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

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AP-G84/04 

Best practice in road use data collection, 

analysis and reporting


BEST PRACTICES IN ROAD USE DATA COLLECTION, 

ANALYSIS AND REPORTING


Best Practices in Road Use Data Collection, Analysis and Reporting 

First Published 2004 

© Austroads Inc. 2004 

This work is copyright. Apart from any use as permitted under the Copyright Act 1968, 

no part may be reproduced by any process without the prior written permission of Austroads. 

National Library of Australia 

Cataloguing-in-Publication data: 


ISBN 0 85588 721 4 

Austroads Project No.BS.A.N.520 

Austroads Publication No. AP-G84/04 

Project Manager 

Flori Mihai, Main Roads Western Australia 

Prepared by 

James Y K Luk, Charles Karl, Tim Martin 

ARRB Transport Research Ltd 

Peer Review Group 

Anthony Brown, Roads & Traffic Authority, NSW 

Raoul Casagrande, VicRoads 

Ray Daltrey, Roads & Traffic Authority, NSW 

Geoff Clarke, Department of Transport and Regional Services 

Jeanine Diederich, Main Roads Western Australia 

Ray Dyer, Department of Infrastructure, Energy & Resources, Tasmania 

Geoff Horni, Department of Infrastructure, Planning & Environment, NT 

Andrew Krause, Department for Transport, Urban Planning and the Arts, SA 

Roger McLeay, Transit New Zealand 

David Pratt, Roads & Traffic Authority, NSW 

Geoff Smith, Queensland Department of Main Roads 

Tim Strickland, VicRoads 

Published by Austroads Incorporated 

Level 9, Robell House 

287 Elizabeth Street 

Sydney NSW 2000 Australia 

Phone: +61 2 9264 7088 

Fax: +61 2 9264 1657 

Email: austroads@austroads.com.au 

www.austroads.com.au 

This guide is produced by Austroads as a general guide. Its application is discretionary. Road 

authorities may vary their practice according to local circumstances and policies. 

Austroads believes this publication to be correct at the time of printing and does not accept 

responsibility for any consequences arising from the use of information herein. Readers should 

rely on their own skill and judgement to apply information to particular issues.


BEST PRACTICES IN ROAD USE DATA COLLECTION, 

ANALYSIS AND REPORTING 

Sydney 2004


Austroads profile 

Austroads is the association of Australian and New Zealand road transport and traffic 

authorities whose purpose is to contribute to the achievement of improved Australian and 

New Zealand road transport outcomes by: 

♦ undertaking nationally strategic research on behalf of Australasian road agencies and 

communicating outcomes 

♦ promoting improved practice by Australasian road agencies 

♦ facilitating collaboration between road agencies to avoid duplication 

♦ promoting harmonisation, consistency and uniformity in road and related operations 

♦ providing expert advice to the Australian Transport Council (ATC) and the Standing 

Committee on Transport (SCOT). 

Austroads membership 

Austroads membership comprises the six state and two territory road transport and traffic 

authorities and the Commonwealth Department of Transport and Regional Services in Australia, 

the Australian Local Government Association and Transit New Zealand. It is governed by a council 

consisting of the chief executive officer (or an alternative senior executive officer) of each of its 

eleven member organisations: 

♦ Roads and Traffic Authority New South Wales 

♦ Roads Corporation Victoria 

♦ Department of Main Roads Queensland 

♦ Main Roads Western Australia 

♦ Department of Transport and Urban Planning South Australia 

♦ Department of Infrastructure, Energy and Resources Tasmania 

♦ Department of Infrastructure, Planning and Environment Northern Territory 

♦ Department of Urban Services Australian Capital Territory 

♦ Commonwealth Department of Transport and Regional Services 

♦ Australian Local Government Association 

♦ Transit New Zealand 

The success of Austroads is derived from the collaboration of member organisations and others in 

the road industry. It aims to be the Australasian leader in providing high quality information, advice 

and fostering research in the road sector.


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.


The use of a single axle sensor for traffic count collection is quite common amongst RAs, even 

though most have recognised the need for classified counts. It is recommended that traffic counts, 

as a default, be reported in vehicle numbers. If the raw data is in the form of axles or axle-pairs, 

then the count should be converted to vehicles with the conversion factor also reported if possible, 

adopting the principle of transparency. 

The issue of utilising weigh-in-motion (WIM) equipment as part of a traffic counting program is also 

discussed in some detail. As long as all lanes of traffic are monitored at a WIM site, the traffic 

count data (and WIM data) should add extra value to a counting program. The related issue of 

correlating classified count data with vehicle mass data is more complex and there is not much 

information published on this topic. In practice, 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 can be determined. 

Road use data can come from many sources meeting a variety of needs. This leads to the issues 

of data integration and accessibility. Many stakeholders are also involved. This report provides a 

stakeholder consultation model from Main Roads WA. These issues are rather involved, and RAs 

at present have quite varied policies on data availability and its pricing. As the trend to outsource 

data continues amongst RAs, third parties may become both supplier and purchaser of road use 

data. 

In summary, this report has provided materials on best practices amongst RAs not previously 

covered in NAASRA (1982) and the recently updated Guide to Traffic Engineering Practice Part 3 

(Austroads 1988a and 2004). In particular, the materials cover topics such as: error bin (bin 13) in 

the Austroads vehicle classification system by axle configurations, vehicle classification by lengths, 

defining a homogeneous traffic section, correlation of classified counts with WIM data, publicprivate 

partnership, quality checks, dealing with missing data, calibration of a WIM system using 

CULWAY as an example, and a stakeholder consultation model. A list of thirteen 

recommendations is summarised in the report with future research identified in the following areas: 

Determine the threshold values for automatic vehicle classification system by lengths; 

Develop a model for the correlation of WIM data with classified counts; 

Develop a policy or business model on data integration, accessibility and pricing. 

It takes time to achieve consistency amongst RAs in this important area of road use data. This 

report represents a step in this on-going process.


TABLE OF CONTENTS 

1 INTRODUCTION ...................................................................................................................1 

2 BEST PRACTICE PRINCIPLES............................................................................................2 

3 VEHICLE DETECTION AND CLASSIFICATION...................................................................3 

3.1 Axle Sensor and Axle Counts .......................................................................................3 

3.2 Inductive Loop Sensor and Vehicle Counts..................................................................5 

3.3 Vehicle Classification System.......................................................................................6 

3.4 Other Considerations..................................................................................................10 

4 TRAFFIC COUNTING PROGRAM ......................................................................................13 

4.1 Background.................................................................................................................13 

4.2 AADT and VKT Estimation .........................................................................................14 

4.3 Network Coverage ......................................................................................................23 

4.4 Reporting ....................................................................................................................29 

5 DATA INTEGRATION..........................................................................................................33 

5.1 Road Use Data Types for Integration .........................................................................33 

5.2 Correlation of Traffic Composition and WIM Data ......................................................35 

5.3 Public-Private Partnership ..........................................................................................38 

6 DATA INTEGRITY ...............................................................................................................39 

6.1 Issues .........................................................................................................................39 

6.2 Specifications for Data Collection Activities................................................................40 

6.3 Quality Check and Dealing with Missing Data ............................................................41 

6.4 Calibration of a WIM System ......................................................................................44 

6.5 Accuracy Specifications..............................................................................................48 

7 STAKEHOLDER CONSULTATION .....................................................................................49 

8 CONCLUSIONS ..................................................................................................................51 

REFERENCES ...........................................................................................................................53 

APPENDIX A – NATIONAL GLOSSARY OF TERMS ................................................................53 

APPENDIX B – NUMBER OF AADT OBSERVATIONS .............................................................53 

APPENDIX C – EXECUTIVE SUMMARY OF LITERATURE REVIEW REPORT (RC2050-2) ..53 

APPENDIX D – EXECUTIVE SUMMARY OF CURRENT AND FUTURE ROAD USE REPORT 

(RC2050-3) .................................................................................................................................53 

Page


TABLES 

Table 1 Use of axle sensors under different AADT values......................................................4 

Table 2 Current Austroads vehicle classification systems (updated in 1994) .........................7 

Table 3 Vehicle classification systems currently in use...........................................................8 

Table 4 Vehicle classification systems by lengths...................................................................9 

Table 5 Accuracy requirements for individual sites ...............................................................17 

Table 6 Accuracy requirements for groups of roads .............................................................17 

Table 7 Examples of rounding AADT ....................................................................................18 

Table 8 Adjustment factors by jurisdictions ...........................................................................19 

Table 9 Some adjustment factors from short-term counts to AADT estimates......................20 

Table 10 Densities for traffic counting stations........................................................................24 

Table 11 National highway traffic counting station density by location and status..................24 

Table 12 Groups by functional classes: ..................................................................................26 

Table 13 Suggested volume stratification of road segments...................................................27 

Table 14 Suggested volume stratification based on % change in AADT ................................28 

Table 15 Complete list of data classes collected/computed and stored in Queensland .........31 

Table 16 Mean axle group and gross vehicle mass ................................................................35 

Table 17 RTA NSW rejection rules on classified counts .........................................................42 

Table18 High-speed Weigh-in-Motion Systems Used and available in Australia 

(source: Koniditsiotis 2000) ......................................................................................45 

Table 19 Accuracy requirements.............................................................................................48 

Table B1 Calculation of the Number of VKT Stations..............................................................53 

FIGURES 

Figure 1 A pair of pneumatic tubes and a vehicle classifier on a local road monitoring 

two lanes of vehicle traffic and a bicycle path on the nearside of the photograph .....3 

Figure 2 Four positions of a two-axle vehicle passing over two axle sensors ..........................5 

Figure 3 Typical response of a loop detector crossed by a passenger car ..............................6 

Figure 4 Loop configurations for bicycle detection .................................................................10 

Figure 5 A sensor array for separate lane axle counts on a three-lane carriageway .............11 

Figure 6 Survey of road use data needs amongst Road Authorities ......................................15 

Figure 7 Expected percentage error for AADT from ADT and count duration 

(n-Day counts) for a 75% confidence level...............................................................18 

Figure 8 A design for traffic counting program .......................................................................22 

Figure 9 An integrated road use database from Queensland Main Roads.............................34 

Figure 10 Total mass of three-axle prime movers and three-axle semi-trailers.........................36 

Figure 11 Total mass of nine-axle B-doubles ............................................................................37 

Figure 12 Laden and unladen vehicles in each vehicle class at Port Fremantle, WA ...............37 

Figure 13 Flow diagram of the CSIRO method for detection of corrupt traffic data ...................44 

Figure 14 Three axle masses of an articulated six-axle vehicle (Class 9) .................................47 

Figure 15 Adjustment factors for diurnal variation of CULWAY axle mass data 

(Grundy et al. 2002) .................................................................................................47 

Figure 16 Adjustment factors for seasonal variation of CULWAY axle mass data 

(Grundy et al 2002) ..................................................................................................47


ABBREVIATIONS 

(see also Appendix A) 

AADT Annual Average Daily Traffic 

AAWT Annual Average Weekday Traffic 

AASHTO American Association of Highway and Transportation Officials 

ADT Average Daily Traffic 

AWDT Average Weekday Daily Traffic 

AWT Average Weekday Traffic 

DAM Drive Axle Mass 

FOI Freedom of Information 

GIS Geographic Information System 

GPS Global Positioning System 

GTEP Guide to Traffic Engineering Practice 

HMM Hidden Markov Model 

IAP Intelligent Access Program 

ITS Intelligent Transport System 

LoS Level of Service 

MAWDT Monthly Average Weekday Traffic 

MRWA Main Roads Western Australia 

OH&S Occupational Health and Safety 

PCS Permanent Counting Station (or Site) 

RA, RAs Road Authority, Road Authorities 

RTA NSW Roads and Traffic Authority of New South Wales 

SAM Steering Axle Mass 

SCATS Sydney Coordinated Adaptive Traffic System 

SCS Short-term Counting Station 

TAM Tandem Axle Mass 

TRB Transportation Research Board 

UTS Uniform Traffic Section 

VicRoads Roads Corporation Victoria 

VKT Vehicle-Kilometre Travelled 

WIM Weigh-In-Motion 

WRS Weighted Road Segments


1 INTRODUCTION 

Austroads 2004 

— 1 — 


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 (Martin and Chiang 2002), and identification of the 

current and future use of road use data (Roper et al. 2002). The Executive Summaries of those 

two Reports are included in Appendix C and D respectively. 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 


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 

content of this report (Luk and Karl 2003). 

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). The methodologies have largely followed the 

NAASRA (1982) principles. It is recognised that there are differences in applying these principles 

and there are jurisdictional requirements and constraints. Further, each road authority has 

developed its own traffic data counting program over many years and the program would have 

been refined, i.e. already achieving good practices in some ways. 

It is intended that the materials in this report would further improve consistencies 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-private partnership (Section 5), 

Data integrity and dealing with missing data (Section 6), and 

Stakeholder consultation (Section 7).


2 BEST PRACTICE PRINCIPLES 


— 2 — 


This section provides a brief discussion on the principles of best practice. The aim is to provide a 

broad context in making recommendations in this report. These principles also relate to data 

integrity issues, which will be addressed in more detail in Section 6. They are as follows: 

Accuracy – This is a measure of how well the data collected represent true values. In traffic 

data, perfect accuracy is unnecessary and it is actually impossible to achieve. 

Consequently, it is good practice to quote a percentage error and the associated confidence 

level. A trade-off between accuracy with other quality criteria is also necessary – higher 

accuracy often implies higher cost. 

Effectiveness – This is the likelihood that a work program achieves desired objectives. It 

measures how closely outcomes match predefined targets. 

Efficiency – This is the ratio of output to input. A lot of output data with minimal input 

resources is an efficient data collection program. However, collecting the wrong data is an 

ineffective program irrespective of the amount of input resources involved. 

Reliability – This is related to accuracy. A counting program needs to produce consistent and 

repeatable results. Reliability is therefore an outcome of how well a road network is sampled 

or covered to produce statistically consistent data. 

Accessibility – Road use data should be easily accessible within a jurisdiction and to the 

public due to the Freedom of Information (FOI) Act, possibly not in the raw data form but in 

aggregated form. The data structure and formats should be amenable to data accessibility. 

Transparency – A clear enunciation of the procedures for collection, analysis and reporting 

should help reducing differences amongst RAs and other users in the use and interpretation 

of road use data. 

Timeliness – This relates to the frequency of data collection, its reporting and updates. A 

yearly update and reporting should be the minimal update period. With more and better 

database technologies, it is expected that road use data would become more timely in the 

near future. 

Relevance – The data collected should meet the needs of a user, i.e. should fulfil the 

principle of fitness of purpose. There is always a great need for traffic counts and weigh-inmotion 

(WIM) data. A current issue is what other types of data should be included in a 

comprehensive road use database. 

The above list is certainly not exhaustive but does cover most of the important elements in 

identifying best practices. These principles will be applied in the following sections to identify and 

recommend practices in the final road use data report. 

Further, a glossary of terms is included in Appendix A. The glossary is intended to be a ‘national 

glossary’ to promote consistency. It is recognised that individual jurisdictions would adopt slight 

variations in definitions and therefore practices due to constraints specific to a jurisdiction. Over 

time, the national glossary also requires updates and is a reference towards which jurisdictional 

practices would migrate.


3 VEHICLE DETECTION AND CLASSIFICATION 


— 3 — 


The Austroads (1988a and 2004) Guide to Traffic Engineering Practice (GTEP) Part 3 on Traffic 

Studies provides a broad background to vehicle detection and classification and other traffic 

studies. Discussions on technologies such as image processing and Global Positioning System 

(GPS) will only be mentioned in passing in this report. The focus of this section is on getting the 

most out of currently used technologies, which are axle and inductive loop sensors. 

3.1 Axle Sensor and Axle Counts 

The most common type of axle sensor is the pneumatic tube with an air switch. It is suitable in 

both urban and rural environments for temporary axle counts. Two tubes are usually needed for 

speed measurement and vehicle classification. A two-tube installation for vehicle classification on a 

local road is shown in Figure 1. The photograph shows the set-up for the monitoring of two lanes of 

vehicular traffic and a bicycle path on the near side of the photograph. 

Figure 1 – A pair of pneumatic tubes and a vehicle classifier on a local road monitoring 

two lanes of vehicle traffic and a bicycle path on the nearside of the photograph 

A single axle sensor has been found useful in a wide range of road traffic conditions. For single 

lane traffic, one axle sensor usually presents no problems in obtaining axle counts. At remote sites, 

single axle sensors are sometimes used in permanent count stations because they are reasonably 

reliable and repair costs at remote sites are expensive. For multi-lane traffic (in one or two 

directions), axle counts are missed when the wheels of two different vehicles arrive at an axle 

sensor at the same time. Installing two axle sensors could improve accuracy but the installation of 

two axle sensors also increase the Occupational Health and Safety (OH&S) risk.



— 4 — 


With the prevalent use of single axle sensor for traffic counts, it is important that the number of axle 

pairs obtained be corrected for vehicles with more than two axles. The correction factor is 

dependent on traffic composition and site conditions and should be calibrated on-site. Some 

guidelines on the correction factor are given in GTEP Part 3. For example, if heavy vehicles 

account for 12% of the total traffic on an urban highway, the number of axle-pairs obtained should 

be reduced by about 6%, i.e. the correction factor is 0.94 ±0.15 at the 95 % confidence limits. 

