National Academies Press: OpenBook

Equipment for Collecting Traffic Load Data (2004)

Chapter: Chapter 5 - Best Practices for Equipment Use

« Previous: Chapter 4 - A Process for Selecting Equipment
Page 46
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 46
Page 47
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 47
Page 48
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 48
Page 49
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 49
Page 50
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 50
Page 51
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 51
Page 52
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 52
Page 53
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 53
Page 54
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 54
Page 55
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 55
Page 56
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 56
Page 57
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 57
Page 58
Suggested Citation:"Chapter 5 - Best Practices for Equipment Use." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
×
Page 58

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.

46 CHAPTER 5 BEST PRACTICES FOR EQUIPMENT USE The collection of traffic load data required by the pave- ment design software is just one of a variety of traffic data collection tasks that highway agencies must perform. The traffic load data collection effort cannot be done as an inde- pendent activity. It must be performed within the context of the entire traffic data collection effort undertaken by a high- way agency. Determination of what equipment to purchase and how to install, calibrate, and maintain it, as well as what data to col- lect, how equipment and staffing resources are efficiently used to collect it, and how the collected data are manipulated, stored, and reported once they are available, must be done within the context of the entire agency’s traffic data needs. Separation of the pavement design needs from the other traf- fic data needs leads to considerable inefficiency in traffic data collection. Therefore, a need for good data practices applies throughout the agency’s traffic data collection program. In general, good data collection practice can be summa- rized as nine basic steps: 1. Identify user requirements and develop an implemen- tation plan. 2. Determine location and system requirements. 3. Determine site design life and accuracy necessary to support the end user. 4. Budget the resources necessary to support the selected site design life and accuracy requirements. 5. Develop and maintain a thorough quality assurance and performance measurement program. 6. Purchase the WIM or classification equipment with a warranty. 7. Manage the equipment installation. 8. Calibrate and maintain calibration of equipment. 9. Conduct preventive and corrective maintenance at the data collection sites. It is important to remember that good traffic data collec- tion practice requires the agency to also consider the impact on data collection of other traffic data needs. This section expands on the traffic data collection and equipment needs discussed elsewhere in the report by explaining how pave- ment design data needs fit together with other agency needs. 5.1 IDENTIFY USER REQUIREMENTS The pavement design process requires an accurate estimate of the number of heavy vehicles projected to use the roadway lane being designed and the number, type, and weight distri- bution of the axles on those trucks. These data will come from a combination of project-specific counts and the sum- mary tables developed from the general truck counting and weighing program performed by the state highway agency. The level of reliability desired by pavement design engi- neers (and the budget available to them for data collection) will result in their selection of the level of data collection per- formed for pavement design projects. The level will define the amount of truck volume and weight data collected specif- ically to meet the needs of pavement design efforts. These needs will become requests to collect specific data that are sent to the traffic data collection section of an agency. Traffic data collection units will need to develop mecha- nisms that allow them to efficiently respond to both these spe- cific requests (which will vary from request to request) and the need to collect the more general data that are used to create the summary statistics and tables used when project-specific data are not required or cannot be affordably collected. To create a cost-effective data collection program, both of the above needs must be efficiently coordinated with the other truck volume and weight data needs of the highway agency. Collecting the data needed for general summary tables is part of routine data collection programs, and direc- tions for this are included in the FHWA’s Traffic Monitor- ing Guide, Sections 4 and 5. Responding efficiently to the need for project-specific counts is a more difficult under- taking. Often it can most effectively be accomplished by set- ting up one or more meetings during each year between traf- fic data collection staff and pavement design staff to discuss roadway sections that will most likely be the subject of new pavement designs in the next year or two. These sections will require truck traffic data collection, and a 1- to 2-year time- frame should allow efficient scheduling of the data collec- tion effort. Scheduling this meeting (or meetings) to take place prior to the development of each year’s traffic data collection program allows data collection staff to efficiently schedule their data collection resources. This significantly decreases the cost of data collection efforts, and this scheduling efficiency more

