i

University of Southern Queensland

Faculty of Health, Engineering and Sciences

THE IMPACT OF COMPACT URBAN FORM ON THE PROVISION OF PUBLIC

ROAD TRANSPORT

A dissertation submitted by

Martin Franz Stager

in fulfilment of the requirements of

Courses ENG4111 and ENG4112 Research Project

towards the degree of

Bachelor of Engineering (Civil)

Submitted: 30th October, 2014

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Abstract

With an ever increasing global urban population, cities around the world are

searching for urban planning schemes that will aid them in accommodating this

future growth whilst limiting boundary expansion. This along with the increased

pressure from resource prices, climate change and the consumption of surrounding

productive rural areas has led to Compact Urban Form becoming part of many city

planning schemes. Planning for efficient transportation networks within the cities has

also become essential for a city becoming more sustainable. Feelings are mixed to

whether Compact Urban Form will actually reduce the reliance on automobiles and

help to produce a self-sufficient public bus system.

This dissertation aimed to investigate the effects on public bus patronage if compact

urban form was implemented within a single city suburb statistical area. The

available literature was reviewed and a justifiable method of modelling modal choice

for transport was selected. A case study statistical area of Chermside was selected

within the Brisbane City Council area. The statistics for the boundary area of

Brisbane City were collected from several sources and collated. A distribution model

was created utilizing this data. Trial employment and residential densities were

applied to the study area to identify whether the public bus patronage can be

modelled to either increase or reduce. A Modal Choice model was attempted without

success.

Through the mixed method research it was found that there is a link between

employment and residential densities and public bus patronage. The assessment of

the data indicated that variables such as household income and average travel

distances of Brisbane city residents do follow the trends found in other cities from

other research. It was concluded that the simplified method of modelling could show

favorable results for increasing densities based on journey to work figures. This

conclusion however is limited by the available collected data.

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University of Southern Queensland

Faculty of Health, Engineering & Sciences

ENG4111/ENG4112 Research Project

Limitations of Use

The Council of the University of Southern Queensland, its Faculty of Health,

Engineering & Sciences, and the staff of the University of Southern Queensland, do

not accept any responsibility for the truth, accuracy or completeness of material

contained within or associated with this dissertation.

Persons using all or any part of this material do so at their own risk, and not at the

risk of the Council of the University of Southern Queensland, its Faculty of Health,

Engineering & Sciences or the staff of the University of Southern Queensland.

This dissertation reports an educational exercise and has no purpose or validity

beyond this exercise. The sole purpose of the course pair entitled “Research Project”

is to contribute to the overall education within the student’s chosen degree program.

This document, the associated hardware, software, drawings, and other material set

out in the associated appendices should not be used for any other purpose: if they are

so used, it is entirely at the risk of the user.

Dean

Faculty of Health, Engineering & Sciences

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Certification of Dissertation

I certify that the ideas, designs and experimental work, results, analysis and conclusions set out in this dissertation are entirely my own effort, except where otherwise indicated and acknowledged. I further certify that the work is original and has not been previously submitted for assessment in any other course or institution, except where specifically stated.

Martin Franz Stager

Student Number: 0019820581 _______________________

Signature

30/10/2014

Date

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Acknowledgements

I like to acknowledge a number of people and departments for their contributions in

aiding me with completing this dissertation:

• My three supervisors Ms Marita Basson, Mrs Paula Grant and Dr Nateque

Mahmood for their advice and support throughout the development of this

project.

• USQ staff, especially Annette Nanka for her patience and help arranging

organisational access to required data from the different government

departments.

• Mr Jaco van den Berg, Mr Kerneels Olivier and the Policy and Planning

Branch of the Department of Transport and Main Roads for their time and

effort in making the BSTM__MM available and Mr Olivier’s time in

attempting to convert it into a format that I was able to use.

• Australian Bureau of Statistics for providing access to their extended

organisational census database and providing, over the phone, ‘Tablebuilder’

training.

• INRO, for the use of the trail version of EMME 4 modeling program.

Lastly, I would like to thank my entire family for their understanding and support

over the last eight years. Especially my wife, Meike, and our two daughters, Klara

and Eva for their support, patience and sacrifices they have made so I could complete

this degree. I owe you all so much.

Martin Stager

University of Southern Queensland

October, 2014

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Table of Contents

Abstract ............................................................................................................................... ii

Limitations of Use .............................................................................................................. iii

Certification of Dissertation ............................................................................................... iv

Acknowledgements ............................................................................................................. v

Table of Contents ............................................................................................................... vi

List of Figures .................................................................................................................. viii

List of Tables....................................................................................................................... x

Glossary of Terms .............................................................................................................. xi

1.0 Introduction .......................................................................................................... 1

1.1 Background ........................................................................................................ 3

1.2 Project Aims ....................................................................................................... 4

1.3 Objectives ........................................................................................................... 5

1.4 Project Justification ............................................................................................ 5

1.5 Project Methodology ........................................................................................ 10

2.0 Literature Review ............................................................................................... 13

2.1 Introduction ...................................................................................................... 13

2.2 Definitions ........................................................................................................ 13

2.2.1 Urban Forms ............................................................................................. 13 2.2.2 Urban Sprawl ............................................................................................ 14 2.2.3 Compact Urban Form ................................................................................ 17 2.2.4 Transit Oriented Development .................................................................. 20 2.2.5 Public Road Transport............................................................................... 22

2.3 Economic benefits for transport with CUF ...................................................... 23

2.4 Environmental benefits for transport with CUF ............................................... 24

2.5 Social benefits for transport with CUF ............................................................. 25

2.6 Planning ............................................................................................................ 26

2.7 Transport Modelling ......................................................................................... 28

2.8 Conclusion ........................................................................................................ 31

3.0 Compact Urban Form and Transport Trends ..................................................... 32

3.1 Bogotà, Colombia ............................................................................................. 32

3.2 Curitiba, Brazil ................................................................................................. 34

3.3 Hong Kong, China ............................................................................................ 36

3.4 London, England .............................................................................................. 37

3.5 Conclusion ........................................................................................................ 38

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4.0 Case Study – Chermside ..................................................................................... 39

4.1 Introduction ...................................................................................................... 39

4.2 Study area ......................................................................................................... 39

4.3 Methodology .................................................................................................... 43

4.4 Data Analysis ................................................................................................... 49

4.4.1 ‘Journey to Work’ Trip Figures ................................................................ 49 4.4.2 Population ................................................................................................. 50 4.4.3 Employment .............................................................................................. 52 4.4.4 Demographics ........................................................................................... 55 4.4.5 Transport Modes ....................................................................................... 56 4.4.6 Future employment and population projection ......................................... 60 4.4.7 Analysis Results ........................................................................................ 62

5.0 Recommended Framework ................................................................................. 63

6.0 Conclusion .................................................................................................................. 64

6.1 Limitations ........................................................................................................ 64

6.2 Future Research ................................................................................................ 65

6.3 Conclusions ...................................................................................................... 66

7.0 References ................................................................................................................... 67

Appendix A ....................................................................................................................... 77

Appendix B ....................................................................................................................... 79

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List of Figures

Figure 1.1 Percentage of urban population 1980 6

Figure 1.2 Percentage of urban population 2011 7

Figure 1.3 Percentage of urban population 2025 8

Figure 2.1 Aerial view of Sprawl 15

Figure 2.2 Required components of CUF 17

Figure 2.3 Varying forms of CUF within self-boundary 18

Figure 2.4 Stages of density 19

Figure 2.5 Artistic impression of TOD 20

Figure 2.6 TOD Precinct plan 21

Figure 2.7 City densities compared to CO2 emissions 24

Figure 2.8 BSTM_MM coverage area 29

Figure 2.9 Planning Strategies 30

Figure 3.1 View over Bogota with Bus Rapid Busway 32

Figure 3.2 Bogota – Modal Split 33

Figure 3.3 Bogota – Modal Split 33

Figure 3.4 View over Curitiba 34

Figure 3.5 Curitiba – Rapid Bus Transit 35

Figure 3.6 Curitiba – Modal Split 35

Figure 3.7 View over Hong Kong 36

Figure 3.8 Hong Kong – Modal Split 37

Figure 3.9 London Bus 37

Figure 4.1 Levels of Statistical Areas 39

Figure 4.2 Map of Brisbane SA4 Areas 40

Figure 4.3 Map of Brisbane North (302) SA3 Area 41

Figure 4.4 Map of Chermside (30202) SA2 Area 42

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Figure 4.5 Map outlining Chermside (colour) 43

Figure 4.6 Modal Split of Brisbane (6 modes) 57

Figure 4.7 Modal Split of Brisbane (3 modes) 57

Figure 4.8 Modal Split Chermside (6 modes) 58

Figure 4.9 Modal Split Chermside (3 modes) 58

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List of Tables

Table 4.1 Modal Matrix 45

Table 4.2 SA4 areas with nominated centroid 46

Table 4.3 Trip distribution of Journey to Work 49

Table 4.4 Population of Chermside 50

Table 4.5 Percentage of population compared to residential density 51

Table 4.6 Population of suburb compared to average annual income 51

Table 4.7 Employment figures for Chermside 53

Table 4.8 Trip distribution Journey to Work 53

Table 4.9 Trip distribution of Journey to Work 54

Table 4.10 Trip distribution of Journey to Work 55

Table 4.11 Trip distance of Journey to Work v density 56

Table 4.12 Household Demographics 59

Table 4.13 Work from home compared to residential density 60

Table 4.14 Work from home percentage compared to r. density 61

Table 4.15 Modal choice vs residential density 62

Table 4.16 Trip distribution of Journey to Work 62

Table 4.17 Summary Table 63

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Glossary of Terms

ABS Australian Bureau of Statistics

BCC Brisbane City Council

BITRE Bureau of Infrastructure, Transport and Regional Economics

BRT Bus Rapid Transit

BSTM__MM Brisbane Strategic Transport Model – Multi Modal

CBD Central Business District

CUF Compact Urban Form

DTMR Department of Transport and Main Roads

ED Employment Decentralisation

EMME name of modelling software designed by INRO

HK Hong Kong

INRO name of transport modelling software company

PT Public Transport

PRT Public Road Transport

QLD Queensland

SPA Sustainable Planning Act 2009

SEQ South East Queensland

SEQRP South East Queensland Region Plan 2009-2031

TL Translink Transit Authority

TOD Transit Oriented Development

UF Urban Forms

UN United Nations

UPT Urban Public Transport

USA United States of America

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1.0 Introduction

Urban Form (UF) is the term given to the built living environments of our cities and

towns. UF affects every one of us, either directly in where we live or indirectly in

how our global environment functions (Michelson 1966; Williams (ed) 2005; Jenks

& Burgess 2000). UF governs the maintenance and distribution costs of basic

services provided by municipalities such as water, electricity and waste (Williams

(ed) 2005). UF therefore controls the pricing of transportation and distribution of

labour and goods (Ottensmann 1977) which is then passed onto the final consumer.

This project will assess whether Compact Urban Form (CUF) will have a negative or

positive effect on Public Road Transport (PRT) and whether the population densities

of a suburb can be altered to make PRT more viable.