There are no standard practices amongst RAs regarding when axle pair counts are collected rather 

than classified counts. At the Austroads Road Use Data Workshop, there was general agreement 

that classified counts be collected wherever possible. As mentioned earlier, a pair of axle sensors 

is essential for classified counts in spite of higher installation and maintenance costs. An axle 

sensor pair is also more appropriate than a single axle sensor in obtaining bi-directional counts as 

traffic volume increases. 

Table 1 recommends what could be used under different axle arrangements and AADT values. 

In Table 1, the threshold AADT values correspond to different Levels of Service (LoS) for two-lane 

rural roads and arterial roads (GTEP Part 2, Austroads 1988b; TRB 2000). At a LoS of E, a road 

facility is operating at capacity. Usually a facility is designed for a LoS of C. Delay is frequently 

experienced at LoS of D and E. The values in Table 1 are for a peak-hour to daily factor of 0.15. 

Road environment 

Combined direction (e.g. two-lane 

two-way rural roads) 

Separate direction (e.g. multi-lane 

carriageway at 80 km/h speed limit) 

Table 1 - Use of axle sensors under different AADT values 

Indicative maximum AADT (veh) 

One axle Two axles 

9,000 (two lanes) 

(Level of Service = D) 

8,500 per lane 

(Level of Service = C) 

15,000 (two lanes) 

(Level of Service = E 

or at capacity) 

11,000 per lane 

(Level of Service = D) 

On multi-lane highways, temporary surface mounted pneumatic tube sensors may not be suitable 

under heavy traffic conditions, poor pavement surface or OH&S problems for field staff. The count 

accuracy also decreases with increase in traffic flow and the number of lanes that an axle sensor 

(or an axle pair) has to cover. In these situations, it could be better to use in-ground loop sensors 

described in Section 3.2. 

The development of piezo-cables for axle detection continues to progress (Luk and Brown 1987; 

ARRB TR 2003). The cost is higher than pneumatic tubes but should be able to offer a working life 

similar to that of an in-ground inductive loop - four to five years depending on traffic volume, its 

composition and pavement type and condition. Piezo-cables can be installed lane-by-lane so lanebased 

counts are possible. The piezo-sensor needs to cover only half to three-quarters of a lane 

width to reduce cost and possibly improve accuracy with ‘cleaner’ axle actuations. 

Axle sensors give more distinct axle actuation times and therefore better accuracy than loops in 

speed measurement and vehicle classification. Figure 2 shows the four timing pulses for speed 

calculation and hence vehicle classification. For very slow or stationary traffic, it is difficult to 

separate vehicles using the axle actuation times T1 to T4 and determine speeds and classify 

vehicles, especially if a vehicle straddles over both axle sensors for a long period of time. Inductive 

loops and the related electronic circuitry are designed to monitor loop occupancy (i.e. the time that 

a vehicle is on top of a loop) and are more suitable for slow traffic, e.g. urban traffic in peak hours.



— 5 — 


The ability to detect gaps between adjacent vehicles and obtain a valid vehicle count is dependent 

on the length of the inductive loop in the direction of travel along a lane. Undercounting will occur 

when the loop detection zone is too long. 

Mendigorin et al. (2003) described studies in Sydney on using axle sensors for classified counts in 

urban traffic. Their results suggested that a separation of 4 m between two axle sensors provide 

good accuracy and these results are consistent with those obtained in Luk and Besley (1985) using 

different counting equipment. The optimal separation is a balance between equipment accuracy 

and fluctuations of vehicle speeds between the two axle sensors. It is dependent on the type of 

equipment and the associated software used in a study. 

T 1 

T 3 

T 2 

T 4 

Pair of axle 

sensors 

Vehicle actuation 

pulses 

T 1 

Figure 2 – Four positions of a two-axle vehicle passing over two axle sensors 

In summary, a pair of axle sensors should be considered where possible to give better accuracy, 

better distinction by direction, and the ability to produce classified counts through axle 

configurations. If a single axle is used, it is a recommended practice to correct for traffic 

composition and convert axle-pairs into vehicle counts for reporting purposes. 

3.2 Inductive Loop Sensor and Vehicle Counts 

The operation of a loop sensor requires an alternating current passing through the inductive loop. 

When a mass of metal such as a vehicle chassis and an engine passes through the 

electromagnetic field of the loop, the inductance of the loop reduces. These reductions are used to 

indicate the passage or presence of a vehicle. 

Figure 3 shows the typical response of a loop detector when crossed by a passenger car. The 

reduction in loop inductance depends on the size and metallic content of the vehicle. For example, 

a passenger car generates a change of about five per cent for the loops typically used in traffic 

counting. The inductive loop detector is used extensively for automatic traffic counting, traffic 

surveillance and traffic signal control. The analysis of inductive loop profiles can also be used for 

vehicle classification but is seldom used amongst RAs. 

− 

T 13 

T 3 

− 

T 2 

− 

T 34 

T 4 

− 

Time



— 6 — 


Figure 3 - Typical response of a loop detector crossed by a passenger car 

Two types of loop designs are commonly used by road authorities for vehicle counting on a lane of 

traffic flow. A loop for counting at mid-block is often a square loop of 2 m × 2 m, and a pair of 

these loops at a known distance (4 - 5 m) apart is usually used for speed measurement and 

vehicle classification. A second loop design is the SCATS loop for signal operation and traffic 

counting. The SCATS loop has a width of 2 m and an overall length of about 4.5 m and is located 

near the stopline of an approach. It provides reasonably accurate counts over a measurement 

period of, say, 15 minutes but the counting accuracy is less over the length of a signal cycle of one 

to two minutes due to the loop location and length. 

For accurate counting, care needs to be taken in selecting the size, shape and positioning of the 

loop within the traffic lanes. Over-counting can occur when loops in adjacent lanes are too close 

and the same vehicle is detected in both lanes. On the other hand, motor cycles or small vehicles, 

straddling both lanes may be missed by loops placed too far apart. 

The ability of the loop detection circuit to hold the change in inductance (or electric charges) allows 

a loop sensor to be a presence detector. This property allows a loop sensor to measure volume 

and speed at very low speed and is therefore suitable for congested urban traffic conditions. 

However, speed measurement using loops is not as accurate as axle sensors due to more 

uncertainty in the actuation timings. This also affects the accuracy of vehicle classification using a 

pair of loops. The classification has to be by vehicle lengths and in a smaller number of classes or 

bins (see Section 3.3). Also, inductive loop detectors are not suitable for unsealed roads in 

general. 

3.3 Vehicle Classification System 

Vehicle classification data is important for all transport engineering applications. Comprehensive 

classification data is now possible using new technologies. Vehicle classification surveys are 

concerned with the distribution of traffic data into different types of vehicles. Classified data can be 

used for design and performance of road pavements and bridges, traffic capacity and operations 

analysis, vehicle categories for traffic legislation and regulation, road safety studies, and economic 

studies.



— 7 — 


A number of classification systems are currently in use. These systems include the 1994 Austroads 

system (Table 2) and a few other systems mainly for urban traffic, which are also shown in Table 3. 

Table 2 - Current Austroads vehicle classification systems 

(updated in 1994) 

Level 1 Level 2 Level 3 

Length 

(indicative) 

Axles and 

Axle Groups 

Vehicle Type 

Austroads 

Classification 

Type Axle Groups Description 

LIGHT VEHICLES 

Class Parameters 

Short 

Up to 5.5m 2 1 or 2 

Medium 

5.5m to 14.5m 

Long 

11.5m to 19.0m 

3, 4 or 5 3 

Short 

Sedan, Wagon, 4WD, 

Utility, Light Van, Bicycle, 

Motorcycle, etc. 

Short – Towing 

Trailer, Caravan, Boat, etc. 

1 

2 

d1 ≤ 3.2m 

and Axles = 2 

Groups = 3, 

2.1 m ≤ d1 ≤ 3.2m 

d2 ≥ 2.1m, 

and Axles = 3, 4 or 5 

2 2 

HEAVY VEHICLES 

Two Axle Truck or Bus 3 d1 > 3.2m and Axles = 2 

3 2 Three Axle Truck or Bus 4 Axles = 3 and Groups = 2 

> 3 2 Four Axle Truck 5 Axles > 3 and Groups = 2 

3 3 

4 > 2 

5 > 2 

6 

> 6 

> 2 

3 

Medium 

Combination 

> 6 4 

17.5m to 36.5m > 6 5 or 6 

Long 

Combination 

Over 33 m 

> 6 > 6 

Three Axle Articulated or 

Rigid vehicle & trailer 

Four Axle Articulated or 


Five Axle Articulated or 


Six Axle (or more) Articulated 

or Rigid vehicle & trailer 

'B' Double or 

Heavy truck trailer 

Double Road Train or 

Heavy truck and trailers 

Triple Road Train or 

Heavy truck and three trailers 

Definitions: Group: (axle group) - where adjacent axles are less than 2.1m apart 

Groups: number of axle groups 

Axles: number of axles (maximum axle spacing of 10 m) 

d1: distance between first and second axle 

d2: distance between second and third axle 

6 

7 

8 

9 

d1 > 3.2m, Axles = 3 

and Groups = 3 

d2 ≤ 2.1m, 

2.1 m ≤ d1 ≤ 3.2 m 

Axles = 4 and Groups > 2 

d2 ≤ 2.1m, 

2.1 m ≤ d1 ≤ 3.2 m 

Axles = 5 and Groups > 2 

Axle = 6 and Groups > 2 

or Axles > 6 and Groups = 3 

10 Groups = 4 and Axles > 6 

11 

Groups = 5 or 6 

and Axles > 6 

12 Groups > 6 and Axles > 6


Table 3 – Vehicle classification systems currently in use 


— 8 — 


Methodology Urban traffic Rural traffic 

By axle configuration using two axle 

sensors 

By vehicle length using two inductive 

loops 

By manual observation, loop 

inductance profiling or video imaging 

Not suitable for congested roads, turning 

traffic, vehicles changing lanes 

3- or 4-bin systems 

Austroads 12-bin system (Table 2) 

Suitable for sites where loops can be 

embedded into a sealed carriageway; 

requires good pavement surface 

2-bin system (light and heavy vehicles; Table 2) and various vehicle classification 

schemes 

3.3.1 Current Austroads Vehicle Classification System 

As already mentioned, the Austroads vehicle classification system currently in use is based on 

classifying axle configurations. Bins 1 and 2 are classified as light vehicles (two axle vehicles with 

or without towing a caravan, trailer, etc.) and bins 3 to 12 are called heavy vehicles. This two-bin 

system is suitable for manual classified counts but now used mainly for intersection movement 

counts. 

The 12-bin system has been found valuable since its inception and has provided useful data for all 

RAs. The system has provided consistency across jurisdictions and the data can also be analysed 

over time. There have been requests for changes that include: 

Different jurisdictions require variations to cater for their own needs; sub-classifications within 

a bin would be a solution. Examples are motor cycles in bin 1 and sand trucks in bin 9. 

These sub-classes can be easily aggregated to provide the standard 12-bin system for 

consistent reporting and trend analysis. 

New vehicle-axle configurations such as the performance-based standard (PBS) vehicles are 

being developed. These vehicles aim to carry heavier loads with better safety capability and 

more user-friendly suspension. It is expected that PBS vehicles would still fit into the 12-bin 

system but their development needs to be monitored. It is anticipated that the migration of 

the current heavy vehicle fleet to a PBS regime will take some time. 

There are requests to create additional classification bins within Austroads bins 11 and 12 

(Type 1 and Type 2 road trains respectively; Roper et al. 2002). 

It is therefore recommended that the current Austroads vehicle classification be maintained but 

allowing sub-classes as variations of the basic system. This was also endorsed at the Austroads 

Road Use Data Workshop held in Melbourne. It may be appropriate to introduce a new notation to 

designate different jurisdictional versions, e.g. the Austroads (Vic) for the Victoria version. 

The issue of introducing a separate bin to collect errors in classified counts is discussed below. 

3.3.2 The Error Bin 

It is good practice to always check where the errors come from, e.g. poor installation, 

malfunctioning of sensors, etc. Different ways are currently used to treat an error or ‘nonclassifiable’ 

vehicle count. Error counts can be lumped into a separate error bin (say, bin 13) that 

also acts as a quality check of the classified data. Methods for managing the error counts include 

the following possible actions:


Put them into bin 1 (passenger-car bin); 


— 9 — 


Distribute across the 12 bins in proportion to the measured vehicle numbers in each bin (i.e. 

new bin j vehicles = old bin j vehicles × sum of bins 1 to 13 / sum of bins 1 to 12); and 

Discard them. 

Each of these methods has its own inadequacy. The last method in discarding the error counts 

would underestimate the true traffic demand in a road network. The second method of proportional 

distribution across all bins may significantly distort the true distribution if the sum of vehicles in bins 

1 to 12 is small relative to the vehicles in the error bin. The first method of loading error counts into 

the passenger-car bin (bin 1) would not significantly affect the true count in bin 1 only if there are 

relatively large numbers of vehicles in this bin. 

It is thus recommended that: 

(a) An error bin (no. 13) be introduced into the Austroads system as good practice to indicate the 

quality of the classified counts; 

(b) The inclusion of error counts in bin 13 in the total counts requires some judgement. If the 

number of error counts is high relative to the total counts, it is necessary to identify the 

reasons for such a situation before error counts are included; 

(c) If it is deemed appropriate to distribute bin 13 vehicles, the two distribution methods 

mentioned previously can be considered, or both methods are used and guided by relevant 

local knowledge. 

(d) The number of error counts should be monitored and, if these remain high relative to the 

traffic stream, the reasons for these errors should be identified and the problem rectified. 

3.3.3 Vehicle Classification in Urban Traffic 

As indicated in Table 3, loop sensors are more suitable for collecting vehicle classified counts on 

congested multi-lane highways. These sensors are usually located mid-block on arterial roads and 

data retrieved remotely using modems. Another source of classified counts is the freeway 

management systems now in place in capital cities. Loop sensors or virtual loops using imaging 

technologies are installed at regular intervals (e.g. 500 m) to monitor traffic flow and identify 

incidents. 

These loop pairs calculate the spot speed and classify vehicles by length. Both 3-bin and 4-bin 

systems have been employed in various jurisdictions. VicRoads, Main Roads Queensland and 

Transit New Zealand have reported using a 4-bin system. Transit New Zealand and RTA NSW 

also use three bins to classify vehicles by lengths. Table 4 shows the threshold values of these 

systems. 

Vehicle class Main Roads 

Queensland 

Table 4 – Vehicle classification systems by lengths 

Vehicle lengths (m) 

VicRoads Transit New Zealand RTA NSW 

Bin 1 (short) < 5.8 < 6.0 < 5.5 11.5 

Bin 4 (combination) > 21.2 > 17.5 > 17.0 -



— 10 — 


Transit New Zealand’s 3-bin system was developed for vehicle classification on motorways using 

video imaging, and found to give adequate information to handle traffic flows and understand the 

makeup of fleet in each lane. 

A variety of threshold values are therefore being used for vehicle classification by lengths. It is 

recommended that RAs come to agreement on a single classification system by lengths. Some 

empirical work using a standard loop design would appear necessary. On reaching agreement, the 

classification system in Table 2 should incorporate a definitive multi-bin system by vehicle lengths 

as a component of the total Austroads classification system. This harmonisation should facilitate 

equipment manufactures to design their equipment for a single specification. 

3.4 Other Considerations 

The following aspects of traffic counting/classifying require further discussion: 

(a) Bicycles – Cycling is now recognised as an important mode of road transport. Its counting 

and therefore management in a mixed stream of traffic is a challenging task (see also GTEP 

Part 3). It can be detected by inductive loops, pneumatic tubes or piezoelectric cables. 

Leschinski (1994) reported that the standard symmetripole loop (configuration 1 in Figure 4) 

for signal operation is not suitable for bicycles and that the best configuration is an 

asymmetric loop slanting at a 45o angle to the direction of traffic (configuration 4 in Figure 4). 

Golden River (2001) also reported the feasibility of separating bicycles from other traffic by 

measuring the actual tyre contact width. 

The detection of bicycles (and pedestrians) is an area of on-going research, and a recent 

study can be found in FHWA (2003). This study tested a range of technologies including 

loop, infrared, video, microwave and magnetic field in a controlled environment. The results 

show that most of these technologies are promising, but the challenge is really in undertaking 

these tests in a real-world, mixed traffic environment. 

Figure 4 - Loop configurations for bicycle detection



— 11 — 


(b) Vehicle concatenation – When the space between two consecutive vehicles is small, it 

becomes difficult to separate the two vehicles using, say, a standard two axle sensor 

classifier. Where very high accuracy is required in a traffic operation (e.g. toll collection), a 

loop sensor may be useful in separating vehicles by monitoring the loop occupancy time. The 

loop can be placed between the two axles. The software for classification must be capable of 

using the information from the loop sensor. An infrared beam can also perform the function of 

monitoring occupancy as an alternative to the loop (see (d) below). 

