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Latest release

# Survey of Motor Vehicle Use, Australia methodology

Reference period
12 months ended 30 June 2018
Released
20/03/2019
Next release Unknown
First release

## Explanatory notes

### Introduction

1 This publication presents estimates of motor vehicle use in Australia between 1 July 2017 and 30 June 2018. Estimates are compiled from the ABS' Survey of Motor Vehicle Use (SMVU). This survey included a sample of vehicles registered in Australia during this period.

### Scope and frame

2 The scope of the survey comprised all vehicles registered with a motor vehicle authority for road use during the 12 months ended 30 June 2018. Not included were caravans, trailers, tractors, plant and equipment, vehicles belonging to the defence services and vehicles with diplomatic or consular plates. Where they were registered as such, vintage and veteran cars were also excluded from the survey. Unregistered vehicles were out of scope.

3 Vehicles were identified using information obtained from state and territory motor vehicle registration authorities as part of the annual ABS Motor Vehicle Census (see Motor Vehicle Census, Australia (cat. no. 9309.0)). The Motor Vehicle Census (MVC) provides a snapshot of registered vehicles at 31 January each year. There were 19 million vehicles identified from the MVC at 31 January 2017. These vehicles provided the population of vehicles, or survey frame, for the 2018 SMVU.

### Methodology

4 For the 2018 SMVU, a sample of 16,000 vehicles was selected for inclusion in the survey. The survey sample consisted of passenger vehicles (17.42%), motor cycles (5.27%), freight vehicles (65.73%), buses (8.34%) and non-freight carrying vehicles (3.24%). The sample size was chosen to give a suitable level of reliability for estimates of total distance travelled and tonne-kilometres travelled for each state/territory of registration by type of vehicle category over the survey period.

5 Vehicles were selected for one of three data collection periods, each 4 months in duration.

6 Owners of vehicles selected in the survey were asked to complete two questionnaires, either paper or web, tailored to their vehicle type. The first, at the beginning of the survey period, asked for selected vehicle characteristics and the vehicle odometer reading. Owners were also advised they would receive a follow up questionnaire at the end of the period, with examples of the main items included. The second questionnaire requested details about the use of the vehicle over the four month period and a second odometer reading.

7 When questionnaires were returned to the ABS they were checked for completeness and accuracy and, where possible, follow-up contact was made with owners to resolve reporting problems. Where contact with owners could not be made, missing items on incomplete questionnaires were filled by using data from like vehicles for which data were obtained.

8 Where the selected vehicle owner had not owned the vehicle for the whole four month survey period, the usage details provided for the period of ownership were adjusted to give a four-month equivalent. Where the vehicle was deregistered during the four month survey period, only usage up to the date of deregistration was included.

9 In addition, adjustments were made in the estimation process to account for the use of new motor vehicles registered after the survey population was identified, as well as the re-registration of other vehicles during this time. More information about these adjustments is provided in paragraph 27 of the Technical Note.

10 Estimates from information reported in each four month collection period were produced and these were then aggregated into annual estimates relating to the use of vehicles during the period 1 July 2017 and 30 June 2018.

### Reliability of estimates

11 When interpreting the results of a survey it is important to take into account factors that may affect the reliability of estimates. Such factors can be classified as either survey methodology, sampling error or non-sampling error. Information on these factors is provided in the Technical Note.

### Fuel consumption question

12 The 2018 survey instrument included a change to the question design and wording relating to fuel consumption over the four month collection period. The question related to fuel consumption has historically been difficult for respondents to answer.

13 The change removed the burden of multiple reporting options. The previous question asked respondents to report, or calculate and record their average rate of fuel consumption (litres per 100 kilometres) over the four month reporting period. This was replaced with a simpler question asking for the total amount of fuel used over the four month collection period. To assist respondent recall, an optional log sheet was also provided to record the amount of fuel purchased over the collection period.

14 As a result of the change to the fuel consumption question, care should be taken when interpreting the estimates for total fuel consumption and average rate of fuel consumption.

