DATA AND DATA SOURCE ISSUES IN ASSESSING THE TRANSPORT ...eprints.qut.edu.au/archive/00002777/01/2777.pdf · DATA AND DATA SOURCE ISSUES IN ASSESSING THE TRANSPORT IMPACTS OF E

Working Paper 5 of the E-Business and Transport Project

for

The National Transport Secretariat

DATA AND DATA SOURCE ISSUES IN ASSESSING THE TRANSPORT IMPACTS OF E-BUSINESS

by

Nariida Smith CSIRO - Building, Construction and Engineering

&

Elspeth Mead Queensland University of Technology

&

Luis Ferreira Queensland University of Technology

31 January 2001

For further information or clarification please contact:

Dr Nariida Smith Principal Research Scientist CSIRO Building, Construction and Engineering PO Box 310 North Ryde NSW 1670 Phone 02 9490 5466 Fax 02 9490 5777 e-mail [email protected] This research was undertaken in collaboration with the Built Environment Research Unit of the Queensland Department of Public Works. © CSIRO/QUT 2001

Data and Data Source Issues Working Paper 5

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EXECUTIVE SUMMARY

Both the rapid speed of change and the changes in society due to the information revolution in general, poses special challenges for forecasting the future of e-business and assessing its impacts. The traditional processes of collecting information over time, describing a base case and projecting of future trends, or assessing change scenarios, encounters problems in all stages.

These challenges apply to studies of impacts on the economy in general but are greater in studies of impacts in specific industries, such as transport. More detailed information is required and more detailed outputs are expected. Almost always such studies are looking for sufficiently detailed insights on future conditions to allow plans to be put in place. This requires predictions to be linked closely to time frames and specifics of localities.

Network data adds connectivity, type of link and link capacity information to geographic mapping data of roads and railway lines. The road authorities in each state hold complete network data for their states often augmented by road condition data.

Linking capacity and linking condition may be particularly relevant to assessment of the future ability of the road or rail system to accommodate future e-commerce. This might be applied to consideration of capacity of local roads for urban freight in cities or to measure highway or railway capacity for inter-urban freight movements.

In common with workplace and industrial locations, commodity flows are likely to change with a rise in e-commerce. It is possible these changes may occur within a five-year framework.

Examples of available data for tracking activities, which may change rapidly within a five- year period, have been divided for convenience into those relating to personal activity and those relating to business activity. Both types of data are discussed in section three of this Working Paper.

Vehicle registration and licensing databases show locations and numbers of registered vehicle types by location. While they provide information on the passenger fleet they are of particular value in the e-business context in tracking changes in numbers and types of commercial vehicle and also in holders of commercial vehicle licences. Individual states and territories hold these databases. In addition, the ABS also collects the data to report on an Australia wide basis.

Consideration of changes in the urban freight task is particularly important in estimating the impacts of e-business on the road system and vice versa. For example, will e-commerce add to congestion and equally what will be the impacts of congestion on e-commerce? Until recently transport planners concentrated on commuter travel. Estimates of future demands were modelled on passenger transport needs and the extra demands of freight were simply ‘loaded onto the network’.


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TABLE OF CONTENTS

EXECUTIVE SUMMARY ................................................................................................ II

TABLE OF CONTENTS ..................................................................................................III

FIGURES ...........................................................................................................................IV

TABLES .............................................................................................................................IV

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

1.1 TRACKING CHANGE .........................................................................................................1

1.2 DATA NEEDS FOR FORECASTING..................................................................................2

2 AVAILABLE DATA AND SOURCES – STATIC/SEMI-STATIC ................... 3

2.1 INFRASTRUCTURE ............................................................................................................3

2.2 SOCIO-DEMOGRAPHICS...................................................................................................5

2.3 INDUSTRY AND WORKPLACE LOCATIONS.................................................................7

2.4 COMMODITY FLOWS ........................................................................................................7

3 DATA AND SOURCES – DYNAMIC RE: PERSONAL ACTIVITY............... 8

3.1 PERSONAL INTERNET USAGE ........................................................................................8

3.2 MAKING PERSONAL TRIPS..............................................................................................9

3.3 MOTOR VEHICLE USE.....................................................................................................11

3.4 TOURISM DATA................................................................................................................11

4 DATA AND SOURCES – DYNAMIC RE: BUSINESS ACTIVITY ............... 14

4.1 TELECOMMUNICATIONS NETWORKS AND FACILITIES........................................14

4.2 BUSINESS INTERNET USE..............................................................................................15

4.3 FREIGHT/ COMMERCIAL VEHICLE ACTIVITY..........................................................17

4.4 AIR AND SEA FREIGHT...................................................................................................19

4.5 MARKET DATA.................................................................................................................20


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5 DATA LIMITATIONS AND SOLUTIONS ....................................................... 21

5.1 DATA GAPS .......................................................................................................................21

5.2 COLLECTION OF APPROPRIATE DATA.......................................................................23

5.3 A SYSTEMATIC APPROACH TO DATA SHORTFALLS..............................................25

BIBLIOGRAPHY AND REFERENCES ........................................................................ 27

GLOSSARY ....................................................................................................................... 30

FIGURES

Figure 1: Growth in Adult Internet Users by Region 1995-2005. ......................................................................2

Figure 2: Annual Aircraft Movements From Australian Airports in 1997-98....................................................5

Figure 3: Use of Personal Computers and the Internet Access in Australia [August 2000]. ..............................8

Figure 4: Characteristics of Purchases and Orders From the Internet, August 2000. .........................................9

Figure 5: Change in Distance and Number of Trips by Purpose in Sydney, 1991-1997. .................................10

Figure 6: International Visitors by Main Purpose of Journey to Australia, 1990-1999....................................12

Figure 7: Visitor Arrivals per State 1980 - 2000. .............................................................................................12

Figure 8: Distribution of Telecommunications Services in Eastern Victoria Central Highlands. ....................14

Figure 9: Australian Businesses with Computers, Internet and Web Sites/Home Pages, 1993 - 2000.............15

Figure 10: Percentages of Computer Use, Internet Access, Web Sites and Industry Types.............................16

Figure 11: Purposes of Internet Use by Industry Types. ..................................................................................16

Figure 12: Loaded Kilometres Travelled by Freight Vehicle Type per State of Registration. .........................17

Figure 13: Total Tonnes Carried by Freight Vehicle Type per State of Registration. ......................................17

Figure 14: Coastal Freight Loaded By State Compared By Year 1998 - 1999.................................................20

Figure 15: Telephone Services to Household Ratio to Estimate Regional Employment..................................24

Figure 16: Data Management System for Assessing e-Business Impacts on Transport. ..................................26

TABLES

Table 1: Australian Roads in 1996. ....................................................................................................................4

Table 2: Age Group by some Postcodes in New South Wales, Australia...........................................................6

Table 3: Purpose of Overnight Travel by Australians 1999. ............................................................................13

Table 4: Increase in Vehicles between 1995 and 1998 Based on Registration Figures....................................18

Table 5: Commodity Exports by Mode 1997-98. .............................................................................................20


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1 INTRODUCTION

1.1 Tracking Change

E-Business encompassing e-commerce between businesses [B2B] or from business to consumer

[B2C] and the use of electronic technology within business is already changing the way

business is conducted internationally, nationally, within states and at the local area level.