(c) Axle sensor array for multi-lane highway – There are situations when short-term classified 

counts are needed for each lane on multi-lane highways. An array of axle sensors may be 

needed if the equipment is unable to distinguish traffic travelling in each lane, as shown in 

Figure 5. By deduction, lane traffic can be determined. For simplicity, only the configuration 

for axle or axle-pair counts is shown. An extra parallel set of sensors is needed for classified 

counts. An alternative arrangement is to locate an extra data logger in the median. This 

counter or logger can monitor one or more lanes of traffic closest to the median. Instructions 

from equipment manufacturers should be followed in a study. 

tube sensors 

and data logger 

median 

kerb 

Figure 5 – A sensor array for separate lane axle counts on a three-lane carriageway 

(d) Infrared axle sensor – Infrared beams are routinely used in commerce and industry, e.g. as a 

‘doorbell’ in many shops. A beam is aimed from a transmitter to a receiving unit, and any 

break in the beam is processed to produce an electric signal, which can be used to 

increment a counter or sound a buzzer. In traffic applications a beam may be aimed across a 

roadway or reflected from a raised pavement marker. The reflectors can be on the centreline 

(counting one direction), or the far side of a two-lane road (counting both directions). 

The beams should be close to the road surface as an alternative to a surface-mounted axle 

sensor. Potential problems include detector robustness and the maintenance of beam 

alignment. The concept does offer the advantage of minimum interference to the pavement 

and traffic stream, but it is still to be proven for multi-lane highway applications.



— 12 — 


(e) Principle of uncertainty - Completely accurate counts do not exist due to human errors, 

mechanical failures, environmental factors such as rain and magnetic fields, vehicles 

straddling two lanes, interference from opposing traffic, multi-axled vehicles and other factors 

which affect the reliability of raw traffic counts. The best practice is often a compromise 

between accuracy and cost. 

(f) Known test data set – the uncertainty mentioned in (e) can be minimised with a known test 

data set. A file with a know distribution of classified counts can test out the accuracy of a 

vehicle classifier. This approach was adopted in the development of the ARRB Vehicle 

Detection Data Acquisition System in the late 1970s, and should again be deployed in testing 

a new generation of software and hardware. 

As described in GTEP Part 3, the counting personnel should be well prepared in terms of counting 

equipment, ancillary equipment such as tools and seats if necessary, appropriate clothing including 

contingency provisions for adverse weather and amenities (e.g. food and toilets), and carry 

authority certificates. Conspicuous signage is not recommended as it may affect count results. 

Other issues of quality control are addressed in Section 6.


4 TRAFFIC COUNTING PROGRAM 


— 13 — 


This section addresses best practices in a traffic counting program. It begins with a brief 

description of the current situation in traffic counting programs amongst road authorities (RAs). 

The description is from the perspective of traffic counting techniques and methods as well as from 

the perspective of the RAs having to balance rigour in their traffic counting programs against an 

increasing rate of change and variation in traffic flows caused by economic activity, increased 

scrutiny and user demands as well as budgetary cost pressures (Section 4.1). This section covers 

best practices in the three main areas of traffic counting programs: obtaining the raw data (Section 

4.2), transforming the raw data into AADT and VKT (Section 4.3) and reporting of the raw and 

transformed data (Section 4.4). 

4.1 Background 

Traffic counts and classifications are the basic raw materials for the study and analysis of traffic, 

forming a key input and influencing the design, maintenance and management decision making of 

every RA. In fact, the importance of traffic data extends beyond the interests of each RA to include 

various commercial users (for planning and commercial studies) and other government agencies at 

a national level on policies leading to an integrated national land transport infrastructure network. 

In general, there are three main themes of traffic data: 

Variation of traffic flow by location, 

Time-variation of traffic flow, 

Composition of the traffic flow. 

The above data are collected by counting stations. Counting stations are located at various points 

in the road network to ensure the collection of a representative sample of data that will, in isolation, 

report on particular road segments or corridors and, in aggregate, report on the entire road network 

itself. Two types of counting stations are employed. They are the Permanent Counting Stations 

(PCS, also known as ‘Pattern Stations’) and Short-term Counting Stations (SCS, also known as 

‘Sample’ or ‘Coverage’ Stations). Pattern Stations continuously monitor or frequently sample traffic 

to determine patterns and seasonal fluctuations. Such stations are permanent or semi-permanent 

for counting over a defined ‘season’ (usually a year). Short-term stations are counting stations that 

are utilised for brief traffic surveys ranging from periods of a few weeks to one hour, with a typical 

sample site counting traffic for seven consecutive days. 

Traffic counting faces pressure from users who want additional data for reports and projects, as 

well as from owners who want improved cost efficiencies. Out-sourcing many aspects of data 

collection and maintenance is more commonplace today and one particular traffic data unit in a RA 

has become totally off-budget, requiring it to secure customers amongst the various divisions 

within the parent RA. 

At the same time, rapid changes in the economy, the development of toll roads, increased freight 

flows, changing vehicle regulation and technology have resulted in potentially large changes in 

traffic patterns as well as the potential to use new technology to track and count vehicles. 

In this environment, it is timely to revisit the fundamentals of traffic counting programs. The 

contribution of the practitioners in this field through the earlier fieldwork and the recent Austroads 

Road Use Data Workshop has been invaluable towards a common sense approach in addressing 

and developing best practice for traffic counting programs.



— 14 — 


Traffic counting programs are well established and documented through a series of guides 

beginning with NAASRA (1982) and the two versions of GTEP Part 3 (Austroads 1998a and 2004). 

A national review of traffic counting procedures (Roper 2001) and subsequent recommendations 

(Bennett 2002), addressed the issues of additional data collection facilities and consistency across 

the reporting RAs. The studies of Roper (2001), Bennett (2002), a parallel research activity 

addressing an international literature review on consistency of data (Martin and Chiang 2002), and 

a review of the current traffic data of the RAs (Roper et al. 2002), led to the Road Use Data 

Workshop in October 2003 involving representatives from the RAs specifically addressing the key 

issues in traffic data (Luk and Karl 2003). 

In addition to the studies and Workshop already mentioned, a range of other sources (such as the 

US FHWA, the European Commission and other reports) were accessed in the preparation of this 

section. 

The NAASRA (1982) Guide provided a solid grounding for RAs on which to base their traffic 

counting operations and, in general, this has resulted in a consistent and diverse set of traffic data 

available from RAs, as can be seen in 

Figure 6 (Roper et al. 2002). A cursory examination of the tables will show that most of the 

tabulated data items of interest are already reported by most of the RAs. 

4.2 AADT and VKT Estimation 

Annual Average Daily Traffic (AADT) and Vehicle-Kilometres Travelled (VKT) are two of the most 

commonly used descriptors of traffic derived from raw traffic counts. Data expressed in AADT and 

VKT are universally understood. This section begins with brief introductions to AADT and VKT, 

followed by discussions on accuracy and adjustment factors. 

4.2.1 AADT 

AADT is the Average Annual Daily Traffic passing a roadside observation point over the period of a 

calendar year. It can be obtained by a number of ways. For example, it is obtained by measuring 

the total volume of traffic passing the observation point over a year and then divided by the number 

of days in that year, i.e. 

AADT 

= 

n 

∑Vi 

/ n 

i= 

1 

where Vi = the total traffic counts on day i and n is the number of days in that year (n can be 365 and 366 days). 

Some jurisdictions adopt n = 365.25 days but the difference should be small. 

The production of AADT data would be straightforward if all counting stations were to operate 

without fail for every day of the year, recording daily traffic data. However, in reality, reliability is 

not 100% and not every counting station is a permanent station that operates all year round. 

AADTs derived from permanent or pattern sites may require ‘plugging the gaps’ due to missing 

data. Such adjustments are preferably minor and involve a few days of lost or corrupt data over a 

twelve month period. Permanent sites, however, comprise only a small amount of the traffic 

counting network. The issue of handling missing data will be addressed in Section 6.


(a) Traffic volume data items by jurisdiction 

Data Item ACT NSW NT QLD SA TAS VIC WA NZ 

ADT 

AADT 

AWT 

AAWT 

Midweek Volume 

VKT 

DHV 

Growth & Trends in 

AADT 

Growth & Trends in 

VKT 

 

 

(b) Traffic flow data items by jurisdiction 


Ranking in highest 

hourly flows 

Distribution of Hourly 

Flows through the 

year 

 

Directional Patterns 

Directional/Lane 

Distribution of traffic 

 

Seasonal Patterns 

Use of Aggregated 

Data by Time or 

Location (e.g.: 15, 

30, 60-minute 

intervals etc.) 

 

(c ) Traffic composition data items by jurisdiction 


Use of Austroads 

Vehicle Classification 

schemes 

 

 

Other schemes 

Traffic Composition 

(e.g. % HV, % Artic. 

% Rigid, % 

Combination, etc.) 

 


— 15 — 


(d) Weigh-in-motion data items by jurisdiction 


Weigh-in-Motion (WIM) 

Truck Weight Data 

Freight Mass 

(e) Other data items by jurisdiction 


Functional Classification of 

Road 

 

Geographical Regions 

Types of Counts Collected 

(e.g., Turning Counts, Axle 

Counts, Vehicle Counts, 

Bicycle Counts) 

Other Types of Traffic 

Data Collected (e.g., 

Travel Time Surveys) 

 

 

(f) Adjustment factors by jurisdiction 


Seasonal Adjustment 

Factors 

Expansion Factors (e.g.. 

12 hour count to 24 hour 

count) 

Other Adjustment Factors 

(e.g.. public holiday, 

weekends, etc ) 

Daily Flow Factors 

Figure 6 – Survey of road use data needs amongst Road Authorities



— 16 — 


All AADTs are estimates. A large amount of AADT data is derived from sample or short-term sites 

and estimated by utilising short-term counts to calculate an Average Daily Traffic (ADT) and 

applying an adjustment factor. This factor is derived from a seasonal (12-month) count on the road 

in question, or on a road exhibiting similar seasonal traffic variations. 

Other related traffic parameters are also processed by RAs. These include (see Appendix A): 

Annual Average Weekday Traffic (AAWT), Average Weekday Daily Traffic (AWDT) or Average 

Weekday Traffic (AWT). Different jurisdictions at present adopt slightly different definitions and 

hence procedures in calculating these quantities. The actual values should not be significantly 

different. However, it would be good practice for RAs to state the methods used to generate AADT 

and other values. Further, the definition of AADT or other quantities should not imply a particular 

method of calculation or estimation. It is, however, important that the definitions in Appendix A be 

accepted as a national glossary. 

4.2.2 VKT 

VKT on a network is estimated by measuring or estimating AADT on selected road sections, and 

making an estimate of the total network travel on the basis of travel on each road section. Simply 

expressed, 

VKT 

= 

m 

∑ AADT × 

j= 

1 

j l j 

where 

VKT = the vehicle-kilometre travelled on a network of road sections j = 1, … m 

AADTj = the AADT for road section j, and 

= the length of road section j in km. 

lj 

The total daily travel (the daily VKT) on all segments in a road network is the sum of the product of 

AADT on each road section and the section length. The yearly VKT or total yearly travel is 

therefore the total daily travel multiplied by the number of days in that year (365 or 366 days). It is 

good practice to specify units to distinguish daily VKT compared to annual VKT. 

The accuracy of the estimation of VKT depends primarily on how close the sample of road sections 

represent the traffic flow for the entire road (or road network) and the accuracy of the estimates of 

AADT. It also depends on the dispersion or ‘scatter’ of the estimates. Accuracy can be improved 

by grouping together road segments with similar traffic characteristics. These groups may be 

geographic regions, functional categories, or volume strata (see also Section 4.3.2). For example, 

seasonal variations have the greatest impact on AADT estimates and regional groupings are 

therefore preferable. On the other hand, volume stratification is used to improve the accuracy in 

estimating a VKT and will be described further below. 

A number of different forms of VKT are commonly used: 

Total VKT, 

VKT by vehicle class, 

VKT by road or route, 

VKT by road type, 

VKT by region.



— 17 — 


These breakdowns provide further meaningful information for users. VKT by vehicle class is 

particularly useful in differentiating freight flows. The classification of traffic counts, a necessary 

prerequisite in order to produce this data, has been discussed earlier in Section 3. The correlation 

of classified counts and VKT by vehicle classes with weigh-in-motion data (e.g. from CULWAY) 

facilitates the estimation of pavement and bridge loading and is discussed in Section 5.2. 

4.2.3 Accuracy and Rounding 

Traffic counts taken at any one location are affected by occurrences elsewhere in the road network 

(RTA 2003). Some effects may be known, such as a new road opening or major disruption to traffic 

due to a recent major incident. Others may be minor and unknown, such as minor road crashes. 

Other effects include weather conditions, equipment malfunctioning and miscoding, and community 

effects such as day-of-week, school and public holidays. Each traffic count taken, say, daily should 

be viewed as a sample from an unknowable distribution. AADT, whether estimated from short-term 

or permanent count stations, should not be viewed as a uniquely defined quantity, but rather as a 

mean value with a distribution of errors. 

Accuracy requirements for both AADT and VKT have been previously reported in NAASRA (1982) 

and are given in Table 5 and Table 6. As can be seen, larger error bands are acceptable at low 

levels of AADT and 68% confidence limits represent ± one standard deviation from the mean. 

Traffic characteristic 

Traffic characteristic 

AADT < 100 

101-300 

301-1100 

>1100 

Trend in AADT 

Traffic composition 

VKT - Annual total 

VKT - Annual change 

VKT - Annual composition 

Table 5 - Accuracy requirements for individual sites 

Table 6 - Accuracy requirements for groups of roads 

Maximum error requirements 

(at 68 % confidence limits) 

50% 

35% 

25% 

15% 

10% 

20% 

Errors at 95 % confidence limits 

Australia State City* 

3% 

1% 

3% 

5-7% 

3% 

5% 

* If these accuracies are achieved, those for individual State and Australia will also be obtained. 

10% 

5% 

10% 

Rural 

area* 

10% 

5% 

10% 

AADT range 

(Table 13) 

Errors are reduced if the number of days that traffic is counted is increased, as can be seen in 

Figure 7 (NAASRA 1982). For all AADTs, percentage error decreases as the number of days 

counted increases and the six-day count will produce less than 12.5% error for AADTs over 1000. 

While error bands are naturally higher for smaller AADTs, this has to be realistically acceptable 

and is tolerable in terms of the limited impact on the overall traffic volume. 

25% 

10% 

15%



— 18 — 


Figure 7 - Expected percentage error for AADT from ADT 

and count duration (n-Day counts) for a 75% confidence level. 

An appreciation of the accuracy of AADT data permits the consideration of the rounding of 

numbers. 

Table 7 provides some examples of rounding and compares the percentage change in AADT 

against the maximum error requirements stipulated in NAASRA (1982). It shows that, in all cases, 

rounding is within the permitted error range. The maximum error permitted is based on random 

sampling and rounding should be carried out only if necessary, e.g. for reporting purposes. 

Table 7 - Examples of rounding AADT 

AADT Example Rounding Percentage change Maximum error permitted 

0-100 46 50 8.7% 50% 

101-300 157 200 27.4% 35% 

301-1100 678 700 3.2% 25% 

>1100 2345 2300 1.9% 15% 

4.2.4 Adjustment Factors 

Traffic counts from both permanent and short-term sites often are incomplete. Seasonal 

adjustment factors, conversion and expansion factors are typically applied to traffic counts in order 

to statistically convert shorter counts to annual averages. FHWA (2001) describes it as follows: 

Assuming that temporal characteristics affect all roads and since continuous 

temporal data exist at several points (the permanent/pattern sites) to describe 

temporal variation, it is possible to transfer knowledge by developing factoring 

mechanisms.



— 19 — 


Most RAs have utilised a range of adjustment factors, as can be seen in Table 8 (Roper et al. 

2002). 

Table 8 - Adjustment factors by jurisdictions 


Seasonal Adjustment Factors 

Expansion factors (e.g. 12 hour count to 

24 hour count) 

Other adjustment factors (e.g. public 

holiday, weekends, or daily flow factors 

from control sites) 

 

 

A number of adjustment factors have been applied, in various jurisdictions, which include: 

Seasonal adjustment factors, 

Hour-of the day expansion factors, 

Day-of-week adjustment factor, 

Lane distribution factors, 

Growth trends at that location, 

Factors for short-term counts, 

Factors for total volume and estimates for trucks, 

Visual 8, 12 hour expansion factors, 

Visual truck expansion factors. 

GTEP Part 3 provides a general guideline, noting that where a road counting program has been 

established and seasonal patterns identified, the AADT at a particular location may be estimated 

by multiplying a sample count (say, two to six days duration) by the Seasonal Adjustment Factor 

derived from a Pattern Station representative of the required location by: 

(AADT)j = ADT ij × (SAF) i,k 

where 

(AADT)j = the AADT at the required location j; 

(ADT)ij = the sample count in the season (or month, week, day, etc.) i at the location j; 

(SAF)ik = the Seasonal Adjustment Factor for the season (or month, week, day, etc.) i at a Pattern Station, k, 

representative of the required location j. 

As conditions will vary in each jurisdiction, the specific adjustment factors utilised by RAs will each 

be unique to their jurisdiction. It is good practice for each RA to derive its own adjustment factors. 

Also, adjustment factors should be clearly detailed, say, in appendices to provide clarity and 

transparency to the process of adjusting the raw data.



— 20 — 


Transfund New Zealand (2001) provides the adjustment factors to convert short-term counts to 

estimates of AADT and some of these factors are shown in Table 9. These factors are categorised 

by road types, day-of-week, time periods of day (part-days) and vehicle types. The day and partday 

conversion factors are shown in Table 2.4 for Auckland and non-Auckland roads in New 

Zealand. The validity of these factors for other places has yet to be established, and the error for 

AADT estimates using part-day factors can be greater than 30% and exceed Austroads (2004) 

guidelines. 

