Travel Demand & Urban Morphology: 10 Train Station Precincts in Melbourne

BOWLING CLUB

DON ARCADE

AQUATIC & LEISURE CENTRE AUSDOC OVAL

WARRAWEE PARK OVAL PIONEER CEMETERY

SWINBURNE COLLEGE

LIBRARY

CENTRAL GARDENS

SECONDARY COLLEGE

Oakleigh

Glenferrie SWINBURNE SECONDARY COLLEGE SWINBURNE UNIVERSITY

BUNNINGS GLENFERRIE PRIMARY SCHOOL

St Albans

OAKLEIGH CENTRAL MEDICAL CENTRES

SACRED HEART GIRLS’ COLLEGE

TOWN HALL

MCKECHNIE RESERVE VICTORIA UNIVERSITY

SACRED HEART PRIMARY SCHOOL

SKULLIN PARK

CABRINI HEALTH

TIGERS CLUB HOUSE

ERRINGTON RESERVE

GRASSLANDS RESERVE

PARK

PIONEER RESERVE

RSL MILTON GRAY RESERVE PLAZA

WOODVILLE PRIMARY SCHOOL

Hoppers Crossing

PLENTY RIVER TRAIL & RESERVE

Malvern

Greensborough

MERCY HOSPITAL ST VINCENTS PRIVATE HOSPITAL WATER PUMPING STATION

UNIVERSITY OF MELBOURNE VETERINARY VICTORIA UNIVERSITY

COMMUNITY HEALTH CENTRE GRACE VILLA AGED CARE

CAULFIELD PARK

WILLINDA PARK

INNER EAST COMMUNITY HEALTH

GAHAN RESERVE

GAHANS RESERVE

RICHMOND HILL MEDICAL CENTRE SERVICED APARTMENTS

FA ANDREWS RESERVE

BRUNSWICK PARK

East Richmond WHITE ST RESERVE

BARKLY GARDENS CITYLINK TUNNEL

ALLAN BAIN RESERVE

HOCKEY CLUB

JK GRANT RESERVE CRICKET CLUB BOWLING CLUB

PRIMARY SCHOOL

THE PIER

CLIFTON PARK

North Richmond

Brunswick

GILPIN PARK POWLETT RESERVE DARLING SQUARE

SECONDARY COLLEGE SOCIAL HOUSING RMIT UNIVERSITY

WOMENS HOSPITAL

SYDNEY RD COMMUNITY SCHOOL

CHERRY LAKE TENNIS CLUB COUNCIL

Altona

MATERNAL & CHILD HEALTH CENTRES ARTS & COMMUNITY CENTRE RJ LOGAN RESERVE

MERCY HOSPITAL

Travel Demand & Urban Morphology !

THE ESPLANADE

10 Train Station Precincts in Melbourne

!

Travel Demand & Urban Morphology: 10 Train Station Precincts in Melbourne

! ! ! ! !

Pamela Caspani

!

Student ID: 555262

! !

! !

A Thesis Submitted for Partial Fulfilment of the Requirements for the Degree of Master of Urban Design At the University of Melbourne Semester 01, 2014

! ! ! ! ! ! ! !

Acknowledgement!

I would like to thank my advisor Professor Kim Dovey who spent countless hours giving me feedback and guidance over the last semester. 

Contents!

Introduction!........................................................................................................................................................5!

Travel Demand in Melbourne, Victoria!...............................................................................................................5!

Methodology!......................................................................................................................................................9!

Data Analysis!...................................................................................................................................................13!

Pedestrian Network & Walkable Catchment Areas!................................................................................13!

Density!........................................................................................................................................................14!

Dwelling & Population Density!...............................................................................................................14!

Employment Density!..............................................................................................................................15!

Mix!..............................................................................................................................................................15!

Grain Mix!...............................................................................................................................................15!

Functional Mix!.......................................................................................................................................16!

Access!........................................................................................................................................................16!

Public Transport Network!......................................................................................................................16!

Road Network & Parking!.......................................................................................................................17!

Findings!...........................................................................................................................................................18!

