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ESTIMATION OF VEHICLEKILOMETRES TRAVELLED ON

ARTERIAL AND LOCAL ROADS

INFORMATION PAPER

May 2005

Prepared by

ARRB Transport Research Ltd


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National Transport CommissionEstimation of Vehicle Kilometres Travelled on Arterial and Local Roads Information

Paper

Report Prepared by: ARRB Transport Research Ltd

ISBN: 1 877093 64 5


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REPORT OUTLINE

Date: May 2005

ISBN: 1 877093 64 5

Title: Estimation of Vehicle Kilometres Travelled onArterial and Local Roads Information Paper

Address: National Transport Commission

Level 15/628 Bourke Street

MELBOURNE VIC 3000

E-mail: [email protected]

Website: www.ntc.gov.au

Type of report: Information Paper

Objectives: To estimate the vehicle kilometres travelled (VKT) by

different vehicle types on arterial and local roads

across Australia. The data will be used in conjunction

with estimates of spending on local roads to calculate

the heavy vehicles' share of local road expenditure for

inclusion in their charges.

NTC Programs: Efficiency

Key Milestones: National Transport Commission, input to the Third

Heavy Vehicle Road Pricing Determination

calculations.

Abstract: The report provides estimates of VKT for heavy,

medium and light vehicles, in metropolitan and ruralareas, on arterial and local roads, and details of the

method used to calculate the estimates.

Purpose: For information

Key words: vehicle kilometres travelled, local roads, arterial

roads, heavy vehicles, medium vehicles, light

vehicles, road use.


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FOREWORD

The National Transport Commission (NTC) is an independent body established under an

Inter-Governmental Agreement, and funded jointly by the Australian Government, States

and Territories. The NTC has an on-going responsibility to develop, monitor and maintain

uniform or nationally consistent regulatory and operational reforms relating to road

transport, rail transport and intermodal transport.

The NTCs heavy vehicle road pricing work contributes to strategies pursuing transport as

a more sustainable activity, and in devising smarter approaches to regulation, provides both

increased flexibility and greater certainty about results achieved.

National heavy vehicle road use charges were first introduced in 1995-96 following the

First Heavy Vehicle Road Pricing Determination. A Second Heavy Vehicle Road Pricing

Determination was agreed and implemented in 2000. The Third Heavy Vehicle Road

Pricing Determination is an efficiency initiative on the NTCs national regulatory reform

agenda, and is focussed on:

1. Ensuring the prices paid by heavy vehicles for use of the road system reflect thePricing Principles agreed by the Australian Transport Council (ATC), and in

particular, continue to recover their share of the costs of providing and maintaining

roads; and

2. The implementation of more flexible road pricing arrangements namelyIncremental Pricing which provides opportunities for charges to be levied for heavy

vehicles operating above current statutory mass limits.

Work on the Third Heavy Vehicle Road Pricing Determination has involved a major

overhaul of the input data to the cost allocation model used in the Second Determination.

The cost allocation model uses road expenditure and road usage data as inputs, and

attributes expenditure by vehicle class as an output. The input data has been reviewed and

revised through a series of projects that have taken place over the last two years. These

projects are being reported in a series of Information Papers this report being one ofthem.

The main purpose of this Information Paper is to provide a more accurate indication of the

split of total Vehicle Kilometres Travelled (VKT) between local roads and arterial roads

for different vehicle classes than was used in previous charges Determinations. The VKT

split between local and urban roads is an extremely important factor in the allocation of

road expenditure to each vehicle class. The data will be used in conjunction with estimates

of spending on local roads to calculate heavy vehicles share of local road expenditure for

inclusion in their charges.

A Third Determination Policy and Technical Report Discussion Paper is currently being

prepared. It will incorporate the results of this Paper and the work reported in the other

Information Papers. Sensitivity tests which explore the impacts of moving to the revisedestimates of vehicle kilometres travelled on local and arterial roads will be reported in the

Discussion Paper.

The NTC acknowledges the work of George Giummarra of ARRB Transport Research Ltd

as the major contributor to this report, as well as the contributions of the NTC Road

Pricing team comprising Chris Egger, Fiona Calvert, Kerry Todero and Keith Lloyd.

Stuart Hicks

Chairman


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SUMMARY

ARRB Transport Research has undertaken the project Estimation of Vehicle Kilometres

Travelled on Arterial and Local Roads for the National Transport Commission (NTC).

This study includes the supplementary review of the earlier work undertaken in June 2004

covering a check of the VKT estimates from the previous data sets, use of more actual

traffic count data and additional remote councils in the sample. A summary of the

Supplementary work is included as Appendix D.

The results provide an estimate of VKT on arterial and local roads based on a

representative selection of municipal councils road lengths and traffic information on

various road classifications across Australia. This information was factored up to provide

an overall estimate of VKT travelled across Australia for various municipal groups, road

and vehicle types.

The data, a comparable estimate of local and arterial road use, will be used in conjunction

with estimates of spending on local roads to calculate the heavy vehicles' share of local

road expenditure for inclusion in their charges. Differences between vehicle classes are

particularly important to estimate, as these will have a significant impact on the process ofallocating road expenditure to each vehicle class.

A statistically based sampling technique was used to obtain representative traffic

information. It was assumed that by taking up to a 10 per cent selection of councils and

making calculations of VKT on representative roads, that an estimate representative of

VKT for urban and rural areas across Australia could be made.

The statistical sampling technique used to provide a representative estimate of VKT by

vehicle class and road type was as follows:

Select a representative sample of councils.

Classify the road networks for each council selected, and calculate the length of roadper classification for selected councils.

Classify categories of vehicles.

Select representative traffic count locations for each road class and obtain data.

Calculate estimates of VKT.

Ninety councils were invited to take part in the study (12 per cent sample). Responses were

received from 54 councils, representing a 7.5 per cent sample across states and council

categories. Councils that did not respond after follow up reported a lack of time, resources

available or suitable data to contribute.

The key finding from this study on local road use for the Third Heavy Vehicle Road

Pricing Determination is that there is a much higher percentage of travel on local roadsthan had been previously assumed. Results from this study compared to estimates used in

the Second Heavy Vehicles Road Pricing Determination are as follows:

Light vehicles (cars, motorcycles, vans) 37 per cent of their travel is on localroads, compared to the Second Determination estimate of 35 per cent.

Medium vehicles (rigid trucks, buses) 30 per cent of their travel is on localroads, compared to the Second Determination estimate of 10 to 30 per cent

depending on vehicle class with an average of 25 per cent.


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Heavy vehicles (articulated trucks, B-doubles and road trains) 16 per cent oftheir travel is on local roads, compared to the Second Determination estimate of 5

to 8 per cent depending on vehicle class with an average of 5 per cent.

All vehicles - 35.5 per centof travel is on local roads, compared to the SecondDetermination estimate of 34 per cent.

Light vehicle traffic accounts for 90 per cent of total VKT, 57 per cent on arterial roads

and 33 per cent on local roads. In addition it is estimated that in terms of travel by vehicle

class, local road travel makes up 15 per cent of all heavy vehicle travel, 30.5 per cent of all

medium vehicle travel and 37 per cent of all light vehicle travel.

These estimates provide a useful indication of the split of total VKT between local roads

and arterial roads for different vehicle classes, an important factor in the allocation of road

expenditure to each vehicle class.