Table 9 - Some adjustment factors from short-term counts to AADT estimates 

Road types 

Day factors 

Mon Tues Wed Thu Fri Sat Sun 

Urban arterial 1 (Auckland) 0.98 0.95 0.93 0.92 0.88 1.17 1.37 

Urban arterial 1 (non-Auckland) 1.01 0.98 0.95 0.94 0.89 1.09 1.24 

Urban arterial 2 (Auckland) 0.99 0.96 0.95 0.93 0.89 1.13 1.31 

Urban arterial 2 (non-Auckland) 1.00 0.97 0.94 0.93 0.88 1.11 1.30 

Urban CBD (Auckland) 1.02 1.00 0.97 0.94 0.86 1.07 1.34 

Urban Industrial (Auckland) 0.85 0.85 0.84 0.84 0.84 1.84 2.66 

Rural urban fringe 1.14 1.14 1.10 1.07 1.07 0.94 0.86 

Rural strategic 1 1.05 1.01 0.99 0.97 0.97 1.09 1.10 

Rural strategic 2 1.11 1.14 1.10 1.05 1.05 1.03 0.91 

Rural recreation summer 1.07 1.17 1.13 1.05 1.05 1.11 0.88 

Rural recreation winter 1.15 1.25 1.21 1.12 1.12 1.03 0.82 

Road types 

Part-day factors for typical Monday - Thursday 

7-9 a.m. 9a.m.-12p.m. 1-4 p.m. 4-6 p.m. 

Urban arterial (Auckland) 5.63 5.66 5.10 5.78 

Urban arterial (non-Auckland) 7.31 5.17 4.60 6.00 

Urban CBD (Auckland) 7.86 5.77 5.22 6.36 

Urban Industrial (Auckland) 5.57 4.13 3.72 5.97 

Rural urban fringe 8.61 5.59 5.49 6.64 

Rural strategic 1 8.40 4.74 4.41 5.99 

Rural strategic 2 10.2 5.17 4.89 6.88 

Rural recreation summer 16.9 4.84 4.08 7.77 

Rural recreation winter 13.8 5.24 4.76 7.91 

(Source: Transfund New Zealand 2001)



— 21 — 


Some examples of using Table 9 are as follows: 

(a) If the short-term day count on a Wednesday on a rural urban fringe site is 10,000 vehicles 

per 24 hour, then the estimated AADT is 1.10 × 10,000 = 11,000 vehicles. 

(b) If the short-term part-day (7 a.m. – 9 a.m.) count on a Wednesday at the same rural urban 

fringe site is 1,300 vehicle per 2 hour, then the estimated AADT is 8.61 × 1300 = 11,193 

vehicles. 

In general, 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 – collecting data over a consecutive period of, say, seven 

days. 

Based on some NSW count data, Botterill and Luk (1998) reported that regression analysis is 

suitable for determining an adjustment factor to convert a sample count to an estimated AADT and 

estimating the relevant accuracy. Regression analysis could be preferable to cluster analysis for 

some users because the statistics from regression are well-known. The NSW data were separated 

into monthly counts and regression equations were derived relating AADT to road type, count type 

(axle counts or classified counts), and AADT strata. It was found that AADT strata had minimal 

effect and a recommended regression equation is as follows (without using the constant term): 

AADT = β × Vol + ε 

where AADT is the count for each Permanent Station, 

β is the seasonal adjustment factor to be estimated, 

Vol is the daily volume for each nominal period (either a three-day or a six-day week) in the four weeks 

in any month for each permanent stations, 

ε is the error term representing the deviation from the regression line. 

Botterill and Luk (1998) further provided a systematic view of the tasks involved in a traffic counting 

program and the calculation of VKTs (Figure 8). The framework highlights the following issues: 

Need for a strategic view in implementing a counting program (funding issues and on-going 

maintenance); 

Appropriate analysis techniques (cluster or regression for pattern recognition, seasonal 

adjustments, etc.); 

Reliability check over many years and between years; 

Clear operational producers. 

Some of the issues have been mentioned earlier in Section 2 and are addressed further below.


Stages 

Set up network 

Set up stations 

Data 

Relate counters 

Relate data 

Network VKT 

Figure 8 - A design for traffic counting program 


— 22 — 


Concepts Feedback 

Operations 

Road 

network 

Grouping 

variables 

Strategy: 

funds, maint’e, 

continuity 

Count 

duration, 

timing 

Cluster, 

regression 

method 

Conversion 

(seasonalising) 

method 

Reliability 

over many 

years 

Reliability 

between 

years 

Improve 

accuracy 

PCS site 

choice 

- reliability 

Yearly 

count data 

Determine 

road sections 

SCS site 

choice 

- coverage 

Sample 

count data 

Group PCS Allocate to 

PCS group 

Conversion 

factors 

(Seasonal, etc) 

- by group 

Total VKT 

on network 

- accuracy 

Adjusted 

estimates of 

VKT, AADT 

- by group



— 23 — 


4.3 Network Coverage 

Reports of AADT and VKT relate to the network where traffic is counted. As traffic counts are 

almost always a sample of the road network, the issue of network coverage or link-node 

development is important. The key issues are as follows: 

Difficulty in prescribing rules like number of permanent sites per km as each RA network is 

unique. Good judgement is required to define road sections or links; 

Use of performance measures such as an indication of the number of permanent sites for a 

given number of homogeneous sections; 

Frequency of reviewing uniform traffic sections (UTS) or road sections; and 

Definition of homogeneous or uniform road sections. 

The accuracy of AADTs at sample stations depends critically on the selection of Pattern Stations, 

which alone provide the full year of traffic counts and from which the related short-term sites 

depend for their adjustment factors. In order to increase the confidence in the pattern information 

gained, the collection of data at all Short-term Stations in a pattern group should be made at the 

same time as the related Pattern Station (if it is seasonal) is being sampled. 

The accuracy of an AADT estimate depends partly on the reliability of grouping Short-term Stations 

with Pattern Stations, and the method of grouping is important. Areas with similar economy, 

culture and development tend to show similar traffic patterns, and proximity on its own may not be 

a good indicator. 

This section deals with those issues and discusses good practices found in each area. The 

following sub-sections cover sampling (Section 4.3.1), frequency (Section 4.3.2), defining 

homogeneous sections (Section 4.3.3) and directionality (Section 4.3.4). 

4.3.1 Sampling 

Sampling in a traffic count program relates to two separate yet inter-related issues; the sampling 

unit and the frequency of sampling. Both issues are discussed in this section. 

Sampling Unit 

Pattern stations are best located by randomly selecting the number of sites on roads from each of 

a number of AADT strata, within each geographical region. As a general rule, the density of 

stations in each stratum should be proportional to the product of the total length of road and the 

square root of the mean AADT in that stratum (Austroads 2004). 

In practice, the number of Short-term Stations is determined by the extent of the road system and 

the availability of funds. The number of Pattern Stations, and the ratio of Pattern Stations to Shortterm 

Stations, cannot be determined in advance, because the assessment will depend on the 

regional grouping and the reliability of matched patterns. Thus, the achievement of consistent and 

specified accuracy levels in AADT estimation is essentially an iterative process that develops over 

time and with experience. 

The guidelines for the densities of traffic counting stations are shown in Table 10.


Rural 

Type of road 

Table 10 - Densities for traffic counting stations 


— 24 — 


Counting station density (km/station) 

Pattern Stations 

Permanent* Seasonal* 

Freeways, arterials 100-200 200-300 

Local roads 5,000-9,000 1,000-2,000 

Urban 

Freeways, arterials 20-50 30-50 

Collectors, distributors 100-150 50-100 

Local roads >1,000 4,000-6,000 

* lower figures (or higher densities) applicable if relatively few Seasonal Stations used 

Short-term 

Stations 

Up to 25 

(usually 13-17) 

Short-term Stations may be 

characterised by one Pattern 

Station 

In a recent review of traffic counting stations, Roper (2001) found that RAs generally exceed the 

guidelines in Table 11 for traffic counting station densities. In most cases, densities were higher 

than the guide of 100 – 200 km for rural road and 20 - 50 km for urban count stations. 

Rural 

Urban 

Table 11 – National highway traffic counting station density by location and status 

Counting station type Station density (km/site) 

(location and status) NSW VIC QLD WA SA TAS NT ACT NZ 

Permanent 68.3 68.1 19.3 217.6 221.9 51.0 175.4 6.3 111 

Temporary 10.3 6.4 74.1 25.8 35.0 2.7 202.4 9.5 9.8 

Overall 9.0 5.9 15.3 23.1 30.3 2.6 94.0 3.8 9.1 

Permanent 2.9 2.6 3.2 -* 43.5 7.0 3.3 - 22.5 

Temporary 3.0 4.2 20.6 2.8 7.3 0.5 3.9 - 4.5 

Overall 1.5 1.6 2.8 2.8 6.2 0.5 1.8 - 3.1 

* there are 40 sites in metropolitan Perth although none on National Highways due to the urban form of Perth 

Because judgemental sampling is often employed to locate Permanent and Short-term Stations, 

the AADT values obtained cannot be regarded as random samples. The estimation of VKT from 

these AADT values is therefore not as representative as those obtained by, say, the sampling 

frame of the Survey of Motor Vehicle Usage (SMVU) of the Australian Bureau of Statistics (ABS 

1963 and other years). The VKT from a road authority counting program (e.g. the NSW program) 

is comparable to SMVU estimates if VKT from all road types in a State are accounted for, 

especially the VKT from local roads. 

The NAASRA (1982) method of calculating the number of counting stations to determine the VKT 

of a region is shown in Appendix B. The method has been developed further in Botterill and Luk 

(1998) by including accuracy and statistical significance requirements and relaxing the requirement 

on preset AADT strata. Using the original method, the number of stations required could be 

underestimated. The derivation of the new formulae is quite involved and will not be included in 

Appendix B. In any case, as mentioned previously, the provision of counting stations is more than 

generally recommended in NAASRA (1982).



— 25 — 


Frequency of Sampling 

The issue of frequency of traffic counts relates in particular to the frequency at which short-term 

counts are conducted at specific sites. In determining the frequency of data collection, a road 

authority will need to consider the rate of change of values of particular data types. The accuracy, 

quality and currency of the data should be determined with reference to the cost of collecting those 

data and the value and benefit of that data (Western European Road Directorate 2003). The issues 

are summarised as follows: 

Within a year – number of sampling periods, e.g. once, twice or more times per year; 

duration of each sampling period, e.g. 1 h, 1 day, 1 week or more; when to survey, e.g. 

randomly, outside school holidays or at each season; consistency with previous surveys; 

Between years – predictability and consistency of traffic growth; predictability and 

consistency of factors leading to traffic growth; existence or otherwise of commodity in traffic 

flows between areas or road types, e.g. rural versus urban or National Highways versus main 

roads; 

External demands – internal road authority requirement; external government agency 

requirements; political or public relations demands. 

The frequency of collecting short-term traffic counts varies amongst RAs. The current practices 

range from a frequency of a sample once every year to as infrequent as ten years apart. Most RAs 

sample at a frequency of once every two to three years. Within this sample program, one half or 

one third of the sites are sampled in any year, resulting in all sites covered over a two to three year 

cycle (described as a ‘rolling’ program). Increasing budgetary pressures are leading RAs to lower 

collection frequencies, e.g. from three to four years to five to six years in South Australia. 

Further ‘fine-tuning’ is applied within a sampling frequency regime, by differentiating between 

classes of road (urban and rural) as well as within classes of road (mainly rural). Thus, Western 

Australia reported a two year metropolitan cycle and a five year rural cycle while Tasmania 

reported sampling 100 sites every two years, 300 sites every five years and 450 sites every ten 

years. 

It is good practice that the sampling interval should not extend beyond three years, under what can 

generally be described as ‘normal’ conditions. In practice, sampling frequency is a balance of 

costs and accuracy, and recognition of changing traffic conditions, best determined by the users of 

the data themselves. As such, for rural roads where traffic counts hardly change, a ten year 

sampling frequency may be justifiable, as with some roads in Tasmania. This, of course, would not 

apply to rural roads closer to urban areas, where it is likely that conditions will change more rapidly. 

The local knowledge of RAs enables fine tuning within portfolios of count stations, to achieve cost 

effectiveness in the counting program. 

4.3.2 Defining a Homogeneous Section 

Homogeneous sections are utilised to transpose AADTs calculated for specific survey locations, as 

part of the sample traffic counting program, across to road sections in the total road network. By 

assigning an AADT for each road section in the network, a calculation for VKT can be made, 

multiplying the road section length by the AADT and adding up across all road sections. 

Homogeneous sections are groups of roads or ‘strata’ or ‘uniform traffic segments’ (UTS), each of 

which includes a relatively large number of segments that can be regarded as a ‘population’ for 

statistical sampling. Homogeneous sections also relate to groups of roads to which adjustment 

factors, described earlier, can be applied. FHWA (2001) describes the creation of ‘factor groups’ 

(similar to homogeneous sections) as follows:



— 26 — 


The factoring process defines a set of roads as a ‘group’. All roads within that 

group are assumed to behave similarly. Then a sample of locations on roads 

from within that group is taken and data are collected. The mean condition for 

that sample is computed and that mean value is used as the ‘best’ measure of 

how all roads in the group behave. If the sample of data collection sites is 

randomly selected and moderately large, the distribution of that measure about 

the mean is a good measure of how well that mean applies to road sections in 

the group (FHWA 2001). 

The definition of a homogeneous road section is complicated by differences between rural and 

urban sections across a State and region. Homogeneity can be derived from functional classes as 

well as from stratification by volume. Various homogeneous groupings include those in Table 12 

by functional classes and Table 13 by AADT strata. 

US 

FHWA (2001): 

• interstate rural 

• other rural 

• interstate urban 

• other urban 

• recreational 

Table 12 - Groups by functional classes: 

Canada 

(Martin and Chiang 2002) 

• regional commuter 

• average rural 

• partially recreational 

• recreational 

New Zealand 

(Transfund NZ 2001) 

• urban arterial – major, minor 

• urban – commercial, industrial, 

other 

• rural urban fringe 

• rural strategic 

• rural recreational – winter, summer 

VicRoads is implementing a classification system for its declared road types based on uniform road 

geometry and performance specifications to invoke consistent user expectations. The road network 

then becomes a set of clearly stratified sub-networks each of reasonably similar AADT. 

Table 13 provides the current Austroads guide on deriving homogeneous sections based on AADT 

volume data, categorised by urban and rural roads. In the study on traffic counts for RTA, Botterill 

and Luk (1998) found that the rural AADT strata of (0, 100) and 7,000-plus could be expanded to 

give better estimation.


Table 13 - Suggested volume stratification of road segments 

Stratum Suggested range 

for urban roads 

(AADT) 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

0-300 

301-1,100 

1,101-2,000 

2,001-4,000 

4,001-7,000 

7,001-16,000 

16,001-20,000 

20,001-35,000 

35,001-50,000 

50,000 plus 


— 27 — 


Suggested range 

for rural roads 

(AADT) 

0-100 

101-300 

301-700 

701-1,100 

1,101-2,000 

2,001-4,000 

4,001-7,000 

7,000 plus 

Homogeneous or uniform traffic sections should be determined in a consistent way based on a 

predefined set of rules. Road authorities would have their own individual procedures in defining 

and processing homogeneous sections. An example of rules for defining homogeneous traffic 

section, sourced from Main Roads WA and Transit NZ and reported below. those rules can be 

used as a starting point to generate traffic sections that can be reviewed by stakeholders with local 

knowledge for consistency and accuracy. Each homogeneous section to be selected up to its 

maximum length, including ramps; 

Homogeneous sections to be delimited by intersections with arterial and sub-arterial roads; 

A homogeneous section should not extend over two roads; 

A homogeneous section should not contain a single and dual carriage way or a ‘one-way’ 

road; 

A homogeneous section cannot span across different speed zones 

The start and end points of homogeneous sections should not coincide with start and end 

points of strategic links (cannot have a homogeneous section overlapping the lengths of two 

consecutive strategic links); and 

A difference in AADT volumes between two consecutive count sites of 15 - 100% generates 

a new traffic section, as shown in Table 14. Similar rules can be applied in regard to traffic 

composition.



— 28 — 


Table 14 - Suggested volume stratification based on % change in AADT 

From AADT or AAWT To AADT or AAWT 

(Source: Main Roads WA) 

% change above which a new traffic 

section is required 

40,000 50,000 15% 

30,000 40,000 20% 

20,000 30,000 25% 

10,000 20,000 30% 

7500 10,000 35% 

5,000 7,500 40% 

4,000 5,000 45% 

200 400 50% 

10 20 100% 

Road Authorities deal with a total number of homogeneous road sections varying from 500 

segments to 5,000 segments. These segments will change from time to time and various recent 

techniques have offered potentially new solutions in the updating of road segments. Thoresen and 

Michel (2002) reported on the use of hourly flow histograms from Pattern Stations to develop 

default hourly histograms for other sites and ultimately across the entire network. Such a 

technique provides, indirectly, another means of deriving homogeneous sections across the 

network. 

