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Publication Number:  FHWA-HRT-13-090    Date:  April 2016
Publication Number: FHWA-HRT-13-090
Date: April 2016


MEPDG Traffic Loading Defaults Derived From Traffic Pooled Fund Study


A number of improvements to LTPP WIM data have occurred within the last 13 years, as summarized in the following sections.


Study Objective

Since the original defaults were developed, LTPP undertook the SPS TPF study that focused on installing highly reliable permanent WIM systems and collecting axle loading data using a uniform vehicle classification scheme and rigorous quality control (QC) procedures to produce research-quality traffic data (classification and weight) to support LTPP analysis projects.(2) The SPS TPF study was designed with the support of the Transportation Research Board Traffic Expert Task Group (ETG). The effort consisted of two principal elements: shifting the data collection from highway agencies to a national, centralized effort and standardizing data collection equipment and procedures. Additionally, guidelines for pavement smoothness, equipment calibration checks, equipment model specifications, and LTPP vehicle classification scheme were developed and implemented for SPS TPF sites.(2)

WIM Equipment

Table 1 provides the location, road type, and WIM technology description for each SPS TPF site. Two types of weighing sensors typically were used for the sites: bending plate and quartz piezo. Both sensors have a proven history of reliable performance. In addition, two Ohio sites use load cell technology.

Table 1. SPS WIM site locations.
State SPS Site Route and Site Location WIM Sensor Road Functional Class
1. Arizona 040100 US-93 North at M.P. 52.62 Bending plate Rural principal arterial-other
2. Arizona 040200 I-10 East at M.P. 108.6 Quartz piezo Rural principal arterial-interstate
3. Arkansas 050200 I-30 North of SR74 overpass Bending plate Rural principal arterial- interstate
4. California 060200 SR-99 at M.P. 32.5 Bending plate Rural principal arterial-other
5. Colorado 080200 I-76 East at M.P. 39.7 Bending plate Rural principal arterial-interstate
6. Delaware 100100 US-113 Southbound north of SR 579 Quartz piezo Rural principal arterial-other
7. Florida 120100 US-27 at M.P. 12.03 Quartz piezo Rural principal arterial-other
8. Florida 120500 US-1 Quartz piezo Rural principal arterial-other
9. Illinois 170600 I-57 at M.P. 225.6 Bending plate Rural principal arterial-interstate
10. Indiana 180600 US-31 North at M.P. 216.9 Bending plate Rural principal arterial-other
11. Kansas 200200 I-70 West at M.P. 287.48 Bending Plate Rural principal arterial-interstate
12. Louisiana 220100 US-171 at M.P. 8.4 Quartz piezo Rural principal arterial-other
13. Maine 230500 I-95 at M.P. 200.1 Quartz piezo Rural principal arterial-interstate
14. Maryland 240500 US-15 North at M.P. 4.62 Bending plate Rural principal arterial-other
15. Michigan 260100 US-27 South Quartz piezo Rural principal arterial-other
16. Minnesota 270500 US-2 at M.P. 91.8 Quartz piezo Rural principal arterial-other
17. New Mexico 350100 I-25 North at M.P. 36.1 Quartz piezo Rural principal arterial-Interstate
18. New Mexico 350500 I-10 East at M.P. 50.2 Quartz piezo Rural principal arterial-interstate
19. Ohio 390100 US-23 at M.P. 19.7 Load cell Rural principal arterial-other
20. Ohio 390200 US-23 at M.P. 19.7 Load cell Rural principal arterial-other
21. Pennsylvania 420600 I -80 at M.P. 158.2 Quartz piezo Rural principal arterial-interstate
22. Tennessee 470600 I-40 West at M.P. 91.67 Quartz piezo Rural principal arterial-interstate
23. Texas 480100 US-281 South Bending plate Rural principal arterial-other
24. Virginia 510100 US-29 bypass at M.P. 12.8 Bending plate Rural principal arterial-other
25. Washington 530200 US-395 at M.P. 93.01 Quartz piezo Urban principal arterial-other freeways or expressways
26. Wisconsin 550100 US-29 at M.P. 189.8 Bending plate Urban principal arterial-other

M.P. = Milepost.

Study Coverage

Figure 4 shows the distribution of SPS TPF sites on a map, illustrating good coverage across the United States. However, only two functional road types have adequate representation in the SPS TPF study: rural principal arterial interstate and rural principal arterial other non-interstate highways. No SPS TPF site was located on an urban interstate or on minor arterials and collectors, and only two sites were located on urban roads (principal arterial other and expressways). This is a limitation in developing alternate NALS for the MEPDG. In other words, the alternate NALS developed from these sites may be restricted to certain truck traffic conditions.

