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Equipment for Collecting Traffic Load Data (2004)

Chapter: Chapter 3 - Technology Descriptions

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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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Suggested Citation:"Chapter 3 - Technology Descriptions." National Academies of Sciences, Engineering, and Medicine. 2004. Equipment for Collecting Traffic Load Data. Washington, DC: The National Academies Press. doi: 10.17226/13717.
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21 CHAPTER 3 TECHNOLOGY DESCRIPTIONS Tables 3.1 and 3.2 list the most commonly used technolo- gies for vehicle classification and WIM, respectively, together with their primary strengths and related concerns. Strengths and concerns are summaries of material found in the literature. Opinions of the strengths, weaknesses, or level of expected performance for any given technology or piece of equipment often differ from one expert to another, usually based on the experience that individual has had with a specific piece of equipment. The performance of any specific device may differ from these summaries. This chapter provides further informa- tion about • How these technologies work, • The types of data they can provide, • Installation conditions required for accurate performance, • Specific weaknesses, and • Typical uses (e.g., portable versus permanent data col- lection). As noted earlier, sensor technology is constantly under development. This chapter includes summaries of published research. For more current information, readers should consult resources such as the Vehicle Detector Clearinghouse at New Mexico State University (http://www.nmsu.edu/~traffic/), the FHWA’s Demonstration Project 121 web site on WIM Technology (http://www.ornl.gov/dp121/) maintained by Oak Ridge National Laboratories, and the European WIM of Axles and Vehicles for Europe (WAVE) project web site (http:// wim.zag.si/wave/).1 In addition, excellent written documen- tation exists that should be used when learning about equip- ment attributes and selection. Useful documents include the FHWA’s States Successful Practices WIM Handbook,2 the Traffic Detector Handbook,3 the FHWA’s Traffic Monitoring Guide,4 and the ASTM E 1318 WIM5 standard. This report should serve primarily as a starting point to the selection and operation of vehicle classification and WIM equipment. 3.1 VEHICLE CLASSIFICATION The descriptions of technologies for vehicle classification are grouped on the basis of whether they use intrusive or non-intrusive sensors. Technologies using temporary, sur- face-mounted sensors are considered intrusive technologies, because they involve access to the roadway structure. 3.1.1 Intrusive Technologies This section covers sensor technologies that are placed either in or on top of the pavement and, at a minimum, provide the ability to classify vehicles into passenger vehicles and trucks. Portable Operations Portable sensor technologies used for classification include • Road tubes, • Piezoelectric sensors (BL [brass linguini], ceramic cable, and quartz), • Fiber-optic cable, • Other pressure sensors, • Preformed inductance loops, • Magnetometers, and • Side-fired radar and other non-intrusive sensors. Road tubes, piezoelectric sensors, and fiber-optic cable technologies are pressure sensitive. That is, they deflect as vehicle tires pass over them, and the deflection causes a sig- nal that is detected and interpreted. Inductance loop and mag- netometer technologies are presence detectors that detect the presence of a vehicle (by changes in the sensor’s inductance or the earth’s magnetic field) as a result of the presence of metal in the vehicle. Pressure-sensitive technologies have several strengths and weaknesses. These technologies count vehicle axles and mea- sure axle spacings. Most classification systems that use intru- sive sensors base their classification on these variables. Hence, the performance of the equipment is a function of how accu- rately these measurements are made and how well they assign 1 All web sites referenced in this report were active as of June 20, 2003. 2 B. McCall and W.C. Vodrazka Jr., States Successful Practices WIM Handbook, Cen- ter for Transportation Research and Education (CTRE), Iowa State University, Decem- ber 15, 1997, http://www.ctre.iastate.edu/research/wim_pdf/index.htm. 3 J.H. Kell and I.J. Fullerton, Traffic Detector Handbook, Second Edition, U.S. Depart- ment of Transportation, Federal Highway Administration, Washington, D.C., 1992. 4 Traffic Monitoring Guide, U.S. Department of Transportation, Federal Highway Administration, Office of Highway Policy, January 2001, http://www.fhwa.dot.gov/ ohim/tmguide/index.htm. 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 WIM (WIM) Systems with User Requirements and Test Method.

vehicles to the desired classes. Differentiation between closely spaced vehicles is often improved by using pressure sensors in conjunction with an inductive loop. Where traffic, geometric, or environmental conditions make it difficult to count axles and measure axle spacings cor- rectly, pressure-sensitive sensors do not work effectively. The three most common problems associated with the use of this type of sensor are • Very rough pavement (which causes axles to bounce over the sensors); • Roadway conditions that cause braking or vehicle accel- eration while vehicles are crossing sensors (interfering with the estimation of axle spacing); and 22 • Poor lane discipline, resulting in vehicles changing lanes as they cross sensors or traveling with one tire outside of the established lane lines (and striking sensors in adja- cent lanes). Traffic signals, major interchanges, and congestion can cause the last two conditions. As a result, it is difficult to use these technologies for collecting classification counts at many urban locations or at rural locations immediately adjacent to major interchanges. Another problem with equipment accuracy is a poor cor- respondence between the variables measured and the vehicle classes of interest. Pressure-sensitive technologies, by them- selves, have difficulty distinguishing between vehicles in the Type of Sensor Strengths Concerns Portable Vehicle Classification Sensors Road Tubes (axle-based classification) Inexpensive Very common Easy to use Inaccurate under high volumes Difficult to install on multi-lane facilities Conventional road tubes can only measure classifications in lanes next to shoulders or medians. Multi-lane road tube technology is relatively new to the market Inductance Loops (preformed) – (total length-based classification) Inexpensive More difficult to place than road tubes Difficult to install under high-volume conditions Accuracy degrades with tight headways Magnetometer (total length-based classification) Ease of deployment Simple installation Difficult to deploy in high-volume conditions Little reliability information published Accuracy degrades with tight headways Eight length-based classification bins Conventional Pressure Sensors includes various piezo technologies and tape switches (axle-based classification) Well supported by vendor community Ease of deployment in low-volume conditions and when measurement lane is accessible from a shoulder Reliable Can be difficult to place in high-volume conditions (may require traffic control) Meticulous installation required Easy to break sensor/wiring connection if used for lanes not bordering on shoulders Susceptible to lightning Fiber-Optic Cable (axle-based classification) Can monitor multiple lanes Not susceptible to lightning Relatively new technology with little performance history Often requires traffic control to install if deployed across multiple lanes Side-Fired Radar (total length-based classification) Non-intrusive sensor Use in a portable configuration is relatively uncommon TABLE 3.1 Sensors commonly used for vehicle classification

FHWA Classes 2 (cars), 3 (light-duty trucks), and 5 (six-tire, two-axle, single-unit trucks). Many of these vehicles have axle spacings that overlap the boundaries that are commonly used to distinguish vehicles in these classes. Various types of recreational vehicles are also difficult to distinguish based on their axle configurations. In some cases, these errors are irrel- evant in terms of traffic load estimation (e.g., misclassifica- tion of cars as light duty trucks). 23 A related problem is differentiating between two closely following vehicles (often two cars) and a truck pulling a trailer. Traffic signals tend to create platoons of closely spaced vehi- cles. These vehicle platoons are often miscounted as multi-unit trucks. These types of errors have more significant impacts on traffic load estimates. Presence detectors have some of these same problems. In particular, presence detectors rely on constant vehicle speeds TABLE 3.1 (Continued) Type of Sensor Strengths Concerns Permanent Vehicle Classification Sensors Intrusive Sensors (General Comments) Sensors installed in the pavement tend to be adversely impacted by poor pavement condition Poor lane discipline limits accuracy Must be reinstalled if channelization changes Snow can badly degrade lane discipline and consequently classification count accuracy Axle sensor-based systems allow use of FHWA 13-category system and similar state classification systems When traffic flow conditions are unstable, as often occurs in urban areas, simpler, more aggregated, length-based classification schemes often work more accurately than the more complex, axle- based classification systems Inductive Loop (conventional) (total length-based classification) Widely supported technology Inexpensive Length classification not as detailed as axle- based classifications Loses accuracy in areas with closely spaced vehicles Inductive Loop (undercarriage profile) New technology Relatively new technology with little performance history Higher traffic volumes deteriorate accuracy Requires well-tuned loops Piezo Cable (ceramic, polymer [film], or quartz) Widely used and supported Best practices information available Ease of deployment Can work well in areas of high volume, if speeds are stable Requires regular maintenance Difficult to maintain in areas of high traffic volumes Fiber-Optic Promising new technology Immune to lightning Inexpensive if amortized for moderate period of time Little data available for accuracy and reliability Other Pressure Sensors Sensors are generally immune to lighting Technology is generally well understood Used frequently in toll applications along with loops, which allows accuracy in low- speed, unstable (stop-and-go) conditions Not widely deployed Requires new interfaces from several manufacturers Magnetometer Ease of deployment Limited classification bins based on length Little reliability data available Data retrieval from some models can require wireless communications (continued on next page)

