Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
41 The agencies that responded to the questionnaire use one or more of the three calibration methods described; namely, by means of test trucks, traffic stream vehicles of known static weight, and weigh-in-motion (WIM) traffic data quality con- trol (QC). A summary of the fraction of agencies that use par- ticular WIM calibration methods is given by WIM application in Table 60. Most agencies perform test truck WIM calibrations on a routine basis at intervals ranging from 6 to 24 months, with the majority of them doing so every 12 months. Most agen- cies use a single Class 9 test truck. Others use a Class 5, 6, 7, or 10 truck, either by itself or with a Class 9 truck. Although the majority of these agencies report considering pavement roughness, only about 25% does so objectively (i.e., 11.1% perform the straightedge/circular plate test described by ASTM E1318-02, 3.6% simulate this test using software that accepts the pavement profile as input, and 14.8% simply use the International Roughness Index). Even fewer agencies objectively consider the structural condition of the foundation of the sensors. Most agencies use fixed weigh scales for obtaining the static loads of test trucks; however, more than 40% of agencies using WIM for both data collec- tion and enforcement screening use portable scales. Most agencies perform the static measurements only once. About half of the WIM systems, regardless of application, perform test truck runs using the site median traffic speed, whereas the remainder use either the posted speed limit or multiple speeds. The majority of agencies administering dual-use WIM systems use multiple test speeds. Most agencies using WIM for traffic data collection only conduct ten test runs per vehicle speed, but agencies using WIM for either traffic data/ enforcement or enforcement alone conduct three test runs per vehicle speed. Responders indicated that overall 87% of agencies carry out calibration calculations on site. The method for computing calibration factors is equally split between agency software, vendor software, and short-hand calculations. For combined-use WIM systems, most agen- cies use short-hand calculations, but for enforcement-only screening systems, about two-thirds of the agencies use vendor software. The main load data elements for which WIM errors are computed are gross vehicle weight (GVW), the individual axle loads, and the tandem axle loads. Most agencies compute calibration factors by setting the mean GVW equal to zero, or by setting a combination of the mean GVW and the mean axle load errors equal to zero. Few agen- cies compute calibration factors by minimizing the least square errors between WIM and static axle loads through zero-intercept regression. Depending on the WIM applica- tion, up to 67% of the agencies reported deriving speed- specific calibration factors, although a significant percent- age reported inputting their average value in all speed bins after calibration. Agencies that use traffic stream vehicles of known static weight for WIM calibration obtain static weights largely by permanent static scales at truck inspection stations. Only about one-third of agencies perform these calibrations on a routine basis at intervals ranging from 1 to 12 months. The majority does so only when there is an indication of calibration drift. There is roughly an equal division between the methods used for selecting the number of traffic stream vehicles used. Where a fixed number of vehicles is specified, it varies between 1 and 100, with the majority of agencies using 50 vehicles. Where a fixed time interval is used, it ranges between 1 and 168 hours, with the majority of agencies using data collected over a period of from 1 to 4 hours. The type of vehicles included in this sample varies; most agencies using WIM for traffic data or traffic data/enforcement favor select- ing vehicles in certain classes regardless of speed, but most agencies using WIM for enforcement-only screening use a random selection of vehicle classes. Axle spacing is measured mostly by manual means. The responses to the questions on where error calculations are performed vary; some agencies always do so at the site, others do so at the office. Interest- ingly, enforcement agencies are more likely to perform the error/calibration computations at the site, which is explained by their ready access to static scale data. The actual method for performing the calculations varies; however, most often vendor software is used. The most common traffic elements for which errors are computed are GVW, individual axle loads, and tandem axle loads. The most commonly used approach for computing calibration factors for traffic data WIM systems is by setting the mean GVW to zero. For traffic data/enforcement and enforcement only WIM systems, the most common calibration approach is by setting the combined errors for the GVW and individual axle loads to zero. About 16% of the agencies that operate traffic data WIM use regres- sion for computing calibration factors. Most agencies do not compute multiple calibration factors corresponding to different traffic speeds. Monitoring of the traffic stream WIM data is used by many agencies as a means of detecting WIM calibration status and, CHAPTER FIVE CONCLUSIONS
as a result, is used as the trigger for more detailed on-site cali- bration by the other two on-site methods previously described. As shown in Table 60, some agencies use this approach as the only WIM calibration method. Agencies that operate dual-use WIM systems download data automatically, but most agencies that manage enforcement-only screening WIM systems do so manually. The actual WIM data QC analysis frequency ranges from daily to monthly, or it is decided on the basis of person- nel availability or perceived calibration need. It is performed by manual or automated means, or a combination of the two. With few exceptions, almost all of the agencies that responded believe that WIM data QC are capable of identifying system operational problems (e.g., vehicle errors, system errors, and unclassified vehicles). Most agencies, regardless of WIM data application, focus their traffic stream WIM data analysis for the purpose of calibration monitoring on either Class 9 trucks or, more specifically, the 3S2 configuration. The most com- mon load-related truck properties being monitored are the steering axle load, the left-side/right-side wheel loads of the steering axle, GVW for empty versus loaded trucks, and GVW by vehicle speed. Interestingly, the steering axle load standard deviation (SD) and the GVW SD are monitored primarily by agencies that manage enforcement-only screening WIM sys- tems. The most common distance measure being monitored is the axle spacing of the tractor tandem axles of 3S2 trucks and, less frequently, the total wheelbase versus the sum of the axle space data. As shown in Table 58 in chapter four, which sum- marizes the responses of agencies regarding their actions when WIM data QC indicates calibration drift, approximately 5% of 42 the agencies that use WIM for traffic data collection suggest that they take no action. In concluding this synthesis, the following future research needs and suggestions are made: ⢠There is a need to field test the AASHTO MP 14-05 pro- visional standard to ensure that the pavement smooth- ness levels specified can reliably differentiate between suitable and unsuitable WIM sites. ⢠For Type I WIM systems, there is a need to test the effec- tiveness of the speed-specific calibration approach in reducing in-service traffic WIM errors in GVW and axle load measurements. This issue needs to be studied in terms of the vehicle class and suspension type of the test trucks and the dominant vehicle class and suspension type of the in-service truck traffic. ⢠There is a need for research to develop software stan- dardizing the WIM system calibration process involving test trucks. This software, developed in coordination with WIM vendors, will facilitate and homogenize on- site WIM system calibration. ⢠Research is needed to establish simplified WIM data QC criteria for triggering test truck WIM calibration. ⢠Finally, it is suggested to study the potential for provid- ing a system to train and certify WIM technicians. This could be organized along the lines of the AASHTO R18 accreditation process, which covers a variety of labora- tory testing procedures. TABLE 60 SUMMARY OF WIM CALIBRATION METHODOLOGY USED BY APPLICATION Method Traffic Data Only Both Enforcement Only Test Truck Only 22/34 (65%) 7/34 (21%) 20 of 34 (59%) Traffic Stream Trucks of Known Weight Only 6/7 (86%) 4/7 (57%) 6 of 7 (86%) WIM Data QC Only 2/11 (18%) 10/11 (91%) 6 of 11 (55%)