The term CUF refers to a compact, mixed use urban environment with moderate to

high population densities. The planning of the CUF focuses on reducing the reliance

on automobiles through providing and promoting alternative modes of transport such

as active and public transport. In recent decades this approach has been applied by

cities around the world as a method of achieving a ‘sustainable’ urban form with an

ever increasing urban population (Dock 2003; Jenks & Burgess 2000; Williams (ed)

2005) with some debate (Williams (ed) 2005).

Since the days of the Industrial Revolution, people have made the migratory path

from the country side to urbanised areas in search of employment. These original

industrial cities of Europe were planned and built for employees to live within

walking distance of their places of work. As the industries expanded over time a

greater volume of labour was required. This led to overcrowding, lack of services

and spread of disease throughout these densely populated areas. (Au-Yeung,

Yigitcanlar & Mayere 2009). Currently thirty-three percent of world’s population

lives within urban areas. Trends indicate that this will rise to sixty-six percent by

2030 (UNHabitat 2012). This requires adequate planning to contain the urban

boundaries whilst maintaining or gaining a good standard of living for the cities

inhabitants.

Post World War II Australia also followed the United States of America’s (USA)

lead in urban design (Brown 1981, Shay & Khattak 2012). USA’s model of

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automobile dependency has been copied by many first and almost all ‘Third World’

countries (Brown 1981). The ‘Australian Dream’ of the affordable quarter acre

blocks with comfortable homes and room for a car or two has provided us with our

current urban landscape (Moran 2006). ‘Progress’ would have been the term happily

used for the expansion of the outer city suburbs and not ‘Blight’ or ‘Sprawl’. As can

be seen in most new residential suburbs throughout Australia with oversized blocks

of land and minimum density, the ‘Australian Dream’ continues today (Moran 2006).

Easy access to cheap transportation has led to high percentage of the developed

world’s population owning personal automobiles (Mackett & Edwards 1997, Brown

1981). This fact has shaped our economic system, the places where we live and how

we live in them (Brown 1981, Williams ed. 2005). By having a faster and more

flexible means of transport, individual households are provided with a greater range

of choices of employment, shopping and leisure (Mackett & Edwards 1997, Shay &

Khattak 2012). This results in Public Transport (PT) not being able to respond to

changing market needs and therefore has a negative effect on the services for all PT

patrons (Hensher 1998). With a gradual decrease in patronage, PT providers are left

with the loss in market share and the option of reducing the total available services

and/or increasing fares to cover the running costs (Hensher 1998). It is the remaining

patrons that feel the effects and are then often forced into sourcing other means of

transport. This may even be purchasing an automobile. This then becomes a ‘vicious

circle’ and places more pressure on the already weakened PT system (Hensher 1998).

Due to Public Road Transport (PRT) having the majority share of trips over other PT

modes and the largest city coverage in the South East Queensland (SEQ) area

(Department of Transport and Main Roads 2012), the resulting effects are felt

throughout the entire community.

This chapter includes the following sections:

• Background – supporting details on research topic

• Project Aims – states the broad aims of the research

• Objectives – outlines of goals of research

• Project Justification – provides supporting details of need for research

• Project Methodology – outlines the original and revised procedure

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1.1 Background

Brisbane is one of the fastest-growing mature cities in the world (Brisbane City

Council 2014). With many businesses making Brisbane their global headquarters, it

is also set to be one of the world’s most prosperous cities (Brisbane City Council

2014). City governing departments have realised they must market themselves on

the global stage. Cities need to be a place where people would visit, work and live or

where they are willing to do business or invest. Creating a greener city image

through implementing planning schemes that promote sustainable travel options, is

the direction Brisbane is taking to cover the market needs (Brisbane City Council

2014).

The Bureau of Infrastructure, Transport and Regional Economics (BITRE)(2013) has

recently estimated that the demand for Urban Public Transport (UPT) will grow by

thirty-three percent over the 20 years from 2011 to 2031 (BITRE 2013). This

positive growth will be experienced by both Brisbane and Perth. Planners within

SEQ have understood the importance of planning an efficient UPT network. This can

be seen by the early implementation of the Bus Rapid Transit (BRT) in 2000

(Department of Transport and Main Roads 2014). BRT is a form of public mass

transport that offers the dependable quality of a light rail system with the flexibility

of buses. This consists of segregated and dedicated transit corridors with rail like

boarding ‘platforms’ to aid in fast transitions in loading and unloading of bus

passengers (Translink Transit Authority 2012).

Urban Sprawl (US) is the expansion of city or town boundaries with low density

development (Miller & Spoolman 2009, G18). Australian city councils understand

and agree that US is no longer a viable method of accommodating a growing

population. To help reduce the city expansion, Brisbane City Council (BCC) (2012)

has stated that 138,000 of the 156,000 projected new dwellings will be allocated to

infill or redevelopment projects (Brisbane City Council 2012). With an increase in

density there will be additional demand placed on all services especially transport

(Brisbane City Council 2012).

Transport planning within SEQ utilises data collected from several means. The first

being the Australian census conducted by the Australian Bureau of Statistics (ABS)

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every five years. The most recent of these was conducted in 2011. Future censuses

will include a broader range of questions to cover trip distribution and trip generation

across a broader demographic (ABS 2013). These questions will cover similar topics

to the second source of information, being surveys conducted by Translink Transit

Authority (TL) (ABS 2013; Translink Transit Authority 2010; Translink Transit

Authority 2011). The Department of Transport and Main Roads are able to utilising

both of these sources of information to complete a range of transport models and as a

method of calibration of transport models which aid in assessing public transport

demand.

Public Road Transport (PRT) is term coined for this report. It is the combination of

Public Transport (PT) and Road Transport (RT). For this project PRT will be

exclusively related to public buses operated by Translink. TL is a division of the

Department of Transport and Main Roads and is responsible for all of the PRT

systems throughout Queensland. This includes the Brisbane City bus service which is

the focus of this research (Translink Transit Authority 2014).

1.2 Project Aims

This project aimed to assess the demand that urban sprawl places on public road

transport systems within the growing city of Brisbane. The economic, environmental

and social benefits that a successful public bus network and compact urban form can

bring to a city and in-turn a nation will be outlined. These benefits will provide the

justification of a transport and suburb planning model that will be built from data

from the Brisbane statistical area of Chermside. In doing this, it will determine

whether compact urban form enables a more effective provision of those services or

adds to the traffic congestion on an individual statistical level. This transport model

will be utilised to show how future planning can have a beneficial effect on the

public and the economy.

5

1.3 Objectives

The specific objectives of the project include:

• Undertake a comparison of demographics within case study boundaries.

• Create a quantitative transport model utilising the existing data from the case

study area utilising the statistical area boundaries.

• Conduct modelling under future population and employment predictions for

the case study area.

• Analyse resulting figures for effects of an increase in the population and

employment that is isolated to the case study area only.

• Conduct trial modelling with increasing compactness to oscillate the point

where the case study area is compact enough to produce a declining trend in

public road transport usage (Utilising BSTM_MM therefore not achieved, see

Section 1.5).

• Assess whether the found population and employment densities are viable to

be implemented in the case study area.

1.4 Project Justification

Current projected population figures indicate that today’s world population of 7.2

billion (Worldometers 2014; Geohive 2014) will peak at 9.22 billion by the year

2075 (Economic & Social Affairs – Population Division 2004). Whilst the total

population in some countries such as European countries like Germany and Demark

is set to decrease, the total population in Australia is predicted to rise. (Economic &

Social Affairs – Population Division 2004)

Migration from rural to urban environments has been around since the days of the

Romans and continues still today especially in developing countries (UNHabitat

2012). Over the next 16 years the urban population will increase from 33 percent to

66 percent of the world’s total (UNHabitat 2012). Australia is already one of the

most urbanised countries in the World (ABS 2012). In 1980 Australia was one of a

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small amount of countries that had a population that was over 75 percent urbanised

(Economic and Social Affairs – Population Division 2012). A visual representation

of the World’s urban populations in 1980 can be seen in Figure 1.1.

Figure 1.1 Past - Percentage of urban population and agglomerations by size class 1980

indicating that Australia was one of only a handful of countries with greater than 75 percent

of the population living in an urban environment (Economic and Social Affairs – Population

Division 2012)

In the thirty-one years between 1980 and 2011 the percentage of world’s urban

population has increased. When the 2011 figures (see Figure 1.2) are compared with

the 1980 figure there is an increase in countries located in Europe, North Africa,

Middle East, North and South America are now over 75 percent urbanized. What is

also noticeable is the birth of over 20 mega cities (>10 million residents)

predominantly in Asia during this period (Economic and Social Affairs – Population

Division 2012, Keivani 2010).

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Figure 1.2 Present - Percentage of urban population and agglomerations by size class 2011.

There has been a large increase in urbanization since 1980 with most of North and South

America now having greater than 75 percent urbanized as a country. However the greatest

difference is the birth of over 20 mega cities with populations greater than 10 million.

(Economic and Social Affairs – Population Division 2012)

Predictions show our urbanised population shall continue to increase (UNHabitat

2012). The Economic and Social Affairs – Population Division has predicted that the

world’s global urban population percentages in 2025 will reach the level shown in

Figure 1.3. Russia, Northern and Eastern European countries are included in the top

urban populated counties. One other point to note is the number of mega cities

predicted for India while the urban percentage of the country is still less than 50

percent. Left to run its course, this concentration of people in such small areas will

have flow-on effects in the future (UNHabitat 2012).

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Figure 1.3 Future - Percentage of urban population and agglomerations by size class 2025.

The UN has predicted a massive increase in urbanization since 1980 which now includes

Russia, some Northern and Eastern European countries. The total number of megacities will

increase throughout the India-Asia area (Economic and Social Affairs – Population Division

2012)

As Brookes et al. have stated, it would be easy to point the finger and draw attention

to the environmental impacts produced by high growth rate cities in the developing

world. Brookes et al. also points out that this is far from the truth as it is the

developed world consuming resources at a disproportionate rate thus leading to

major global impact (Brookes et al 1996). One of the largest consumption of

resources in western cities is land. Urban Sprawl is the fringe expansion of cities and

town with low-density development (Miller & Spoolman 2009) that consumes

sometimes productive land. CUF has been said to halt sprawl and improve the

liveability of the urban environment (Nueman 2005; Dock 2003; Jenks & Burgess

2000; Williams (ed) 2005). This aligns with the United Nations definition of a

prosperous city.

Along with economic growth and stability, the United Nations define a city as

prosperous when it:

• Provides adequate infrastructure – water, sanitation, roads, information and

communication technology in order to improve urban living and enhance

productivity, mobility and connectivity

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• Enhances the use of public spaces in order to increase community cohesion,

civic identity, and guarantees the safety and security of lives and property

• Ensures the equitable distribution and redistribution of the benefits of a

prosperous city, reduces poverty and the incidence of slums, protects the

rights of minority and vulnerable groups, enhances gender equality, and

ensures civic participation in the social, political and cultural spheres

• Values the protection of the urban environment and natural assets while

ensuring growth, and seeking ways to use energy more efficiently, minimize

pressure on surrounding land and natural resources, minimize environmental

losses by generating creative solutions to enhance the quality of the

environment

(UNHabitat 2012 Table 1.1 p14)

Planning of public transport system in cities around the world has been

predominately focussed on increasing patronage numbers to relieve traffic

congestion (Mackett & Edwards 1997) in utilising public transport (Mackett &

Edwards 1997; Williams (ed) 2005). It has been found that an increase in efficiency

and occupancy in public transport can lead to resurgence in private automobile

transport (Mackett & Edwards 1997). But wouldn’t it be of more benefit to a city to

actually reduce the necessity to use private transport while maintaining or even

reducing the operatable level of public transport?