in order to accurately measure vehicle length (and correctly classify vehicles). Acceleration and deceleration interfere with this measurement. Presence detectors also have difficulty sep- arating closely spaced vehicles and differentiating between tailgating vehicles and vehicles pulling trailers. However, by limiting the number of length classes used, overall accuracy from presence detectors tends to be higher than with axle detectors in areas with only modest changes in vehicle speed. 24 The other major limitation of most presence detectors is that they are not capable of detecting axles,6 so they cannot be used to classify vehicles into the axle-configuration cate- 6 One new loop-based technology, “Undercarriage Profile Loops,” currently under development for use at permanent sites, is designed to detect axles. This technology is discussed in the next subsection. TABLE 3.1 (Continued) Type of Sensor Strengths Concerns Permanent Vehicle Classification Sensors (Continued) Non-Intrusive Sensors (General Comments) Easily adjusts to new channelization Accuracy normally not affected by deteriorating pavement conditions Normally cannot provide FHWA 13-category classification information Requires mounting structure (bridge, sign bridge, pole) Accuracy tends to be significantly affected by mounting height and angle of view Stability of mounting platform affects accuracy Video Allows multiple lanes of data collection from a single camera Easy to deploy Widely accepted technology Well supported Affected by visibility problems (snow, fog, heavy mist/rain) Camera lenses must be protected from the elements Less accurate in multi-lane environment Generally, only performs length-based classification accurately Microwave Radar Accuracy not affected by weather or poor pavement conditions Allows multiple lanes of data collection from a single device Easy to deploy Widely accepted technology Well supported Under good conditions is generally less accurate in multi-lane environment than traditional sensors Only performs length-based classification Infrared New technology – appears promising Multiple lanes can be measured by one device Affected by visibility problems (snow, fog, heavy mist/rain) Requires regular maintenance Not as accurate in multi-lane environment Little reliability data available Ultrasonic New technology - appears promising Little reliability data available Requires multiple sensor installation Accuracy deteriorates as traffic volumes increase Some environmental conditions (air turbulence) can decrease system accuracy Acoustic New technology Little reliability data available Accuracy deteriorates with increasing variability in traffic speeds

gories used by most WIM systems. Instead, length-based classes are used, producing somewhat less accurate estimates of axle loads experienced by pavements. Additional details about these technologies follow. Road tubes. Road tubes are by far the most frequently used portable classification sensors. Like most pressure sensors, the most common configuration is two road tubes placed in paral- 25 lel, a measured distance apart, perpendicular to and within a single lane of traffic. The time differential between these two known sensor positions allows the computation of vehicle speed and, consequently, the spacing between axles. Road tubes are air switches. As an axle crosses each tube, the tube collapses and pushes air through a switch at the counter. The air switch generates an electrical signal that is used to record the time each axle crosses the sensor. TABLE 3.2 Sensors commonly used for WIM Type of Sensor Strengths Concerns Permanent WIM Sensors General Comments Permanent sensors are placed flush with the road surface, increasing the accuracy of the sensor outputs The accuracy of all WIM sensors decreases with decreasing pavement conditions Unstable speeds, which are common in urban areas, result in significant decreases in WIM accuracy, regardless of the technology chosen Piezoceramic Cable Easier, faster installation than most other WIM systems Generally lower cost than most other WIM systems Well supported by industry Sensitive to temperature changes Accuracy affected by structural response of roadway Susceptible to lightning Meticulous installation required Low cost and ease of installation often result in placement in slightly rutted pavements, resulting in loss of accuracy Piezopolymer Easier, faster installation than most other WIM systems Generally lower cost than most other WIM systems Well supported by industry Sensitive to temperature changes Accuracy affected by structural response of roadway Susceptible to lightning Meticulous installation required Low cost and ease of installation often result in placement in slightly rutted pavements, resulting in loss of accuracy Piezoquartz Easier, faster installation than many other WIM systems May be more cost-effective (long term) if sensors prove to be long lived Very accurate sensor Sensor is not temperature sensitive Growing support by industry More expensive than other piezo technologies Requires multiple sensors per lane Above average maintenance requirement Sensor longevity data not available Accuracy affected by structural response of roadway Bending Plate Frame separates sensor from pavement structure Entire tire fits onto sensor Moderate sensor cost Sensor is not temperature sensitive Extensive industry experience with the technology Longer installation time required than piezo systems Some systems have experienced premature failure, while others have been very long lived (continued on next page)

Tubes used for classification purposes must be placed par- allel to each other and perpendicular to the direction of travel. (If the tube is not placed perpendicular to the direction of travel, a single axle may generate more than one air pulse, resulting in an inaccurate count of axles.) Both tubes must be the same length, or the timing of the air pulse at the air switches will not be equal, and the time differential between the first and second sensors will be inaccurate, resulting in inaccurate esti- mation of speed and, consequently, axle spacing. 26 Traditional road tubes were limited to outside travel lanes for classification purposes. This is because placing a single tube across more than one lane of travel generates signals from each lane. Several tube makers have solved this prob- lem by making road tubes that have only a limited section of tubing that produces air pulses. These tubes are lane sensi- tive and can be used in multi-lane applications. Also, it is possible to use a multi-tube configuration with certain detec- tor products to obtain classification and lane volumes across TABLE 3.2 (Continued) Type of Sensor Strengths Concerns Permanent WIM Sensors (Continued) Load Cell Entire tire fits onto sensor Frequently considered the “most accurate” of conventional WIM technologies Some systems have demonstrated very long life spans Most expensive WIM system Requires significant construction effort to install Becomes cost effective if constructed and maintained for a long life span Fiber-Optic Promising technology Not susceptible to lightning New technology, no longevity history Not well supported yet by industry Accuracy affected by structural response of roadway Subsurface Frame Strain-Gauge System System designed to eliminate impact loads on sensor, increasing expected design life Buried design increases “time on sensor” for an axle Very new technology, currently undergoing testing in the United States No data on longevity of system, or accuracy of output using current software design Unclear if variation in structural response of pavement will affect system accuracy Expensive, long-duration installation Multiple Sensor Systems (piezo, bending plate) Increasing the number of sensors used increases accuracy, everything else held constant System performance only somewhat degraded if one sensor fails, thus increasing system reliability Increase in the number of sensors increases the chance that at least one sensor will fail Higher number of sensors increases installation time and maintenance costs Bridge WIM (includes CULWAY) Bridge platform limits the effect of vehicle dynamics Recent European advances offer significant improvements over previous U.S. versions Only proven to work consistently on a limited set of bridge designs (mostly short-span girder bridges) Needs truck isolated on bridge to weigh accurately Not actively marketed in the United States Capacitance Mats Modest sensor cost Frame separates sensor from pavement structure Most common configuration only measures one wheel path

multiple lanes in the same direction. However, multi-lane installations are prone to error because it is difficult to anchor them tightly enough to keep them from bowing in the middle, violating the requirement that they remain perpendicular to traffic. The primary advantages of road tubes are that they are very inexpensive to purchase and are easy to install. They also are frequently used for traditional volume counting. Piezoelectric sensors (BL and ceramic). Piezoelectric sen- sors come in a variety of shapes and materials. For classifica- tion purposes, each of the most common sensor styles has fairly similar properties. When a mechanical force is applied to a piezoelectric device, it generates a voltage by causing electrical charges of opposite polarity to appear at the paral- lel faces of the piezoelectric material. An electronic compo- nent of the counter detects this signal and uses it to indicate the passage of an axle. The measured voltage from the sen- sor is proportional to the force or weight of the wheel or axle as it is applied to the sensor. This allows the piezo sensor to be used as a scale. Sophisticated vehicle classifiers use this measure of axle weight to improve the accuracy of the vehi- cle classification algorithm; however, many classifiers use the strength of the sensor output signal only to separate sig- nal noise from the passage of an axle. 27 The piezoelectric effect is dynamic; i.e., charge is gener- ated only when the forces applied to the sensor are changing. Thus, piezoelectric sensor systems can only be used in appli- cations where vehicles are moving at speeds above 10 mph. Piezoelectric sensor systems cannot be used at locations with either slow-moving or stop-and-go traffic. Some piezoelectric materials (and sensors) are sensitive to temperature and do not perform well in very cold temperatures. As with road tubes, the most common portable piezo sen- sor installations consist of two sensors, parallel to each other and perpendicular to the roadway, a measured distance apart. Unlike conventional road tubes, piezoelectric sensors are lane specific. Thus, they can be used to monitor inner lanes; however, for portable operations, lead wires to the sensors must be placed across the outer lane(s). This increases the potential for damage to sensor connections to lead wires, one of the more common causes of sensor failure. Fiber-optic cable. Use of fiber-optic sensor technology for axle detection is fairly new and relatively uncommon in com- parison with other intrusive technologies. Fiber-optic sensors detect the presence of a load by measuring the decrease in optical transmission caused by constriction of the fibers when tires pass over the sensors. Fiber-optic sensor systems contain a light transmitter (usually a light-emitting diode), a photon TABLE 3.2 (Continued) Type of Sensor Strengths Concerns Portable WIM Sensors General Comments Sensors placed on top of the pavement create a “bump” that decreases the accuracy of static weight estimates Placement of portable sensors without performing new, “in place” calibration effort is likely to lead to unreliable weight estimates Bridge WIM (includes CULWAY) In most “portable” configurations, only axle counting sensors must be placed on the roadway, leading to easy, short- duration equipment set up Recent European advances offer significant improvements over previous U.S. versions Only proven to work consistently on a limited subset of bridge designs (mostly short-span girder bridges) Generally needs truck isolated on bridge to weigh accurately Not actively marketed in the United States Piezo (ceramic cable, BL-polymer film) Ease of deployment Inexpensive sensor cost Susceptible to variations in temperature More accurate if used in permanent installation Capacitance Mats Ease of deployment Modest sensor cost Only measures one wheel path Creates the largest “bump” of the portable technologies