### Comparison with motor vehicle census data

15 Survey estimates of the numbers of vehicles, by vehicle type for SMVU are not fully comparable with ABS Motor Vehicle Census data (see Motor Vehicle Census, Australia (cat. no. 9309.0)). The main differences are:

• Survey estimates of the numbers of vehicles relate to the average number of vehicles registered for road use during the period 1 July 2017 to 30 June 2018, not to the number of vehicles registered at a specific date, as is the case for the Motor Vehicle Census.
• Characteristics of the vehicle reported in the survey information may differ from those recorded by the motor vehicle registries.

### Concepts of averages

16 Most tables in this publication include statistics presented as averages. The denominator used in calculating these averages varies depending on the characteristics of interest. The method of calculating each average is noted in the table where it is presented. As the denominators used to calculate each average are different it should be noted that the averages along a table row cannot be used to derive the total column entry for that row.

### Historical comparisons

17 This publication includes estimates of vehicle use for earlier years. However, it should be noted the survey was designed to produce reliable estimates of key data items for a point in time, not for year-to-year changes. Estimates of movement over time are subject to high sampling error and care should be taken in drawing inferences from these comparisons. See paragraphs 9-13 of the Technical Note for further information.

18 Users may also wish to refer to the following publications and products which contain information relating to motor vehicles in Australia:

Microdata: Motor Vehicle Use, Australia, 2018 (cat. no. 9208.0.55.008)
Motor Vehicle Census, Australia (cat. no. 9309.0)
Sales of New Motor Vehicles, Australia (cat. no. 9314.0)
Road Freight Movements, Australia (cat. no. 9223.0)

### ABS data available on request

19 As well as the statistics included in this publication, the ABS has other relevant data available on request. Inquiries should be made to the National Information and Referral Service on 1300 135 070.

## Technical note - data quality indicators

### Data quality

1 When interpreting the results of a survey it is important to take into account factors that may affect the reliability of estimates. The survey procedures as well as sampling and non-sampling errors should be considered. Examination of the following quality indicators will assist users in determining fitness for purpose of estimates produced from the Survey of Motor Vehicle Use (SMVU).

### Sampling error

2 Estimates from the SMVU are based on information collected for a sample of registered motor vehicles, rather than all registered vehicles. The estimates may differ from those that would have been produced if all registered motor vehicles had been included in the survey. This difference is referred to as sampling error.

3 One measure of sampling error is the Relative Standard Error (RSE), which indicates the extent to which a survey estimate is likely to deviate from the true population, expressed as a percentage of the estimate. Estimates with a RSE of 25% or greater are subject to high sampling error and should be used with caution.

4 In the datacube associated with this release, estimates are presented side by side with their RSE. It is important to consider the RSEs when using estimates produced from the SMVU as it affects the reliability of the estimates, and therefore the importance that can be placed on interpretations drawn from the data.

5 Another measure of sampling variability is the Standard Error (SE), which is an indication of the sampling error expressed in numeric terms.

6 The reliability of estimates can also be assessed in terms of a confidence interval. Confidence intervals represent the range in which the population value is likely to lie. They are constructed using the estimate of the population value and its associated standard error. For example, there is approximately a 95% chance (i.e. 19 chances in 20) that the population value lies within two standard errors of the estimates, so the 95% confidence interval is equal to the estimate plus or minus two standard errors.

7 The example below demonstrates how each of the reliability measures described above can be calculated and interpreted:

Relative Standard Error (RSE)
From Table 4 of the datacube:
Total kilometres travelled by passenger vehicles, Australia, 2018
Estimate = 179,761 million kilometres
RSE = 2.44%

Since the RSE on the estimate is less than 25%, the estimate would be considered reliable enough for general use.

Standard Error (SE)
SE = RSE x estimate / 100
SE (Total kilometres travelled by passenger vehicles, Australia, 2018) = 2.44 x 179,761 / 100 = 4,386 million kilometres

95% Confidence Interval
95% confidence interval = Estimate plus or minus 2 x SE
Lower limit of the interval = 179,761 - (2 x 4,386 ) = 170,989 million kilometres
Upper limit of the interval = 179,761 + (2 x 4,386 ) = 188,533 million kilometres
95% Confidence Interval = 170,989 to 188,533 million kilometres

It can, therefore, be considered with 95% reliability that the true distance travelled by registered passenger vehicles in Australia is between 170,989 and 188,533 million kilometres.

8 It is important to note that estimates at more detailed levels than the above are subject to higher RSEs and therefore are less reliable.

