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of day, day of week, and time of year. Estimating the number of vehicles (by class) is addressed
in the next chapter, and equipment calibration is covered in a companion report.2
Seasonal variation in axle weights are of particular interest in pavement design because the
pavement damage caused by axle loads is significantly affected by seasonal variations in soil
conditions such as wetness, freezing, and thawing. As a result, the Pavement Design Guide
software is designed to use separate sets of load spectra for each month of the year and to
incorporate the effects of seasonal variations in the load spectra in the resulting pavement
designs, and TrafLoad has been designed to produce such seasonally varying load spectra.
Because WIM data collection tends to be difficult and expensive, highway agencies cannot
afford to collect all the data needed to precisely measure and account for each source of vari-
ation for all pavement design efforts. Consequently, a series of data-collection options are pro-
vided to help states optimize the amount of data they collect, given the accuracy of the load
estimate they require and the funding available for data collection.
In general, the recommended program for collecting WIM data is stratified into three levels of
data collection. Each level corresponds to how well an agency understands the location com-
ponent of truck weight variation. Level 1 design is for sites where site-specific WIM data are
available, and thus errors associated with locational differences are negligible. Level 2 design
is for sites where some general knowledge of loading rates can be applied but actual WIM data
have not been collected. Level 3 design is for sites where knowledge of loading rates is lim-
ited enough that statewide average loading rates are the best available. Each of these three
general conditions is discussed further in the remainder of this chapter.
2.2 Alternative Data-Collection Programs
There are three levels of axle-load distribution (or load spectra) data in the data collection and
analysis:
· Site specific,
· Truck weight road group (TWRG), and
· Statewide averages.
These different levels of data collection and application are introduced below and discussed
in more detail in subsequent sections of this chapter.
Site-Specific Load Distribution Data
Site-specific data collection means that the state is able to accurately weigh trucks on the road
on which the new pavement will be laid. The axle-weight data-collection site must be located
2
Ibid.
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so that the traffic measured by the WIM scale is basically the same as the traffic that operates
in the design lane of the roadway segment being designed.
The intent of the definition of "site specific" is to allow a state to collect data on the same road-
way as, but possibly at a location somewhat removed from, the pavement project's segment
limits. For example, the WIM scale used to provide data on I80 in Wyoming might be located
at the Utah-Wyoming border on I80. The corresponding pavement project could easily be
50 (or more) miles away on I80 in Wyoming. Because little change in trucking activity occurs
between those locations, data from the border-crossing WIM would be considered site spe-
cific. However, data collected on I80 20 miles east of Salt Lake City would not be considered
site specific for a pavement 20 miles west of Salt Lake City on I80 because considerable change
in loading rates can occur within major metropolitan areas.
In addition, Level 1 data collection assumes that the highway agency will use the WIM data
collected to calculate the axle-load distribution tables directly. This means that the agency
must be satisfied with the performance of the WIM scale being used. The scales used must be
properly calibrated, and quality control checks must indicate that the data are valid. (If the agency uses
a device that is not adequately calibrated, the site-specific data should be used only to iden-
tify which TWRG dataset is most appropriate for that pavement project; and a Level 2 design,
as discussed in Section 2.4, should be performed.)
TrafLoad accepts WIM data collected over a 12- to 24-month period as well as WIM data col-
lected over shorter periods of time. The software uses seasonal load spectra datasets that con-
tain data for the 12 months of the year as the basis for imputing seasonal adjustments to data
collected for periods of less than 12 months. The process for performing these adjustments (pre-
sented in Part 4, which is available online at http://trb.org/news/blurb_detail.asp?id=4403)
uses seasonal load spectra factor groups, which are discussed in Section 2.3. These factor
groups are similar to automatic traffic recorder (ATR) factor groups except that sites are
assigned to a group on the basis of seasonal variation in axle weights instead of seasonal vari-
ation in the traffic volume.
Errors in the axle distributions at site-specific locations usually are due to equipment and cal-
ibration errors and errors caused by sampling of the traffic stream. In general, these site-specific
estimates are relatively good.
TWRG Data
TWRG axle-load distributions are summary load distributions that represent axle loads found
on roads with similar truck weight characteristics. These groups are similar to ATR factor
groups, in which all roads in a group have similar seasonal volume patterns. In the case of
TWRGs, however, all roads in a group have similar axle-load distributions. Because TWRGs
are designed to have similar axle-load distributions rather than similar volume patterns,
TWRGs generally will differ from the seasonal and day-of-week (DOW) factor groups.