detector, and signal analysis hardware and software in addi- tion to the fiber sensor itself. Fiber-optic sensors are used in the same way as piezo- electric sensors. The sensor itself is normally the width of a lane. Like road tubes, sensor manufacturers have also designed specific sensors that allow for collection of data on all lanes of a multi-lane facility. Fiber-optic sensors are more responsive than road tubes, theoretically making them more accurate under both very slow speed conditions and very high volume conditions. The advantage of fiber-optic sensors over piezo sensors is that the former are not temperature sensitive and the sensors them- selves do not conduct electricity, thus making devices using these sensors less susceptible to lightning strikes. Other pressure sensors. A variety of other pressure sen- sors have been used at one time or another as portable axle sensors. All share the basic functionality of producing an elec- trical signal when the pressure from a passing axle closes a cir- cuit. The most common of these is probably the tape switch. Most portable pressure sensors, like the tape switch, are laid on top of the travel lane and held in place by asphalt tape. Preformed inductance loops. Inductance loops are used in traffic signal operations, making them the most common permanent vehicle sensors. When two loops are placed in series, they allow passing vehicles to be classified on the basis of their overall length. This is done by determining the difference in time between activation of the first and second loops. This time difference, and the distance between loops, allows for the computation of vehicle speed. Using vehicle speed and the total time one of the loops stays active allows overall vehicle length to be derived. It is possible to use preformed inductance loops (most com- monly, wire loops attached to a thin solid frame) as portable sensors. Preformed loops are taped to the road surface a pre- determined distance apart in order to create the required sen- sor configuration. Lead wires can also be taped to the road surface, allowing preformed loops to be placed on multi-lane facilities. Loops have the advantage of being placed in the center of the lane and so are not subject to the same level of impact loading as pressure-sensitive portable sensors. Thus, they are less likely to be knocked loose by passing traffic, and they can frequently be used for longer counting periods than pressure- sensitive detectors. Dual-loop installations, however, are limited in the accu- racy of the data they can provide. Because inductance loops actually measure the presence of metal, and signal strength is a function of the amount and proximity of the metal, not all vehicles are detected at the same distance from the loop. Vehi- cles that contain large amounts of metal tend to be detected for a longer time period than vehicles with little metal. This means that inductance loops tend to overestimate the length of vehicles with a lot of metal and underestimate the length of vehicles with less metal. 28 Limitations in the accuracy of the overall length measure- ment restrict how many vehicle categories are normally col- lected. In addition, considerable error exists in the correlation between overall vehicle length and the FHWA’s 13-category classification system (or the state-specific variations of that system) used by most WIM scales. As a result, most dual-loop systems normally classify traffic into only three or four broad length categories. This reduces the number of classification errors, while still providing an excellent measure of the num- ber of large trucks versus the number of smaller trucks and passenger cars. However, it does not provide other potentially useful information, such as distinctions between the number of heavy single-unit trucks with three or more axles and the number of usually less-damaging two-axle trucks. Magnetometers. Magnetometers measure changes in the magnetic field surrounding sensors to determine the presence of passing vehicles. Like dual-inductance loop technology, magnetometers use estimates of vehicle speed and the dura- tion of the signal to determine the length of vehicles. Vehi- cle length is then used to classify vehicles into defined length categories. Portable magnetometers are commonly used throughout the United States for volume counting and, to a lesser extent, vehicle classification. They are placed on top of the pave- ment in the center of each traffic lane, much like portable inductance loops. However, they are much smaller, making them easier and faster to place. In other respects, their char- acteristics are similar to those of inductance loops. Side-fired radar and other non-intrusive sensors. Because intrusive sensors cannot be placed in many locations due to high traffic volumes, a variety of non-intrusive sensors have been developed. These sensor technologies are described in Section 3.1.2. The vast majority of these technologies are currently designed strictly for permanent operation. A number of vendors are currently working on developing portable ver- sions of their existing non-intrusive detectors. In addition, a number of enterprising efforts have already been undertaken to create portable devices using these sen- sor technologies. For example, Ohio Department of Trans- portation (DOT) has developed the ability to use a side-fired microwave radar system as a portable traffic counter. In this case, the radar sensor is mounted on an extendable pole that is mounted on a trailer. The trailer can be parked in a safe location beside a roadway. The pole is then raised, and the radar system aimed and operated. Power for the system is supplied by batteries. Permanent Operations Except for road tubes, the portable sensor technologies described above can also be permanently installed in the pavement and used for continuous data collection. For this purpose, the sensors are placed in a pavement cut, which is

then sealed with an epoxy or tar and used for data collection over extended periods of time. Road tubes, by design, must be placed on top of the pavement, where they do not have a long enough fatigue life to be used as permanent sensors. Sensors placed in the pavement for long-duration count- ing have particular attributes. The primary advantage of in- pavement sensors is that the impact loads associated with surface-mounted sensors are no longer present. This greatly increases sensor life. However, placing sensors in the roadway has some disad- vantages. A road closure is needed to initially place the sen- sor, as well as every time the sensor needs to be examined or maintained. Road closures are both expensive and publicly unpopular, particularly on high-volume roads. Once placed, in-pavement sensors normally cannot be moved. Thus, if channelization changes (i.e., the lane lines are moved), the sensors are no longer correctly located in the lanes and new sensors must be installed. This makes intru- sive sensors a poor choice for those locations where lane lines will be moved in the near future. Permanent sensors can fail because of fatigue or because of environmental effects such as moisture getting into the sensor or a nearby lighting strike that shorts out the sensor or its electronics. Also, failure of the surrounding pavement can destroy a sensor or render its output unusable. Successful practices designed to limit failures and extend sensor life are discussed in Chapter 5. In summary, initial site selection and installation are the key to achieving long sen- sor life. Placing an intrusive sensor in pavement that is in poor condition is likely to result in poor sensor performance and short sensor life, regardless of the technology chosen. Similarly, haphazard sensor installation (e.g., poorly cleaned or dried pavement cuts) can also lead to early sensor failure. Placement of sensors in pavement that is badly deterio- rated also leads to inaccurate results. Vehicle axles that are bouncing badly “jump” over pressure sensors. Concrete slabs that rock because of joint failure cause pressure sensors to pick up spurious signals and report “ghost axles.” In these cases, the sensors are actually working correctly; they are just functioning in an operating environment that prevents them from counting axles accurately. Descriptions of intrusive sensor technologies that can be permanently installed follow. Piezoelectric sensors. The various types of permanent piezoelectric sensors have similar layouts and slightly differ- ent operating characteristics but different installation require- ments and performance history. The minimum layout is two parallel sensors. An inductance loop can be added to this basic installation (usually placed mid-way between the two parallel sensors), which is used to help separate vehicles. (That is, the loop presence is used to tell the data collection equipment when one vehicle ends and the next begins.) An alternative to this sensor layout is to place two inductance loops (to measure vehicle speed and presence) with a single 29 piezo sensor in between (to count axles and determine the spacing between those axles). Also, a four-sensor layout can be used (two loops and two piezo sensors) in order to allow for loss of one sensor (either a loop or piezo) without loss of classification capability. The differences in piezo sensor operating characteristics are more important for weighing accuracy than they are for classification capabilities. In general, BL sensors require the smallest pavement cut. Quartz sensors are the least affected by temperature change and forces (stresses) that move hori- zontally through the pavement. Quartz sensors are also the most expensive and are primarily used as WIM sensors, rather than simply for classification. Piezo sensors can often be paved over and still function correctly. That is, most piezo sensors are sensitive enough that they can be covered by an asphalt overlay and still be used to detect passing axles (so long as the sensor and its lead wire and connections are not damaged in the process of lay- ing the new pavement). Other pressure sensors. There are a variety of other pres- sure sensors available for use as permanent classification sensors. Fiber-optic cable and older pressure switch tech- nologies belong to this category. Like the piezo sensors, other pressure sensors are typically placed into small saw cuts in existing pavement and held in place by some type of epoxy or other bonding agent. How- ever, unlike piezo sensors, most of these pressure sensors are not sensitive enough to function correctly underneath an asphalt overlay layer. Other pressure sensors generally are less expensive to pur- chase than piezo sensors, though installation time and effort tends to be very similar. Dual-inductance loops. Dual-inductance loops were the first mechanism used to collect long-duration classification data. While the number of these systems in rural areas has been declining in favor of axle sensor-based systems (in order to collect data using the FHWA’s 13-category classifi- cation scheme), they are still commonly used in urban areas. Because urban environments often involve congested traffic conditions, many agencies are unwilling to spend the money needed to place the more expensive sensors required to per- form axle-based classification. At the same time, in many urban areas, volume, speed, and lane occupancy data are needed to operate modern traffic control systems. By plac- ing dual loops in the roadway, these data can be obtained. Loop systems also offer the potential for collecting length- based classification data. Loops have an advantage over pressure-sensitive technolo- gies in that they do not involve contact with vehicle axles and so are not subject to the impact loading that leads to sensor failure. Sensor failure for loops is more commonly tied to freeze-thaw conditions that result in pavement movements

