Section 5

Truck Weight Monitoring

**SECTION 5**

**CONTENTS**

*Section* *Page*

**CHAPTER 1---INTRODUCTION TO TRUCK WEIGHT DATA COLLECTION 5-1**

Weigh-in-Motion (WIM) Data Collection 5-1

Weigh-in-Motion Equipment Calibration 5-3

**CHAPTER 2---TRUCK WEIGHT USER NEEDS 5-4**

Basic Truck Weight Data Summaries 5-4

Truck Loading Estimates 5-5

**CHAPTER 3---TRUCK WEIGHT DATA COLLECTION 5-9**

Truck Weight Group Formation 5-10

Testing the Quality of Selected Truck Weight Groups 5-15

Determining the Precision of Estimates from Truck Weight Groups 5-15

Determining the Number of WIM Sites per Group 5-17

Determining the Number of Days that should Be Counted At a Given WIM
Site 5-22

WIM Site Installation by Lane and Direction of Travel 5-24

Site Selection 5-25

New WIM Site Selection Criteria 5-26

Integrating the WIM Sites with the Remaining Count Program 5-27

Total Size of the Weight Data Collection Program 5-28

**CHAPTER 4---TRUCK WEIGHT DATA SUMMARIZATION 5-29**

**APPENDIX 5-A---WIM EQUIPMENT ISSUES 5-32**

WIM Sensor Calibration 5-32

Monitoring of WIM Data Output 5-38

Front Axle Weights of Five-Axle, Tractor Semi-Trailer Trucks 5-38

Gross Vehicle Weight Distributions of 5-Axle Tractor Semi-Trailer Trucks
5-39

Both Peaks Shifted 5-40

One Peak Shifted 5-40

Number of Vehicles Heavier than 80,000 Pounds 5-41

Changes in Tandem Axle Spacings 5-41

Changes in Measured Truck Volumes 5-42

**APPENDIX 5-B---FREQUENTLY ASKED QUESTIONS 5-44**

**REFERENCES 5-45**

**FIGURES**

*Figure* *Page*

5-2-1 Example GVW Flow Map 5-7

5-3-1 Tandem Axle Load Distributions At Three Sites With Different\
Loading Conditions. The Case For Truck Weight Road Groups 5-11

5-A-1 Effect of Weigh-in-Motion Scale Calibration Drift on the Accuracy
of\
ESAL Calculations 5-33

5-A-2 Variation of Axle Forces with Distance and the Consequential
Effect on\
WIM Scale Calibration 5-35

5-A-3 Variation of Axle Forces with Distance and the Consequential
Effect on\
WIM Scale Calibration 5-36

**TABLES**

*Table* *Page*

5-2-1 Example Daily Load Distribution Table (All Vehicle Classes
Combined)\
and Computation of Total ESAL Loading 5-8

5-3-1 Example Truck Loading Groups 5-14

5-3-2 Example of Statistic Computation for Precision Estimates 5-16

5-3-3 Statistics Used For Sample Size Computation 5-19

5-3-4 Example Effects of Sample Size on the Precision of GVW Estimates
5-20

5-3-5 Example Effects of Sample Size and Confidence Interval on
Precision\
of GVW Estimates for the Revised Truck Weight Group 5-21

Section 5\
Truck Weight Monitoring

Chapter 1\
Introduction To Truck Weight Data Collection

The last of the primary traffic monitoring activities is truck weight
data collection. Gathering truck weight data is the most difficult and
costly of the three primary data collection activities. However, in many
respects these data are the most important.

Data on the weight carried by trucks are used as a primary input to a
number of a State highway agency's most significant tasks. For example,
traffic loading is a primary factor in determining the depth of pavement
sections. It is used as a primary determinant in the selection of
pavement maintenance treatments. The total tonnage moved on roads is
used to estimate the value of freight traveling on the roadway system
and is a major input into calculations for determining the costs of
congestion and benefits to be gained from new construction and operating
strategies. Truck classification and weight information is also a key
component in studies that determine the relative cost responsibility of
different road users.

This section discusses the alternatives for collecting truck weight
information. This first chapter introduces truck weight data collection
technology and data collection strategies. The second chapter discusses
the basic user needs for truck weight data and describes how those uses
affect the data collection and summarization strategy. Chapter 3
recommends a truck weight data collection program that meets the needs
identified in Chapter 2. Chapter 4 presents a variety of ways to
summarize weight data. Finally, a discussion of the need for calibration
of WIM devices is presented as an Appendix.

Weigh-in-Motion (WIM) Data Collection

Of all the traffic monitoring activities, WIM requires the most
sophisticated data collection sensors, the most controlled operating
environment (strong, smooth, level pavement in good condition), and the
most costly equipment set up and calibration.[^1] WIM systems are
designed to measure the vertical forces applied by axles to sensors in
the roadway. This measurement helps estimate the weight of those axles
if the truck being weighed were stationary. The task is complicated by a
number of factors, including the following:

• Each sensor "feels" the vertical force of each axle for only a brief
time.

• The "weight" applied to the sensor during that time period is normally
[not]{.underline} equal to the static weight of that axle. This is
because while the vehicle is in motion, the truck and its components
bounce up and down. If the truck mass is moving upward when an axle
crosses the WIM sensor, the weight applied by that axle is lower than
the static value. If the truck mass is landing, the weight applied is
greater than the static value.[^2]

• Some sensors (strip) feel only a portion of the tire weight at any
given time. Because the sensor is smaller than the footprint of the
tire, the pavement surrounding the sensor physically supports some
portion of the axle weight throughout the axle weight measurement.

• The tread on some tires is so well defined that very high
concentrations of force are generated under those portions of the tread
that are actually in contact with the ground. This is also mostly a
problem for strip sensors.

• Sensors must be capable of weighing more than one axle in quick
succession. That is, the scale must be able to "recover" quickly enough
so that one axle weight does not affect the measurement of the following
axle.

• Roadway geometry (horizontal and vertical curves) can cause shifts in
vehicle weight from one axle to another.

• Vehicle acceleration or braking, torque from the drive axles, wind,
the style and condition of vehicle's suspension system, and a variety of
other factors can also cause shifts of weight from one axle to another.

The effects of many of these factors can be minimized through careful
design of the WIM site. The site should be selected and designed to
reduce the dynamic motion of passing vehicles. However, achieving these
design controls requires restrictions on site selection, which means
that WIM systems cannot be placed as easily or as universally as other
traffic monitoring equipment.

WIM scales work most accurately when they are placed flush with the
roadway. Sensors that sit on top of the roadway cause two problems with
WIM system accuracy: 1) They induce additional dynamic motion in the
vehicle, and 2) they can cause the sensor to measure the force of tire
deformation (which includes a horizontal component not related to the
weight of the axle) in addition to the axle weight. This means that
permanent installation of the sensors and/or frames that hold the
sensors is normally better for consistent, accurate weighing results.
The use of permanently installed WIM sensors is recommended as a means
of improving the quality of the data.[^3]

weigh-in-motion equipment calibration

Calibration of WIM equipment is also more demanding than calibration of
other types of traffic monitoring equipment. WIM scale calibration must
account for the vehicle dynamics at the data collection site. Because
vehicle dynamics are affected by pavement roughness, the "correct"
calibration value for a scale is a function of the pavement condition
and the sensor installation at each site. Since these differ with each
placement, a significant calibration effort is required each time WIM
equipment is placed on the ground. If the scale is not calibrated, the
static weight estimates provided by the scale can be very inaccurate,
even if the scale accurately reports the vertical forces applied to its
surface. The expense of calibrating portable WIM scales each time they
are installed is another significant detriment to their use**.**

Because pavement conditions change over time, and because those changes
affect WIM scale performance, even permanently installed WIM sensors
need to be periodically calibrated.

To ensure that the equipment is operating effectively, the data produced
must be promptly produced and analyzed. Changes in vehicle weight over
time must be examined quickly to understand whether the equipment is
malfunctioning, calibration is needed, or the scales are simply
reflecting changes in freight movement. Software systems that allow
rapid monitoring and retrieval of WIM system output are an important
consideration of WIM data collection. The FHWA Vehicle Travel
Information System (VTRIS) allows quick examination of WIM data. More
information on WIM site requirements and WIM calibration requirements is
included in Appendix 5-A.

Chapter 2\
Truck Weight User Needs

Truck weight data are used for a wide variety of tasks. These tasks
include, but are not limited to, the following:

• pavement design

• pavement maintenance

• bridge design

• pavement and bridge loading restrictions

• development and application of equitable tax structures

• determination of the need for and success of weight law enforcement
actions

• determination of the need for geometric improvements related to
vehicle size, weight, and speed

• determination of the economic value of freight being moved on roadways

• determination of the need for and effect of appropriate safety
improvements.

Basic Truck Weight Data Summaries

State highway agencies summarize and report truck weight data in many
ways. Three types of summaries are commonly used including:

• gross vehicle weight (GVW) per vehicle (usually by vehicle class)

• axle load distribution (by type of axle) for specific vehicle types

• equivalent standard axle load[^4] (ESAL) for specific vehicle types.

Basic statistics such as the GVW or ESAL for a given vehicle
classification can be expressed as distributions, as mean values, or as
mean values with specified confidence intervals, depending on the needs
of the analysis that will use this information. Each of these summary
statistics can be developed for a specific site, a group of sites, or an
entire State or geographic region, depending on the needs of the
analysis and the data collection and reporting procedures. The role of
the traffic monitoring program is to provide the user with whichever of
these data summaries is needed. The summaries can be required for any
one of several levels of summarization. For example, it may be
appropriate to maintain axle loading distributions for each of the FHWA
heavy vehicle classes (classes 4 through 13)[^5] so that these
statistics are available when needed for pavement design. However, even
if a more aggregated classification scheme is used, such as single-unit
trucks, combination trucks, and multi-trailer trucks, the more detailed
summary should be retained for WIM data. These summaries can be computed
with FHWA's VTRIS software, with software supplied by the WIM system
vendor, or with software developed specifically for use by the State
highway agency as part of its traffic database.

A single statewide average statistic may not be applicable to all parts
of the State. Trucking characteristics vary significantly by type of
road. When a single statewide summary is not representative of all
roads, it is important to collect data and maintain summary statistics
for different regions or roads in the State. For example, the truck
traffic in urban areas often has different truck weight characteristics
than those in rural areas. Roads that serve major agricultural regions
often have different loading characteristics than roads that serve
resource extraction industries. Roads that serve major industrial areas
within an urban area tend to carry much heavier trucks than roads that
serve general urban and suburban areas. Roads that serve major
through-truck movements often experience very different truck weights
than roads that serve primarily local truck traffic. An effective truck
weight program must identify these differences and include a data
reporting mechanism to provide users with data summaries that correctly
describe specific characteristics.

Truck Loading Estimates

Axle load distribution tables and average gross vehicle weights per
vehicle are useful statistics, but they are rarely the end product that
many users need. Instead, most users are interested in total load
estimates for a given period (e.g., total ESAL per year, or total number
of axle loads by type and weight range in the last ten years). These
statistics can be derived directly only from WIM sites. Unfortunately,
because WIM equipment is expensive to install and maintain, WIM data are
available at only a few locations in the State. Thus, at most road
sites, these WIM data items cannot be measured directly. Instead, the
data are normally computed from a summary weight data set, as previously
described, and a site-specific count of volume by vehicle classification
category. The WIM data are imputed to the site-specific classification
count to estimate total loading.

