West Byron Development Transport Studywestbyronproject.com.au/wordpress/wp-content/uploads/2011/10/1… · Home-based Trip Production Model The trip production model for home-based

March 2011

West Byron Development Transport Study

Prepared for Byron Bay West Landowners Association

By Veitch Lister Consulting Pty Ltd


VLC Project No. 10-008

Final Report

Project No. Report Name Version Date Completed by Checked by

10-008 West Byron Development Transport Study

V1 March 2011 MV/NV MV

Table of Contents

1.0 INTRODUCTION ......................................................................................................................... 1

1.1 BACKGROUND ............................................................................................................................. 1 1.2 STRUCTURE OF REPORT .............................................................................................................. 2

2.0 DESCRIPTION OF THE ZENITH MODEL ............................................................................ 3

2.1 INTRODUCTION ......................................................................................................................... 3

2.2 STRUCTURE OF THE ZENITH MODEL ........................................................................................... 5 2.2.1 Household Segmentation Model ....................................................................................... 5 2.2.2 Household Trip Production Model ................................................................................... 7 2.2.3 Trip Attraction Model ..................................................................................................... 11 2.2.4 Travel Market Segmentation by Vehicle Ownership ....................................................... 12 2.2.5 Trip Distribution Model .................................................................................................. 12 2.2.6 Period Allocation Model ................................................................................................. 15 2.2.7 Mode Choice Model ........................................................................................................ 15 2.2.8 Trip Assignment .............................................................................................................. 17

3.0 DEVELOPMENT OF 2008 TRAVEL MODEL AND VALIDATION .................................. 19

3.1 INTRODUCTION......................................................................................................................... 19 3.2 ZENITH MODEL VALIDATION AGAINST 2008 SEQ SCREENLINE TRAFFIC COUNTS ................... 20 3.3 ZENITH MODEL VALIDATION AGAINST 2008 BYRON BAY TRAFFIC COUNTS ........................... 25 3.4 CURRENT TRAFFIC DEMAND (2008) IN BYRON BAY (LOW TOURIST SEASON) ........................... 27 3.5 HISTORICAL TRAFFIC GROWTH ................................................................................................ 27

4.0 FUTURE LAND USE AND ROAD NETWORK ASSUMPTIONS 2018 AND 2028............ 31

4.1 POPULATION AND EMPLOYMENT GROWTH PROJECTIONS ......................................................... 31 4.2 TOURISM GROWTH ASSUMPTIONS ............................................................................................ 32 4.3 WEST BYRON BAY DEVELOPMENT ASSUMPTIONS ................................................................... 32

5.0 2018 AND 2028 BASE CASE TRAFFIC FORECASTS (NO WEST BYRON

DEVELOPMENT SCENARIO) ................................................................................................ 35

5.1 2018 MODELLING RESULTS (NO WEST BYRON DEVELOPMENT) ............................................... 35 5.2 2028 MODELLING RESULTS (NO WEST BYRON DEVELOPMENT) ............................................... 47

6.0 2018 AND 2028 TRAFFIC FORECASTS WITH THE WEST BYRON ..................................

DEVELOPMENT ....................................................................................................................... 57

7.0 SUMMARY OF MAJOR FINDINGS AND RECOMMENDATIONS .................................. 75

West Byron Development Transport Study Prepared by Veitch Lister Consulting Pty Ltd

10-008 West Byron Development Transport Study Final Report_Mar2011.Docx 1

1.0 Introduction

1.1 Background

In March 2010 the Byron Bay West Landowners Association (BBWLA) commissioned

Veitch Lister Consulting (VLC) to undertake a transport study for a 108 hectare

greenfield urban development at West Byron/Belongil Fields, Byron Bay. The site

location is shown in Figure 1.

Figure 1: Location of the West Byron Development Site

In broad terms, VLC’s commission has involved

• calibration and validation of a travel forecasting model for the subject area for a

“base year” of 2008 for the off-peak tourism period (i.e. during school term

time);

• establishment of a travel forecasting capability for two planning horizons (2018

and 2028);

• production of travel demand forecasts for alternative land use/road network

scenarios for 2018 and 2028, with a view to developing an appropriate transport

strategy for Ewingsdale Road (including site access arrangements and provisions

for pedestrians and cyclists) should development of the subject site proceed;

and



• examination of the need for the mini-bypass and/or full bypass of the Byron Bay

Town Centre.

VLC’s proprietary travel forecasting model of South East Queensland and Northern New

South Wales (Zenith) has been used to produce all the travel demand forecasts

presented in this report. The Zenith model runs within the OmniTRANS transport

modelling software platform. The OmniTRANS software is available commercially

throughout the world, and is the result of a joint development collaboration between

VLC and OmniTRANS International of the Netherlands.

1.2 Structure of Report

The balance of this report is structured as follows:

Section 2: Description of the Zenith travel demand forecasting model

Section 3: Validation of the Zenith model for Byron Shire and contiguous areas for

2008

Section 4: Future land use assumptions (2018 and 2028)

Section 5: 2018 and 2028 Base Case travel forecasts (no West Byron development

scenario)

Section 6: 2018 and 2028 travel demand forecasts with the West Byron

development

Section 7: Summary of major findings and recommendations

This report should be read in conjunction with a Supplementary Report that addresses

issues raised by the Director, Strategic Assessment of the NSW Department of Planning

in a letter to the BBWLA dated 27th

January 2011. In this letter it was requested that

“The traffic study should include current and future levels of service of key surrounding

intersections”.

VLC’s Supplementary Report on Level of Service Changes (dated March 2011) addresses

these issues.



2.0 Description of the Zenith Model

2.1 Introduction

The initial version of the Zenith model of South East Queensland and Northern New

South Wales was developed by VLC in 1995. It operated by application of travel

behaviour relationships derived from Brisbane, Adelaide and Melbourne household

travel surveys, applied to local transport demand and supply conditions.

The calibration parameters used by the model have been progressively updated when

the results of government household travel surveys are released.

The Zenith model’s footprint covers the whole of South East Queensland (SEQ) and

Northern New South Wales (NNSW), as shown in Figure 2.

The standard SEQ/NNSW model contains 1,533 travel zones and over 55,000 transport

network links.

All public transport services operating in the modelled area are included in the model.

This includes the route of each service, its stop locations and stopping patterns, and

service frequency at various times of the day.

The model produces travel forecasts for the following modes of travel.

• Car as a driver or passenger

• Commercial vehicle travel

• Public transport travel (train, bus and ferry)

• Walking and cycling

The Zenith model produces average weekday travel demands during school term time,

and outputs its forecasts for three time periods - AM peak two hours, PM peak two

hours and off-peak. The model produces traffic demands on every link in the road

network, as well as public transport passenger demands and pedestrian/cyclist flows on

every link in their respective networks.

The original Zenith model was mainly focussed on producing accurate travel forecasts

for South East Queensland and Tweed Shire. Byron Shire, Ballina and Lismore were

included in the model, but only as part of a “buffer” network. In other words, only a

simple representation of the transport network, and a fairly crude travel zone system,

were included in the model for these three municipalities.

For the specific purposes of this study the resolution of the Zenith model has been

significantly increased in Byron, Ballina and Lismore. This will result in far more accurate

assessment of the transport impacts of future development within the region. This

aspect of the refinement of the Zenith model is discussed in more detail in Section 3.1 of

this report.



