National Academies Press: OpenBook
« Previous: Chapter 1 - Introduction
Page 10
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2020. Use of Weigh-in-Motion Data for Pavement, Bridge, Weight Enforcement, and Freight Logistics Applications. Washington, DC: The National Academies Press. doi: 10.17226/25793.
Page 11
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2020. Use of Weigh-in-Motion Data for Pavement, Bridge, Weight Enforcement, and Freight Logistics Applications. Washington, DC: The National Academies Press. doi: 10.17226/25793.
Page 12
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2020. Use of Weigh-in-Motion Data for Pavement, Bridge, Weight Enforcement, and Freight Logistics Applications. Washington, DC: The National Academies Press. doi: 10.17226/25793.
Page 13
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2020. Use of Weigh-in-Motion Data for Pavement, Bridge, Weight Enforcement, and Freight Logistics Applications. Washington, DC: The National Academies Press. doi: 10.17226/25793.
Page 14
Suggested Citation:"Chapter 2 - Literature Review." National Academies of Sciences, Engineering, and Medicine. 2020. Use of Weigh-in-Motion Data for Pavement, Bridge, Weight Enforcement, and Freight Logistics Applications. Washington, DC: The National Academies Press. doi: 10.17226/25793.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

10 Current WIM systems serve as traffic monitoring technology that can capture, calculate, store, and even transmit to a remote location these types of information: vehicle axle weights, gross vehicle weights, axle spacing, vehicle classification, vehicle speed, and traffic counts—all obtained by measuring vehicle weights at highway speeds. As WIM technology has advanced, WIM applications have grown from the original axle weight measurements to now include information for pavement and bridge design, asset management and load ratings; commercial vehicle weight enforcement screening; and freight planning and logistics. This literature review documents these uses of WIM. Pavement Design, Bridge Design, and Asset Management and Load Rating The use of WIM in pavement design, bridge design, and asset management and load rating actually fulfills the initial desire for acquiring vehicle weight information. From start of the AASHO Road Test to today, the need to design pavements and bridges to a specific design life requires a knowledge of the traffic using the facility for that chosen design life. Pavement Design All pavement design procedures from the 1960s to 1993, as well as all of AASHTO’s pavement design guides, were based on the empirical performance equations developed in the AASHO Road Test and all have used the concept of the 18,000 lb. ESAL. It is the unit of measure chosen as the standard to compare all axle loads. WIM data can be used to determine ESALs from measured traffic levels. Pavement design procedures have aimed for adequate performance for a specified number of ESAL passes. One shortcoming of the AASHO Road Test is that the conditions (soil types, materials, weather, etc.) are specific to those in 1950s Ottawa, Illinois—where and when the test took place. Additionally, the traffic levels studied amounted to only a little over 1 million ESALs. These limitations and the extrapolations to apply to today’s traffic usually produce pavement designs that are deemed over-designed (AASHTO, 2008; Pierce, 2015; TRB Session #360, 2017). With improved understanding and characterization of traffic, materials, climate effects on materials, pavement performance, data collection, and computing power, design procedures can account for many more variables. In 1998, the National Cooperative Highway Research Program (NCHRP) began a 6-year research effort to address a new, more mechanistic type of pavement design procedure by fund- ing NCHRP 1-37A. The product of this research was the MEPDG and companion software in C H A P T E R 2 Literature Review