Moreover, the pace of change is increasing. The planning for transport infrastructure and

services needs to accommodate such change. A study of e-Business and the Australian

Transport System by Queensland University of Technology [QUT] and CSIRO, with the

support of Queensland Department of Public Works, for the National Transport Secretariat, is to

investigate:

• how will the transport task change;

• what will be affected; and

• how can the transport system respond?

To effectively answer any of these questions, we need processes for assessing the current state

of play, tracking changes and predicting future trends. To answer the third question effectively

the relative importance of changes and associated responses need to be addressed. All these

processes need appropriate data. This Working Paper is the fifth in a series reporting this study

and addresses issues of data and associated sources, which may be applied in ongoing

assessment of e-business impacts. We emphasise that time and resources were not available for

a complete study of all possible data sources.

The rapid rate of change makes even the task of assessing the present difficult. As can be seen

from Figure 1 showing expected growth of Internet use in different regions of the world, some

data will be out of date almost before it can be reported. Tracking change over time is

complicated, due to large changes in scope and scale of use. Predicting the future is even more

difficult in view of exponential change and the multiplicity of impacts. After a brief discussion

of the data requirement for forecasting in this section, we review some data available currently

in sections 2, 3 and 4, pointing to its sources and giving some example outputs. Section 5 then

considers the strengths and weakness of types of available data for tracking impacts of e-

business, noting particular data gaps and provides suggestions for compensating for this lack of

data including drawing on non-traditional sources of data. The paper concludes with some

recommendation on transport metadata, requirements for tracking e-business impacts and


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provides a figure showing possible processes for integrating data from multiple sources into

transport planning frameworks.

Figure 1: Growth in Adult Internet Users by Region 1995-2005.

[Source: Computer Industry Almanac, 2000]

1.2 Data Needs for Forecasting

Both the rapid speed of change and the changes in society due to the information revolution in

general, poses special challenges for forecasting the future of e-business and assessing its

impacts. The traditional processes of collecting information over time, describing a base case

and projecting of future trends, or assessing change scenarios, encounters problems in all stages.

Problems arise in comparing information year-to-year, where scope and scale change, with the

description of ‘the numbers of users and uses’ at a given point in time becoming rapidly

outdated. Projection of trends, which resemble exponential growth curves rather than a linear

trend, is difficult. Change scenario assessments are based on impacts of a particular change of

interest ‘if other things are unchanged’, yet the only certainty about the future at the moment is

that many things will change.

These challenges apply to studies of impacts on the economy in general, but are greater in

studies of impacts in specific industries, such as transport. More detailed information is required

and more detailed outputs are expected. Almost always such studies are looking for sufficiently

detailed insights on future conditions to allow plans to be put in place. This requires predictions

to be linked closely to time frames and specific characteristics of localities.


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2 AVAILABLE DATA AND SOURCES – STATIC/SEMI-STATIC

2.1 Infrastructure

While most data is time dependent, that which describes systems or situations that change

slowly over time may be considered almost static in comparison to other rapidly changing

items. For example, transport networks may remain virtually unchanged over a five to ten year

period, with changes being small adjustments for capacity or links. In contrast, while basic

telephone networks are in place across Australia, telecommunications networks are still

expanding services, so still need to be classified as varying.

Transport Networks

Network data adds connectivity, type of link and link capacity information to geographic

mapping data of roads and railway lines. The road authorities in each state hold complete

network data for their states often augmented by road condition data. For example, the

Transport Data Centre [TDC] from the New South Wales Department of Transport is now

conducting their fourth Household Travel Survey, compiling information on travel trips,

purpose, sources and destinations. The TDC is also collecting data for a Commercial Transport

Study [CTS] to update their Commercial Vehicle Survey [CVS] conducted in 1991/1992. The

expected outputs from this data are trip tables of commercial vehicle flows and available

regional commodity flows between industries to produce travel zone level trip tables that are

predictable [TDC, 2000].

Additionally, relevant transport authorities hold networks for future planning. Both commercial

providers of information and providers of emergency services hold urban networks information.

A larger number of organisations hold highways and major link networks. However, to the best

of our knowledge, there is not a detailed road network in use that covers all of the 813,769

kilometres of road open to general traffic across Australia [ABS, 2000f]. Table 1 from the

National Road Transport Commission showing the distribution of roads across Australia in 1996

shows that such a network would be a massive undertaking.


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NSW VIC QLD WA SA TAS NT ACT TOTAL

National highways 2,900 1,031 3,898 4,640 2,910 320 2,677 15 18,391

Rural arterials 30,162 18,536 18,438 15,616 8,690 2,540 2,837 20 96,839

Urban arterials 4,237 2,978 1,482 1,815 1,250 190 84 188 12,224

Rural locals 121,400 115,500 127,900 107,300 75,700 14,500 25,210 280 587,790

Urban locals 20,100 22,100 13,400 8,200 7,500 1,200 580 2,100 75,180

TOTAL 178,000 160,100 165,100 137,500 96,100 18,800 31,390 2,600 789,590

Table 1: Australian Roads in 1996.

[Source: NRTC, 1996]

Linking capacity and linking condition may be particularly relevant to assessment of the future

ability of the road or rail system to accommodate future e-commerce. This might be applied to

consideration of capacity of local roads for urban freight in cities or to measure highway or

railway capacity for inter-urban freight movements. A recent American study by Southworth

and Peterson [2000], has assessed the overall freight capacity across the network in North

America and identified potential bottlenecks.

Transport Facilities

These include both public and private facilities such as ports, airports and intermodal

interchanges. AVSTATS the Department of Transport and Regional Services’ database

[DOTRS, 1999] provides information about both airport location and current traffic. Figure 2

gives an example of the annual aircraft movements from Australian airports in 1997 to 1998.


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Figure 2: Annual Aircraft Movements From Australian Airports in 1997-98.

[Source: DOTRS, 1999]

Intermodal Road/Rail Terminals. Data available from rail/terminal operators on infrastructure

and networks includes static information on location and capacities as well as Operational Data

such as:

• tonnages in/out and number of containers in/out;

• train performance [on-time arrivals];

• availability of freight to customers [on-time performance];

• road truck movements in/out; and

• road truck turnaround times.