RTA NSW employs an alternative technique in defining a homogeneous section and a network is 

divided at points of major flow changes. RTA (2003) proposed a computational solution based on a 

statistical set of rules to update UTSs (Uniform Traffic Sections or Segments) automatically with 

minimal manual maintenance. The techniques are based on working with an alternative set of 

road sections, called ‘Weighted Road Segments’ (WRS) that encompass a number of UTSs and 

defining UTSs in terms of changes in AADTs within each WRS by a 95% statistically significant 

measure of twice the square root of AADT, based on the assumption that AADT is distributed as a 

Poisson variable. 

While it is easy to define a road section on AADT volume stratification, the same road section may 

fall under a number of road classifications (urban, rural, etc). Furthermore, the definition of the 

group changes depending on the vehicle classification. This raises another implication for good 

practice - the need to maintain a historical record of changes to homogeneous sections and 

classifications over time. 

In summary, a good practice among RAs in defining homogeneous sections, and hence the 

calculation of AADT and VKT in a statewide counting program, would involve the following iterative 

steps: 

(a) Locate permanent and short-term count stations based on judgement and road functional 

classes (e.g. Table 12); 

(b) Collect counts; 

(c) Divide road network using volume stratification and traffic pattern behaviour; 

(d) Determine the number of stations required by applying a volume stratification technique (e.g. 

Tables 13 and 14);



— 29 — 


(e) Review count station density and location; 

(f) Calculate adjustment factors to convert short-term counts to AADT estimates; 

(g) Identify homogenous traffic sections and calculate VKT; and 

(h) Check, review and prepare a rolling program with stakeholders who have good local 

knowledge. 

4.3.3 Directionality 

The counting of traffic on road sections can be either directional or bi-directional (two-way). 

Typically, most RAs report directional AADTs for urban road sections and two-way AADTs for rural 

road sections. Directional VKT for rural environment has some value and should continue to be 

measured where possible and most permanent counting stations can provide the information on 

directionality. 

More specifically, directionality reporting is usually based on carriageway or the sum of all lanes in 

the reported direction. In greater detail, VicRoads reported AADT by direction with breakdowns for 

a.m., p.m. and other time periods, while RTA NSW reported only by direction. 

In general, directionality should be specified as reported either in one direction or bi-directionally, 

and that traffic counts are the sum of counts from all lanes in the direction reported. When 

directionality is unspecified, then an AADT value always refers to a bi-directional (two-way) count. 

4.4 Reporting 

Reporting of traffic counts reaches a wide audience. While internal users within a road authority 

may be considered to be familiar users of such data, an increasing number of external users have 

varying levels of familiarity from novice to experienced. Furthermore, user familiarity with traffic 

count data and reports may be based on experience with reports and data from other jurisdictions, 

locally and internationally, where procedures and methods may be different. In some cases, users 

may be interested in further aggregating data, across a number of States, or comparing such data 

with similar data from other States. 

Road Authorities have on-going demand from external users for road use data, on an ad-hoc feefor-service 

basis or under Freedom of Information (FOI) legislation. At the same time, RAs are 

themselves providing users with more access through the Internet, by CDs and publications (both 

hard and soft copies). 

The October 2003 Road Use Data Workshop addressed issues in consistency of reports and this 

section covers examples of good practice in reporting (Section 4.4.1), making data available to 

users (Section 4.4.2). Data integration and public-private partnership in making data available are 

mentioned in this section and will be addressed more fully in a separate progress report. 

4.4.1 Examples of Good Practices 

Estimates of AADT and VKT, together with their trend projections, are of key interest to road 

agencies. These data are kept in numerical tabulation and graphical forms. Primary information 

such as location of the counting site, location plan, weather features and public holidays should be 

documented with the respective data. The common presentation of the following data parameters 

is discussed below. 

AADT 

Published maps with historical and current AADT shown at each pattern and short-term 

counting station.



— 30 — 


Plotting of AADT as ordinates on map of urban road network, against each segment as an 

abscissa. 

Axle pairs or Vehicle Counts 

Provide raw data on axle pairs and adjusted vehicle counts if a single axle sensor is used; 

Record the method of adjustment; 

Mention the level of accuracy. 

Seasonal Patterns and Trends of Traffic 

Monthly, weekly and daily variation patterns in traffic can be illustrated in graphical and 

tabular forms; 

Information on the variation about the average level of traffic can be drawn from the graphical 

data arranged in decreasing order. 

Turning Movements at Intersections 

A tabular method is used when classification by vehicle type is required to be present; 

Peak and daily hour turning movements can be illustrated graphically. 

Other issues that should be considered are: 

Reporting interval, 

What data are reported, 

Geographic referencing of the information, and 

Report presentation format. 

As a minimum, it is recommended that reporting should be at yearly intervals, providing AADT for 

each section of the road network, in terms of vehicles per section per time period. Each traffic 

counting station should be identified by geographic referencing using maps or coordinates. 

Minimum reporting format should include location, AADT by road section and vehicle class, details 

of homogeneous road sections, VKT (total, by vehicle class, by road type, by region), details of 

duration of counting and traffic growth over the previous year. 

An example of the data classes collected and stored for reporting in Queensland is detailed in 

Table 15. 

It is also good practice that statistical results be reported jointly in terms of percentage error and 

confidence interval. If measurements could be made over the totality of a target population rather 

than over just a sample then there would be no need to specify a confidence interval. Because 

usually only a sample is taken, there remains the possibility that this sample is unrepresentative of 

the population, so that the error is likewise unrepresentative. It is also good practice to specify a 

risk factor that the sample could be unrepresentative. 

For example, a risk of 1 in 20 chance (sample) is taken, which translates to the 95% confidence 

interval and this happens with the confidence limits at ±1.96 standard deviations from the mean. 

Similarly, with confidence limits at ±1 standard deviation from the mean, the confidence level is at 

68 %. Risk should be specified as a percentage error for determining confidence limits.



— 31 — 


Table 15 - Complete list of data classes collected/computed and stored in Queensland 

Class Description Class Description 

0 Volume 33 Free Speed – Distribution – Low Range 

1 Annual Average Daily Traffic 34 Free Speed – Distribution – Mid Range 

2 Highest Hourly Volumes 35 Free Speed – Distribution – High Range 

3 AM and PM Peaks 40 Gross Vehicle Mass – Mean 

5 Exponential Growth 41 Gross Vehicle Mass – Standard Deviation 

6 Set Factors 42 Gross Vehicle Mass - 95%< 

10 Speed – Mean 43 Gross Vehicle Mass – Distribution 

11 Speed – Standard Deviation 50 Axle Group Mass – Mean 

12 Speed – 85%< 51 Axle Group Mass – Standard Deviation 

13 Speed Distribution – Low Range 52 Axle Group Mass – 95%< 

14 Speed Distribution – Mid Range 53 Axle Group Mass – Distribution 

15 Speed Distribution – High Range 60 Unloaded 

20 Percentage Following 61 Loaded 

21 Gap Acceptance Overtaking 62 Overloaded Axle Group 

22 Headway – Mean 63 Overloaded Vehicle – by Axle Group 

23 Headway – Standard Deviation 64 Overloaded Vehicle – by Gross Vehicle Mass 

24 Headway – 85%< 69 Mean Net Load – (Total Freight tonne) 

25 Headway – Distribution 70 ESA – Unloaded 

30 Free Speed – Mean 71 ESA – Loaded 

31 Free Speed – Standard Deviation 72 ESA – Overloaded 

32 Free Speed – 85%< 73 ESA – Total 

80 Vehicle-Kilometre Travelled 

4.4.2 Data Availability 

Reporting also entails making data available to a range of users at different levels of access. Such 

considerations include: 

Storage formats which should be clearly stated for easy unpacking and processing; 

Download formats which should included the most commonly used applications (pdf, 

spreadsheets, zipped, etc); 

Methods of access which should include traditional hard copies and more common electronic 

forms of access (Internet, dial up, help desk, etc); 

Limitations on sensitive and commercial information (if any); and 

Copyright and legal issues, regarding the on-sale and distribution of such information to third 

parties. 

The existence of Freedom of Information Acts in each RAs’ jurisdiction implies that road data 

should be available to the public. Indeed, this is already the experience of most RAs in dealing 

with an increasing amount of requests for such information. As requests can vary from simple 

queries to detailed investigations requiring further effort, processing or analysis of the data, it is 

reasonable that a user-pay policy applies.



— 32 — 


It is good practice to develop a schedule of standard data and information that are available (such 

as audited and processed data required for normal RA reporting purposes) and the cost of 

provision of those data to the public in the required formats (hard copy or electronic copy). 

A schedule of charges for labour and overheads should also be prepared for non-standard 

requests that involve utilising RA resources in fulfilling a private or commercial inquiry. 

Further sources of road use data are available through infrastructure deployed for other purposes. 

These include WIM data and other data collected under the Austroads program on national 

performance indicators (Austroads 2001). More recently, SCATS, freeway incident management 

systems and other Intelligent Transport Systems now also provide ‘cheap’ traffic data efficiently. 

Commercial organisations also, increasingly, have access to traffic data through the use of tracking 

systems such as GPS and mobile phone positioning technology. 

New sources of ITS data will originate, not only from RAs, but also from commercial organisations. 

The commercial structures of such arrangements (licensing, joint venture) will need to be 

examined. This may lead further down the path of disaggregating traffic counting into its 

component activities, i.e. collection, aggregation and dissemination. Already, out-sourcing occurs 

at the collection level in many RAs. 

Furthermore, the delivery or dissemination channels for this expanded data and information will 

include specialist third parties such as the media, Internet and Telematics service providers as well 

as mobile phone operators. 

In this new environment, reporting of traffic data would become highly customised and 

personalised, with service providers creating value by differentiating and customising traffic data 

for their user base. There could be complementary roles where the RAs provide the basic raw or 

simply processed data for internal users. The general public and the service providers undertake 

further analysis and transformation of traffic data for their customers. 

Data accessibility and its pricing will continue to be an issue facing RAs in the years to come. 

Policies need to be established. RTA NSW has in place manuals for the sale of data and finance 

manual on pricing. The development of a pricing policy especially for external users is complex and 

common amongst RAs. It is an area recommended for further research. 

The issue of data integration and public-private partnership are further addressed in Sections 5 

and 6 respectively.


5 DATA INTEGRATION 


— 33 — 


Road authorities are increasingly involved in collecting a wide range of road use data. These data 

types can come from a number of sources. There is also an increasing need to efficiently access 

the data within a RA, or from external parties including the public. This raises the issue that road 

use data should be better integrated for easy access. This section describes the issues involved, 

current practices within RAs and future directions. 

Data integration can be broadly defined as the bringing together of different types of road use data 

and other road data such as the inventory of road conditions in a central database. The integration 

can utilise several databases, with the capability of referencing the data using a unique referencing 

system such as road numbers and Geographical Information System (GIS) coordinates. 

By combining data, it is possible to generate further information than in its parts, e.g. the Austroads 

(2001) National Performance Indicators Program. However, integration should not be interpreted 

as a simple combination of data such as averaging data sets, merging data from different time 

periods of a day or aggregating data across lanes. Also, raw data should be maintained in a 

database as far as possible. 

5.1 Road Use Data Types for Integration 

The range of road use and related data generally suitable for integration, and reflecting the 

diversity of traffic information needs, includes the following: 

Traffic volumes, traffic composition (i.e. classified counts) and turning movement counts; 

Pavement loading data from weigh-in-motion (WIM) systems and weigh bridges; 

Pavement condition rating; 

Transport demand data such as origin-destination data; 

Route performance data, e.g. travel time, level of service or congestion index; 

Traffic management data from ITS, signal systems or freeway management systems, 

Road crash data, e.g. exposure measure, risk or black spots; and 

Asset management data such as bridges, tunnels, signs and markings and guard rails. 

The above list is not exhaustive and other data types can be included in an integrated database 

now and in the future. Various RAs have already started the integration process, although there 

are no standard procedures, software or types of data for integration. Efficient access using 

database technologies will benefit a wide range of users within and external to a jurisdiction for 

many different needs. An example is the need for a road crash exposure indicator and one that 

can correlate with road crashes already stored in, say, a GIS framework. For urban roads, that 

exposure indicator can be a low-cost indicator available from road traffic counts in a signal control 

system, assuming that signal counts are stored routinely. 

Figure 9 shows the framework of the ARMIS database of the Queensland Department of Main 

Roads. The ARMIS framework covers most of the data types mentioned above, supplemented with 

road and bridge inventory data. These data types are entered into a database, which has an audit 

utility. At the core of the database is a road reference and road inventory that specifies locations for 

traffic analysis, road crash analysis, pavement condition, road maintenance performance contracts 

and construction management. After an auditing process, the data is sent to the Road Information 

Data Centre to support a wide range of road data users.


ARMIS 

Remote Bridge 

Logging Systems 

Road Crash 

Traffic 

Counting Tools 

Condition Data 

Acquisition 

Remote Feature 

Logging Systems 

Traffic Analysis 

& Reporting 

Pavement 

Condition 

External Maintenance 

Management Systems 


— 34 — 


Figure 9 – An integrated road use database from Queensland Main Roads 

It is therefore recommended as a good practice that traffic data from counting stations, manual 

counts, WIM sites and signal systems be integrated as a first step. Other data to follow should 

include data previously mentioned - Austroads National Performance Indicators, road management 

data, crash rates and others. 

Main Roads WA also reported the use of a relational database management system to receive and 

validate raw traffic count data (Mihai 2004). The data is then uploaded to the Integrated Road 

Information System (IRIS) which has the functions of: 

Maintaining traffic section definitions on a road network; 

Managing the editing, storage and access to data on each traffic section, and at each count 

site; 

Maintaining count site locations; 

Road Crash 2 

Road Reference 

and 

Road Inventory 

Road Maintenance 

Performance Contracts 

Bridge 

Information 

Construction 

Management 

System 

Maintaining relationships between count sites; and 

Roads 

Information 

Data 

Centre 

Roads Info 

Directory 

Service 

Chartview 

Maintaining and publishing high level annual traffic statistics for count sites and road 

sections. 

Some road authorities further reported the use of proprietary weigh-in-motion databases (e.g. WIM 

Net) to facilitate the processing, storage and access of weigh-in-motion data. These databases 

perform additional functions such as calibrating for weight drift and time clock inaccuracies (see 

Section 6 on quality assurance). They can assist with identifying overloading trend and associated 

axle configuration patterns. 

RIPA 

Financial 

Data 

Presentation 

& Analysis 

RIO 

ARMIS GIS 

Mapview 

Road Crash 

GIS 

Databrowser 

Query Tool 

Spreadsheet 

Scenario



— 35 — 


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 

[0 00 000] 

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 


Gross Vehicle Mass (GVM) 23.5 25.3 25.3 24.9 

Class 10 B-double 

[0 00 000 000] 




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.



— 36 — 


Figure 10 shows the distribution for six-axle Class 9 (‘123’) vehicles in all WIM sites surveyed. It 

has two peaks, one at around 16 tonnes most likely for an unladen vehicle. Another peak is at 42 

tonnes most likely for a fully laden vehicle (the maximum mass limit is 42.5 tonnes). 

Figure 11 shows a similar bi-polar distribution of GVM distribution for B-doubles (‘1233’ or nineaxle 

Class 10 vehicle). The first peak is at 26 tonnes and the second at 58 tonnes (the maximum 

mass limit of a B-double is 62.5 tonnes). 

Note that the y-axis showing the distributions were not scaled in the original document (Ramsay et 

al. 2001). This omission is due to the sensitive nature of the data. 

Peters (2002) showed similar results for all heavy vehicles entering and exiting the Port of 

Fremantle in WA (Figure 12). He also reported that 8% of six-axle vehicles, 13% of B-doubles and 

4% of double road-trains were overloaded. The majority of overloading occurred on the in-bound 

route into the Port. The degree of overloading varied and static weighbridges are needed for 

enforcing compliance. 

Distribution of ’123’ vehicles 

0 10 20 30 40 50 60 70 80 

Gross Vehicle Mass (t) 

Figure 10 – Total mass of three-axle prime movers and three-axle semi-trailers 

(Source: Ramsay et al. 2001)


Vehicles 


— 37 — 


Distribution of ’1233’ vehicles 

0 10 20 30 40 50 60 70 80 90 100 

Gross Vehicle Mass (t) 

Figure 11 – Total mass of nine-axle B-doubles 

(Source: Ramsay et al. 2001) 

Figure 12 – Laden and unladen vehicles in each vehicle class at Port Fremantle, WA 

It is thus concluded that mean values such as those in Table 16 can only 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.



— 38 — 


Road authorities have undertaken correlation work on WIM data with classified counts to determine 

equivalent standard axle factors and other data for pavement deterioration modelling. According to 

Transit New Zealand, a key issue is that the storage, retrieval and processing of the large amount 

of WIM data consume a lot of time and stretches the capacity of the computer system managing 

the data. 

5.3 Public-Private Partnership 

The future development of road use data as a corporate asset is dependent on how much data 

collection and management is carried out by the government sector. Budgetary constraints within 

RAs may limit a data collection program. Many data collection activities have also been 

outsourced. At the same time, the private sector may see opportunities to participate in the data 

collection program, add value and market the data as a commercially viable business. 

