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Correlation of Traffic Composition and WIM Data

WIM sites are mainly located on major freight corridors and many are permanent sites, e.g. Culway sites. These counting sites can be used to calculate AADTs if all lanes of traffic are monitored.  They can be treated as ‘pattern stations’, although they may not be at the best statistically representative locations.  

Short-term WIM sites can certainly provide an extra sample of counts to supplement current short-term stations.  A good practice is therefore treating them as part of an integrated counting program. 

Previous research (Austroads 2004) showed that the mean values of axle group and GVM can give an approximate indication of bridge and pavement loading from axle configurations.  At a particular counting station, an indication of whether a heavy vehicle is laden or unladen can substantially improve the accuracy of load estimation from axle configurations.  This can be obtained from freight surveys, proximity to an existing WIM site, on-site calibration using portable WIM equipment, and subjective observations.  For example, if the ratio of loaded and unloaded vehicles at a counting station is consistent with a nearby WIM site, then an accurate estimate of pavement loading could be determined.

Austroads 2010 discusses the methodology to identify, from a list of candidate WIM sites (therefore with known GVM frequency distributions), the one that can give the best indication of the GVM distribution at a classifier site.  Some data from WA, Queensland and NSW were used to test the method. 

The main findings were:

  • Based on the KSS and the nine-step method proposed in the report, the WIM data sets considered (from WA, Queensland and NSW) were found suitable for the identification of a WIM site to provide the GVM frequency distribution for a classifier site in the road network of a jurisdiction.  It is likely that the WIM data from all jurisdictions would be suitable.
  • The classifier site needs to use piezo-cables to indicate the level of unladenness, i.e. it should be an intelligent classifier site.  From the analysis carried out so far, it appears that the proposed method is not particularly sensitive to the threshold value and therefore can make use of the standard quality piezo-cable to indicate unladenness.  This issue, however, would need some further investigation with on-site data.
  • The proposed correlation method can be improved with the local knowledge of the location of the classifier site, proximity to candidate WIM sites and freight movement information such as the categorisation of freight routes and the use of freight models and data.

It is therefore recommended that jurisdictions evaluate the method with their own WIM data.  When the method is found satisfactory, a jurisdiction should consider implementing intelligent classifier sites with piezoelectric cables to determine the loading frequency distribution at any site in its road network with information (or assumed information) on the level of unladenness.

The correlation methodology investigated in this project is quite general and can be employed for other applications in road use data collection.  For example, some further research can extend the framework for identifying the permanent count site that provides the best count statistics for a temporary count site, or any site that is in need of comprehensive count data.



Further Reading

Austroads 2010, ‘A method to correlate weigh-in-motion and classification data’, AP-T161-10, Austroads, Sydney, NSW