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Representative Weigh-In-Motion (WIM) System Accuracy and Guidelines for Equipment Selection Based on Sensor, Site, and Calibration-Related Factors

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International Journal of Pavement Research and Technology Aims and scope Submit manuscript

Abstract

Weigh-in-motion (WIM) technology provides accurate information about road network traffic, including vehicle class and speed, vehicle count, gross vehicle weight (GVW), wheel and axle weights, axle spacing, date, and time of each vehicle passage over WIM sensors. Several factors can affect the WIM system accuracy (i.e., measurement error). The potential site-related influences include road geometry, pavement stiffness, surface distresses, roughness, and climate. Further, the WIM calibration and equipment-related factors also have a substantial effect, including sensor type and array, calibration speed, and speed points used by the WIM controller. The long-term pavement performance (LTPP) database was used to study the relative importance of these factors. The WIM calibration data were available for bending plate (BP), load cell (LC), quartz piezo (QP), and polymer piezo cable (PC) sensors. The representative values of GVW measurement errors were estimated using WIM equipment calibration data for all sensors. The BP sensor showed the lowest errors, followed by LC and QP sensors. The PC sensor indicated the highest WIM measurement errors among all sensor types. Decision tree models developed in this paper illustrate a potential for estimating the expected WIM measurement error range using information about the WIM site and sensor-related factors. The results show that the sensor array and types are the most important predictors, followed by WIM controller functionality (speed points). The data analysis and results also show that for some sensor types, the climate is important. One can integrate this information with equipment installation and life cycle costs to determine the most reliable and economical equipment while also considering WIM data accuracy requirements by WIM data users.

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Acknowledgements

The authors would like to acknowledge the National Cooperative Highway Research Program (NCHRP) for funding the study under Grant [NCHRP 20-50(20)].

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Michigan State University, Engineering Building, 428 S. Shaw Lane, Room 3546, East Lansing, MI, 48824, USA

    Muhamad Munum Masud & Syed Waqar Haider

  2. Transportation – Mid-Atlantic Division, Applied Research Associates, Inc., 7184 Troy Hill Drive, Suite C, Elkridge, MD, 21075-7057, USA

    Olga Selezneva & Dean J. Wolf

Authors
  1. Muhamad Munum Masud
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  2. Syed Waqar Haider
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  3. Olga Selezneva
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Contributions

The authors confirm contribution to the paper as follows: Study conception and design: SWH, OS, and MMM. Data collection: MMM and DJW. Analysis and interpretation of results: SWH, MMM, and OS. Draft manuscript preparation: SWH, OS, and MMM.

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Correspondence to Syed Waqar Haider.

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Masud, M.M., Haider, S.W., Selezneva, O. et al. Representative Weigh-In-Motion (WIM) System Accuracy and Guidelines for Equipment Selection Based on Sensor, Site, and Calibration-Related Factors. Int. J. Pavement Res. Technol. (2023). https://doi.org/10.1007/s42947-023-00291-1

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