The results from this study reveal that heavy vehicles travel more on local roads than was

originally assumed in the Second Charges Determination. For the Third Determination

this means higher charges for heavy vehicles because of the higher unit costs for local

roads compared with arterial roads.

This study comprised a 7.5 per cent sample of Australian councils. The methodology used

is believed to be adequate in the calculation of estimates, where other methods of

estimation would prove to need even greater resources. Further, more extensive studies of

this nature are suggested to improve results.


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CONTENTS

1. INTRODUCTION ....................................................................................................11.1 Background.................................................................................................................1

2. METHODOLOGY ...................................................................................................2

2.1 Selection of sample councils ......................................................................................22.1.1 Segmentation of councils..............................................................................................22.1.2 Determination of required number of councils for each state........................................32.1.3 Calculation of average populations...............................................................................42.1.4 Selection of councils by comparison to average population..........................................4

2.2 Classification of roads.................................................................................................42.3 Classification of vehicles.............................................................................................42.4 Identification of representative count locations...........................................................52.5 Remote councils .........................................................................................................52.6 Calculations ................................................................................................................6

3. RESULTS ...............................................................................................................63.1 Results of sampling process.......................................................................................63.2 Data results.................................................................................................................7

3.2.1 Sensitivity .....................................................................................................................8

4. CONCLUSIONS ...................................................................................................13

5. REFERENCES .....................................................................................................14

APPENDIX A: LOCAL GOVERNMENT CATEGORIES.........................................15A1 NOLG Local Government Categories.......................................................................15A2 Number of Councils by category and by state, June 2003 .......................................16A3 Average population of local government categories.................................................17

APPENDIX B: ROAD CLASSIFICATION...............................................................19

APPENDIX C: VEHICLE CLASSIFICATION..........................................................21

APPENDIX D: SUPPLEMENTARY REPORT ........................................................23

LIST OF TABLES

Table 1 Categories of Australian local government ........................................3

Table 2 Response rate from councils compared to sample aim andsample contacted ........................................................................................................7

Table 3 VKT on arterial and local roads by vehicle type, for metropolitanand rural areas (average vkt per day) .......................................................................9

Table 4 VKT as percentages on arterial and local roads by vehicle type,for metropolitan and rural areas (average vkt per day) ........................................10

Table 5 Heavy and medium vehicle VKT on arterial and local roads(average vkt per day) ................................................................................................11


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LIST OF FIGURES

Figure 1 Example of VKT calculation ................................................................. 6


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Estimation of Vehicle Kilometres Travelled on Arterial and Local Roads- Final Report - January 2005 Page 1

1. INTRODUCTION

ARRB Transport Research has undertaken the project Estimation of Vehicle Kilometres

Travelled on Arterial and Local Roads for the National Transport Commission (NTC).

This project provides estimates of the vehicle kilometres travelled (VKT) by different

vehicle types on arterial and local roads across Australia.

The information will be used by the NTC, in conjunction with estimates of spending on

local roads, to work out the heavy vehicles share of local road expenditure for inclusion in

their charges.

This study provides an estimate of VKT on arterial and local roads based on a

representative selection of municipal councils and traffic information on various road

classifications across Australia. This information was factored up to provide an overall

estimate of VKT travelled across Australia for various municipal groups, road and vehicle

types.

This study includes additional work, to an earlier report in June 2004, covering a check onthe analysis of VKT from the data sets, confirmation of the traffic estimates made in some

cases and an increased number in the sample of remote councils.

1.1 Background

The NTC sought estimates of either the kilometres (kms) travelled by different vehicle

types on local roads, or the proportion of their annual travel that is on local roads.

Local roads constitute around 85 per cent of the length of the national roadway. According

to the Australian Bureau of Statistics, almost three-quarters of the nation's road traffic

(vehicle-kilometres) are carried on arterial and sub-arterial roads which together comprise

only about 16 per cent of the total length of road. (ABS, 2002a).

The data will be used in conjunction with estimates of spending on local roads to calculate

the heavy vehicles' share of local road expenditure for inclusion in their charges. The NTC

have reasonable estimates of the total kms travelled by each class of vehicle, but the main

problem is splitting this between local roads and arterial roads. Consequently, what is

needed is a comparable estimate of local and arterial road use. Differences between vehicle

classes are particularly important to estimate, as these will have a significant impact on the

process of allocating road expenditure to each vehicle class.

The cost allocation process works by assigning the expenditure on the different types of

road works between vehicle classes according to how much they use the roads. Different

measures of road use are used to assign the different types of expenditure, eg. tonne kms,

Passenger Car Units (PCU) kms, Equivalent Standard Axles (ESA) kms, etc. However, allof these road use measures relate to kms and estimates of vehicle classifications. Arterial

and local road expenditure are treated separately as not all local road expenditure is driven

by use considerations - much of it relates to providing a minimum standard of access.

Consequently local road use estimates are vitally important.


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Page 2 Estimation of Vehicle Kilometres Travelled on Local Roads

2. METHODOLOGY

This section describes the methodology used to estimate VKT on local and arterial roads.

A statistically based sampling technique was used to obtain traffic information. It was

assumed that by taking a selection of councils and making calculations of VKT on

representative roads, that an estimate representative of VKT for urban and rural areas

across Australia could be made. The statistical sampling technique used to provide arepresentative estimate of VKT by vehicle class and road type (local and arterial) is

outlined below:

Select a representative sample of councils.

Classify the road networks for each council selected.

Calculate the length of road per classification for selected councils.

Classify categories of vehicles.

Select representative traffic count locations for each road class and obtain data.

Calculate estimates of VKT.

Each of these steps is further explained in the following sections.

2.1 Selection of sample counci ls

Australia currently has 721 local councils across the states and territories, all of which have

varying characteristics, such as size, population, location, roads and traffic. A

representative sample of these councils was needed for this project. The sampling process

used is targeted to select an initial 10 per cent sample that is representative of the whole. It

was expected that from this initial 10 per cent sample, it would be possible to have a 5-10

per cent sample with data usable in estimates. This is in accordance to the contract

proposal and the resources made available.

2.1.1 Segmentation of councils

Local councils were segmented so that a representative sample could be taken across

council types. The National Office of Local Government (NOLG, 2003) categorises the

721 local governments in Australia into typical types, based on population, densities, and

the percentage of the local government area that is urban (see Appendix A.1 for category

details). For the purpose of this project, these categories were considered to be of 5 basic

groups, as shown in Table 1.


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Table 1 Categories of Australian local government

(Source NOLG 2003)

2.1.2 Determination of required number of counci ls for each stateFrom each of the categories, an approximate ten percent sample was taken, as shown in

Table 1. This 10 per cent sample was distributed across the states to ensure fair

representation according to the number of councils in each state of that category.

For instance, there are 95 local councils classified as Urban Regional Small (UDS). A ten

percent sample is to be selected. This is 9.5 councils (i.e. 10 councils) that will be selected.

The 10 councils that were selected are proportionally spread across the states according to

existing councils of that type. Refer to Table 2. See Appendix A.2 for number of councils

by category and by state.