Figure 2: The Zenith Modelled Transport Network (SEQ and Northern New South Wales)



2.2 Structure of the Zenith Model

The Zenith model is a traditional four-step model embracing trip generation, trip

distribution, modal choice and trip assignment.

The model comprises the following sub-models:

• Household segmentation

• Trip production

• Trip attraction

• Travel market segmentation by car availability

• Trip distribution

• Time period trip allocation

• Mode choice

• Trip assignment to the network

The Zenith sub-models are now briefly described.

2.2.1 Household Segmentation Model

For each travel zone, this sub-model generates the proportion of households that will

have discrete socio-economic attributes, based on the average values reported in the

ABS Census. For example, if a travel zone is reported in the Census as having an average

car ownership of 1.4 cars per household, this model estimates the proportion of

households that will have zero cars, 1 car, 2 cars, 3 cars and 4+ cars.

How the model performs against household attribute profiles reported at census

collector district level in the 2006 Census for SEQ and NNSW is shown in Figure 3.



Figure Error! No text of specified style in document.1 Household segmentation curves from ABS Census 2006

against SEQ and NNSW data

(a) Persons

(d) Dependents 0-17

(b) Workers (White Collar)

(e) Dependents 18-64

(c) Workers (Blue Collar)

(f) Dependents 65+

Source:

Data and regression lines shown for each CCD in SEQ

and NNSW from ABS Census 2006, Australian Bureau

of Statistics

(g) Cars



The distribution of household socio-economic profiles within travel zones are important,

as they largely determine the propensity of a zone to generate trips for various purposes

(work, shopping, recreation, education, etc.), as well as influencing choice of destination

and mode of travel.

The household segmentation model provides a means of reflecting the influence of

varying household characteristics on travel behaviour more precisely than using average

zonal household attribute values. The outputs of this sub-model feed directly into the

household trip production model.

2.2.2 Household Trip Production Model

The household trip production model calculates the number of trips that will be

generated by the residents of each travel zone during a typical weekday. These trips are

calculated for a large number of demand segments, enabling the model to reflect more

precisely the travel made by households of a particular type for a range of activities.

Home-based Trip Production Model

The trip production model for home-based travel forecasts the number of trips that will

be made each weekday by households of different type using a “stratified dummy-

variable regression” technique. These trips are estimated for 8 journey purposes defined

by the activity for which the travel is undertaken, and calibrated using the various

household travel surveys undertaken in Australia’s capital cities.

The home-based trip production model predicts household travel demands for 8

purposes:

• HBWW - White collar worker commuting

• HBWB - Blue collar worker commuting

• HBEP - Primary education

• HBES - Secondary education

• HBET - Tertiary education

• HBS - Shopping and personal business

• HBR - Recreation and social

• HBO - Other (including serving the needs of a passenger)

The home-based trip production models are disaggregate, in the sense that they are

calibrated to reflect behaviour of individual households belonging to each household

attribute category, although they are generally applied to forecast the aggregate travel

demands for all households in each zone.

A series of linear-additive relationships have been derived to forecast the number of

trips made by households where each variable represents a certain attribute. These

variables are themselves linear additive functions of dummy variables representing the

discrete attribute levels.



Each household category will be described by only one of the attribute levels, for each

attribute, and thus can be described using dummy variables, which take a value of either

0 or 1.

This enables a complex multi-dimensional set of relationships to be represented by a

family of hierarchic linear relationships, and enable them to be applied in either

aggregate or disaggregate form without the need to forecast the joint distribution of

household attributes. This provides an efficient means of reflecting the behaviour of

different household types and permits a greater degree of market segmentation.

The coefficients used to estimate household and zonal person trip productions are

presented in Table 1.

Table 1: Coefficients used in the Home-based Trip Production Model

Attribute Level Trip production coefficients for purpose

HBWW HBWB HBEP HBES HBET HBS HBR HBO

Persons 1

2 -0.9219

3 -0.6983

4 0.034 0.2812 -0.3888

5 -0.1404 0.6545 -0.1069

6+ -0.0326 1.0380 0.0798

Workers

(White

Collar)

0

1 1.1809 0.0613 0.2909 0.2292

2 2.1699 0.1384 0.5698 0.37

3+ 3.4356 0.3255 1.4263 1.0184

Workers

(Blue Collar)

0 0.1737

1 1.4401 0.0547 0.25 0.0316

2 2.7981 0.1105 0.47 0.1472

3+ 4.2038 0.1789 0.71 0.6458

Dependents

(Aged 0-17)

0 0.1186 0.0560 -5.0447

1 0.4319 0.3171 0.24 0.3034 -3.6353

2 1.4864 0.5285 0.3673 0.6455 -2.0958

3+ 3.1081 0.7097 0.6642 0.9031

Dependents

(Aged 18-64)

0 -0.1268

1 0.1367 0.5675 0.3731

2 0.3976 1.2239 0.6492

3+ 0.9204 1.56 0.91

Dependents

(Aged 65+)

0

1 0.97 0.624

2+ 0.0173 1.76 1.035

Cars 0 -0.0170 -

0.4052

-0.3535 -0.2475

1 -0.0779 -

0.3053

-0.2052 0.1426

2 0.0274 -0.1067 -

0.0039

-0.0344 0.1241

3+ 0.1446 0.0504

Households 0.003 1.130 0.3756 6.0408



Non-home-based Trip Production Model

A significant proportion of travel on an average weekday takes place where the home is

at neither end of the trip. The spatial and technical characteristics of these trips can be

quite different to those for home-based travel. These trips include travel for business,

short trips in the locality of the workplace for lunch, shopping or personal business, trips

comprising part of a daily tour, such as continuing to work after dropping the kids at

school, travelling from one shopping location to another, from work to participate in

leisure or social activities such as the gym before going home, and so on.

The accessibility offered by different modes for these trips can be quite different to that

closer to home. Many of these trips will have an origin or destination within major

business and activity centres, and occur during business hours, when public transport

may be less crowded but also less frequent than in the peak periods.

The Zenith demand models identify a number of key activities by which non-home-

based travel is segmented. These are defined in Table 2.

Table 2: Activities and Purposes for Non-home-based Travel

Primary

activity

Secondary activities Purpose Label

Work Work, business meeting or other

employers business

Work-based work WBW

Shopping and personal business Work-based shopping WBS

All other non-home-based activities,

including education, recreation,

dining and entertainment, and serve

passenger

Work-based other WBO

Shopping Shopping and personal business Shopping-based

shopping

SBS

All other non-home-based activities

(as above)

Shopping-based other SBO

Other All other non-home-based activities

(other than work and shopping as

defined above)

Other-based other OBO

The models used for non-home based trip production make use of aggregate variables

describing the land use and other indicators of activity intensity within each zone. The

variables used within the non-home-based trip production are households, education

enrolments and employment by industry category. The employment categories used in

the non-home-based travel model are presented in Table 3, while the model’s

coefficients are given in Table 4.



Table 3: Employment Variables used in Zenith Non-home-based Travel Model

Variable ANZSIC Description

EAGR A Agriculture, fishing and forestry

EMNG B Mining, oil and gas extraction and supporting services

EMNF C Manufacturing of all products

EEGW D Services related to electricity, gas, water supply and waste management

ECNS E Building and civil engineering construction and services

EWHS F Wholesaling of all goods

ERTL G Retailing of all goods

ETST I Transport postal and warehousing

ECOM J Information, media and telecommunications

EFBS K, L, M, N Financial and business services

EPAD O Public administration

ECSS P, Q Community and social services including education and training, and

healthcare

ERPS H, R, S Recreation and personal services including accommodation and food,

arts and other

EALL All Total employment

.

