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
9. Missing data – it is good practice to edit and provide missing data. For estimating missing
data, it is necessary to refer to historical and current information from previous and
subsequent weeks and available data for the pattern relating to weekdays flows. The
management of missing data still requires substantially more research. A vital point is to flag
amended data and ensure that the original raw data sets can be recovered should better
estimation techniques become available.
10. Short duration counts - Counts of very short durations of a few hours or even 12 hours are
not reliable for traffic forecasting. With automatic counting equipment now commonly
deployed, the extra cost of collecting data on more days should be minimal. The best way to
ensure consistency amongst RAs is for each RA to collect robust data sets, i.e. collecting
data over a consecutive period of, say, seven days at Short-term Stations.
11. Weigh-in-motion data - WIM site selection and equipment calibration ensure data accuracy
and are requirements of good practice. The potential impact of weather conditions on some
pavement surfaces should be noted. As long as all lanes of traffic are monitored at a WIM
site, the traffic count and WIM data should add extra value to a counting program.
12. Stakeholder consultation - good practice is achieved by adopting a corporate road use data
collection framework and engaging stakeholders. Many activities require road use data and a
diverse group of stakeholders are involved. As such, road use data is a corporate asset that
requires its share of corporate planning and management.
13. Future research - the following areas are identified:
− Determine the threshold values for automatic vehicle classification system by lengths;
− Develop a model for the correlation of WIM data with classified counts; and
− Develop a policy or business model on data integration, accessibility and pricing.