Pedestrian Network & Walkable Catchment Areas!................................................................................18!

Density!........................................................................................................................................................22!

Intensity!.................................................................................................................................................22!

People & Jobs!.......................................................................................................................................26!

Mix!..............................................................................................................................................................28!

Grain Mix!...............................................................................................................................................28!

Functional Mix!.......................................................................................................................................30!

Residential & Commercial Mix!...............................................................................................................30!

Access!........................................................................................................................................................34!

Public Transport!.....................................................................................................................................34!

Parking!..................................................................................................................................................37!

Road Network !.......................................................................................................................................37!

Assemblages of Density, Mix & Access!......................................................................................................40!

Conclusion!.......................................................................................................................................................45!

Bibliography!.....................................................................................................................................................47

! !

List of Figures!

Figure 1: Melbourne’s Train Travel Demand Forecast to 2031 (Adapted from PTV, 2012, p. 39)!.....................6!

Figure 2: Melbourne’s Planned Urban Renewal Precincts (Adapted from Plan Melbourne, 2014, p. 48)!.........7!

Figure 3: Melbourne’s Weekly Train Station Patronage FY2011/12 (Source: Author)!.......................................8!

Figure 4: Selected Stations across Melbourne (Source: Author)!.....................................................................12!

Figure 5: Pedestrian Access by Station (Source: Author)!................................................................................19!

Figure 6: Walkable Catchment by Station (Source: Author)!............................................................................20!

Figure 7: Dwelling Density by Station Precinct (Source: Author)!.....................................................................23!

Figure 8: Population Density by Station Precinct (Source: Author)!.................................................................24!

Figure 9: Employment Density by Station Precinct (Source: Author)!..............................................................25!

Figure 10: Proportions of Grain Sizes by Station Precinct (Source: Author)!...................................................28!

Figure 11: Grain Mix by Station Precinct (Source: Author)!..............................................................................31!

Figure 12: Functional Mix by Station Precinct (Source: Author)!......................................................................31!

Figure 13: Proportions of Industry of Employment by Station Precinct (Source: Author)!................................32!

Figure 14: Proportions of Residential Typologies by Station Precinct (Source: Author)!..................................32!

Figure 15: Public Transport Network by Station Precinct (Source: Author)!.....................................................35!

Figure 16: Road Network & Parking by Station (Source: Author)!....................................................................38!

Figure 17: Density, Mix, Access & Patronage RANKS (Source: Author)!.........................................................40!

Figure 18: Further Work (Adapted from Plan Melbourne, 2014, pp. 24 & 48)!.................................................46

List of Tables!

Table 1: Summary of Station Patronage Data against Criteria (Source: Author)!.............................................11!

Table 2: Function Attributed to Planning Scheme Zone Code (Source: Author)!..............................................16!

Table 3: Road Hierarchy Classification (Source: Author)!.................................................................................17!

Table 4: Walkable Catchment Area (WCA) & Pedestrian Catchment Ratio (PCR) Calculations by Station (Source: Author)!...............................................................................................................................................18!

Table 5: Count of Dwellings, Persons & Jobs by Station Precinct (Source: Author)!........................................26!

Table 6: Summary of Density Scoring by Station Precinct (Source: Author)!...................................................27!

Table 7: Summary of Mix Scoring by Station Precinct (Source: Author)!..........................................................33!

Table 8: Public Transport Connections (Source: Author)!.................................................................................36!

Table 9: Designated Parking Facilities by Station (Source: PTV, 2013)!..........................................................37!

Table 10: Summary of Access Scoring by Station Precinct (Source: Author)!..................................................39!

Table 11: Ranking Density, Mix, Access & Patronage SCORES (Source: Author)!..........................................40

!

! !

Introduction!

Many contend that dense, mixed-use, transit accessible and walk-friendly urban development can significantly influence the transport modes people choose to travel. These characteristics of development, derived from popular urban design philosophies of New Urbanism and Transit-Oriented Development (TOD) are suggested to be ways of shaping travel demand by degenerating trips and weaning people from their cars (Cervero, 2002). The effects of density on travel demand have long been acknowledged (e.g. Levinson and Wynn, 1963). Yet the effects of land use mixtures and access (in and around trip origins and destinations) have just as long been ignored. Ignoring the potential effects of mix and access on travel demand is a significant oversight. It means we do not test the possible transportation benefits of land use initiatives, be they TOD or infill housing.