Table 4: Coefficients for Non-home-based Trip Production Model

Category Variable Trip production coefficients for purpose

WBW WBS WBO SBS SBO OBO

Demographics HLDS 0.1104 0.3506

Enrolment

ENRP 0.6988

ENRS 0.1849 0.2964

ENRT 0.0816 0.0681

Employment EMNF 0.213

EEGW 1.2326 0.213

ECNS 1.0297 0.4251

ERTL 1.0297 0.4962 2.15 2.94 0.3267

ETST 0.213

ECOM 0.4251

EFBS 0.44 1.249

EPAD 1.2326 1.911

ECSS 0.2659 0.213

ERPS 0.4493 0.4251 1.3549 2.0099 0.9333

EALL 0.34



2.2.3 Trip Attraction Model

The trip attraction model calculates the relative level of attraction of each zone for

satisfying the travel generated by the home based trip production model. Obviously

zones containing high levels of retail employment are more likely to satisfy the travel

needs of people undertaking shopping trips. Similarly, zones with high primary school

enrolments will be the major attractors of primary school travel.

The level of “trip attraction” that each travel zone has will vary by journey purpose,

depending on the nature and scale of land use in each zone.

The Trip Attraction Model comprises a series of linear additive functions (one for each

activity/purpose) derived from average attribute values describing the land use and

other indicators of activity intensity within each zone.

The model has the following analytical form for each of the home-based travel markets:

�� = � ��

where Aj is the level of attraction associated with destination j, estimated from the set

of k available land use variables Zkj , with parameters βk determined from linear

regression.

The coefficients of the trip attraction model for home-based travel are shown in Table 5.

Table 5: Coefficients used in the Home-based Trip Attraction Model

Category Variable Trip attraction coefficients for purpose

HWW HWB HBEP HBES HBET HBS HBR HBO

Demographics HLDS 0.083 0.615 0.689

Enrolment

ENRP 1.269 3.3

ENRS 1.075 0.206 0.363

ENRT 0.495 0.038 0.107

Employment EWHS 0.58

ERTL 7.930 0.307 1.010

ECSS 0.381

ERPS 2.000 0.948 1.030

Occupation OCCW 1.236 0.580

OCCB 0.025 0.868

Special generators

(Productions)

0.3

Constant 0 0 0 0 0 0 0 0

F-statistic 2866 246 675 1171 1166 220 91 203



2.2.4 Travel Market Segmentation by Vehicle Ownership

Government household travel surveys clearly show that the level of accessibility a

household has to private motor vehicles influences both the choice of destination and

the mode that will be used to get there.

In order to account for this phenomenon the Zenith model further disaggregates the

home-based travel market by journey purpose into journeys made by non-car owning

households, 1 car, 2 car and 3+ car owning households. The relationships used to

perform this travel market disaggregation are presented in Figure 4, and were derived

from government household travel surveys.

This further travel market segmentation by level of household car availability is

performed prior to trip distribution and modal choice. Zenith uses separately calibrated

trip distribution and modal choice functions for each home-based journey purpose and

level of car availability.

2.2.5 Trip Distribution Model

Zenith employs a ‘traditional’ gravity trip distribution model to reflect the choice of

destinations made by groups of households or individuals sharing the same origin and,

for home-based trips, the same car ownership level.

The trip distribution model is used to allocate the trips produced in each zone to the

relevant destinations for each activity in accordance with the number of opportunities

(attraction level) available and the generalised cost of travel to reach them.

The model is applied simultaneously for each of the travel market segment groups

defined by journey purpose (8) and household car ownership level (4), resulting in 32

separate trip distribution functions for home-based travel.

For a trip from origin zone i, the model determines the probability that each destination

j will be chosen from all possible alternatives k.

The model has the following analytical form:

Pr�� = �� ∝ ��

∑ �� ∝ ��

where Aj is the level of attraction associated with destination j, and Cij is the generalised

cost of travelling from zone i to zone j. The generalised costs of travel used in the

distribution model are derived from the inclusive values calculated in the hierarchical

mode choice model, described later in Section 2.2.7. These reflect the demand-

weighted disutility of travel for each of the market segments, inclusive of all modes.



Figure 4 Segmentation of travel demands by car ownership for SEQ

(a) HBW (White Collar)

(b) HBW (Blue Collar)

(c) HBE (Primary)

(d) HBE (Secondary)

(e) HBE (Tertiary)

(f) HBS

(g) HBR

(h) HBO



The parameter β is used to set the relative influence of travel cost against the

attractiveness of the destination, while the parameter α provides ‘fine-tuning’ for

shorter trips and has less influence over longer trips. Both parameters are calibrated

using a maximum log-likelihood technique.

The parameter values for home-based travel are provided in Table 6. All of the β

parameters are negative, while all α parameters except those for education travel are

negative.

Table 6: Parameters of Trip Distribution Model for Home-based Trips

Activity Segment Parameters

α β x 100

HBW (White Collar)

1 0 cars -1.2995 -0.2036

2 1 car -0.9033 -0.1904

3 2 cars -1.1648 -0.1623

4 3 cars -1.2901 -0.1828

HBW (Blue Collar)

5 0 cars -1.4756 -0.2018

6 1 car -0.5980 -0.2492

7 2 cars -0.6407 -0.2884

8 3 cars -1.0845 -0.2271

HBE (Primary) 9 0 cars -2.0459 -1.0605

10 1 car 0.4298 -1.4505

11 2 cars 1.3347 -1.6068

12 3 cars 4.8232 -3.3488

HBE (Secondary) 13 0 cars 2.3914 -1.3125

14 1 car 0.2902 -0.5933

15 2 cars 0.8325 -0.7105

16 3 cars 0.3868 -0.5703

HBE (Tertiary) 17 0 cars 3.0411 -1.1538

18 1 car 2.2085 -0.7776

19 2 cars 0.8303 -0.4594

20 3 cars -0.0743 -0.2155

HBS 21 0 cars -1.3387 -0.5271

22 1 car -0.7845 -0.6796

23 2 cars -2.8127 -0.0915

24 3 cars -2.7393 -0.0461

HBR 25 0 cars -1.3299 -0.3460

26 1 car -1.0647 -0.3573

27 2 cars -2.1609 -0.0992

28 3 cars -1.8176 -0.1420

HBO 29 0 cars -2.3661 -0.0645

30 1 car -2.0552 -0.9031

31 2 cars -1.8052 -1.0870

32 3 cars -1.6911 -0.9887



2.2.6 Period Allocation Model

The Period Allocation Model allocates trips forecast for each market segment for an

average weekday, into individual periods of the day, including the early morning,

morning peak, inter-peak, afternoon peak and evening periods.

This is to take account of differences in trip making behaviour by purpose and time of

day to enable robust estimates of both peak and off-peak travel demand. It is applied

immediately after Trip Distribution and before Modal Choice.

The proportion of trips allocated to the various time periods is shown by trip purpose in

Figure 5.

Figure 5: Period Allocation of Trips by Travel Market Segment

2.2.7 Mode Choice Model

The mode choice model in Zenith is applied to reflect the full range of options available

for door-to-door travel. It considers public transport as an integrated system, and the

alternative means of access to and egress from the system (walking/cycling or car access

- park-and-ride/kiss-and-ride).