These calculations assume that the basic weight distribution developed
at available WIM sites is representative of all roads within a specified
group. For example, all rural Interstates are assumed to have similar
truck loading conditions. Rural Interstate loading conditions are then
measured at three different WIM sites and the data combined to provide
the weight distribution estimate to represent all segments in the group.

Site-specific volume counts (by classification) are used to "size" the
weight distribution. That is, the site-specific classification count
(adjusted for day-of-week and seasonal variation) is used to determine
how many trucks of a particular type actually travel on the road. The
volume by classification determines how many axles of each type are
present. (For example, if a road section carries 100 Class 9 trucks in a
day, it experiences approximately 100 single axles and 200 sets of
tandem axles.)

Multiplying the number of trucks within a given class by the average GVW
for vehicles of that class yields the total number of tons[^6] applied
by that class on that roadway. Adding these values across all vehicle
classes yields the total number of tons carried by that road. These
values can be plotted graphically, creating an image very similar to a
traffic volume flow map[^7] (Figure 5-2-1). The graphics are useful for
both public presentations and as an information tool for decision
makers. Map displays allow decision makers to graphically compare roads
that carry large freight volumes with roads with light freight
movements. The information can also be used to help prioritize potential
road improvement projects.

Multiplying the total number of vehicles in a given class by the number
of axles (by type of axle) associated with that class and by the axle
weight distribution associated with that class, yields the total number
of axles applied at that site by that vehicle class. Adding these weight
distribution tables across vehicle classes results in the total number
of axles, by weight class, applied to that roadway. This type of summary
table will be one of the primary data inputs for the pavement design
guide being readied by AASHTO.

The axle distribution by axle weight range can also be easily converted
into equivalent standard axle loads (ESAL), the most common pavement
design loading value currently used in the United States. To make this
conversion, an ESAL[^8] value is assigned to each axle weight category
for each type of axle (single, tandem, tridem). This value times the
number of axles within that weight range yields the total ESAL load for
that type and weight range of axles. Summing these values across all
axle types and weight ranges yields the total number of ESALs applied to
that roadway (Table 5-2-1).

Finally, understanding and accounting for seasonal variations in vehicle
weights is becoming increasingly important for both economic analyses
and pavement design procedures. New pavement design procedures being
developed and refined require traffic loading data for specific times of
the year. For example, in many colder regions proposed pavement design
procedures will require the average daily loading rate during the spring
thaw period because the pavement will be designed to withstand loads
when the roadway structure is at its weakest. Since pavement strength
changes with many environmental conditions, the pavement designers are
likely to require data on loads at different sites at different times
during the year. If loads vary (because the numbers of trucks or the
weights of individual trucks vary during the year), the traffic data
collection process must be able to detect and report these differences.
Otherwise, the pavement design procedures will be unreliable.

![](media/image1.wmf){width="5.320138888888889in"
height="7.041666666666667in"}

# Figure 5-2-1: Example GVW Flow Map

**Table 5-2-1: Example Daily Load Distribution Table (All Vehicle
Classes Combined) and Computation of Total (Flexible) ESAL Loading**

  --------------- -------- -------- ----------- -------- -------- -------- ----------- -------- -------- -------- ----------
  Single Axles                                  Tandem                                 Tridem                     
                                                Axles                                  Axles                      

  Lower Weight    Upper    ESAL Per Number of   Lower    Upper    ESAL Per Number of   Lower    Upper    ESAL Per Number of
  Range (kgs)     Weight   Axle     Axles       Weight   Weight   Axle     Axles       Weight   Weight   Axle     Axles
                  Range                         Range    Range                         Range    Range             
                  (kgs)                         (kgs)    (kgs)                         (kgs)    (kgs)             

  0               1,363    0.000    5           0        2,727    0.001    4           0        5,454    0.001    0

  1,364           1,818    0.001    7           2,728    3,636    0.002    16          5,455    6,818    0.006    0

  1,819           2,272    0.003    51          3,637    4,545    0.005    24          6,819    8,181    0.014    0

  2,273           2,727    0.007    31          4,546    5,454    0.010    36          8,182    9,545    0.027    0

  2,728           3,181    0.014    37          5,455    6,363    0.020    34          9,546    10,909   0.048    0

  3,182           3,636    0.026    75          6,364    7,272    0.036    37          10,910   12,272   0.079    0

  3,637           4,090    0.044    99          7,273    8,181    0.061    33          12,273   13,636   0.126    0

  4,091           4,545    0.071    97          8,182    9,090    0.097    28          13,637   15,000   0.191    0

  4,546           5,000    0.108    78          9,091    10,000   0.148    23          15,001   16,363   0.278    0

  5,001           5,454    0.158    56          10,001   10,909   0.217    19          16,364   17,727   0.393    0

  5,455           5,909    0.224    40          10,910   11,818   0.309    20          17,728   19,090   0.539    0

  5,910           6,363    0.310    22          11,819   12,727   0.425    22          19,091   20,454   0.722    1

  6,364           6,818    0.416    16          12,728   13,636   0.572    29          20,455   21,818   0.947    0

  6,819           7,272    0.547    16          13,637   14,545   0.752    29          21,819   23,181   1.217    0

  7,273           7,727    0.706    13          14,546   15,454   0.757    30          23,182   24,545   1.537    2

  7,728           8,181    0.894    13          15,455   16,363   1.229    25          24,546   25,909   1.912    1

  8,182           8,636    1.115    11          16,364   17,272   1.532    17          25,910   27,272   2.346    3

  8,637           9,090    1.371    10          17,273   18,181   1.884    15          27,273   28,636   2.843    1

  9,091           9,545    1.664    7           18,182   19,090   2.288    8           28,637   30,000   3.408    0

  9,546           10,000   1.999    6           19,091   20,000   2.747    7           30,001   31,363   4.046    0

  10,001          10,454   2.376    5           20,001   20,909   3.267    5           31,364   32,727   4.763    0

  10,455          10,909   2.801    3           20,910   21,818   3.850    2           32,728   34,090   5.563    0

  10,910          11,363   3.275    1           21,819   22,727   4.502    3           34,091   35,454   6.453    0

  11,364          11,818   3.804    1           22,728   23,636   5.229    1           35,455   36,818   7.441    0

  11,819          12,272   4.390    1           23,637   24,545   6.035    1           36,819   38,181   8.534    0

  12,273          12,727   5.039    1           24,546   25,454   6.927    1           38,182   39,545   9.740    0

  12,728          13,181   5.756    0           25,455   26,363   7.913    0           39,546   40,909   11.070   0

  13,182          13,636   6.546    0           26,364   27,272   8.999    0           40,910   42,272   12.532   0

  13,637          14,090   7.416    0           27,273   28,181   10.194   0           42,273   43,636   14.138   0

  14,091          14,545   8.371    0           28,182   29,090   11.506   0           43,637   45,000   15.900   0

  14,546          15,000   9.419    0           29,091   30,000   12.947   0           45,001   46,363   17.831   0

  15,001          15,454   10.567   0           30,001   30,909   14.525   0           46,364   47,727   19.942   0

  15,455          15,909   11.824   0           30,910   31,818   16.253   0           47,728   49,090   22.250   0

  15,910          16,363   13.197   0           31,819   32,727   18.140   0           49,091   50,454   24.769   0

  16,364          16,818   14.696   0           32,728   33,636   20.201   0           50,455   51,818   27.514   0

  16,819          17,272   16.331   0           33,637   34,545   22.448   0           51,819   53,181   30.503   0

  17,273          17,727   18.111   0           34,546   35,454   24.895   0           53,182   54,545   33.753   0

  17,728          18,181   20.047   0           35,455   36,363   27.556   0           54,546   55,909   37.283   0

  18,182          none     22.149   0           36,364   none     30.446   0           55,910   none     41.111   0

  **Total ESAL by                   **169.8**                              **269.7**                              **15.6**
  type of axle\                                                                                                   
  **∑(ESAL/axle                                                                                                   
  \* Total Axles)                                                                                                 

  **Total ESAL\                     **455.1**                                                                     
  **(all axle                                                                                                     
  types combined)                                                                                                 
  --------------- -------- -------- ----------- -------- -------- -------- ----------- -------- -------- -------- ----------

Chapter 3\
Truck Weight Data Collection

**The objective of the truck weight data collection program is to obtain
a reliable estimate of the distribution of vehicle and axle loads per
vehicle for truck categories within defined roadway groups.**

The data collection plan for truck weight accounts for:

• the statistical needs of State and federal agencies

• the capabilities and limitations of WIM equipment

• the resource constraints found at most State highway agencies

• the variability of truck weight data, as discussed in the literature
and as observed in data submitted to the FHWA.

The truck weight data collection program is based on creating summary
axle load distributions that can be applied with confidence and
statistical precision to all roads in a State. The procedure is to group
the State's roads into categories, so that each group experiences
freight traffic with reasonably similar characteristics. For example,
roads that experience trucks carrying heavy natural resources should be
grouped separately from roads carrying only light, urban delivery loads.
The truck weight data collection program is closely analogous to the
permanent, continuous count programs for collecting seasonal and
day-of-week pattern information for volume and vehicle classification
data. The primary difference is that most of the truck weight data
collection sites do not need to be operated in a continuous manner.

Within each of these groups of roads, the State should operate a number
of WIM sites. These sites will be used to identify truck weight patterns
that apply to all roads in the group. At least one of the WIM sites
within each group should operate continuously throughout the year to
measure seasonal changes in the loads carried by trucks operating on
those roads. Where possible (given budget and staffing limitations),
more than one location within each group should be monitored
continuously to provide more reliable measures of seasonal change. The
proper number of additional continuous sites is primarily a function of:

• each State's ability to supply the resources needed to monitor the
sites to ensure the provision of accurate data throughout the year

• the proven need to monitor differences in seasonal weight
characteristics.[^9]

Performing additional vehicle weighing, both by operating more
continuous WIM scales and by collecting data at more than the minimum
number of scale sites, will allow a State to determine whether the
initial groups selected do, in fact, carry similar truck traffic. Where
new data collection shows that monitored roads do not carry traffic with
loading characteristics similar to those of other roads in the group,
the State will either need to create new road groups (and collect more
truck weight information) or revise the existing road groups to create
more homogeneous groups.

Truck Weight Group Formation

Truck weight road groups should be based on a combination of known
geographic, industrial, agricultural, and commercial patterns, along
with knowledge of the trucking patterns that occur on specific roads.
Road groups or systems for truck weight data collection should: 1) be
easily applied within each State, and 2) provide a logical means for
discriminating between roads that are likely to have very high load
factors and roads that have lower load factors (that is, between roads
where most trucks are fully loaded and roads where a large percentage of
trucks are either partially loaded or empty).

In addition, States should incorporate into their truck weight grouping
process knowledge about specific types of heavy trucks, so that roads
that carry those heavy trucks are grouped together, and roads that are
not likely to carry those trucks are treated separately. For example,
roads leading to and from major port facilities might be treated
separately from other roads in that same geographic area, simply because
of the high load factor that is common to roads leading to/from most
port facilities.

Figure 5-3-1 illustrates the reason why roads should be stratified into
road groups. It shows the distribution of tandem axle weights for Class
9 trucks from three different truck weight sites. Each of these three
sites exhibits a very different set of loading conditions, ranging from
heavily loaded to very lightly loaded. Use of loading information from
one of these sites at either of the other two sites would result in very
poor load estimates. The average flexible ESAL per tandem axle at the
heavily loaded site is 0.66, while the moderately loaded site has a
flexible ESAL per tandem axle of 0.35, and the lightly loaded site has
an ESAL per tandem axle of 0.19. Thus, use of the "heavy" load
distribution at the "lightly" loaded site would result in an
overestimation of actual loading rates by a factor of over 3.