sufficient to “cut” the wire placed in the pavement. They also fail as a result of failing roadside amplifiers. Given the weaknesses inherent in the collection of length- based vehicle classes, the primary drawback to loop systems (other than their susceptibility to freeze-thaw failure) is the fact that classification accuracy degrades significantly under con- gested conditions. Thus, significant quality assurance efforts are needed before data collected at congested, urban sites are accepted as accurate measurements of truck volumes. Undercarriage profile loops. A new technology has recently been released by several manufacturers that uses the shape of the inductance signature of passing vehicles to clas- sify the vehicle. While the specific technical approaches used by the different manufacturers appear to be somewhat dif- ferent, the overriding concepts appear to be similar. In one approach, additional loops are used to help detect axles (by detecting the change in inductance caused by presence of the metal in the axles), while in another approach, the shape of the primary inductance pattern itself is matched against the known shape of specific vehicle types. This approach shows more promise to allow sophisticated classification capabilities than previously available using loop technology. At this time, however, these systems are rela- tively new, and little practical experience is available to deter- mine their accuracy and reliability. Magnetometers. As with undercarriage profile loop clas- sifiers, several different versions of permanent magnetome- ters are being marketed currently. Some are placed directly in the pavement, and others are inserted into conduits placed underneath the pavement. Both styles of magnetometers mea- sure vehicle presence by monitoring changes in the earth’s magnetic field. The sensors are capable of estimating vehicle speed and use that measure along with the duration of vehicle detection to estimate vehicle length. This length estimate is used to classify vehicles. Note that the conduit style of magnetometer is frequently considered to be “non-intrusive” because the conduit can be placed (by drilling under the pavement from the roadway shoulder) without closing the lane of travel. The sensor can be placed in the conduit without disrupting traffic, and the sensor can be repositioned within the conduit if lane geome- try is changed. 3.1.2 Non-Intrusive Technologies for Classification While the majority of vehicle classification counting is performed with intrusive or surface-mounted sensors, an increasing percentage of classification counting is being per- formed with non-intrusive sensors. Non-intrusive technolo- gies include sensors that can be mounted overhead or to the side of the roadway. 30 Non-intrusive technologies have been available for vehi- cle detection and volume counting for a number of years,7 and improvements in computer processing power have allowed these technologies to be extended to the more com- plex task of vehicle classification. In addition, with both the reduction in computer costs and the increased production of non-intrusive sensors resulting in economies of scale for their manufacture, the cost of many of these technologies has declined considerably in the last 10 years. Non-intrusive technologies have a number of distinct advan- tages over technologies that must be placed in or on the road- way surface, including the following: • Increased staff safety (as staff do not need to be in the roadway in order to place the sensors), • Less traffic disruption during sensor installation (as sen- sors can be placed with little or no traffic disruption, even on high-volume roadways), • The ability to reorient the sensor to adjust for changing lane configurations or other geometric changes without having to physically replace sensors, • The capability of some non-intrusive sensors of collect- ing data on more than one lane at a time from a single sensor (e.g., camera), • Ease of maintenance and repair of above-ground sensors in comparison with sensors that are placed in ground, and • Not being subjected to many types of environmental damage that commonly reduce the sensor life of intru- sive sensors (e.g., freeze-thaw damage, tire impacts on exposed sensors, and pavement failure around sensors). Non-intrusive sensors also have weaknesses. The biggest drawback is the difficulty for non-intrusive sensors to count vehicle axles accurately, which is a key aspect of traffic load estimation for pavement design. Some non-intrusive sensors do count vehicle axles, but these systems are limited in their application and have either installation problems similar to intrusive sensors (i.e., they can only measure one lane of traf- fic without being placed at roadway level on the lane lines) or suffer from occlusion that occurs when a system cannot “see” one vehicle or axle because the sensor’s “view” of that vehicle is blocked by an intervening vehicle. As a result of their inability to easily count axles, most non-intrusive sensors classify vehicles by overall vehicle length, similar to dual-inductance loop technology. While this does not correlate directly with the vehicle classes com- monly collected by WIM systems, it does provide useful data for pavement design purposes. Use of vehicle classifications based on overall vehicle length does require an additional data manipulation step for correlating these classes to those used by an agency’s WIM equipment. The staff time and the 7 Field Test of Monitoring of Urban Vehicle Operations Using Non-Intrusive Tech- nologies, FHWA, May 1997.

potential for error associated with this extra data processing step must be traded off against the benefits obtained from use of non-intrusive technologies. Currently, most non-intrusive classification counting is done with permanently mounted sensors. Some vendors and several highway agencies have been exploring the develop- ment of portable versions of non-intrusive devices. These devices usually consist of one of two designs. In one design, sensor arrays are mounted to poles, which are in turn mounted onto trailers fitted with a power source.8 The trailer is then towed to the desired roadside location, and the pole is lifted into position. This allows side-fired detection systems to operate. The second style of system is designed to be tem- porarily mounted on existing highway infrastructure, usually light standards or highway signs.9 These portable systems are not actively marketed in the United States. As with intrusive sensors, the accuracy of non-intrusive classification systems is a function of the quality of the clas- sifier’s sensing system, the proper installation of the sensor, the placement of the sensor in an environment that is con- ducive to the proper operation of that specific technology, and the vendor’s algorithm used to process the raw sensor data. The placement of the sensor in a location where it will work correctly is the most important variable that is within the control of the data collection agency. The starting point for this process is the ability to place the sensors where they can properly sense the vehicles they are intended to classify. When sensors are mounted on the side of the road, it usually means that they must be placed high enough to sense over vehicles in nearby lanes in order to count and classify vehi- cles in lanes that are farther away. Sensor height and angle of view are also important for overhead-mounted sensors that collect data on more than one lane of travel. The specific sen- sor mounting locations required by each device will vary with the device and are not discussed in this report. Specific guidance on these details should be obtained from the ven- dor of each device. (However, it will be noted that overhead- mounted sensors tend to perform somewhat more accurately than the same sensor mounted at the roadside, all other things being equal. This is most likely a result of the overhead posi- tion generally having a better “field of view” than the road- side position.) The specific technologies presented in this section include • Video, • Radar, • Doppler microwave radar, • Passive infrared, • Active infrared, • Passive acoustic, and • Ultrasonic. 31 The accuracy of specific implementations of these tech- nologies was recently studied by a project jointly sponsored by the U.S. DOT and the Minnesota DOT.10 The first round of field tests was completed in 2001, and the second round was completed in September 2002. While the study focused on the collection of volume data using non-intrusive devices, the results of these tests can be of considerable use to agencies interested in using non-intrusive data collection equipment. A pooled fund study specifically looking at portable use of non- intrusive devices has been proposed and is actively being pur- sued. Information on the completed and ongoing non-intrusive detector tests can be obtained from http://www.dot.state.mn. us/guidestar/projects/nitd.html. Video Video detection is the most widely used of the non-intrusive detection technologies. Video devices convert camera images into digital representations (pixel images) and then use micro- processors to analyze those representations. There are two primary video image analysis techniques, trip line and image tracking, with the trip line approach being the oldest and most commonly used. In the trip line technique, a specific portion of the video image is defined as a “zone.” Pixels within this zone are mon- itored for change, and changes in pixels are used to determine when vehicles are entering or leaving the zone. (Zones in video images can be considered “virtual inductance loops.”) Activations of virtual zones can be used to determine volume and lane occupancy. Two or more consecutive zones (set at a known distance apart) can be used, just as dual-inductance loops are used, to measure vehicle speed and consequently overall vehicle length. This allows for vehicle classification based on total vehicle length. Image tracking relies on pattern recognition algorithms to detect, recognize, and track specific kinds of vehicles. These systems allow for more detailed data collection. (For exam- ple, they examine pixel images to detect axles, not just the presence of a vehicle, in order to provide axle-based classifi- cations.) However, the complexity of the algorithms and short- comings of video image quality place additional constraints on their operation. Video detectors of both types are sold by a number of dif- ferent vendors, and these systems can have very different capabilities. These differences are caused primarily by the use of a variety of different data processing algorithms, each of which has different strengths and weaknesses. While con- siderable experience has been gained as a result of the cur- rent use of these devices, the differences in specific vendor implementations make it difficult to identify the differences of those experiences. 8 This style of system has been used or tested in Ohio and New York among other states. 9 This style of system has been used or tested in Virginia and Minnesota among other states. 10 The Minnesota Guidestar Non-Intrusive Traffic Detection Tests http://www.dot. state.mn.us/guidestar/projects/nitd.html.