Cervero (2002) provides reasons for this oversight, suggesting that transportation and land-use data are usually compiled by separate entities for different purposes and are not always compatible, and that there is typically an absence of rich, Mesh Block-level data on built environments. He identifies that past transport studies have ignored land-use factors resulting in regional travel demand models overvaluing or undervaluing the importance of travel time and cost, and past land use studies on travel demand have misinterpreted (by ignoring generalised costs) the importance of the built environment (Cervero 2002).

That is, past studies have failed to identify relationships between density, mix and access for the purpose of drawing inferences about the importance of built environment factors in shaping mode choice.

This thesis therefore analyses built-environment in terms of its three core dimensions: density, mix, and access. It analyses the urban morphology to study the outcomes of ideas and intensions as they take shape on the ground and mould the city. Specifically 10 train station precincts in Melbourne, Victoria, have been mapped and analysed to examine density, mix and access of the built environment. Furthermore, this thesis explores whether assemblages of density, mix and access effect train station patronage by scoring measures of each to shed light on the sensitivity of mode choice to changes in the built environment.

Travel Demand in Melbourne, Victoria!

Travel is a derived demand based on the demand for activities and goods that require travel (Boarnet & Crane, 2001). The demand for travel is increasing in Melbourne with population growing rapidly in the north, west and south-east on the city fringe and absolute employment growing in the Central Business District (CBD) (PTV, 2012). The demand for public transport has increased at an average rate of 3.9% per annum over the last decade and is expected to increase by an average of 4.4% per annum over the next decade (PTV, 2012). This represents only a slight increase in modal share as the total demand for travel is anticipated to grow in line with population growth (i.e. 1.2-1.5% per annum). This data highlights that the economic and social changes in the mid-2000s that saw demand for public transport growth exceed 6% per annum have not been sustained (PTV, 2012). To sustain city development it is crucial for Melbourne to significantly increase growth in public transport and non-motorised mode shares (Kenworthy, 2006).

Page 5

The statutory authority responsible for providing, coordinating and promoting public transport in Victoria is Public Transport Victoria (PTV). In 2012 PTV released ‘Metropolitan Public Transport Demand Forecast Report’ presenting the forecasts of demand for public transport in Melbourne. The report provides the basis for network planning and business case development by PTV during 2012 – 2013 and subsequent years.

Figure 1 highlights PTV’s long-term train travel demand forecast based on regional transport modelling to 2031. This plan suggests that train travel demand is expected to be highest in all inner city areas, and along the west (i.e. Sunbury railway line) and south-east (i.e. Cranbourne railway line) corridors.

FIGURE 1: MELBOURNE’S TRAIN TRAVEL DEMAND FORECAST TO 2031 (ADAPTED FROM PTV, 2012, P. 39)!

!

In the past decade 60 per cent of Melbourne’s population growth has occurred on the city fringe (DTPLI, 2014). The 2014 Metropolitan Planning Strategy ‘Plan Melbourne’ which sets out the State Government’s vision that will guide the city’s growth to 2050, now recognises the need to re-focus growth into the central city, activity areas and adjacent to the rail network as an alternative to fringe developments. Figure 2 illustrates the State Government’s proposal to accommodate this growth by focusing on medium and high density development in existing urban areas. This represents an increase in development surrounding train stations, particularly those located in close proximity to activity centres.

!

Page 6

FIGURE 2: MELBOURNE’S PLANNED URBAN RENEWAL PRECINCTS (ADAPTED FROM PLAN MELBOURNE, 2014, P. 48)!

!