2.2 Socio-Demographics

It has been noted by numbers of authors, [for example, Stimson et al., 1999], that the structure

of Australian households is changing over time with a general ageing of the population and a

move to smaller households. However, such change is slow relative to the rate of change in e-

commerce uptake. Thus socio-demographic base data may be considered as static at least

across the five-year period between census collections.

ABS Census of Population and Housing

The Australian Bureau of Statistics Census of Population and Housing [ABS, 2000b] is carried

out every five years and is the only complete enumeration of the entire population of Australia.

Thus, apart from the value of the data in its own right, it provides the basis necessary for

expanding information collected in surveys by ABS and others to estimates for the entire


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population. Data is collected for households and individuals and the dwellings in which they

live.

The census data is available via the product CDATA96 [ABS, 1997] which includes mapping

facilities to link socio-demographic information to location. Table 2 shows a sample of

numbers of people within age groups for some postcodes in New South Wales, Australia.

PC 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 90plus2006 6 6 6 581 595 69 36 20 23 9 9 15 6 3 3 6 0 0 02007 98 77 60 171 570 433 309 222 223 176 129 94 65 72 56 36 12 11 32008 144 93 79 258 843 676 556 326 275 215 155 118 106 97 85 62 32 8 32009 93 69 77 215 792 731 449 280 218 175 123 99 52 57 61 39 17 11 32010 461 365 335 672 2403 3010 2341 1743 1294 1176 852 781 651 695 529 386 233 144 742011 416 333 346 691 2100 2982 2422 1813 1450 1276 1031 815 676 619 492 360 291 186 1352015 208 161 124 175 356 498 529 436 351 254 255 137 114 110 98 58 43 17 32016 429 381 305 463 1214 1270 1059 862 697 638 507 501 472 485 392 322 189 76 322017 302 349 327 360 379 408 368 398 380 372 275 358 318 314 305 212 138 83 242018 864 829 773 849 1057 1073 1132 1028 884 852 821 731 644 627 489 309 234 106 332019 456 434 355 465 477 550 594 577 493 525 441 419 288 315 267 204 154 58 272020 459 452 454 477 556 697 677 690 632 584 522 430 382 360 235 172 120 41 102021 619 461 421 616 1435 2360 1935 1392 1145 1213 955 740 542 441 386 296 258 118 532022 517 402 403 584 1076 1275 1071 878 705 714 540 420 348 316 346 283 201 79 282023 424 454 567 769 725 831 768 706 628 761 533 370 304 323 370 243 202 104 312024 577 500 519 551 755 1030 1098 983 779 793 580 457 407 382 386 353 349 252 1292025 261 270 270 340 571 835 751 563 483 596 512 410 281 283 272 213 159 102 36

Table 2: Age Group by some Postcodes in New South Wales, Australia.

[Source: Derived from CDATA96, Table.B03]

Figure 3 maps by postcode the numbers of households in Sydney with weekly incomes greater

than $2000. The areas of darker shading show numbers of households with higher incomes.

SydPOA96by 2000+

590 to 2,560 (68)330 to 590 (51)200 to 330 (63)

90 to 200 (66)0 to 90 (65)

Figure 3: Number of Households in Sydney Postcodes with Weekly Incomes $2000+.

[Source: Derived from CDATA96, table B23]


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2.3 Industry and Workplace Locations

We argued in Working Paper One of this series [Smith et al., 2000] that change to a knowledge

based economy or information age would result in changes to types of industry, occupations and

job locations. However, these changes will be slower than many other changes due to uptake of

e-commerce. Thus data gleaned from the census and other ABS special collections might be

considered as approximating to static over five- year periods.

Journey to Work Tables

Sets of special tables showing origins and destination of work trips by mode of travel together

with gender, occupations and industries of the workers are derived from the Journey to Work

[JTW] question on the ABS census [ABS, 2000b]. They are available from ABS and state

department of transport for capital cities and surrounding areas at travel zone level [larger than a

Census Collector District but generally smaller than a postcode area]. Apart from their use in

tracking work trips the destination information is also useful for mapping industry by location.

Most other information on workers and industries from the census is linked to home location.

Macquarie Park Mascot Chatswood

manufacturing19%

wholesale17%

retail15%

property/bus serv15%

education11%

other23%

manufacturing17%

transport/store57%

other26%

retail20%

finance13%

property/bus serv24%

other43%

Figure 4: Examples of Industry Distribution by Centres in Sydney From JTW Tables.

[Source: calculated from Sydney 1996 JTW tables]

2.4 Commodity Flows

In common with workplace and industrial locations considered above, commodity flows are

likely to change with a rise in e-commerce. It is possible these changes may occur within a

five-year framework. However, they are included in this section because even though change

may be rapid, data collection and update of Input/Output tables to reflect that change will be


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slow. Thus they are of value as baseline statistics rather than as a source of dynamic

information.

3 DATA AND SOURCES – DYNAMIC RE: PERSONAL ACTIVITY

Examples of available data for tracking activities, which may change rapidly within a five-year

period, have been divided for convenience into those relating to personal activity reported here

and those relating to business activity in the next section.

3.1 Personal Internet Usage

Data pertaining to personal Internet use is varied, dependent on the sources and relevant time

frame, due to the rapid explosion of personal computers and Internet access introduced into

Australian households. In the year prior to August 2000, the ABS [2000e] recorded an increase

from 48 to 54 percent of Australian households having personal computers. During this period

access to the Internet also increased from 23 to 34 percent of Australian households [ABS,

2000e]. Generally, computer ownership and Internet access is more predominant in

metropolitan areas than in regional areas.

In December 2000, Nielsen NetRatings [2001], estimated that Australian household accessed

the Internet for up to seven hours per month, visiting up to 14 Internet sites a month, for up to

28 minutes per site.

Figure 3: Use of Personal Computers and the Internet Access in Australia [August 2000].

[Source: ABS, 2000e]

0

10

20

30

40

50

60

70

80

90

100

Australia'spopulation

Households withcomputers

Urban householdswith computers

Regionalhouseholds with

computers

Households withInternet access

Urban householdswith Internet access

Regionalhouseholds withInternet access


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Figure 4: Characteristics of Purchases and Orders From the Internet, August 2000.

[Source: ABS, 2000e]

The most popular online purchases for the year ending in August 2000, were books and

magazines [27 percent] and music [also 27 percent]. Over half of the online purchases were

sourced from within Australia, 33 percent exclusively from overseas and 13 percent, a

combination of national and internationally sourced purchases. Eighty-five percent of all online

purchases were paid for online with credit cards [ABS, 2000e].

