MEPDG Traffic Loading Defaults Derived From Traffic Pooled Fund Study

CHAPTER 3-SUMMARY OF IMPROVEMENTS IN LTPP TRAFFIC DATA SINCE ORIGINAL MEPDG DEFAULTS

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

OVERVIEW OF SPS TPF STUDY

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.

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.

AXLE LOADING DATA FROM SPS TPF STUDY

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⁾

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.

CONCLUSIONS

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.