Survey of Motor Vehicle Use, Australia methodology

This release has ceased

Reference period

12 Months ended 30 June 2020

Released

21/12/2020

Next release Ceased

How the data is collected

Source

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 19.5 million vehicles identified from the MVC at 31 January 2019. These vehicles provided the population of vehicles, or survey frame, for the 2020 Survey of Motor Vehicle Use (SMVU).

Scope and coverage

The scope of the survey comprised all vehicles registered with a motor vehicle authority for road use during the 12 months ended 30 June 2020. 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.

Collection method

For the 2020 SMVU, a sample of 16,180 vehicles was selected for inclusion in the survey. The survey sample consisted of passenger vehicles (18.0%), motor cycles (5.0%), freight vehicles (65.9%), buses (8.1%) and non-freight carrying trucks (3.0%). 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.

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

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.

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.

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.

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 'How the data is processed'.

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 2019 and 30 June 2020.

For this release of the SMVU, the information reported in each four month collection period between 1 July 2017 to 30 June 2018 and 1 July 2019 to 30 June 2020 have been individually aggregated to produce sub-annual estimates relating to the use of vehicles. Sub-annual estimates are available in the download tab and cover the following collection periods:

2018 Sub-annual estimates

1 July 2017 to 31 October 2017
1 November 2017 to 28 February 2018
1 March 2018 to 30 June 2018

2020 Sub-annual estimates

1 July 2019 to 31 October 2019
1 November 2019 to 29 February 2020
1 March 2020 to 30 June 2020

How the data is processed

Sampling errors

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.

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.

In the data cube 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.

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

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.

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 data cube:
Total kilometres travelled by passenger vehicles, Australia, 2020
Estimate = 162,983 million kilometres
RSE = 2.65%

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, 2020) = 2.65 x 162,983 / 100 = 4,319 million kilometres

95% Confidence Interval
95% confidence interval = Estimate plus or minus 2 x SE
Lower limit of the interval = 162,983 - (2 x 4,319) = 154,345 million kilometres
Upper limit of the interval = 162,983 + (2 x 4,319) = 171,621 million kilometres
95% Confidence Interval = 154,345 to 171,621 million kilometres

It can, therefore, be considered with 95% reliability that the true distance travelled by registered passenger vehicles in Australia is between 154,345 and 171,621 million kilometres.

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

Data movements estimated by comparing SMVU data from different time periods are also subject to sampling error.

Sub-annual level estimates may be subject to additional sampling error due to the smaller sample sizes. Estimates produced for each collection period at detailed levels are not designed for comparing movements of data over different time periods. At more detailed levels higher RSEs are expected and care should be given when interpreting these estimates.

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

\(SE(M_{t}) = \sqrt {(RSE(Y_{x}) \times Y_{x}/100)^2 + (RSE(Y_{u}) \times Y_{u}/100)^2}\)

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 \(M_{t} = Y_{x} - Y_{u}\)

SE Movement = \(\sqrt {(SE(Y_{x})^2 + SE(Y_{u})^2)}\)

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.

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, 2018 = 179,761 million kilometres (RSE = 2.44%), SE = 4,386 million kilometres
Total kilometres travelled by passenger vehicles, Australia, 2020 = 162,983 million kilometres (RSE = 2.65%), SE = 4,319 million kilometres
Movement between estimates (2020 estimate - 2018 estimate) = -16,778 million kilometres
SE Movement (Total kilometres travelled by passenger vehicles, Australia, 2020) = \(\sqrt{(4,386^2 + 4,319^2)}\) = 6,156 million kilometres

95% Confidence Interval of movement
95% confidence interval = Estimate plus or minus 2 x SE
Lower limit of the interval = -16,778 - (2 x 6,156) = -29,090 million kilometres
Upper limit of the interval =-16,778 + (2 x 6,156) = -4,466 million kilometres

It can, therefore, be considered with 95% reliability that the true movement in distance travelled by registered passenger vehicles in Australia from 2018 to 2020 is a decrease that lies between 29,090 million kilometres and 4,466 million kilometres.

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 2018 SMVU to the 2020 SMVU using unrounded estimates and RSEs.