The key to the design of the truck weight data collection effort, and
the use of the data that results from that process, is for the highway
agency to be able to successfully recognize these differences in loading
patterns, and to collect sufficient data to be able to estimate the
loads that are occurring under these different conditions.

![](media/image2.wmf){width="5.993055555555555in" height="5.35in"}Figure
5-3-1: Tandem Axle Load Distributions At Three Sites With Different
Loading Conditions. The Case For Truck Weight Road Groups

Australia recently proposed a similar grouping technique in the chapter
on traffic data collection in its pavement design guide.[^10] In the
Australian guide, 25 different truck loading patterns are identified
nationwide. These patterns are structured both by type of trucking
movement, and the infrastructure linkages being served. The Australian's
use the following categories of haul activities:

• General Freight

• General Freight in a Heavy Vehicle Increased Mass Permit Environment

• Predominately Industrial

• Quarry Products

• Predominately Farm Produce

• Live-Stock

• Logging Products

To further aid in classifying any given road section to one of the truck
loading patterns, the Australian guide also provides a simplified
description of what types of links a given roadway provides (e.g., the
road connects a major port to other regional cities).
"Characterizations" of the trucking patterns used include the following:

• Long-haul, inter-capital

• Long-haul inter-capital at remote sites

• Inter-regional within state/territory or nearby region

• Near town and/or where local freight movement occurs

• Developing area

• Entering and exiting port/loading sites

• Entering and exiting capitol city

This report does not recommend specific roadway grouping criteria. The
Australian system has significant merit, can be applied fairly easily,
and requires only a modest understanding of the traffic on a given
highway. However, the Australian groupings are not directly applicable
to U.S. roads because our economy and geographic distribution of cities
are considerably different. Instead, States should consider creating
similar styles of roadway groups that are characterized by
industrial/roadway traits that fit their economic infrastructure. For
example, States may want to differentiate among roads affected by
specific types of industrial or agricultural activity (such as areas
that grow wheat or areas that support steel manufacturing).

It may also be reasonable to start with a less detailed truck weight
stratification than used by the Australians. In fact, unless extensive
State data suggest the need for a more definitive grouping process, it
is recommended that initial groups be based on a much more simplistic
approach. This simplistic approach would then be improved (as needed)
over time as more weight data are collected and analysis carried out.

Where more detailed information is not available, the initial grouping
of roads into truck weight categories should be based on the percentage
of through-trucks that exist on a roadway and distinct geographic
regions within a State that can be associated with specific types of
economic activity. The vehicle classification data provide much
information as to what types of trucks are found on which roads. Other
factors that can/should be used to differentiate roads into truck weight
groups may include the following:

• The presence of agricultural products that create specific loading
patterns and are carried in specific types of trucks. For example, wheat
growing areas might need to be grouped separately from those that grow
cherries because these two products have different densities, different
weights on a truck and because their harvest and hauling seasons are
different.

• The types of industrial areas, such as resource extraction operations
that ship large amounts of material by truck. For example, coal truck
traffic roads may be grouped separately from roads that experience few
coal trucks.

• The distance over which the trucks are likely to travel. For example,
roads where trucks deliver cargo over long distances across multiple
States, or roads with truck travel between cities within a region where
drivers can make a round trip in one day, or roads with truck travel
within a general urbanized area where drivers make multiple trips in a
day. Trucks traveling longer distances are more likely to be full, and
thus heavier, than trucks operating within half a day of their base,
which are likely to be full leaving their depot but are often empty when
returning.

• Urban or rural roads, because urban areas often have considerably
higher numbers of partially loaded trucks and trucks that travel empty
after unloading at urban destinations. Note that some roads functionally
classified as "rural" that are located between two large cities (say
within 300 km or 180 miles of each other) may experience urban rather
than rural trucking patterns because trucks routinely make day-trips
between those cities, traveling full in one direction and empty in the
other.

A State may also be interested in discriminating between roads because
of the industrial activity they serve. For example, roads leading into
and out of major seaports may experience far heavier traffic (higher
load factors) than other roads in the same area. Much information can be
extracted from existing truck weight databases and planning programs to
determine logical and statistical differences that can be accounted for
in the formation of truck weight groups.

As an example of a weight factor group, Washington State developed five
basic truck loading patterns as part of a study to determine total
freight tonnage carried by all State highways. These five groups were
defined as

Group A - serves major statewide and interstate truck travel. These
routes are the major regional haul facilities

Group B - serves primarily intercity freight movements, with minor
amounts of regional hauling. These routes also serve as produce transfer
routes, serving rail and barge loading facilities.

Group C - serves farm to market routes and regional commerce.

Group D - serves suburban industrial activity.

Group E - serves primarily local goods movement and specialized
products.

A starting point for developing truck weight groups is shown in Table
5-3-1. The example begins with the groups identified in the vehicle
classification section. The truck loading groups defined should be
coordinated with the vehicle classification groups identified in section
4. Differences in the two sets of groups are likely since the groups are
defined to meet different purposes (seasonal differences in volume and
loading variation). However, they both reflect truck travel
characteristics that are directly related. A similar group definition
will greatly simplify the understanding and applicability of the
patterns. The groups will need further redefinition over time as
information is gained.

Table 5-3-1: Example Truck Loading Groups[^11]

  ----------------------------------- -----------------------------------
  Rural                               Urban

  Interstate and arterial major\      Interstate and arterial major\
  through-truck routes                truck routes

  Other roads (e.g., regional         Interstate and other freeways
  agricultural with little            serving primarily local truck
  through-trucks)                     traffic

  Other non-restricted truck routes   Other non-restricted truck routes

  Other rural roads (mining areas)    Other roads (non-truck routes)

  Special cases (e.g., recreational,  
  ports)                              
  ----------------------------------- -----------------------------------

The number of groups selected is a key consideration because of the
impact on the number of WIM installations needed. The higher the number
of groups, the higher the number of WIM sites needed. For large States
with an established base of WIM sites, a higher number of groups is
appropriate. For small States with limited number of WIM installations,
smaller numbers of groups should be tried. Since the character of
trucking patterns does not change at State boundaries, pursuing the
establishment of regional groups in combination with neighboring States
could serve to reduce the individual State level of effort required
while still providing the basic information needed.

Given the fact that much needs to be learned, starting the process with
a small number of groups seems very reasonable. This can be accomplished
by defining the truck loading groups as would be appropriate if WIM
resources were not a constraint. The groups can then be combined and
aggregated until the number of groups dwindles down to the appropriate
number given the currently available WIM sites. In some cases, groups
could be formed with smaller number of WIM sites than recommended and
then WIM installations added in the future as resources become
available. It is very likely that the study of truck patterns will
highlight the need for additional WIM installations in the future.

Testing the Quality of Selected Truck Weight Groups

Just as with the formation of groups used for factoring volume and
classification counts, the initial formation of truck weight groups must
be reviewed to determine whether the road segments grouped together
actually have similar truck weight characteristics. Examining available
data from the existing truck weight sites is the first step. A
substantial amount of judgment is required since the data is likely to
be limited to that currently available from existing WIM sites.

For example, a State highway agency may find that in one group of roads,
the class 9 trucks all have similar characteristics, but the class 11
truck characteristics are very different from each other. By changing
the road groups, it may be possible to classify roads so that all class
9 and 11 trucks within a road group have similar characteristics. More
likely it will not be possible to form homogenous groups for different
truck classes, and trade-offs will have to be made. The type of vehicle
considered the most important should be given priority.

The trade-offs can be made based on the relative importance of each
weight statistic to the data user. In many cases this is simply a
function of determining the relative importance of different truck
statistics. For example, if 95 percent of all trucks are in class 9,
then having truck weight road groups that accurately describe class 9
truck weight characteristics may be more important than having road
groups that accurately describe class 11.

Determining the Precision of Estimates From Truck Weight Groups

An estimate of the "precision" of the mean of a variable that any truck
weight road group will provide can be found by computing the standard
deviation when computing the mean statistic for that variable (refer to
equation 3-3). For example, the precision of the mean gross vehicle
weight for a Class 9 truck within a truck weight group can be estimated
while computing the mean GVW per Class 9 truck from all of the WIM sites
within that group. The standard deviation of the estimate and the number
of sites provide an approximate measure of the accuracy of the mean of
the group.

An example of this computation is shown below. In the example, assume
that a State has determined that all rural Interstate roads have similar
truck weight characteristics based on seven WIM sites. Statistics from
those WIM sites are shown in Table 5-3-2. On the basis of these data, it
can be assumed that all rural Interstate roads in the group have a mean
gross vehicle weight of 25,000 kg for class 9 trucks. Each class 9 truck
can also be assumed to apply an average of 1.63 ESAL.[^12]

The precision of the group mean, referred to as the standard error of
the mean, can be estimated with 95 percent confidence as
approximately[^13] plus or minus 1.96 times the standard deviation
divided by the square root of the number of sites.

Table 5-3-2: Example of Statistic Computation for Precision Estimates

  -------------------------- ---------------------- ---------------------
  **Site**                   **Mean Class 9 GVW**   **Mean Class 9 ESAL**

  1                          23000 kg               1.64

  2                          26000 kg               1.72

  3                          29000 kg               1.84

  4                          21000 kg               1.45

  5                          21000 kg               1.34

  6                          25000 kg               1.65

  7                          28000 kg               1.78

  **Group Mean**             **25000 kg**           **1.63**

  **Group Standard           **3200 kg**            **0.18**
  Deviation**                                       

  **Coefficient of           **0.13**               **0.11**
  Variation**                                       
  -------------------------- ---------------------- ---------------------

In the above example, note that the coefficient of variation for the two
statistics (GVW/vehicle and ESAL/vehicle) are different, even though
both variables come from the same set of vehicle weights. Each statistic
computed for a truck weight group is likely to have different
statistical reliability because of the different levels of variation
found in axle weights, GVW, and the various other statistics computed
from weight records.

To complicate matters further, each statistic has a different level of
precision for each different vehicle class. Thus, the precision of the
ESAL/vehicle value for Class 9 trucks will be different than that of the
ESAL/vehicle value for Class 11 trucks.

In sampling applications, increasing the number of samples increases the
precision of the mean estimate being computed. Thus, increasing the
number of WIM sample locations within a given truck weight group will
improve the precision of the mean value computed within a weight group.
This is an important result when calculating system-level summary
variables, such as annual ton-kilometers.

Increasing the number of WIM sites will improve the system-wide averages
for each group. However, increasing the sample size only marginally
improves the precision of estimates used as default values for loading
rates on specific roadway sections. When a mean value of a distribution
is assumed to be the "best" estimate of a value at a specific point, the
variability of that estimate is measured by the standard deviation of
the distribution. The error bounds can only be reduced by creating truck
weight groups that have tighter distributions, or by taking
site-specific WIM counts. Taking site-specific measurements ensures that
the data apply directly to the site in question. This is why
site-specific vehicle classification counts are requested for most
pavement design projects since they provide the only cost-effective
method for obtaining the accuracy needed at a specific location.
Unfortunately, because portable WIM data is difficult to collect
accurately, it is very difficult to obtain site-specific values for
truck weights.