3.2 Making Personal Trips

Household Travel Surveys

The TDC from the New South Wales Department of Transport is now conducting the fourth

annual wave of their ‘continuous’ Household Travel Survey, compiling information on day-to-

day household travel trips in the Greater Sydney Metropolitan Region [GSMR]. Data collected

includes information on trip details, from origin to destination with travel times, duration and

distances, modes of transport, fare type, ticket cost, trip purpose and socio-demographics of the

survey participants. As the survey was designed to allow comparison with earlier surveys in

1981 and 1991, changes over 10 year periods can also be tracked. Figure 5 depicts the change

in distances travelled and the number of trips in relation to trip purpose from 1991 to 1997. The

largest increase for kilometres travelled are for personal business, social/recreational and then

passenger purposes, while the greatest increase in the amount of travel trips are for passenger

requirements, social/recreational and educational/childcare purposes [TDC, 1999]. Of particular

0

5

10

15

20

25

30

35Pr

opor

tion

of In

tern

et S

hopp

ers

[%]

1Characteristics of Goods/Services Purchased Online

Other Goods/Services

Music

Books/Magazines

Computer Software

Entertainment/Tickets

Computer hardware

Clothing/Shoes

Food/Groceries

Holidays


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interest in relation to e-commerce is a 16 percent rise in distance travelled for shopping trips is a

fourfold increase over the four percent rise in numbers of trips. The TDC data also show a

growth in multi-purpose trips, for example, shopping while dropping someone at a station.

Substituting such shopping with e-shopping deliveries would increase rather than decrease total

trips.

Figure 5: Change in Distance and Number of Trips by Purpose in Sydney, 1991-1997.

[Source: TDC, 1999]

Activity Surveys

Activity surveys differ from travel surveys in that they emphasise the activities undertaken at

different locations or the ‘stops’ made by persons surveyed over a day. Thus, they acquire

richer information about daily lifestyles than the simple trip purpose obtained by travel surveys.

The Victorian Activity and Travel Survey [1999] is conducted by the Transport Reseach Centre

[TRC] at the Royal Melbourne Institute of Technology [RMIT]. On average, about 5,000

households respond to the survey each year. Household details, personal details and the

activities undertaken at ‘stops’ during a day of travel are recorded. This yields a total of

approximately 12,000 person records and 50,000 stop records per year [TRC, 1999]. Since the

survey covers all 365 days of the year, both daily and seasonal variations in travel and activity

patterns can be observed. Figure 6 shows variation by time of day and season of the year in the

population of Bayside, a predominantly residential area along Port Philip Bay, 14 kilometres

south of the Melbourne CBD. Largely, people leave the residential area to work elsewhere

during the day.

0

5

10

15

20

25

30

35

Per

cen

t

Kms 17.7 16.8 13.2 16.2 12.7 32.4 25.8 25.1

Trips 14.2 16.1 4.5 20.1 3.8 10.7 22.4 25.9

Home Com mute Busines s Education/ childcare

Shopping Personal business

Socical/ Recreation

Serve passenger


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Figure 6: Seasonal and Hourly Population Variation of Bayside, Victoria.

[Source: Based on VATS data reported by Marquez et al. 2000]

3.3 Motor Vehicle Use

The ABS conducted a survey of motor vehicle use for the year ending in July 1999 [ABS,

2000d]. This is the second annual survey of this type, with information collated quarterly. For

the year ending in July 1999, 51 percent [70,267 million kilometres] of personal travel trips on

Australian roads were by passenger vehicle. Other passenger vehicle travel was for trips to and

from work [24 percent] and for business use [25 percent]. Vehicles registered in the Australian

Capital Territory and the Northern Territory recorded the highest average vehicle usage for

private purposes while vehicles registered in South Australia recording the lowest average

private purpose vehicle usage [ABS, 2000d].

3.4 Tourism Data

Inbound International Visitors

International Visitor Survey: The International Visitor Survey [IVS] draws on personal

interviews with 20,000 overseas visitors aged 15 or more, departing from major Australian

airports [Sydney, Melbourne, Brisbane, Cairns, Perth, Adelaide and Darwin] each year. It is the

most comprehensive source of information available on the travel patterns of visitors to

Australia. Summary information is published on a quarterly basis, while detailed results are

published for each calendar year. In addition, a large range of unpublished data is available on

request from the Bureau of Transport Research [BTR, 2000b].


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Figure 6: International Visitors by Main Purpose of Journey to Australia, 1990-1999.

[Source: BTR, 2000a]

Overseas Arrivals and Departures [OAD] is an ongoing monthly, quarterly and annual profile

of the characteristics of travellers to and from Australia from the ABS [ABS, 2000c]. In the

month of December 2000, there were 565,700 visitors arrive in Australia, which is a 23 percent

increase on the amount of visitors arriving in Australia the same month 1999. New South

Wales, Queensland and Victoria were the three most popular states visited.

Figure 7: Visitor Arrivals per State 1980 - 2000.

[Source: ABS, 2000c]

0

500000

1000000

1500000

2000000

2500000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

Num

ber o

f Int

erna

tiona

l Vis

itors

Holiday

Visitingrelatives

Other

Business

Convention


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Domestic Visitation

The National Visitor Survey [NVS] replaced BTR’s earlier Domestic Tourism Monitor in

January 1998. The NVS involves interviews with approximately 80,000 Australian residents

aged 15 years and over, each year. Domestic travel is by far the largest part of the Australian

tourism market, and the NVS provides details of the travel patterns and behaviour of Australian

residents. Details are obtained of all travel in Australia, including trips involving nights away

from home, as well as daytrips and trips by Australians overseas. Results from the survey are

published on a quarterly and calendar year basis, with a further range of unpublished data

available for detailed analysis from the Bureau of Transport Research [BTR, 2000c].

Holiday and leisure travel, business travel, visits to friends and relatives, and other types of

tourism are included. Previously unavailable data, such as domestic tourism expenditure and

activities on trips, as well as improved regional data, are now available from the NVS. Example

outputs are given in Table 3.

Table 3: Purpose of Overnight Travel by Australians 1999.

[Source: BTR, 2000d]

Accommodation

Survey of Tourist Accommodation [STA]: An ongoing monthly data collection, published

quarterly. It is a census of supply and levels of utilisation of selected tourist accommodation by

the ABS.

Aviation

International Air Transport: Data is for the annual and financial year, [plus some monthly data

available] for, air traffic to and from Australia with International passengers by operator,

operator market shares, airline passenger capacity and utilisation, International airport traffic

and aircraft movements [inbound and outbound], and city pairs [inbound and

outbound],[DOTRS, 1999].