The mode choice model is a hierarchical binary logit model, containing levels (or a

nesting of alternatives) which address the following choices explicitly:

• Whether to use a form of motorised transport or to walk/cycle

• If using motorised transport, whether to use public or private transport

• If using public transport, how to access and egress from the system, by

walking/cycling or by car



The mode choice model has the structure shown in

Figure 6: Structure of the

The random utility

above hierarchical tree ha

where Vjm is the utility of alternative

consideration, being one of a feasible set of alternatives

parameters λm and the alternative specific constant (ASC) for each nest are provided in

Table 8.

.

Development Transport Study Prepared by Veitch Lister

West Byron Development Transport Study Final Report_Mar2011.Docx

model has the structure shown in Figure 6.

Structure of the Mode Choice Model

The random utility function that describes the choice of mode for each “nest” in the

above hierarchical tree has the following analytical form:

Pr�� | �� = ��

∑ ��

is the utility of alternative j within nest m, and i is the alternative under

consideration, being one of a feasible set of alternatives Jm associated with nest

and the alternative specific constant (ASC) for each nest are provided in

Veitch Lister Consulting Pty Ltd

16

of mode for each “nest” in the

is the alternative under

associated with nest m. The

and the alternative specific constant (ASC) for each nest are provided in



Table 8: Parameters of Mode Choice Model

Segment Access to public transport Public/private transport Motorised Transport

λ x 100 ASC λ x 100 ASC λ x 100 ASC

1 HBWW01 0.1605 -49 0.2007 631 0.2127 1031

2 HBWW2 0.1594 450 0.2473 318 0.2838 401

3 HBWW3 0.3300 138 0.3072 201 0.3807 328

4 HBW B01 0.1605 -49 0.2007 708 0.2127 1031

5 HBWB2 0.1594 450 0.2473 395 0.2838 401

6 HBWB3 0.3300 138 0.3072 278 0.3807 328

7 HBEP

8 HBES 0.2622 -257 0.0850 653 0.2233 1107

9 HBET 0.1433 -97 0.0975 1375 0.1706 1121

10 HBS01 0.2810 -226 0.1850 403 0.2133 1389

11 HBS23 0.3015 -52 0.2350 299 0.2972 720

12 HBR01 0.2810 -226 0.1900 266 0.2133 1389

13 HBR23 0.3015 -52 0.2400 162 0.2972 720

14 HBO 0.2785 158 0.1553 -1335 0.3374 987

15 WBW 0.3220 -75 0.2412 -868 0.2668 830

16 WBS 0.3220 -75 0.2262 -315 0.2668 830

17 WBO 0.3220 -75 0.2262 -294 0.2668 830

18 SBZ 0.2445 -108 0.2020 -20 0.2044 1089

19 OBO 0.2445 -108 0.1500 -274 0.2044 1089

20 APT 0.3015 -52 0.2400 162 0.2972 720

21 SRC 0.3015 -52 0.2400 162 0.2972 720

22 GV

23 HBV 0.2810 -226 0.1225 -28 0.2133 1389

24 OBV 0.2810 -226 0.1225 -28 0.2133 1389

25 EXT

The model calibration parameters in Table 8 were derived from government household

travel surveys.

2.2.8 Trip Assignment

The Zenith model is a “link based” model. Travel speeds on the dedicated public

transport network in SEQ/NNSW - i.e. the rail system, busways and the ferry system -

are coded to reflect the published timetables.

Travel speeds on the road network are estimated for each link as a function of the traffic

demand on the link, its capacity and free flow speed.

As the model does not specifically analyse intersection delays by turning movement, link

capacities are set to reflect the form of traffic operation on the approaches to

intersections - number of lanes and whether the form of operation is a traffic signal,

roundabout or priority junction.



The general form of

where V and V0 are the calculated and free

average ratio of volume to capacity during the model period, and α, β, γ, δ and ε are

parameters estimated for each road segment accordin

represent the conditions referred to above.

At present, the link parameters are distinguished only for roads with at

(urban roads) and for grade

These relationships are illustrated in

Figure 7: Speed/flow

The Zenith uses an ”

congestion to affect driver route choice. The assignment is run for a number of

iterations until all road users are taking routes that minimise their travel cost under the

prevailing traffic conditions.

Development Transport Study Prepared by Veitch Lister

West Byron Development Transport Study Final Report_Mar2011.Docx

the model used to forecast road travel times in the Zenith model is:

� = � !" # �1 % " /�1 # ' ()�*

+

are the calculated and free-flow travel speed respectively,


parameters estimated for each road segment according to its classification which

represent the conditions referred to above.

At present, the link parameters are distinguished only for roads with at

and for grade-separated, limited access roads such as

nships are illustrated in Figure 7.

Speed/flow Relationships

”equilibrium” traffic assignment technique that allows delays due to



prevailing traffic conditions.

Veitch Lister Consulting Pty Ltd

18

travel times in the Zenith model is:

( * # ,) -

flow travel speed respectively, x is the


g to its classification which

At present, the link parameters are distinguished only for roads with at-grade junctions

separated, limited access roads such as freeways.

allows delays due to





3.0 Development of 2008 Travel Model and Validation

3.1 Introduction

The Zenith model of South East Queensland (SEQ) has been comprehensively validated

against traffic counts undertaken by the Queensland Department of Transport and Main

Roads (QDTMR) and the various Councils in SEQ. QDTMR also provides traffic counts on

all roads crossing a comprehensive set of screenlines in Brisbane, that enables observed

traffic flows between the various areas of the region to be compared with modelled

estimates.

Section 3.2 presents the results of the 2008 validation of the Zenith model’s traffic

estimates for Brisbane’s screenlines.

For this study however, the more pertinent issue is how accurately does the model

replicate observed traffic flows in the Byron Bay Township and immediate environs?

Section 3.3 presents the model validation results when comparing 2008 modelled traffic

volumes within the Byron Bay Township and surrounds with 2008 traffic counts

undertaken by Council and the RTA.

In order to more accurately predict traffic volumes within Byron Bay, Ballina and

Lismore, the travel zone system within the standard Zenith model has been

disaggregated. This initially involved the adoption of Australian Bureau of Statistics

census collector districts as the basis for the travel zone system. The travel zone system

within the Byron Bay Township was then further disaggregated to sub town block level.

The finally adopted travel zone system within the 2008 base year model, within Byron

Bay, is shown in Figure 8.

Figure 8: Zenith Model Travel Zone System in Byron Bay



The version of the Zenith model used for this study has a total of 1,943 travel zones -

including 199 in Byron Shire, 91 in Ballina and 121 in Lismore.

The base year (2008) modelled transport network contains 58,968 links.

3.2 Zenith Model Validation against 2008 SEQ Screenline Traffic Counts

The 2008 version of the Zenith model includes in its model inputs the population and

household demographic data, and employment estimates, derived from the Australian

Bureau of Statistics (ABS) for the 2006 national census and ABS released Estimated

Residential Population (ERP) figures for 2008. The model’s transport network also

reflects the road network and public transport system as it was in 2008.

Table 9 compares the Zenith model’s estimates of 2008 traffic volumes on all roads

crossing the SEQ screenlines shown in Figure 9 with Government traffic counts.

In terms of the total traffic crossing each screenline, the modelled and observed traffic

flows are extremely close - as evidenced in Figure 10, where an R2

correlation of 0.9964

has been achieved. This suggests that the model is extremely closely replicating

observed traffic demands between the various regions of South East Queensland.