Determining The Number of WIM Sites Per Group

The precision calculations can be used to determine how many WIM systems
should be included within each truck weight group. The State highway
agency should determine what statistic it wants to use as the key to the
analysis, select how precisely it wishes to estimate that statistic, and
compute the number of WIM locations needed to obtain the desired degree
of confidence.

The first step involves several decisions. The State highway agency
should determine whether the truck weight groups will be developed to
produce mean statistics within each group with a given level of
precision (e.g., the mean ESAL/class 9 truck for rural interstates is
1.56 [+]{.underline} .15 with 95 percent confidence).

This decision primarily affects the grouping process. If the intention
is to develop precise mean values for the group as a whole, the key
tends to be the number of data collection locations included in each
group. If the intention is to develop good default values for individual
sites, the key to the grouping process is to have more and very
homogenous groups (groups in which truck weights are very similar for
all sites within the group, making standard deviations very small).
States that emphasize predicting mean values for groups will have fewer
groups but larger numbers of data collection sites within each group,
whereas States that emphasize site-specific estimates will have more
truck weight groups but fewer sites within each group.

The second decision that affects the grouping process is the selection
of the statistic to be the basis for the precision estimates. Because
the precision of each statistic will vary, the State should select a
single statistic to use as its benchmark. Normally, this means selecting
a specific vehicle classification and a specific weight variable. The
recommended statistics for use in selecting sample sizes are either the
mean ESAL[^14]/class 9 trucks or better the mean GVW for class 9 trucks.
Class 9 trucks are recommended because they are the most common
throughout the country, and they tend to carry a high percentage of the
loadings on most major roads.

The two most likely weight variables that can be used are the average
gross weight (by class) and the average ESAL per vehicle (by class).
Both measures are acceptable statistics for this purpose. GVW is easily
understood by technical and non-technical people and does not change. It
is reasonably well correlated to pavement damage and is commonly used as
a measure of the size of commodity movements. ESAL are a much better
measure of pavement damage than GVW. However, ESAL are not easily
converted to measures of commodity flow, and current pavement research
is not emphasizing their use in the design process.

The next decision is how precise to estimate the target statistic.
Precision levels are normally stated in terms of percentage of error
within a given level of confidence (e.g., the GVW/vehicle estimate is
within [+]{.underline}15 percent with 95 percent confidence). Decreasing
the size of the acceptable error or requiring higher levels of
confidence both increase the number of samples required. Conversely,
accepting lower levels of precision and/or confidence allows smaller
sample sizes and lower data collection costs.

Selecting the acceptable level of error is an iterative process. First,
the desired target precision is selected. Next, the variability of data
in the truck weight groups is examined. This examination may result in
either the need to collect more data or to adjust the assignment of
roads within truck weight groups. If the State can not meet the
initially selected precision levels (either because it can not create
sufficiently homogenous groups or because it can not collect data at
enough sites), the desired precision levels have to be relaxed to
reflect the quality of the estimates that can be obtained. The last step
is to compute the number of weighing locations needed to meet the
desired precision level. The number of WIM sites within a group is
estimated as:

n = (*t~(α/2)~*)^2^ (C^2^) / (D^2^) (5-1)

where: n = the number of samples taken (in this case, the number of
sites in the group),

*t* = the Student\'s *t* distribution for the selected level of
confidence (α) and appropriate degrees of freedom (one less than the
number of samples, n),\
α = the selected level of confidence,

C = the coefficient of variation (COV) for the sample as a proportion,

D = the desired accuracy as a proportion of the estimate.

This equation can be manipulated to solve for any variable. COV (the
ratio of the standard deviation to the mean) is usually computed from
available truck weight data. D is selected as part of the previous step
(see above). The number of sites, n, can be computed after selecting the
value for alpha (α) and looking up the appropriate term for *t*~α/2~
with n-1 degrees of freedom. Similarly, if n is given, it is possible to
solve directly for the value of *t*~α/2~ and thus α.  The example given
below illustrates the basic process of comparing sample size with the
precision levels each sample size achieves.

Table 5-3-3 shows the same truck weight statistics used in Table 5-3-2,
except two additional weigh sites have been added. These two sites
experience heavy vehicle weights and, consequently, have increased the
mean values for GVW/vehicle and ESAL/vehicle for the group.

Table 5-3-3:\
Statistics Used For Sample Size Computation

  -------------------------- ---------------------- ---------------------
  **Site**                   **Mean Class 9 GVW**   **Mean Class 9 ESAL**

  1                          23000 kg               1.64

  2                          26000 kg               1.72

  3                          29000 kg               1.84

  4                          21000 kg               1.45

  5                          21000 kg               1.34

  6                          25000 kg               1.65

  7                          28000 kg               1.78

  8                          35000 kg               2.01

  9                          34000 kg               1.95

  **Group Mean**             **27000 kg**           **1.71**

  **Standard Deviation**     **5100 kg**            **0.22**

  **Coefficient of           **0.19**               **0.13**
  Variation**                                       

  **Standard Error of Mean** **1700**               **0.07**
  -------------------------- ---------------------- ---------------------

Using this table, the following facts can be determined:

• The average GVW of Class 9 trucks for this group is 27,000 kg.

• This estimate is [+]{.underline} 3,900 kg with 95 percent confidence
(1700 multiplied[^15] by 2.31).

Increasing the number of WIM stations included in the sample to 15 sites
(and assuming that those stations do not change the standard deviation
of the sample) would change the standard error of the mean to 1300 kg.
(5100 divided by the square root of 15). This would improve the
confidence in the mean value of the GVW/vehicle estimate for the truck
weight group to 27,000 kg [+]{.underline} 2,800 kg with 95 percent
confidence.

The improvement comes from two sources. The first is the increased
precision in the mean value provided by the increase in the number of
samples. The second is the decrease in the value of *t*~α/2~ used to
compute the multiplier in the confidence interval by having a greater
sample size upon which to perform the statistical computation.

Table 5-3-4 shows the effect of different sample sizes and confidence
intervals estimates of the group mean. Note that increases beyond about
six sites in the group sample size has only a marginal effect on the
precision of the group mean.

Table 5-3-4\
Example Effects of Sample Size on the\
Precision of GVW Estimates

  ---------------- -- ----------------- --------------------- ----------------------
                                        **Precision of the    
                                        Mean Value Itself\    
                                        (Standard Error )**   

  Number of Weigh     Mean Value        **80% Level of        95% Level of
  Sites[^16]                            Confidence**[^17]     Confidence[^18]

  3                   27,000 kg         [+]{.underline}5600   [+]{.underline}12800
                                        kg                    kg

  5                   27,000 kg         [+]{.underline}3500   [+]{.underline}6400 kg
                                        kg                    

  9                   27,000 kg         [+]{.underline}2400   [+]{.underline}3900 kg
                                        kg                    

  15                  27,000 kg         [+]{.underline}1800   [+]{.underline}2800 kg
                                        kg                    

  30                  27,000 kg         [+]{.underline}1200   [+]{.underline}1900 kg
                                        kg                    

  60                  27,000 kg         [+]{.underline}850 kg [+]{.underline}1300 kg

  90                  27,000 kg         [+]{.underline}700 kg [+]{.underline}1100 kg
  ---------------- -- ----------------- --------------------- ----------------------

If tighter confidence intervals are deemed necessary, it is always
possible to modify the truck weight road groups. Looking at Table 5-3-3,
it is apparent that sites 8 and 9 have much higher loads than the
remaining seven sites. If these sites are removed from the truck weight
group, the computed standard deviation of the GVW per vehicle computed
for sites in the group drops from 5100 kg to 3200 kg. This has a
dramatic impact on the precision of the estimates computed for the
group.

Table 5-3-5 shows the precision level of the truck weight group after
removal of these sites. However, note that in order to remove these two
sites from the truck weight road group, they must represent some
identifiable set of roads. For example, they could be located on the
State's only north/south rural Interstate, while the remaining seven
sites are on east/eest interstates. Thus the "rural Interstate" truck
weight grouping could be divided into two separate truck weight
groupings, "rural east/west Interstate" and "rural north/south
Interstate."

Table 5-3-5:\
Example Effects of Sample Size and Confidence Interval\
on Precision of GVW Estimates for the Revised Truck Weight Group

  ---------------- -- ----------------- ---------------------- ---------------------
                                        **Precision of the     
                                        Mean Value Itself\     
                                        (Standard Error )**    

  Number of Weigh     Mean Value        **80% Level of         95% Level of
  Sites[^19]                            Confidence**[^20]      Confidence[^21]

  3                   25,000 kg         [+]{.underline}3,500   [+]{.underline}8000
                                        kg                     kg

  5                   25,000 kg         [+]{.underline}2,200   [+]{.underline}4000
                                        kg                     kg

  9                   25,000 kg         [+]{.underline}1,500   [+]{.underline}2500
                                        kg                     kg

  15                  25,000 kg         [+]{.underline}1,100   [+]{.underline}1800
                                        kg                     kg

  30                  25,000 kg         [+]{.underline}800 kg  [+]{.underline}1200
                                                               kg

  60                  25,000 kg         [+]{.underline}500 kg  [+]{.underline}800 kg

  90                  25,000 kg         [+]{.underline}400 kg  [+]{.underline}700 kg
  ---------------- -- ----------------- ---------------------- ---------------------

The key to correctly creating these truck weight groups is that sites
should only be removed from a truck weight group when they can be
readily identified with a specific set of roads that experience those
loads. All of those roads need to be moved to the new truck weight
group.

From the above examples, it is possible to see that changing the number
of sites included in a truck weight road group has three effects:

• It changes the computed sample standard deviation for the group (which
serves as the estimate of the standard deviation for the entire road
group).

• It changes the denominator used to computed the standard error, the
statistic used to determine "how well" the mean value computed from that
group of roads estimates the mean value for the population being
sampled.

• It changes the value of *t* used to compute the size of the confidence
interval applied to estimates produced for that group.

In general, the more sites included in a group, the better the estimates
produced by that group, although the benefit of adding sites decreases
as the number of sites within a group increases. The effect of using the
Student's *t* distribution to compute confidence intervals means that a
significant decrease in the value of *t* can be obtained by simply
adding locations up to a sample size of six. A sample size of six sites
has a 10 percent smaller confidence interval at the 95 percent level of
confidence than a sample size of five sites, all other things being
equal. Beyond six sites, the benefits gained by adding sites begin to
decrease quickly. More than six sites in a group may be appropriate,
particularly if the State is unsure of its truck weight patterns.

**Based on this analysis, six sites per group are recommended.** The
exception to the six-site rule is for truck weight road groups that
contain very few roads. These will tend to be specialty roads (e.g.,
roads leading into and out of gravel pits) that have unusual loading
conditions but that are not applicable to many other roads in the State.

If improvements in precision are needed beyond what affordable increases
in sample size will achieve, the primary option is to change the make-up
of the truck weight groups, i.e., create new subsets of roads that will
serve as the truck weight groups. If this change produces a significant
decrease in the standard deviation that offsets the increase in *t*~α/2~
caused by the lower sample size, then the State will benefit from an
improvement in the precision of its weight estimates along with a
smaller data collection sample size.

Determining the number of Days that should Be Counted At a Given WIM
Site

All of the statistics discussed above start with the critical assumption
that each WIM site in a truck weight group produces an accurate estimate
of vehicle weights for that location, so that the mean value calculated
for the group is accurate. The "accuracy" assumed for the data provided
by each WIM scale is not just that the scale weighs the passing trucks
correctly but that those weight estimates are representative of weights
at that site throughout the year.