SE of the movement of total kilometres travelled - 2018 and 2020(a)(b)
Vehicle Type	Level Estimates				Movement Estimates
	2018 (million)	2018 RSE (%)	2020 (million)	2020 RSE (%)	Movement (million)	SE of Movement (million)	95% Confidence Interval of Movement (million)
	2018 (million)	2018 RSE (%)	2020 (million)	2020 RSE (%)	Movement (million)	SE of Movement (million)	Lower Limit	Upper Limit
Passenger vehicles	179,761	2.44	162,983	2.65	-16,778	6,156	-29,090	-4,466
Motor cycles	2,193	13.24	1,683	10.6	-510	341	-1,192	172
Light commercial vehicles	52,307	3.35	52,229	3.25	-78	2,440	-4,958	4,802
Rigid trucks	10,274	2.48	10,975	3.15	702	429	-156	1,560
Articulated trucks	7,917	1.74	8,181	1.84	264	204	-144	672
Non-freight carrying trucks	313	11.77	321	11.24	8	52	-96	112
Buses	2,266	4.65	2,126	4.77	-140	146	-432	152
Total	255,031	1.83	238,499	1.89	-16,532	6,488	-29,508	-3,556

a. Data for 2018 and 2020 are for 12 months ended 30 June.
b. Some data calculated on unrounded estimates and RSEs.

Non sampling error

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.

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

Imputation

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.

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 travelled (%)	Percentage of total tonne-kilometres travelled (%)	Percentage of fuel consumption (%)
New South Wales	23	22	46
Victoria	21	31	46
Queensland	21	24	51
South Australia	18	22	43
Western Australia	20	31	45
Tasmania	20	22	41
Northern Territory	25	23	52
Australian Capital Territory	20	25	40
Australia	21	26	46

a. Includes both partial and full imputation.

Contribution to estimates from imputation (a), type of vehicle
	Percentage of total kilometres travelled (%)	Percentage of total tonne-kilometres travelled (%)	Percentage of fuel consumption (%)
Passenger vehicles	21	. .	47
Motor cycles	26	. .	38
Light commercial vehicles	22	38	51
Rigid trucks	21	28	45
Articulated trucks	19	25	41
Non-freight carrying trucks	17	. .	51
Buses	14	. .	25
Total	21	26	46

. . not applicable.
a. Includes both partial and full imputation.

Response and non response

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 2020, comprised of 76% from registered vehicles and 3% from unregistered vehicles, out of scope and duplicates.

Response and non-response by category
		Percentage of selections 2020 (%)
Response received
	Registered vehicle	76
	Unregistered vehicle(a)	3
Non-response
	Untraceable - mailing address unknown	4
	Other(b)	17
Total selections		100

After removing those vehicles that had been found to be deregistered or out of scope, the response rate for the 2020 SMVU was 78.5%.

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	79
Victoria	79
Queensland	79
South Australia	79
Western Australia	78
Tasmania	80
Northern Territory	76
Australian Capital Territory	76
Australia	79

Response rates, type of vehicle
	Response rate (%)
Passenger vehicles	78
Motor cycles	76
Light commercial vehicles	77
Rigid trucks	78
Articulated trucks	79
Non-freight carrying trucks	83
Buses	82
Total	79

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 motor cycles (76%).

Frame quality

A population or survey frame of 19.5 million vehicles was identified on 31 January 2019 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).

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 2020, approximately 0.2% 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.

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.

An assessment of vehicle classification anomalies from 2020 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, 21.5% were found to be other vehicle types and 13.5% of vehicles listed as buses were found to be other vehicle types. This issue was not significant for other vehicle types on the frame.

Adjustments

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.

Sub-annual estimates were created using the three independent samples employed in the compilation of the annual survey. Estimates were then aggregated separately from each other rather than together. As with the annual estimates, each of the sub-annual level estimates have been adjusted for new motor vehicles and re-registrations of vehicles.

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 2020 SMVU, the frame was created on 31 January 2019. 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.

For the sub-annual estimates, the adjustment for re-registrations has been applied in line with the annual estimates. However, adjustments to account for new motor vehicles have been applied differently to account for the different time periods of the three samples. As a result of the difference in application of this adjustment the sub-annual estimates will not total to exactly match the annual estimates.

Contribution of adjustments for re-registrations (a), Australia - 2012, 2014, 2016, 2018 and 2020 (b)
Type of Vehicle	Percentage of total kilometres travelled
Type of Vehicle	2012 (%)	2014 (%)	2016 (%)	2018 (%)	2020 (%)
Passenger vehicles	1	-	-	-	-
Motor cycles	7	1	3	3	5
Light commercial vehicles	2	-	1	1	1
Rigid trucks	3	-	2	2	1
Articulated trucks	4	-1	2	1	2
Non-freight carrying trucks	1	1	2	5	-2
Buses	5	2	2	4	3
Total	1	-	-	1	1

- nil or rounded to zero (including null cells).
a. Estimates for 2014 were produced using a different method than in 2012, 2016, 2018 and 2020. The contribution of adjustments for re-registrations in 2014 is not comparable with other years.
b. Data for 2014 are for 12 months ended 31 October. Data for 2012, 2016, 2018 and 2020 are for 12 months ended 30 June.