Figure 11 shows a scatter plot of modelled traffic volumes versus counts for the

individual traffic count locations on the screenlines. Again the correspondence is

excellent - with an R2 of 0.9747 being achieved.



Figure 9: SEQ Screenlines for 2008 Zenith Model Validation



Screenline Name # Counts Count Volume Modelled Volume Difference (%)

BCC - Brisbane River 20 513,834 519,003 1.0%

BCC - CBD North 18 370,273 323,118 -12.7%

BCC - East Bdy 18 266,163 269,060 1.1%

BCC - Inner North & West 34 449,391 415,323 -7.6%

BCC - Inner South & East 38 687,651 695,029 1.1%

BCC - North Bdy 22 291,258 298,305 2.4%

BCC - South Bdy 6 76,119 89,652 17.8%

BCC - West Bdy 16 147,968 161,896 9.4%

Boonah-Beaudesert Bdy. 4 1,578 2,031 28.7%

Caloundra - Maroochy Bdy 10 98,413 93,709 -4.8%

GC - EW0 Albert River 8 127,840 136,877 7.1%

GC - EW3 Helensvale 16 218,825 215,567 -1.5%

GC - EW6 Tallebudgera Crk 10 122,859 135,322 10.1%

GC-EW1 - Hotham Creek 8 98,339 111,855 13.7%

GC-EW2 - Coomera River 8 135,427 143,799 6.2%

GC-EW4 - Nerang River 24 297,963 283,823 -4.7%

GC-EW5 - Miami-Robina 14 111,597 113,522 1.7%

GC-EW7 - Tweed Bdy. 8 70,440 79,164 12.4%

GC-NS1 - Coomera-Tugun Rai 40 442,429 404,903 -8.5%

GC-NS2 - Paradise Point-Ro 24 159,759 141,271 -11.6%

GC-NS3 - Southport-Coolang 14 126,130 115,843 -8.2%

Gold Coast - Beaudesert Bd 14 29,904 38,094 27.4%

ICC - CBD Ring 22 146,598 136,811 -6.7%

ICC - East 17 110,984 104,450 -5.9%

ICC - Inner East 18 132,006 115,391 -12.6%

ICC - Inner West 12 74,378 73,567 -1.1%

ICC - North 18 42,322 43,365 2.5%

Ipswich - Boonah Bdy. 6 12,740 8,728 -31.5%

Ipswich-Logan Bdy 8 51,649 56,255 8.9%

Maroochy - Noosa Bdy 18 58,774 49,144 -16.4%

Pine - Caboolture Bdy. 16 142,952 134,554 -5.9%

Pumicestone (N/S) 8 27,533 25,189 -8.5%

Redcliffe - Pine Bdy. 8 94,452 93,880 -0.6%

Redland (N/S) 16 101,653 98,021 -3.6%

Redland (Redland Bay Road) 14 147,532 131,078 -11.2%

Redland - BCC Bdy 8 114,409 122,827 7.4%

Redland - Logan Bdy 10 19,074 28,138 47.5%

Redland Bay (E/W) 12 56,430 57,859 2.5%

SEQ Inner Bdy. - North 10 57,717 51,053 -11.5%

SEQ Inner Bdy. - South 14 194,900 217,852 11.8%

SEQ Inner Bdy. - West 16 54,364 56,713 4.3%

SEQ Outer Bdy. - North 4 16,901 9,383 -44.5%

SEQ Outer Bdy. - South 14 88,001 92,436 5.0%

SEQ Outer Bdy. - West 14 32,136 24,134 -24.9%

SSC-EW1: Maroochy River 4 46,879 45,039 -3.9%

SSC-NS1 26 148,988 156,639 5.1%

TOTAL 6,817,532 6,719,674 -1.4%

Table 9: Screenline Totals for 2008 (Modelled versus Count)

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Count Id Name Location Direction Counts Modelled

Abs difference

(mod-counts)

% difference

(mod-counts)

5018 Ewingsdale Rd East of Woodford Ln Eastb. 7329 7509 180 2%

5019 Ewingsdale Rd East of Woodford Ln Westb. 7329 7234 -95 -1%

5020 Ewingsdale Rd West of Bayshore Dr Westb. 7272 7441 169 2%

5021 Ewingsdale Rd West of Bayshore Dr Eastb. 7272 7712 440 6%

5022 Ewingsdale Rd East of Melaleuca Rd Westb. 7043 6954 -89 -1%

5023 Ewingsdale Rd East of Melaleuca Rd Eastb. 7043 7167 124 2%

5024 Ewingsdale Rd East of Sunrise Bvd Eastb. 8170 7531 -640 -8%

5025 Ewingsdale Rd East of Sunrise Bvd Westb. 8170 7288 -882 -11%

5026 Lawson St Railway Crossing Eastb. 9059 8526 -533 -6%

5027 Lawson St Railway Crossing Westb. 9059 8305 -753 -8%

5028 Bangalow Rd South of Browning St Southb. 5449 7007 1558 29%

5029 Bangalow Rd South of Browning St Northb. 5449 6906 1456 27%

5030 Bangalow Rd North of Old Bangalow Rd Southb. 7666 6630 -1036 -14%

5031 Bangalow Rd North of Old Bangalow Rd Northb. 7666 6544 -1123 -15%

5046 Pacific Hwy North of Ross Ln Northb. 7746 7442 -303 -4%

5047 Pacific Hwy North of Ross Ln Southb. 7746 7469 -277 -4%

5066 Pacific Hwy South of Ewingsdale Rd Northb. 9036 9376 340 4%

5067 Pacific Hwy South of Ewingsdale Rd Southb. 9036 9239 203 2%

5068 Pacific Hwy North of Ewingsdale Rd Northb. 13214 12858 -356 -3%

5069 Pacific Hwy North of Ewingsdale Rd Southb. 13214 13013 -200 -2%

5074 Ballina-Ewingsdale North of Broken Head Rd Northb. 7445 6348 -1097 -15%

5075 Ballina-Ewingsdale North of Broken Head Rd Southb. 7445 6435 -1010 -14%

5086 Ballina-Ewingsdale Lennox Head, The Coast Road Southb. 3241 3477 236 7%

5087 Ballina-Ewingsdale Lennox Head, The Coast Road Northb. 3241 3544 303 9%

5090 Ballina-Ewingsdale Byron Bay Rd - North of Lennox Head Southb. 2676 2269 -407 -15%

5091 Ballina-Ewingsdale Byron Bay Rd - North of Lennox Head Northb. 2676 2337 -339 -13%

5092 Bangalow Rd East of Pacific Hwy Eastb. 2592 2391 -201 -8%

5093 Bangalow Rd East of Pacific Hwy Westb. 2592 2549 -43 -2%

5100 Lismore-Bangalow Rd West of Granuaille Rd Eastb. 4433 3660 -773 -17%

5101 Lismore-Bangalow Rd West of Granuaille Rd Westb. 4433 3648 -784 -18%

3.3 Zenith Model Validation against 2008 Byron Bay Traffic Counts

Table 10 compares the Zenith model’s 2008 traffic forecasts with RTA and Council traffic

counts in the Byron Bay Township and on its approach roads. The count locations used

to validate the model are shown in Figure 13.

Both the counts and the modelled traffic volumes presented in Table 10 are for the low

tourism season (i.e. during school term time).

Referring to Figure 12, which presents a plot of counted and modelled traffic volumes, it

is evident that the Zenith model’s traffic forecasts for 2008 are closely replicating

observed traffic flows within Byron Bay and on its approaches.