9 The movement estimated by comparing SMVU data from different time periods is also subject to sampling error.

10 The standard error for the movement between two years can be approximated for the SMVU using the following formula

$$S E(M_t)={\sqrt{(RSE(Y_x) \times Y_x/100)^2+(RSE(Y_u) \times Y_u/100)^2}}$$

where $$Y_u$$ is an estimate of total of the variable of interest, obtained from the 1st time point $$Y_x$$ is an estimate of total of the same variable of interest, obtained from the 2nd time point $$M_t$$ is an estimate of movement of the total of the variable of interest from the 1st time point to the 2nd time point, ie $$M_t=Y_x-Y_u$$

11 Estimates of movement produced from the SMVU are subject to significant sampling error, and particular caution should be used when making inferences about differences between estimates over time.

12 The example below demonstrates how the reliability of movement in the SMVU estimates can be calculated and interpreted:

Standard Error (SE) of movement
Total kilometres travelled by passenger vehicles, Australia, 2016 = 175,899 million kilometres (RSE = 2.74%), SE = 4,820 million kilometres
Total kilometres travelled by passenger vehicles, Australia, 2018 = 179,761 million kilometres (RSE = 2.44%), SE = 4,386 million kilometres
Movement between estimates (2018 estimate - 2016 estimate) = 3,862 million kilometres
SE Movement = sqrt(SE(x)²+SE(y)²)
SE Movement (Total kilometres travelled by passenger vehicles, Australia, 2018) = sqrt((4820)² + (4386)²) = 6,517 million kilometres

95% Confidence Interval of movement
95% confidence interval = Estimate plus or minus 2 x SE
Lower limit of the interval = 3,862 - (2 x 6,517) = -9,172 million kilometres
Upper limit of the interval = 3,862 + (2 x 6,517) = 16,896 million kilometres

It can, therefore, be considered with 95% reliability that the true movement in distance travelled by registered passenger vehicles in Australia from 2016 to 2018 is between a decrease of 9,172 million kilometres and an increase of 16,896 million kilometres.

13 The table below presents the standard error and 95% confidence intervals for the estimated movement in total kilometres travelled by type of vehicle from the 2016 SMVU to the 2018 SMVU using unrounded estimates and RSEs.

### SE of the movement of total kilometres travelled - 2016 and 2018(a)

LEVEL ESTIMATES (b)MOVEMENT ESTIMATES (b)
2016RSE (2016)2018RSE (2018)MovementSE (Movement)95% Confidence Interval of movement
Lower LimitUpper Limit
mill.%mill.%mill.mill.mill.mill.
Type of vehicle
Passenger vehicles
175 899
2.74
179 761
2.44
3 863
6 515
-9 167
16 893
Motor cycles
2 176
9.71
2 193
13.26
17
359
-701
735
Light commercial vehicles
50 778
3.14
52 307
3.34
1 529
2 362
-3 195
6 253
Rigid trucks
10 301
2.9
10 274
2.49
-27
393
-813
759
Articulated trucks
7 613
1.84
7 917
1.74
304
196
-88
696
Non-freight trucks
290
15.63
313
11.78
23
58
-93
139
Buses
2 456
4.41
2 266
4.66
-190
151
-492
112
Total
249 512
2.09
255 031
1.83
5 518
6 997
-8 476
19 512
a. Data for 2016 and 2018 are for 12 months ended 30 June.
b. Calculated on unrounded estimates and RSEs.

### Non-sampling error

14 Non-sampling error covers the range of errors that are not caused by sampling and can occur in any statistical collection whether it is based on full enumeration or a sample. For example, non-sampling error can occur because of non-response to the statistical collection, errors or omissions in reporting, definition or classification difficulties, errors in transcribing and processing data and under-coverage of the frame from which the sample was selected. If these errors are systematic (not random) then the survey results will be distorted in one direction and therefore will be unrepresentative of the target population. Systematic errors result in bias.

15 A number of indicators of possible non-sampling error are outlined below.

### Imputation

16 Imputation is the process whereby a value is generated for missing data. Data may be missing for a particular data item (partial imputation), or for a unit which has not responded to the questionnaire (full imputation). For the SMVU, imputed values are based on responses for similar vehicles which were operating for the reference period.