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Air Transport Statistics - Domestic Airlines: Data available is similar information to

international collections but is concerning domestic carriers, for the annual and financial year,

with some monthly information available [DOTRS, 1999].

World Tourism

Yearbook of Tourism Statistics: The yearbook and other related publications provide ongoing

yearly visitor numbers and origins, plus accommodation used in selected countries [WTO,

2000].

4 DATA AND SOURCES – DYNAMIC RE: BUSINESS ACTIVITY

4.1 Telecommunications Networks and Facilities

Data on the distribution of telecommunication services can be obtained from Telstra’s White

Pages, Yellow Pages and other telecommunication organisations. Coverage is Australia-wide

since all businesses paying rentals on their telephone service are included. Each entry usually

includes the business name, street address, suburb/town, postcode, type of business, as well as

one or more ASIC industry codes [ASIC, 2000]. Where multiple entries for a business location

occur, the Telstra software usually identifies duplicates to avoid double counts.

Tele-services data is updated frequently allowing longitudinal studies of change to be made.

Moreover, since tele-services are listed at the level of the business location, the resolution of

employment distribution can be estimated down to the business address or PABX level.

Figure 8: Distribution of Telecommunications Services in Eastern Victoria Central Highlands.

[Source: McNamara et al., 2000b, based on Telstra data.]


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The use of mobile telephone information to track both personal and business activity is currently

being investigated in both Japan and America. As the mobile phone registers in a new cell, the

user is tracked. Apart from obvious concerns about privacy implications of such tracking,

accuracy of tracking also depends on cell size. However, if both these problems can be resolved

this may provide a rich source of dynamic data in the future. Especially as such phones and

other mobile devices are used for e-commerce transactions.

4.2 Business Internet Use

Figure 9: Australian Businesses with Computers, Internet and Web Sites/Home Pages, 1993 - 2000.

[Source: ABS 2000a]

The ABS has collected information in the financial years ending in 1994, 1998 and 2000 from

Australian businesses to ascertain their use of information technology. The survey, entitled

Business Use of Information Technology [ABS, 2000a] has evolved from collecting data on

computer use to measuring the extent and uses of Internet and web technologies. During this

period the growth of computer use in Australian businesses has expanded from 49 percent to 76

percent. Statistics collected for the financial periods ending in 1998 and 2000 reveal that the

proportion of businesses having access to the Internet has nearly doubled and the proportion of

Australian businesses with web sites or home pages has tripled [ABS, 2000a].

0

10

20

30

40

50

60

70

80

1993-1994 1997-1998 1999-2000

Years

Businesses with ComputersBusiness with Internet AccessBusiness with Web sites/Home pages


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Different categories of businesses using the Internet were surveyed to ascertain their Internet

business, comparing the extent of their Internet access [excluding e-mail and searching] and

their web page or home page prescience. The Mining, Electricity, Gas and Water Supply, and

Cultural and Recreational Services industry types all ranked highly in relation to their

computers use, Internet access and web presence [ABS, 2000a].

Figure 10: Percentages of Computer Use, Internet Access, Web Sites and Industry Types.

[Source: ABS, 2000a].

Communication Services rated the highest for Internet activities, for online selling related

activities and banking purposes. Health and Community Services rated the least for online

buying related activities and Mining, rated the least for online selling related activities.

Electricity, Gas and Water supply rated the highest while Cultural and Recreational Services

ranked the lowest for online buying related activities [ABS, 2000a].

Figure 11: Purposes of Internet Use by Industry Types.

[Source: ABS, 2000a]

0

10

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Min

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Man

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Industry Type

Perc

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Use ComputersHave Internet AccessHave Web Site/Home Page

0

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Sale Related ActivitiesBuying Related ActivitiesBanking ActivitiesE-mail/Information Searches Only


CSIRO & QUT Page 17

4.3 Freight/ Commercial Vehicle Activity

ABS Motor Vehicle Use: In the year preceding July 1999, loaded freight vehicles travelled

19,905 million kilometres within Australian and carried 1,421 million tonnes of goods. This

represents an increase of five percent of the area travelled and 11 percent of total tonnes carried

the previous year. Articulated and rigid trucks carried the largest proportion of freight [653 and

660 million tonnes respectively, with light commercial vehicles [LCV] carrying 107 million

tonnes of goods [ABS, 2000d].

Figure 12: Loaded Kilometres Travelled by Freight Vehicle Type per State of Registration.

[Source: ABS, 2000d]

Figure 13: Total Tonnes Carried by Freight Vehicle Type per State of Registration.


0

500

1000

1500

2000

2500

3000

3500

4000

NSW VIC QLD SA WA TAS NT ACT

Light commercial vehiclesRigid trucksArticulated trucks

0

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NSW VIC QLD SA WA TAS NT ACT

Rigid trucksArticulated trucksLight commercial vehicles


CSIRO & QUT Page 18

Registration Data: Vehicle registration and licensing databases show locations and numbers of

registered vehicle types by location. While they provide information on the passenger fleet they

are of particular value in the e-business context in tracking changes in numbers and types of

commercial vehicle and also in holders of commercial vehicle licences. Individual states and

territories hold these databases. In addition, the ABS also collects the data to report on an

Australia wide basis as shown in Table 4 below.

Year Passenger

Vehicles

[‘000]

Light

commercial

vehicles [‘000]

Trucks

[‘000]

Buses

[‘000]

Motor

Cycles

[‘000]

Total

[‘000]

1995 8,660.60 1,527.20 410.90 52.20 296.60 10,947.50

1996 9,021.50 1,601.60 415.40 58.80 303.90 11,401.20

1997 9,239.50 1,632.20 418.40 61.10 313.10 11,664.30

1998 9,560.60 1,686.40 426.90 64.00 328.80 12,066.70

Table 4: Increase in Vehicles between 1995 and 1998 Based on Registration Figures.


Interurban Freight

FreightInfo is an ACCESS based database [FreightInfo, 2000]. This database relies on data

supplied by companies which make up the entire supply chain. It provides, tonnage by

commodity and mode for each origin-destination pair. The database uses 134 zones and includes

freight flows to and from Australia, as well as those within the country.

Ad-Hoc and Industry Specific Freight Survey Data: Regional freight studies have been

undertaken on ad-hoc basis across Australia. For example, the Southern Cairns: Integrated

Land Use and Transport Study by Queensland Government [1999], gives an overview of current

trends in Far North Queensland. The study investigates future land use and identifies future

transport needs in order to develop an integrated strategy to meet likely needs [Queensland

Government 1999].

Specific Industry Studies: Studies to investigate demand for specific industries such as a series

of forestry, cattle, cotton and sugar studies in Queensland to highlight the specific needs and use

of roads by industry types.