Can Compact Urban Form (CUF) deliver the desired result? Understanding the

issues, modelling our possibilities, planning the solutions to benefit all parties (Batts

2011), providing public awareness (Anable, Lane & Kelay 2006) and moving

quickly (Lerner 2007) are the steps to produce a working solution. Promoting

sustainability is a requirement contained within Engineers Australia’s Code of

Ethics. It is therefore justifiable that this combined focus of urban planning and

transport engineering is undertaken (Engineers Australia 2010).

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1.5 Project Methodology

This section contains a description of the methodology that was originally developed

as part of the initial plan and also the revised final method required to meet the

project goals. The method has been divided into six stages.

Stage one was the review of literature from around the world on urban sprawl,

compact urban form, sprawl, transport oriented development and the impacts of

different urban forms on modelling and planning public road transport. It also

outlines the benefits that a compact urban form and successful transport systems may

bring to a growing city.

The next stage was to research current trends in urban planning densities involving

public transport from around the world. This focussed on example cities that have set

the benchmarks in combining integrated transport systems with densely populated

living areas to overcome the pressure that urban sprawl places on services. The cities

contained in this research are Bogotà (Colombia), Curitiba (Brazil), Hong Kong

(China) and London (England). This section also includes a review of the densities in

South East Queensland, specifically to cover the boundaries in the case study area.

The Brisbane suburb of Chermside has been selected as the case study area due its

location in relationship to the city centre and the current focus on becoming a future

transport node within Brisbane’s RBT network. The planning of TODs and UPT

within the suburb of Chermside has also become a resent hot topic of debate between

planners and the general public (Brisbane City Council 2013). A summary of the

findings and conclusion completes this section of the report.

Collection of secondary data from the case study area was the third stage of the

project. Part of the data collected was a compilation of surveyed Australian Bureau

of Statistics (ABS) census data. The ABS data included:-

• Mode of Transport to Work (MTW)

• Place of Work (POW)

• Place of Usual Residence (PUR)

• Full Time or Part Time employment (FTE)(PTE)

• Number of job places within the suburb (EMP)

11

• Number of residents in suburb (POP)

• Journey to Work (JTW)

• Area (km²) of study area

Most of this secondary data was compiled in Table Builder Pro within the ABS data

resource website. This was extracted at the Statistical Area Level 2 (SA2) which is

based on the officially gazetted suburbs (ABS 2011b). The information that could not

be automatically extracted was manually input into a database. The SA2 level was

selected as it provided enough detail for the purpose of this study. Further support for

this selection is contained in Section 4.2 of this document.

The assembling of the projected population and employment figures was the next

step in this section of the report. A clear analysis was required to compare the

sources of the population projections so that a valid figure of growth could be

applied to the model that was attempted in the next stage.

Stage 4 of the project was modelling. The preferred method of modelling was to

utilise the existing modelling methods of Policy and Planning Department from the

Department of Main Roads and Transport in Brisbane. Access to the model was

arranged with an agreement between USQ and DMRT. It was a drawn out process

and a copy of the Brisbane Strategic Transport Model – Multi Modal (BSTM_MM)

was finally received in mid-September. To operate the BSTMM__MM the

appropriate software was required. Due to the project budget, a trial version of the

EMME software from INRO was arranged to run the model. Contrary to the advice

from the manufacturer, this version did not have sufficient capacity to operate the

BSTM_MM. An older version of EMME was uncovered at the University at the end

of September. This too had insufficient capacity to operate the model. The

limitations of the EMME trial are based mainly on the number of nodes and links of

the model. A series of attempts were made to reduce the size of the model by

selecting an area within these limits that included Chermside whilst retaining the

active links to the surrounding area. This was a time consuming process. The

alternative approach was for DTMR to complete the modelling. Due to project time

constraints and DMRT’s capacity, the decision to drop this approach was made. In an

attempt to provide results for the project, it was decided to construct a model of the

study area utilising the selected information from stage 3 would be made. The model

12

selected was to be as close to the BSTM__MM as possible. Therefore it was decided

that a four-step transport model would be undertaken. As the results required were

the modal choice figures, the final step being traffic assignment would not be

required.

A gravity distribution model was completed as Step 2 for the existing and future

density figures. Step 3 of the model was creating the modal split. This was attempted

by extracting the equations and coefficients from the BSTM__MM. This was not

successfully achieved. More detail is contained in Chapter 4 of this document. To

utilise the already collected data, a comparison between all statistical areas within the

boundary was devised. Travel times and distances between suburbs and the Central

Business District (CBD) were extracted from Google maps. The analysis

incorporated household demographics, travel times and physical data.

The 5th stage was to entail utilising the working model with the projected future

population figures to project demand on PRT. Different scenarios were to be tested

by the model to examine the effects that density and workplaces availability can have

on a residential/multi-use suburb. The base requirements of population and

employment were to be increased in search of a saturation point of PTR patronage.

The resulting data was to be arranged in an employment/residential density versus

PRT, private and active transport figures graph to find whether there is a point where

the private vehicle and PRT both follow downward trends. The corresponding

density values were to be compared to those prescribed in the suburb planning

scheme. This stage was not completed due lack of modelling tools.

The final stage of project specification compliance was to recommend a framework

for optimal urban built form patterns that provided the most efficient and effective

PRT service for the Chermside area. This was completed utilizing the research and

data analysis. The single statistical area modelling method was then reviewed to

whether it can be utilised as a quick study into PRT patronage.

13

2.0 Literature Review

2.1 Introduction

The previous chapter provided justification of the importance in planning city spatial

structures. This chapter provides definitions for the components of Urban Form

planning relating to Public Road Transport as it is defined in the relevant literature

from reputable sources from around the world. This was completed by using web

based search engines, utilising databases and library registers. A combinations of the

terms and synonyms of ‘urban forms’, ‘urban sprawl’, ‘compact urban form’,

‘transit oriented development’, ‘public road transport’, ‘economical benefits’,

‘environmental benefits’, ‘planning’ and ‘transport modelling’ were used.

This chapter includes the following sections:

• Definitions – Urban Forms, Urban Sprawl, Compact Urban Form, Transit

Oriented Development and Public Road Transport.

• Economic benefits – defines economic benefits from review.

• Environmental benefits – defines environmental benefits from review.

• Social benefits – defines social benefits from review.

• Planning – discuss planning and requirements.

• Transport Modelling – discusses options of modelling.

• Conclusion

2.2 Definitions

2.2.1 Urban Forms

Urban Form (UF) can be described as the ‘general pattern of building height and

development intensity’ within an urban environment (Envicom Corporation 1995,

ch.5 par. 2). This definition can be expanded to state that it is the spatial pattern of

14

human activities (Anderson et al. citied in Tsai 2005) at a particular point in time

(Tsai 2005). South East Queensland Regional Plan 2009 - 2031(2009) describes the

UFs of Brisbane as ‘high-density apartments, well-established ‘timber and tin’

suburbs and new communities’ (Hinchcliffe 2009, p. 17).

UF can be classified on different levels such as metropolitan, city, suburb and

neighbourhood (Tsai 2005). Cities are becoming more aware that many of our

current UFs are not sustainable (Au_Yeung, Yigitcanlar & Mayere 2009; Hickman,

et al. 2010, Brueckner 2000). Expanding city footprints, caused by an increasing

population, places pressure on our natural and communal resources (Hickman, et al.

2010). Current city fringe UFs will not sustain these additional pressures without

some modification (Hickman, et al. 2010). Most local governments have come to

realize this in recent years and have formed planning and management schemes.

These schemes target planning for more sustainable new city suburbs and to remodel

some of the existing problem areas (Au_Yeung, Yigitcanlar & Mayere 2009).

The types of UF that will be considered and discussed in this project are Urban

Sprawl, Compact Urban Form and Transit Oriented Development.

2.2.2 Urban Sprawl

Urban Sprawl (US) is the ‘growth of low-density development on the edge of cities or

towns’ (Miller & Spoolman 2009, G18). The expansion of city boundaries through

this type of urban form has provided us with new spatial terms such as satellite town,

edge city or exurb (Neuman 2005, Brueckner 2000). Tsai (2005) states that there a

lack of agreement when it comes to the definition of US. Three types of spatial-

structure-based phenomena of US can also be defined. These are ‘scattered

development’, being low density decentralized sprawl. ‘Commercial strip

development’, being concentrated to main arterials. ‘Leapfrog development’ being

new developed areas disconnected from the city boundaries (Tsai 2005; Hickman et

al. 2010; Ewing cited in Tsai 2005, Malpezzi & Guo in Tsai 2005).

The fact is that US is more menacing than the simple description leads us to believe.

US causes the ‘formation of an environmentally dangerous, socially divided city

15

where economic and ecological disparities reinforced each other, with differential

effects on particular social groups’ (Forsyth 1997 p54, Brueckner 2000) therefore

making public transport less effective (Lowe 2014; Economist Intelligence Unit

2011). See Figure 2.1 for an aerial view of what an example of US looks like from

above. With the low density housing, lack of communal spaces, long curved streets,

ring roads and single arterials, this form of road network easily increases simple trip

journey times.

Figure 2.1 An aerial view of a housing subdivision in Nevada, USA provides a good

example of the patterns typical sprawl type urban planning makes on urban landscapes. The

long curved blocks, cul-de-sacs, ring roads and single arterials increase the journey time

within and out of the suburb (Gielen 2010).

Through the literature it became more evident that the relationship between sprawl

and the automobile is a ‘vicious circle’ (Neuman 2005, Lowe 2014). Lowe (2014)

stated that Australia has become car dependant due to the urban planning methods

leading to sprawl (Lowe 2014) and yet it was the desire of owning a car in the quest

for a better lifestyle that necessitated the need of sprawl (Forsyth 1997).

We are not the only developed country with this problem. Australia followed the

same path as America’s single land use, auto-oriented development model (Dock

2003) with its social values and physical nature (Siksna 2006). Social polarization

had become evident and as Ismail (2013) has stated, Australian’s cities are and will

be more prone than Western European and North American cities to be affected by

16

income and then social segregation (Ismail 2013). Australian followers of expansion,

state that sprawl provided Australia with an egalitarian society because the working

class were able to obtain the highest quality of housing for a reduced price due to

land on the outer suburbs being cheaper (Forsyth 1997). One of the reasons the land

in these new developments was so cheap, was the fact that residents were not

confronted by the true full costs that a better planned development would have

required disregarding future costs (Ottensmann 1977).

