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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. 2-8

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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 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 2-9

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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. 2-10

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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. 2-11