Figure 4. Illustration. Map of SPS TPF study sites. This map shows the location of all Specific Pavement Studies Transportation Pooled Fund (SPS TPF) study sites in the United States. Pins are used to mark the location of the sites on the map, and the sites are identified by a six digit number in a rectangular box. The first two digits represent the State code, and the last four represent the Strategic Highway Research Program (SHRP) ID. An example of a site ID is 240500, where 24 represents the State code and 0500 represents the SHRP ID.

Figure 4. Illustration. Map of SPS TPF study sites.


LTPP Definition of Research-Quality Traffic Data

Under the LTPP SPS TPF study, research-quality traffic data are defined as at least 210 days of data (in a year) of known calibration meeting LTPP's precision requirements for single axles, axle groups, gross vehicle weight (GVW), vehicle length (bumper-to-bumper), vehicle speed, and axle spacing, as detailed in table 2.(2)

Table 2. LTPP WIM system performance requirements.
SPS TPF Factors 95 Percent Confidence Limit of Error
Loaded single axles ±20 percent
Loaded axle groups ±15 percent
GVW ±10 percent
Vehicle length greater of ±1.5 ft or ±3 percent
Vehicle speed ±1 mi/h
Axle spacing length ±0.5 ft

As a result of enforcing the criteria for research-quality data, the SPS TPF sites have had more direct calibration and performance monitoring reviews performed as part of the data collection effort than any other WIM sites in the United States. Because LTPP requires regional contractors to periodically download and verify the collected traffic data, anomalies are identified quickly, and actions are taken to ensure accurate performance of WIM systems. That is, if performance problems are noted in the equipment, the repair/calibration is performed, and problem data are not processed and stored. The SPS TPF WIM equipment is also installed in pavement that supports accurate WIM system performance, ensuring the accuracy of the collected data. This means that the SPS TPF dataset is among the most trustworthy WIM data in the country.

LTPP Traffic Data QC Process

The LTPP data processing and QA programs ensure that WIM data being collected at SPS TPF sites are reviewed in a timely manner using a systematic, comprehensive, and well-documented internal process. Implementation of the new and improved LTPP Traffic Analysis Software (LTAS) for traffic data QC and processing, along with rigorous and systematic WIM scale validation and calibration process for SPS TPF sites, has greatly improved the quality of WIM data.

For equipment measurements, QC procedures include routine calibrations, data checks during acquisition, and data checks prior to loading data into the LTAS database. Once WIM data are downloaded to LTAS, they undergo several levels of data QC checks developed by the LTPP Program for completeness and validity.

Overview of Relevant LTPP Data Tables

LTPP Standard Data Release (SDR) 24 was the primary source of data for this study.(11) The LTAS DD* series of tables contain daily axle load (DD_AX table) and truck volume (DD_WT_CT table) data for all SPS TPF sites. Axle load and vehicle volume data in these tables have one-to-one correspondence on a daily basis, which is important for computing APC coefficients. These daily data were used as the primary source of data.

The DD_AX table contains axle data by site, year, month, day of the month, day of the week (DOW), lane, direction, vehicle class, axle group, and load bin. This table was created by accumulating the axle distributions over all hours by vehicle class in a calendar day. The data are summarized in 1,000-lb bins for single axles, 2,000-lb bins for tandem axles, and 3,000-lb bins for tridems and quads. (Quad axles are any axle group with four or more axles.)

The DD_WT_CT table summarizes the number of vehicles by class. This table contains count data by site, year, month, day, lane, and direction for each day for which weight data exist for estimating loads. This table uses the calendar day to define a day of data.


SPS TPF data represent a unique national traffic loading data sample. Currently, this dataset is the best quality national loading data sample available in the United States. The primary benefit of the SPS TPF data is that they are collected using WIM devices that are routinely monitored and periodically calibrated using uniform procedures to monitor changes in load spectra over time. Additional benefits of these data are the extended periods of data collection (using continuously operating WIM scales). Also, a uniform vehicle classification scheme is used at most sites (some minor deviations from the algorithm are observed in data from Florida, Ohio, and Washington).

The SPS TPF data provide an opportunity to improve the MEPDG traffic loading defaults. The quality and quantity of data affect the reliability of loading defaults, as do consistency in data collection and data processing protocols and the uniformity of the vehicle classification scheme. However, the limited scope of SPS TPF WIM data may limit the utility of the alternate NALS defaults. Currently, data are available for only 26 sites, and they do not cover all road types.

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