Developers regarded the limited access to employment and services as only a short-

term problem. However in most cases, no future allowance for suburb remodelling

was ever made (Forsyth 1997, Ottensmann 1977). This was seen as a trade-off for

the volume and quality of housing being provided (Forsyth 1997). Even in 1977,

Ottensmann (1997) wrote that there should be an allowance of land reserved for

future development within the new growth areas. This would be used for intensive

uses, such as higher density residential or commercial requirements. Thus creating a

more efficient pattern of growth that could be expanded later (Ottensmann 1977).

Through the early 1990’s many parties in government came to realize the cost of

non-‘future proof’ growth caused by poorly planned US (Forsyth 1997).

In the public’s eye, sprawl is to blame for the reputation that cities take up too much

space and are encroaching excessively on agricultural land (Brueckner 2000,

Neuman 2005, Ewing 2008). This has come about due to the fact that developers

were able to acquire farming land at a fraction of the cost of residential land. This

indicates that we do not value our means of food production as much as we value our

residential suburbs and lifestyles (Brueckner 2000). Brueckner (2000) also states that

‘excessive’ should be included in the definition of US. There is no escaping the fact

that cities may need to grow spatially. It is how that future growth is controlled is the

key to a sustainable city (Brueckner 2000).

Moran (2006) found the consensus was that city centres are seen as noisy, dirty, lack

services, lack space and are not family friendly areas full of temporary residents such

as students (Moran 2006). Therefore US had always been seen to be suited for the

nuclear family (Forsyth 1997). But can a city centre be both productive and attractive

17

for the whole community? The next section will define the solution of Compact

Urban Form.

2.2.3 Compact Urban Form

Compact Urban Form (CUF) can be described as compact mixed use urban

environment with moderate to high population density with the focus on reducing

reliance on automobiles through providing self and public transport options. The mix

of uses includes residential, employment, shopping and recreation (Dock 2003; Jenks

& Burgess 2000; Williams (ed) 2005). Public open spaces, schools and cultural

facilities are also required to be allowed for during the planning of these dense

developments (see Figure 2.2) (Chakrabarti 2013; Williams (ed) 2005). CUF has

been seen as a viable alternative to sprawl for some time (Dock 2003; Jenks &

Burgess 2000; Williams (ed) 2005). However there is still mixed sentiment

(Neuman, 2005).

Figure 2.2 Required components of Compact Urban Form (Chakrabarti 2013).

There is an ongoing debate on the true definition of CUF. Most researches define it

with different names (Chakrabarti 2013). CUF can be known as Compact Cities

18

(CC), Healthy Communities (HC), Smart Growth (SG), New Urbanism (NU) and on

a lesser scale, Transit-Oriented Development (TOD) (Neuman 2005; Newman &

Kenworthy 2006). TOD will be explained in the subsequent chapter.

The compact city model has been heavily based on the densely urbanised historical

city centres of Europe (Williams (ed) 2005). These early cities based their security

on the confines of a city wall (Moran 2006). To avoid the taxes that came with the

protection, settlements started to sprawl outside of the safe confines. Their safety was

the price that these settlers were willing to pay for their space (Moran 2006). The

thought of applying CUF to our cities came to rise since the 1970’s. At this time we

faced an energy and growth crisis. Australia came to realise too that our world and

resources were actually finite (Dock 2003).

Chakrabarti (2013) points out that the perception of compact cities is often negative.

This is due to the merits being misunderstood and the confusion in how to apply the

best built density (Chakrabarti 2013). An example of the difference in spatial layout

can be seen in the three examples in Figure 2.3. Each having the same density

however arranged to be either horizontal, a combination of horizontal - vertical and

intense vertical.

Figure 2.3 Varying forms of CUF within a set boundary (Chakrabarti 2013).

19

Neuman (2005) stated that CUF is not necessary and that attempting to make a city

sustainable in all areas by using urban for strategies is ‘counterproductive’ (Neuman

2005 p 23). Throughout Neuman’s (2005) study the argument against CUF was

based on many projects taking the name of CUF being focused on residential

requirements only. This is does not align with the defined description. Also he makes

a point stating that there are no open spaces and crowding. However as can be seen

in Chakrabarti’s (2013) example (Figure 2.4) of levels of density, open spaces,

employment opportunities and transport networks can be achieved with the correct

spatial planning strategies.

Figure 2.4 Stages of densities in favor of Hyperdensity (Chakrabarti 2013).

20

2.2.4 Transit Oriented Development

Transit Oriented Development (TOD) is becoming a common method of planning for

both city planners and developers alike (Batts 2011; Burke, Li & Dodson 2011;

Wheeldon 2011). Muley, Bunker & Ferreira (2007) define TOD as a ‘moderate or

high density development, located within an easy walk of a major transit stop’

(Muley, Bunker & Ferreira 2007). Lund states that it usually includes ‘a mix of

residential, employment and shopping opportunities designed for pedestrians,

without necessarily excluding the car’ (Lund 2006). Therefore with these

descriptions it can be simplified by describing TOD as a small scale CUF planned

with focus being close to a major transport node.

Similar to CUF, planners hope that TOD’s will encourage walking, cycling and the

use of public transport along with revitalising neighbourhoods and providing new

housing possibilities (Lund 2006; Muley, Bunker & Ferreira 2007; Wheeldon 2011).

Surveys conducted have also found that the residents moving to these areas are

expecting the same goals (Lund 2006). The marketing for private investors is

targeting this desire (Wheeldon 2011).

The Queensland Government has focused its policies

on utilising TOD to accommodate a large part of

SEQ’s rapid growth since including it in the regional

plan in 2005 (Batts 2011, Hinchchliffe 2009, Burke,

Li & Dodson 2011). As Burke, Li and Dodson

pointed out there have been few true residential TODs

built in Australian cities prior to their 2011 study

(Burke, Li & Dodson 2011). Therefore it is hard to

gauge TOD’s actual effectiveness in increasing Public

Transport (PT) patronage. They also raised the

question of the effectiveness of TOD compared to

Employment Decentralisation (ED) within the urban

landscape (Burke, Li & Dodson 2011).

Figure 2.5 Artistic impression of ‘Milton Apartments TOD (Wheeldon 2011).

21

As TOD centres around and along Public Transport (PT) nodes, a lot of planning is

required. For these areas to be successful it requires the consensus between the

government agencies, the private sector and the community (Batts 2011). In the past

most of the private sector has focused on delivering single use and single-site

developments (Batts 2011) and haven’t been exposed to the planning and integration

demands that a mixed used site requires (Batts 2011).

The first of Brisbane’s true TODs under the South East Queensland Region Plan

2009 – 2031 is being constructed in the suburb of Milton, Brisbane, starting with

‘The Milton Apartments’ (Wheeldon 2011; FKP 2011). Through the Milton Precinct

Plan development process it found that impacts on amenities are easier managed the

larger the site becomes (AECOM 2014). Following true to the principles of TOD, the

plan also states to reduce the amount of available car parking spaces, whilst

providing communal areas and additional developer contributions going to the

railway station upgrade and public art (Queensland Government 2010). Figure 2.5

presents an artistic impression of one of the planned buildings ‘Milton Apartments’

and Figure 2.6 shows the full precinct.

Figure 2.6 TOD Precinct plan of Milton, Brisbane (AECOM 2014)

22

2.2.5 Public Road Transport

Public Road Transport is the term used in this study to describe the Public Bus

System. With the appropriate regulations, organization and capital investment, PRT

or Bus Rapid Transit (BRT) systems have the capabilities to handle a large volume

of patrons and more cost effective in comparison to other methods (Department of

Transport and Main Roads 2012d; Department of Transport and Main Roads 2012).

BRT also has the potential of offering the greatest reductions in CO2 emissions

(Vincent & Callaghan Jerram 2006).

Brisbane has been active in implementing BRT busways since the opening of the

South-East Busway in 2000 to coincide with the Sydney Olympic Games

(Department of Transport and Main Roads, 2014). Since this initial construction,

Brisbane has expanded its busway network by constructing the Eastern, Inner

Northern and Northern Busways. The Northern Busway currently terminates at

Kedron. At this point in time, the department is revising cost-effective and

deliverable option assessments for the long-term planning of extending the Northern

Busway from Kedron to Bracken Ridge. This will be contained within the Gympie

Road corridor that can be seen on Figure 4.5 (Department of Transport and Main

Roads 2014). This would include servicing the suburb of Chermside with rapid

transit linking it to the rest of Brisbane (Department of Transport and Main Roads

2012d).

Dissatisfaction with the service provider is the highest cause of patrons not using the

public transport systems (McMahon 2013). Patrons are able to forgive unforeseen

delays caused by emergencies, mechanical problems or even traffic. However delays

caused by other events are often blamed directly on the provider. These could vary

from incorrect real-time information to long wait times at transfer stops (McMahon

2013).

23

2.3 Economic benefits for transport with CUF

In the last Queensland State budget the total funding allocated to public transport

(bus, rail and ferry) subsides in SEQ for the 2013-14 year was $ 1.0328 billion. This

was an increase of $ 14.5 million from the previous year. It equates to 19.9% of the

total state concession budget. (Queensland Treasury and Trade, table 1 p 5 & 27

2013)

The current Queensland government has promised to increase the fares by only 7.5%

during the next year instead of the 15% annual increase nominated by the former

government. There is also the introduced ‘free’ tenth trip for gocard users after nine

trips within the week. The reduction in public fare costs combined with a free tenth

trip for gocard users equates to an estimated $6.40 cost of concessions (Queensland

Treasury and Trade 2013). Twenty six percent of the costs of providing the services

will be covered by the collected passenger fares (Queensland Treasury and Trade

2013). Therefore with a decrease in required services it would provide a reduction in

government funding dependency and the benefits can be either passed onto the

passenger directly or indirectly through the funding applied in other areas or

expansion of existing services.

More than one in four Australians were obese in 2011/12 (Heart Foundation 2012).

Even though this is outside to scope of the report, the costs that obesity places on the

Australia society must be considered. In 2008 it was estimated that obesity cost

Australia $58.2 billion (Australian Bureau of Statistics 2011a). The major component

of these costs was comprised of $49.9 billion for the burden of disease, disability,

loss of well-being and premature death caused by health problems. These problems

include cardiovascular disease, high blood pressure and Type 2 diabetes. Making up

the balance is the $8.3 billion in financial costs from short and long term

employment impacts ($3.6 billion), heath care system costs ($2 billion) and carer

costs ($1.9 billion)(Australian Bureau of Statistics 2011a). There are many factors

that influence obesity. For example a diet of processed and high energy foods and

our sedentary or ‘Couch potato’ lifestyles are putting us into the top five of obese

nations around the world (Australian Bureau of Statistics 2011a; Lowe 2014)

24

Public and private businesses will benefit from an efficient transport network and a

healthy workforce. Lost time from sickness and stress related recovery should

decrease and productivity should increase within the workable hours due to a

reduced daily load.

2.4 Environmental benefits for transport with CUF

Even though Lowe (2014) has been previously quoted in regards to health benefits

surrounding active modes of transport, Lowe’s (2014) paper also focussed on the co-

benefits that a policy promoting active or public transport can provide (Lowe 2014).

With the proper policies in place to apply CUF, thus promoting active and public

transport, the cities have the ability to:

• Reduce land clearing

• Incorporate green belts into planning cellular forms

• Reduced fuel consumption and emissions through less, shorter and quicker

trip journeys.