47 than makes up for any “extra” counts that are taken but not actually used because expected pavement projects are delayed. Data collection staff have the responsibility of coordinat- ing the needs of different users. A key to this function is knowing where flexibility exists in the collection and report- ing of data. In a simple example, if two users request the same data for the same road but for road segments one-half mile apart, the data collection staff need to be able to determine if those two data collection requests can be met by a single count, halving the number of counts that need to be taken. Where flexibility exists is a function of the roadway char- acteristics and the uses of the data. If the road is a rural high- way with limited activity, the two requests can likely be met with one count. If a major freeway interchange occurs between the two locations, it is unlikely that the two counts can be combined. Still, the same data collection crew will probably collect both counts, and by collecting both counts in the same trip at least the travel time and cost associated with the data collection can be halved. Traditionally, this type of coordination has been difficult to perform because pavement project selection processes were not done early enough to fit into traffic data collection sched- ules. However, most states now operate pavement manage- ment systems that identify roadway sections in need of repair or rehabilitation in the near future. These program outputs can be used to create a short list of projects that are likely to occur in the next 2 years. The state’s transportation improvement plan (TIP) may also provide such a list. If the actual pavement design list is not available when the traffic data collection pro- gram needs to be developed, this slightly larger list can be used as a surrogate for the actual list. It may require a minor increase in the number of pavement design counts that need to be col- lected, but the slight increase in counts is more than offset by the decrease in cost per count due not only to the coordination efforts, but also to the more timely manner in which data can be made available to the pavement design team. Implementation of the recommendation to enhance the communication and coordination of pavement design engi- neers and traffic data collectors is more of an administrative and institutional problem than a technical problem. If an agency succeeds, four positive changes should take place: • The availability of traffic load data for pavement design purposes should improve. • The cost of collecting traffic load data for pavement design should decline. • Data quality should improve as more staff review and use the data collected. • Internal support for traffic data collection activities should improve as the users of the data improve their understanding of the value and limitations of the traffic data they are receiving. The keys to all of these improvements are (1) achieving a high level of communication between pavement design engi- neers and traffic data collectors and (2) combining that com- munication with strong advance planning. Both pavement design engineers and traffic data collectors obtain consider- able benefits from improving communications. Well-run traf- fic counting programs invariably have strong connections to their users, and the pavement design section is a very impor- tant user group. 5.2 DETERMINE SITE LOCATION AND SYSTEM REQUIREMENTS As noted in the example in the previous section, a key component of the data collection process is understanding what, where, and why data are being collected. Understand- ing these factors is necessary for determining exactly what, when, and how data need to be collected and for selecting the equipment to be used. Traffic data are collected from a given location either because data from that point are important to a specific design or project, or because data from that location are needed to help develop a default or average value that can be used at many sites where site-specific information cannot be afford- ably collected. The first of these count efforts is generally referred to as “project counts.” These generally are data col- lected (1) to describe the current traffic stream crossing the design lane for a project and (2) to serve as a baseline upon which to forecast the future traffic stream. The second data collection effort is often thought of as planning counts, which are performed as part of general agency data collection efforts. While also meeting general agency needs, these data are used to compute the Level 2 and Level 3 load-spectra defaults1 used by the pavement design software. These counts include WIM efforts used to compute truck weight road group (TWRG2) axle-load distributions, and continuous classifica- tion counts are used to determine seasonal truck volume and other truck traffic patterns. Some flexibility exists in the col- lection of both of these types of data. Ideally, project-specific counts are taken at the project site, as this provides the most reliable estimate of current traffic crossing the design pavement. However, the actual data col- lection effort can be moved upstream or downstream from the project location if the project location is not conducive to accurate traffic data collection or if other circumstances war- rant such a move. One good reason to move a project-specific count is that the pavement at the project site is in such poor condition that the available traffic sensors will not work accu- rately. In general, having accurate, representative data is more important than having data from the exact site of the pave- ment project. 1 Level 2 load-spectra defaults are those axle weight distributions used when site- specific data for a project site are not available, but when the site can be identified with a regional average. (Level 2 is the regional average.) Level 3 represents the statewide average and is used only when no better information is available for a pavement design. 2 TWRGs are groups of roads that have trucks with similar loading conditions. A sample of vehicle weights is collected and used to represent the axle load distribution for all roads that belong to that group when site-specific load information is not available.

48 If the count is moved, it should be placed so that truck traf- fic being measured is as closely related to the actual project traffic as possible. If the project location, for example, is on I-80 in Wyoming, a valid data collection location could be sev- eral miles away. However, if the project location is on I-95 in New Jersey, the count most likely needs to be taken within the same set of interchanges as the pavement project. Selecting data collection locations for pavement design purposes can also be affected by the need to coordinate with other data collection needs. A highway agency may be will- ing to accept some minor error in the traffic loading estimate in order to reduce the total counting burden of the state, and the agency thus may choose to use an existing count that is slightly removed from the project location rather than go to the expense of collecting newer, more precise information. Even broader flexibility is available to highway agencies as they select those locations where data are collected to compute the TWRGs. The primary goal of the TWRG is to provide an accurate measure of average conditions for a given set of roads. Given the lack of weight data available to most highway agencies and the cost and difficulty of col- lecting accurate weight data, most agencies know relatively little about the vehicle weights present on specific roads. Thus, considerable latitude is available in the selection of data collection sites that are included in the TWRG compu- tations because most agencies have little information upon which to judge alternative locations and any valid data are better than no data. The first criterion of TWRG formation is that the sites be similar in characteristics to the other roads they represent. (For example, the shape of the axle load distribution associ- ated with FHWA Class 9 trucks should be similar at all sites within the TWRG.) The second criterion for data collection is that the sites selected be conducive to accurate weight data collection. This means that the pavement should be in good condition. It should be flat, with no ruts. The pavement should be strong enough to support weight sensors effectively under whatever environmental conditions are present when weight data are being collected. It is recommended that, at least initially, data collection for TWRG development be oriented toward sites at which accu- rate data can most confidently be collected. As budgets permit, the weight data collection program should then be expanded or moved to other locations around the state (where WIM equipment can be accurately operated) in order to gain a more complete picture of truck weights around the state. 5.3 DETERMINE DESIGN LIFE AND ACCURACY REQUIREMENTS Another key to efficient expenditure of data collection resources is to match the design life of equipment to the life of pavement and select the equipment accordingly. It is rarely a wise decision to select a WIM sensor that is expected to out- live the pavement in which it is placed. Few WIM installa- tions can be removed intact from the roadway and reused. (This does not include technologies such as bending plates, where the sensor itself can be removed, but the frames into which the plates are set are not removable.) Thus, it makes little sense to design a 5-year WIM site for a pavement that will be repaved in 3 years. For WIM data collection, site failure is often the result of failure of the pavement condition around the site, not just the failure of sensors themselves. Thus, site design life is a func- tion of the fatigue life of the sensor itself, the installation quality of the sensor, the initial site condition and design, and the expected wear on the pavement. Sensor fatigue life is usually a function of the sensor design and the traffic loadings. Vendors normally warranty their sensors for a specified period, and obtaining a warranty is itself a recommended best practice. Sensors with longer fatigue lives are usually more expensive than shorter-lived sensors. However, many WIM systems become inoperable not because of sensor failure, but because of the failure of pave- ment around them. This includes both when the pavement/ sensor bond fails and when pavement deterioration such as rutting exposes the sensor to impact loads (e.g., snowplow blades) that cause catastrophic failure. A primary cause of premature pavement/sensor bond failure is poor initial instal- lation quality. This includes such errors as poor mixing of adhesives, poorly cleaned or dried pavement cuts, incompat- ibility of sealants and pavement, and inappropriate tempera- ture conditions. Site condition and site design are key areas that successful programs examine as part of WIM site design and imple- mentation. Where remaining pavement life is only modest, strong consideration should be given to rehabilitating the pavement prior to WIM sensor installation if an extended design life for the site is desired. Unfortunately, pavement rehabilitation is a costly addition to WIM installation. How- ever, if a scale site is expected to have a long life, life-cycle costs are far lower if the pavement at the site is rehabilitated prior to initial sensor installation. In many cases, highway agencies have found it to be a wise investment to build 300-foot concrete pavement sec- tions into which WIM scales are placed. This gives agencies smooth, strong, maintainable platforms in which to place sen- sors. Strong concrete pavements generally do not change structural strength with changing temperatures and tend to deteriorate slowly. Thus, strong concrete pavements are gen- erally considered to be good locations for scale sensors. A pavement with high-durability characteristics provides for a long design life and low maintenance costs for the scale sys- tem. (However, it is important to note that the pavement must be smooth as well as durable to be good for vehicle weighing.) Not all WIM installations are intended to last many years. In many cases, an agency only wishes to collect data for a year or two at a location before moving the agency’s scarce WIM resources to another location. In such a situation, the design