Literature Review 11 2004. AASHTO adopted this product as the AASHTO MEPDG (Sharpe, 2004; Transportation Research Board, n.d.). The AASHTO MEPDG software, first introduced in 2004 and updated several times to the most current release called AASHTOWare Pavement ME Design, can account for many vari- ables and their effect on pavement performance. One set of variables is for traffic. AASHTOWare Pavement ME Design can use WIM data in the form of a traffic axle load spectra for traffic inputs. A WIM traffic axle load spectra is a measurement of all axle loads measured and binned by axle group for the 13 vehicle classifications specified in the FHWA Traffic Monitoring Guide across time (classes 1 to 3 are usually not included in pavement design as the weights and consequent pavement damage are minimal). In this way, it can account for the axle configuration and weights of each vehicle class using a roadway representing hourly, daily, monthly, and seasonal traffic variations. The software recognizes three levels of data input representing the accuracy of the output and the cost of the data collection. Level 1 requires the greatest number of and most accurate inputs; this would include site-specific WIM data. Level 2 has fewer input requirements, using regional or statewide average WIM traffic spectra. Level 3 produces the least accurate design outputs, but requires less effort for collecting input data, using default WIM data from average LTPP sites (Pierce, 2015; FHWA, 2016; TRB, 2017). Since the AASHTOWare Pavement ME Design’s introduction, FHWA and state DOTs have conducted research to see the effects that using this software would have on their operations and the level of effort DOTs would need to extend in order to acquire the requisite design inputs. This research has been used in DOT decisions to adopt the software (Hong and Prozzi, 2006; Papagiannakis et al., 2006; Prozzi and Hong, 2006; Khanum et al., 2008; Cottrell and Kweon, 2011; Michigan DOT, 2017). The new AASHTOWare Pavement ME Design software that can use advanced traffic data collected by WIM is beginning to replace the older design methods at DOTs. Bridge Design Since 2007, bridges in the United States have been designed according to the AASHTO Load and Resistance Factor Design (LRFD) method. This method uses live load factors derived from truck data recorded in Ontario, Canada, in 1975. The assumptions for these live load factors were chosen to be conservative because they would be used to represent live loads for design in the whole of the United States. The LRFD process does allow for the use of site-specific or more local live load factors based on actual traffic conditions calculated using WIM data. The default load factors in the LRFD must account for variations in traffic (reliability) that effectively increase design conservatism. Site-specific load factors, developed using WIM, represent the actual traffic spectrum and thus have to account for less variability and consequently produce a design more tailored for the traffic using a bridge. Site-specific load factors are used mostly in the design of signature bridges. This is where significant cost savings can be achieved and the cost of acquiring the required WIM data can be justified. The requirements for developing site-specific load factors are generally not cost-efficient for most bridges (Sivakumar et al., 2011; Grubb et al., 2015). Some researchers have studied the use of site-specific load factors using WIM data, finding in nearly all cases that the AASHTO LRFD default live load factors produce more conservative designs. In only a few locations in the country—such as port areas with consistently heavier truck spectra than standard—were site-specific load factors needed. In those few locations, site- specific factors would actually produce designs that are more conservative. For this reason, most states do not develop site-specific live load factors; instead, they use the standard AASHTO

12 Use of Weigh-in-Motion Data for Pavement, Bridge, Weight Enforcement, and Freight Logistics Applications default load factors (Kozikowski, 2009; Ghosn et al., 2011; Transportation Research Board Session #360, 2017). Asset Management and Load Rating Some DOTs currently use WIM data for asset management and load rating. As WIM data become more available, they should become even more useful for this purpose. State DOTs are required to report certain information to the FHWA for the Highway Per- formance Monitoring System (HPMS). This information system is a national high-level asset management system with traffic and performance data reported. FHWA uses this informa- tion to inform Congress of highway use and condition, and it factors into funding legislation. Collected WIM data can satisfy the traffic data reporting requirement (FHWA, 2016). States can use WIM in their own asset management systems or may conduct special studies on road and bridge consumption, as illustrated in the following examples. Texas researchers installed a WIM system in Texas to monitor freight movements across the Texas–Mexico border for the purposes of estimating pavement damage due to Mexican trucks traveling beyond a border zone into the United States. The researchers noted that although Mexican truck weight limits are higher than those in the United States, Mexican trucks driving past border zones would have to comply with U.S. truck weight limits, effectively not damaging pavements any more than current U.S. trucks (Prozzi et al., 2008). AASHTO’s Load and Resistance Factor Rating (LRFR) is a program to load-rate bridges in service and can be used to monitor the health of bridges. The state of New York has many bridges and more heavy trucks using their bridges than many other states. The LRFR has the ability to use standard load factors or to develop site-specific load factors using WIM data. The New York DOT has funded research to develop site load factors specific to New York State and uses the methodology to load rate and post understrength bridges (Ghosn et al., 2011). The New York DOT has also used WIM to estimate the number of overweight trucks on roads and bridges and to estimate the damage they cause. Researchers found that 18% of trucks are overweight, with 6% illegally overweight (no permit). Researchers estimated $100 million in damage to roads and bridges from illegal overweight trucks (Ghosn et al., 2015). Several states and countries are beginning to use Bridge WIM or B-WIM. B-WIM is effec- tively instrumenting a bridge, usually with strain gauges, to monitor bridge member deflections and estimate the weight of the vehicle producing the deflections. This analysis is more compli- cated than regular WIM in that multiple vehicles may be on the bridge at once. B-WIM, while it might be a growing application, is not in the scope of this present synthesis study (Treacy and Brühwiler, 2012; Cantero and González, 2014; Skokandić et al., 2017). Commercial Vehicle Weight Enforcement Support Heavy vehicles consume more pavement and bridge life than lighter vehicles, so load limits are in place to prevent premature deterioration and shortened useful life. WIM can be used to enforce vehicle weight limits. Most DOTs do not have law enforcement authority, but can pro- vide information to law enforcement about the presence and prevalence of probable overweight vehicles. Law enforcement can use WIM for identifying possible overweight vehicles and stop only those vehicles likely overweight for static legal weighing. This reduces the time delay for any legally loaded vehicle.