The R2 correlation coefficient is 0.9422, which is an excellent outcome.

The only major discrepancy between modelled volumes and Council’s traffic counts is

on Bangalow Rd (south of Browning St). However, VLC believes Council’s traffic count

may be in error, as it is inconsistent with the nearby traffic count on Bangalow Rd (North

of Old Bangalow Rd).

Table 10: Modelled and Observed 2008 Traffic Volumes (weekday - 24 hour)



y = 1.0233x

R² = 0.9422

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Individual Counts - Byron Bay area

Figure 12: Scatter Plot for 2008 (Count versus Modelled)

Figure 13: Count Locations for Model Validation in Byron Bay



3.4 Current Traffic Demand (2008) in Byron Bay (low tourist season)

Based on traffic counts and Zenith modelled traffic volumes, the highest traffic volume

within Byron Bay occurs at the Lawson Street level crossing, with a daily traffic volume

of about 18,000 vehicles (average weekday during the low tourism season). This,

combined with weekday traffic volumes exceeding 10,000 vehicles per day (vpd) on

Jonson Street, results in the Lawson Street/Jonson Street roundabout operating at close

to capacity during the morning and evening peak periods.

During peak holiday periods traffic demand in Byron Bay increases by 20-30 percent.

This results in the Lawson Street/Jonson Street roundabout being seriously overloaded.

This can cause long traffic queues to develop on all approaches to the roundabout for

extended periods of the day. During such times a queue of traffic extending several

kilometres along Ewingsdale Road is a not uncommon occurrence, causing significant

delays to traffic attempting to enter the Town Centre from the west.

Council traffic counts indicate that Ewingsdale Road is heavily trafficked on all sections

from the Pacific Highway through to Shirley Street. Traffic demands in 2008 on its

various sections are as follows:

• East of the Pacific Highway - 14,660 vpd

• Between BayshoreDrive and Sunrise Boulevard - 14,080 vpd

• East of Sunrise Boulevard - 16,340 vpd

Figures 14, 15 and 16 show the Zenith model’s base year (2008) traffic forecasts for the

modelled road network within the Byron Bay Township, and on Ewingsdale Road

extending as far as the Pacific Highway interchange.

3.5 Historical Traffic Growth

Between 1996 and 2008 traffic demand on the Pacific Highway north of the Ewingsdale

Road interchange increased dramatically - from about 15,000 vpd to 26,500 vpd (a 77%

increase in just 12 years). The progressive upgrading of the Highway, and the opening

of the Tugun Bypass, have undoubtedly contributed toward this growth by making both

Byron Bay and Ballina far more accessible from the South East Queensland Region.

In the same 12 year period (1996-2008) traffic on Ewingsdale Road has increased by

over 50 percent. Average weekday traffic demand on Ewingsdale has been increasing

by almost 1,000 vpd every two years - i.e. demand is increasing by about 475 vehicles

each year.

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4.0 Future Land Use and Road Network Assumptions 2018 and 2028

VLC has set up 2018 and 2028 versions of the Zenith model that reflect State

Government projections of population and in Northern New South Wales and the South

East Queensland Region. Both the 2018 and 2028 road networks used in the modelling

include the RTA upgrade of the Pacific Highway between Ballina and Byron Bay.

4.1 Population and Employment Growth Projections

Table 11 present the population and employment growth projections that were used in

the travel modelling - for Byron, Ballina and Lismore Shires. The Byron Shire

projections do not include any allowance for the proposed development at West Byron

Bay.

The 2008 population figures for the three Shires were supplied by the Australian Bureau

of Statistics (ABS), and are derived from the ABS 2006 national census and residential

building completion data in the period 2006-2008.

The 2018 and 2028 population projections were provided by the Department of

Planning NSW.

The 2008 employment used in the modelling has adopted ABS 2006 Journey to Work

employment numbers as a base, and then adjusted them upward to take account of

2006-2008 population growth, assuming a constant workforce participation rate.

The 2018 and 2028 employment projections also assume a constant workforce

participation rate.

Table 11: Population and Employment Growth Projections

Year LGA Population Households Employment

2008 Byron 31,505 12,987 12,073

2018 Byron 36,038 14,865 13,912

2028 Byron 40,661 16,778 15,793

2008 Ballina 41,679 17,456 14,297

2018 Ballina 46,878 19,503 16,080

2028 Ballina 52,285 21,631 17,935

2008 Lismore 44,989 18,199 20,800

2018 Lismore 46,224 18,676 21,371

2028 Lismore 47,990 19,359 22,187

2008 TOTAL 118,173 48,642 47,170

2018 TOTAL 129,140 53,045 51,363

2028 TOTAL 140,936 57,768 55,916



4.2 Tourism Growth Assumptions

Tourism is a major driver of the local economy, and a major contributor to travel activity

and traffic growth in the region. Consequently, accurately forecasting future tourism

activity is important for the robust planning of new or expanded road network

infrastructure.

Peter Valerio of Tourism Strategy Development Services (TSDS) was engaged by the

Byron Bay West Landholders Association to provide estimates of future tourism activity

in Byron Shire that would be used in the travel modelling. His assessment of the growth

in tourism that will occur in Byron Shire by 2018 and 2028 is presented in Table 12.

Table 12: Tourism Growth Projections (source: TSDS)

The visitor related travel markets within the Zenith model were adjusted in 2018 and

2028 to reflect the growth projections contained in Table 12.

4.3 West Byron Bay Development Assumptions

In order to assess the traffic impact of the development at West Byron Bay required an

additional 18 travel zones to be added to the Zenith Model. The subject site was

originally modelled as a single travel zone. The now 19 zone travel zone system within

the site is shown in Figure 17.

Table 13 details the population and employment assumptions used in the modelling for

each of the 19 travel zones.

2008 2018 2028 2018 2028

Domestic Overnight 887,000 908,000 856,000 2.4 -3.5

Domestic Day 2,635,000 3,030,000 3,337,000 15 26.6

International Overnight 183,000 208,000 235,000 13.7 28.4

Growth from 2008 (%)Annual Visitor Numbers to Byron BayType of Visitors

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Zone ID Population Households Ave. HH Size Employment

1717 59 22 2.66 0

1939 0 0 0 129

1940 0 0 0 10

1941 173 65 2.66 0

1942 226 85 2.66 0

1943 189 71 2.66 0

1944 0 0 0 2

1945 263 99 2.66 0

1946 173 65 2.66 0

1947 165 62 2.66 0

1948 170 64 2.66 0

1949 144 54 2.66 0

1950 170 64 2.66 0

1951 51 19 2.66 0

1952 154 58 2.66 0

1953 51 19 2.66 0

1954 0 0 0 10

1955 81 45 1.79 100

1956 115 64 1.79 128

West Byron TOTAL 2,182 856 2.55 379

Table 13: West Byron Development Assumptions

The development has been assumed to contain about 855 households. Assuming all

households to be occupied, this translates into the development accommodating about

2,180 residents at an average household size of 2.55; however, the average household

size does vary across the site as indicated in Table 13.

VLC considers it appropriate to assume that all households will be occupied for traffic

modelling and planning purposes, although it may lead to slightly higher modelled traffic

generation than might actually occur.

For modelling purposes it has been assumed that West Byron will be fully developed in

both 2018 and 2028 model runs. The reality is, however, that it will only be partially

developed by 2018.