Table 2 Example of counci l selection across States/Territories

Urban Regional Small

NSW VIC QLD WA SA TAS NT Total

Existing 15 13 43 8 7 5 4 95

Selected 2 2 4 1 1 0 0 10

Group Category Abbreviation Totalnumber

Samplenumber

Urban Urban Capital CityUrban Development SmallUrban Development Medium

Urban Development LargeUrban Development Very Large

(UCC)(UDS)(UDM)

(UDL)(UDV)

71926

232095

123

2210

Outer Urban Urban Fringe SmallUrban Fringe MediumUrban Fringe LargeUrban Fringe Very Large

(UFS)(UFM)(UFL)(UFV)

816101448

12115

Urban Regional Urban Regional SmallUrban Regional MediumUrban Regional LargeUrban Regional Very Large

(URS)(URM)(URL)(URV)

953596145

1041116

Rural Rural Significant Growth

Rural Agricultural SmallRural Agricultural MediumRural Agricultural LargeRural Agricultural Very Large

(RSG)

(RAS)(RAM)(RAL)(RAV)

13

76967063318

1

8107632

Remote Rural Remote Extra SmallRural Remote SmallRural Remote MediumRural Remote Large

(RTX)(RTS)(RTM)(RTL)

46312712116

533112

Totals 721 75


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2.1.3 Calculation of average populations

A sample number of councils were taken from each category. Samples were selected that

had populations around the average population for that category. It was assumed that the

selection of councils of around average population was the best method for selecting

typical councils for the sample. The ABS Preliminary Local Government Area (LGA)

populations were utilised to calculate the average populations for each category, as

summarised in Appendix A.3 (ABS, 2002b).

2.1.4 Selection of councils by comparison to average population

Representative councils were chosen by selecting the appropriate number of councils close

to average population for that category. The average population for each state was also

considered, as for some categories, the states averages varied considerably. In this case

selections were made for each state by comparing council populations to the state average,

to achieve an overall average around the category average. Appendix A.4 shows the

councils selected using this process, by category and by state. By chance, some councils

selected using this process are contiguous, however this is not expected to have any effect

on the results.

2.2 Classification of roads

Councils selected using the above process provided maps of their municipality, showing

the road network, road classification or hierarchy and locations for which recent traffic

count data exists.

For the selected councils, the road network, including both arterial and local roads, were

reviewed with respect to their nominated classifications. Councils across Australia use a

variety of methods to classify their road networks. Council hierarchies were translated into

a common classification system to allow a consistent approach to be taken. The Austroads

Functional Road Classification (Austroads, 1989, see Appendix B) was used to break down

state and local roads into classes. This breakdown was performed to allow sampling fromeach class of road to be included in the vehicle kilometre travelled estimates.

Once the classifications were established for all arterial and local roads, the total kilometre

length of each road class was then established. This was determined by using a number of

methods, as appropriate to each council map. Methods used included:

Measuring road lengths from a hardcopy map.

Making calculations from GIS Arc View file data (where these had been supplied).

Consulting the council contact for information from council records.

Kilometre lengths of each road classification were recorded in a spreadsheet for later

calculations.

2.3 Classification of vehicles

Once road classifications and road lengths were established, a vehicle classification was

established. The Austroads Vehicle Classification System (Austroads, 1994; refer also to

Appendix C) was used to break vehicle types into three categories:

Light vehicles - all cars, vans, utilities, bicycles and motorcycles, including trailers

(Austroads Class 1 and 2).


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Medium vehicles - range between 2 axle trucks or buses up to 4 axle trucks (Austroads

Class 3 to 5).

Heavy vehicles - 3-6 axle articulated vehicles, B doubles, and double/triple road trains

(Austroads Class 6 to 12).

2.4 Identification of representative count locationsThe next task was to identify appropriate representative count locations for vehicle count

data. It was assumed that by selecting several count locations on roads of a particular

classification, that an average AADT calculated would be representative of travel on that

road classification.

For each class of road, a sample number of count locations were chosen. Typically, 3

locations were selected. If less than 3 roads made up the total class road length, less

locations were selected. A sample proportional to the length of each road class was not

feasible, due to the large difference between total lengths for different road classes. In

addition, typically the class with the longest length (access roads classes 4C and 8C) also

has the least amount of available traffic count data.

Councils had previously indicated locations for which they have current count data, and

this existing data was utilised for this project where appropriate. Traffic data was

considered to be appropriate if it was less than three years old and counts were undertaken

in a normal period of activity (i.e. not in holiday periods or other seasonal conditions).

For each road classification, count locations were selected by first selecting a number of

roads that were assumed to have average characteristics for that classification. Count

locations were then selected that, it was assumed, would give an average depiction of

traffic on that road (i.e. a location with a mid- level traffic volume for that road).

Councils were then requested to supply count information for each of the locations

identified. This count information included:

AADT, or average daily two-way traffic volume taken from traffic counts of at least afew days duration, outside of any seasonal factors.

The percentage breakdown for light vehicles, medium vehicles and heavy vehiclesaccording to the classification described in section 2.3.

The same information was requested from the state road authorities for the arterial (state)

roads for each of the selected councils.

2.5 Remote councils

A modified approach was required to collect information from remote councils. Remote

councils generally have few resources and little information about their road networks. Tosimplify the road network and the information required for these councils, remote council

networks were broken down as follows:

Arterial Roads classes 1, 2 and 3 (where applicable).

Local Roads sealed and unsealed.

Sample traffic counts or estimates were requested for average sealed and unsealed roads

within the council area. There was a need to rely on the best estimates from council staff

for these councils as traffic count data is rare for local roads in these areas.


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2.6 Calculations

Data collected was assembled in an Excel spreadsheet to allow calculation of VKT. The

data entered into the spreadsheet for each council was:

Kilometre length for each road classification.

Count data for a number of locations of each road classification.

VKT was calculated by multiplying an average AADT for each vehicle classification by

the total kilometre length of each road classification. An example is shown in

Figure 1. The total length of road classed 4A in a council is 245 km. Three sample count

locations are taken to produce an average daily traffic volume for 4A roads in that council.

These averages are multiplied by total kilometre length to calculate VKT by vehicle class.

Figure 1 Example of VKT calculation

road

class km length

vehicle

t ype sample 1 sample 2 sample 3

avg o

samples vkt

4A 245 km L 92.2% 85.6% 88.5% 88.8% 248,288

M 5.4% 9.3% 7.9% 7.5% 21,071H 2.4% 5.1% 3.6% 3.7% 10,349

total 1300 900 1225 1141.67

Further calculations are made to determine estimates of:

Average VKT by vehicle class, for arterial and local road classes, for each council.

Average VKT by vehicle class, for arterial and local roads, for each council category(UCC, UDS, etc) by aggregating council results. These results were then factored up by

the total number of councils in Australia under each category.

Average VKT by vehicle class, for arterial and local roads, for each council group(urban, outer urban, etc) by aggregating category results.

The results of these calculations are discussed in the following section.

3. RESULTS

This section provides a discussion of the resulting estimates, results of the sampling

process, council involvement, data results and discussion of result sensitivity.