CSIRO & QUT Page 19

Weigh Station Data: Road Authorities such as Main Roads in Queensland, The Roads and

Traffic Authority in New South Wales and VicRoads in Victoria collect weigh-in-motion data

on heavy vehicle characteristics such as vehicle type and weight per axle groups.

Urban Freight

Consideration of changes in the urban freight task is particularly important in estimating the

impacts of e-business on the road system and vice versa. For example, will e-commerce add to

congestion and equally what will be the impacts of congestion on e-commerce? Until recently

transport planners concentrated on commuter travel. Estimates of future demands were

modelled on passenger transport needs and the extra demands of freight were simply ‘loaded

onto the network’. However major freight studies have carried out for example:

SEQ/Brisbane metropolitan freight study, includes roadside interview survey data, with details

such as mode, commodity, origin, and destination.

Cordon Counts in all cities and Automated Vehicle Counts: measure numbers of vehicles,

vehicle type, or number of axles, speed, by time of day and are routinely collected by road

authorities in all states.

Commercial Vehicle Study: There is now growing realisation in view of the increasing

importance of commercial traffic and the need to estimate traffic impacts across the day and not

just at commuter peak periods. In the context of development to a new travel model for Sydney

the New South Wales TDC are carrying out a study of commercial traffic. It is expected that the

study, in line with their household travel study, would provide ongoing data to monitor

commercial vehicle traffic growth [TDC, 2000].

4.4 Air and Sea Freight State Trade Statistics for each port are held by state transport departments. These normally will

normally include tonnage by commodity flows through each port.

Databases of Freight are maintained by the BTE. For example, these databases hold particulars

of freight loaded and discharged by coastal vessels at Australian ports [BTE, 2000a]. In general,

the data is unpublished and reported via BTE publications. The following figure is an example

of the sort of information available from such sources.


CSIRO & QUT Page 20

Figure 14: Coastal Freight Loaded By State Compared By Year 1998 - 1999.

[Source: BTE, 2000a]

In a similar manner databases for airfreight are held. Some examples are discussed below in the

context of exports

4.5 Market Data

Exports: Cargo Statistics

As can be seen from Table 5 while the sea freight carries a far higher tonnage of freight,

airfreight is of relatively greater value.

Table 5: Commodity Exports by Mode 1997-98.

[Source: BTE, 2000b]

Moreover, while the BTE [2000b] reports that Australia exported 64 different commodity

groups by air in 1998 based on the two digit commodity group codes of the ABS. Vegetables

and fruit, at 81.8 thousand tonnes, comprised 27 percent of the total exports and together with


CSIRO & QUT Page 21

the next two largest commodity groups, fish, crustaceans and molluscs and meat and meat

preparations account for just under half [47 percent] of the total weight of air freight exports.

In terms of the value of exports, gold and non monetary make up 36 percent of the total [$7.6

billion] with the next clear category being office machines and automatic data processing

[ADP] equipment 6.6 percent [$1.4 billion].

E-commerce may increase market access for the produce fish and meat. As this makes up the

largest volume of exports, hence, data for monitoring airfreight is particularly important in

considering capacity bottlenecks. In contrast, the export dollars won from an increase in sale of

ADP equipment will be sufficiently important to want to ensure the transport of such goods is

expedited. While outside the scope of this paper considering data sources, we note that the BTE

paper from which this information is drawn considers supply and demand balances in airfreight

to Asia.

5 DATA LIMITATIONS AND SOLUTIONS

5.1 Data Gaps

The diverse data sets reviewed above have one element in common. They are all secondary data

sets collected for other purposes. Thus in line with all secondary data sets, they are planned to

meet other requirements. Thus, it is not surprising that the special data requirements for

tracking e-business impacts on transport are not met.

Road freight flow data is particularly limited. Most data on freight vehicles has been collected

for regulatory purposes and thus while types of vehicles and mass/volumes carried are recorded,

there has been less interest in tracking the type of goods carried. Commodity flows are

measured for economic reasons. But as noted, input-output tables are held at levels of spatial

aggregation far too course for predicting traffic movements. For example, an entire city can fall

within a single commodity flow zone. Data about commercial vehicle movements related to

services rather than freight are even less detailed.

To measure changes to transport demand due to e-business, data about network flows of specific

goods and services is needed. For example, expectation of increased deliveries in suburban

streets can only be tested if such trips are measured. Likewise, increases can only be attributed

to e-business/e-commerce if a causal link to provision of those goods or services can be

established. This requires detailed identification of services and goods delivered. It is also clear

that e-business has significant potential to change the mix as well as the amount of goods and


CSIRO & QUT Page 22

services delivered. Planning by both private industry and government authorities require this

information for infrastructure planning that can range from appropriate loading bays though

modal interchanges to highways.

The other freight modes, air, rail and sea, collect somewhat better data on goods carried.

However, in general, the level of data is better for bulk commodities that are less subject to e-

business change, such as coal or wheat. In other cases, both privacy and commercial

considerations make abstraction of data, from, for example bills of lading, impractical.

Passenger Travel Data: In general better data is available to track travel by people. Peak hour

traffic due to travel-to-work has traditionally been measured to plan transport infrastructure and

services for cities. This type of network flow data is now being expanded and enhanced as

interest grows in both applying transport demand management to limit increasing traffic and in

the overall environmental impacts of travel. Studies such as the Victorian Activity and Travel

Survey described in section 3.2 can provide a sample of activity for strategic modelling.

Individual in-depth studies target local area shopping. However, the detailed level of activity

data needed to track shopping trends or leisure activities and relate them to specific localities is

not available.

Tourism Data Collections: have targeted accommodation in terms of visitor nights. Although

these collections provide quite good estimates of visits to popular destinations but are less

valuable for destinations ‘off the beaten track’. These sampling limitations mean that increases

in such tourism due to e-commerce marketing will not be picked up. Similarly tourism travel by

air is better measured than tourism travel by car. The increases in car tourism to regional areas

may thus be missed. Moreover, an emphasis on accommodation may miss the impact of

passing traffic using services from meals to specific tourism attractions.

Timely Data: Most of the data sets described above are collected at intervals. Often complexity

and detail of the data set is inversely proportional to the frequency of collection. For example,

the ABS Census of Population and Housing is collected at five-year intervals, the ABS Survey

of Motor Vehicle Use is annual and some trade figures are available quarterly. The entire

Internet revolution has occurred between the 1996 and 2001 census. Thus, annual changes in

both the scope and scale of e-business impacts have been too large to use annual collections to

assess trends.