The FHWA's Traffic Monitoring Guide recommends TWRGs as a way for highway agencies to
collect, summarize, and report summary statistics for groups of roads. The intent is to group
roads by their trucking characteristics so that the load spectra on all the roads in a group are
fairly similar. Road characteristics that can be used to define road groups include the region
of the state, particularly where the economic activity in a state differs from region to region
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(e.g., agricultural areas versus mining areas); the nature of the commodities being carried (e.g.,
roads leading to a port versus roads in other parts of an urban area); and sometimes the type
and location of the facility (e.g., urban freeway versus suburban arterial).
The Traffic Monitoring Guide expects TWRGs to be state specific, but multiple states can work
together to create regional load distribution tables if the states involved in the regional effort
have similar truck weight laws. (Different truck size and weight laws would invariably lead
to different truck weight characteristics and thus different axle-load distribution tables.)
TWRG axle-loading tables are needed because most states do not have (and cannot afford to col-
lect) site-specific WIM data for the majority of pavements they design each year. However, these
tables produce poorer estimates of pavement stresses than tables derived using site-specific
data. Recent analyses of California data indicate that, for combination trucks, TWRGs produce
mean absolute percentage errors (MAPEs) for pavement stresses (as measured in 18,000-
pound equivalent single-axle loads) of 1720 percent,3 varying with the degree of disaggre-
gation of the TWRGs and the care with which they are constructed. In contrast, the analyses
indicate that site-specific WIM data collected over a 48-hour period produces a MAPE of only
7 percent (exclusive of equipment and calibration error).
Because load distributions vary significantly, it is important that the pavement designer
understand the approximate range of loads being applied. Using such knowledge greatly
improves the reliability of the pavement design. Figure 2.1 shows the tandem-axle distribu-
tions found at three different WIM scales. Figure 2.1(a) represents a site where a large per-
centage of trucks are operating empty or in a partially loaded condition. Figure 2.1(b) repre-
sents a moderate loading condition, while Figure 2.1(c) illustrates a site with very heavy (but
predominantly legal) loading. Ideally, each of these three sites should be in a different TWRG.
If each of the three roads carries the same number of trucks, the different loading conditions
should result in three very different pavement designs. The challenge for each state is to deter-
mine which roads (and directions of travel in some cases) are typified by which of these (or
other) basic loading conditions. This grouping process requires analysis of a state's existing
weight data and trucking patterns, and it results in the creation of appropriate TWRGs. (Note
that states may easily have more than three loading conditions. Also note that the TWRGs gen-
erally will not correspond to the groups used for factoring classification counts. The number
of TWRGs distinguished in the analyses of California data varied between 3 and 10, with the
best results obtained using a set of 10 TWRGs that distinguished functional system, region,
and direction of travel.)
For each TWRG, a set of tables for the axle-load spectra will be created by the software. These
tables summarize the distribution of all axle loads measured for trucks weighed at scales
within each group of roads. In addition, for each TWRG, the average number of axles for each
VC will be computed.
All or most WIM scales in a state or multi-state region should be assigned to a TWRG. For each
truck class, the axle weights of all trucks in the class weighed at the scales in a given TWRG
are then used by the software to compute a corresponding set of load spectra.
3
Cambridge Systematics, Inc., Accuracy of Traffic Load Monitoring and Projections, Volume II, The Accu-
racy of ESALs Estimates, prepared for FHWA, February 2003, Tables 4.5 and 5.1.
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Figure 2.1 Load Distributions for Tandem Axles of
FHWA Class 9 Trucks at Three Different Sites
Figure 2.1(a) Lightly Loaded Trucks
Fraction of Tandem Axles in Weight Group
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0
6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Figure 2.1(b) Moderately Loaded Trucks
Fraction of Tandem Axles in Weight Group
0.12
0.10
0.08
0.06
0.04
0.02
0
6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Figure 2.1(c) Heavily Loaded Trucks
Fraction of Tandem Axles in Weight Group
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0
6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Axle Weight Groupa
a Each group is identified by the maximum weight, in thousands of pounds, for the group.
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