Jenks and Burgess (2000) stated that contrary to western models the effects to the

environment are caused by the high densities of a city like Hong Kong. They then go

on to state that benefits can be gained but to achieve them requires finding the

optimal level of compaction (Jenks & Burgess 2000). It is finding this point that

cities can maximise the advantages whilst minimising the disadvantages will lead to

a more sustainable urban form.

It is becoming clearer that a buffer is required between our new and existing

urbanization and water. This benefits the health of the vegetation, animals and

human population (Bosselmann 2008; Hinchcliffe 2009). These would include

providing reserves along natural water drainage paths that include drains, creeks,

rivers and the foreshores of bays and estuaries (Bosselmann 2008; Forsyth 1997,

Hinchcliffe 2009). This entails breaking up the continuous urbanization into pockets

of urban settlement. (Bosselmann 2008)

25

With an increase in density brings a reduction in emissions (Haas et. al 2010). A

visual presentation to support this claim can be seen in Chakrabarti (2013) where a

graph originally generated by Haas et al. was modified to include an additional

model. Figure 2.7 shows the typical American suburb densities plotted against three

selected cities being Stockholm, Olso and Barcelona. It also includes the Hyperdense

city model. As can be seen Chakrabarti (2013) and Haas et al. (2010) drew the

conclusion that a denser population produces less carbon dioxide emission

(Chakrabarti 2013; Haas et al. 2010).

Figure 2.7 City densities compared to carbon dioxide emissions (Chakrabarti 2013).

2.5 Social benefits for transport with CUF

There are many social benefits that the implementation of CUF can bring to a suburb

or community. Some of these are as follows:

• Reduced social separation compared to current fringe suburb areas.

• CUF’s ability to support a mix of ages.

• Shopping, working and living locally promotes community

• Public transport promotes social interaction.

• Self-transport promotes social interaction.

26

(Forsyth 1997, Brueckner 2000, Hinchcliffe 2009 SEQRP 2009-2031 Cl. Cl 6.1-

Social Planning, Cl 6.2-Addressing Disadvantage, Cl 10.8 Social Infrastructure, Cl

12.2 Sustainable travel and improved accessibility)

Along with the physical health benefits described in the economic benefit section of

this report, it has been found that physical exercise can provide mental health

benefits (Martinsen 1990). As the journey to work is part of the daily grind, Weston,

Gray, Qu & Stanton found the trend that the more hours worked leads to lower job

satisfaction that then has flow-on effects to a family’s well-being (Weston, Gray, Qu

& Stanton 2004).The community wellbeing is also impacted upon.

Most people would answer that they do not have time for regular sport or exercise

(Australian Bureau of Statistics 2007). No time for exercise, time to cook proper

meals or time to relax. This is not from our working hours increasing as trends show

that the average total hours have been declining since 2001(Australian Bureau of

Statistics 2010). Thus our time spent traveling to and from work on average is too

high. As the benefits from exercise are well documented it can be said that a

multimodal transport network combined with a compact form will provide to the

opportunity to fill in the exercising gap (Grant 2013; Lowe 2014).

2.6 Planning

Planning of transport and land developments has challenged urban planners, policy

makers, transportation engineers, land developers and air quality modellers for

decades (Zhou & Kockelman 2009; Anable, Lane & Kelay 2006). The key to a

successful sustainable urbanized form is through the right polices and management

(Brookes et al 1996; Anable, Lane & Kelay 2006). Hickman et al. found during a

study in England that even though integrated land use and transport planning is often

part of planning polices these practices are seldom followed (Hickman, et al., 2010)

Dock found that conventional planning is largely based around cost-effectively

transporting commuters to the cities’ core employment centres (Dock 2003). This in

itself can add to the problems of congestion.

27

The Office of Economic and Statistical Research are predicting Brisbane’s total

population to be within 1.20 and 1.33 million people by 2031 (The Office of

Economic and Statistical Research 2012). As of 2011 the total population was 1.08

million. This gives approximately an additional 200,000 residents over 20 years. This

is annual change of 10,000 per year is almost half of the 19,269 persons per year

increase that Brisbane has had to accommodate over the 2003 to 2009 period (The

Office of Economic and Statistical Research 2012). Historically SEQ was

categorized as dispersed, low-density urban development coupled with spatially

uneven distribution of industries and population settlements (Speritt 2009). This

increase in residents will require an additional 156 000 dwellings above the 2006

total (Hinchliffe 2009).

The Connecting SEQ 2031 Plan states that land use planning and transport planning

go hand in hand (Department of Transport and Main Roads 2011). Creating higher

density areas and improving people mobility is planned to achieve the infill level

required. Mobility improvements will come from a change in resident travel patterns.

The plan states the following to achieve this:

• Active transport (walking and cycling) to increase from 10% to 20% of all

trips

• Public transport to increase from 7% to 14% of all trips

• Private motor vehicles reduced from 83% to 66%

(Department of Transport and Main Roads 2011).

Connecting SEQ 2031 Plan (2011) goes on to state that these goals are ambitious but

can be met if SEQ residents change their mode of transport from car to public or

active transport for just three out of 17 weekday trips. This requires improvements in

the safety and appeal in both public and active transport (Department of Transport

and Main Roads 2011).

Gordon and Richardson (1997) stated that there are three types of planning

approaches, macro, micro and spatial structure (Gordon & Richardson 1997). The

macro method is based on high densities at a city level but can be also applied to a

28

freestanding town. SEQRP 2009 – 2031 states as part of SEQ’s macro planning,

future residential and employment growth will be accommodated through a

combination of methods (Hinchcliffe 2009).

Residential growth will be covered by a redevelopment and use of:

• underutilised land within the broader urban framework and established

urban areas

• remnant broadhectare land

• broadhectare development

• limited rural living.

(Hinchcliffe 2009 p 13)

Employment required to meet the populations requirements will be directed within

urban areas by a combination of:

• activity centres

• specialised employment precincts

• limited home-based business

(Hinchcliffe 2009 p 13)

2.7 Transport Modelling

Transport Modelling has become a major part of urban planning strategies within

major Australian cities. The goal of such strategies being to promote CUF and TOD,

reduce sprawl and reduce resource consumption (Hinchliffe 2009). SEQ regards

achieving their goals as their method of becoming a sustainable (Burke & Li 2010;

Hunkin 2009).

The Department of Transport and Main Roads is responsible for Transport Modelling

for the South East Queensland region. They use the Brisbane Strategic Transport

Model – Multi Modal (BSTM_MM) (Pool 2014; Hunkin 2009). BSTM_MM is a

computerised, calibrated transport planning model that was based on a model

originally from Perth, Australia. It requires a travel demand modelling system such

as EMME from INRO to run the model (INRO Software 2014; Pool 2014) . It is

able to forecast demand and traffic flows based on land use and demographic

parameters and transport network characteristics (SKM Connell Wagner JV 2008).

29

This includes the ability of forecasting traffic conditions on a specific year with

varying demographics (Pool 2014).

Figure 2.8 BSTM_MM boundary area (Pool 2014).

The coverage of BSTM_MM can be seen in the insert in Figure 2.8 above. The

current BSTM-MM is currently going through model improvement program. Some

of these modification include changes to the adult ages due to the official retirement

ages increasing over the coming years. They are also changing it to a single adult

category instead of the existing blue and white collar categories (Pool 2014).

BSTM_MM is a four-step strategic model.

These stages include:

• trip generation – calculating the number of trips originating from each

geographical area – based on land use, population and employment

forecasts;

• trip distribution – determining the linkages between trip origins and

destinations;

• mode choice – estimating the proportion of travel by each transport mode

(eg. car, public transport) between each origin and destination;

• assignment – determining the roads and public transport services used by

each traveller between each origin and destination.

(Hunkin 2009 p 2)

30

Modelling doesn’t always predict a perfect world (Davies & Marinelli 2011). It was

found that during a construction works traffic restriction on Coronation Drive in

Brisbane that delays were not as long as predicted. Davies and Marinelli (2011)

concluded that people are able to adapt to change by altering travel route, time, mode

or postponing (Davies & Marinelli 2011). They also found that when delays are

introduced to a roadway there is a direct modal shift to viable public and active

transport options. This could be used as method of achieving a long term modal shift

(Davies & Marinelli 2011). This was supported by a survey undertaken after the

restrictions had been lifted. The results of a maintained change of mode were that of

those surveyed 19 percent change all trips, 38 percent changed most trips, 16 percent

changed some trips and the remaining 28 percent returned to the original road

network (Davies & Marinelli 2011).

Within any project involving planning, as mentioned in the previous chapter, a

method of achieving the goal is required. It is the ability to maintain and monitor the

performance of such a project which is then the key to success. Figure 2.8 below

provides a good example of a modelling strategy (Espada 2010).

Figure 2.8 Planning strategies (Espada 2010).

The planning strategy in Figure 2.8 shows the two separate bodies of the urban form and the

transport network both being altered by planning strategies to suit future predictions. After

this the factors are both required to construct the transport demand model. Once the model is

in place it then turns to maintaining and monitoring.

31

2.8 Conclusion

The importance of urban forms and transport networks has become known to all

developed cities around the world. With proper inception, compact urban form can

be applied to both new and existing city spatial patterns. The potential of benefits

that CUF can offer the economy, environment and society were questioned however

supported through the research undertaken. Promoting these advantages to the public

will aid in gaining a positive consensus.

The literature reviewed in this chapter demonstrates the multiple variables that

require to be taken into account when planning for the relationship between urban

forms and public transport. The global cities contained in the next chapter will be

reviewed by targeting the main variables being densities and modal choice.

32

3.0 Compact Urban Form and Transport Trends

During the undertaking of the literature review there were several cities in past

studies from around the world being referenced in regards to their residential

densities and public transport. Ferreira (1999) stated that population distribution,

land use development patterns, household structure, house hold income and car

ownership are what govern our city success (Ferreira 1999).

The sections contained in this chapter will provide background information on the

current adopted methods of regional transport that incorporate compact urban areas.

Employment and population density will be the focus as a means of fair comparison.

These will be compiled into a comparison table contained in section 4.4.5.

3.1 Bogotà, Colombia

Bogotá is the Capital city of Colombia, South America. It has become well known

throughout the world for its TransMilenio mass transit system that it implemented in

December of 2000. The system is made up of privately run buses with approximately

18,000 being legal and somewhere between 30 and 50 percent more ‘Pirates’

(Cracknell 2003)

33

Figure 3.1 View over Bogotá with Bus Rapid Busway in the foreground (Network 2014)

The system has some of the largest bus flows in the world. Some counts have been

over 1,000 buses per hour per direction on a single road (Cracknell 2003). The secret

of the system success lays in the Busway as seen in Figure 3.1 and Figure 3.2. The

segregation between Bus-traffic and Normal-traffic and efficient boarding ‘Stations’

is the key to be able to reach these volumes (Cracknell 2003).

Figure 3.2 Bogotá, Busway design (Cracknell 2003)

For comparison purposes the modal split of Bogotá was collected from Smith (2013)

and Cracknell (2003) and used to produce the graph in Figure 3.3 (Smith 2013;

Cracknell 2003).