49 life of the system can be fairly short and pavement rehabilita- tion may not be warranted, so long as the pavement condition is adequate for collecting accurate weight data. In such cases, it may be unwarranted to construct a new 300-foot pavement slab for a WIM installation that is needed only to provide an accurate week-long sample during a particular commodity movement (for example, during a harvest season) and where the existing pavement is reasonably smooth. 5.4 BUDGET NECESSARY RESOURCES Initial site and equipment costs are not the only budgetary requirements of truck volume and weight data collection. While a large portion of the data collection budget is associ- ated with initial system purchase and installation, these funds are poorly spent if the other tasks associated with data col- lection are not also adequately funded. Staffing and other resources are needed to collect, review, and summarize the data being collected. They are also needed for calibration, routine scale calibration verification, site maintenance, and site repair in order to obtain the maximum value from the funds spent on initial site implementation. Good data collection is not necessarily achieved by pur- chasing the most expensive technology. What is necessary is to correctly budget the resources needed to buy reliable equip- ment, install that equipment properly, calibrate the equip- ment, and maintain and operate the equipment. The cost of performing these tasks will almost always be returned to the highway agency in improved reliability in the pavement design process. Similarly, the cost of poor data collection is most likely to be made apparent in costs incurred as a result of poor pave- ment design. (That is, poor design resulting from bad input data is ultimately more expensive than collecting the data needed to create a good design.) Table 5.1 (based on vendor- and state-supplied data) pro- vides general equipment costs. (Note that these costs will TABLE 5.1 WIM equipment estimated initial and recurring costs1 Site Cost Considerations Piezo Piezo Quartz Bending Plate Deep Pit Load Cell Initial Costs Pavement Rehabilitation2 ?? ?? ?? ?? Sensor Costs, Per Lane3 $2,500 $17,000 $10,000 $39,000 Roadside Electronics 7,500 8,500 8,000 8,000 Roadside Cabinet 3,500 3,500 3,500 3,500 Installation Costs/Lane Labor and Materials 6,500 12,000 13,500 20,800 Traffic Control 0.5 days 1 day 2 days 3+ days Calibration 2,600 2,600 2,600 2,600 Annual Recurring Costs/Lane Site Maintenance 4,750 7,500 5,300 6,200 Recalibration 2,600 2,600 2,600 2,600 Notes: 1. 2. 3. These cost estimates have been developed based on a variety of published sources. However, costs vary over time and especially from vendor bid to vendor bid. Thus, actual costs can vary considerably from what is presented here. Pavement rehabilitation costs are a function of current pavement condition, desired smoothness, desired site life, and desired WIM system accuracy. Consequently, they differ dramatically from site to site. At a given site, however, they will be similar for all technologies. These costs can vary considerably based on the exact sensor configuration chosen for a given site, as well as the specific bid prices provided by vendors.