The current Austroads Intelligent Access Program (IAP) is an example of exploiting this publicprivate 

partnership (Koniditsiotis 2003). This project invites third party service providers (the 

private sector) to monitor in real time the compliance of heavy vehicles in following jurisdictional 

regulations such as speed, load and route access. In exchange for being monitored with the use 

of ITS technologies such as on-board GPS units, the transport industry expects to gain a 

concession in accessing a road network. The service provider charges the transport industry for 

tracking vehicles that subscribe to the service, and also have the freight/heavy vehicle data that 

can potentially be another source of income. The IAP is currently in the early stages of 

implementation after a two-year feasibility study. 

Integration and analysis of these new sources of data will be costly and clear business cases will 

be required to support investment in this area. Progress in data fusion will most likely occur in 

close consultation and cooperation with users to ensure that specific customer-driven traffic data 

needs are being met in aggregating such data. In most cases, there will not be one single user, 

but potentially multi-user scenarios, bringing benefits, for example, to traffic management 

operations as well as to road users who want to know the impact of incidents along their routes. 

Other similar public-private business models have been promoted for the future development of 

various ITS services. These services include, in particular, the provision of travel time information 

(see, e.g. Karl et al. 2003). Others could be speed camera operation and administration, toll roads, 

operations of traffic information centre. 

These trends require policy and pricing decisions for RAs in terms of the extent of private sector 

involvement and price or cost of services or data to be provided to the private sector. Road 

authorities at present adopt a variety of policies on this issue. Some have gone further down the 

path of out-sourcing and are charging fees for providing data to other parties. A best practice on 

this issue has yet to be developed, but the emerging trends suggest that the public-private 

partnership could be a win-win business model.


6 DATA INTEGRITY 


— 39 — 


6.1 Issues 

Data integrity principally means the provision of accurate data in a user-friendly format on demand, 

but it can often mean different things to different people (Daltrey 2002). 

There is little doubt that integrity or quality is of major concern in road use data collection and 

processing. Consider the issues faced in RTA NSW (and similarly issues in other RAs). Traffic 

data in NSW are collected at 1,300 Short-term Stations every year and from 500 Pattern Stations 

on a continuous basis. Both types of data must then be edited to remove corrupt data and adjust 

for missing data prior to release and preparation of summary statistics. Editing is a timeconsuming 

task that is partly computer-assisted. There could be different levels of consistency in 

editing, which would impact upon the quality of the processed data. The task can be onerous, 

repetitive, on-going and time-consuming. The manual editing needs to be replaced with an 

automated process. RTA NSW has been testing some editing software jointly developed by RTA 

NSW and CSIRO (Donnelly et al. 2003) that could be of use to other RAs when thoroughly tested. 

Data quality can be affected by a variety of factors. These factors could be environmental factors 

such as rain and magnetic fields that may influence equipment accuracy or abnormal traffic events 

in the connecting road network. In consequence, it is necessary to treat events in traffic systems as 

random variables and use statistical theory to investigate the impact of interacting factors. Unusual 

variability within traffic statistics derived from one survey station should be verified if possible from 

surrounding survey stations or previous data at the same station. The success of a statistical 

investigation depends largely on the size and validity of the traffic survey used to collect the data. 

Daltrey (2002) further suggested that quality is also defined in terms of fitness of purpose. 

Consider the definition of AADT as the total yearly count divided by the number of days. If there is 

a data loss because of equipment failure, then the missing data can be estimated by established 

procedures. However, if a road is closed for some proportions of a year, the AADT statistics based 

on sample counts (estimated by factoring from associated permanent traffic count stations) would 

differ from those statistics obtained from a permanent in-situ site. As mentioned previously, 

weather, road works and other environmental factors can cause transient reductions in traffic 

levels. These factors could lead to AADT values that differ from trend and expectation in the 

context of AADT values from surrounding traffic survey sites. The issue is whether the trend-based 

AADT value should be used (e.g. for planning purposes), or whether the incident-affected values 

be used (e.g. as an exposure measure in road crash research). Users of data should therefore pay 

attention to this issue of fitness of purpose. 

This section discusses the following key quality issues: 

Specifying data collection Quality Assurance (QA) procedures for field survey activities, 

Dealing with missing data, 

Calibration of a WIM site using CULWAY as an example, 

Specification of accuracies. 

It is worth repeating that road use data collection and processing is not a new discipline but an 

established operation of a RA over many years. Each RA has established its own data quality 

procedures refined over many years, and these procedures are therefore good practices. This 

section highlights some of these practices as examples of best practices.



— 40 — 


6.2 Specifications for Data Collection Activities 

It is recommended that specifications for data collection activities be developed irrespective of 

whether the survey is undertaken by in-house staff or contractors. These specifications can be 

used to ensure consistency in data collection and provide a basis for assessing data quality. 

Surveys undertaken by third parties define the work by means of a brief. It is recommended that a 

modified version of the brief be used for surveys undertaken by in-house staff. GTEP Part 3 states 

that the following points must be included in a brief: 

General outline of type of survey to be undertaken and purpose; 

Details of field survey and survey design (if included) required; 

Competencies required and field instruction detailed; 

Criteria for data acceptance; 

Structure of data outcomes; and 

Provision of method for conducting survey, data quality plan and training proposed by 

consultants. 

Tenders should then be assessed on the basis of management capability (scheduling, contingency 

planning, staff recruitment and training, quality plan, resource programming), technical capabilities 

(project management skills of key personnel and level of technical support), price and documented 

prior experience. 

The key to successful data collection lies in the attention paid by the study manager to the details 

of survey administration. The following checklist may help to reduce the severity of problems 

arising during a traffic study. The checklist is particularly suitable for the manual collection of data, 

but the principles are suitable for the collection of most road use data. 

(a) Personnel must receive training in the purpose of the survey and in the methods of 

measurements to be employed. 

(b) Replacement field staff should be available, especially on the first day of a multi-day survey. 

During the survey, rosters of rest periods for observers are essential, and replacement staff 

will be needed to cover these periods. 

(c) Survey forms should be prepared and distributed, as far as possible, on the eve of the 

survey. The pre-study briefing is ideal for this distribution. 

(d) Procedures should be in place for early checking of data quality and for consequential 

modifications to the survey operations to be made. Plans and procedures should be in place 

to accommodate failure to obtain information. 

(e) Occupational health and safety policies must be followed. Rest and meal breaks must 

comply with relevant industrial awards. These are essential in maintaining the alertness and 

concentration of field personnel and reducing the errors of observation and recording. 

(f) Privacy legislation must be followed. Data of confidential nature should not be disclosed 

without proper clearance. 

Transit New Zealand has out-sourced the collection of traffic data for a number of years. It has 89 

Pattern Stations and 1,400 Short-term Stations. A single contractor provides the service of 

collecting data from the Pattern Stations which are all telemetry sites throughout New Zealand. 

Several independent contractors collect data from the Short-term Stations. The specifications 

provided to these contractors follow the principles outlined above and represent a set of best 

practice specifications (Transit New Zealand 2002). The following two procedures adopted by 

Transit New Zealand are of interest in data quality control: 

(a) Data validation against independent means – the contractor specifications include a traffic 

counter operational check. The contractor is to fax the form to Transit New Zealand within 48



— 41 — 


hours of the counter being installed in the field. This allows Transit to undertake independent 

visual surveys at random to ensure the contractor is performing as expected and providing 

quality data. Any variation between recorded data and the visual surveys of ± 5% will result in 

the contractor having to repeat the survey at their cost. 

(b) Cross-checks - The contractors responsible for the Short-term Stations are asked to 

undertake random (once very three years) classified counts at each of the permanent 

telemetry sites as a way of independently validating the accuracy of each other’s equipment 

and data collected. Any variance of ± 5% is then the source of investigation to assess why 

the results are different. As both parties are independent, the contractors have a strong 

incentive to ensure their methodology and equipment accurately record traffic at these sites. 

In summary, the counting personnel whether contractors or in-house staff should be well prepared 

in terms of counting equipment, suitable seats, appropriate clothing including contingency 

provisions for adverse weather, amenities (food, toilet), and carry authority certificates. 

Conspicuous signage is not recommended as it may affect count results. 

6.3 Quality Check and Dealing with Missing Data 

The principle of editing data and identifying missing data is endorsed as a good practice at the 

October 2003 Road Use Data Workshop. The Department of Infrastructure, Energy and 

Resources in Tasmania recommended the need to describe clearly how the editing is carried out. 

This may involve the use of metadata to provide traceability (metadata is ‘data on data’ used to 

describe the content, condition and other characteristics in the data). In US, Smith et al. (2003) 

endorsed a similar principle and suggested the need to reconsider the AASHTO (1992) policy of 

not estimating missing (or ‘imputing’) data. 

Each RA has its own data quality checking procedures, which are also different for different data 

types. It is beyond the scope of this report to record all procedures; however, few examples of 

current practices from Queensland Main Roads, RTA NSW, Main Roads WA and the CSIRO 

software developed for RTA, are presented below for illustration. 

The current Queensland Main Roads practice is to ensure complete data sets at Pattern Stations 

during annual processing. Missing data are computed at the hourly level using weekly and hourly 

adjustment factors, i.e. 

Missing hour volume = Anticipated AADT × Week factor × Hour factor 

RTA NSW pays special attention to classified counts and the data rejection rules for classified 

counts at RTA NSW are shown in Table 17.


Table 17 – RTA NSW rejection rules on classified counts 

Daily reject criterion ≤ 5 veh / day or 

Hourly reject criterion 0 veh for more than 12 hours 


— 42 — 


> 14% Class 3 (two-axle truck or bus with wheelbase more than 3.2 m) or 

> 20% Class 13 (error bin, with less than 1,000 error vehicles per day) 

> 20% Class 13 (error bin with less than 100 error vehicles per hour) 

Main Roads WA adopts a definition of AADT slightly different from GTEP Part 3. AADT is 

determined using the Monthly Average Day of the Week Traffic (MADW) and Annual Average Days 

of the Week (AADW) as follows: 

MADW 

ij 

= 

V 

n w ij 

ij 

∑ 

w= 1 nij 

12 

∑ 

j= 

1 

(84 values in a year) 

1 

AADW i = MADWij 

(7 values in a year) 

12 

1 

AADT = 

7 

7 

∑ AADWi 

i= 

1 

where i is the weekday index with i = 1 to 7 (Monday to Sunday), 

j is the monthly index with j = 1 to 12 (January to December), 

w is the week index with w = 1 to nij = 1 to 5, 

nij is the number of a weekday in a month; (e.g. one to five Mondays in a month), 

w 

V ij is the traffic volume on ith weekday in the wth week in the jth month, 

MADW is the monthly average traffic for each day of the week, over the period of one month, and 

AADW is the annual average traffic for each day of the week, over the period of one year. 

Main Roads WA has a well-defined set of quality check rules. Using the above three traffic volume 

statistics for illustration, the minimum number of statistics for a site to be treated as active is as 

follows: 

At least one weekday must exist for MADW to be calculated for that weekday in the month, 

e.g. at least one Monday out of four or five Mondays in a month; 

At least nine MADWs for a weekday must exist before AADW is calculated for that weekday 

(i.e. nine out of twelve); 

All seven AADWs must exist before AADT is calculated for a Pattern Count Site (PCS); 

In all other circumstances, if a site has insufficient data, it will be treated as inactive unless 

missing data is estimated. 

Other similar rules apply to average weekday traffic and other related quantities when Saturdays, 

Sundays and public holidays are not considered.



— 43 — 


A PCS can exceed the minimum data requirement and still be unsuitable to calculate AADT or be 

used in seasonal adjustment. Sites which have unusual or significant seasonal variations fall into 

this category. Therefore all data for all PCS must be reviewed prior to acceptance of the data and 

production of final AADT and seasonal factors. The initial procedures are as follows: 

Identify and remove all anomalous data. 

Re-run monthly/ annual reports and identify which sites have AADT affected by missing data. 

Identify which of these sites and months are required for seasonal adjustment. These 

sites/months will have estimates submitted. 

Estimates will be submitted to meet minimum data requirements and to produce seasonal 

factors for months where associated short-term data requires adjustment. 

Estimate MADW data for these sites. 

It is not necessary to submit estimates for missing data where the minimum requirement is already 

achieved, seasonal factors are not required and the missing data has the same result as the 

MADW. For example, a PCS has nine MADW values and July’s MADW for Monday is missing. If 

the AADW for Monday is 6225 and the estimated MADW for July is 6228 then it is not necessary to 

submit this MADW. If there is no data for a specific weekday in a month where estimates are 

required then an MADW must be submitted. 

If an actual MADW exists for a specific weekday for a month but this has been derived from less 

than the minimum number of those weekdays required in that month, then the MADW must be 

reviewed and where required, an estimate will replace the actual value. 

Public holidays have a significant effect on MADW. Any months with missing daily flows for 

weekdays on which public holidays occur (particularly Mondays) will usually require an estimate to 

replace the actual value. 

For the estimation of missing data, it is necessary to refer to historical information, current 

information for previous and subsequent weeks and available data for the pattern relating to 

weekday flows. Estimation of the value is either by computer modelling or manual means using 

graphical analysis. 

The framework of the RTA-CSIRO software for detecting and correcting corrupt data is shown in 

Figure 13. It fits a linear model on all available data as follows (Donnelly 2003): 

Predicted value = grand median + trend + season + day + hour + school + public 

The model then uses a Hidden Markov Model (HMM) to detect periods of erratic counts. If any 

detected period exceeds seven days then that period is removed from the dataset in the HMM 

modelling process. A second linear model is fitted to the remaining data and outlier detection 

method is used to detect and correct individual extreme counts. If no periods of erratic counts are 

detected then the outlier method can be run using the original linear model. The second linear 

model is used to predict counts that are missing from the dataset, either because they were 

removed during this process (before linear modelling or during the HMM process) or because they 

were missing from the original data. 

In summary, 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. There is much expectation that the CSIRO 

software developed for RTA NSW would soon be in production phase and become useful also to 

other RAs.


Input raw 

data 

Add calendar 

information 

Fit linear 

model 

Run Hidden 

Markov 

Model 

7+ days 

block of 

bad data 

found? 

Remove these 

bad periods 

from data 

Refit linear 

model 


— 44 — 


Run outlier 

method 

Predict 

missing 

counts 

If hourly 

aggregate to 

daily 

Output 

Figure 13 – Flow diagram of the CSIRO method for detection of corrupt traffic data 

6.4 Calibration of a WIM System 

Table 18 shows the range of (high-speed) WIM technologies employed in Australia (Koniditsiotis 

2000). These technologies include plate-in-ground, load cell, piezo-cable and strain gauge. The 

supplier/manufacturer of a WIM system will provide detailed instructions for calibration. In view of 

the fact that most of the WIM sites in Australia employ CULWAY, with 142 in operation in year 

2000, this section therefore uses CULWAY as an example to illustrate some aspects of good 

practices in WIM calibration. Again, each RA has its own operational guidelines on CULWAY 

installation and operation and it is unnecessary to repeat the full procedures in this report. 

CULWAY weighs and classifies traffic by using mechanical strain gauges deployed in a box 

culvert. Vehicles passing over the culvert cause the roof to deflect producing strain forces that can 

be converted to axle loads. The CULWAY system employs two piezoelectric sensors, placed 10 m 

apart in each instrumented lane to detect vehicle passage and synchronise the strain 

measurement at each axle. The accuracy of CULWAY relative to other WIM systems currently in 

use is also shown in Table 18. CULWAY and all other WIM sites should be carefully calibrated to 

achieve optimal use of the system.



— 45 — 


This section highlights one general aspect and one specific aspect in the use of CULWAY: 

General guidelines on site selection; and 

Corrections for the time-dependent drift between calibrations. 

Table18 – High-speed Weigh-in-Motion Systems Used and available in Australia 

(source: Koniditsiotis 2000) 

Mass sensor type Bending plate Load cell Piezoelectric 

cable 

WIM system name PAT DAW100 ARRB TR HSEMU ARRB TR Expressweigh 

Installation mode Semi-permanent 

flush mounted 

Semi-permanent 

flush mounted 

Permanent flush 

mounted 

Strain gauge 

ARRB TR CULWAY 

Semi-permanent 

within pavement 

culvert installed 

Max. no. of lanes 4 User-defined 2 1 to 4 

Axles 

weighed 

Weighing capacity for 

axle load (tonnes) 

All All All All 

20 80 - 50 

Temperature (°C) -40 to +75 -20 to +70 -50 to +80 -10 to +70 

Sensor life span 

(years) 

Accuracy 

Vendor 

3 rd party 

Installations in 

Australia (as reported 

in year 2000) 

− 15 − 10 

*GVM 

(± 10% at 95%) 

− 

10 

(another 6 using 

other bending plates) 

GVM 

(± 5% at 95%) 

GVM 

(± 3% at 66%) 

GVM 

(± 10% at 95%) 

− 

GVM 

(± 10% at 95%) 

GVM 

(± 7% at 95%) 

2 1 140 

(another 2 for multilane 

operation) 

* GVM (±10% at 95%) means that the accuracy of measuring gross vehicle mass is ±10% for 95% of vehicles weighed. 

6.4.1 Site Location 

The selection of a suitable location for a CULWAY installation is critical to the accuracy of the 

information collected. The following criteria (from Main Roads WA) are recommended when 

assessing the suitability of a site for CULWAY: 

(a) Alignment and overtaking opportunities - a minimum of 120 m of straight and level roadway 

should be on each side of the CULWAY. Sight distance should not be so good as to make it 

an attractive overtaking opportunity for motorists. This encourages poor lane discipline 

resulting in poor detection rates and low levels of data confidence. Avoid placing CULWAY 

too close to curves since vehicles travelling at speed may swing wide taking the curve and 

consequently affect the CULWAY results by invalid vehicle actuations. 

