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

Accessed by AR - ARRB TRANSPORT RESEARCH on 04 Feb 2005
EXECUTIVE SUMMARY
The objective of Austroads Project BS.A.N.520 is to investigate the consistency of data on traffic
loading and traffic mix. Previous work delivered from the project includes a review of local and
overseas practices in road use data collection, and identification of the use of current and future
road use data. The final task, and the aim of this report, is to develop a best practice report on the
collection, analysis and reporting of road use data.
An Austroads Road Use Data Workshop was held in October 2003 to share information amongst
Road Authorities (RAs), address issues of data inconsistencies, and reach agreement on the
contents of this report. The Workshop confirmed that RAs carry out their road use data tasks with
similar methodologies and are quite consistent in key aspects including the calculation of Annual
Average Daily Traffic (AADT) and Vehicle-Kilometre Travelled (VKT). Each road authority has
developed its own traffic data counting program over many years and these programs would have
achieved good practices in some ways.
It is intended that the materials in this report would further improve consistency in road use data
collection, analysis and reporting amongst RAs. The contents of this report are as follows:
General principles in identifying best practices (Section 2),
Technologies and practices for traffic detection and classification (Section 3),
Key elements of a road use counting program (Section 4),
Data integration including the issue of public-transport partnership (Section 5),
Data integrity and dealing with missing data (Section 6), and
Stakeholder consultation (Section 7).
The principles of best practice include accuracy, effectiveness, efficiency, reliability, accessibility,
transparency, timeliness and relevance. They also relate to the issue of data integrity. The issue of
transparency deserves special attention because it is a key means at enhancing consistency in
data collection, analysis and reporting amongst RAs and facilitate accessibility of current and
potential users of road use data.
Another key data integrity issue is the method of managing missing data. It is recognised that
each RA has its own procedure in handling missing data, and that there is no standard way of
addressing the issue. This report provides the current procedures adopted in RTA NSW and Main
Roads WA as examples of good practices. In particular, the software developed by RTA-CSIRO
using the method of Hidden Markov Model could prove useful amongst RAs after the necessary
tests are completed.
The topics of vehicle detection and classification are also discussed. Most issues related to these
topics have been addressed over many years. It is generally accepted that the Austroads 12-bin
classification by axle configuration is stable and well received since its inception. Minor variations
are recommended as follows:
Introduction of an error bin (bin 13) to account for the quality of classified counts;
Distribution of vehicles in the error bin into either bin 1 (the passenger car bin) or across all
twelve bins depending on the error bin size, or using both methods making use of knowledge
of on-site traffic conditions;
Standardisation of classifying vehicles into four or five bins by vehicle lengths;
Recognition of minor variations amongst RAs but sub-classified counts by either axle
configurations or vehicle lengths must be capable of aggregating into the Austroads system
(13 bins by axles or 4 or 5 bins by lengths).
The issue of vehicle classification by vehicle lengths into three, four or five bins as an integral part
of the Austroads classification system is discussed. The current practices amongst RAs are
presented but have not been harmonised in this study.