50 change from vendor to vendor and from site to site.) However, when budgeting for new sites, initial costs should also include any necessary pavement rehabilitation costs (although those costs are often paid out of other funding sources). Pavement rehabilitation to achieve necessary smoothness levels is not a function of the equipment technology selected. Accuracy degrades for all types of WIM equipment when they are placed in rough pavement. Other initial costs include vehi- cle presence and weight sensors, roadside electronics, road- side cabinets, and installation. Annual recurring costs include site maintenance, system maintenance, calibration, and per- formance evaluation. Site design life and expected sensor life can be combined to predict the estimated initial cost per lane and the estimated average cost per lane over the selected site design life. For example, Table 5.2 provides an estimate of system perfor- mance, initial cost per lane, and average annual cost per lane (not including pavement rehabilitation costs). This compari- son of performance and cost is based on the information ini- tially provided in the States’ Successful Practices Weigh-in- Motion Handbook, dated December 1997. The performance of the systems is given as a percent error on gross vehicle weight (GVW) at highway speed and is contingent on the site’s meeting ASTM E 1318 standards. The estimated initial cost per lane includes the equipment and installation costs, cal- ibration, and initial performance checks. It does not include the cost of traffic control. The estimated average cost per lane is based on a 12-year site design life and includes expected maintenance and the cost of periodic calibration and valida- tion checks. The system maintenance is based on a service contract with the system provider. A more detailed method (including a simple cost-calculation spreadsheet) that includes all the site cost considerations listed in Table 5.2 has been developed for LTPP. A brief dis- cussion of the method was presented in Appendix 2 of the States’ Successful Practices Weigh-in-Motion Handbook. The LTPP calculation allows inclusion of specified pave- ment rehabilitation and maintenance. The spreadsheet used by LTPP to compute WIM cost estimates is available through the LTPP web site at http://www.tfhrc.gov/pavement/ltpp/spstraf- fic/index.htm. While now several years old, the spreadsheet allows input of up-to-date cost components (including pave- ment rehabilitation costs), as well as the costs and character- istics of new WIM equipment. 5.5 DEVELOP, USE, AND MAINTAIN A QUALITY ASSURANCE PROGRAM No matter how much money is budgeted and spent for the initial purchase and installation of a WIM site, all WIM equipment requires continual care and attention. Without ongoing attention to equipment performance and data col- lection site conditions, equipment performance will degrade over time. While vehicle classification equipment tends to be more robust (it is less sensitive to calibration drift), it requires periodic attention and continuous monitoring. Consequently, another key practice is for highway agen- cies to implement and use a quality assurance program that monitors data being collected and reported. It is poor practice to simply place equipment and hope that an autocalibration function will soon bring the system into calibration. While autocalibration has some important uses, all autocalibration functions have significant shortcomings. Each relies on the concept that some particular traffic value will remain constant over time, and that constant value can be used to tune the calibration of the data collection device. TABLE 5.2 WIM system accuracy and cost comparison WIM System Performance (Percent Error on GVW at Highway Speed) Estimated Initial Cost Per Lane (Equipment and Installation Only)1 Estimated Average Cost Per Lane Per Year1 (12-Year Life Span2 Including Maintenance) Piezoelectric Sensor ± 10% $22,600 $7,350 Bending-Plate Scale ± 5% $37,600 $7,900 Piezoquartz Sensor ± 5% $43,600 $10,100 Single Load Cell ± 3% $73,900 $8,800 Notes: 1. 2. Pavement rehabilitation costs are not incorporated in this estimate or the average annual cost. Some of these systems are unlikely to reach a 12-year life span due to early sensor failure, failure of the pavement/sensor bond, or deterioration of the pavement condition itself.

51 (The most common value used is the mean front-axle weight of FHWA Class 9 trucks.) Unfortunately, these values often are not constant. Even more importantly, there often are site- specific variations in the values of these variables. Thus, unless the autocalibration function is first independently measured and tracked at a site, the equipment controlled entirely by an automatic self-calibration function will be miscalibrated, pro- ducing biases in the data collected. Calibration problems identified by a quality assurance pro- gram may also not be solved through simple adjustment of the calibration factor for the scale. In many cases, calibration drift is a symptom of a larger problem (pavement deteriora- tion, sensor degradation, etc.) that requires a site visit and equipment or site maintenance action. Quality assurance programs are designed to review col- lected data and report unusual or unexpected results. In many ways, this is similar to how many autocalibration systems work. Where they differ is that quality assurance programs should not result in automatic changes to the data collection equipment or collected data. Instead, problems identified by the quality assurance process should result in an independent review of the operation of the equipment. Only after this independent check takes place should data and equipment be discarded or adjusted. For permanently installed sensors, unusual data flagged by the quality assurance process normally means that a site visit should occur to check the performance of sensors and their connected electronics. Such a site visit should include a visual review of pavement and sensor condition and a short, manual classification count that can be compared with reported traf- fic counts. For WIM equipment, it is often necessary to val- idate the calibration setting for the site. The following types of data checks are often used in the quality assurance process: • Has the location of either the loaded or unloaded peak in the GVW distribution for the FHWA Class 9 trucks changed since calibrated data were last collected at this site? (See Figure 5.1.) Other vehicle classes that exhibit a common loading characteristic at the site in question can also be used in this data review. • Has the mean front-axle weight for loaded FHWA Class 9 trucks changed since calibrated data were last collected at this site? • Has the percentage of all weekday trucks that are clas- sified as FHWA Class 9 changed significantly from pre- vious counts at this site? Did percentages increase in classifications that indicate malfunctioning classification equipment (e.g., an increase in FHWA Class 8 would indicate a missed axle)? • Did the number of unclassified vehicles increase to unex- pected levels? • Did the number of counting errors reported by the equipment increase to unexpected levels? • Are the left and right wheel path sensors (for those scales with multiple sensors) reporting similar axle weights? • Has the measured distance between axles for tractor drive tandem axles changed? 0 2 4 6 8 10 12 14 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 104 108 112 GVW (thousands of pounds) P er ce n t o f T ru ck s Original Calibration Shift Indicating Calibration Drift Figure 5.1. Use of GVW of FHWA Class 9 trucks to detect scale calibration drift.