To provide further clarity into the situation ‘Station Patronage Research’ undertaken by PTV and published in July 2013 is a resource that delivers for the first time a comprehensive, more rounded view of the public transport network and the people who use it. The research, which ranks all 204 metropolitan Melbourne stations by the number of people using them, shows data for four financial years 2008/09 to 2011/12. To date this research has been subjected to little investigation outside PTV. Figure 3 maps the weekly station patronage for FY2011/12 from the research data. Figure 3 shows that station patronage is highest in the CBD (e.g. 539,532 entries per week at Flinders Street Station and 329,921 entries per week at Southern Cross Station) and at train interchange stations in inner Melbourne (e.g. 88,080 entries per week at Footscray Station and 78,704 entries per week at Caulfield Station). The remainder is scattered across the network, with most stations in the 5,000-10,000 and 10,000-20,000 entries (persons) range.

Melbourne’s weekly train station patronage across the network is relatively low in a global context when compared to Shinjuku Station (Tokyo, Japan) which gets 742,833 entries per day, Ikebukuro Station (Tokyo, Japan) which gets 550,756 entries per day or Shibuya Station (Tokyo, Japan) which gets 412,009 entries per day (East Japan Railway Company, 2012).

!

Page 7

!

FIGURE 3: MELBOURNE’S WEEKLY TRAIN STATION PATRONAGE FY2011/12 (SOURCE: AUTHOR)!

The challenge for Melbourne lies in its ability to accommodate population growth in and around train stations whilst increasing train patronage to make the most of its rail infrastructure and sustaining city development. With a newly acknowledged effort from the State Government to encourage increased development adjacent to train stations, there is now an opportunity to improve the structure and design of new developments located in these areas. This also provides opportunity to apply lessons learned from local contexts to improve the liveability, vitality, and desirability of areas surrounding train stations in Melbourne. 

Page 8

Methodology!

The chief database used to carry out this research was PTV’s 2013 ‘Station Patronage Research’. The research, which provides number of people using each station also includes research into how people get to the station and their reasons for travelling. To collect this data PTV carried out surveys with passengers on the network, and summarised data obtained from the call centre and the myki ticketing system, as well as operator performance results.

Train station precincts were selected based on a common typology in Melbourne; stations co-located with activity centres. To compare like centres only those identified in Melbourne 2030 as Major Activity Centre (MAC) type and size centre were considered. Stations co-located with activity centres were initially identified by cross checking activity centres listed in the State Government’s 2002 Metropolitan Planning Strategy ‘Melbourne 2030’ with Local Government Structure Plans across the metropolitan region. Structure Plans were individually assessed to determine if the listed activity centre incorporated the train station within the activity centre plan.1 To narrow the study to a representative sample of train stations precincts, a selection criteria was applied. The criteria is explained below and summarised in Table 1.

It should be noted that a limitation of the PTV data was that surveys were only conducted between 07:00 and 19:00, and that due to privacy and policy issues uniformed school children or those who looked to be under 16 were unable to be interviewed. This means the data is missing access mode and journey purpose information before 07:00 and after 19:00, and all detailed information on the population under 16 years of age who, for example are likely travelling by walking to the station for school but are not included in the ‘walk all the way’ access mode or ‘education’ journey purpose figures.

Criterial 1 - Station Patronage!

Criteria 1 included selecting a sample of weekly station patronage numbers ranging from 0-10,000, 10,000-20,000, 20,000-30,000, 30,000-40,000, and 40,000-50,000 persons. Glenferrie with 48,872 weekly entries was the only station co-located with an activity centre with patronage over 40,000 entries. Altona with 5,597 weekly entries was the station co-located with an activity centre with the lowest patronage (with the exception of Diamond Creek with 4,499 weekly entries that was not chosen as it is planned but not yet developed into a comparable MAC type and size).

Criteria 2 - Proportions of People Walking to the Station !

Criteria 2 included selecting a sample of proportional weekday entries by access mode - walked all the way, ranging 0-10%, 10-20%, 20-30%, 30-40%, 40-50%, 50-60%, 60-70%, 70-80%, 80-90% and 90-100%. Walkability is an essential complement to transit access, mixed land uses, and higher densities, and is a major component of efforts in designing to reduce auto dependence (Canepa, 1992; Leyden, 2003; Al-Hagla,

! This centre hierarchy has since been dropped in the State Government’s 2014 Metropolitan Planning Strategy ‘Plan 1 Melbourne’, with all former Principle and Major Activity Centres now referred to as activity centres. For comparison of built environment the previous activity centre hierarchy based on key characteristics and future strategic development directions provided a useful basis for determining the role of the individual centres for review.