Figure 3.3 Bogotá – Percentages of Transport Modal Split (Smith 2013)

34

3.2 Curitiba, Brazil

Curitiba is the capital of the state of Paraná in southern Brazil. It has become well

known throughout the rest of the world because of its many environmental and social

planning initiatives. These include waste recycling schemes, parks and green area

expansions, environmental education and heritage preservation. The main initiative

that has gained most of the attention is the public transport planning and land use

management (Rabinovitch 1992; Lerner 2007). Figure 3.4 shows an example area of

the city with high density multi-purpose structures spaced apart, pockets of green and

a designated public transport link.

Figure 3.4 View over Curitiba with Bus Rapid Transit lanes in the foreground (Viajarpelamundo 2012)

It was during the 1960’s Curitiba’s method of planning required modification to meet

the rapid rise in urban population. A method devised was to combine all the required

elements that a population needs and based it around a transport system. Curitiba has

generated a global following with the introduction of the Rapid Bus Transit system

(Lerner 2007). The designated lanes and railway style loading tubes can be clearly

seen in Figure 3.5.

35

Figure 3.5 Curitiba – Rapid Bus Transit – Tube Stops (Places 2012)

For comparison purposes the modal split of Curitiba was collected from LTA

Academy (2011) and used to produce the graph in Figure 3.6 (LTA Academy 2011).

Figure 3.6 Curitiba – Percentages of Transport Modal Split (LTA Academy 2011)

36

3.3 Hong Kong, China

Over 50 years ago Hong Kong’s policy for urban development changed from

dispersion to compaction (Jenks & Burgess, 2000). This all took place at a time when

western planners were blaming high rises and compact form for social problems such

as crime, vandalism and social dysfunction (Jenks & Burgess, 2000). These factors

were also considered in Hong Kong but they were however outweighed by the

problems of land resources and a booming population (Jenks & Burgess, 2000).

Figure 3.7 View of the skyline of Hong Kong (Hong Kong – Asia’s World City, 2014)

Hong Kong has now become a model example of a compact city. It boasts the

highest urban density in the world (Jenks & Burgess, 2000). This density can be

easily seen skyline of the city shown in Figure 3.7. According to the Economist

Intelligence Unit (2011), Hong Kong was ranked as the number one city in the world

by their Liveability Index (Economist Intelligence Unit 2011). It did however fall

down in one of the scoring criteria in this report. Hong Kong received the

penultimate ranking for pollution within the top ranked twenty cities. This rating

supports Jenks & Burgess (2000) statement that cities with higher densities can have

a negative effect on the environment (Jenks & Burgess 2000).

37

For comparison purposes the modal split of Hong Kong was collected from Smith

(2013) and used to produce the graph in Figure 3.8.

Figure 3.8 Hong Kong – Percentages of Transport Modal Split (Smith 2013)

3.4 London, England

London has an iconic PRT system. From 1956 until 2005 the residents and streets

were served by the Routemaster London Bus (see Figure 3.9) (Double Decker Bus

2013). London has still continues to provide a more modern version of the classic

Double Decker bus. This design has been instrumental in moving high volumes of

commuters through narrow streets aiding in holding off congestion (Double Decker

Bus 2013).

Figure 3.9 ‘Routemaster’ London Bus (Double Decker Bus 2013)

38

The main source of policy on transport is the Mayor’s Transport Strategy (MTS).

This sets six goals, which are linked to the six themes of ‘The London Plan’:

• Supporting economic development and population growth

• Enhancing the quality of life for all Londoners

• Improving the safety and security of

• Improving transport opportunities for all Londoners

• Reducing transport’s contribution to climate change, and improving its

resilience

• Supporting delivery of the London2012 Olympic and Paralympic Games and

its legacy.

(Greater London Authority 2014, p.197)

For comparison purposes the modal split of London was collected from Smith (2013)

and used to produce the graph in Figure 3.10.

Figure 3.10 London – Percentages of Transport Modal Split (Smith 2013)

3.5 Conclusion

From this information it can be seen that the example cities cover a wide range of

conditions and resulting figures. Bogotá, Curitiba and Hong Kong all show higher

modal share with public transport. Whilst London is the closest to being evenly

shared between the three modes.

The next chapter provides background and analysis of the case study area with

reference to the example cities findings.

39

4.0 Case Study – Chermside

4.1 Introduction

In the previous chapter a comparative analysis of the different example cities was

undertaken. From this it can be seen that there is potential in the theory that modal

choice of transport can be changed by true CUF. To test this theory the most

practical method is to undertake modelling of an example city area.

This chapter provides the details on the selection and modelling for the case study

area. This includes

• Study area – description of area selection.

• Methodology – description of modelling and steps carried out.

• Current figures – summary of collected patronage data, population and

employment figures and the comparisons.

• Future figures – description of future modelling parameters.

• Modelling results – resulting outcomes.

4.2 Study area

As detailed within the background section of the

introductory chapter, it is predicted that SEQ will be

subjected to a constant growth of population over the

forthcoming decades. The first step was to select the

boundary of the full study area. As the data to be utilised

was to be sourced from the Australian Bureau of Statistics

all the boundaries needed to match the statistical areas.

The levels that data can be obtained from ABS is as per the

stages shown in Figure 4.1.

Figure 4.1 Levels of Statistical Areas from ABS

(Australian Bureau of Statistics 2014a)

40

The sub-state regions, Statistical Areas Level 4 (SA4s) of Brisbane were selected.

These were Brisbane – North, Brisbane – East, Brisbane – South, Brisbane – West

and Brisbane – Inner City (Australian Bureau of Statistics 2014). This covers an area

of 1626 km² and had a population of 1,110,906 residents during the last Australia

Census of 2011. These areas can be seen in the Figure 4.2.

Figure 4.2 Map of Statistical Areas Level 4 from ABS (Australian Bureau of Statistics 2014a)

41

The next step was to select a smaller region within the outer boundary. Statistical

Areas Level 2 (SA2s) was chosen as a boundary for the case study area. Within the

four SA4 areas mentioned above, there is a total of 137 SA2 areas. SA2 provides a

level of detail that is sufficient to provide results for this study. Throughout Australia

SA2s include a population range of 3,000 to 25,000 persons with the average being

10,000 persons (Australian Bureau of Statistics 2014). For modelling, BSTM__MM

utilises SA1 which is the smallest level of statistical area (Department of Main Roads

and Transport 2014).

Figure 4.3 Map of Brisbane North (302) containing Statistical Areas Level 3

(Australian Bureau of Statistics 2014a)

The study SA2 was required to have attributes that displayed potential for growth in

both employment and population. Chermside, a 3.4 square kilometre suburb located

ten kilometres north of Brisbane’s CBD was selected (Australian Bureau of Statistics

2013). It is located in the Brisbane North (302) SA4 area. This can be seen in Figure

4.2 divided into the SA3 areas. The SA3 area of Chermside is further divided in the

SA2 areas. See Figure 4.3 for SA2 area of Chermside.

42

Chermside has been named as one of SEQ’s 18 principal activity centres (Hinchcliffe

2009, p. 107, Department of Transport and Main Roads 2012a). It is one of two of

the principal areas not connected to the rest of the city by high quality transport such

as rail, busway or priority buses (Department of Transport and Main Roads 2012d).

The suburb contains two large trip attractions being the Westfield Shopping Centre

(Westfield Retail Trust 2014) and The Prince Charles Hospital (Hinchcliffe 2009, p.

114) employing approximately 3500 staff (Queensland Health 2013). See Figure 4.5

for attraction locations. Both of these lend themselves to future expansions,

especially the shopping centre as it has zoning that has been changed to allow for the

construction of multiuse buildings to a height of 15 storeys (Hinchcliffe 2009 Cl

7.2.3.6 Chermside centre neighbourhood plan code). It currently stands as a single to

double storey shopping complex with approximately 6500 car park spaces set on

22.1 hectares (Westfield Retail Trust 2014). As this lends itself for redevelopment, it

is justifiable to utilise this one node to accommodate the future expansions in

population and employment without disturbing the remaining low density post war

housing within the remaining suburb. As there are no ferries or train stops within

Chermside, the suburb is almost solely serviced by buses (Australian Bureau of

Statistics 2013). Therefore Chermside lends itself to an ideal comparison between

CUF and PRT. The method of comparison is detailed in Section 4.3.

Figure 4.4 Map of Chermside (30202) containing Statistical Areas Level 2 (ABS 2014a)

43

Figure 4.5 below provides a clearer image of the defining boundary of Chermside,

the case study suburb. The white area marked with (1) is the Westfield Shopping

Centre. Note its close proximately to the Seventh Brigade Park. The pink area

marked with (2) is The Prince Charles Hospital complex. The Gympie Road corridor

mentioned in Section 2.2.5 can be seen here as the main arterial heading north and

south.

Figure 4.5 Map outlining Chermside Statistical Areas Level 2. Containing location for (1) Westfield Shopping

Centre and (2) The Prince Charles Hospital (Chermside QLD 2014)

4.3 Methodology

Once selection of Chermside was confirmed, a means of data collection was

required. As this research requires data on a city wide scale, the search for a current

quantitative data set was required. The initial method was to request actual recorded

data from all PRT services that pass through the study area. The live primary data

collected by the transport provider in the Brisbane City area should reveal the

demographical cross section and total volume of current patrons using the services.

44

After this information was received from Translink, it was realised that it did not

lend itself easily to constructing a working model. While a high percentage of the

patrons utilised the go card ticketing system, it did not provide a broad range of

information within the data set. This data set still has the ability of providing figures

on patron demographics such as whether they are students, adults or seniors and

travel times. The main issue was that it lacked vital information such as purpose of

journey, place of work, place of residence and trip termination location. A second

data set for all other trips of active and private transport would need to be gathered to

combine with this information. Therefore this direction was dropped from the

methodology.

Next step was to request access to the Brisbane Strategic Transport Model – Multi

Modal (BSTM_MM) from DMRT. It was decided that utilising the actual and

current model from the area would be the preferred method of choice. Burke and Li

(2010) from Griffith University had also conducted modelling utilising the

BSTM_MM in their research into the effects of employment decentralisation (Burke

& Li 2010). Their method of modelling included the running a base run using

DMRT’s projected 2031 model. They then created a second run with adjusted

decentralized employment figures over 13 individual suburbs and were able to

produce results (Burke & Li 2010). It was therefore seen as a viable approach to be

used for this study. Due to a prolonged attempt to access this model a new method of

analysis was required.

The next approach in data collection was to search for surveys that would cover the

gaps in the information. Two such surveys were found. The first being the ‘Public

Transport User Survey’ from TL (Translink Transit Authority 2010). This was

created by TL to collect movement information such as origin-destination, modes of

movement, transfer location, trip purposes and passenger profiles (Translink Transit

Authority 2010, Department of Transport and Main Roads 2012). The most recent of

these surveys had been carried out in May 2010. This was over a twenty weekday

period with the exclusion of public and school holidays. The response rate was not

high. The total number of records collected during the survey was 56,513 responses.

This covered only 19 percent of bus and ferry patrons and only 10 percent of train

patrons for the study area (Translink Transit Authority 2010). These low percentages

of coverage combined with the irregular survey period intervals and unbalanced

45

suburb figures lead to this data set being excluded from the base modelling

application.