Factors that have been shown to affect video system per- formance adversely include • Shadows (both stationary and moving shadows cast by vehicles); • Direct sunlight; • Reflections caused by wet pavement and headlights; • Transition from light to dark or dark to light; • Wind-induced pole movement; • Environmental degradation of the video image caused by (1) water on the camera lens, (2) icicles hanging in front of the camera lens, (3) salt grime on the camera lens, or (4) cobwebs on the camera lens; and • Limited visibility caused by such phenomena as heavy snow, heavy mist, or dust storms. Each of these factors creates artificial changes in pixels within the camera image (i.e., a change not caused by a vehi- cle passing through the image). Some of these causes are transient environmental conditions, while others are more per- manent and require corrective maintenance action. (Note: camera-based systems may require more frequent mainte- nance activity than conventional loop-based systems.) The accuracy of counts obtained from these systems is largely dependent upon how effectively each system can deal with these situations. It is also apparent that the design and construction of sen- sor installations must take into account performance limita- tions. Camera lenses need to be protected as much as possi- ble from the elements. Similarly, placement of the cameras to minimize the effects of changing lighting conditions is also important for maximizing the performance of video-based systems. The two primary strengths of video image detection are (1) the ability to easily move “virtual sensors” to adapt to changing lane configurations or to the need for new sensor locations and (2) the ability of field staff to use a video mon- itor to observe what the sensor is actually observing and to consequently (and easily) make adjustments to the operation of the sensor. Video detection has also the advantage of ability to collect, from a single video image, data on more than one lane of traf- fic at a time. The keys to collecting multiple lanes of data from a single camera are (1) the ability to obtain a clear video image of the lanes with sufficient pixel resolution to accu- rately monitor vehicle presence and (2) sufficient computing power to monitor all “virtual detectors” in the image. Radar Conventional radar-based detection uses pulsed, frequency- modulated, or phase-modulated signals to detect vehicles. This technology is currently the only other non-intrusive technology that is designed to collect data from more than 32 one lane at a time with a single sensor. Radar technology has been in use in the United States for a number of years. Radar sensors can be either side-fired (mounted beside the roadway) or overhead mounted. A single, side-fired radar unit can collect data on more than one lane, but a unit is required for each lane if overhead mounting is selected. (Overhead mounting is more accurate, according to the manufacturer.) Because radar technology is relatively immune to weather conditions (snow, fog, etc.), it is used in a number of loca- tions where poor visibility conditions make video impracti- cal. Radar is easy to place, because side-fired systems can be pole mounted at a height of only 5 meters (15 feet), which is considerably lower than for video systems that must often be mounted as high as 10.7 meters (35 feet). Finally, conventional radar has the ability to detect slow- moving and non-moving vehicles. This means that system count accuracy does not degrade significantly in stop-and-go traffic conditions. In some system tests, radar has slightly undercounted vehicles relative to counts made using conventional loop detectors.11 Doppler Microwave Radar Doppler microwave radar is a variation on conventional radar systems. Doppler technology employs a continuous wave signal and measures the wave’s Doppler shift as it is reflected by passing vehicles. These detectors provide vehicle counts and speeds, but are not capable of detecting stopped vehicles and may be less applicable for the classification of vehicles other than non-intrusive detectors. Passive Infrared Passive infrared devices detect the presence of vehicles by comparing the infrared energy naturally emanating from the road surface with the change in energy caused by the pres- ence of the vehicle. Because the roadway may generate either more or less radiation than a vehicle depending on the sea- son, the contrast in heat energy is detected. As with radar detectors, passive infrared detectors can be mounted either on the side or overhead for data collection. These sensors provide the same detector output as conven- tional loops: vehicle volumes and presence. Monitoring these from two consecutive sensor locations allows the computa- tion of vehicle speed and consequently overall vehicle length. Sensor output from passive infrared appear to be unaf- fected by changes in weather conditions. While several ven- dors sell these devices on the U.S. market, there are a rela- tively small number of current installations. 11 Lawrence Klein, Michael Kelley, and Milton Mills, “Traffic Detection Technolo- gies for a Modern Transportation Infrastructure,” SPIE Conference 2592, Collision Avoidance and Automated Traffic Management Sensors, October 25–26, 1995, Philadel- phia, Pennsylvania.

Active Infrared Active infrared sensors differ from passive sensors in that a low-power laser beam is directed from the data collection device to the road surface. Measurement of the time lapsed until the reflected signal returns to the device is used to deter- mine the presence of a vehicle. By splitting the laser beam into two separate signals from a single sensor, it is possible to compute vehicle speed and overall length. This allows length- based classification from a single active infrared sensor. Active infrared systems are also capable of measuring vehicle height and can thus create two- and three-dimensional images of passing vehicles. This allows even more compre- hensive vehicle classification capability. Infrared sensors do have signal degradation during weather conditions that reduce visibility. A good rule of thumb recom- mended by the Vehicle Detector Clearinghouse12 is that if vis- ibility drops to the point where the human eye does not see an object clearly, then infrared sensors are also likely to experi- ence difficulties. Passive Acoustic Passive acoustic devices consist of an array of microphones aimed at the traffic stream. The devices are passive in that they are listening for the sound energy of passing vehicles. The primary source of sound is the noise generated by the contact between tires and road surface. At slower vehicle speeds, the sound of the vehicle’s engine is more prominent. Passive acoustic devices are best used in a side-fired position, pointed at the tire track in a lane of traffic. Acoustic detectors physically measure the changes in sound energy radiating from the roadway. Increases in energy indi- cate the arrival of a vehicle, and decreases in energy indicate its departure. From these data, it is possible to determine lane occupancy. By using multiple detection zones, it is possible to estimate vehicle speed and length, thus allowing vehicle classification by length. Some models of acoustic sensors have been shown to be sensitive to undercounting in cold temperatures. In addition, some acoustic sensors have a loss of accuracy when vehicles are stopped or moving very slowly. These sensors are not com- monly used for classification purposes in the United States at this time. Ultrasonic Pulse ultrasonic devices emit pulses of ultrasonic sound energy and measure the time lapsed until the signal returns to the device. When the sound energy returns more quickly 33 than the normal road surface energy returns, a vehicle is pres- ent. Signal analysis allows determination of vehicle presence and occupancy. Using two closely spaced beams aimed a known distance apart allows for computation of vehicle speed and consequently vehicle length. Pulse ultrasonic devices are capable of high count accuracy when optimally mounted. An overhead mounting location provides a perpendicular reflec- tive surface, offering the best signal return. Tests indicate that great changes in temperature and extreme air turbulence may inhibit accuracy of ultrasonic devices. Such devices are not commonly used in the United States at this time. 3.2 WIM This section discusses sensor technologies that provide the ability to weigh vehicles. The systems must be capable of supplying axle weights and classifying vehicles into at least the 13 FHWA vehicle classification categories. All WIM sensors currently used in the United States mea- sure transient forces applied by tires to sensors as vehicles pass over. They use the measured force to predict the weight applied by the tire (axle) when the vehicle is at rest. The sen- sors used to perform this measurement include very thin, nar- row sensors placed directly in the pavement (fiber-optics, piezo cables); large plates resting in frames that are in turn imbedded in the pavement (bending plates, hydraulic load cells); instrumented roadway structures (bridge and culvert WIM); and flat sensors placed on top of the road surface (capacitance pads). Selecting a specific technology requires considering the following factors: • Cost of the sensors and their installation, • Locations where a given technology can be successfully installed, • Sensitivity of sensors to various factors (temperature, vehicle dynamics, traffic volume, and speed), • Expected life span of sensors, and • Robustness of sensor installation (e.g., the ability to continue to collect data if one or more sensors fail or to compare output of one sensor against another). The WIM task is heavily complicated by the dynamic motion of trucks being weighed. As trucks move, they bounce. The degree to which each truck bounces is a function of pavement roughness, vehicle load, environmental conditions such as wind, and each vehicle’s design and suspension sys- tems. The greater the amount of vertical motion exhibited by trucks, the more difficult the task for WIM systems to accu- rately estimate static axle loads. Thus, for all WIM technologies, a key issue for collecting accurate weight data is to select locations for data collection that minimize the dynamic motion of trucks being weighed. The lower the vertical dynamic motion of passing trucks, the 12 Mimbela and Klein, A Summary of Vehicle Detection and Surveillance Technolo- gies Used in Intelligent Transportation Systems, prepared by the Vehicle Detector Clearinghouse, for FHWA, Fall 2000.