3.1 Results of sampling process

From the sampling process, 90 councils were invited to take part in the study, a higher

number than the 72 required for a 5 to 10 percent sample, to allow for some lack of council

response. Responses were received from 54 councils, representing a 7.5 per cent sample

across states and council categories.Table compares the response rate of councils across

the groups to the numbers for a 10 per cent sample, and the 12 per cent sample contacted in

regards to the study. Response rates for urban areas were better than response rates for

rural areas. However the large number of councils taken, for example in rural areas, still

returned a good amount of data allowing a spread of results across regional categories.


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Councils who did not respond after follow up mainly had a lack of time and resources

available or reported that they did not have suitable data to contribute. The issue of data

was a particular problem for remote communities, who had very few or no traffic counts,

very small amounts of traffic and sometimes indistinct local road networks.

Table 2 Response rate from counc ils compared to sample aim and sample

contacted

Group Sample aim, max(10%)

Contacted f orstudy (12%)

Replied, includedin s tudy (7.5%)

Urban 10 12 9

Outer Urban 5 8 5

Regional 16 19 12

Rural 32 36 18

Remote 12 14 10

A series of follow ups were conducted through the project, proving to be a difficultprocess. Initial letters that were sent to councils were followed by an email and by phone

call, and by information being sent again on council request. Further follow ups were

conducted if councils indicated that they would send information. Councils who sent

responses to the initial letter were sent a letter requesting traffic data. Many councils

provided a swift response. A second round of following up via phone call and email was

conducted in an effort to collect more results, however some councils failed to respond.

The quality of information received for the first stages of the study (classification of road

networks and length of roads) was varied. This was partly due to the wide variety of

councils that it was necessary to include in the study and their available resources. Many

councils did not supply maps of good quality and also had limited capacity to map traffic

count locations.The majority of councils that responded could supply the requested traffic data. Forty eight

councils returned local roads data. Most of the information was drawn from existing traffic

count data files from recent years. Some councils assisted by conducting new counts to

complete the set of data requested. Twelve councils had to rely, in part, on their own

estimates to complete datasets for their council. Typically, these councils made estimates

for only a few locations, and these estimates were mainly for the traffic composition

numbers along with a measured traffic volume. Councils were requested to base estimates

on information on roads of a similar nature to that being sampled. If this was not possible,

judgement was used to provide the estimate.

Traffic count data was also included from state road authorities for arterial roads for each

of the 54 councils.

3.2 Data resul ts

The results of VKT calculations are shown in Table 3.2, Table 3.3 and Table 3.4. Table

3.2 shows VKT on arterial and local roads by vehicle type, for metropolitan and rural

areas. Table 3.3 shows the breakdown of percentages for VKT by area, vehicle type and

road type. Table 3.4 shows the VKT by heavy and medium vehicles by area, vehicle type

and road type, illustrating the proportion of heavy and medium vehicle travel on arterial

and local roads, as a share of all road travel by these vehicles.


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It is estimated that in total, 35.5 per cent of VKT by all vehicle and road types is on local

roads (see Table 3.3). Out of the total VKT, around 1 per cent of VKT is heavy vehicles on

local roads, and 2 per cent is medium vehicles on local roads. Heavy and medium vehicle

VKT is slightly higher on arterial roads, at 3 and 4 per cent of total VKT by all vehicle and

road types. Light vehicle traffic accounts for 90 per cent of total VKT, 57 percent on

arterial roads and 33 percent on local roads.

In metropolitan areas, 7 per cent of all VKT is by medium and heavy vehicles. Close to a

third of the VKT by medium and heavy vehicles in metropolitan areas is on local roads (27

percent). In rural areas, 12 per cent of all VKT is by medium and heavy vehicles, with 25

per cent of this travel being on local roads.

The percentage of VKT on local roads drops in rural areas. Local road VKT in

metropolitan areas accounts for 38 per cent of VKT, where in rural areas local road VKT

accounts for 33 per cent. Average traffic volumes on local roads are significantly lower in

rural areas. This in turn produces low numbers for VKT, even though the local road

network is typically considerably longer than the arterial network.

In addition it is estimated that in terms of travel by vehicle class, local road travel makes

up 15 per cent of all heavy vehicle travel, 30.5 per cent of all medium vehicle travel and 37per cent of all light vehicle travel.

3.2.1 Sensitivity

The accuracy of the results for the study is dependent on:

The appropriate representative sample size selected.

The classification of the road network.

Sample number and location of the representative traffic counts.

Reliability of the traffic volumes and break down by vehicle type supplied.The more significant sensitivity factor is considered to be traffic volume estimates

provided. The other factors of sample size of representative councils, road lengths by road

class are not seen to have a major influence. As about a third of the councils supplied

some data partly by way of an engineers estimate rather that on actual counts there is

some reservation on the overall accuracy of the final results.

With additional time and resources to collect actual traffic volumes by vehicle type the

estimates on VKT the accuracy of the result can be significantly improved.

Note that the focus of the survey method was to estimate relative use of the road network

rather than estimate the absolute level of use of the network. According to the Survey of

Motor Vehicle Use, vehicles in Australia travelled 190,152 million kilometres fromNovember 2000 to October 2001, or on average, 521 million kilometres a day (ABS,

2003). This study estimates VKT at 655 million a day, a higher number than the SMVU

figures. However, this was to be expected due to the method used to calculate the

estimates. The calculation method based on vehicle counts or observation estimates

assumes that vehicles travel the full length of the road in question. While this results in an

overestimation of VKT, it is an effective method to simply estimate the relative use of the

road network.


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Estimation of Vehicle Kilometres Travelled on Arterial and Local Roads Information Paper

Table 3 VKT on arterial and local roads by vehicle type, for metropolitan and rural areas (averag

METROPOLITAN AREAS

URBAN H M L total %ARTERIAL 3,815,831 7,845,066 125,349,317 137,010,214 20.9%

LOCAL 716,805 3,448,655 63,274,488 67,439,948 10.3%

TOTALS 4,532,636 11,293,721 188,623,804 204,450,162 31.2% Urban + Outer Urban

OUTER URBAN H M L total % METRO TOTALS H M

ARTERIAL 1,795,789 2,534,957 52,819,572 57,150,318 8.7% ARTERIAL 5,611,621 10,380,023 178,

LOCAL 401,610 1,480,221 49,952,924 51,834,755 7.9% LOCAL 1,118,415 4,928,875 113,

TOTALS 2,197,399 4,015,178 102,772,496 108,985,073 16.6% TOTALS 6,730,036 15,308,899 291,

RURAL AREAS

REGIONAL H M L total %

ARTERIAL 4,218,519 5,550,213 84,576,524 94,345,256 14.4%

LOCAL 1,165,041 2,588,433 55,179,108 58,932,582 9.0%

TOTALS 5,383,560 8,138,646 139,755,632 153,277,838 23.4%

RURAL H M L total %ARTERIAL 11,088,947 10,818,529 108,145,003 130,052,479 19.8%

LOCAL 1,639,931 4,139,758 45,855,894 51,635,583 7.9%

TOTALS 12,728,879 14,958,287 154,000,897 181,688,063 27.7% Regional + Rural + Remote

REMOTE H M L total % RURAL TOTALS H M

ARTERIAL 418,938 362,065 3,417,102 4,198,105 0.6% ARTERIAL 15,726,404 16,730,808 196,

LOCAL 172,541 161,874 2,383,074 2,717,490 0 .4% LOCAL 2,977,514 6,890,065 103,

TOTALS 591,479 523,940 5,800,176 6,915,594 1.1% TOTALS 18,703,918 23,620,872 299,

TOTALS

TOTALS H M L total % TOTALS H M

ARTERIAL 21,338,025 27,110,831 374,307,517 422,756,373 64.5% = ARTERIAL 21,338,025 27,110,831 374,

LOCAL 4,095,929 11,818,940 216,645,488 232,560,357 35.5% LOCAL 4,095,929 11,818,940 216,

TOTALS 25,433,953 38,929,771 590,953,005 655,316,730 100.0% TOTALS 25,433,953 38,929,771 590,

L =Light vehicles- all cars, vans, utilities, bicycles and motorcycles, including trailers (Austroads Class 1 and 2).M = Medium vehicles- range between 2 axle trucks or buses up to 4 axle trucks (Austroads Class 3 to 5).