The 2011 Australian Census conducted on the 9th of August 2011 by the Australian

Bureaus of Statistics was then reviewed (Australian Bureau of Statistics 2014). The

initial review from the information provided on the ABS website concluded that

there was insufficient data collected. After this a second review was undertaken and

the Census question sheet was located. It was found that the Census did contain a

question relating to Place of Work (POW). Due to a privacy acts this information

was not made available to the general public. To access this information an

organization access agreement was arranged between ABS and the University of

Southern Queensland.

With this new supply of information it was decided that the data had sufficient range

for undertaking area reviews alone but it did not contain enough information to

complete modelling. To be able to complete the modelling requirement of the

objectives a simplified 15 x 15 matrix was devised. This was due to the full SA2

matrix being 137 x 137 in size. As most of the additional information required for

modelling required manual entry this size matrix was not justifiable. The origins and

destinations for the 15 x 15 matrix was broken down to the statistical areas shown in

Table 4.1 below.

Table 4.1 Model matrix defined destinations and origins

SA Level Statistical Area Name

Aspley

Chermside

Chermside West

Geebung

Kedron - Gordon Park

Stafford

Stafford Heights

Wavell Heights

Bald Hills - Everton Park

Nundah

Sandgate

Brisbane - East

Brisbane - South

Brisbane - West

Brisbane Inner City

SA4

SA2

SA3

46

The SA4 region of Brisbane – North has been first divided into the SA3 areas

Chermside, Bald Hills – Everton Park, Nundah and Sandgate. SA3 Chermside is then

divided in to the SA2 regions of Aspley, Chermside, Chermside West, Geebung,

Kedron – Gordon Park, Stafford, Stafford Heights and Wavell Heights. The other

SA4 areas of Brisbane – East, Brisbane – South, Brisbane – West and Brisbane Inner

City can be seen at the bottom of the table. The full distribution matrix can be seen in

Table 4.3 in Section 4.4.1.

It was decided for the larger origin and destination areas (SA4) a centroid was

nominated by overlaying the statistical boundaries with Google Maps and selecting

the middle of the built area. The centre of the smaller suburb is listed against the

corresponding SA4 region in Table 4.2 below.

Table 4.2 SA4 areas with nominated centroid.

With the boundary and parameters set further data collection could be completed.

The following tables were compiled for the purpose of modelling:

• Distribution (Journey to Work)

• Distances between centroids

• Total daily distances travelled

• Car travel time between centroids

• Bus travel time between centroids

• Walk travel time between centroids

• Bicycle travel time between centroids

Using the matrix of distances between centroids, a Gravity Model was then created

for predicted future employment/residential figures. Two adopted future models were

created. Next was to create the third step of the four-step transport model.

To define the modal choice the coefficients and utility functions are required to be

able to calculate the probabilities of figures for the choice of modes. The utility

SA4

Brisbane - East (Alexandra Hills)

Brisbane - South (Archerfield)

Brisbane - West (Indooroopilly)

Brisbane Inner City (Kelvin Grove)

47

functions are calculated by a variation of the following formula to suit the

application:

��� =�����

����

where ��� = total utility provided by mode m to a traveler k

��� = coefficient calculated from travel survey data for mode m

corresponding to the characteristics of mode or n

���� = mode or traveler characteristics n (e.g. travel time of mode,

income) for mode m of traveler k.

(CIV3703 Transport Engineering: study book 2012)

To utilise this formula the coefficients need to be defined first. Each of these are

individual for every single link in the network and require a travel survey to be

defined. The only method found to gain these was to extract them from the

BSTM_MM coding. An example of the coding for White Collar worker Utility

(wwu3 highlighted) formula from the ‘modch – wkw’ file contained within

BSTM_MM.

~##########################################################################

~# PUBLIC TRANSPORT USER

~~/ PnR

~# Park & Ride access

~# ~# restrictions added to exclude the alternative:

~# ~# 1. If the car access time is excessive (>98%ile in SEQTS)

~# ~# 2. If the walk egress time is excessive (>98%ile in SEQTS)

~# ~# 3. If the model estimates zero in-vehicle time or wait time (because

~# ~# it is shorter to walk the rest of the way)

~+|1|y|”wwu3”|n|”wkmppt”+”watpt”*(“amchwt”+”amcaet”)

~+|+”wcost”*(“amchwc”+”amcfar”)+”wmmtt”*”amcivt”+”wwtpt”*”amcwat”

~+|+”wadvh”*((“zoneah”-“zonevh”).max.0)+”wshftv”+”wempd”*”empden”

~+|-99*((“amchwt”.gt.33).or.(“amcaet”.gt.28).or.(“amcivt”.eq.0)

~+|.or.(“amcwat”.eq.0).or.(p.eq.q))|||n|2

48

Some of the variables are easily obtained however most are derived from

calculations contained within the model, rendering them inaccessible to the

researcher.

The final derived method of analysis was to combine all of the below factors into the

matrixs for comparison.

• Distribution (Journey to Work) (ABS)

• Distances between centroids (Google maps / small matrix only)

• Total daily distances travelled (ABS & Google Maps)

• Car travel time between centroids (Google Maps)

• Car travel time to CBD (Google Maps)

• Bus travel time between centroids (Google Maps)

• Bus travel time to CBD (Google Maps)

• Walk travel time between centroids (Google Maps)

• Walk travel time to CBD (Google Maps)

• Average income (ABS)

• Method of travel to work (ABS)

The original project specification also stimulated the inclusion of frequency of

services, dedicated routes, multi-modal transport, weekday peak, weekend peak and

off-peak period options into the study. This was not fulfilled due to the reliance on

the BSTM__MM to complete this task.

The next section presents the results from the analysis.

49

4.4 Data Analysis

This section provides the findings from the analysis completed using the variables

listed in the previous chapter. The sources of the information area the Australian

Bureau of Statistics 2011 Census (Australian Bureau of Statistics 2013) and Google

Maps ‘Routeplaner’ (Chermside QLD 2014).

4.4.1 ‘Journey to Work’ Trip Figures

The current Journey to Work (JTW) trip figures to be used in the assessment of the

study area were taken directly from the 2011 Australian Census. These figures have

been extracted utilising the Organisational `Tablebuilder Pro and Standard’. The

table constructed was of Place of Usual Residence (PUR) vs Place of Work (POW).

Whilst creating the table, there was also the additional option of splitting the working

figures between Full Time (FT) and Part Time (PT) work. It was assumed for this

study that part time work was conducted over a 5 weekday period. The original Table

4.3 is contained in Appendix B. It is not possible to display the full SA2 version

within this format. This is contained within the available file.

Table 4.3 Trip Distribution of Journey to Work.

50

4.4.2 Population

This section includes the presentation of generated graphs relating to population

which have be plotted in different combinations against other variable such as time,

density and income.

The first of the population figures is Chermside’s population growth from 1991 to

2013 (see Table 4.4). Note the increase in growth since the 2006 ABS Census.

Table 4.4 Population of Chermside

Using the full SA2 matrix, Table 4.5 was created by plotting each of the 137 zones

percentage of the total resident population against the density of that particular zone.

The initial zero values have been left in the comparison. Note that the bulk of the

population is dispersed between 0 – 3000 residents/km².

Table 4.6 is a comparison between the population within a zone and the zones

average annual income. It includes the total population, female and male population

for each of the zone. The trendline does show a slight trend that lower income

suburbs have a higher population. This however could be effected by either

pensioners or families with one wage.

51

Table 4.5 Percentage of population compared to residential density.

Table 4.6 Population of Suburb compared to Average Annual Income.

52

4.4.3 Employment

Table 4.7 below shows the total number of workers from other suburbs, SA3 and

SA4 areas working in the suburb of Chermside. It can be seen that only 8.6 percent

of the jobs in Chermside are actual filled by a resident of the suburb. This already

shows that the promoting of CUF beliefs could aid in reducing this difference.

Table 4.7 Employment figures for Chermside

The next table, Table 4.8, the calculated percentage of working residents that are

employed in their home suburb or region. It can be seen that Chermside does fair

better than some of the other areas but it still falls short of the levels of CUF.

Place of Work

Chermside

Aspley 470

Chermside 678

Chermside West 294

Geebung 136

Kedron - Gordon Park 335

Stafford 113

Stafford Heights 165

Wavell Heights 258

Bald Hills - Everton Park 1075

Nundah 634

Sandgate 1155

Brisbane - East 185

Brisbane - South 389

Brisbane - West 578

Brisbane Inner City 1392

7857Total

POW - PUR

PLACE OF

RESIDENCE

SA2

SA3

SA4

53

Table 4.8 Trip Distribution of Journey to Work.

Table 4.9 Below shows the percentage of workers from the population within an

individual suburb plotted against the distance to the CBD. The trendline indicates

that the closer a suburb is the CBD the higher the percentage of workers.

Table 4.9 Percentage of Working Population vs Distance to CBD

Aspley 737 4777 15%

Chermside 678 3078 22%

Chermside West 229 2395 10%

Geebung 238 1684 14%

Kedron - Gordon Park 668 5942 11%

Stafford 337 2519 13%

Stafford Heights 228 2637 9%

Wavell Heights 377 4114 9%

Bald Hills - Everton Park 2151 15344 14%

Nundah 5057 15057 34%

Sandgate 4086 19854 21%

Brisbane - East 46022 83485 55%

Brisbane - South 57543 121646 47%

Brisbane - West 25024 70222 36%

Brisbane Inner City 81144 109288 74%

224519 462042 49%

Total

Employed

Residents

Percentage of

residents

employed in

home suburb

Jobs Filled by

Suburb

Residents

Existing Figures POR - POW

PLACE OF

RESIDENCE

SA2

SA3

SA4

Total

54

Table 4.10 below shows the percentage of the workforce that work in their home

suburb. This has been plotted in regards to the home suburbs residential density. It

can be seen on average the less dense the suburb the more people work in that suburb

with some exceptions.

Table 4.10 Percentage of workforce that work at place of residence according to density.

The final employment related graph is Table 4.11. It proves the point that the denser

the suburb the shorter the travelling distance to work. The point that the denser

suburbs are also closer to the CBD where the employment density is at its highest is

effecting this plot.

55

Table 4.11 Distance of JTW compared to residential density .

4.4.4 Demographics

Through the manipulation of the data it was found that car ownership figures have

increased. In 1999, Ferreira estimated for the Brisbane City Council that there was to

be 500 cars per 1000 people in 2011. This was taken from the figures of 444 cars per

1000 people in 1986 and 462 cars per 1000 people (Ferreira 1999). From the data

analysis, it was found that the current 653 cars per 1000.

Ferreira stated that population distribution, land use development patterns, household

structure, household income and car ownership are the most influential variables that

effect PT modal share (Ferreira 1999).

From Table 4.12 it can be seen that car ownership follows almost the same slope of

trendline as people in household when they are compared to density. One other

interesting point is the children per family shows only a slight decline in regards to

density. Therefore families are accustomed to living in highly dense areas.

56

Table 4.12 Household demographics compared to residential density.