52 • Is the total number of vehicles counted within expected ranges? (Note that the range used should be fairly large because truck volumes in particular can vary significantly from day to day.) • Are there any unusual time-of-day traffic patterns that would indicate the potential for some type of counter failure or inappropriate counter setting? (For example, does the volume at 1:00 a.m. exceed the volume present at 1:00 p.m.?) • Are hours of data missing from the dataset? • Have the scale’s diagnostics reported any problems? Most of these checks assume that a trusted dataset exists against which new data can be compared in order to deter- mine the presence of unusual data. For permanent data col- lection sites, the best place to get these trusted datasets is immediately after the site is first installed and calibrated. The initial calibration effort should ensure that the site is working correctly, that the vehicles crossing the sensors are being cor- rectly counted and classified, and that the weights are accu- rate. Data collected immediately after calibration should then serve as the initial trusted dataset. If traffic patterns change over time (and the validity of these changes is independently confirmed), additional trusted data patterns can be developed, stored, and used as part of the quality assurance process. For short-duration counts, it is important that data collec- tion crews that set equipment confirm the equipment’s proper operation at the outset before leaving the site and then recon- firm the equipment’s proper operation prior to picking up the sensors at the end of the count. (That is, the crews should per- form a consistent, routine check to ensure that the counter is correctly counting and classifying vehicles each time it is placed.) Office-based reviews can then compare the col- lected data against the short-duration counts made to confirm equipment operation, as well as against earlier (historical) counts made at that site or at nearby sites on that roadway. In addition to these basic data checks, a number of addi- tional routines are often provided by equipment vendors or developed by individual agencies. These should be reviewed, tested, and used whenever they offer cost-effective improve- ments to available procedures. The key to any quality assurance program is that the rou- tines available are tested and used. This requires resources and effort, but results in substantial improvement in the qual- ity of data collected and supplied to users. Over time, qual- ity assurance practices will help identify poor equipment and poor data collection practices, which can then be discarded or modified as appropriate. These practices also will help agencies improve their knowledge of traffic patterns in the state, which is a major benefit to an agency. Wherever possible, quality assurance tests should be auto- mated. However, the automated tests should primarily be limited to • Creating easy-to-use data summaries, • Flagging questionable data, and • Removing poor data after their quality has been con- firmed to be poor by qualified staff review. Staff review of these summaries and the performance of independent reviews of irregular data collection results should still be done for all collected data. As noted earlier, traffic can vary considerably from location to location, and knowledge of site-specific traffic patterns and independent review of ques- tionable data are keys to successful quality assurance programs. 5.6 PURCHASE EQUIPMENT WITH A WARRANTY When purchasing equipment, it is good practice to obtain a warranty on the life of that equipment. The warranty should specify the expected life of the sensor given specific uses of that sensor. For example, a 5-year warranty on bending-plate weigh pads might be specified given a lane volume of less than 5,000 trucks per day. Warranties provide agencies insurance against poor manu- facturing quality control and also provide incentives to ven- dors and manufacturers to improve the quality of their equip- ment. Warranties are not free, but limit the cost of equipment replacement. For a vendor, the added revenue obtained in return for a warranty becomes profit, so long as the equipment performs as specified. However, if equipment fails prema- turely, a significant loss to the vendor occurs. This approach provides a significant incentive to correctly predict the life span of sensors and other equipment. Warranties of overall system performance have been suc- cessfully used by some states. These warranties extend pro- tection beyond the sensor and accompanying electronics to the quality of the data produced by the data collection systems. These warranties are only effective when the highway agency can supply appropriate site conditions (e.g., smooth enough pavement) to make the warranty valid and when a mechanism to monitor compliance with the warranty is in place. For example, with WIM equipment, it is likely that the equipment vendor subject to this type of warranty requirement will require the site to meet the site specifications defined in ASTM E 1318 specifications. While these conditions may be met immediately after sensor installation, it may be nearly impossible to meet these conditions after two additional years of pavement deterioration. Such difficulties can make performance warranties unenforceable. This example points out that if a highway agency chooses to require data quality warranties from outside vendors, it is necessary to set up a quality assurance program that can be used both to detect equipment that needs repair and to determine when the site conditions no longer meet warranty specifications. The specifications developed for LTPP SPS

53 WIM data collection are a good first step toward this type of program.3 5.7 MANAGE EQUIPMENT INSTALLATION Proper installation of sensors is key to both performance and life span, regardless of the technology involved. To ensure the quality of any given installation, it is good practice to have at least one agency representative and one vendor representative oversee the sensor installation process at permanent sites. This ensures that both the state’s and the vendor’s requirements are met during the installation process. This is particularly important when warranties are used to ensure system performance, in that it ensures that both par- ties are satisfied with the initial site conditions and installa- tion. (For WIM performance warranties, site conditions must usually match ASTM E 1318 site condition specifications. These site conditions should be verified by both parties when the site is first selected, well prior to the beginning of the installation process.) Installation of sensors does not just involve placement. For permanently mounted sensors, installation also involves (among other items) placement of conduit for lead wires, placement and design of junction boxes, design and place- ment of cabinets that hold data collection electronics, and provision of environmental protection (lighting and electri- cal surge protection, moisture protection, temperature con- trols, defenses against insect and rodent infestation) for the entire system. Poor installation of any features can lead to early system failure and significant increases in both sensor downtime and maintenance and repair costs. Good practice for equipment installation includes choos- ing good equipment and sensor locations in the first place. For intrusive sensors, this means placing them in or on pave- ment that is in good condition and likely to last well past the design life of the sensors being installed. For both intrusive and non-intrusive sensors, it means understanding the envi- ronmental conditions that occur at a site and designing sen- sor installations so that sensors are protected as much as pos- sible from environmental effects on system performance. (For example, video cameras need to be placed so that glare and other lighting problems are minimized and so that the cam- eras are protected from rain, snow, and spray generated by vehicles. Similarly, intrusive sensors need to be protected from moisture intrusion, with particular attention paid to in-pave- ment wiring when freeze-thaw conditions exist.) A variety of techniques exist for protection of sensors from environmental conditions. Good management practice is to document those practices that are successful (for future use by new staff within the agency) and to share those successes with other agencies. 5.8 CALIBRATE AND MAINTAIN CALIBRATION OF EQUIPMENT Installation of equipment should not be considered com- plete until that equipment has been calibrated and acceptance testing of the device in that location has been completed. Both WIM and vehicle classification devices require calibra- tion, although WIM calibration is far more complex and dif- ficult than vehicle classification. 5.8.1 Initial Calibration A number of procedures for calibrating WIM scales exist. Appendix 5-A in the 2001 FHWA Traffic Monitoring Guide4 provides a reasonably complete description of the current state-of-the-art in WIM system calibration. Some material from this appendix is reprinted below. In addition, ASTM5 and the FHWA’s Long Term Pavement Performance Project6 have recommended the use of two test trucks of known weight but different vehicle characteristics (different classi- fications and/or suspension types) for performing WIM scale calibration. The two-test-truck calibration technique consists of obtain- ing static weights for two distinctly different vehicles and then repeatedly driving those vehicles over the WIM scale. Scale calibration factors are then adjusted to minimize the mean error obtained when comparing static and dynamic weights. (Both the ASTM and LTPP documents provide step-by-step directions for calibrating scales using this technique.) Ideally, during the calibration effort, the two test trucks should be driven over the WIM scale at a variety of speeds and under varied pavement temperature conditions in order to ensure that the scale operates correctly under all expected operat- ing conditions. The use of two calibration vehicles is specifically designed to limit calibration biases that can be caused by the use of a single test vehicle. Biased calibration when using a single test truck comes from the fact that every truck has its own unique dynamic interaction with a given road profile given a specific load. Calibration of a scale to a single vehicle’s dynamic per- formance (motion) is acceptable when the motion of that vehicle is representative of the traffic stream. Unfortunately, it is extremely difficult to determine if a given test truck is representative of the traffic stream, and consequently use of a single vehicle can cause a calibration bias that forces the scale to weigh most vehicles inaccurately. The source of this calibration bias can be explained with two figures. Figure 5.2 illustrates how the force applied by a 3 http://www.tfhrc.gov/pavement/ltpp/spstraffic/index.htm (active as of June 20, 2003). 4 http://www.fhwa.dot.gov/ohim/tmguide/index.htm (active as of June 20, 2003). 5 American Society of Testing Materials, Annual Book of ASTM Standards 2000, Sec- tion 4, Construction, Volume 04.03, Designation: E 1318-02—Standard Specification for Highway Weigh-In-Motion (WIM) Systems with User Requirements and Test Method, ASTM. 6 Long Term Pavement Performance Program Protocol for Calibrating Traffic Data Collection Equipment, April 1998 (with 5/10/98 revisions). http://www.tfhrc.gov/pave- ment/ltpp/pdf/trfcal.pdf (active as of June 20, 2003).

