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

Dawid Rys

doi:10.3390/s19153272

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.

Highlights

Determination of Steering Axle Load Spectra (SALS) and Normality Check recognised vehicles was below 2% of the total number of axles, due to the strength of the Lilliefors test and largedetermined number offor observations, had an impact on for the WIM
The presented analysis was based on data delivered from 77 weigh-in-motion stations, which are in normal service on motorways and national roads in Poland
In order to include several factors—such as the type of axle load sensor, pavement temperature and vehicle speed—various cases of steering axle load spectra were calculated from a data set selected using a series of filters

Summary

Introduction

Weigh-in-motion (WIM) systems are installed in pavements or on bridges for two main purposes:. Application of actual data from WIM for pavement distress analysis results in more reliable estimations of maintenance treatment schedule and agency costs [16]. Use of WIM data in pavement analysis requires due attention to data quality. Efforts to improve WIM data quality are laid both on the development of new solutions for WIM construction, like multi-sensors systems [17,18], and in developing new procedures for data processing [19]. The significance of data quality in pavement design was studied in [16,20,21,22], Farkhideh and Nassiri [20] investigated weight measurements from

Methods

Results

Conclusion