17 Imputation introduces non-sampling error, and the contribution to estimates from imputed data provides one measure of the reliability of the estimates. As for previous surveys, the need for imputation of unanswered items on the returned questionnaires remained quite high. The tables below show the percentage contribution to the estimates from both partial and full imputation.

### Contribution to estimates from imputation (a), state/territory of registration

Percentage of total kilometres travelledPercentage of total tonne-kilometres travelledPercentage of fuel consumption
%%%
New South Wales
22
27
49
Victoria
20
28
49
Queensland
22
26
44
South Australia
19
18
41
Western Australia
22
26
44
Tasmania
22
23
47
Northern Territory
27
35
53
Australian Capital Territory
21
22
39
Australia
21
26
47
a. Includes both partial and full imputation

### Contribution to estimates from imputation (a), type of vehicle

Percentage of total kilometres travelledPercentage of total tonne-kilometres travelledPercentage of fuel consumption
%%%
Passenger vehicles
21
. .
48
Motor cycles
24
. .
52
Light commercial vehicles
19
46
43
Rigid trucks
20
24
40
Articulated trucks
15
26
49
Non-freight carrying vehicles
12
. .
16
Buses
23
. .
36
Total
21
26
47
. . not applicable
a. Includes both partial and full imputation

### Response and non-response

18 An important factor that affects non-sampling error is the response rate. The ABS makes all reasonable efforts to maximise response rates. For the SMVU, mail reminders and telephone follow-up were used to attempt to contact non-responding vehicle owners. Usable responses were received from 79% of all of the selections for 2018, comprised of 77% from registered vehicles and 3% from unregistered vehicles, out of scope and duplicates.

### Response and non-response by category

Percentage of selections 2018
%
Registered vehicle
77
Unregistered vehicle(a)
3
Non-response
4
Other(b)
16
Total selections
100
a. Includes deregistration, out of scope and duplicates.
b. Includes: responses that were unusable because of unresolved queries or where the vehicle was sold during the reference third and the reported data covered less than 14 days; non-response where no listing could be found to enable contact by telephone; and owner contacted by telephone but response still not secured.

19 After removing those vehicles that had been found to be deregistered or out of scope, the response rate for the 2018 SMVU was 79%.

20 Response rates for each State and Territory, and for each vehicle type, are shown in the following tables:

### Response rates, state/territory

Response rate
%
New South Wales
80
Victoria
79
Queensland
80
South Australia
82
Western Australia
79
Tasmania
80
Northern Territory
76
Australian Capital Territory
80
Australia
79

### Response rates, type of vehicle

Response rate
%
Passenger vehicle
77
Motor cycles
77
Light commercial vehicles
76
Rigid trucks
80
Articulated trucks
81
Non-freight carrying trucks
84
Buses
84
Total
79

21 For the SMVU, it is assumed that the characteristics of non-responding vehicles are the same as for like responding vehicles. Non-response has the potential to cause non-response bias, which occurs if the usage patterns of the non-responding vehicles differ from those of the responding vehicles. For example, the lowest response rate achieved by vehicle type was for light commercial vehicles (76%). This could result in the estimates for light commercial vehicles being of a lower quality than other vehicle types.

### Frame quality

22 A population or survey frame of 19 million vehicles was identified on 31 January 2017 using information obtained from the state and territory motor vehicle registration authorities, as part of the annual ABS Motor Vehicle Census (MVC) (cat. no. 9309.0).

23 The reliability of this frame in providing an accurate number of vehicles in scope of the survey is indicated by the number of duplicate vehicle registrations, vehicle de-registrations prior to frame extract, and out-of-scope vehicles identified. For 2018, approximately 0.5% of the total frame were identified as such. This indicates the frame was reliable in terms of providing an accurate number of registered vehicles in Australia.

24 Another indicator of frame quality is the number of units identified as in scope with different characteristics compared to what was recorded on the frame. For the SMVU, this can arise when respondents indicate an alteration has been made to the vehicle body, resulting in a different body type to that recorded on the frame. These changes can happen during the time-lag between finalising the frame and collection of SMVU data (between 5 and 17 months). Vehicle classification anomalies can also result from data supplied by state and territory vehicle registration authorities.