more accurate the WIM scale, regardless of the technology selected. The other step required to account for truck dynamics is to calibrate the WIM scale to the unique traffic characteristics of each data collection site. While it is possible to calibrate each sensor in the laboratory, it is not possible to account for the dynamic motion of trucks at a specific roadway site with- out measuring those forces in the field at the location where the sensor is being placed. Only direct comparison of WIM system output against known axle weights for specific vehi- cles allows the calibration needed to enable a WIM system to accurately predict static axle weights. While many vendors supply autocalibration features with their WIM systems, auto- calibration depends on one or more key assumptions that also must be calibrated to each specific site. 3.2.1 Portable WIM Operations There are only two technologies commonly used for por- table WIM data collection in the United States: capacitance mats and piezoelectric (BL-style) sensors, although a number of states used bridge WIM systems in a portable fashion in the late 1980s and early 1990s. These three technologies are dis- cussed in this section. Finally, some states perform portable operations by moving electronics from one set of perma- nently mounted sensors to another. This style of “portable” data collection will be treated as permanent operations sim- ply because the sensors themselves are permanently placed in the roadway. Capacitance Mats A capacitance mat consists of two metal sheets separated by dielectric material. An outer surface layer surrounds the sensor, protects the steel plates, and allows the sensor to be placed on the pavement. A voltage is applied across the two metal plates. When a vehicle crosses over the plate system, it causes the distance between the two plates to decrease, which increases the capacitance of the system. Measure- ments of the resonance frequency of the circuit allow the esti- mation of axle weight as it is applied to the sensor system. A typical portable capacitance mat system covers one-half of a lane and measures one side of each passing axle. It is usually secured to the roadway surface using a combination of asphalt nails and tape. Portable loops are usually also placed as part of the system installation in order to provide measures of vehicle presence and vehicle speed. Capacitance mats are moderately priced (each pad is about $10,000, not counting data collection electronics) and light- weight. Installation requires several people, however, both to help place the sensors and to provide traffic control and cal- ibration assistance. Use of portable capacitance mats allows WIM data collection to take place on the outside lane of almost any level roadway that has a reasonable shoulder. 34 (Capacitance mats are difficult to use on inside lanes because the lead wires and sensor connections must be exposed to traffic in those positions.) Portable capacitance mats have significant shortcomings in terms of overall system accuracy. The primary ones are that (1) the system only weighs one side of passing axles and (2) the sensor itself is fairly thick, creating a “bump” in the road that both increases vehicle dynamics and causes an impact load on the sensor that degrades system accuracy. These shortcomings cause accuracy from portable systems to fall below that of flush-mounted, full-lane width, permanent WIM systems. Accuracy limitations are also inherent in the placement of mats on the roadway. While mats can be initially calibrated at a control location, the effects of vehicle dynamics at each given data collection location can only be determined by site- specific calibration efforts. The cost of these efforts often far exceeds the cost of placing and retrieving the data collection sensors and greatly increases the cost of collecting accurate weight data with these systems. While many states limit the amount of site-specific calibration done with their portable mat systems, the lack of site-specific calibration significantly affects the mat’s ability to accurately estimate the static axle weights needed for the pavement design process. Piezoelectric Sensors (BL and Ceramic Cable) The primary alternative to capacitance mats currently used by state highway agencies for high-speed portable WIM data collection is thin-strip piezo sensors. There are two basic styles of thin-strip piezo sensors: a flat plate configuration (the BL sensor) and unmounted piezoceramic coaxial cable. Both systems operate on the same basic principle. When a mechanical force is applied to a piezoelectric device, it gen- erates a voltage by causing electrical charges of opposite polarity to appear at the parallel faces of the piezoelectric crystalline material. The measured voltage is proportional to the force or weight of the wheel or axle. The piezoelectric effect is dynamic (i.e., charge is generated only when the forces are changing); piezoelectric sensor systems can only be used in applications where vehicles are moving at speeds not less than 10 mph. Piezoelectric sensor systems cannot be used in applications having either slow-moving traffic or stop- and-go traffic. For portable weighing operations, sensors (each sensor is roughly one lane width in length) are taped to the roadway, perpendicular to traffic. Normally, two sensors are placed a measured distance apart. The time difference between axle contact on the two cables is used to determine vehicle speed, which is then used to determine the axle spacings needed for vehicle classification. These systems are relatively easy to set up, although, like capacitance mats, they are routinely placed only in the out- side lane of traffic in order to allow the lead wires to be placed

on the roadway shoulder. The sensors themselves are less expensive than individual capacitance mats. However, like the capacitance mat systems, piezo cables used in portable operations suffer from significant limitations in accuracy for the following reasons: • Sensors are temperature sensitive, making it difficult to keep them in calibration when temperature changes dur- ing the day. • The sensors’ narrowness allows tires being weighed to “fold” over them, meaning that at no time during the axle weighing process is an entire tire isolated on the sensor. Therefore, changes in tire pressure or tire-tread patterns affect the force measured by this type of sensor more significantly than many other WIM technologies. • The same site-specific calibration problems that affect all portable WIM systems also affect these systems (cal- ibration of the sensors to site-specific vehicle dynamics is necessary to obtain the level of accuracy needed for direct inclusion of these data into the pavement design process). • Sensors have a relatively high signal-to-noise ratio. Bridge WIM In the 1980s and early 1990s, a number of states instru- mented bridges for use as WIM platforms. The technology works by measuring the response to traffic loads as measured by strain gauges attached to girders under the bridge. A num- ber of European countries are still strong supporters of bridge WIM, and Australia extensively uses a similar system based on the deflection of culverts. Portable operations were achieved by attaching the strain gauges, either with C-clamps or permanently, and then con- necting portable roadside electronics to those gauges when data collection was desired. Bridge WIM has the advantage of having a very large weighing platform: the bridge deck itself. This helps limit the effects of vehicle dynamics. Unfortunately, various other fac- tors degrade the signal from the strain gauges and limit the accuracy of data from bridge WIM. The most significant of these factors are the presence of other traffic on the bridge at the same time a truck is being weighed (which significantly increases the noise in the weight signal) and the fact that states could not adequately define the expected response of many bridges to given loading conditions (which limits the accuracy of the computation of loads based on bridge response). Currently, without extensive site-specific set-up, calibra- tion, and testing, bridge WIM is considered reliable only on short-span, simply supported, steel girder bridges where trucks can be isolated on the span during the weighing process. How- ever, the Australians use a similar WIM technology called “CULWAY” that works on a similar principle (measuring the strain of the underside of a structure), but is attached to the underside of large culverts rather than to bridge girders. Use of 35 this technology may increase the number of structures suitable for use as weight sensors. Conventional Static (Loadometer) Scales For low-volume roadways, it is also possible to collect truck weight data with portable static scales used for truck weight law enforcement. Any scale that meets Handbook 44 standards13 is acceptable for truck weight data collection. Collecting data with static scales requires either a perma- nent scale facility or a level, paved surface where trucks can be pulled off the road safely. This requirement significantly limits the locations at which data can be collected. Significant numbers of workers are needed to perform this task. Weighing vehicles statically is a slow process and results in a dataset of limited size. On high-volume roads, this small dataset can easily represent a biased estimate of actual traffic loads, especially if by-pass routes exist, that might bias the weights of trucks being sampled. However, on low-volume roads with little bypass opportunity, this approach to weight data collection can provide an accurate and complete mea- sure of truck traffic for the periods for which the highway agency can afford to collect data. A number of low-speed WIM systems can be used to speed up this process, while maintaining good data quality. Infor- mation on such systems can be found on the FHWA Demon- stration Project 121 web site http://www.ornl.gov/dp121/. 3.2.2 Permanent WIM Operations The majority of WIM data collection is now done with permanently installed weight sensors, although many states do not collect data continuously at these sites. Instead, they attach data collection electronics to previously mounted sen- sors when data collection is desired. The scale sensors are then calibrated (or should be calibrated), and data are col- lected for the desired time period. Permanently mounting WIM sensors allows them to be installed flush with the roadway surface. When done prop- erly, this eliminates the bump that vehicles experience when crossing surface-mounted sensors. The removal of impact loads on sensors and the elimination of extra vertical motion caused by bumps result in improved system accuracy. Permanently mounting sensors flush with the pavement sur- face also decreases the impact loads on sensors themselves, which in turn increases sensor life. One common cause of sen- sor failure is when sensors become directly exposed to hori- zontal forces from tire contact. This exposure often leads to early fatigue failure for both sensors and the bonds between 13 Tina G. Butcher et al., Specifications, Tolerances, and Other Technical Require- ments for Weighing and Measuring Devices: As Adopted by the 83rd National Con- ference on Weights and Measures, 1998, ISBN #0-16-049825-2.