(b) Longitudinal grade - ideally the longitudinal gradient shall be less than 1%. However, the 

gradient may exceed this where considered appropriate but gradients of more than 2% are 

not suitable.



— 46 — 


(c) Crossfall - ideally the crossfall shall be between 2% and 3% with a minimum of 2%. The 

designer needs to balance the needs of CULWAY, i.e. as flat and smooth as possible against 

the engineering necessity of adequately draining the road. 

(d) Open speed section - the speed of the traffic may also impact CULWAY results. Normal 

highway speeds are desirable for optimal operation. Slow speed, in particular speeds of less 

than 70 km/h may affect accuracy. Vehicles travelling below 30 km/h will probably be 

overweighed. The presence of nearby intersecting roads or any other road geometry that 

causes traffic acceleration or deceleration over the CULWAY is undesirable. 

(e) Culvert cover - a section of fill providing adequate cover for the culvert after ensuring a 

culvert of minimum height 600 mm can be placed above natural surface. 

(f) Culvert to be dry - the CULWAY should be located in a section where the fall is sufficient to 

achieve essentially what will be a dry culvert or at least will ensure that any water will drain 

away quickly from the CULWAY. 

6.4.2 Time-Dependent Drift between Calibrations 

It has been known for some time that the CULWAY data is affected by time-of-day and week-ofyear 

(Lim 1992). The variation or drift is dependent on site and environment conditions. The 

strength of the bituminous surface on top of a culvert box is affected by temperature, and how well 

air and water are drained through the surface. 

Grundy (2002) reported the study of two CULWAY sites in Victoria, making use of the property that 

the steering axles mass (SAM) of an articulated six-axle is the least affected by temperature and 

drainage conditions. This is because there is no direct load on the steering axle ( 

Figure 14). It is therefore possible to use this constancy of average SAM to correct for diurnal and 

seasonal drift in WIM sensitivity. 

The results are shown in Figure 15 for the diurnal effect of hour for the sites – Western Ring Road 

in Melbourne and Yarra Glen just outside Melbourne. Figure 16 shows the seasonal variation over 

six months (July to December). The variation can be substantial in the case of the Western Ring 

Road. The correction factor varied from 0.93 (winter) to 1.1 (summer). The Yarra Glen site 

showed less variation, possibly due to the use of a more traditional seal whereas the more porous 

surface of the Western Ring Road exhibited more variation with temperature and drainage. 

A CULWAY system has its own temperature-compensating mechanism in its electronic hardware 

and provides reasonable results with acceptable accuracy levels but does not consider 

temperature/climatic variations of the pavement on top of the culvert box. It is therefore good 

practice to pay attention to site conditions and how seasonal changes in climate could affect a WIM 

installation. Some RAs already allow for seasonal and diurnal drifts of CULWAY.


Steering 

Axle 

Mass 

SAM 

Drive Axle 

Mass DAM 


— 47 — 


Tandem 

Axle Mass 

TAM 

Figure 14 – Three axle masses of an articulated six-axle vehicle (Class 9) 

Figure 15 – Adjustment factors for diurnal variation of CULWAY axle mass data 

(Grundy et al. 2002) 

Figure 16 – Adjustment factors for seasonal variation of CULWAY axle mass data 

(Grundy et al 2002)



— 48 — 


6.5 Accuracy Specifications 

Section 6.1 mentions the complexity of traffic phenomena, which often arises by chance from a 

combination of many factors. These factors have high degrees of variability. Completely accurate 

counts do not exist and all traffic quantities are statistics with distributions of errors. 

Table 19 provides broad guidelines on accuracy requirements for different traffic statistics. The 

values were adapted mainly from GTEP Part 3. Note that the quality of the data obtained depends 

on the survey method selected and the amount of quality control performed. More precise survey 

techniques with higher quality control generally consume more survey resources. A trade-off must 

be made on the basis of time, money and people so that a feasible survey method and sample size 

can be found for the successful completion of a survey. Also, the specification of accuracy should 

also follow the principle of fitness of purpose discussed in Section 6.1. 

Table 19 – Accuracy requirements 

Traffic and other quantities Maximum error 

AADT < 100 

101-300 

301-1100 

> 1100 

±50% 

±35% 

±25% 

±15% 

Confidence level and 

comments 

90% confidence limits 

Trends in AADT ±10% 90% confidence limits 

Classified counts 

(% error in vehicle class proportions) 

VKT (annual total for National and State 

roads) 

WIM applications 

Economic analysis, transport studies, 

vehicle classification 

Road and bridge management, 

overload warning, road safety 

Enforcement compliance 

±20% 

±3 (Australia) 

±7% (State) 

±10% (City) 

±20% 

±15% 

±5% 



Gross Vehicle Mass for 95% of 

vehicles


7 STAKEHOLDER CONSULTATION 


— 49 — 


Many technical issues have been addressed in previous sections. This section is a summary 

section that serves to illustrate key components of a best practice road use data program. It must 

be emphasised again that many activities require road use data and a diverse group of road use 

data stakeholders are involved. These stakeholders can be internal or external to a RA. Examples 

of external stakeholders are road safety agencies, Local Governments and planning authorities. 

As such, road use data is a corporate asset that requires its share of corporate planning and 

management. The following model developed by Mihai (2004) for Main Roads WA is used for 

illustration and is shown in Figure 17. 

The governance structure in Figure 17 aims to bring together all parties interested in road use data 

collection, and ensures their needs are considered. District or regional interests within a RA must 

be represented. A good governance structure should facilitate securing corporate support. Such a 

structure would include the following three groups: 

Business reference groups that includes expert practitioners, strategic planners external and 

internal to a RA; 

A steering committee that includes an RA’s corporate executives; and 

Cross-regional team that includes representatives from regions and districts in a RA. 

With a governance structure in place, a road use data planning and management process can be 

put into operation. Such a process consists of six key elements as shown in Figure 17. The 

process is an on-going process with regular reviews and updates. Some of the elements have 

been mentioned in previous sections and brief descriptions are given below. 

(a) Stakeholder consultation – a register of stakeholders should be kept and their needs in terms 

of data types and formats periodically updated. Main Roads WA conducts comprehensive 

stakeholder consultation every five years. The needs can have priorities set at various levels 

to determine funding priorities. 

(b) Data planning – necessary activities include the review of needs, review of existing data 

collection sites, setting (five-year) goals, estimation of costs and benefits, setting and 

updating of policies on issues such as the level of coverage (Section 4.3), defining a uniform 

traffic section (Section 4.3) and data quality (Section 6). 

(c) Budget and programming – this task includes preparing estimates of capital equipment costs 

and on-going maintenance costs, rolling programs over, say, five years according to different 

scenarios (outsourced, in-house or a combination), data processing costs, and costs of 

monitoring counting sites periodically. 

(d) Data management – this process is as described previously on the collection, validation, 

processing, integration and storage. 

(e) Data delivery – after data management, the key issue is facilitating the efficient and effective 

delivery of data to users. The tasks of data reporting and accessibility have been discussed 

in earlier sections. In particular, a public-private partnership could be an important means to 

make data more accessible. Training sessions on data usage should be provided. 

(f) Review and audit – reviews and audits of the data under management are part of a corporate 

process. Feedback in the form of stakeholder surveys should also be considered.


STAKEHOLDER 

CONSULTATION 

- Stakeholder 

Register 

- Stakeholder 

Needs Matrix 

DATA PLANNING 

- Needs Review 

- Existing Sites 

- Policies 

- 5 Years Goals 

- Costs and Benefits 

BUDGET AND 

PROGRAMMING 

- Equipment Costs 

- Maintenance 

Scenarios 

- Maintenance Costs 

- Other Costs 

Figure 17 – Road use data planning and management framework (Source: Mihai 2004) 


— 50 — 

DATA 

MANAGEMENT 

- Data Collection 

- Data Validation 

- Data Processing 

- Data Storage 


DATA DELIVERY 

- Statistics 

- Data Release 

- Data Access 

- Training Sessions 

REVIEW AND 

AUDIT 

- Data Audit 

- Process Review 

- Stakeholder 

Satisfaction Surveys 

Steering Committee Business Reference Group Cross Regional Team 

Procedures and Guidelines


8 CONCLUSIONS 


— 51 — 


This report has successfully compiled and highlighted best practices in road use data collection, 

analysis and reporting. Road use data is a complex topic and the materials in this report are the 

findings of an Austroads Road Use Data Workshop held in October 2003 in Melbourne, and the 

many feedback and suggestions from the Project Working Group members. A key finding of this 

project is that RAs have been following similar principles in traffic count data collection and 

analysis. Road authorities have refined their counting programs and in many ways have achieved 

good practices. 

The principles of best practices are enunciated in the report (Section 2). These principles include 

accuracy, effectiveness, efficiency, reliability, accessibility, transparency, timeliness and relevance. 

They also relate to the issue of data integrity (Section 6). 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 


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 in 1994. Minor 

variations are recommended as follows: 

Introduction of an error bin (bin 13) to account for the quality of classified counts; 

Distribution of vehicles from 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; and 

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). 

In the case of vehicle classification by lengths, there is no standard established system yet. 

Indeed, it may be appropriate to adopt a 5-bin system to sub-classify combination vehicles so 

manufacturers can design their equipment accordingly. The five bins can be aggregated into four 

bins where necessary. Some field tests and calibration to determine the appropriate threshold 

values of vehicle lengths for each bin are recommended. 

The use of a single axle sensor for traffic count collection is quite common amongst RAs, even 

though most have recognised the need for classified counts. It is recommended that traffic counts, 

as a default, be reported in vehicle numbers. If the raw data is in the form of axles or axle-pairs, 

then the count should be converted to vehicles with the conversion factor also reported if possible, 

adopting the principle of transparency.



— 52 — 


The issue of utilising WIM equipment as part of a traffic counting program is also discussed in 

some detail. As long as all lanes of traffic are monitored at a WIM site, the traffic count data (and 

WIM data) should add extra value to a counting program. The related issue of correlating classified 

count data with vehicle mass data is more complex and there is not much information published on 

this topic. In practice, 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 can be determined. 

Road use data can come from many sources meeting a variety of needs. This leads to the issues 

of data integration and accessibility. Many stakeholders are consequently involved. This report 

provides a stakeholder consultation model from Main Roads WA. These issues are rather involved, 

and RAs at present have quite varied policies on data availability and its pricing. As the trend to 

outsource data continues amongst RAs, third parties may become both supplier and purchaser of 

road use data. While these issues were discussed at the October 2003 Workshop and described in 

some detail in this report, they will continue to confront RAs in the future. It is appropriate to 

develop, as a future research task, an appropriate policy or business model in this area of data 

integration, accessibility and pricing. 

In summary, a list of recommendations is as follows: 

1. Vehicle detection - a pair of axle sensors should be considered where possible to give better 

accuracy, better distinction by direction, and the ability to produce classified counts through 

axle configurations. If a single axle is used, it is a recommended practice to correct for traffic 

composition and convert axle-pairs into vehicle counts for reporting purposes. 

2. Vehicle classification - the current Austroads vehicle classification be maintained but allowing 

sub-classes as variations of the basic system. It is appropriate to introduce a new notation to 

designate different jurisdictional versions, e.g. the Austroads (Vic) for the Victoria version. 

3. Error bin - an error bin (no. 13) be introduced into the Austroads system as good practice to 

indicate the quality of the classified counts. The number of error counts should be monitored 

and, if these remain high relative to the traffic stream, the reasons for these errors should be 

identified and the problem rectified. The inclusion of error counts in bin 13 in the total counts 

requires some judgement. If the number of error counts is high relative to the total counts, it 

is necessary to identify the reasons for such a situation before error counts are included. If it 

is deemed appropriate to distribute bin 13 vehicles, the method of distributing across all bins 

from 1 to 12 or only to bin 1 can be considered, or both methods are used and guided by 

relevant local knowledge. 

4. Vehicle classification by length - a variety of threshold values are being used for vehicle 

classification by lengths. It is recommended that RAs come to agreement on a single 

classification system by lengths. Some empirical work using a standard loop design would 

appear necessary. On reaching agreement, the Austroads system should incorporate a 

definitive multi-bin system by vehicle lengths as a component of the total Austroads 

classification system. This harmonisation should facilitate equipment manufactures to design 

their equipment for a single specification.



— 53 — 


5. Transparency - Different jurisdictions at present adopt slightly different definitions and hence 

procedures in calculating traffic statistics. The actual values should not be significantly 

different. However, it is good practice for RAs to state the methods used to generate AADT 

and other values. Further, the definition of AADT or other quantities should not imply a 

particular method of calculation or estimation. It is, however, important that the national 

glossary in Appendix A be adopted. 

6. Counting program - a statewide counting program should involve the following iterative steps: 

− Locate permanent and short-term count stations based on judgement and road 

functional classes; 

− Collect counts; 

− Divide road network using volume stratification and traffic pattern behaviour; 

− Determine the number of stations required by applying a volume stratification 

technique; 

− Review count station density and location; 

− Calculate adjustment factors to convert short-term counts to AADT estimates; 

− Identify homogenous traffic sections and calculate VKT; and 

− Check, review and prepare a rolling program with stakeholders who have good local 

knowledge. 

7. Reporting of statistics - statistical results be reported jointly in terms of percentage error and 

confidence interval. If measurements could be made over the totality of a target population 

rather than over just a sample then there would be no need to specify a confidence interval. 

Because usually only a sample is taken, there remains the possibility that this sample is 

unrepresentative of the population, so that the error is likewise unrepresentative. 

8. Data quality control – the following two procedures be considered when employing a 

contractor for data collection or undertaking the task in-house: 

− Data should be validated against independent means. The contractor specifications 

include a traffic counter operational check. The contractor is to fax the form to a RA 

within 48 hours of the counter being installed in the field. This allows the RA to 

undertake independent visual surveys at random to ensure the contractor is performing 

as expected and providing quality data. Any variation between recorded data and the 

visual surveys of ± 5% will result in the contractor having to repeat the survey at their 

cost. 

− There should be cross-checks. The contractors responsible for the Short-term Stations 

are asked to undertake random (once every three years) classified counts at each of 

the permanent telemetry sites as a way of independently validating the accuracy of 

each other’s equipment and data collected. Any variance of ± 5% is then the source of 

investigation to assess why the results are different. As both parties are independent, 

the contractors have a strong incentive to ensure their methodology and equipment 

accurately record traffic at these sites.



— 54 — 


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.


REFERENCES 


— 55 — 


ARRB Transport Research Ltd (2003). Express Weigh 

 

AASHTO (1992). Guidelines for traffic data programs. American Association of Highway and 

Transport Officials, Washington, DC. 

Australian Bureau of Statistics (1963). Survey of Motor Vehicle Usage. Catalogue No. 9202.0. 

ABS, Sydney. 

Austroads (1988a). Guide to Traffic Engineering Practice Part 3 – Traffic Studies. Report No. 

AP-11.3/88. Austroads, Sydney. 

Austroads (1988b). Guide to Traffic Engineering Practice Part 2 – Roadway Capacity. Report No. 

AP-11.2/88. Austroads, Sydney. 

Austroads (2001). The Australian and New Zealand road system and road authorities - national 

performance indicators 2000. Report No. AP-43/01. Austroads, Sydney (and earlier annual 

editions: 43/96, 43A/96, 43/98, 43/99, 43/00). 

Austroads (2004). Guide to Traffic Engineering Practice Part 3 – Traffic Studies. Report No. 

AP-G11.3/04. Austroads, Sydney. 

Bennett, D. (2002). Assessment of national highway traffic counting. Contract Report RC2219-1. 

ARRB Transport Research, Vermont South. 

Botterill, R and Luk, J.Y.K. (1998). Investigation of a new method of estimating vehicle kilometres 

of travel. Contract Report RC6028. ARRB Transport Research, Vermont South. 

Daltrey, R. (2002). Data integrity. Discussion Paper, Roads and Traffic Authority, Sydney. 

Donnelly, J., Chan, D., Duncan, J., Keighley, T. and Angelovski, K. (2003). SSOFTRAC – a 

statistical software package for detection and correction of corrupt traffic counts. User’s Manual 

Version 2.2. CMIS Report No. 03/157. CSIRO Division of Mathematical and Information Sciences, 

North Ryde. 

Federal Highway Administration (2001). Traffic monitoring guide. US Department of 

Transportation, FHWA, Office of Highway Policy Information, Washington DC 

 

Federal Highway Administration (2003). Bicycle and pedestrian detection. Final Report prepared 

by SRF consulting Group, Minneapolis. 

Golden River Traffic Ltd (2001). M410 Bicycle Counter Mixed Traffic counting. 

 

Karl, C.A., Powell, T. and Luk, J.Y.K. (2003). Development of a predictive travel time model for 

Singapore based on GLIDES. Proc. 21 st ARRB and 11 th REAAA Conf. 18-23 May 2003, Cairns. 

Koniditsiotis, C. (2000). Weigh-in-motion technology. Publication No. AP-R168/00. Austroads, 

Sydney. 

Leschinski, R. (1994). Bicycle detection at signalised intersections. Research Report ARR No. 245, 

Australian Road Research Board, Vermont South.