25 An assessment of vehicle classification anomalies from 2018 data shows that while there was no bias towards specific states or territories, there were marked discrepancies for some vehicle types. For vehicles on the frame that were listed as non-freight carrying trucks, 17.2% were found to be other vehicle types and 14.2% of vehicles listed as buses were found to be other vehicle types. This issue was not significant for other vehicle types on the frame.

### Survey procedures

26 The survey is comprised of three independent samples, with a different sample used for each four month period in the overall 12 month survey period. Estimates from each of these samples are aggregated and adjusted for new motor vehicles and re-registrations of vehicles to produce an annual estimate.

27 The SMVU aims to measure the use of all vehicles registered during the reference year. Because selections are taken from vehicles registered some time before the beginning of each collection period, adjustments are made to account for the change in size of the registered motor vehicle fleet since the population frame was created. For the 2018 SMVU, the frame was created on 31 January 2017. These adjustments involved two categories:

• re-registrations - older vehicles that are returning to the registered vehicle fleet after a period of de-registration, and
• new motor vehicles - vehicles which have not been previously registered.

### Contribution of adjustments for re-registrations (a), Australia - 2010, 2012, 2014, 2016 and 2018(b)

PERCENTAGE OF TOTAL KILOMETRES TRAVELLED
20102012201420162018
%%%%%
Type of Vehicle
Passenger vehicles
2
1
-
-
0
Motor cycles
8
7
1
3
3
Light commercial vehicles
2
2
-
1
1
Rigid trucks
3
3
-
2
2
Articulated trucks
4
4
-1
2
1
Non-freight carrying vehicles
6
1
1
2
5
Buses
6
5
2
2
4
Total
2
1
-
-
1
- nil or rounded to zero (including null cells)
a. Estimates for 2014 were produced using a different method than in 2010, 2012, 2016 and 2018. The contribution of adjustments for re-registrations in 2014 is not comparable with other years.
b. Data for 2010 and 2014 are for 12 months ended 31 October. Data for 2012, 2016 and 2018 are for 12 months ended 30 June.

28 These activities occur continuously and the adjustments are made to account for the registrations that are estimated to have been added to or removed from the registered vehicle fleet between the population frame date and the end of the reference period. The adjustment process also accounts for de-registrations. This means it is possible for the re-registration factor to be negative.

### Contribution of new vehicles registered after frame creation - 2010, 2012, 2014, 2016 and 2018(a)

PERCENTAGE OF TOTAL KILOMETRES TRAVELLED
20102012201420162018
%%%%%
Type of Vehicle Passenger vehicles
9
7
10
7
7
Motor cycles
11
9
11
8
8
Light commercial vehicles
10
8
11
7
8
Rigid trucks
8
6
6
6
11
Articulated trucks
11
9
16
8
7
Non-freight carrying trucks
8
13
13
11
3
Buses
5
5
3
4
6
Total
9
7
10
7
7
a. Data for 2010 and 2014 are for 12 months ended 31 October. Data for 2012, 2016 and 2018 are for 12 months ended 30 June.

### Nil use

29 Some providers may report nil use for the 4 month reference period in which they were selected. Nil use vehicles are registered vehicles that report no travel during that specific reference period. Nil use vehicles are included in the survey as their reported nil use is representative of other vehicles in the population. Vehicles may have nil use due to factors such as seasonal usage, mechanical faults or economic conditions. Where a provider gives a nil use response, a follow-up phone call is used to check the veracity of the response.

### Nil use, vehicle type - 2010, 2012, 2014, 2016 and 2018(a)

20102012201420162018
NUMBER OF REGISTERED VEHICLES WITH NIL USE
Passenger vehicles
561 613
479 179
476 348
315 089
482 959
Motor cycles
148 217
182 308
196 887
231 039
246 877
Light commercial vehicles
122 227
71 292
103 727
99 456
140 684
Rigid trucks
34 647
36 549
38 541
39 461
36 788
Articulated trucks
5 165
6 162
6 652
5 092
2 191
Non-freight carrying trucks
2 424
3 157
2 566
1 532
2 498
Buses
2 831
1 809
2 006
2 644
246 877
Total
877 123
780 455
826 725
694 315
918 362
PROPORTION OF REGISTERED VEHICLES WITH NIL USE (%)
Light commercial vehicles
5
5
3
3
4
Rigid trucks
9
8
8
8
7
Articulated trucks
5
6
7
5
6
Non-freight carrying trucks
7
11
15
7
9
Buses
2
4
2
3
3
Total
5
6
5
4
5
a. Data for 2010 and 2014 are for 12 months ended 31 October. Data for 2012, 2016 and 2018 are for 12 months ended 30 June.

