AP-G84/04 Best practice in road use data collection, analysis ... - WIM

Accessed by AR - ARRB TRANSPORT RESEARCH on 04 Feb 2005
Austroads 2004
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Best Practices in Road Use Data Collection, Analysis and Reporting
Other RAs have also reported the use of database technologies to facilitate data integration. The
frameworks currently adopted (or being developed) in Queensland Main Roads and Main Roads
WA represent examples of good practice in data integration.
Many other opportunities are possible and can be exploited perhaps with private sector funding in
a public-private partnership arrangement (Section 5.3). Also, the many applications of road use
data necessitate consultation with various stakeholders, and this issue is addressed in Section 7.
5.2 Correlation of Traffic Composition and WIM Data
WIM sites can provide classified counts, gross vehicle masses and axle group masses, with some
WIM systems also providing individual axle load information. WIM sites are mainly located on
major freight corridors, and many of them 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.
An important issue is whether traffic composition at a count site can correlate with axle/vehicle load
data at a nearby or related WIM site. In other words, the issue is whether it is possible to relate the
tonnage of a heavy vehicle from its axle configuration using historical WIM data. Pearson and
Foley (2001) identified a relationship shown in Table 16 from data collected at 16 CULWAY sites in
Victoria from 1995-1998 for two vehicle classes.
Class 9 Articulated vehicle
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Table 16 - Mean axle group and gross vehicle mass
Mean mass (tonne)
1995 1996 1997 1998
Axle Group Mass 2 8.9 9.4 9.5 9.2
Axle Group Mass 3 10.1 10.7 10.7 10.5
Gross Vehicle Mass (GVM) 23.5 25.3 25.3 24.9
Class 10 B-double
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Axle Group Mass 2 8.6 9.5 9.5 9.3
Axle Group Mass 3 9.7 10.8 10.9 10.7
Axle Group Mass 4 9.2 10.4 10.4 10.2
Gross Vehicle Mass (GVM) 32.0 35.9 36.0 35.6
Table 16 shows that the average GVMs of the two vehicle types were 25 tonnes and 36 tonnes
respectively. It is therefore possible to gain an approximate indication of the axle and gross vehicle
load from particular axle configurations. However, further analysis of the heavy vehicle data
reported for the National Transport Commission (formerly the National Road Transport
Commission, Ramsay et al. 2001) showed that the mass distribution for a particular vehicle class is
not a normal distribution. Ramsay et al. reported that the data were compiled from the WIM
databases of each State, totalling some 29 million records of vehicle spacings and weights. The
data were collected in the period 1993 to 2000, with predominantly pre-1999 data.