H = Heavy vehicles- 3-6 axle articulated vehicles, B doubles, and double/triple road trains (Austroads Class 6 to 12).


20/36

Page 10 Estimation of Veh

Table 4 VKT as percentages on arterial and local roads by vehicle type, for metropoli tan and rper day)

METROPOLITAN AREAS

URBAN H M L totalARTERIAL 1.9% 3.8% 61.3% 67.0%

LOCAL 0.4% 1.7% 30.9% 33.0%

TOTALS 2.2% 5.5% 92.2% 99.9% Urban + Outer Urban

OUTER URBAN H M L total METRO H M L total

ARTERIAL 1.6% 2.3% 48.5% 52.4% ARTERIAL 1.8% 3.3% 56.8% 61.9%

LOCAL 0.4% 1.4% 45.8% 47.6% LOCAL 0.4% 1.6% 36.1% 38.1%

TOTALS 2.0% 3.7% 94.3% 100.0% TOTALS 2.2% 4.9% 93.0% 100.0%

RURAL AREAS

REGIONAL H M L total

ARTERIAL 2.8% 3.6% 55.2% 61.6%

LOCAL 0.8% 1.7% 36.0% 38.5%

TOTALS 3.5% 5.3% 91.2% 100.0%

RURAL H M L total

ARTERIAL 6.1% 6.0% 59.5% 71.6%

LOCAL 0.9% 2.2% 25.2% 28.3%

TOTALS 7.0% 8.2% 84.8% 100.0% Regional + Rural + Remote

REMOTE H M L total RURAL H M L total

ARTERIAL 6.1% 5.2% 49.4% 60.7% ARTERIAL 4.5% 4.9% 57.4% 66.8%

LOCAL 2.5% 2.3% 34.5% 39.3% LOCAL 0.9% 2.0% 30.2% 33.1%

TOTALS 8.6% 7.6% 83.9% 100.0% TOTALS 5.4% 6.9% 87.6% 100%

TOTALS H M L total TOTALS H M L total

ARTERIAL 3.3% 4.1% 57.1% 64.5% = ARTERIAL 3.3% 4.1% 57.1% 64.5%

LOCAL 0.6% 1.8% 33.0% 35.4% LOCAL 0.6% 1.8% 33.0% 35.4%TOTALS 3.9% 5.9% 90.1% 99.9% TOTALS 3.9% 5.9% 90.1% 99.9%

L =Light vehicles- all cars, vans, utilities, bicycles and motorcycles, including trailers (Austroads Class 1 and 2).M = Medium vehicles- range between 2 axle trucks or buses up to 4 axle trucks (Austroads Class 3 to 5).H = Heavy vehicles- 3-6 axle articulated vehicles, B doubles, and double/triple road trains (Austroads Class 6 to 12).


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Estimation of Vehicle Kilometres Travelled on Arterial and Local Roads Information Paper

Table 5 Heavy and medium vehicle VKT on arterial and local roads (average vkt per day)

METROPOLITAN AREAS

URB N H M total %ARTERIAL 3,815,831 7,845,066 11,660,897 74%

LOCAL 716,805 3,448,655 4,165,460 26%

TOTALS 4,532,636 11,293,721 15,826,357 100% Urban + Outer Urban

OUTER URB N H M total % METRO TOTALS H MARTERIAL 1,795,789 2,534,957 4,330,746 70% ARTERIAL 5,611,621 10,380,023

LOCAL 401,610 1,480,221 1,881,831 30% LOCAL 1,118,415 4,928,875

TOTALS 2,197,399 4,015,178 6,212,577 100% TOTALS 6,730,036 15,308,899 2

RURAL AREAS

REGION L H M total %ARTERIAL 4,218,519 5,550,213 9,768,732 72%

LOCAL 1,165,041 2,588,433 3,753,474 28%

TOTALS 5,383,560 8,138,646 13,522,206 100%

RUR L H M total %ARTERIAL 11,088,947 10,818,529 21,907,476 79%

LOCAL 1,639,931 4,139,758 5,779,689 21%

TOTALS 12,728,879 14,958,287 27,687,165 100% Regional + Rural + Remote

REMOTE H M total % RURAL TOTALS H MARTERIAL 418,938 362,065 781,003 70% ARTERIAL 15,726,404 16,730,808

LOCAL 172,541 161,874 334,415 30% LOCAL 2,977,514 6,890,065

TOTALS 591,479 523,940 1,115,419 100% TOTALS 18,703,918 23,620,872 4

TOTALS

TOT LS H M total % TOT LS H MARTERIAL 21,338,025 27,110,831 48,448,855 75% = ARTERIAL 21,338,025 27,110,831 4

LOCAL 4,095,929 11,818,940 15,914,869 25% LOCAL 4,095,929 11,818,940

TOTALS 25,433,953 38,929,771 64,363,724 100% TOTALS 25,433,953 38,929,771

M = Medium vehicles- range between 2 axle trucks or buses up to 4 axle trucks (Austroads Class 3 to 5).

H = Heavy vehicles- 3-6 axle articulated vehicles, B doubles, and double/triple road trains (Austroads Class 6 to 12).


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Estimation of Vehicle Kilometres Travelled on Arterial and Local Roads Information Paper Page 13

4. CONCLUSIONS

This study provides an estimate of VKT on arterial and local roads based on a

representative selection of municipal councils and traffic information on various road

classifications across Australia. This information was factored up to provide an overall

estimate of VKT travelled across Australia for various municipal groups, road and vehicle

types.

These estimates provide a useful indication of the split of total VKT between local roads

and arterial roads for different vehicle classes, an important factor in the allocation of road

expenditure to each vehicle class.

The key finding from this study on local road use for the Third Heavy Vehicle Road

Pricing Determination is that there is a much higher percentage of travel on local roads

than had been previously assumed. Results from this study compared to estimates used in

the Second Heavy Vehicles Road Pricing Determination are as follows:

Light vehicles (cars, motorcycles, vans) 37 per cent of their travel is on localroads, compared to the Second Determination estimate of 35 per cent.

Medium vehicles (rigid trucks, buses) 30 per cent of their travel is on local roads,compared to the Second Determination estimate of 10 to 30 per cent depending on

vehicle class with an average of 25 per cent.

Heavy vehicles (articulated trucks, B-doubles and road trains) 16 per centof theirtravel is on local roads, compared to the Second Determination estimate of 5 to 8

per cent depending on vehicle class with an average of 5 per cent.