5.0 2018 and 2028 Base Case Traffic Forecasts (no West Byron Development Scenario)

This section of the report presents the Zenith model traffic forecasts for 2018 and 2028

assuming no development occurs at West Byron. The upgrading of the Pacific Highway

between Ballina and Byron Bay has been incorporated in the road networks used for all

these model runs.

A primary aims of the analysis presented in this section of the report is to examine the

impact of various configurations of the Town Centre Bypass, and to assess the need for

upgrading of Ewingsdale Road.

5.1 2018 Modelling Results (no West Byron Development)

Five 2018 model runs have been performed, as follows:

a) 2018 Base Case - assumes no upgrading of the road network within Byron Bay

b) As for (a) above, but with Ewingsdale Road upgraded to 4 lanes

c) Ewingsdale Road 4-laned, but including the Mini-Bypass proposal using the

Butler Street alignment, then crossing the railway line and connecting into a

roundabout at the intersection of Jonson Street and Marvel Street.

d) Ewingsdale Road 4-laned in combination with the Long Bypass proposal, with no

intermediate connection to Jonson Street at Marvel Street

e) As for (d) above, but with an intermediate connection to Jonson Street at

Marvel Street.

All the traffic forecasts presented in this section of the report are for average weekday

conditions during school term time (i.e. low tourism season).

2018 Base Case Traffic Forecasts

Assuming no development occurs at West Byron, and no upgrading of the road network

takes place, the Zenith model predicts that by 2018 traffic on Ewingsdale Road, west of

Bayshore Drive will increase to 18,820 vpd - a 24 percent increase on 2008 traffic levels

(refer Figures 15 and 18).

Similar levels of traffic increase are forecast along the entire length of Ewingsdale Road,

and at the Lawson Street level crossing. Traffic demand at the Lawson Street level

crossing will be approaching 20,000 vpd.

Predicted 2018 Base Case daily traffic flows within the Town Centre are presented in

Figure 19.

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Based on these modelling results, VLC concludes that the Lawson Street/Jonson Street

roundabout will be operating at, or close to, capacity during the low tourism period.

Instances where traffic banks up on all approaches to the roundabout will be far more

prevalent, and pedestrian/vehicle conflict will increase in the heart of the Town Centre.

Safely entering and leaving the higher speed limit sections of Ewingsdale via side road

connections will also become an increasing safety problem - at McGettigans Lane,

Bayshore Drive and Sunrise Boulevard. The introduction of roundabouts at key access

locations to Ewingsdale Road will significantly reduce safety concerns by slowing traffic

speeds and providing for more efficient side road access - particularly for those

attempting to access Ewingsdale Road by making a right hand turn.

Four-Laning of Ewingsdale Road

A model run has been performed to assess the impact of 4-laning Ewingsdale Road.

While a 4 lane, divided carriageway (with central median), and roundabouts at key side

access road locations, will provide a safer road traffic environment for both vehicles and

pedestrians crossing Ewingsdale Road - this alone is not the answer to Byron Bay’s

traffic and pedestrian problems.

Traffic Impact of the Mini-Bypass

When the 4-laning of Ewingsdale Road is tested in combination with the Mini-Bypass

proposal results in the 2018 traffic predictions shown in Figure 20.

Figure 21 shows the change in traffic volumes relative to the 4-laning of Ewingsdale

Road scenario. In this figure red indicates where traffic is predicted to increase as a

result of the Mini-Bypass proposal, while blue indicates a traffic reduction. The main

conclusions are:

• The Mini-Bypass, in 2018, is predicted to reduce traffic at the Lawson Street

level crossing by 6,680 vpd (a reduction of 32%).

• Traffic using the northern end of Jonson Street reduces by 5,610 vpd (a 48%

reduction).

• The number of vehicles entering the critical Lawson Street/Jonson Street

roundabout reduces from 19,720 vpd to 13,620 (a 31% reduction) - or 17%

lower than present day conditions.

• The main effect of the Mini-Bypass is to significantly reduce traffic in the areas

of the Town Centre where pedestrian activity is the most intense - i.e. in the

vicinity of the Lawson Street roundabout and the northern end of Jonson Street.

• As expected, the Mini-Bypass has little impact on traffic flows in the southern

half of the Town Centre.

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Traffic Impact of the Long Bypass

Figures 22 and 23 show the 2018 traffic predictions should the Long Bypass, with no

intermediate connection to Marvel Street, be built instead of the Mini-Bypass. It’s

impacts are as follows:

• The Long Bypass, in 2018, is predicted to reduce traffic at the Lawson Street

level crossing by 4,710 vpd (a reduction of 22%).


reduction)


roundabout reduces from 19,720 vpd to 15,280 (a 20% reduction).

• Traffic entering the roundabout in 2018, with the Long Bypass operational, is 6

percent lower than present day conditions.

• Traffic is reduced in the vicinity of the Lawson Street/Jonson Street roundabout

where pedestrian activity is highest, but its impact is not as pronounced as the

Mini-Bypass.

• The Long Bypass has more impact than the Mini-Bypass in reducing traffic on

the southern end of Jonson Street.

Based on the above, VLC concludes that the Mini-Bypass is more effective than the long

bypass in solving Byron Bay’s traffic problems. The Mini-Bypass is also a cheaper option

to construct.

Traffic Impact of the Long Bypass with Intermediate Connection to Marvel

Street

VLC has also tested an option which combines the Mini-Bypass and Long Bypass options

- i.e. the Long Bypass with an intermediate connection to Marvel Street. The modelling

results are presented in Figures 24 and 25.

The impacts of this more expansive project are as follows:

• In 2018, it is predicted to reduce traffic at the Lawson Street level crossing by

7,420 vpd (a reduction of 35%).


reduction)


roundabout reduces from 19,720 vpd to 12,970 (a 34% reduction).

• Traffic entering the roundabout in 2018, with both bypasses operational, is 21%

lower than present day conditions.



• Traffic is reduced in the vicinity of the Lawson Street/Jonson Street roundabout

where pedestrian activity is highest. In this regard it has the highest beneficial

impact of all the options tested.

• The combined bypass option reduces traffic throughout the Town Centre, east

of the railway line.

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5.2 2028 Modelling Results (no West Byron Development)

The 5 road network options tested for a 2018 planning horizon (as described in Section

5.1) have also been tested for a 2028 planning year. The major findings from these

model runs are now summarised.

2028 Base Case Traffic Forecasts and 4-Laning of Ewingsdale Road

Assuming no development occurs at West Byron, and no upgrading of the road network

takes place, the Zenith model predicts that by 2028 traffic on Ewingsdale Road, west of

Bayshore Drive, will increase to 20,340 vpd (from 15,150 vpd in 2008) - a 35 percent

increase (refer Figure 26).

Similar levels of traffic increase are forecast along the entire length of Ewingsdale Road.

The Lawson Street level crossing is expected to carry about 21,000 vpd. However, if

Ewingsdale Road is upgraded to 4 lanes then traffic at the level crossing is expected to

increase to over 23,000 vpd. This level of daily traffic demand is comparable with what

currently occurs during the peak of the holiday season.

Predicted 2028 Base Case daily traffic flows within the Town Centre are presented in

Figure 27.

VLC concludes from these results that by 2028, if Lawson Street is still the sole road

crossing of the railway line then the Lawson Street/Jonson Street roundabout will be

operating well beyond its traffic carrying capacity, not just during peak tourism periods

but also during peak traffic periods throughout the year.