For WIM sites where less than a year of data are collected, the
assumption is that the time period measured gives an accurate
measurement of weights for the entire year. If the weight data
collection period is only 24 or 48-hours long, it assumes that there is
no day-of-week difference in the loading condition of trucks passing the
site (that is, that trucks traveling on weekends carry the same
distribution of payloads as trucks traveling on weekdays), as well as
the hypothesis that there are no seasonal differences in truck loading
patterns.

At some WIM sites in some States, extensive data collection has shown
that these assumptions are quite reasonable. (Butler 1993) At other
sites and in other States these assumptions are incorrect (Hallenbeck
and Kim 1993). Where truck weights are not stable across days of the
week or seasons, the weight monitoring effort has to be extended to
account for these differences. For example, the count duration may be
extended from two days to seven days to incorporate day-of-week
differences. Seasonal differences can be detected and incorporated in
the annual estimates by collecting data at each site more than once per
year, such as once per quarter.

It is also possible to factor data collected during short duration WIM
sessions on the basis of findings from permanent, continuous WIM sites,
much like the seasonal adjustments recommended for volume and vehicle
classification counts. For example, if one data collection site is
operated continuously, the information learned about seasonal patterns
from that one site can be applied to other weight data collected within
that truck weight group. This step requires the assumption that all
sites within the truck weight group experience the same seasonal
variation. This process is doable for summary statistics, such as GVW,
if sufficient data is available, but it becomes more mathematically
complex to seasonally adjust axle weight distribution tables. As
seasonality analysis for total volume and vehicle classification data
have shown, a large database is needed to identify and quantify temporal
variation patterns.

If two or more continuous sites are present in a truck weight group, the
seasonal adjustments for both sites can be averaged before being applied
to the data collected at the short duration sites. However, if the
seasonal adjustments for those sites are significantly different, it is
likely that the truck weight road group consists of more than one truck
weight pattern. In these cases, splitting the truck weight road group
into two or more new groups could be considered.

To date, little work has been published on the seasonal differences in
axle weight distributions found in the nation's truck fleet, let alone
on the weight characteristics of particular trucking movements found in
individual States. However, these seasonal and day-of-week weight
changes can have dramatic effects on the selection of the pavement
designs that rely on them. The collection and analysis of continuous
data collection is the easiest method to begin to understand the
temporal variation.

The key for the weight data collection program is to measure and account
for both day-of-week and seasonal differences in vehicle weights within
each truck weight group. The only way to do this adequately is to have
each WIM station providing continuous WIM data, unless analysis has
shown that temporal variability is not present. For States with large
numbers of continuous WIM stations, there may exist sufficient stations
to populate the groups. For smaller States facing resource limitations,
the installation of many continuous WIM sites is not an option. **The
general recommendation is that each truck weight group should have at
least one**[^22] **permanent WIM device collecting continuous data.**
This site should be maintained in a calibrated condition, and the data
obtained from it should be used to determine whether significant
differences exist between vehicle weights (by vehicle class) for
different days of the week and different seasons of the year.

The remaining sites within a group can have either short duration counts
or additional continuous counts. As with vehicle classification and
volume counting, a minimum of 48 hours is recommended. Weight data have
been shown to vary by time of day, day of week, weekdays and weekends.
As with vehicle classification and volume counts, it is acceptable to
use different data collection periods as needs and constraints allow.
Because of differences in weekday and weekend vehicle weights, the data
collection program should be designed to cover those differences and
account for them when statistics are produced. Counts taken for a period
of one week eliminate the need for day-of-week adjustment, allow the
equipment and traffic conditions to stabilize, provide data verification
capabilities, and identify weekday/weekend differences in average
weights. **A monitoring period of seven continuous days is recommended
for all WIM sites that do not provide continuous data.**

Short duration WIM measurements should be collected with permanently
mounted sensors because permanent[^23] sensors can be mounted flush to
the road surface, providing a more accurate weight measurement. Use of
permanently mounted sensors also allows data collection periods to be
lengthened at relatively little additional cost.

Portable sensors although not completely ruled out, introduce accuracy
issues that may compromise the validity of the data. Organizations using
portable WIM sensors, must carefully ensure that the data collected is
sufficiently accurate to meet user needs.

WIM Site Installation by Lane and Direction of Travel

There are many issues to consider when installing WIM sites. Current
installations range from full coverage for all lanes and directions of
travel to the LTPP standard of a single lane in one direction. Some of
the issues that should be reviewed when selecting the number of lanes of
WIM to install include: available funding, the cost of installation,
program objectives to be met, the design of current installations in the
State, the trade-offs between obtaining more complete coverage at each
site versus less coverage at each site but getting more sites covered,
prior experience with WIM equipment, the type of equipment being
installed, equipment installation options, specific site
characteristics, truck volumes present at the roadway being monitored,
use of the scale for or influence from nearby enforcement activities,
the ability to perform maintenance on equipment at that site, and the
ability to perform calibration of the scales.

Analyses of available WIM data have shown that significant differences
in loads by direction of travel often occur. The collection of WIM data
in at least one lane in each direction of travel at each site allows a
clear assessment of directional differences in weights and loadings.

WIM differences by travel lane are generally less significant and
difficult to generalize, although previous analyses have shown that the
outside lanes tend to carry heavier vehicles. More analysis of current
installations is needed before a determination of the cost-effectiveness
of covering several lanes at some of the WIM sites or at all sites can
be made.

A WIM site covering all lanes and direction of travel provides the most
accurate data collection coverage. **At least one continuous WIM station
in each weight group should provide WIM coverage for all or a minimum of
two travel lanes in each direction.** This will allow future pavement
design analysis to cover most possibilities. For multi-lane facilities,
covering two lanes in each direction provides the most cost-effective
alternative. If all lanes are not monitored by WIM scales, each WIM site
should have, at a minimum, a short classification count by direction and
travel lane in order to measure truck travel in the lanes not being
monitored with WIM. Continuous classification in those lanes may even be
preferable.

**For new WIM site installations, at least one lane in each direction of
travel is recommended.** Additional lane/direction installations at
current sites, such as LTPP sites, depend on many other considerations
and should be made based on careful analysis including the examination
of vehicle classification data at each site to determine
cost-effectiveness. The VTRIS package allows these types of analyses by
direction and lane for both vehicle classification and WIM data.

Site Selection

Most WIM systems also provide counts of vehicle volume by classification
and total volume. Consequently, most WIM data collection locations can
also provide volume and vehicle classification count data that can take
the place of counts required to meet the needs discussed in Sections 3
and 4. Unfortunately, for a variety of technical reasons, WIM data
cannot be collected on all roadway sections. Physical constraints on
many road sections prevent the collection of accurate weight data. In
addition, most States do not have the resources to collect weight data
at more than a modest number of locations. Finally, most States already
have a significant investment in WIM sites, either as part of their
existing truck weight monitoring program or as part of the Long Term
Pavement Performance project (LTPP).

Each State should begin to apply the procedures discussed with its
existing WIM data collection sites. As a result of the study the
addition of sites may become necessary. As existing sites require
attention because of failure of the pavement surrounding the WIM sensors
or failure of the WIM equipment itself, the need for that WIM station or
site should be reevaluated. Sites that are still necessary should be
reinstalled. If that site is no longer needed or if other higher
priority locations exist, the WIM equipment should be moved to another
site.

New WIM Site Selection Criteria

The selection of new WIM sites should be based on the needs of the data
collection program and the site characteristics of the roadway sections
that meet those needs. The needs of the data collection program include,
but are not limited to, the following:

• the need to obtain more vehicle weight data on roads within a given
truck weight roadway group

• the need to collect data in geographic regions that are poorly
represented in the existing WIM data collection effort

• the need to collect data on specific facilities of high importance
(e.g., Interstate highways or other National Highway System routes)

• the need to collect data for specific research projects or other
special needs of the State

• the need to collect weight information on specific commodity movements
of importance to the State.

However, just because a roadway section meets some or all of the above
characteristics does not make it a good WIM site. With current
technologies, WIM systems only accurately weigh trucks when the
equipment is located in a physical environment that meets specific
criteria. Thus, States should place WIM equipment only in pavements that
allow for accurate vehicle weighing. While individual equipment vendors
may require slightly different pavement characteristics to achieve
specified results, in general all WIM sites should have the
following[^24]:

• smooth, flat (in all planes) pavement

• pavement that is in good condition and that has enough strength to
adequately support axle weight sensors

• vehicles traveling at constant speeds over the sensors

• access to power and communications (although these can be supplied
from solar panels, and through various forms of wireless
communications).

In addition, there should be sufficient truck traffic at the site to
justify the installation of a WIM data collection site. The actual sites
can be selected randomly or judgmentally (using the previous list of
criteria) from sites that meet all of the site requirements.

Smooth, strong pavement is needed to reduce the effect of vehicle
dynamics. Although placing multiple sensors in series (Cebon 1999) can
significantly reduce the error that vehicle dynamics produce in
individual weight measurements, placement of WIM sensors on smooth, flat
pavements that reduce vehicle dynamics significantly improves WIM
accuracy, regardless of the equipment used.

Pavement strength can affect sensor accuracy. Weight estimates produced
by strip sensors (such as piezo-cables) that are embedded directly into
pavements are often affected by changes in pavement strength caused by
changes in environmental conditions (e.g., spring thaw periods). A
decrease in pavement strength invariably decreases system accuracy.
Therefore, WIM sensors should only be placed in strong pavements that
are not subject to significant changes in structural response during
different seasons. Similarly, WIM sensors begin to become inaccurate as
soon as pavements start to rut. In most cases, installations in
pavements likely to rut are a poor investment of limited data collection
funds.

The requirement for constant vehicle speed (which limits the use of WIM
equipment in many urban and suburban areas where routine congestion
occurs) is primarily due to the fact that braking and acceleration
causes shifts in load from one set of axles to another. This shifting
causes "inaccurate" comparison of WIM estimates against static loads.

The availability of power and communications allows extended operation
of the WIM equipment. While this is not as crucial for sites intended
for short duration WIM counts, the availability of power allows the
collection of longer duration WIM measurements. This is particularly
helpful for research studies intended to confirm or refute the ability
of short duration counts to meet the accuracy needs of the data
collection plan. It also allows the WIM site to be used as a continuous
classifier or ATR even while weight data are not being collected.

Integrating the WIM Sites with the Remaining Count Program

Even with all of the constraints described above, most of the existing
sites can be used to meet a given weight data collection need. When
exploring alternative sites, the "deciding vote" can often be cast by
examining how well these alternative sites fit within the existing State
traffic monitoring program.

Sites selected for WIM data collection should be located within HPMS
volume sample section, if at all possible. If two alternative sites
exist to meet a specific need and one is already an HPMS sample site, it
should be given priority over the alternative (all other factors being
equal). If neither site falls on an HPMS sample section, the selected
WIM site should become an HPMS sample section the next time the HPMS
sample is revised. The HPMS volume and classification data should be
collected at the same time as the WIM data, using the same equipment
where practical. This reduces the staffing and resources needed to
collect these HPMS data and directly ties the different data items.

Total Size of the Weight Data Collection Program

The recommendations discussed above lead to the conclusion that the size
of the weight data collection program will be a function of the
variability of the truck weights and the accuracy and precision desired
to monitor and report on those weights.