All vehicles 35.5 per cent of travel is on local roads, compared to the SecondDetermination estimate of 34 per cent.

This study eventuated in a 7.5 per cent sample of Australian councils, compared to the aim

for up to a 10 per cent sample. The lower response rate was primarily due to councils not

providing the necessary information or lacking available resources to do so. The

methodology used is believed to be adequate in the calculation of estimates, where other

methods of estimation would prove to need even greater resources. Further, more extensive

studies of this nature using this methodology are suggested to improve results and to verify

the credibility of the results from this study. Some suggestions from this study include:

The inclusion of a larger sample of councils. The selection of councils based onaverage population provided a good basis for calculating average VKT. Including a

larger sample of councils in the study (say 30 per cent of all councils contacted, over a

12 month project period) would further improve the reliability of results.

The inclusion of a larger number of representative traffic counts. The daily traffic onroads of the same class can vary greatly within a council. Including a larger number of

traffic count locations would refine the averages.

It would be an advantage to councils and to research if more councils had GIS-baseddata systems that could quickly facilitate such a study by providing more accurate data

on the road network in a consistent format, including information on the road

hierarchy, road lengths and data such as traffic counts.


24/36

Page 14 Estimation of Vehicle Kilometres Travelled on Arterial and Local Roads Information Paper

5. REFERENCES

ABS (2002a). Special Article: History of Roads in Australia Year Book Australia 2002:

Transport.

http://www.abs.gov.au/Ausstats/[email protected]/0/2e904c15091c39a5ca2569de0028b416?OpenDocument

ABS (2002b). Preliminary Local Government Area (LGA) populations (at 30 June 2002)and final median ages (at 30 June 2001) fromRegional Population Growth, Australia and

New Zealand, 2001-02(ABS cat. no. 3218.0)http://www.abs.gov.au/websitedbs/c311215.nsf/20564c23f3183fdaca25672100813ef1/3c556306c6a3b803ca256b5500799b10!OpenDocument

AUSTROADS (1989).Rural Road Design: guide to the Geometric Design of Rural Roads.

Austroads, Sydney.

AUSTROADS (1994). Guide to Traffic Engineering Practices Part 3 Traffic Studies.

Austroads, Sydney.

NOLG (2003). 2001-02Report on the Operation of the Local Government (Financial

Assistance) Act 1995- Appendix F- Australian Classification of Local Governments.

National Office of Local Government.http://www.nolg.gov.au/publications/national_report/01_02/appendix_f/index.htm


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APPENDIX A: LOCAL GOVERNMENT CATEGORIES

A1 NOLG Local Government Categories

Step 1 Step 2 Step 3 Identifiers Category

URBAN (U) Capital City (CC) UCC

Population more than

20 000

Metropolitan

Developed (D)

Small (S) up to 30 000 UDS

Part of an urban centre

of more

Medium (M) 30 00170 000 UDM

than 1 000 000 or

population

Large (L) 70 001120 000 UDL

density more than 600/sq

km

Very Large (V) more than 120 000 UDV

OR

Regional Towns/City

(R)

Small (S) up to 30 000 URS

Part of an urban centrewith population less than

Medium (M) 30 00170 000 URM

Large (L) 70 001120 000 URL

Very Large (V) more than 120 000 URV

OR

Fringe (F) Small (S) up to 30 000 UFS

A developing LGA on the

margin of a developed or

Medium (M) 30 00170 000 UFM

Large (L) 70 001120 000 UFL

Very Large (V) more than 120 000 UFV

RURAL (R)

Significant Growth

(SG)

Average annual

population growth more

AND

Small (S) up to 2 000 RAS

Medium (M) 2 0015 000 RAM

Large (L) 5 00110 000 RAL

Very Large (V) 10 00120 000 RAV

AND

Extra Small (X) up to 400 RTX

Small (S) 4011 000 RTS

Medium (M) 1 0013 000 RTM

Large (L) 3 00120 000 RTL

RSG

Population density

more than 30

persons per sq km

90 per cent or more

of LGA population is

urban

An LGA with

population less than

20 000

Not applicable

Population density

less than 30 persons

per sq km

Agr icul tu ral (A)

Less than 90 per

cent of LGA

population is urban

Remote (T)

Source: NOLG (2003)


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A2 Number of Councils by category and by state, June 2003

Source: NOLG (2003)


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A3 Average populat ion of local government categor ies

Category State State avg Category State State avg Category State State avg

UCC NSW 30681 URM NSW 46668 RTX NT 341

NT 68378 QLD 45719 QLD 320

QLD 917216 TAS 52020 WA 292

SA 13501 VIC 41975 RTX avg 330

TAS 47439 WA 40176 RTS NT 823

VIC 53956 URM avg 45575 QLD 686

WA 8733 URL NSW 82815 WA 892UCC avg 162843 QLD 85263 RTS avg 792

UDS NSW 23678 VIC 82410 RTM NSW 2424

SA 13169 URL avg 83768 NT 1697

WA 16944 URV NSW 174403 QLD 1674

UDS avg 17647 QLD 278865 SA 0

UDM NSW 51140 VIC 198164 WA 1526

QLD 50718 URV avg 213184 RTM avg 1671

SA 44150 RSG QLD 12640 RTL NSW 5157

VIC 65549 VIC 21617 QLD 4730

WA 47346 WA 13290 WA 7549

UDM avg 50058 RSG avg 13730 RTL avg 6306

UDL NSW 90331 RAS NSW 1710

SA 100371 QLD 1260

VIC 100659 SA 1446

WA 87771 TAS 1279

UDL avg 96332 WA 1042

UDV NSW 174396 RAS avg 1165QLD 169433 RAM NSW 3548

VIC 144020 QLD 3458

WA 177962 SA 3193

UDV avg 159138 TAS 2848

UFS NT 22818 WA 3751

QLD 23585 RAM avg 3499

SA 18657 RAL NSW 7716

TAS 20475 QLD 7460

VIC 26774 SA 7645

WA 22118 TAS 6876

UFS avg 22700 VIC 7250

UFM NSW 50066 WA 7910

QLD 51764 RAL avg 7497

SA 54151 RAV NSW 13969

VIC 56174 NT 15738

WA 56883 QLD 13717

UFM avg 54170 SA 13998

UFL NSW 77426 TAS 13767

QLD 121601 VIC 14798

VIC 116414 WA 11767

WA 87015 RAV avg 14018

UFL avg 105252

UFV NSW 156876

QLD 130186

SA 152106

VIC 158580

WA 156964

UFV avg 153094

URS NSW 21908

NT 9826

QLD 4111

SA 14777

TAS 21385

VIC 21739

WA 14797URS avg 12810

Source: ABS (2002b).


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A4 Councils selected for study

Group Category Council name State Pop. Group Category Council name State Pop.