The scale of traffic increase predicted by 2028 suggests that a corridor should be

preserved along Ewingsdale Road that will allow for the provision of a 4-lane divided

carriageway at some point in the future. 4-laning is also necessary for the efficient

functioning of the road and reduction of pedestrian/vehicle conflicts.

Traffic Impact of the Mini-Bypass

The impact of the Mini-Bypass in 2028 is shown in Figures 28 and 29.

In essence, the impact of the Mini-Bypass in 2028 is similar to that reported in Section

5.1 for 2018. It provides substantial traffic relief for the Lawson Street/Jonson Street

roundabout. Traffic entering the roundabout reduces from 22,360 vpd in the 4-laning of

Ewingsdale Road scenario to 14,540 vpd (a 35% reduction). This level of 2028 traffic

demand at the roundabout is 11% lower than 2008 low tourism season demand.

Substantial traffic reduction on the northern end of Jonson Street is still evident.

Traffic using the Bypass increases from 6,590 vpd in 2018 to 7,820 vpd in 2028 (a 19%

increase).

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Traffic Impact of the Long Bypass

The traffic impacts of the Long Bypass in 2028, without an intermediate connection to

Marvel Street, are presented in Figures 30 and 31.

While traffic using the Long Bypass increases between 2018 and 2028 by about 19%,

traffic volumes on the Lawson Street level crossing also increase - to levels exceeding

2008 low tourism season demand.

This leads VLC to conclude that the Long Bypass, on its own, is not the optimal long term

solution to Byron Bay’s traffic problems.

Traffic Impact of the Long Bypass with Intermediate Connection to Marvel

Street

The 2028 traffic impacts of the Long Bypass, with intermediate connection to Marvel

Street, are presented in Figures 32 and 33.

This option performs the best in terms of reducing traffic on the Lawson Street level

crossing and the northern end of Jonson Street.

It also reduces traffic in the southern portion of the Town Centre, with resultant

amenity benefits.

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10-008 West Byron Development Transport Study Final Report_Mar2011.Docx

6.0 2018 and 2028 Traffic Forecasts with the West Byron Development

This section of the report presents the modelling results for a series of model runs that

include the full West Byron Development. The results are presented in Figures 34

through 49.

The model runs that were performed, and the corresponding figure numbers that

contain their respective results, were as follows:

2018 Model Runs with West Byron Development

• Road network including the Mini-Bypass (Figures 34-37)

• Road network including both Bypasses (Figures 38-41)

2028 Model Runs with West Byron Development

• Road network including the Mini-Bypass (Figures 42-45)

• Road network including both Bypasses (Figures 46-49)

Each group of 4 figures presents the forecast average weekday traffic (low tourism

season) for Ewingsdale Road and the Town Centre, and the change in traffic volume

attributable the West Byron Development.

The main observations and findings to emerge from these figures are as follows:

1. The West Byron Development, as proposed, will contain 856 residential

dwellings housing a population of 2,182. It will also accommodate business and

light industry with an estimated employment of about 379.

2. The Development, at this scale, is predicted to generate about 6,000 vehicle

trips each weekday that will either enter or leave the site via Ewingsdale Road.

3. This scale of traffic interaction with Ewingsdale Road is best accommodated by

providing two site entry/exits.

4. There is little difference between 2018 and 2028 as to how this traffic disperses

once leaving the West Byron Development. Also, whether the Mini-Bypass is

built, or both Bypasses (Mini and Long) does not alter how traffic behaves when

leaving the Development.


10-008 West Byron Development Transport Study Final Report_Mar2011.Docx

5. The most significant traffic impacts of the traffic generated by the West Byron

Bay Development are on Ewingsdale Road, in close proximity to the site. In

2018 these impacts are:

• East of Sunrise Boulevard traffic increases by about 1,600 vpd as a result

of the development (or by 8%)

• West of Bayshore Drive traffic increases by 1,350 vpd (a 6.6% increase)

• On the section of Ewingsdale Road between the two access points to

the development traffic increases by 1,270 vpd (a 6.7% increase)

6. In percentage terms, these impacts on Ewingsdale Road impacts are slightly less

in 2028.

7. As traffic generated by the Development disperses across the road network its

impact gradually diminishes.

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7.0 Summary of Major Findings and Recommendations

This section of the report summarises the study’s major findings, and identifies the key

elements of road infrastructure investment that VLC believes will be required in the

future to improve traffic efficiency and safety within Byron Bay.

The West Byron Development, as proposed, will contain 856 residential dwellings

housing a population of 2,182. It will also accommodate business and light industry with

an estimated employment of about 379.

The Development, at this scale, is predicted to generate about 6,000 vehicle trips each

weekday that will either enter or leave the site via Ewingsdale Road.

This scale of traffic interaction with Ewingsdale Road is best accommodated by

providing two site entry/exits.

The West Byron Development will increase traffic on Ewingsdale Road. In the vicinity of

the site the impact is estimated to be about a 7-8% increase in traffic in 2018 and 2028.

VLC’s assessment of other road infrastructure requirements now follows.

Road Infrastructure Requirements 2010-2018

1. Construct Mini-Bypass on the Butler Street alignment with a connection across the

railway line to Jonson Street at Marvel Street.

• Traffic forecasts suggest that only a two-lane road (one lane in each

direction) is required.

• A two lane roundabout will be required at the Butler Street/Shirley/Lawson

Street intersection.

• A single lane roundabout will be required at the Jonson Street/Marvel

Street/Bypass intersection, but with dual lane approaches to facilitate

dedicated left turn lanes.

• A single lane roundabout will be required at the Butler Street/Bypass

intersection.

2. Construct dual lane roundabouts at the intersections of Ewingsdale Road with

McGettigans Lane and Bayshore Drive, and at the SAE Institute. These works will be

required for traffic safety reasons as traffic volumes on Ewingsdale Road continue to

grow and entering from side roads becomes more hazardous (particularly right

turns entering Ewingsdale Road).

3. Complete pedestrian/cycleway along the south side of Ewingsdale Road.



4. Provide two accesses to the West Byron Development - at Bayshore Drive and the

SAE Institute.

Items 1-3 above, will be required to accommodate future traffic growth, even under a

“no West Byron Bay Development” scenario; however, the urgency of these works will

increase should the Development proceed.

With the above works implemented VLC’s traffic modelling indicates that no further

works will be required to accommodate traffic generated by the proposed West Byron

Bay Development.

Road Infrastructure Requirements Post 2018

1. Maintain the option to construct a 4-lane divided carriageway along Ewingsdale Road.

The 2028 modelling suggests that this initiative may be necessary within the next 20-30

years whether the West Byron Bay Development proceeds or not. The project can be

staged, and the need for the works (in terms of timing) will depend on whether the

Development proceeds.

2. Progressively introduce parking restrictions in Shirley Street on the western approach

to, and exit from, the Butler Street Bypass to provide 4 moving traffic lanes during peak

traffic periods.

3. Maintain the option to implement the Full Bypass (two lanes - one in each direction)

while retaining the intermediate connection to Marvel Street. The extended Bypass can

be connected across the railway line to Browning Street via a single lane roundabout. It

is unlikely that the Full Bypass can be justified on purely traffic efficiency grounds. One

of the major benefits of the project is improved amenity in the southern portion of the

Byron Bay Township east of the railway line resulting from the diversion of through

traffic to the Bypass.

Documents

West Byron Development Transport Studywestbyronproject.com.au/wordpress/wp-content/uploads/2011/10/1… · Home-based Trip Production Model The trip production model for home-based