Investigation of Weigh-in-Motion Measurement Accuracy on the Basis of Steering Axle Load Spectra

Abstract

Weigh-in-motion systems are installed in pavements or on bridges to identify and reduce the number of overloaded vehicles and minimise their adverse effect on road infrastructure. Moreover, the collected traffic data are used to obtain axle load characteristics, which are very useful in road infrastructure design. Practical application of data from weigh-in-motion has become more common recently, which calls for adequate attention to data quality. This issue is addressed in the presented paper. The aim of the article is to investigate the accuracy of 77 operative weigh-in-motion stations by analysing steering axle load spectra. The proposed methodology and analysis enabled the identification of scale and source of errors that occur in measurements delivered from weigh-in-motion systems. For this purpose, selected factors were investigated, including the type of axle load sensor, air temperature and vehicle speed. The results of the analysis indicated the obvious effect of the axle load sensor type on the measurement results. It was noted that systematic error increases during winter, causing underestimation of axle loads by 5% to 10% for quartz piezoelectric and bending beam load sensors, respectively. A deterioration of system accuracy is also visible when vehicle speed decreases to 30 km/h. For 25% to 35% of cases, depending on the type of sensor, random error increases for lower speeds, while it remains at a constant level at higher speeds. The analysis also delivered a standard steering axle load distribution, which can have practical meaning in the improvement of weigh-in-motion accuracy and traffic data quality.

A new approach for varied speed weigh-in-motion vehicle based on smartphone inertial sensors

“…However, the overall accuracy of the three classes is 91.2% that shows the ability of this approach to classify correctly the load level of the tested vehicle. Error tolerance of implemented approach (8.8%) is within permissible error limit (5-15%) [20]- [22].…”

“…The weight sensors may be embedded in roads, bridges or installed in vehicles (on-board WIM). LS-WIM can be implemented using road sensors while HS-WIM can be implemented using all three systems as illustrated in Figure 1 [21], [22].…”

Assessment of Factors Affecting Measurement Accuracy for High-Quality Weigh-in-Motion Sites in the Long-Term Pavement Performance Database

“…Similar findings were reported by Darestani et al ( 10 ). Several other studies documented that regardless of the WIM system calibration, the WIM accuracy can deteriorate over time because of several factors including temperature, pavement roughness, and fatigue of load sensors ( 3, 11–13 ).…”

Optimised Autocalibration Algorithm of Weigh-In-Motion Systems for Direct Mass Enforcement

“…This phenomenon is observed regardless of the technology used for the manufacture of the axle load sensors. However, due to a variety of interfering factors [15][16][17], the weighing error in WIM systems can amount to from 5% to 10% and, what is worse, this can change while the system is in operation [18]. We have written about this topic previously, demonstrating that the main causes of the instability of the system are changes in pavement temperature and the speed of the weighed vehicle [17].…”