Page 9

2009; Carnoske et al., 2010). Hoppers Crossing with 16.5% was the only station co-located with an activity centre with a proportion of less than 20% of patrons who walked all the way to the station.

Criteria 3 - Reason for Travelling!

Criteria 3 included selecting a sample of proportional weekday entries by journey purpose. Any proportion over roughly 20% was considered to be a key function of a station. Glenferrie was the station with the highest proportion of education function (50%) and East Richmond was the station with the highest proportion of work function (71.8%). Notably 5 of the top 6 highest weekly patronage stations had education as a key function, and Glenferrie and St Albans both with high proportions of education functions also had high work functions.

Criteria 4 - Distance from the Centre!

Criteria 4 included selecting a sample of stations that had a distance from Southern Cross Station of 0-10km, 10-20km and 20-30km. This was significant for considering other factors such as feasibility of competing transport modes (e.g. 4km is typically more walkable or cycle-able to the centre than 17km).

For this criteria, a visual assessment of the stations on a plan of Melbourne was also undertaken to ensure the sample was a geographic representation covering north, south, east and west of Melbourne (see Figure 4).

Criteria 5 - Public Transport Connections!

Criteria 5 was a selection of types of public transport connections. Despite the Station Patronage Research identifying stations as having an interchange of either a connecting bus, connecting tram or connecting bus and tram, site inspections revealed the level-of-service of each interchange varied. A more appropriate level-of-service classification system was therefore created, and defined the public transport interchange as either a connecting tram, connecting tram and bus stop, connecting bus interchange, or connecting bus stop. Notably, no stations co-located with activity centres presented a connecting tram and bus interchange typology, and it was determined that some bus stops at stations (e.g. Malvern station) had been overlooked altogether in the Station Patronage Research.

!

Page 10

Criteria 1

Criteria 2

Criteria 3

Criteria 4

Criteria 5

Patronage FY2011-12 (Weekly)

Proportional Weekday Entries by Access Mode FY2011-12

Proportional Weekday Entries

by Journey Purpose FY2011-12

Distance from Southern Cross Station

Interchange*

48,872 (Highest for MAC)

84.6% Walked all the way

50% Education (Highest) (33.2% Work)

8.1km

Tram + bus

OAKLEIGH

34,247

42.4% Walked all the way (29.7% Bus & 25.4% Car)

46.9% Work (21.4% Education)

16.6km

Bus

ST ALBANS

33,196

55.7% Walked all the way (29.9% Car)

45.9% Education (40.8% Work)

17.8km

bus

HOPPERS CROSSING

28,481

16.5% Walked all the way (49.5% Car & 31.1% Bus)

63.3% Work (19.3% Education)

27.7km (Furthest)

Bus

MALVERN

26,029

76.2% Walked all the way

70.8% Work

10.1km

Tram + bus

GREENSBOROUGH

18,932

29.9% Walked all the way (44.8% Car & 22.4% Bus)

61% Work (19.9% Education)

22.9km

bus

EAST RICHMOND

14,412

93.6% Walked all the way

71.8% Work (Highest)

4.4km

Tram

NORTH RICHMOND

11,683

73.8% Walked all the way (16.7% Tram)

62% Work

4.2km (Closest)

Tram + bus

BRUNSWICK

5,675

84.5% Walked all the way

58.3% Work

7.3km

Tram + bus

5,597 (Lowest for MAC)

62.5% Walked all the way (23.9% Car)

59.2% Work

17.1km

bus

GLENFERRIE

ALTONA

*Bus denotes interchange, bus denotes bus stop TABLE 1: SUMMARY OF STATION PATRONAGE DATA AGAINST CRITERIA (SOURCE: AUTHOR)!

! ! !