While e-business technologies themselves have resulted in continuous data collection by

devices from e-tag readers for tolling to bus ticketing machines, this data has not yet been


CSIRO & QUT Page 23

systematically applied for wide scale assembly of data for tracking e-business impacts beyond

their specific applications.

5.2 Collection of Appropriate Data

When secondary data proves unsatisfactory the traditional response is to recommend specific

purpose collections, but, this is yet another situation related to impacts of e-business where

traditional solutions do not apply.

Two major data requirements for assessing impacts are:

• frequent update of data, in line with rapid change ;and

• broad spatial coverage to allow for differences across cities regions and the nation.

The expense of special purpose collections with such a temporal and spatial scope could not be

justified. It would be both more effective and efficient to:

• seek modifications to existing data collections to target necessary information;

• make better use of data collected continuously by electronic devices; and

• explore the use of multiple and complementary sources of data to build information.

None of these solutions is straightforward.

Modifying Existing Collections: There are always problems in adding questions to existing

survey instruments. While both the TDC in New South Wales and the TRC in Victoria are

including questions regarding Internet use in their updated surveys, the degree of detail that can

be requested is limited within a total travel survey. Even the NOIE specific surveys of Internet

use face limitations in distinguishing amounts and types of use. For example, a common

solution in internet-use surveys, both here and overseas, is to group shopping that results in

purchases with Internet ‘window shopping’. However, a clear distinction is needed for

assessing impacts on transport and further information about whether that purchase replaced a

store trip and, if so, the location of that store. Further, the answers to such questions would need

to be held individually for each purpose rather than aggregated purposes.

Another difficulty applies when obtaining Internet information in any survey, which extends

over time. It is reasonable to assume household shopping patterns will be not vary significantly

over the time between the survey of the first and last households in a travel survey, but, this may

not hold where internet opportunities vary rapidly over time. However the value in having

internet use questions linked with travel data would justify special efforts in question design and

sampling.


CSIRO & QUT Page 24

Collecting extra information in commercial vehicle use collection also faces a range of

difficulties, including issues of commercially sensitive and proprietary information. However,

at least in some collections there is loss of detail in data processing rather than collection.

Types of commodities carried are grouped for convenience because of sample size constraints.

Further investigation may show that more detail in some categories of freight might be obtained

from existing samples.

Better Use of Electronic Data: E-business systems are generating masses of data daily. In

numbers of cases the massive volume of data seems to defy use for other than its primary

purpose. However, an important resource is being lost.

In addition to continuous collections devices directly related to transport itself, in intelligent

transport systems and logistics track and trace applications, there are a range of relevant data

collected by business systems that update change as it happens. Concerns about privacy and

commercial sensitivity in use of such data sets will need to be addressed via access controls and

reporting allowed. Figure 15 shows an example of the use of up-to-date data sources, in this

case, telephone connections as a proxy for concentrations of employment. Census journey-to-

work data, where available, gives better estimates but can only be used to track change at five-

yearly intervals.

Figure 15: Telephone Services to Household Ratio to Estimate Regional Employment.

[Source: McNamara et al., 2000]


CSIRO & QUT Page 25

Data Fusion: However, the most useful approach would involve a mix of the methods above

and, particularly, innovative approaches to the use of data from multiple sources. This could

involve combination of transport data such as commercial operators information on vehicle

schedules and road authorities information on traffic for improved intelligent transport systems

applications. However a wider range of business data such as Internet purchase information

would also be relevant. It is usual to stress the confidentiality concerns in use of such sources,

but equally, or perhaps even more important, are the needs:

• firstly to ensure the data can be extracted in the manner needed with minimal cost and

disruption to the holder; and

• secondly to demonstrate some advantages, beyond payment in most cases, for the

organisations providing data.

The difficulties are not trivial but not in-surmountable. Improved standard of data

interoperability will be required. Data models will be self-describing, allowing them to be

transferred readily between systems. Numbers of e-business technologies such as intelligent

agents to automatically source and repackage information as required will help. Unfortunately

there is no ‘technology’ to convince businesses to share data. They will need to see direct

returns in terms of better transport infrastructure and services.

5.3 A Systematic Approach to Data Shortfalls

We have found there is insufficient data available to accurately track the current impacts of e-

business and transport. However, there is sufficient data and experience to see that changes are

occurring. Transport supply and demand management require a capability to go beyond mere

tracking of change, to prediction of change, and to an understanding of the factors driving

change.

In particular data must be:

• up-to-date and comparable over time,

• geographically specific to allow for locational diversity; and

• have sufficient contextual information for analysis of impacts.

No one central data collection could satisfy these requirements. Figure 16 is a schematic

impression of the sort of data system needed.


CSIRO & QUT Page 26

Figure 16: Data Management System for Assessing e-Business Impacts on Transport.

An up-to-date meta-data set holds information about available data and access processes. The

data is then sourced as needed, with provision of payment to the holder, where appropriate, and

then prepared for analysis. New web based access systems mean that such systems are now

theoretically viable. A system of this type would be very helpful for both industry and

government in successful handling of the transport impacts of e-business.

META-DATABASE

Information about allData sets relevant to

E-business

REQUEST

for data

WEB BASEDCOLLECTION

Applicationto bring datafrom owners

DATA

At many different

sites PAYMENT

if needed

REQUIRED DATA

for E-business ImpactAssessment

REQUESTS for extra data collection


CSIRO & QUT Page 27

BIBLIOGRAPHY AND REFERENCES

ABS. [1997]. CDATA96 [CD Rom]. Australian Bureau of Statistics, Canberra, Australia.

ABS. [2000a]. Business Use of Information Technology, Australia, 8129.0. Australian Bureau of Statistics, Canberra, Australia.

ABS. [2000b]. How Australia Takes a Census, 2903.0. Australian Bureau of Statistics, Canberra, Australia.

ABS. [2000c]. Overseas Arrivals and Departures, December 2000, 3401.0. Australian Bureau of Statistics, Canberra, Australia.

ABS. [2000c]. Survey of Motor Vehicle Use 9208.0. Australian Bureau of Statistics, Canberra, Australia.

ABS. [2000d]. Survey of Motor Vehicle Use, 12 months ended 31 July 1999, 9208.0. Australian Bureau of Statistics, Canberra, Australia.

ABS. [2000e]. Use of the Internet by Householders, August 2000, 8147.0. Australian Bureau of Statistics, Canberra, Australia.

ABS. [2000e]. Use of the Internet by Householders, August 2000, 8147.0. Australian Bureau of Statistics, Canberra, Australia.

ABS. [2000f]. Australian Bureau of Statistics: Annual Report 1999-2000. Australian Bureau of Statistics, Canberra, Australia.