Literature Review 13 The law enforcement aspect of WIM is not in the scope of this study, but here are some examples of WIM use by either law enforcement or a DOT to support law enforcement. The AASHTO Technology Group studied virtual weigh stations. Its Lead States Team made presentations around the United States in support of adoption of VWS technology. VWS is a WIM station with additional sensors to include cameras for identification and license-plate reading and three-dimensional scanning sensors to identify over-height vehicles. These systems can gather the standard WIM data of axle loadings, vehicles classification, and traffic spectrum for asset design and management, but can also identify overweight and over-height vehicles for law enforcement purposes. The Lead States Team, which included North Dakota, California, Florida, Indiana, and Nevada, promoted the technology and was in the process of implementing these systems (AASHTO Technology Implementation Group, 2006–2007). VWS are currently implemented in a number of states. Michigan researchers reviewed Michigan’s weight and size enforcement methods. Michigan was using permanent weigh stations that had several drawbacks (disruptive, expensive, labor intensive, and may be circumvented). The Michigan DOT funds the weight enforcement equip- ment for the Michigan State Police. The researchers recommended closing some permanent weigh stations and using VWS equipment instead. This highlights a beneficial push toward new technology; DOTs may fund WIM equipment for law enforcement, but will gain further benefit from the WIM data for design and asset management (Kwigizile et al., 2015). Norway has performed testing of VWS systems for weight enforcement as a proof of concept. Tests included using VWS for pre-selection of possible overweight trucks, only stopping trucks likely to be overweight. Researchers also tested various sensors for VWS systems. The Norwegian environment is harsh and studded tires are allowed, although they produce pavement erosion and sensor damage. Researchers noted several areas for improvement, including sensor durability, pavement wear, calibration, and security (Haugen et al., 2016). Freight Planning and Logistics Freight planning and logistics is a relatively new area for the use of WIM data and has many growth possibilities. Some work in this area is described below. TRB conducted a peer exchange in 2005 entitled “Freight Data for State Agencies.” Participants included representatives from Alaska, California, Colorado, Florida, Georgia, Idaho, Kentucky, Minnesota, New York, Ohio, Pennsylvania, Washington, Wisconsin, and the Port of Portland. Comments included that some commercial third-party freight data that are available for pur- chase can vary substantially from those measured by DOTs. Colorado and Florida use WIM data in state freight plans. Minnesota was adding WIM stations to its traffic monitoring system and will purchase third-party freight data to use in their freight plan. Ohio also uses WIM data and third-party freight data. A summary of agencies represented cited a lack of data to help with DOT freight plans, including WIM data and automated vehicle classification data, but mostly freight data (volume, weight, value, origin–destination, routing, etc.) (Hall, 2005). A report for the USDOT Southwest Region University Transportation Centers Program provided a theoretical framework for future commercial vehicle user charging using real- time vehicle weight and configuration information collected using WIM systems. This policy paper studied alternatives to replace or augment gasoline taxes in order to develop the fund- ing stream to properly build and maintain transportation infrastructure. The study cites a policy statement in place in the European Union that would allow tolling by weight-distance (W-D) for trucks that can also include construction, maintenance, and development costs and allow for congestion and emissions mitigation. The authors suggest W-D tolling with a base toll