sensors and pavement. Exposed sensors are also highly sus- ceptible to damage from contact with snowplow blades. As noted in Section 2.2.1, a variety of other factors play important roles in both the output accuracy and the life of permanently mounted sensors. Pavement at permanent sen- sor locations should be • Flat (no horizontal or vertical curves), • Smooth (no bumps or other surface conditions that increase dynamic vehicle motion), • Strong (to reduce pavement flex underneath the WIM sensor), and • In good condition. Flat, smooth pavement reduces vehicle dynamic motion and increases the accuracy of all WIM sensors. Strong pavement results in longer lived pavement, which in turn increases sen- sor life. Strong pavement is especially important for strip sensors that are embedded directly into the pavement. The output from these sensors depends on the performance of the pavement itself. If pavement strength varies significantly over time (e.g., with environmental conditions), sensor out- put will also vary, and this greatly decreases the likelihood of accurate sensor calibration. Some researchers have sug- gested criteria for the pavement strength required for installing WIM equipment based on falling weight deflectometer (FWD) measurements. These criteria stipulate a maximum deflec- tion under the center of the applied load and a minimum deflection basin area. Good condition pavement reduces vehicle dynamics and makes the bond between sensors and pavement more likely to last. A common cause of sensor failure is the failure of sensor/ pavement bonds, which is often traced to poor pavement con- dition. Poor installation is another common cause of this fail- ure. Poor cleaning or drying of pavement cuts results in a weak bond that allows moisture intrusion and further deterioration of the bond. Moisture is also a common cause of equipment failure because of intrusion into either the sensor itself or the com- munication lines connecting the sensor to the data collection electronics. Each vendor and each state highway agency has its own procedures for fighting moisture intrusion. Similarly, agencies and vendors have equipment and procedures for protecting permanent equipment from lightning strikes, other environ- mental effects (extreme temperatures, humidity, dust), insects, power surges, and various other causes of equipment or com- munications failure. No single document exists that lists best practices for protecting equipment from these common prob- lems. The U.S. Department of Transportation has recently started promoting information exchanges between state high- way agencies in order to increase the sharing of knowledge in these areas. Specific WIM system technologies that can be used for permanent, continuous weight data collection are 36 • Piezoceramic sensors, • Piezopolymer sensors, • Piezoquartz sensors, • Bending plates, • Hydraulic load cells, • Bridge and culvert WIM systems, • Capacitance mats, and • Other WIM technologies (fiber-optic, subsurface strain gauge, multi-sensor). Each of these technologies is introduced briefly below. Piezoceramic Sensors As noted above, piezoelectric WIM sensors come in vari- ous forms, but all systems operate on the same basic princi- ple. When a mechanical force is applied to a piezoelectric device, it generates a voltage by causing electrical charges of opposite polarity to appear at the parallel faces of the piezo- electric material. The measured voltage is proportional to the force or weight of the wheel or axle and is transmitted by the sensor to electronics that measure and interpret the voltage signal. The first piezo traffic sensor marketed in the United States uses a ceramic powder compressed between a solid core and an outer sheath of copper. The cable is about the size of con- ventional coaxial cable. When used as permanent WIM scale sensors, the cable is most commonly placed in aluminum channels filled with epoxy resin or another substance. The channel is then placed so that the top of the sensor is flush with the road surface, in a slot cut into the pavement, less than 2 inches wide. (Different vendors use slightly different sensor mounting techniques.) Routine site installations can consist of two piezoceramic sensors, two sensors plus an inductance loop, or one piezo sensor and two inductance loops. Each of these configurations allows for the computation of vehicle speed and, consequently, axle spacing, which in turn permits vehicle classification as well as axle weighing. The installations using two piezo sensors tend to provide better estimates of static axle weights because each sensor provides an independent measure of axle weight during a different time period associated with the vertical motion of the vehicle being weighed. Combining the two independent weight esti- mates generally improves the accuracy of the static weight estimate. The piezoelectric effect generated by the sensor is dynamic. That is, the charge is generated only when the forces applied to the sensor are changing. As a result, piezoelectric sensor systems can only be used in applications where vehicles are moving at speeds not less than 10 mph; they are not reliable in slow-moving or stop-and-go traffic. In addition, it is difficult to construct a cable for these sen- sors that has uniform response across its entire length. Strict laboratory testing is done to ensure that cables used for weigh- ing meet uniformity standards. Cables that successfully pass

uniformity tests are called “Class 1” sensors. Sensors that function correctly but do not meet the highest signal unifor- mity standards are designated as “Class 2” sensors and can be used for vehicle classification purposes, but not for weighing. Piezoceramic sensors produce weight estimates of average quality. They suffer from three significant limitations in sys- tem accuracy: temperature sensitivity, reliance on the pave- ment itself for structural support, and narrow sensor design. Because the piezoceramic sensor is temperature sensitive, piezoceramic WIM systems must include various algorithms and/or additional sensor inputs that allow the WIM system to account for temperature changes when estimating weights. Each equipment vendor tends to approach this problem dif- ferently, and different levels of success are achieved. The technical process is further complicated by the fact that sen- sors are placed directly in the pavement structure and, because many pavement structures have structural responses that are also temperature dependent, this also affects the piezo’s signal strength for a given load. Consequently, the sensor response is affected by two independent (but related) sources of varia- tion in signal strength, and these lead to errors in estimated axle weights. The narrow-sensor design is an advantage when it comes to the time and cost required for installation. However, the narrow sensor also means that tires being weighed are never isolated on the sensor. That is, during all points in the weigh- ing process, at least part of the tire is being supported by the pavement surrounding the sensor and not the sensor itself. Thus, the sensor never senses the entire force applied by a tire. This effect is exacerbated by some tire tread designs that can concentrate forces on small surface areas, and those sur- faces may or may not be directly on the sensor itself. The combination of these effects is that the sensor can sense a variety of different forces, and this results in a larger error when estimating static weights than with some other WIM technologies. Piezopolymer Sensors The second common piezo technology uses a piezoelectric polymer surrounded by a flat brass casing. This sensor, com- monly called the BL sensor, is placed directly on the road for portable weighing but, like the piezoceramic cable, is com- monly placed into an aluminum channel filled with epoxy resin when being used as a permanent WIM sensor. This sensor is used exactly as the piezoceramic cable is used and has essentially the same benefits and drawbacks. The BL sensor is also temperature sensitive, and the piezo- electric effect it generates is dynamic. It is not a reliable sen- sor in slow or stop-and-go conditions, and additional steps are needed when processing sensor output to account for changes in sensitivity because of changing temperatures. Finally, like the piezoceramic cable, it comes in Class 1 and Class 2 con- figurations, which indicates the degree of sensor uniformity. 37 Piezoquartz Sensors The piezoquartz sensor was recently introduced in the United States. It differs from the other piezoelectric sensors both in the piezoelectric material used and in the design of the sensor itself, although it still fits into a pavement cut gen- erally less than 2 inches wide. While it is more expensive per sensor than the other piezo- style sensors, the quartz sensor has the distinct advantage of being insensitive to changes in temperature. It is therefore generally more accurate than other piezo sensors. However, because the sensor still relies on structural support from the pavement, if the pavement structure is sensitive to tempera- ture, the sensor will show some change in response to a given axle load simply as a result of the change in pavement strength with changing environmental conditions. This sensor is not sensitive to changes in temperature or soil moisture if placed in a thick portland cement concrete pavement. However, out- put from this sensor is likely to be sensitive to changes in temperature, although not as much as other piezo sensors would be, if placed in a moderately thin asphalt pavement. Like other piezo sensors, this sensor is placed into a rela- tively small slot cut into the pavement. Each sensor is roughly 1 meter (3 feet) long, so four sensors are placed in an end-to- end arrangement to instrument an entire 12-foot traffic lane. The site installation can consist of two lines (eight sensors) of piezoquartz sensors, two lines plus an inductance loop, or one line of piezo sensors and two inductance loops. As with other piezo installations, each of these configura- tions allows for the computation of vehicle speed and, conse- quently, axle spacing, which in turn allows vehicle classifica- tion. The installations using two piezo lines tend to provide better estimates of static axle weights, because each line pro- vides an independent measure of axle weight, and the aver- aged weight estimate can be used to account for the dynamic motion of the vehicle more effectively than a single line of sensors. Real-world experience with piezoquartz sensors is still being gained in the United States, but the sensor appears to offer accuracy on a par with bending-plate systems when installed in structurally strong pavements. Bending Plates Bending-plate WIM systems use plates with strain gauges bonded to the underside. As axles pass over the bending plate, the system measures the strain on the plate and calcu- lates the load required to induce that level of strain. Individual bending plates are generally 6 feet long and roughly 2 feet wide. One bending plate is generally installed in each wheel path. In some cases they are installed aligned, while in other cases the right and left wheel path plates are staggered in order to measure tire loads at two different points in the vehicle’s dynamic path. A typical bending-plate site also includes two inductance loops used to detect approaching