For a small State that has only two basic truck weight road groups, the
basic recommendation would be for a minimum of about 12 weighing
locations and two to four continuously operating weigh-in-motion sites.
The number of locations could be further reduced if the State worked
with surrounding States to collect "joint" vehicle weight data. A larger
state with diverse trucking characteristics might have as many as 10 or
15 distinct truck weight road groups, and thus 60 to 90 WIM sites, with
a corresponding increase in the number of continuously operating WIM
locations. Most States will be between the two extremes presented, and
the number of weighing locations should fall somewhere between 12 and 90
locations.

Chapter 4\
Truck Weight Data Summarization

WIM data collection provides a number of important summary statistics.
These statistics are computed both from individual vehicle weight
records and from the axle weight summary distributions that are
developed from the scales. The following statistical summaries should be
routinely computed and used by States:

-   the average number of specific axle groups (i.e., the number of
    single, tandem, and triple axles) per vehicle for each vehicle
    (truck) class

-   the average number of axles (total) per vehicle for vehicle classes
    that do not have constant numbers of axles

-   the average weight distribution for each type of axle for each
    vehicle class used by the State highway agency for pavement design.

The AASHTO 2002 Pavement Design Guide (being developed under NCHRP
Project 1-37, and currently in draft form) uses inputs of axle load
distributions and volumes by vehicle classification to determine the
traffic load inputs to the design process. One important input variable
that the State highway agency needs to compute and use in that process
is the average number of axles (by type of axle) found in each vehicle
type. For example, if the state uses the 13 FHWA vehicle classes, it
needs to track how many of the 7+axles in Class 13 trucks are single
axles and how many are tandem axles. These factors are easily computed
as part of the load distribution process. All valid axle weights are
counted (by type) for a given vehicle class, and that total is divided
by the total number of vehicles weighed in that class. This yields the
average number of axles within each axle type for that vehicle class.

The second category of statistical summaries allows the State highway
agency to produce a more accurate axle correction factor from vehicle
classification data. Several of the FHWA 13 vehicle classes do not
contain specifically defined numbers of axles on each vehicle. Class 7
allows four or more axles per single unit truck. Class 8 allows four or
fewer axles. Class 10 allows six or more, and class 13 allows seven or
more axles per truck. Individual vehicle weight records allow the
computation of more precise measures of the mean number of axles per
vehicle for each of these types of truck classes.

The NCHRP Project 1-37 draft pavement design guide is designed to allow
engineers to account for variation in both traffic load and material
properties as environmental conditions change. This allows State highway
agencies to account for the effects of spring load restrictions and
seasonal changes in commodity flows as part of the pavement design
process. However, to take advantage of these new design capabilities,
the State highway agency must have the data that describe these load
changes.

These data come from collecting and summarizing data from continuously
operating WIM sites at different times of the year. Ideally, the State
highway agency should create axle weight distribution tables by vehicle
class for each period when axle weight distributions change. These axle
load distribution tables (by vehicle class) can then be read directly
into the new Pavement Design Guide software, where they supply the load
information needed to complement vehicle classification volume data
collected on different roads.

When analyzed, WIM data will also determine whether changes in axle load
distributions occur by season of the year. These changes can (and
should) then be used in the pavement design process to improve the
reliability of the pavement designs. Seasonal WIM data for each truck
road group should be analyzed, since each road group may exhibit
different seasonal patterns. This process can result in a considerable
number of axle load distribution tables (one per vehicle class, per
season, per road group). While this may seem like an excessive amount of
data summarization, it is necessary to understand the nature of and
account for truck loading patterns in the design process. In addition,
by automating the collection and reporting of WIM data, the resources
required to perform these tasks can be minimized.

State highway agencies also need to compute axle distribution tables for
each vehicle classification scheme the agency intends to use in the
pavement design procedures. At a minimum, axle load distribution tables
for each of the ten FHWA heavy vehicle categories (Classes 4 -- 13)
should be used. In order to use a more aggregated vehicle classification
scheme (such as the four category scheme "cars, single unit trucks,
combination trucks, and multi-trailer trucks"), axle load distribution
tables and axle frequency tables for these vehicle classes must be
developed. These tables identify how many axles of each type (singles,
tandems, tridems, quads) are present on average for each truck class,
and what the axle load frequency distribution is for each of those axle
types.

In addition to these primary analyses, individual vehicle records from
WIM data collection allow a variety of specialized analyses. For
example, they can be used to monitor changes in axle spacing
configurations that result from changes in vehicle size and weight
regulations. Similarly, changes in the relative proportion of specific
vehicle configurations that fall within the more generalized vehicle
classifications can be examined. For example, what specific vehicle
types are classified within the FHWA Class 13 category? This latter
analysis is particularly important for summary vehicle classification
categories (e.g., "multi-trailer trucks") when limited other data exist
with which to monitor the changing composition of vehicles within
aggregated classes.

Finally, individual vehicle records serve as an excellent data resource
that can be manipulated for a variety of research and planning purposes.
For example, with WIM from most types of scales, changes in overall
vehicle lengths over time can be examined. This has implications for
roadway geometric design and the need for new roadway design standards.
Another example is that with some scales, individual vehicle records can
be used to monitor the variation in loads between axles in a tandem.
Scale data can also be used for a variety of economic studies (fraction
of unloaded trucks) and as an independent measure of the effectiveness
of applied enforcement strategies. Note that because of the effects of
vehicle dynamics, WIM data cannot be used directly to measure the number
of over-weight and/or illegally loaded trucks. However, under controlled
conditions WIM data can be used to determine changes in the presence of
overloaded vehicles. Controlled conditions include the fact that the
scale is well calibrated, changes in pavement roughness do not occur
during the study, and scale by-pass efforts can either be measured or
controlled for. State highway agencies are thus encouraged to collect
and store these data in a manner that allows them to be retrieved and
used as easily as possible.

Appendix 5-A\
WIM Equipment Issues

This appendix discusses two key issues concerning the use of WIM
equipment and data. Both subjects deal with ensuring that the data being
collected represent, to the highest degree possible, the vehicle weights
being experienced by the roadways. These two subjects are 1) the
calibration of WIM equipment and 2) the monitoring of the data reported
by WIM systems as a means of detecting drift in the calibration of
weight sensors.

WIM Sensor Calibration

The FHWA strongly encourages State highway agencies to allocate
resources to the calibration of their WIM systems. Calibration of WIM
sensors is especially important, because even small errors in vehicle
weight measurements caused by poorly calibrated sensors result in
significant errors in estimated pavement damage when those axle weights
are used in pavement design analyses. Traditional pavement damage
calculations use a formula developed as part of the original AASHO Road
Test. This formula is a fourth order polynomial. Its is often simplified
by stating that damage from a single axle can be computed from the
following rule:

Damage = (axle weight in pounds / 18,000 pounds)^4^

Figure 5-A-1 shows the general effects of scale calibration error that
result from the use of this formula. In this graphic, the X-axis is the
percent error in the axle weight, while the Y-axis is the corresponding
error in ESAL values. Although the effect of scale drift varies somewhat
from site to site, the basic trend is that every 1 percent error that a
scale is [under]{.underline}-calibrated results in slightly more than a
3 percent under-estimation of the true ESAL value.[^25] ESAL computed
for heavy axles are affected more by calibration drift than ESALs
computed for light axles. So the ESAL error for a site with mostly heavy
axles is greater than the error for a site with mostly light axles.
Every 1 percent [over]{.underline}-estimation in axle weight represents
a 4.5 percent over-estimation of ESAL values. Thus, even an
over-calibration of merely 10 percent would result in a 45 percent error
in estimated damage.

Unfortunately, at this time, an inexpensive WIM calibration system has
not been developed. The NCHRP has twice attempted to create improved,
lower cost, WIM calibration techniques (Cunagin 1993; Papagiannakis
1995). In both cases, the practices developed have failed to be widely
adopted, primarily because of their cost and complexity. Unfortunately,
this does not remove the need for WIM system calibration. In addition to
the techniques developed by NCHRP, a number of other techniques are used
to ensure that the equipment is initially calibrated and then remains in
calibration. These techniques tend to be less robust than the NCHRP
procedures, but they provide a process that has a more acceptable
balance between the accuracy of the WIM calibration effort and the
resources needed to perform it.

![](media/image3.wmf){width="6.25in" height="5.356944444444444in"}

# **Figure 5-A-1: Effect of Weigh-in-Motion Scale Calibration Drift on the Accuracy of ESAL Calculations**

The most common of these approaches is to make multiple passes over the
WIM scale with one or more test trucks of known (measured) weight. The
scale's performance is then compared with the known weights, and
adjustments are made to the scale's calibration as necessary (McCall and
Vodrazka 1997; Long-Term Pavement Performance Project 1998). Additional
passes are then made to confirm that the performance of the scale has
improved to the level of accuracy desired.

Several variations to this basic approach exist. These variations
usually involve the use of additional vehicles, the performance of test
runs at different speeds, or the performance of test runs under
different environmental conditions (usually different temperatures). All
of these variations have merit. The benefits gained from a specific
variant depends somewhat on the specific scale technology being used,
the types of environmental conditions that occur at the site while the
scale is operating, the type of pavement in which the scale sensors are
installed, and the structural response of that pavement.

Test trucks have the distinct advantage of being relatively easy to use
and only modestly expensive. In most cases, the most common variant of
this technique increases the number of passes performed, and increases
the quality of the scale calibration operating under commonly
experienced conditions. This improves scale calibration, but also
slightly increases calibration costs.

**The drawback to the use of test trucks is the fact that use of a
single vehicle (or even two vehicles) to calibrate a scale can create
bias in the calibration**, and thus additional steps are needed to
ensure the accuracy of the calibration effort. One common method for
testing for scale bias is to examine summary outputs from the scale and
compare those outputs against known weights (e.g., legal load limits)
for trucks commonly found in the road.

Bias in the calibration when a single test truck is used comes from the
fact that each truck has its own unique dynamic interaction with a given
road. Calibration of a scale to a specific vehicle's dynamic performance
(motion) is acceptable when the motion of that vehicle is representative
of the traffic stream. Unfortunately, a single test truck is hardly
representative of the traffic stream and the calibration effort actually
forces the scale to weigh most vehicles inaccurately.

Why this occurs can be explained with a picture. Figure 5-A-2 shows how
the force of a truck (or any given truck axle) varies as it moves down
the road as a result of the interaction between the vehicle's suspension
system(s) and the road's roughness. The vehicle's dynamic motion causes
the weight felt by the road to change from location to location. The
goal of the WIM calibration effort is to measure this varying force at a
specific location (Point A in Figure 5-A-2) and relate it to the truck's
actual static weight. To do it, the scale sensor needs to be able to
measure the weight actually being applied at Point A, and correct for
the bias associated with the fact that at Point A, the truck is actually
producing more force than it does when the truck is at rest (because it
is in the process of landing as it bounces down the road).

By using a test truck, these two tasks can be performed in one pass. The
truck is driven over the scale several times, and the weights estimated
by the scale are compared to actual static values. The scale's
sensitivity is then adjusted until the weight estimated by the scale
equals the known static weight of the truck/axle.

![](media/image4.wmf){width="5.986111111111111in"
height="4.361111111111111in"}

Figure 5-A-2: Variation of Axle Forces with Distance and the\
Consequential Effect on WIM Scale Calibration

The problem with this technique comes from the fact that each truck has
a different dynamic motion. When the test truck has a different set of
dynamics than the other trucks using that road, the scale is calibrated
to the wrong portion of the dynamic curve (Point B shown in Figure
5-A-3). If the scale is calibrated to the dynamic motion of the test
truck, it will cause the scale to overestimate the weights associated
with the majority of trucks on that road.