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 - 2012, 2014, 2016, 2018 and 2020 (a)
Type of vehicle	Percentage of total kilometres travelled
Type of vehicle	2012 (%)	2014 (%)	2016 (%)	2018 (%)	2020 (%)
Passenger vehicles	7	10	7	7	5
Motor cycles	9	11	8	8	5
Light commercial vehicles	8	11	7	8	7
Rigid trucks	6	6	6	11	9
Articulated trucks	9	16	8	7	10
Non-freight carrying trucks	13	13	11	3	9
Buses	5	3	4	6	2
Total	7	10	7	7	6

a. Data for 2014 are for 12 months ended 31 October. Data for 2012, 2016, 2018 and 2020 are for 12 months ended 30 June.

Nil use

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 - 2012, 2014, 2016, 2018 and 2020(a)
	2012	2014	2016	2018	2020
Number of registered vehicles with nil use
Passenger vehicles	479,179	476,348	315,089	482,959	485,349
Motor cycles	182,308	196,887	231,039	246,877	309,820
Light commercial vehicles	71,292	103,727	99,456	140,684	174,152
Rigid trucks	36,549	38,541	39,461	36,788	43,729
Articulated trucks	6,162	6,652	5,092	6,366	7,892
Non-freight carrying trucks	3,157	2,566	1,532	2,191	2,025
Buses	1,809	2,006	2,644	2,498	4,214
Total	780,455	826,725	694,315	918,362	1,027,180
Proportion of registered vehicles with nil use (%)
Passenger vehicles	5	4	2	3	3
Motor cycles	23	26	28	29	35
Light commercial vehicles	5	3	3	4	5
Rigid trucks	8	8	8	7	8
Articulated trucks	6	7	5	6	8
Non-freight carrying trucks	11	15	7	9	9
Buses	4	2	3	3	5
Total	6	5	4	5	5

a. 2014 are for 12 months ended 31 October. Data for 2012, 2016, 2018 and 2020 are for 12 months ended 30 June.

How the data is released

Summary of outputs

The following core estimates of motor vehicle use are produced for each release:

Number of vehicles
Kilometres travelled
Fuel consumed
Tonne kilometres travelled
Tonnes carried

Data cubes containing all tables for this publication in Excel spreadsheet format are available from the Data download section of the main publication. The spreadsheets present tables of estimates and averages, and their corresponding relative standard errors (RSEs).

Any discrepancies between totals and sums of components in this publication are due to rounding.

Motor Vehicle Use data is available as a microdata product through TableBuilder, an online tool for creating tables and graphs. Further information about this service can be found here Microdata Entry Page

Concept of averages

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.

Comparison with motor vehicle census data

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 2019 to 30 June 2020, 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.

Special care should be given when interpreting the survey estimates of the number of vehicles for the sub-annual estimates. The survey is designed for an annual output of the average number of vehicles registered for road use.

Comparisons with previous survey release

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.

The sub-annual estimates included in this publication are based on a design to produce reliable estimates of key data items for a point in time for an annual output, not for four month to four month changes. It is likely that the sub-annual estimates are subject to a higher sampling error due to the smaller sample sizes.

History of changes

Sub-annual estimates

This release will be the first time sub-annual estimates will be published.

The Survey of Motor Vehicle Use, 2020 will be the final release of this publication.

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

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.

Business kilometres

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.

Business use

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.

Laden distance

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.

Unladen distance

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

'000	Thousand
'000 000	Million
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
tkm	Tonne-kilometres
Vic.	Victoria
WA	Western Australia

APA

Survey of Motor Vehicle Use, Australia methodology

APA

How the data is collected

Source

Scope and coverage

Collection method

How the data is processed

Sampling errors

Non sampling error

Imputation

Response and non response

Frame quality

Adjustments

Nil use

How the data is released

Summary of outputs

Concept of averages

Comparison with motor vehicle census data

Comparisons with previous survey release

History of changes

Sub-annual estimates

Glossary

Show all

Articulated trucks

Average load carried

Billion

Buses

Business kilometres

Business use

Capital city

Freight vehicles

Fuel consumption

Fuel consumption (average)

Gross Combination Mass (GCM)

Gross Vehicle Mass (GVM)

Interstate

Laden distance

Light commercial vehicles

Megalitre

Non-freight carrying trucks

Other urban areas

Passenger vehicles

Private use

Rigid trucks

Relative Standard Error (RSE)

Standard Error (SE)

Tonne-kilometres

Tonnes carried

Travel to and from work

Unladen distance

Quality Declaration

Institutional environment

Relevance

Timeliness

Accuracy

Coherence

Interpretability

Accessibility

Abbreviations

Show all