URBAN UCC Melbourne VIC 53,956 RURAL RSG Greenough WA 12,973

URBAN UDS Prospect SA 19,285 RURAL R AS Conargo Shire NSW 1,826

URBAN UDS Bassendean WA 14,175 RURAL RAS Warroo QLD 1,069

URBAN UDS Nedlands WA 21,672 RURAL RAS Le Hunte SA 1,452

RURAL RAS Brookton WA 1,038

URBAN UDM Kogarah Municipal NSW 53,250 RURAL RAS Coorow WA 1,368

URBAN UDM Pittwater NSW 56,790 RURAL RAS Goomalling WA 967

URBAN UDM Campbelltown SA 47,005 RURAL RAS Jerramungup WA 1,228

RURAL RAS Kondinin WA 1,029

URBAN UDL South Sydney City NSW 90,315

URBAN UDL Port Adelaide Enfield SA 102,705 RURAL RAM Harden Shire NSW 3,831

URBAN UDL Maroondah VIC 100,935 RURAL RAM Lockhart Shire NSW 3,605

RURAL RAM Hay Shire NSW 3,555

URBAN UDV Bankstown City NSW 173,370 RURAL RAM Warren Shire NSW 3,310

URBAN UDV Whitehorse VIC 146,751 RURAL RAM Dalrymple QLD 3,480

RURAL RAM Tara QLD 3,917

Group Category Council name State Pop. RURAL RAM Waggamba QLD 2,997

OUTER URBAN UFS Palmerston NT 22,818 RURAL RAM Ceduna SA 3,625

OUTER URBAN UFS Burnett QLD 24,439 RURAL RAM Cent ral Highlands TAS 2,307

RURAL RAM Merredin WA 3,715

OUTER URBAN UFM Melton VIC 58,764 RURAL RAM Waroona WA 3,514

OUTER URBAN UFM Armadale WA 52,288

RURAL RAL Coonabarabran NSW 6,833

OUTER URBAN UFL Wyndham VIC 92,604 RURAL RAL Mulwaree NSW 7,090

OUTER URBAN UFL Swan WA 87,425 RURAL RAL Wellington NSW 8,761

RURAL RAL Wentworth NSW 7,218

OUTER URBAN UFV Hornsby Shire NSW 154,708 RURAL RAL Eacham QLD 6,372

OUTER URBAN UFV Joondalup WA 156,964 RURAL RAL Grant SA 7,827

RURAL RAL Latrobe TAS 8,432

Group Category Council name State Pop. RURAL RAL Towong VIC 8,349

REGIONAL URS Goulburn City NSW 21,303 RURAL RAL Mid Murray SA 6,286

REGIONAL URS Grafton City NSW 17,341

REGIONAL URS Katherine NT 8,824 RURAL RAV Mudgee Shire NSW 18,464

REGIONAL URS Charters Towers QLD 8,790 RURAL RAV Muswellbrook NSW 15,352

REGIONAL URS Goondiwindi QLD 4,888 RURAL RAV Belyando QLD 10,228

REGIONAL URS Roma QLD 6,707

RURAL RAV Jondaryan QLD13,229

REGIONAL URS Torres QLD 3,732 RURAL RAV Loxton Waikerie SA 12,227

REGIONAL URS Port Lincoln SA 14,049 RURAL RAV Waratah - Wynyard TAS 13,568

REGIONAL URS Moorabool VIC 25,412 RURAL RAV Golden Plains VIC 15,360

REGIONAL URS Horsham VIC 18,706

REGIONAL URS Port Hedland WA 12,731 Group Category Council name State Pop.

REMOTE RTX Cox Peninsula NT 284

REGIONAL URM Cessnock City NSW 47,566 REMOTE RTX Mataranka NT 227

REGIONAL URM Great Lakes NSW 45,141 REMOTE RTX Aputula NT 300

REGIONAL URM Bundaberg QLD 45,043 REMOTE RTX Diamantina QLD 321

REGIONAL URM Clarence TAS 49,659 REMOTE RTX Menzies WA 362

REGIONAL URM Wellington VIC 41,374

REMOTE RTS Kunbarllanjnja NT 1200

REGIONAL URL Tweed Shire NSW 76,229 REMOTE RTS Nauiyu Nambiyu NT 900

REGIONAL URL Mackay QLD 77,157 REMOTE RTS Boulia QLD 567

REMOTE RTS Tambo QLD 618

REGIONAL URV Greater Geelong VIC 198,164

REMOTE RTM Coomalie NT 1070

REMOTE RTM Thamurrurr NT 2,500

REMOTE RTM Barcaldine QLD 1,732

REMOTE RTM Blackall QLD 1,784

REMOTE RTL Ashburton WA 5,816

Bold= councils included in study


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APPENDIX B: ROAD CLASSIFICATION

Roads in Rural Areas -Arterial

Class 1 Those roads, which form the principal avenue of communication between, major regions ofAustralia, including direct connections between capital cities.

Class 2 Those roads, not being Class 1 whose main function is to form the principal avenue ofcommunication for movements between:

A capital city and adjoining states and their capital cities; or

A capital city and key towns; or

Key towns.

Class 3 Those roads, not being Class 1 or 2 whose main function is to form an avenue of communication formovements:

Between important centres and Class 1 and Class 2 roads and/or key towns; or

Of an arterial nature within a rural area.

Roads in Rural Areas Local Roads

Class 4 Those roads, not being Class 1,2 or 3 whose main function is to provide access to abutting property.

RoadClass

Class Type Service Function Description Brief Description RuralAreas

4A Local ArterialRoad

Provides primarily for the mainconnection from, town centres and localareas to the wider State main roadnetwork.

Two way, two-lane,mainly sealed.

4B CollectorRoad

Provides for collecting and distributingtraffic and acting as a feeder service tolocal arterial roads.

Two way, sealed orunsealed road.

4C Access Road Provides predominantly for direct access

to properties, recreational areas andindustries in town and rural zones

Two way, mainly two lane

sealed or unsealed road.

4D Limitedaccess track

Provides primarily for limited access andin rural areas using four-wheel drivevehicles

Two way, unformed singlelane track with accessrestrictions imposed.

Class 5 Those roads, which provide almost exclusively for one activity or function which, cannot be assignedto Classes 1 to 4 (Eg. access to parks and tourist areas).

(Source Austroads 129)


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Roads in Urban Areas - Arterial

Class 6 Those roads whose main function is to form the principle avenue of communication for massivetraffic movements.

Class 7 Those roads not being Class 6 whose main function is to supplement the Class 6 roads in providingfor traffic movements or which distribute traffic to the local street system.

Urban Areas- Local Roads

Those roads, not being Class 7 whose main function is to provide access to abutting property.

RoadClass

Class Type Service Function Descript ion Brief Description Urban Areas

8A Local ArterialRoad

Provides primarily for the mainconnection from, urban centres and localareas to the wider State main arterialroad network.

Two way, two-lane,mainly sealed.

8B CollectorRoad

Provides for collecting and distributingtraffic and acting as a feeder service tolocal arterial roads.

Two way, sealed orunsealed road.

8C Access Road Provides predominantly for direct accessto properties, recreational areas and

industries in urban zones

Two way, mainly two lanesealed or unsealed road.

Class 8

8D Limitedaccess

Provides primarily for limited access torear of properties or within recreationalparks.

Two way, unformed singlelane track with accessrestrictions imposed.

Class 9 Those roads, which provide exclusively for one activity or function and which cannot be assigned toclasses 6, 7 or 8 (i.e. access to major recreational parks).

(Source Austroads AP 129)


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APPENDIX C: VEHICLE CLASSIFICATION


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APPENDIX D: SUPPLEMENTARY REPORT

Estimation of vehicle kilometres travelled on arterial and local roads: SUPPLEMENTARY

WORK (February, 2005)

A supplementary period of work was undertaken in late 2004 to improve the confidencelevels in the results of the original work completed in early 2004.