To solve this problem there are five basic approaches:

• A scale sensor can be used that physically measures the truck weight
for a long enough time period to be able to account for the truck's
dynamic motion (this is true of the bridge weigh in motion system
approach where the truck is on the "scale" the entire time it is on the
bridge deck).

• Multiple sensors can be used to weigh the truck at different points in
its dynamic motion either to average out the dynamic motion, or to
provide enough data to predict the dynamic motion (so that the true mean
can be estimated accurately).

• The relationship of the test truck to all other trucks can be
determined. This is often done by mathematically modeling the dynamic
motion of the truck being weighed in order to predict where in the
dynamic cycle it is when it reaches the scale.

• More than one type of test truck can be used in the calibration effort
(where each test truck has a different type of dynamic response) in
order to get a sample of the vehicle dynamic effects at that point in
the roadway.

• Independent measurement can be used to ensure that the data being
collected are not biased as a result of the test truck being used.

![](media/image5.wmf){width="5.986111111111111in"
height="4.402777777777778in"}

Figure 5-A-3: Variation of Axle Forces with Distance and the\
Consequential Effect on WIM Scale Calibration

The first of these techniques results in a series of other difficult
technical problems that result in other accuracy problems. The use of
multiple sensors is encouraged from a technical perspective, but most
States dislike the added capital costs associated with this technique,
although theoretically, it has the best long-term chance of success.

The third technique has strong theoretical backing. However, it is very
difficult to perform in the field, both because it requires extensive
knowledge about the test truck (dynamic response is not
easily/inexpensively measured in the field) and because it requires more
technical knowledge than most data collection crews possess.

The FHWA LTPP project recommends the use of multiple test trucks. These
trucks should have suspensions typical of the type carrying loads on
that road. This allows the calibration process to average between the
dynamic relationships that are measured for the different trucks. This
technique was selected as a compromise between the simplicity and low
cost of using only one test truck and the increased confidence but
higher cost of scale calibration performed with larger numbers of
trucks.

The technique used by California DOT and presented in the Best Practices
Handbook (McCall and Vodrazka 1997; Long-Term Pavement Performance
Project 1998) uses independent measures to confirm the scale's
performance and reduce the chance for bias. One of these measures is
developed by varying the speed of the vehicle crossing the scale. This
changes both the period during which the vehicle's tires are in contact
with the scale and the dynamic motion of the truck. Another measure is
to compare the scale's weight outputs with those of expected truck
weights. Specific classes of vehicles in California, primarily the FHWA
classes 9 and 11 vehicles, have consistent weight characteristics that
can be used to confirm the accuracy of the scale's calibration. However,
it is necessary for the individual performing the calibration to
understand these characteristics as they apply to that specific WIM site
in order to use these factors. That is, unusual truck loading patterns
caused by local economic forces (e.g., the presence of a natural
resource mining site) can cause trucks passing that scale to exhibit
unusual loading characteristics.

Another independent measure that is often used for scale calibration is
the front axle of the FHWA class 9 trucks. This measure can be used, but
only where the State actually understands the axle weights found on the
specific road that contains the scale. It has been found that as truck
configurations and weights change, the weight on the front axle of these
trucks varies considerably. Changes in truck configuration that are as
simple as moving the King Pin[^26] connection on a tractor can cause
significant differences in mean front axle weight ([+]{.underline}10 to
15 percent) on any given truck. Without having an independent measure of
the actual axle front weights present at a site, use of this technique
can force the scale's calibration to drift away from the appropriate
calibration factor rather than improving the quality of the scale's
calibration. However, where this technique is used properly, it can
improve scale accuracy.[^27]

Monitoring of WIM Data Output

The use of front axle weight for calibration purposes is as much a
monitoring function as it is a calibration function. The FHWA's LTPP
program and several States have concluded that one of the best methods
for obtaining valid truck weight data is to carefully calibrate the WIM
equipment, then use a comparison of scale output and expected truck
volume and weight statistics to indicate when a scale's calibration or
classification accuracy is drifting.

If a measure being tracked changes, then the staff investigate the
change. The key is to limit the time spent examining "good data" while
concentrating the limited staff time on review of "questionable" results
and the repairs needed to fix malfunctioning equipment. If the monitored
change can be independently verified as being true, the new pattern is
included as an "expected" pattern for that site. When that "new" pattern
re-appears at a later date, it then does not need to be investigated
further.

The most common statistics applied to monitor the "health" of a WIM
scale follow:

• the front axle weight of five-axle, tractor semi-trailer trucks

• the gross vehicle weight distribution of five-axle, tractor
semi-trailer trucks

• the spacing of tandems axles on five-axle, tractor semi-trailer trucks

• traffic volumes for various vehicle classes, with particular emphasis
on the percentage of vehicles that fall within each FHWA vehicle
classification.

Front Axle Weights of Five-Axle, Tractor Semi-Trailer Trucks

For most roads, the mean front axle weight for these trucks should
remain fairly constant. Most statistical tests of this value examine a
rolling average of the last 100 front axle weights for vehicles of this
configuration. If this mean value changes by more than a given amount
(usually determined as a function of the variability of that statistic
on that road), then the scale calibration is suspected of drifting.

As noted above, several factors can affect the front axle weight
statistic. Among the most important of the factors that should be taken
into account when examining changes in front axle weight statistics are
the following:

• the total gross weight of the vehicle (more vehicle weight generally
raises the front axle weight)

• the spacing between the front axle and the drive tandems on the
tractor (generally, the greater the distance between the first and
second axles, the lower the front axle weight)

• the roughness of the road (in general, the rougher the road, the lower
the front axle weight that can be expected)

• State-specific weight laws and truck characteristics (which have a
variety of effects, but often result in significantly different mean
front axle weights for roads in different States).

Each of these factors has spawned improvements in the front-axle
monitoring concept. One improvement is to track front-axle weights by
basic gross vehicle weight category. Another is to monitor front-axle
weight relative to axle spacing. A third is to ensure that site specific
conditions are accounted for in initially setting the target front-axle
weight against which gathered data will be compared.

It is also important to note that the 100 consecutive vehicles must be
weighed within a timeframe in which the scale calibration is not
expected to change in order to use this mechanism for calibration
testing. For example, this statistic is often used as a self-calibration
adjustment for piezo-electric cable WIM systems. It is designed to
adjust the scale's calibration factor as the temperature changes.
Temperature affects both the sensitivity of the piezo-cable itself and
the structural response of the roadway that supports that cable.

When truck volumes are high relative to changes in temperature (for
example, when over 100 of these trucks an hour cross the scale, and
temperatures do not change more than 10 degrees during that hour), then
all vehicles being included within any given set of 100 consecutive
trucks can be considered to have been weighed under the same relative
conditions, and in most cases, the calibration check represents an
excellent measure of the scale's need for calibration adjustment.

However, if that scale experiences only five Class 9 trucks per day, it
takes 20 days for the scale to observe 100 vehicles. The temperature
conditions during those 20 days can be dramatically different for each
of the 100 different measurements. In this case, computing a calibration
adjustment designed to account for temperature changes is inappropriate,
since the conditions under which the adjustment was calculated were not
stable. This specific condition has caused many States to disconnect
this feature on their scales. In many cases that is the correct
decision. However, as noted above, there are situations where this
feature does improve a scale's calibration.

Gross Vehicle Weight Distributions of 5-Axle Tractor Semi-Trailer Trucks

This technique was originally developed by the Minnesota DOT and was
later adopted by the LTPP program (Hallenbeck 1994). The participating
agency must be able to produce a histogram plot of the gross vehicle
weights of class 9 trucks (mostly five-axle tractor semi-trailer
trucks). LTPP uses a 4,000-lb. increment for creating the histogram
plot, but a State highway agency may use any weight increment that meets
its own needs.

The logic underlying the process is based on the expectation of finding
consistent peaks in the GVW distribution at each site. Most sites have
two peaks in the GVW distribution. One represents unloaded tractor
semi-trailers and should occur between 28,000 and 36,000 pounds. This
weight range has been determined from data collected at static scales
around the country and appears to be reasonable for most locations. The
second peak in the GVW distribution represents the most common loaded
vehicle condition at that site. It varies somewhat with the type of
commodity commonly being carried on a given road and each State's weight
limits for five-axle trucks. Generally, the loaded peak falls somewhere
between 72,000 and 80,000 lb.

For most sites, the location of these peaks within the GVW histogram
remains fairly constant, although the height of the two peaks changes
somewhat over time as a result of changing volumes and/or percentages
(depending on whether the participating agency is plotting volume or
percentage on the vertical axis of the frequency distribution; either
will work) of loaded and unloaded vehicles. The reviewer must examine
this distribution and decide whether the vehicle weights illustrated
represent valid data or the scale either is not correctly calibrated or
is malfunctioning. This is easily done when the current graph can be
compared with graphs produced from data collected at that site when the
scale was known to have been operating correctly.

Both Peaks Shifted

If a plot shows both peaks shifted from their expected location in the
same direction (that is, where both peaks are lighter than expected or
heavier than expected), the scale is most likely out of calibration. The
participating agency should then recalibrate that scale at that site and
collect new data.

One Peak Shifted

If a plot shows one peak correctly located but another peak shifted from
its expected location, the site should be reviewed for other potential
scale problems (such as a high number of classified but not weighed
vehicles or scale failure during the data collection session).
Additional information on that site may also be needed to determine
whether the scale is operating correctly. Information that can be very
useful in this investigation includes the types of commodities carried
by class 9 trucks using that road and the load distribution obtained
from that scale when it was last calibrated. For example, it might be
discovered that a cement plant is just down the road from the WIM scale,
and the loaded, five-axle cement trucks are routinely exceeding the
80,000-pound legal weight limit. This might result in acceptance of a
loaded peak at that site that exceeds the normal 80,000-pound upper
limit for the loaded peak.

If additional information indicates the presence of scale problems, the
scale should be recalibrated. If scale calibration shows that the no
calibration shift has occurred, this new pattern should be catalogued so
that it is accepted in the future. For example, it is possible for
shifts in the commodities carried to occur. These shifts can cause the
loaded peak to shift, without changing the unloaded peak.

**[Number of Vehicles Heavier than 80,000 Pounds]{.underline}**

A second check can be performed with the class 9 GVW frequency
distribution by examining the number (and/or percentage) of vehicles
that are heavier than the legal limit for the State. It is particularly
important to look at the number and percentage of class 9 vehicles that
weigh more than 100 kips. If the percentage of overweight vehicles
(particularly vehicles over 100,000 lb.) is high, the scale calibration
is questionable, although some jurisdictions routinely allow these
weights and thus would not question the results. This check must be done
with knowledge of a specific State's weight and permitting laws, as well
as knowledge of the types of commodities carried by trucks operating on
that road.

The 100,000 lb. check is particularly useful in detecting when
piezo-electric scales begin to fail, since these scales often generate
an almost flat GVW distribution when they begin to malfunction. An axle
weight data set produced by such a scale results in an extremely large
(and inaccurate) ESAL computation for a given number of trucks. It is
also highly unusual for the class 9 trucks to carry such heavy loads. In
most cases, trucks legally carrying these heavy weights are required to
use additional axles, and they are thus classified as class 10 (or
higher) and do not appear in the class 9 GVW graph. While illegally
loaded five-axle trucks may be operating at the site in question, most
illegally loaded trucks do not exceed the legal weight limit by more
than several thousand pounds, and the number (or percentage) of these
extremely high weights is usually fairly low. Thus, it is assumed that
high percentages of extremely heavy class 9 trucks are a sign of scale
calibration or operational problems. On the other hand, if a
participating agency routinely permits much higher loads to be carried
on five-axle trucks, this check may not be useful.