54 truck (or any given truck axle) varies as it moves down the road. This sinusoidal oscillation (bouncing) results from the interaction between the vehicle’s suspension system(s) and the road’s roughness. The vehicle’s dynamic motion causes the weight felt by the road (or the scale sensor) to change from one pavement location to the next as the vehicle moves down the road. The goal of the WIM calibration effort is to measure this varying force at a specific location (Point A in Figure 5.2) and relate it to the truck’s actual static weight. To do this, the scale sensor must be able to measure the weight actually being applied at Point A and also to correct for the bias resulting from the fact that, at Point A, the test truck is actually producing more force than it does when the truck is at rest (because it is in the process of landing as it bounces down the road). By using a test truck, it is possible to directly relate the actual weight sensor measurement to the actual static weight in one simple calculation. If the test truck is driven over the WIM scale several times and the weights estimated by the scale are compared with the known static values, it is possi- ble to determine whether the scale is operating consistently and, if it is, to calculate a statistically valid measure of the scale’s ability to estimate that truck’s axle and gross vehicle weights. The scale’s sensitivity is adjusted (the “calibration factor”) until the weights estimated by the scale equal the known static weights of the truck and its axles. The problem with the single test truck technique occurs because each truck has a different dynamic motion. When the test truck has a different set of dynamics than other trucks using that road, the scale is calibrated to the wrong portion of the dynamic curve. In the example illustrated in Figure 5.3, if the scale is calibrated to the dynamic motion of the test truck, it will cause the scale to overestimate the weights asso- ciated with the majority of trucks on that road (Point B). A change in a given vehicle’s speed affects the force applied by that vehicle’s axles at any given point in the road. This is because the oscillation of the suspension and load are pri- marily based on time, not distance. Thus, the load always lands at the same time after a bump in the road is crossed, but if the truck is going slowly, that landing is located closer to the bump than if the truck is moving quickly. Thus, on roads where truck dynamics are high (and the trucks are bouncing a lot), a change in average vehicle speed (e.g., caused by con- gestion or some other factor) can result in a shift in the appro- priate calibration factor for a scale. To solve the calibration problem caused by dynamic vehicles, five basic approaches have been proposed in the literature: • A scale sensor can be used that physically measures the truck weight for a long enough period to be able to account for the truck’s dynamic motion (this is true of Figure 5.2. Variation of axle forces with distance and the resulting effect on WIM scale calibration. Axle force as the truck moves down the road Actual force (axle weight) at Point A Scale Location Bias caused by measuring this axle at Point A Distance (Direction of Travel ---->) Static Weight Le s se r fo rce th an s ta tic weig ht Gre at er f orc e t ha n s ta tic weig ht