vehicles, to differentiate between closely spaced vehicles, and to measure speed. Bending plates are mounted flush with the roadway into steel frames placed in the pavement. The use of steel frames separates the plate sensor from the roadway structure and increases the accuracy of the weight measurement in com- parison with strip sensors. In addition, the weighing platform is large enough to isolate each tire as it is weighed. This also negates the bridging effect from which strip sensors suffer, as well as limiting the effect that different tire pressures and tread designs have on the forces exerted on the scale platform. Tests of system performance generally indicate that bend- ing plates are more accurate than traditional piezo cable and capacitance mat WIM systems and are roughly equivalent in accuracy to piezoquartz sensors, but are less accurate than hydraulic load cells. However, differences in weighing accu- racy that result from technological differences between WIM systems are often overshadowed by problems inherent with specific weighing installations. (For example, a load cell placed in rough pavement will provide less accurate data than a bending-plate system placed in smooth pavement.) The cost and installation time required to place bending- plate systems also falls between that of piezo and load-cell systems. Because placement of the steel frame involves a more substantial pavement cut than is required for the strip sensor installation, the duration of the lane closure required for system installation is far longer than for piezo systems. However, the time required for bending-plate installation is considerably less than that required for load-cell installation. Hydraulic Load Cells As with most of the WIM technologies, there is more than one high-speed hydraulic load-cell WIM system design in the United States. The most common versions operate by transferring wheel weights applied to the weighing platform to one or more hydraulic cylinders containing oil. Changes in the hydraulic pressure are correlated with axle weights. The most common load cell design uses two in-line scale platforms that operate independently and provides weight estimates for the right and left tires of each axle. The system records the weights measured by each scale and sums them to obtain the axle weight. Off-scale detectors are frequently integrated into the scale design to detect any vehicles off the weighing surface. In addition, at least one inductive loop and one axle sensor are usually included as part of the system design. The inductive loop is placed upstream of the load cell to detect vehicles and alert the system of an approaching vehicle. The axle sensor is usually placed downstream of the load cell to determine axle spacings and vehicle speed. If a second inductive loop is used in place of the second axle sen- sor, it is placed downstream of the load cell to determine vehicle speed, which is needed to determine axle spacings. The deep-pit load-cell system is generally considered the most accurate of the available conventional high-speed WIM 38 systems. It is generally insensitive to changes in temperature and can weigh vehicles at both low and high speeds. It is, how- ever, the most expensive WIM system to purchase and install. The term “deep-pit scale” comes from the fact that this load cell itself requires a significant excavation in the roadway for installation. This means long lane closures are required for sensor installation. Heavy construction equipment is needed to dig installation pits and place sensors and associated elec- tronics. However, the fact that considerable construction is involved normally means that sensors are only placed at loca- tions with smooth pavements (or pavements are made smooth at the time of sensor installation). Thus, hydraulic load cells tend to be correlated with “expensive” installations, which in turn result in better system performance. Load cells are contained in a steel frame that is indepen- dent of the pavement. This makes the load cell’s response to axle weights insensitive to changes in pavement strength caused by changes in environmental conditions (i.e., temper- ature and moisture content). In addition, the weighing plat- form is large enough to isolate each tire as it is weighed. This again eliminates the negative effect pavement strength has on strip sensors, as well as limiting the effect different tire pres- sures and tread designs have on the forces exerted on the scale platform. Bridge and Culvert WIM Systems In bridge WIM systems, strain gauges are placed on the underside of bridges or on the girders of bridges. Strain- gauge output is analyzed to determine the loads on specific vehicle axles. While the number of bridge WIM installations has declined in the United States since the late 1990s, con- siderable research on this subject is still being performed in Europe. Information on this research is available at http:// wim.zag.si/wave/download/wp12_report.html. Culvert WIM is a variation of bridge WIM and is exten- sively used in Australia. In this system, strain gauges are attached to the underside of large culverts, and the strain measurements obtained are used to estimate truck axle loads. The short span of the concrete culvert and the relatively sim- plistic design of the culvert make the analysis of the strain signal straightforward, thus eliminating several of the prob- lems experienced by bridge WIM systems used in the United States. While the culvert-based system has been marketed in the United States, it is not widely used at this time. Capacitance Mats Capacitance mats consist of two metal sheets separated by a dielectric material. An outer surface layer surrounds the sensor, protects the steel plates, and allows the sensor to be placed on the pavement or in a mounting frame. A voltage is applied across the two metal plates. When a vehicle crosses

over the plate system, it causes the distance between the two plates to decrease, which increases the capacitance of the system. Measurement of the resonance frequency of this cir- cuit allows the estimation of the weight of each tire as it is applied to the sensor system. Permanently mounted capacitance mats differ from portable mats in that the former mats are placed in steel frames that are installed in the pavement surface. This allows the surface of the mats to be flush with the roadway and improves the accuracy of the system. It also reduces the impact load on the sensor itself, both increasing sensor life and decreasing the potential for the sensor to be dislodged from the roadway. Most capacitance mat systems rely on weighing only one wheel path. This limitation makes them slightly less accurate than other potential WIM system alternatives. However, ven- dors do sell permanent capacitance mat systems that use mats in both wheel paths. Other WIM Technologies New WIM technologies continue to be developed and brought to the market. Many of the new technologies have been developed specifically to address limitations in the cost, performance, and flexibility of current technologies. The sys- tems discussed below are either in active use elsewhere in the world or in active development in the United States: • Fiber-optic sensors detect the presence of a load by measuring the decrease in optical transmission caused by constriction of the fibers when vehicles pass over sensors. Fiber-optic sensor systems contain light trans- mitters (usually a light-emitting diode), photon detec- tors, and signal analysis hardware and software in addi- tion to the fiber sensor itself. The potential advantages of fiber-optic sensors are relative insensitivity to road temperature and low cost. Fiber-optic sensor systems are not fully developed and are not in field operational use. System accuracy and life have not been established. • Capacitance strip sensors have been used in the United Kingdom for a number of years. These sensors use the same basic principle as capacitance mats (described above), but use a thin sensor (instead of the larger mat) designed for in-pavement installation similar to piezo- sensor deployment. The capacitance sensor material was selected to avoid the temperature sensitivity problems associated with piezoceramic and piezopolymer sen- sors. However, only limited testing of this sensor has been done in the United States, and the sensor is not actively marketed in the United States. • Subsurface strain-gauge frame technology is currently being tested at Virginia Polytechnic Institute and State University. This technology places a steel frame fitted with a large number of strain gauges underneath the pave- ment. (The 2-ton frame is installed at least 2 inches under the pavement surface and can be completely below the 39 roadbed.) The scale sensor is placed by removing the existing pavement at the site and repaving once the scale is correctly positioned. The sensor’s strain gauges reg- ister the strain transmitted through the pavement to the steel frame. A neural network computing algorithm then converts these signals to estimates of vehicle and axle weights. The system uses the pavement structure to dampen the effect of vehicle dynamics and to increase sensor life by limiting the fatigue problems associated with repetitive tire contact and pavement maintenance activities. The manufacturer claims that the sensor system is maintenance free. The testing being performed will determine whether the neural network processing algo- rithm is able to accurately estimate weights given the mitigating effects of the overlying pavement structure. • Multi-sensor WIM is one of the bigger research issues in Europe’s WAVE (“WIM of Axles and Vehicles for Europe”) program. The concept is to use a larger num- ber of moderately priced sensors to weigh a given vehi- cle multiple times during a single pass. By stretching these sensors over many meters, it is possible to deter- mine a vehicle’s dynamic motion and thus significantly improve the estimate of vehicle weight. The use of mul- tiple sensors also provides multiple independent mea- sures of the same basic quantity. While this technique shows considerable promise, it is unclear if it is eco- nomically feasible or if the improvements in accuracy achieved warrant the cost of additional sensors and their placement. A number of states and vendors have moved to take advan- tage of the concept of multi-sensor WIM without taking the approach the European WAVE program tested. In Europe, multi-sensor WIM systems deployed a large number of sen- sors (10 or more). In the United States, vendors and states have both increased the number of sensors deployed and changed the location of sensors in order to improve the measurement of vehicle dynamics. However, they have not increased the num- ber of sensors to the extent examined in the European tests. The increase in sensors allows a more accurate measurement of (and accounting for) the variation in axle weight caused by vehicle motion. However, by limiting the number of sen- sors added, the increase in capital cost and installation time required to build the WIM site is moderated. One fairly common approach to multi-sensor WIM in the United States has been to use three half-lane bending plate scales (rather than the traditional two sensors) and to stagger the left wheel path and right wheel path sensors (rather than placing them side by side). This allows measurement of both sides of the vehicle and provides measurements at three dif- ferent points in the dynamic spectrum while only increasing the sensor cost by 50 percent. Another common approach is to place four staggered sets of half-lane piezo sensors. The concept is the same for this system as for the bending plate system, in that staggering the

sensors yields more information on the dynamic variation of axles, while, in this case, there is no actual increase in the num- ber of sensors required when compared with a conventional piezo-based layout (i.e., two full lanes’ worth of sensors). Both of these designs also have the advantage of providing an extra layer of site reliability. This is because the extra sen- sors allow “graceful degradation” of the WIM system. That is, the loss of one sensor does not make the WIM data unus- able; it simply degrades the accuracy of the system somewhat. 40 In the case of the bending-plate system described above, the loss of one of the two right bending plates actually leaves the site as being equivalent to a conventional bending-plate WIM site in terms of sensor accuracy. Note that before such an approach is adopted, the highway agency must make sure that the vendor’s data collection elec- tronics can both handle any additional sensor inputs and cor- rectly interpret the signals coming from sensors placed in a staggered position.

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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.

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