## Glossary

### Show all

#### Articulated trucks

Motor vehicles constructed primarily for load carrying, consisting of a prime mover which has no significant load carrying area, but with a turntable device which is linked to a semitrailer.

Average load carried is calculated by dividing the total weight carried by the number of trips made while carrying a load.

#### Billion

The term 'billion' means 'thousand million' in line with Australian standards.

#### Buses

Motor vehicles constructed for the carriage of passengers. Included are all motor vehicles with 10 or more seats, including the driver's seat.

Distance travelled for hire and reward, or charged to a business expense, or for which an allowance was received. All distances travelled for business purposes, irrespective of actual use, and irrespective of vehicle type, are included in total business kilometres. The laden-unladen dissection of distance travelled for business purposes relates only to freight vehicles, i.e. light commercial vehicles, rigid trucks and articulated trucks.

Use of vehicle for business, professional, farm or government purposes. It includes use for hire or reward, use which is chargeable to business expense and use for which an allowance was received. Travel to and from work is not included.

#### Capital city

Reported areas of use within or around the capital city areas in each State or Territory similar to the Greater Capital City Statistical Areas as defined in the Australian Statistical Geography Standard (ASGS), 2011.

#### Freight vehicles

Consists of light commercial vehicles, rigid trucks and articulated trucks.

#### Fuel consumption

Fuel consumption is calculated by aggregating the total kilometres travelled multiplied by reported average rate of fuel consumption for each vehicle.

#### Fuel consumption (average)

The average rate of fuel consumption is calculated by dividing the total fuel consumption by total kilometres travelled for each type of vehicle.

#### Gross Combination Mass (GCM)

Tare weight (i.e. unladen weight) of the motor vehicle and attached trailers, plus their maximum carrying capacity. In the survey, this was obtained for vehicles operated in combination (e.g. a prime mover/semitrailer combination, or a rigid truck/trailer combination).

#### Gross Vehicle Mass (GVM)

Tare weight (i.e. unladen weight) of the motor vehicle, plus its maximum carrying capacity. In the survey, this was obtained for buses, rigid trucks and light commercial vehicles not usually towing trailers.

#### Interstate

This refers to any travel by vehicles outside their state or territory of registration.

Distance travelled by light commercial vehicles, rigid trucks and articulated trucks from one destination to another when carrying freight.

#### Light commercial vehicles

Motor vehicles constructed for the carriage of goods and which are less than or equal to 3.5 tonnes GVM. Included are utilities, panel vans, cab-chassis and goods carrying vans (whether four-wheel drive or not).

#### Megalitre

The term 'megalitre' means 'one million litres'.

#### Non-freight carrying trucks

Specialist motor vehicles or motor vehicles fitted with special purpose equipment, and having little or no goods carrying capacity, e.g. ambulances, cherry pickers, fire trucks and tow trucks.

#### Other urban areas

Areas of use outside the capital city areas and in urban areas similar to the Significant Urban Areas as defined in the Australian Statistical Geography Standard (ASGS), 2011.

#### Passenger vehicles

Motor vehicles constructed primarily for the carriage of persons and containing up to nine seats (including the driver's seat). Included are cars, station wagons, four-wheel drive passenger vehicles, campervans and passenger vans or mini buses with fewer than 10 seats.

#### Private use

Travel which is not for business purposes. Personal and other use and Travel to and from work categories are considered private use.

#### Rigid trucks

Motor vehicles exceeding 3.5 tonnes GVM, constructed with a load carrying area. Included are normal rigid trucks with a tow bar, draw bar or other non-articulated coupling on the rear of the vehicle.

#### Relative Standard Error (RSE)

The standard error expressed as a percentage of the estimate to which it refers.

#### Standard Error (SE)

Indicates the extent to which an estimate might have varied by chance because only a sample of vehicles was included.

#### Tonne-kilometres

Total tonne-kilometres is the aggregation of the number of tonnes moved multiplied by the distance travelled in kilometres for each individual vehicle carrying freight. Note that it is not the aggregation of the total number of tonnes moved by total kilometres travelled by all vehicles carrying freight.