4.4.5 Transport Modes

To find the current patronage of PRT the mode of transport to work table was

utilised. As the Census data collected covers a combination of modes up to three in

total, a means of separation was devised. For each of the individual SA2 areas the

single methods were added to half of the methods with two modes and a third of the

methods with three modes. The average over the 137 off SA2 areas was plotted in

graph in Figure 4.6.

57

Figure 4.6 Modal Split of Brisbane (6 modes).

To make a clearer comparison to the figures collected in Section 3, the six modes

were aligned to just three being Public, Private and Active Transport. It was assumed

that ‘Work From Home’ was to be grouped with Active Transport. The graph in

Figure 4.7 was then generated.

Figure 4.7 Modal Split of Brisbane Three Modes

As this comparison was completed on a SA2 level, the Modal Split for the

Chermside could be directly extracted from the table. These are plotted in figure 4.7

below.

58

Figure 4.8 Modal Split of Chermside, Brisbane (6 modes).

Figure 4.9 Modal Split of Chermside, Brisbane (3 modes)

Connecting SEQ 2031 Plan states that the modal choice percentages need be the

following:

• Active transport (walking and cycling) to increase from 10% to 20% of all

trips

• Public transport to increase from 7% to 14% of all trips

• Private motor vehicles reduced from 83% to 66%

(Department of Transport and Main Roads 2011).

59

When Figure 4.7 and 4.9 are compared it can be seen that Chermside is slightly

above Brisbane average in regards to percentage of public and active transport usage.

Chermside is also meeting targets of the SEQ 2031 Plan. Therefore this plan may

need to be reviewed to set higher goals for inner city suburbs.

The trendline in Table 4.13 shows that there is in increase in people working from

home as the suburb becomes denser.

Table 4.13 Work from home compared to residential density.

60

Table 4.14 Work from home percentage compared to residential density.

4.4.6 Future employment and population projection

As the original method was to increase the population and employment to a

saturation point the projected figures in the SEQRP 2009-2031 were not going to be

high enough to make a noticeable difference (Hinchcliffe 2009. The recently planned

Kurilpa – Riverfront Renewal projected employment and residential rates were used

as a comparison (Queensland Government 2014). It was found that the current

Westfield Shopping site was approximately the same size, being 220,900m², as the

proposed Kurilpa development being 250,000m² (Westfield Retail Trust 2014;

Queensland Government 2014). The projected increases for Kurilpa is 8,000 jobs and

11,000 residents for the 30 storey building limit. As the limit for the title of the

shopping centre is 15 storey this figure was halved and applied to the base figures to

get values at year 20XX. The full 30 storey values were also applied to the base

figures to provide a maximum possible value to comply with Brisbane’s inner city

benchmark. These figures are shown below:

Base Figures

2011 Base Employment -7,857 people

2011 Base Population – 8,170 people

61

15 Storey Development

20XX Base Employment -11,442 people

20XX Base Population – 13,670 people

30 Storey Development

20YY Employment – 15,027 people

20YY Population -19,170 people

The modal choice percentages for all of the 137 zone and all modes were calculated

and plotted against the residential densities of that zone (see Table 4.15). The

trendlines all indicate that with an increase in density we see a decrease in private

transport usage. This then provides positive growth in both active and public

transport figures.

A larger copy of this graph is contained in Appendix B.

Table 4.15 Journey to Work Modal Choice v Residential Density Comparison.

62

The modal choice percentages for all of the 137 zone and only three modes were

calculated and plotted against the residential densities of that zone. This shows a

clear picture one the modes are combined.

A larger copy of this graph is contained in Appendix B.

Table 4.16 Journey to Work Modal Choice v Residential Density Comparison.

4.4.7 Analysis Results

All of the collected figures for the example cities were compiled together to make

Table 4.17. From the table it can be clearly seen that Brisbane is not able to compete

with the other model cities in modal choice and all the densities.

63

Bogota

Colombia

Curitiba

Brazil

Hong Kong

China

London

England

Brisbane

Australia

Population 6,824,510 1,864,416 3,379,295 8,250,205 1,109,223

Area 414,21 435,27 12730 1294,77 1,636,3

Residential Density 16,476 4,283.4 44,955.6 6,371.9 677.9

Employment

Density Not Found Not Found Not Found Not Found 282.4

Peak Residential

Density 55,800 Not Found 111,100 27,100 5,584

Peak Employment

Density 61,550 Not Found 120,200 141,600 39,048

Public Transport 56 % 45 % 48 % 34 % 18 %

Private Transport 30 % 29 % 7 % 41 % 71 %

Active Transport 14 % 26 % 45 % 26 % 12 %

Table 4.17 Summary Table of city figures (Smith 2013; Rabinovitch 1992; Places 2012;

LTA Academy 2011, Australian Bureau of Statistics 2011)

For a city to reduce its dependency on private transport, Newman & Kenworthy

(2006) arrived a density figure of 35 residents per hectare or 3500 residents per

square kilometre. All of the example cities are above this cut off except for Brisbane.

The proportion of included vacate land within the statistical areas could be effecting

the results found for Brisbane, but it still would not be enough to achieve an equal

score.

5.0 Recommended Framework

The recommended framework should be able to provide possible population,

employment and transport ratios to square kilometres of developable land.

As the optimal employment and residential densities could not be obtained during

modelling, nominating a design or planning framework is quite difficult. Therefore it

has been excluded from this report. However the point of trendline intersection on

Table 4.15 and Table 4.16 could be examined in more detail to see whether this

correlates directly to the optimal density ratios.

64

6.0 Conclusion

The title of the dissertation is ‘the Impact of Compact Urban Form on the Provision

of Public Road Transport’. This document has presented the findings from the

individual steps in research undertaken. A conclusion was presented at the end of

each stage. This chapter aligns these findings and presents them in the following

subsections:

• Limitations – defines the restrictions of research.

• Future Research – details further work that could be continued on from this

research including the incomplete aims.

• Conclusions – presents the results.

6.1 Limitations

Through the literature it was clear that many assumptions are required during

planning and transport modelling. This research project confirmed this and

subsequently made allowances for some assumptions due to limitations in the

available data. These allowances have been mentioned in their corresponding areas

of this document but have also been combined in this section.

The first main limitation throughout the research was the breadth of data. The

collected data from Australian Census in 2011 has some limitations in transport

modelling. It fails to include figures for all other trips outside of Journey to Work. It

excludes all journeys for students, pensioners, recreation and shopping. Translink’s

household survey does cover some of these variables however it had a limited

coverage of total surveyed persons. The Australian Bureau of Statistics has stated

that the upcoming Census in 2016 will include a new range of questions to cover

some of these areas.

The next limitation is the cross boundary trip figures. When a boundary is selected

and defined there is always the issue of residents travelling outside of this area. This

65

is a problem faced by all planning models. Even the BSTM__MM has its own

boundary issues with residents from other townships, crossing over the boundary for

work or leisure related travel.

The last major limitations with the final findings in this report is that it was based on

the facts that peoples choices on how they live was directly governed by the

residential density of their home suburb. This personal choice also goes to the point

of stating that a person in a denser area will choose to utilise public or active

transport even if there is minimal congestion in private transport.

6.2 Future Research

As the findings from the literature review, example cities and modelling

methodology have shown, there is ample justification for future studies in this field.

The literature review only scraped the surface on the body of research and interest in

this field. The theories from on the city and transport planning are

Due to unforeseen delays in receiving information there are several areas incomplete

from the original methodology. Better results would have been derived from the

project if Translink’s BSTM_MM model was able to be fully utilised. Therefore area

of study would be highly recommended to be completed.

ABS, DMRT and TL all understand the importance of good data collection and that

current methods are lacking in execution or detail. The upcoming ABS census in

2016 will include a larger range of question that will cover transport and travel. TL

are also currently working on conducting a new survey during the upcoming year.

This will too include a greater range of survey questions covering the public’s

transport needs.

It would also be interesting to undertake other modelling scenarios. These could

include:

• Adjusting density factors on a single region to create a saturation point in bus

service requirements.

66

• Applying the increases to several areas with a SA3 area without modify the

remaining city area.

Lastly, another suggested study area is measuring the effectiveness of TOD on public

transport patronage. An example being the TOD being developed in Milton.

6.3 Conclusions

The original goal of this dissertations was to investigate the effects on public bus

patronage if compact urban form is implemented within a single statistical area. The

available literature was analysed to select a justifiable method of modelling the

modal choice of transport. Within the literature, a gap was found where this type of

comparison for future planning had not been undertaken before. A case study

statistical area of Chermside was selected within the Brisbane City Council

boundary. All relevant available statistics for the boundary area of Brisbane City

were compiled. Operational access to the current DMRT modelling methods was

attempted. Once this option failed, the project methodology required revision.

Utilising the Australian Census data, the construction of a four step model for modal

choice was attempted. The inability to extract the modal coefficients from the

BSTM_MM model to apply to the manual model then terminated this revised

direction.

This research then attempted to find a correlation between density and transport

modal choice by analysing the available data set. It was found that Brisbane does

follow the trends of a city with a single CDB area. The demographics such as

income, vehicle ownership, distances travel support this argument.

Through the development of this dissertation it became clear that there are endless

variables that directly effect this relationship. The idea of applying compact form

guidelines to a city district for ‘future proofing’ is feasible. Working from the trends

in the results a projected decline in the private vehicle usage and increase in bus

patronage with CUF was found.

67

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Appendix A University Of Southern Queensland

Faculty of Engineering and Surveying ENG 4111/4112 Research Project

PROJECT SPECIFICATION

For: Martin Stager Topic: THE IMPACT OF COMPACT URBAN FORM ON THE PROVISION OF

PUBLIC ROAD TRANPORT Supervisor: Marita Basson/Paula Grant and Nateque Mahmood Enrolment: ENG 4111-S1 2014

ENG 4112-S2 2014 Project Aim: This project aims to assess the demand urban sprawl places on road

transport services and to determine if compact urban form enables more effective and provision of those services.

PROGRAMME : V4 (ISSUE G) 18th July 2014

1. A literature review on Compact Urban Form (CUF), Urban Sprawl (US), Transit-Orientated Development (TOD) and the impact different urban forms have on road transportation services. It will also include the economic, environmental and social benefits in terms of efficient road transport service provision that compact urban form and a successful transport network could bring to the cities across the world.

2. Investigate current trends in compact urban form and the impacts on public road

transport provision within major cities around the world.

3. Determine current levels of road transport service for the population of Chermside, Brisbane (case study) and how that might change with increased density or more compact built form. This includes of frequency of public transport services, dedicated routes and multi-modal transport options.

4. Model the road public transport requirements within the suburb, to the CBD and

industrial precinct during weekday and weekend peak and off-peak periods.

• Increased population in Chermside without an increase in job places within the suburb

• Increased population in Chermside with an increase in job places within the suburb

5. Make future projections on Chermside’s transport requirements considering the

projected population in 2031.

6. Recommend a framework for optimal urban built form patterns to provide the most efficient and effective road public transport service.

7. Submit a dissertation on findings of research.

If time permits:

8. Conduct modeling for another TOD and public road transport node centre and compare to Chermside case study.

78

Agreed: Martin Stager (student)______________________(supervisor)

79

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