— 56 — 


Lim, E. (1992). Secondary uses of CULWAY data: comparison of three Victorian sites. Working 

document WD TE 92/009. Australian Road Research Board Ltd., Vermont South. 

Luk, J.Y.K. and Besley, M.A. (1985). Automatic vehicle identification as a traffic management 

measure, Forum Papers, 10th Australian Transport Research Forum, 10(2), 13-15 May, 

Melbourne, pp.19 - 44. 

Luk, J.Y.K. and Brown, J.I. (1987). The use of piezoelectric cable as an axle sensor. Proc. New 

Zealand Roading Symposium, Wellington, pp297 - 304. 

Luk, J.Y.K. and Karl, C.A. (2003). Austroads Road Use Data Workshop. Progress Report RC3370- 

1. ARRB Transport Research Ltd, Vermont South. 

Main Roads WA (2003). Notes on procedures to determine homogeneous traffic sections. Data 

Planning and Standards Section, Main Roads, East Perth. 

Martin, T. and Chiang, K. (2002). Consistency of data on traffic loading and traffic mix: literature 

review. Contract Report RC2050-2. ARRB Transport Research Ltd, Vermont South. 

Mendgorin L., Peachman, J. and White, R. (2003). The collection of classified counts in an urban 

area – accuracy issues and results. Forum Paper, 26 th Australasian Transport Research Forum, 1- 

3 October, Wellington. Paper No. 27 (CD ROM). 

Mihai, F. (2004). Road use data, vital information for decision making, MRWA framework for data 

management. Discussion Paper, Main Roads WA, East Perth. 

National Association of Australian State Road Authorities (1982). Guide to Traffic Counting in State 

Road Authorities. AP TEC-13. NAASRA, Sydney. 

Peters, B. (2002). Heavy vehicle mass management at the Port of Fremantle. Proc. AITPM 

National Conference, 8-9 August 2002, Perth. pp. 269-285. 

Ramsay, E., Prem, H. and Pearson, B. (2001). Dimensions and mass characterisation of the 

Australian heavy vehicle fleet. NRTC Working Paper. National Road Transport Commission, 

Melbourne. 

Roads and Traffic Authority (2003). Notes on data consistency and the determination of uniform 

traffic sections, Traffic Management Branch, RTA, Sydney. 

Roper, R. (2001). National highway traffic count procedures study: final report. Contract Report 

RC1632-3. ARRB Transport Research, Vermont South. 

Roper, R., Chiang, K. and Martin, T. (2002). Consistency of data on traffic loading and traffic mix: 

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Vermont South. 

Smith, B.L., Scherer, W.T. and Conkin J.H. (2003). Exploring imputation techniques for missing 

data in transportation management systems. Transportation Research Record TRR 1836. pp. 132- 

142. 

Thoresen, T. and Michel, N. (2002). Improved hourly traffic volume measurement. Contract Report 

RC 1346. ARRB Transport Research, Vermont South. 

Transfund New Zealand (2001). Guide to estimation and monitoring of traffic counting and traffic 

growth. Research Report No. 205. Transfund, Wellington, N.Z.



— 57 — 


Transit New Zealand (2002). Standard Professional Services Specifications – State Highway 

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Transportation Research Board (2000). Highway Capacity Manual HCM 2000. TRB, Washington, 

DC. 

Western European Road Directorate (2003). Data management for road administrations: A best 

practice guide. Version 2. WERD, Sub-group Road Data, Brussels.


APPENDIX A – NATIONAL GLOSSARY OF TERMS 


— 58 — 


Annual Average Daily Traffic AADT is a measure of the average daily traffic passing a 

roadside observation point over the period of a calendar 

year. One method of calculating AADT is by counting the 

total volume of traffic passing an observation point and 

divided by the number of days in that year (365 or 366 days). 

Annual Average Weekday Traffic AAWT is the average 24 hour traffic volume on weekdays 

(Mondays to Fridays excluding public holidays) throughout a 

12 month period, at a specific observation point. 

Average Daily Traffic ADT is a sample of the AADT and is the traffic count 

averaged over a particular month, a week or a few days. 

Average Weekday Daily Traffic AWDT is taken as the average 24-hour count over a 

consecutive seven-day period from Monday to Sunday. It is 

often considered because of the empirical observation that 

the longer the counting period used to observe a traffic 

stream, the better the resulting estimates of the design 

parameters AADT or HHV. 

Average Weekday Traffic AWT is taken as the average 24-hour count over the period 

Monday to Friday. In some situations the average seven-day 

weekly traffic may be required and referred to as Average 

Weekday Daily Traffic (AWDT). 

Axle counts This is the number of actuations on an axle sensor such as a 

pneumatic tube as the wheels of vehicles cross over the 

sensor. 

Axle pair counts This is one half of the number of axle counts and is used as 

an indication of the number of vehicle counts. Axle pair 

counts are always less than the number of vehicles and can 

only be an approximation because a traffic stream will have 

vehicles with more than two axles. A correction factor is 

usually applied after calibration. 

Design Hour Volume DHV is the traffic flow rate chosen as the design traffic load 

for a facility over its design life. Common practice is to 

choose an ‘nth’ HHV as the design volume, with the 30 th 

highest hourly volume often used in a rural environment and 

the 80 th HHV in an urban area. The 100 th HV (or 100 HV) is 

used on National Highways. This probabilistic concept is 

chosen because it is uneconomic, if not impossible, to 

design a facility realistically to meet the highest traffic flow 

rate.



— 59 — 


Highest Hourly Volume HHV is the highest hourly volume of any continuing 60 

minute period over a whole year. There are a number of 

these volumes used in road design, and HHV is usually rated 

in terms of an ‘nth’ highest hour volume, meaning the hourly 

traffic volume (veh/h) exceeded in only n hours of a year. 

The 30th and 80th highest hourly volumes (denoted as 30 

HV and 80 HV respectively) are commonly used parameters 

in assessing the design volume for setting the capacity of a 

traffic facility. 

Link count A link count is the number of vehicles passing an 

observation point along a road link over a given period. The 

count may be bi-directional (i.e. a two-way total), or may be 

split into separate counts for the two directions of flow. 

n th Highest Hourly Volume This is the nth largest hourly volume recorded for a year and 

denoted as n HV. The 30 th highest hourly volume in the year 

(30 HV) is often used to as a representative traffic volume for 

road design. 

Pattern Station A Pattern Station is a counting station that has at least 12 

months’ continuous or frequently sampled counting. There 

are two types of Pattern Stations – Seasonal and Permanent 

Stations. They are used for calculating seasonal adjustment 

factors for traffic data collected at Short-term counting 

stations. These factors are used for generating the annual 

seasonally adjusted statistics for those same counting 

stations, e.g. AADT. 

Peak Hour Factor A Peak Hour Factor (PHF) is the ratio of total hourly volume 

to the maximum flow rate over a specific time period within 

that hour. If the time period is 15 minutes, then PHF = hourly 

volume / (4 × maximum 15 minute counts). The ratio 

represents the variation of traffic volume within an hour and 

is less than or equal to 1. PHF = 1 represents an even 

distribution over an hour. 

Peak Hour Volume A Peak Hour Volume is the maximum traffic count observed 

in any 60-minute interval during a day. In rural areas it is 

usually sufficient to quote a single peak hour volume. In 

urban areas two peak hour volumes are often considered: 

one for the morning and one for the evening. This practice is 

adopted because of the likelihood of significant differences in 

the directional flows on urban roads at different times of day. 

Permanent Station A permanent station is a counting station installed for long 

continuous periods, usually many years. 

Seasonal Station This is a counting station installed for a specific period 

(usually one year) to obtain information for factoring shortterm 

counts to establish AADT. 

Short-term Station This is a counting station set up for a short time. The counter 

is then moved to the next site to provide the widest possible



— 60 — 


traffic data coverage within a region. Seasonal factors are 

used to estimate AADT from this data. 

Traffic patterns These are the variation of traffic volumes over a period of 

time. 

Turning movement A turning movement count is the number of vehicles 

observed to make a particular turning movement (left or right 

turn, or through movement) at an intersection over a 

specified period. All traffic counts fall into one or other 

category of link counts or turning movement counts. 

Twelve hour volume The 12-Hour Volume on a road is the number of vehicles 

passing an observation point over a given 12-hour interval 

during a day. 

Vehicle-Kilometres Travelled Vehicle-Kilometres Travelled (VKT) is the amount of travel 

given by the product of the total number of vehicles and their 

distance travelled. VKT is usually expressed as an annual 

statistic, and sometimes expressed as a daily statistic due to 

the convention of expressing traffic volumes as an average 

daily value. The yearly VKT is the daily VKT multiplied by the 

number of days in that year (365 or 366 days).


APPENDIX B – NUMBER OF AADT OBSERVATIONS 


— 61 — 


The recommended method of estimating the number of AADT observations required to calculate 

VKTij in any region i and in stratum j is as follows: 

Calculate the following estimates from the most recent available data: 

VKT i 

SD 

ij 

L 

ij 

ni 

- amount of travel in region i (=∑ VKTij 

), 

j= 

1 

- standard deviation of the AADT observations in region i and stratum j, 

- total length of road in region i and stratum j, 

ni - the number of strata in region i. 

Calculate the required number of AADT observations in region i and stratum j using the following 

expression (NAASRA 1982): 

N 

ij 

Lij 

SDij 

= 

( 0. 

05VKT 

) 

i 

ni 

2 ∑ 

j= 

1 

L 

ij 

SD 

ij 

where: 

N 

ij 

is the required number of AADT observation in region i and stratum j. 

(Equation B1) 

An example calculation to illustrate the procedure is as shown in Table B1 with eight strata (ni = 8). 

The site is on a rural road. The estimates of VKTij, Lij and SDij are from existing inventory data in 

region i. 

AADT stratum 

j =1 to 8 

0-100 

101-300 

301-700 

701-1100 

1101-2000 

2001-4000 

4001-7000 

7000 plus 

VKT ij 

(×10 3 veh-km) 

2586 

2750 

3277 

2409 

3496 

4013 

2574 

2703 

Table B1 - Calculation of the Number of VKT Stations 

Inventory Data in Region i 

L 

ij 

(km) 

94821 

15275 

6931 

2699 

2310 

1428 

496 

275 

SD 

ij 

(veh) 

29 

60 

111 

110 

279 

543 

848 

2459 

L 

ij 

× SD 

ij 

(veh-km) 

2,749,809 

916,500 

769,341 

296,890 

644,490 

775,404 

420,608 

676,225 

No. of stations 

N ij 

14.07 (14) 

4.7 (5) 

3.94 (4) 

1.52 (2) 

3.30 (4) 

3.97 (4) 

2.15 (3) 

3.46 (4) 

Sum 23808 - - 7,249,267 40



— 62 — 


The total VKTi for this region i using eight strata = ∑ VKT ij = 23,808 × 10 

j= 

1 

3 veh-km 

8 

∑ 

j= 

1 

The summation ij ij SD L = 7,249,267 veh-km 

The number of counting stations for each stratum ( N ij ) is calculated using Equation B1. For 

example, Li1 = 94,821 km and SDi1 = 29 veh for j=1 and 

N i 1 

94, 

821× 

29 

= × 7, 

249, 

267 = 14.07 or 14 stations 

3 2 

( 0. 

05 × 23, 

808 × 10 ) 

This method has been developed further in Botterill and Luk (1998) by including accuracy and 

statistical significance requirements and relaxing the requirement on preset AADT strata. Using the 

original method, the number of stations required could be underestimated. 

8



— 63 — 


APPENDIX C – EXECUTIVE SUMMARY OF LITERATURE REVIEW 

REPORT (RC2050-2) 

Scope 

This Report documents a literature review of typical traffic data collection, processing, analysis and 

reporting practices by major road agencies in Australia, Canada, United Kingdom (UK), New 

Zealand and the United States (US). The emphasis of the review was on highlighting practices 

that differ or are in addition to the NAASRA 1982 Guide. The features of the non-Australian 

practices were also summarised and compared with the NAASRA Guide. 

Findings 

In some instances, the review was not able to find some specific information from the practices 

reviewed. For example, useful information about the frequency of traffic data collection was not 

available for New Zealand and the US and information about traffic data presentation was not 

found for New Zealand and the UK. 

Inconsistencies between practices 

Broad inconsistencies between the reviewed road agency practices were identified in regard to: 

the definition of the traffic information to be collected and reported; 

the traffic guidelines and standards on the appropriate amount of traffic counting (continuous 

vs. temporary counting), the duration of temporary traffic counting and the methodology of 

arranging traffic counter sites; 

vehicle classification systems, the type of equipment used and the recording of vehicle types; 

and, 

recommendations for achieving adequate levels of quality control and the required accuracy 

of traffic measurement. 

Future guidance 

The following findings identified as a result of this review may form a basis to resolve the current 

inconsistencies in traffic monitoring procedures: 

the development of a consistent generic framework for traffic measurement, processing and 

reporting that can accommodate variations in equipment technology and differing road 

network characteristics; 

the development of a systematic approach to traffic monitoring and reporting that captures 

changes from previous monitoring and reporting; 

a consistent approach to the estimation of annual average daily traffic (AADT) and other 

fundamental traffic measurement parameters; 

the development of a sound and defensible basis for traffic adjustment factors used during 

data processing; and, 

the development of principles for clear quality control procedures covering equipment 

calibration, quantification of equipment accuracy and the accuracy required for the estimated 

traffic parameters.



— 64 — 


APPENDIX D – EXECUTIVE SUMMARY OF CURRENT AND FUTURE 

ROAD USE REPORT (RC2050-3) 

Scope: 

This Report documents a review of current and future needs for road use data covering the 

following: 

identification of current road use data related to traffic volume and flow, traffic composition, 

weigh-in-motion and other road use characteristics; 

a discussion of the limitations of the current road use data; 

identification of future needs for road use data related to traffic volume and flow, traffic 

composition and other derived traffic parameters; and, 

a discussion of the issues arising from the capture of future road use data. 

This Report forms the next logical step in the process of developing the best practice in road use 

data collection, analysis and its reporting. 

Outcomes: 

Current Road Use Data 

Current road use data collection practices relating to traffic volume, flow, composition, weigh-inmotion 

and other road characteristics across the States are inconsistent because of differences in 

the standards adopted, the budget allocations available, level of network coverage, specific State 

requirements and the technology used. 

Future Road Use Data 

Traffic volume and flow are likely to remain the basic indicators of road use in the foreseeable 

future and it is expected that further advances in the technology available to measure volume and 

flow will continue to evolve. Emphasis will be on improving measurement accuracy and vehicle 

classification across the network and gaining the benefits from new technology for road use data 

collection applications. 

The use of new technologies is likely to be initially targeted towards heavily trafficked routes, major 

freight routes and other strategic routes of commercial or community value where the greatest 

potential exists to gain additional value from the increased knowledge of traffic flows. 

A road use data collection framework needs to be developed for the long-term, including storage 

and reporting standards and systems, implementation plans, audit and review mechanisms and 

long-term strategies. 

Improved definition of traffic composition through new technology will allow for greater detailed 

information about the characteristics of individual vehicles travelling on the road network. This 

additional information could allow an expanded vehicle classification system to be developed. This 

detailed information could be used for enforcement and regulatory purposes, if it is acceptable. 

Issues of privacy and access to this form of detailed data are likely to restrict this use of the data.



— 65 — 


A number of potentially useful traffic parameters can be derived from the detailed traffic volume, 

flow, composition, weigh-in-motion and other road characteristics data if it is made accessible and 

an adequate level of resources are used to extract these parameters. Detailed access to this data 

is not likely to occur unless it is insulated from enforcement. 

Other Issues 

A portion of the future road use data is likely to be in private hands placing access arrangements 

on a contractual basis. Different levels of access to the data also need to be developed to suit the 

individual requirements of data users. Internet based systems are expected to play a major role in 

transferring data to the users. 

Data dissemination systems need to be made in a form compatible and consistent with other 

collecting agencies and data users. Some attention to developing such systems need to be made 

in cooperation with all Austroads Member Authorities (MAs). 

The development of the best road use data collection, analysis and reporting practice will need the 

input of all MAs. The development of this practice should allow the flexibility to introduce new 

technologies and ideas as they emerge to ensure that the final system remains as relevant and 

adaptable as possible. A broad non-prescriptive approach needs to be used that can 

accommodate all these differences and future changes in technology.


INFORMATION RETRIEVAL 


— 66 — 


Austroads (2004), Best Practices in Road Use Data Collection, Analysis 

and Reporting, Sydney, A4, 76pp, AP-G84/04 

KEYWORDS: 

Bicycle, Classification (vehicle), Data Integration, Delay, Dimensions (vehicle), 

Guidelines, Quality Assurance, Traffic, Traffic Survey, Travel Time, Weigh-in- 

Motion. 

ABSTRACT: 

This report describes the current practices of road authorities (RAs) in road 

use data collection, analysis and reporting, and uses examples to demonstrate 

best practices. It cover topics such as: error bin (bin 13) in the Austroads 

vehicle classification system, vehicle classification by lengths, defining a 

homogeneous traffic section, correlation of classified counts with WIM data, 

public-private partnership, quality checks, dealing with missing data, 

calibration of a WIM system using CULWAY as an example, and a stakeholder 

consultation model. A key finding is that RAs have been following refining 

their counting programs and in many ways have achieved good practices, and 

that the best way to ensure consistency amongst RAs is for each road 

authority to collect robust data sets.

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

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