For SMVU, tonne-kilometres are calculated using the laden distance travelled for work purposes and average load weight as reported by the respondent. This is weighted and aggregated to produce total tonnes-kilometres.

#### Tonnes carried

Total tonnes carried is the total weight of goods and freight carried during the survey period. The estimate of total tonnes carried relates to goods and freight uplifted by vehicles and therefore will overstate the actual physical quantity of goods and freight moved during the survey period to the extent that transhipment occurs (i.e. the transfer of goods and freight from one vehicle to another).

For SMVU, Total tonnes carried are calculated using the number of weeks used for business purpose, number of trips per week and the average load weight. This is weighted and aggregated to produce total tonnes carried.

#### Travel to and from work

The travel between place of residence and place of work at the beginning and end of all working days, including travel to and from public transport stations.

Distance travelled by light commercial vehicles, rigid trucks and articulated trucks from one destination to another when not carrying freight.

## Quality declaration - summary

### Institutional environment

For information on the institutional environment of the Australian Bureau of Statistics (ABS), including the legislative obligations of the ABS, financing and governance arrangements, and mechanisms for scrutiny of ABS operations, please see ABS Institutional Environment.

### Relevance

The Survey of Motor Vehicle Use (SMVU) provides a nationwide picture of motor vehicle use which will be used for policy and planning including the allocation of Federal road funding; road planning, building and maintenance; enhancing road safety and other activities.

The SMVU examines motor vehicle use for a reference year.

SMVU collects information from 16,000 registered vehicles in Australia and estimations based on detailed cross classifications are likely to result in high relative standard errors.

Vehicles are classified to state of registration. Area of operation is a data item broadly defined and reported by respondents.

The main output data items are distance travelled and tonne-kilometres. The tonne-kilometres is not obtained directly but is derived for all records from collected distance and load weight information.

### Timeliness

The population frame for sampling the Survey of Motor Vehicle Use was generated from the Motor Vehicle Census (MVC) at the 31 January 2017.

There are 16,000 selections used for the three reference periods. The first period beginning July 2017 with the third and final period finishing in June 2018.

The statistics are generally available 9 months after the end of the reference period.

### Accuracy

The survey is designed to obtain quality estimates for key data items of total distance travelled and tonne-kilometres.

The delay between the date of the frame and the collection period requires adjustments for new vehicles and re-registered vehicles.

The overall response rate for 2018 was 79%. The need for imputation of unanswered questions on returned questionnaires is quite high, with 25% of all responses requiring imputation of one or more questions for 2018.Average rate of fuel consumption had to be imputed for 47% of vehicles which reported travelling some distance during the reference period.

For further information refer to the Data Quality section of the Technical Note.

### Coherence

The ABS has released a coherent set of statistics for motor vehicle use since 1998.

A change to the methodology in 1998 means that care should be taken in making direct comparisons between 1998 data onwards and that from previous surveys.

Additionally, care should be taken in drawing inferences from changes in the data over time for the current time series as the survey is designed for level estimates not movement estimates. Refer to the Technical Note.

### Interpretability

Information on interpreting the results of this survey is provided in the Technical Note.

### Accessibility

The Survey of Motor Vehicle Use (SMVU) release is available on the ABS website with data contained in a Data Cube. If the information you require is not available as a standard product or service, then ABS Consultancy Services may be able to help you with customised services to suit your needs. Inquiries should be made to the National Information and Referral Service on 1300 135 070. The ABS Privacy Policy outlines how the ABS will handle any personal information that you provide to us.

## Abbreviations

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 '000 Thousand ABS Australian Bureau of Statistics ACT Australian Capital Territory ASGS Australian Statistical Geography Standard ATFCC Australian Transport Freight Commodity Classification Aust. Australia CNG Compressed Natural Gas GCM Gross Combination Mass GVM Gross Vehicle Mass km Kilometre LPG Liquefied Petroleum Gas mill. Million no. Number NSW New South Wales NT Northern Territory Qld. Queensland RSE Relative Standard Error SA South Australia SE Standard Error SMVU Survey of Motor Vehicle Use Tas. Tasmania Vic. Victoria WA Western Australia