ASIC. [2000]. Australian Securities and Investments Commission - About the industry codes of practice. Australian Securities and Investments Commission, Australia. Accessed January 2001. Available at: http://fido.asic.gov.au/index.cfm-id=7EED7A8E-F8A8-11D3-A4D2009027DE39A4&Method=Full&story=yes.htm#1

BTE. [2000a]. Coastal Freight in Australia Bureau of Transport Economics Working Paper 46. Bureau of Transport Economics, Australia.

BTE. [2000b]. The Supply of Airfreight Capacity to Asian Markets Bureau of Transport Economics Working Paper 42. Bureau of Transport Economics, Australia.

BTR. [2000a]. Bureau of Transport Research International Visitors by Main Purpose of Journey, 1990 – 1999. Accessed January 2001. Available at: http://www.btr.gov.au/statistics/Datacard/dcpurp.html

BTR. [2000b]. Bureau of Transport Research International Visitor Survey. Accessed January 2001. Available at: http://www.btr.gov.au/info_Svcs/ivs/ivs.cfm

BTR. [2000c]. Bureau of Transport Research National Visitor Survey. Accessed January 2001. Available at: http://www.btr.gov.au/info_Svcs/nvs/nvs.cfm

BTR. [2000d]. Bureau of Transport Research Overnight Travel by Australians 1999. Accessed January 2001. Available at: http://www.btr.gov.au/statistics/Datacard/overnight.html


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Computer Industry Almanac. [2000]. Computer Industry Almanac, 8th Edition. Computer Industry Almanac, Inc., Arlington Heights, IL. Available at: http://www.c-i-a.com

DOTRS. [1999]. AVSTATS. Department of Transport and Regional Services, Canberra. Available at http://www.dot.gov.au/aviation/avstats/

FreightInfo. [2000]. FreightInfo database. FDF Management Pty Ltd, East Melbourne, Victoria. Accessedd January 2001. Available at: http://www.ozemail.com.au/~fdfmgt/index.html

Marquez L, Smith N, Trinidad G and Guo J. [2000]. Seasonality and Population Exposure: A Case Study of Melbourne, Proceedings of the 9th Conference of the International Association of Travel Behaviour Research, Surfers Paradise, Australia, July, 2000.

McNamara C, Marquez L, Smith N and Trinidad G. [2000]. Tele-Density and Employment Distribution, Proceedings of the 22nd Conference of Australian Institutes of Transport Research, Canberra, Australia, December 6-8, 2000.

Neilsen NetRatings. [2001]. Internet Usage Statistics for the Month of December 2000, Australia. Available at: http://209.249.142.57/au/web/NRpublicreports.usagemonthly

NOIE. [2000]. The Current State of Play, November 2000. National Office for the Information Economy, Canberra, Australia. Also available at: http://www.noie.gov.au/projects/information_economy/ecommerce_analysis/ie_stats/StateofPlayNov2000/index.htm

NRTC. [1996]. National Road Transport Commission, Melbourne, Victoria.

Queensland Government. [1999]. Southern Cairns: Integrated Land Use and Transport Study. Queensland Government. Available at: http://www.nqonline.com.au/southerncairns/

Smith N, Ferreira L and Mead E. [2000]. Working Paper 1 – Introducing the Transport Impacts of E-Business Project. QUT/CSIRO, Australia.

Southworth F and Peterson B. [2000]. Intermodal and International Freight network modelling, Transportation Research Part C: Emerging Technologies, v8, Isssue1-6 Feb- Dec pp 147-166.

Stimson R, Mullins P, Baum S, Jenkins O, Gum K and Davis R. [1999]. A Decade of Change: A Social Atlas of Brisbane and the South East Queensland Region. Australian Housing and Urban Research Institute, Queensland University of Technology, Australia.

TDC. [1999]. Household Travel Survey. Transport Data Centre. Department of Transport. New South Wales. Accessed January 2001. Available at: http://www.transport.nsw.gov.au/tdc/tdc.html

TDC. [2000]. TDC News, Transport Data for Sydney. Transport Data Centre, New South Wales Department of Transport.

TRC. [1997]. The VATS Process. Transport Research Centre, Royal Melbourne Institute of Technology, Victoria, Australia.


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TRC. [1999]. Victorian Activity and Travel Survey [VATS]. Royal Melbourne Institute of Technology, Transport Research Centre, Melbourne, Victoria. Available at: http://www.trc.rmit.edu.au/Data_Products/VATS/vats.html

WTO. [2000]. Yearbook of Tourism Statistics. World Tourism Organisation. Accessed January 2001. Available at: http://www.world-tourism.org/omt/wtich.htm


CSIRO & QUT Page 30

GLOSSARY

ABS – Australian Bureau of Statistics.

Articulated truck – is a motor vehicle constructed primarily for load carrying such as a prime mover [with no significant load carrying area] with the capacity to be attached to a semi trailer.

ASIC - Australian Securities and Investments Commission.

ATC – Australian Transport Council.

B2B – Business-to-Business; online transactions conducted between companies.

B2C - Business-to-Consumer; online transactions, conducted between a company and individual consumer.

BTR – Bureau of Transport Research.

CSIRO – Commonwealth Scientific Institute and Research Organisation.

CTS – Commercial Transport Study.

CVS – Commercial Vehicle Study.

DOTRS – Department of Transport and Regional Services.

e-Business – All business transactions conducted on the Internet.

e-Commerce – All transactions involved with the trade of goods and/or services, and payment conducted over the Internet.

GSMR – Greater Sydney Metropolitan Region.

GVM – Gross Vehicle Mass is the tare weight of a vehicle pus its maximum carrying capacity.

i-Commerce – Internet Commerce; commerce transactions conducted over the Internet.

IVS - International Visitors Survey.

JTW – Journey to Work.

LCV - Light Commercial Vehicles – Motor vehicles constructed to carry goods and are less or up to 3.5 tonnes GVM.

m-Commerce – Mobile Commerce; commerce transactions conducted over devices such as mobile phones.

NOIE – National Office for the Information Economy.

NTS – National Transport Secretariat.

NVS – National Visitor Survey.


CSIRO & QUT Page 31

OAD – Overseas Arrivals and Departures.

PABX – Private Automatic Branch Exchange.

QUT – Queensland University of Technology.

Rigid Truck – is a motor vehicle with a load carrying area that exceeds 3.5 tonnes GVM.

RMIT – Royal Melbourne Institute of Technology.

STA – Survey of Tourist Accommodation.

Tare weight - is the unloaded weight of a vehicle.

TDC-Transport Data Centre.

Tonnes carried – is the total weight of goods/freight carried from a source to a destination.

TRB – Transportation Research Board.

TRC – Transport Research Centre.

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