14 Use of Weigh-in-Motion Data for Pavement, Bridge, Weight Enforcement, and Freight Logistics Applications for all vehicles to cover all common costs and added increments for trucks based on axle weights for additional infrastructure costs (Conway and Walton, 2010). A study in Gdynia, Poland, analyzed WIM for overweight enforcement, but also identified additional uses for the data in developing freight plans to limit truck access to certain parts of the city at specific times of the day for traffic and emissions control. Researchers state that legal changes would be necessary to use WIM for this purpose (Oskarbski and Kaszubowski, 2016). FHWA, along with Connecticut, Michigan, North Carolina, Ohio, Pennsylvania, and Texas, initiated a pooled-fund study to use multiple databases to produce a web-based visualization and analytics tool. The work, performed at the Albany Visualization and Infor- matics Lab, University at Albany–SUNY, used multiple databases (including WIM) from the pooled-fund states to generate a comprehensive tool to display data for state DOTs’ planning needs. Data input formats support those currently used for FHWA reporting and DOT data collection. The project functioned as a proof of concept and an example of how information can be combined, consolidated, and made available in visual displays for use by DOT planners and designers (Lawson, 2016). NYCDOT has developed a freight plan for freight movement and delivery in the New York City area, providing specific truck routes and encouraging off-hours delivery of freight. NYCDOT installed a WIM system on the Alexander Hamilton Bridge leading to Manhattan and used the data to monitor truck traffic and weight. NYCDOT found that 7% of trucks using this bridge are overweight. NYCDOT plans to install additional WIM stations to monitor trucks for weight compliance (NYCDOT, 2015, 2016; FHWA, 2018a). Florida commissioned a study on commodity flows in and out of Florida (direction, volume, and value). Researchers fused multiple data sources to include the Freight Analysis Framework created by FHWA, the Transearch database purchased from IHS Global Insight, and FDOT WIM and vehicle classification databases. Researchers used WIM and classification data to develop loaded and unloaded truck information. This information provided a snapshot of the current condition with which to develop ways to facilitate freight movements and understand infrastructure demands (Eluru et al., 2018). Literature Review Summary The newest pavement design method from AASHTO (AASHTOWare Pavement ME Design) is more sophisticated and can use traffic spectrum data developed from WIM systems. Because infrastructure life is highly dependent on loading, WIM data have been useful for bridge design, asset management, and load rating. Heavy vehicles consume more of the pavement’s life, so load limits are enforced to prevent premature road deterioration. WIM can be used to enforce vehicle weight limits, usually as a pre-check to stop only probable overweight trucks for static, legal weighing. Freight planning is a developing use of WIM data by DOTs to anticipate and guide freight movements for efficient use of the transportation system.

Next: Chapter 3 - State of the Practice »
Use of Weigh-in-Motion Data for Pavement, Bridge, Weight Enforcement, and Freight Logistics Applications Get This Book
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

Most U.S. state departments of transportation (DOTs) are collecting weigh-in-motion data with a wide variety of sensor types and using them in a variety of applications. Many agencies use WIM data to aid in pavement design, although most are not currently using a Pavement ME (mechanistic-empirical) Design application. WIM for bridge and asset management purposes is used much less often.

The TRB National Cooperative Highway Research Program's NCHRP Synthesis 546: Use of Weigh-in-Motion Data for Pavement, Bridge, Weight Enforcement, and Freight Logistics Applications documents how DOTs incorporate weigh-in-motion data into such applications as bridge and pavement design and management, load ratings, weight enforcement support, and freight planning and logistics.

  1. ×

    Welcome to OpenBook!

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

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

    No Thanks Take a Tour »
  2. ×

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

    « Back Next »
  3. ×

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

    « Back Next »
  4. ×

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

    « Back Next »
  5. ×

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

    « Back Next »
  6. ×

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

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

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

    « Back Next »
Stay Connected!