In particular, further work was undertaken to:

Improve the confidence levels in the estimate of heavy vehicle local road use, byundertaking a review of the original data set.

Increase the sample size of "Remote" councils to 10 of which only three remotecouncils provided data previously.

Validate previously estimated figures with actual traffic counts from a sample of 5councils.

Review of original data

Given that the results of the original work indicated that 15 per cent of heavy vehicle travel

is on local roads, rather than an average of 5 per cent as has been used to date by the NTC,

further work was completed to gain confidence in the results.

The database used to make the estimates of VKT was reviewed. In particular, data that had

the greatest impact on the overall figures for heavy vehicles was targeted. The aim of this

was to find particular areas that may be improved by additional information.

Heavy vehicle traffic on local roads was estimated to be greater than 5 per cent for the

majority of councils sampled during the study. The review established that only 23 per cent

of councils had heavy vehicle use on their local road of less than 5 per cent. The largerpercentage of councils was estimated to have greater than 5 per cent of heavy vehicle use

on local roads. This indicates that the 5 per cent figure used to date by the NTC may be

too low.

The urban group (UDS and UDV categories) and the regional group (URM category)

returned particularly high proportions of heavy vehicle use on local roads. The review

targeted these areas to refine the input data used. In particular, areas of work included:

Actual traffic count figures were obtained for the representative arterial road trafficcount for the City of Nedlands. A current traffic count was not available at the time

of the original work and an estimate was used. The breakdown percentages

estimated were not significantly different from those in the actual traffic count. The

new traffic count was slightly higher than the estimated count used.

The category of UDV has the major influence over the results for the urban group.Bankstown Council NSW was sampled for the UDV category. The original

estimate was for a very high proportion of VKT by heavy vehicles on local roads in

Bankstown. This was due to the exclusion of the M5 motorway, a private toll road,

from the calculations of arterial road use. This caused the estimate that was made

for Bankstowns arterial roads to be low. Another council (Whitehorse City

Council) was added to this category to provide a more evenly balanced estimate.


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Figures for Great Lakes Council NSW were reviewed to include better informationfrom Arc view maps with road hierarchies and traffic count information.

Traffic count figures for local roads in Wellington Shire Council VIC were soughtto provide more data for this category.

Other councils that had relatively high proportions of heavy vehicle usage on local

roads were reviewed for confirmation of logic of the input data. Road lengths andtraffic count data for the sample councils reviewed all appeared reasonable and

logical.

Remote councils

Additional remote councils were sought to add to the sample. A modified approach was

required to collect information from remote councils. Remote councils generally have few

resources and little information about their road networks. To simplify the road network

and the information required for these councils, remote council networks were broken

down as follows:

Arterial Roads classes 1, 2 and 3 (where applicable).

Local Roads sealed and unsealed.

Sample traffic counts or estimates were requested for average sealed and unsealed roads

within the council area. There was a need to rely on the best estimates from council staff

for these councils as traffic count data is rare for local roads in these areas.

Councils were contacted after consultation with the LGANT about which councils could

possibly supply some data. Councils contacted generally had information on the kilometre

length of their traffic network but as expected, had no actual traffic count data.

Seven additional rural councils were added to the sample. These councils were:

Cox Peninsula, NT (RTX)

Mataranka, NT (RTX)

Naiuyu Nambiyu, NT (RTS)

Kunbarllanjnja, NT (RTS)

Thammurrurr, NT (RTM)

Coomalie, NT (RTM)

Ashburton, WA (RTL)

Adding the data from these remote councils greatly improved the sample size across theremote council categories, and made the overall study sample size 7.5 per cent (up from

6.4 per cent).

Validation of estimated figures

Further work was undertaken to validate previously estimated figures with actual traffic

counts from a sample of councils. It was reasoned that by estimating some local road

traffic figures, over-estimates of the number of heavy vehicles may have been made that

could influence the final estimates of heavy vehicle use on local roads.


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Traffic data from the 12 councils that supplied some estimates was reviewed, in

conjunction with the general review of data. The percentage of heavy vehicle use on local

roads was calculated for each council.

It was determined that councils that had used some estimates were generally not councils

with high estimates of heavy vehicle use on local roads. This means that the use of

estimated traffic data has not caused an overestimation of heavy vehicle use on local roads.One quarter of councils that previously returned some traffic count estimates were rural

councils (4 councils) which were all estimated to have heavy vehicle use on local roads of

around 5 per cent.

To establish the accuracy of traffic data estimation, a sample of councils (5) who

previously estimated some figures were asked to conduct actual traffic counts. The original

estimates were compared with the actual traffic count data.

Joondalup

Estimates were made for two 4c (see Appendix B for definition) road volumes and

percentages. Joondalup council were unable to conduct more traffic counts, but assisted to

find other 4c road traffic counts that would be suitable for use in the study. The council

contact noted that the locations previously selected were under-representative of traffic on

4c roads, so more representative locations were selected. Overall average number of

vehicles on a 4c road was previously 982 vpd, this was revised to an estimate of 1374 vpd.

Percentages were revised from 98 per cent light, 1.9 per cent medium and 0.l per cent

heavy to 98.3 per cent light, 1.7 per cent medium and 0 per cent heavy. These changes

made little difference to the proportion of heavy vehicle use on local roads, but did

increase local road VKT figures for Joondalup and for the Outer Urban category.

Waggamba

The council undertook 5 additional traffic counts (2 counts on 4a roads, 2 counts on 4c

roads and 1 count on 4b roads). Overall, the average number of vehicles on 4a roads wasrevised to 75 vpd (previously 65 vpd), the average number of vehicles on 4b roads was

revised to 43 vpd (previously 57 vpd) and average number of vehicles on 4c roads was

revised to 126 vpd (previously 20). These revised estimates increased the proportion of

heavy vehicle use on local roads from 1.3 per cent to 1.7 per cent for the Rural Agricultural

Medium category.

Coonabarabran and Wellington

Coonabarabran and Wellington councils are both classified Rural Agricultural Large. Both

councils undertook additional traffic counts on their local roads. The Coonabarabran

council undertook 2 additional traffic counts (1 count on a 4a and 1 on a 4c road). The

Council suggested a more representative location for the 4c road. Wellington Council

provided a database of traffic count information, from which estimates for 4a, 4b and 4c

roads were derived. From these revisions, the overall average number of vehicles on 4a

roads for the Rural Agricultural Large category increased to 295 vpd (previously 145 vpd),

the average number of vehicles on 4b roads decreased to 205 vpd (previously 516 vpd),

and average number of vehicles on 4c roads increased to 66 vpd (previously 6 vpd). These

changes also increased the proportion of heavy vehicle use on local roads in the Rural

Agricultural Large category from 4.7 per cent to 5.9 per cent.


36/36


Bankstown

Bankstown undertook two traffic counts on 4c roads and the actual counts were similar to

those previously estimated

Horsham

Horsham Council placed counters down in late 2004 but due to mal function no counts

were able to be obtained. It was decided that to wait for new counters to be purchased by

Council and redo the work would take too long and no further counts were sought.

EstimVehKiloTravArterLocalMay05 (1)

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