55 the bridge WIM system approach where the truck is on the scale the entire time it is on the bridge deck). • Multiple sensors can be used to weigh the truck at dif- ferent points in its dynamic motion either to average out the dynamic motion or to provide enough data to predict the dynamic motion (so that the true mean can be esti- mated accurately). • The relationship of the test truck to all other trucks can be determined. This is often done by mathematically mod- eling the dynamic motion of the truck being weighed in order to predict where in the dynamic cycle the truck is when it reaches the scale. • More than one type of test truck can be used in the cal- ibration effort (where each test truck has a different type of dynamic response) in order to get a sample of the vehicle dynamic effects at that point in the roadway. • Independent measurements can be used to ensure that the data being collected are not biased as a result of the test truck being used. As noted earlier, the current best practice relies on the use of multiple test vehicles (a minimum of two) for initial cali- bration of WIM scales. This technique was chosen over the other methods because of its simplicity and its relatively low costs compared with the other alternatives, though there is appreciable interest in the multi-sensor approach in Europe. 5.8.2 Maintaining Calibration Once a scale is initially calibrated, best practices maintain calibration over time by a combination of periodic on-site calibration verification field tests and an ongoing review of the scale output against known quantities (e.g., have the loca- tions of the loaded and unloaded peaks for Class 9 trucks moved since the scale was calibrated?). When changes are observed in the reported values for these known quantities, scale performance is investigated (i.e., the measured changes trigger one of the periodic field calibration tests) to determine if a change in vehicle characteristics is occurring or if changes in pavement profile or sensor sensitivity have affected the scale’s calibration. 5.8.3 Autocalibration While many WIM systems feature autocalibration func- tions, these are not an acceptable substitute for the initial site calibration, and, even when used for maintaining the calibra- tion of a previously calibrated WIM, they should only be used with caution. Many autocalibration techniques were originally designed to adjust scale calibration factors to account for known sen- sitivities in sensor performance to changing environmental Figure 5.3. Variation of axle forces with distance and the resulting effect on WIM scale calibration. Axle force on single calibration truck Average bias for all trucks is negative Scale Location Bias caused by measuring this axle at Point A Distance (Direction of Travel ---->) Static Weight Le s se r fo rce th an s ta tic weig ht Gre at er f orc e t ha n s ta tic weig ht Average vehicle motion for all trucks on this roadway A B

56 conditions. Others were software adjustments developed to take into account equipment limitations. Common techniques include • Using the average front-axle weight of FHWA Class 9 trucks and • Using the average weight of specific types of vehicles (often loaded five-axle tractor semi-trailers or passen- ger cars). Although these techniques can have considerable value, they are only useful after the conditions they are monitoring at the study site have been confirmed. In fact, tests performed by LTPP7 showed that autocalibration functions were not always successful at maintaining calibration of environ- mentally sensitive sensors when environmental conditions were changing rapidly. Autocalibration functions cannot be expected to calibrate a scale accurately if key autocalibration values have not been independently confirmed at that site. Site-specific confirmation of autocalibration variables is important because research has shown that those key vari- ables are not as constant as thought when autocalibration for WIM was first developed. For example, while the average front axle weight for Class 9 vehicles at most sites stays fairly constant (and can be measured accurately if a large enough sample is taken), the average front axle weight often varies significantly from site to site across the country or even within a state. Part of this variation is due to different weight laws and truck characteristics, part is due to different truck load- ing conditions at each site, and part is due to vehicle charac- teristics that are controlled by vehicle drivers. Most drivers of modern tractors can change the location of the “king pin” (i.e., the point at which the semi-trailer con- nects to the tractor). Setting the king pin close to the cab pulls in the trailer, reducing air resistance and improving fuel effi- ciency. However, this setting also magnifies the roughness of the ride in the cab and increases driver discomfort. Setting the king pin farther away from the cab smoothes the ride in the cab but results in higher fuel consumption. When operat- ing on rough roads, drivers tend to set the king pin farther back than when they operate on smooth roads. If no other changes are made, simply moving the king pin setting from its foremost position to its rearmost position can shift as much as 2,000 pounds onto or away from the front axle of a fully loaded heavy truck. This is a change of 10 to 15 per- cent. By not accounting for these fairly common fleet changes at a specific WIM scale location, similar errors can be auto- calibrated into the WIM system. In fact, LTPP has confirmed several cases in which autocalibration settings forced scales to adopt biased calibration factors simply because the autocali- bration setting was incorrect for a particular site. Autocalibration is not a bad idea. However, before it can be used even to maintain a scale’s calibration, several factors must be understood: • What autocalibration procedure the scale is using, • Whether that procedure is based on assumptions that are true for a particular site, • How that procedure complements the limitations in the axle sensor (and sensor installation) being used, and • Whether enough vehicles being monitored as part of the autocalibration function are crossing the sensor during a given period to allow the calibration technique to function as intended. The highway agency should also thoroughly test the actual performance of an autocalibration system before assuming that a vendor’s claims about its accuracy are valid. Only after testing has been satisfactorily completed should a state rou- tinely use autocalibration. Even then, autocalibration does not eliminate the need for a state to monitor scale output or peri- odically perform calibration verification tests in the field. 5.8.4 Calibration of Vehicle Classification Equipment In theory, calibration of vehicle classification equipment is not as difficult as WIM system calibration. In reality, some specific installation problems can cause problems with clas- sifier output. Compared with WIM equipment, axle-based vehicle classification equipment is generally less sensitive to minor variations in signal strength. However, some non- intrusive sensors can be very sensitive to input parameters and may require careful tuning of sensor performance to work correctly. There are basically three issues related to the performance of classifiers that need to be reviewed as part of the installa- tion calibration: • Sensor configuration and layout information, • Conversion of the outputs into estimates of each passing vehicle’s characteristics (vehicle speed, vehicle length, and distance between axles), and • The conversion of the vehicle characteristic information into estimates of that vehicle’s classification. Automated vehicle classifiers generally require input of information related to the specific layout of the sensors used. For axle classifiers, this generally means the distance between the two axle sensors (or two loops used for vehicle speed computation). For non-intrusive detectors, it may include measurements such as the height of the camera and angle of view or the distance of a sensor from the roadway. These measurements are used as input to the sensor sys- tems to convert the sensor outputs into the estimates of vehi- cle speed, length, and axle spacing, which are in turn used to 7 SPS Traffic Site Evaluation—Pilots Summary and Lessons Learned, May 2, 2002, http://www.tfhrc.gov/pavement/ltpp/reports/lessons/Lessons.pdf (active as of June 20, 2003).