In either case (scale problems or extreme numbers of overloaded trucks),
State personnel should investigate the situation. If the data are valid,
notes to this effect should be written and maintained in the calibration
file, so that future reviewers are aware of this site's unusual travel
characteristics.

Changes in Tandem Axle Spacings

The mean axle spacing of drive tandems on tractors of class 9 trucks are
fairly constant. As a result, several States monitor this statistic to
determine whether WIM scale sensors are working correctly. The scale's
measurement of this statistic is a function of the scale's ability to
accurately measure speed. Speed determination is crucial in several
aspects of the axle weight computation process. Thus if the scale is
unable to accurately measure speed, it is highly likely that it is not
correctly measuring axle weights. Similarly, if the scale cannot
accurately measure speed, it will be apparent in the mean distance
reported between axles 2 and 3 of three-axle tractors on class 9 trucks.

Changes in Measured Truck Volumes

This last category of monitoring data consists of comparing expected
truck volumes by vehicle classification with expected volumes for those
classes. Two different measures are effectively tracked. One is the
total volume of trucks by classification. The other is the percentage of
trucks within each classification.

Routinely monitoring the total volume of trucks at a WIM site is good,
not only because it can provide a key indicator of scale error, but
because it will show when significant changes in truck flows are
occurring. Analysis performed with LTPP truck volume data show that at
many sites, dramatic changes in truck volumes can occur, even on major
truck routes, such as rural Interstates. On lower volume roads, 100
percent changes in class 9 truck volumes are not necessarily an
indicator of scale malfunction, but knowing that such an increase (or
decrease) in truck volume has occurred is critical to understanding the
performance of that roadway and the expected lifespan of that roadway's
pavement.

On the other hand, some dramatic changes in truck volumes, especially on
high volume truck routes, are often an indication of malfunctioning data
collection equipment. Malfunctioning axle detectors can result in both
the undercounting of axles (resulting in the under-estimation of large
truck volumes), and over counting of axles (one common condition is
called "ghost axles"), resulting in the over-estimation of large truck
volumes. Similarly, a malfunctioning loop detector can cause two cars to
be called one truck, or can cause one truck to be split into two or more
cars.

Simply monitoring summary truck volumes, such as average daily or even
average weekly or monthly volumes allows the detection of changes as
they occur. When significant changes occur, independent measures can be
used (for example a short manual count, or a call to a local DOT office
to confirm the presence of large new truck volumes) to determine the
validity of the data. Data that are invalid can then be discarded. Data
that are valid can then be stored and used with confidence later.

Monitoring truck percentages (i.e., the percentage of truck volumes
within each vehicle classification) is another excellent tool for
detecting equipment failures. When sensors fail, trucks are often
misclassified. For example, the loss of one axle normally converts a
class 9 vehicle into a class 8 vehicle. Thus, a significant shift in
truck percentages from class 9 to class 8 is an indicator of possible
equipment error.

Monitoring truck percentages and truck volumes is very beneficial.
However, it is only truly useful if the State highway agency performs
these checks frequently, promptly investigates abnormal conditions, and
repairs or removes malfunctioning data collection equipment. Without
this prompt follow-up it can be difficult to determine whether
abnormalities discovered are caused by real equipment problems or are
the result of changes in local traffic conditions. Quality information
results from the continuous data collection, improvement, and
verification of the data provided.

Appendix 5-B\
FREQUENTLY ASKED QUESTIONS

Should WIM data be collected only on smooth and flat pavements?

> WIM data is needed to address pavement design and other uses involving
> all types of pavement. Data collection mechanisms that provide quality
> data are needed under all conditions. Indeed the dynamic forces that
> vehicles apply to the pavement may increase as the quality of the
> pavement decreases. Research and equipment activities under the
> auspices of the traffic monitoring program must continue under a
> variety of roadway conditions. However, under current equipment
> constraints, the collection of WIM data based on calibrated equipment
> and comparable to static weight data may only be possible on smooth
> and flat pavement. The TMG emphasizes the collection of quality WIM
> data at permanent installations in flat and smooth pavement to insure
> the quality and veracity of the resulting WIM data. The limited WIM
> data at these sites is then expanded based on specific road groups and
> detailed classification data to apply WIM estimates to the complete
> roadway system. Extended information on these issues is available from
> ASTM or the LTPP program.

References

Butler, Jack, "Getting Better Truck Flows and Loads for Pavement
Management, Task B Final Report: Truck Weight Case Study,"
Transportation Statistics Office, Florida Department of Transportation,
August 1993.

Cebon, David, "Multiple-Sensor Weigh-in-Motion," in [Weigh-in-Motion of
Road Vehicles, Proceedings of the Final Symposium of the project
WAVE]{.underline}, May 6-7, 1999.

Cunagin, Wiley, NCHRP 3-39, Evaluation and Calibration Procedures for
Weigh-in-Motion Systems, December 1993

Hallenbeck, Mark, "WIM Data Quality Assurance" in [Volume II of the
National Traffic Data Acquisition Conference]{.underline}, 1994, pages
251-266.

Hallenbeck, Mark, and Kim, Soon-Guam, "Summary of Truck Loading Patterns
in Washington State," Washington State Transportation Center, September
1993.

Long Term Pavement Performance (LTPP) Project, TDP-11, Protocol For
Calibrating Traffic Data Collection Equipment, April 1998.

McCall, Bill, and Vodrazka, Walter, "State's Successful Practices
Weigh-in-Motion Handbook," FHWA, December 1997.

Papagiannakis, A.T., NCHRP 3-39(2), On Site Evaluation and Calibration
Procedures for Weigh-in-Motion Systems, December 1995.

[^1]: An excellent introduction to WIM is provided in the reference
    "State's Successful Practices Weigh-in-Motion Handbook" by McCall,
    Bill, and Vodrazka, Walter, FHWA, December 1997.

[^2]: In addition, truck components, such as shock absorbers, are also
    in motion affecting the axle weight at any given instant in time.

[^3]: This recommendation does not prevent the use of less accurate
    portable equipment.

[^4]: ESAL are a measure of pavement damage developed by AASHTO
    researchers in the 1960s that are used for pavement design by many
    current design procedures.

[^5]: See Appendix 4-C for definitions of the FHWA vehicle classes.

[^6]: Note that this value is the total tons of load carried by the
    roadway, not the total net tonnage of goods carried over that road
    (i.e., gross weight applied, not net commodity weight carried.)

[^7]: The accuracy of these estimates is a function of the quality of
    the volume by vehicle classification estimate and the degree to
    which the GVW/vehicle value represents the trucks actually using
    that roadway. Like all "flow" maps, extrapolation is required to
    produce the map, and users should not assume high levels of
    precision when reading directly from such a map.

[^8]: ESAL varies with pavement characteristics, flexible (asphalt) or
    rigid (Portland cement) pavement.

[^9]: If extensive data collection shows that a group of roads has a
    very stable seasonal pattern, then relatively few continuous
    counters are needed to monitor the pattern. However, if the State
    has limited data on seasonal weight patterns or if prior data
    collection has shown the pattern to be inconsistent, then a larger
    number of continuous counters may be needed.

[^10]: Update of the AUSTROADS Pavement Design Guide -- Traffic Design
    Chapter, Final Draft Working Document, September 1998.

[^11]: These are examples. Each State highway agency should select the
    appropriate number and definition of truck groups based on its
    economic and trucking characteristics.

[^12]: When comparing ESAL values between sites, the ESAL computations
    assume the same pavement type and structure. All ESAL examples in
    this document are computed assuming flexible pavements.

[^13]: This is a relatively crude approximation. The value 1.96 should
    be used only for sample sizes of 30 sites or more. A more
    statistically correct estimate would use the Student's *t*
    distribution, which for six degrees of freedom (seven weigh sites)
    is roughly 2.45.

[^14]: ESAL varies with pavement characteristics, thus the ESAL
    formulation used for this purpose should be a generic formulation
    using default pavement characteristics.

[^15]: This table uses the Student's *t* distribution for 8 degrees of
    freedom because of the small number of sample sites within the truck
    weight road group.

[^16]: This table uses the Student's *t* distribution because of the
    small number of sample sites in the group.

[^17]: The value of t~α/2~ for each sample size using the Student's *t*
    distribution for a two-tailed confidence interval of α = 80% (t~.1~)
    is as follows: n = 3, t~α/2~ = 1.886, n = 5, t~α/2~ = 1.533, n = 9,
    t~α/2~ = 1.397, n = 15, t~α/2~ = 1.345, n = 30, t~α/2~ = 1.282.

[^18]: The value of t~α/2~ using the Student's *t* distribution for a
    two-tailed confidence interval of α = 95% (t~.025~) is: n = 3,
    t~α/2~ = 4.303, n = 5, t~α/2~ = 2.776, n = 9, t~α/2~ = 2.306, n =
    15, t~α/2~ = 2.145, n = 30, t~α/2~ = 1.960.

[^19]: This tables uses the Student's *t* distribution because of the
    small number of sample sites within the truck weight road group.

[^20]: The value of t~α/2~ for each sample size using the Student's *t*
    distribution for a two-tailed confidence interval of α = 80% (t~.1~)
    is as follows: n = 3, t~α/2~ = 1.886, n = 5, t~α/2~ = 1.533, n = 9,
    t~α/2~ = 1.397, n = 15, t~α/2~ = 1.345, n = 30, t~α/2~ = 1.282

[^21]: The value of t~α/2~ for each sample size using the Student's *t*
    distribution for a two-tailed confidence interval of α = 95%
    (t~.025~) is as follows: n = 3, t~α/2~ = 4.303, n = 5, t~α/2~ =
    2.776, n = 9, t~α/2~ = 2.306, n = 15, t~α/2~ = 2.145, n = 30, t~α/2~
    = 1.960

[^22]: Preferably more than one

[^23]: Permanent sensors include sites where the sensors are permanently
    installed but only used periodically; sites where the sensors are
    installed permanently but the electronics removed from the roadside
    when not in use, and sites where semi-permanent sensor frames are
    permanently installed but the actual sensors replaced with a "dummy"
    scale when not in use.

[^24]: An excellent reference for learning about WIM site requirements
    is ASTM Standard E-1318, Highway Weigh-in-Motion (WIM) Systems With
    User Requirements and Test Method.

[^25]: WIM Scale Calibration: A Vital Activity for LTPP Sites,
    FHWA-RD-98-104, July 1998.

[^26]: The King Pin is the main connection between the tractor and the
    semi-trailer it is pulling. On most tractors, the connection point
    for the King Pin can be moved by as much as two feet. The closer the
    pin is set to the vehicle's cab, the better the gas mileage (because
    of decreased air resistance) but the rougher the ride on the driver.
    Thus, on rough roads, these connections tend to be set further back,
    while on smooth roads the connections are closer to the cab.

[^27]: For some types of sensors, other factors such as changes in
    sensor sensitivity due to changing ambient temperatures and changes
    in sensor sensitivity due to changes in pavement response caused by
    changing environmental conditions are attempted using this
    technique. The effectiveness of the technique is a function of its
    application by individual equipment vendors, the characteristics of
    each individual sensor installation, and the nature of the traffic
    crossing the installation.