratio of class 2 vehicles to class 3 vehicles 

anomalous if the number of class 2s per one class 3 these tolerances: 

100 C42 Daily Ratio of Class 9 to 8 by Lane vs. History The daily ratio of class 9 vehicles to class 8 vehicles 

reported as anomalous if the number of class 9s per from the historical minimum or maximum ratio by motolerances: 

101 C19 Daily Ratio of Class 9 to 8 by Direction vs. The daily ratio of class 9 vehicles to class 8 vehicles 

History be reported as anomalous if the number of class 9s 

from the historical minimum or maximum ratio by motolerances: 

102 C41 Daily Sum of Class 8 and 9 vs. History The daily sum of class 8 and class 9 vehicles will be 

anomalous if it differs from the historical minimum or these two classes by more that these tolerances: 

103 C14 Daily Class 8 Directional Split vs. History The daily directional split percentages for class 8 veh

as anomalous if the leading direction's percentage v minimum or maximum by more than these tolerance

104 C13 Daily Class 9 Directional Split vs. History The daily directional split percentages for class 9 veh

as anomalous if the leading direction's percentage v minimum or maximum by more than these tolerance

105 C43 Daily Sum of Class 8 and 9 Directional Split vs. The daily directional split percentages for the sum of 

 History vehicles will be reported as anomalous if the leading 

percentage varies from its historical minimum or maxthese tolerances: 

106 C46 Daily Directional Split of Any Class (not 8 or 9) The daily directional split percentages for any vehicle 

vs. History reported as anomalous if if the leading direction's pe

its historical minimum or maximum by more that thes

Thursday, September 07, 2000 

168
Rule_ID A.3 Rule # Rule Name Rule Description

107 C17 Daily Directional Split of Sum of Class 4 thru The daily directional split percentages for the sum of 

13 vs. History vehicles will be reported as anomalous if the leading 

percentage varies from its historical minimum or maxthese tolerances: 

108 C47 Daily Directional Split of Class Groups vs. The daily directional split percentages for any class g

History truck, semi-truck and multi-trailer) will be reported as 

leading direction's percentage varies from its historicmaximum by more that these tolerances: 

109 C16 Monthly Directional Split of Sum of Class 4 The monthly directional split percentages for the sum 

thru 13 vs. History vehicles will be reported as anomalous if the leading 

percentage varies from its historical minimum or maxthese tolerances: 

110 C47 Monthly Directional Split of Class Groups vs. The monthly directional split percentages for any cla

History truck, semi-truck and multi-trailer) will be reported as 

leading direction's percentage varies from its historicmaximum by more that these tolerances: 

111 W18 Unloaded Class 9 GVW Distribution Peak Shift A shift in the unloaded GVWs for class 9 vehicles wi

central tendancy of the input data is not within these historical central tendancy 

112 W19 Loaded Class 9 GVW Distribution Peak Shift A shift in the loaded GVWs for class 9 vehicles will b

central tendancy of the input data is not within these historical central tendancy 

113 W23 Loaded vs. Unloaded Class 9 GVW Distribution A parallel shift in Class 9 GVWs will be reported if th

 Peaks tendancy's shift from its historical value minus the un

tendancy's shift from its historical value is not within tolerances: 

114 W20 Incidental Class 9 GVW Distribution Peak Shift A shift in the major incidental GVW peak for class 9 

one) will be reported if the central tendancy of the inthese percents of a matching historical central tenda

115 W18 Unloaded Class 11 GVW Distribution Peak A shift in the unloaded GVWs for class 11 vehicles w

Shift central tendancy of the input data is not within these 

historical central tendancy 

Thursday, September 07, 2000 

169
Rule_ID A.3 Rule # Rule Name Rule Description

116 W19 Loaded Class 11 GVW Distribution Peak Shift A shift in the loaded GVWs for class 11 vehicles will 

central tendancy of the input data is not within these historical central tendancy 

117 W23 Loaded vs. Unloaded Class 11 GVW A parallel shift in Class 11 GVWs will be reported if t

Distribution Peaks tendancy's shift from its historical value minus the un

tendancy's shift from its historical value is not within tolerances: 

118 W20 Incidental Class 11 GVW Distribution Peak A shift in the major incidental GVW peak for class 11 

Shift one) will be reported if the central tendancy of the in

these percents of a matching historical central tenda

119 C6 Daily Average Speed per Lane vs. History The average vehicle speed in each lane will be repo

it differs from the historical average speed for that la tolerances: 

Thursday, September 07, 2000 

170
171

APPENDIX C.

This appendix contains a sample set of procedures for WIM data import, specifically 

ASCII truck record data.

Is it difficult to import ASCII truck record data into a spreadsheet? No, not once the 

procedure has been set up. Following is a quick walk-through of how it is done using 

Excel and the ASCII vehicle records conforming to that specified by LTPP’s model 

specifications (following):
172

Each field included in the ASCII file is entered into the Excel worksheet’s Row A in the 

same sequence as those fields in the ASCII file. The column widths can be adjusted and 

the cell formats fixed after playing a bit with the first few imports. To perform the 

import, start with the cursor in cell A2:

Then go to Excel Menu’s 

Data  Import External Data Import Data

Browse to the directory in which the ASCII vehicle record files are stored and locate the 

vehicle records text data file to import. (Remember - need to look in files of “all types” 

to locate text files):
173

In the Text Import Wizard window’s “Step 1 of 3” check “Delimited,” then click “Next”:

In the “Step 2 of 3” window, make sure only the “Comma” box is checked, then click on 

“Finish”:
174

Click “Properties” in the “Import Data” window that appears:
175

Uncheck “Adjust column width” and then click “OK”:

Make sure the “Import Data” box’s “Existing worksheet” = $A$2, then click “OK” to 

perform the import:
176

All of the data field cells for columns A thru AN should populate for the number of 

records contained in the data file (starting in row 2):

At such time that the import works well and the cells have been formatted as desired, 

delete all data from the spreadsheet and save (perhaps as a “template,” or similar) with 

some pertinent file name to use for all future data imports. Remember that once the 

original spreadsheet has been populated with “new” data, save it with a different file 

name (perhaps site identifier and date).