57 compute vehicle classification. While these outputs are the key calibration measure, problems with the estimation of these values are often a function of poor measurement of the sen- sor layout. Adjustment of these parameters may be needed to make the classifier correctly report vehicle speed and conse- quently axle spacing or vehicle length. (Note that, depending on the classifier technology used, other device parameters may also require adjusting to produce correct device output.) The accuracy of vehicle speeds should be determined by comparing device output against independent measures of vehicle speed collected using a calibrated radar gun or sim- ilar device. Vehicle length and axle spacing computations should be compared by comparing these outputs against inde- pendently collected axle spacing and vehicle length data. Correctly classifying a vehicle requires more than an accu- rate measurement of vehicle speed and the distance between axles (or overall vehicle length). The conversion of these vehi- cle characteristics into an estimate of what vehicle type is represented by that set of attributes is the function of the clas- sification algorithm used by the equipment. While many clas- sification errors are caused by poor sensor input, many errors are simply the result of a classification algorithm that incor- rectly associates a given set of vehicle characteristics with the wrong class of vehicle. With axle classification, this occurs in part because some vehicles from different classes have iden- tical axle spacings. For example, FHWA Class 2 (cars) and FHWA Class 3 (light-duty pickups) have considerable axle- spacing overlap. Many small pickups have shorter axle spac- ings than larger cars. Thus, a considerable number of errors occur when classifiers try to differentiate between these two classes of vehicles. Recreational vehicles (especially those pulling trailers or other vehicles) are another class of vehi- cles that have axle spacings that frequently cause them to be misclassified, even when the classifier is working. Errors associated with poor classification must therefore be separated into those due to poor sensor output and those related to the combination of a poor classification algorithm and/or vehicle characteristics that prevent a given set of sen- sor outputs (axle number and spacing or vehicle length) from correctly classifying a vehicle. Poor sensor output can be dealt with at the time of equipment installation, set up, and calibra- tion. The other two problems must be dealt with through the rigorous design and testing of the classification algorithm used by the agency and through an extensive equipment acceptance-testing program that should be performed when a given brand or model of equipment is first selected for use. (That is, the agency needs to make sure the equipment will classify correctly if installed properly before purchasing large quantities of a given device. Only once the classifier has been shown to work as desired should the device be pur- chased in quantity. If, during the acceptance testing, a device appears to be working correctly but cannot classify specific types of vehicles, the agency should carefully examine the classification algorithm being used to determine if the algo- rithm itself needs to be fixed.) 5.8.5 A Final Word on Calibration and Equipment Installation Calibration is a key part of the initial equipment set up. However, calibration alone will not compensate for a poorly installed piece of equipment. Poorly installed sensors often produce inconsistent signal outputs, making calibration either impossible or unstable over time, as sensor performance declines over time. Poor installation also leads to early sys- tem failure and significant increases in both sensor downtime and maintenance costs. A key part of installation and cali- bration efforts is to ensure that the sensors that have been installed are producing consistent signals. 5.9 CONDUCT PREVENTIVE AND CORRECTIVE MAINTENANCE Preventive maintenance keeps equipment operating. Per- haps more importantly, preventive maintenance helps keep data quality problems to a minimum by reducing the number of strange axle detections that occur during early phases of sensor and pavement failure. Preventive maintenance includes tasks such as cleaning electronics cabinets, replacing parts that are showing wear but that have not yet failed, and even doing minor pavement repairs that are designed to improve pavement smoothness and life such as crack sealing on approaches to sensors. Corrective maintenance is the process of bringing a sensor that is not performing properly back into correct operation. Corrective maintenance can include physical changes to sen- sors or pavement (e.g., sealing cracks in the pavement or repairing the bond between sensors and pavement), replace- ment of failing or failed electrical components, or simply adjustments to sensor calibrations used for computing speed, weights, or overall vehicle length. Proper, timely maintenance increases sensor life, improves data quality, and decreases overall system life-cycle cost. States that have snow and ice conditions need to consider the added maintenance needed for traffic monitoring systems that may be affected by sand, snowplows, and corrosive anti- icing materials. Other specific types of environmental condi- tions, such as dust storms and lightning, are also renowned for frequently causing equipment problems that need to be addressed through timely preventive and corrective mainte- nance activities. Understanding when these types of envi- ronmental conditions are occurring and causing equipment or site damage, and timing site reviews to coincide with these conditions, will decrease the amount of data lost to these con- ditions and increase the quality of the data available from data collection devices. Maintenance activity needs to be tied to data quality con- trol systems. Data being collected often provide early warn- ing signs that minor corrective maintenance is needed at a site. Quick intervention when data quality first becomes questionable both increases the amount of good data that are

58 collected and decreases the staff time spent examining ques- tionable data. If a site visit indicates that minor corrective action is unlikely to resolve problems, the data collection site can be shut down until more significant repairs can be performed. This dramatically reduces the effort wasted in retrieving and reviewing invalid data. Ongoing preventive maintenance also provides excellent input into the performance of differ- ent vendors’ equipment and early warning of impending sen- sor failures. This information can be used both in the budget planning process and as part of sensor deployment planning efforts. For example, if it is known from maintenance activ- ities that pavement at a given site has degraded, data collec- tion staff can plan to move electronics to a new location, where pavement conditions are conducive to accurate data collection, until pavement maintenance activities upgrade conditions at the original site. This ensures the most produc- tive use possible from the available data collection resources.

Next: Abbreviations used without definitions in TRB publications »
Equipment for Collecting Traffic Load Data Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

TRB’s National Cooperative Highway Research Program (NCHRP) Report 509: Equipment for Collecting Traffic Load Data identifies the key issues that should be considered by state and other highway operating agencies in selecting traffic equipment for collecting the truck volumes and load spectra needed for analysis and design of pavement structures.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!