Page 11

FIGURE 4: SELECTED STATIONS ACROSS MELBOURNE (SOURCE: AUTHOR)!

!! ! !

Page 12

Data Analysis!

Each of the 10 selected station precincts have been mapped according to the three core dimensions of the built environment: density, mix and access. A variety of geospatial data sourced from www.data.vic.gov.au and ABS Census 2011 has been utilised in conjunction with observations from site inspections and current Nearmap aerial imagery. The data sources, processing requirements and characteristics utilised in the analysis are discussed below.

Pedestrian Network & Walkable Catchment Areas Walkability has many benefits. Moudon et al. (2007), Frank and Kavage (2009), Gebel et al. (2009), McCormack et al. (2009), and Boer et al. (2007), to name a few argue the benefit of healthier communities.

Cervero & Kockelman (1997), Canepa (1992), Frank et al. (2006) and Cerin et al. (2007), and many others argue the benefits of walking as a mode of transportation as access to final destinations or transit.

Throughout the literature, walkability is defined in many different ways. One important aspect of walkability is the quality of the environment, including the safety, comfort and pleasure it instills in pedestrians. Another important aspect of walkability and the main concern of this research is 'pedestrian accessibility' or the ability of pedestrians to access their destination. For this study the analysis considers each train station as the destination.

Over the past few decades train station analysis has evolved from the simple Euclidean distance buffers to more complex network-based buffers. Although current methods assume that the street network is representative of the pedestrian network, a growing body of literature suggests that informal paths are also important components of the pedestrian network. Social paths are informal paths that emerge in grassy areas, car parks or other vacant lots due to pedestrian traffic. By incorporating social paths into the analysis, this thesis creates walkable catchment areas that are more reflective of how pedestrians actually access each of the stations. These walkable catchment maps are the best estimates of walkability and have been used to define the train station precincts.

Analysis of walkable catchments from train stations was undertaken using ArcMap 10 Network Analyst function. This function requires the definition of a base path network which can be traversed by the mode of transport. This study adopted the Road Network Data to build the base network. The base network was then built by developing a series of links (lines), nodes (points) and edges to enable Network Analyst to perform distance-based network analysis.

On undertaking preliminary assessments of the study areas, site visits and comparison with current aerial imagery, it was evident that some paths used by pedestrians were not included in the base Road Network Data and that there were some links that were available to cars but not for pedestrians. A cross referencing exercise was therefore undertaken to update the base Road Network Data to include paths that were able to be traversed by pedestrians. The revised network provided a more accurate representation of walkable catchments within each of the study areas.

Catchment analysis was undertaken on the basis of three different walking times, approximately 10 minutes, 8 minutes and 5 minutes. Given walking speeds for pedestrians range typically (depending on type of pedestrian and the type of network) between 1.2 m/s and 1.5 m/s, a range of distances would be achievPage 13

able. This study has adopted 800m, 600m and 400m walking distances as they correspond to the 10, 8 and 5 minute travel times at a walking speed of approximately 1.3 – 1.4 m/s for analysis. The walking distance chosen for comparative analysis for density, mix and access was 800m, as the density data available was at a relatively large resolution but important for the analysis. Maps have therefore been clipped to the 800m walkable catchment for each station.

Density!

Dwelling & Population Density Densities have been mapped from 2011 ABS Census data. To display dwelling and population densities, ABS Census data from 2011 was obtained at the Mesh Block resolution for each of the study areas. The Mesh Block resolution is the finest geographic region included in the Australian Statistical Geography Standard (ASGS) and the smallest geographic unit for which ABS Census 2011 data is available. The Mesh Block data includes an aggregation of responses from households within the Mesh Block relating to dwelling counts and Usual Persons Resident (UPR).

The base census data of Dwellings and UPR was obtained using ABS Table Builder for all Mesh Block areas in Victoria. Mesh Block data was provided in tabular format, with Dwelling and UPR data linked to each uniquely identified Mesh Block. Given the need to display the data spatially, linking the raw data to the Mesh Block spatial data was required.

In order to complete this process, Mesh Block boundaries were obtained from the ABS Statistical Geography web portal and the datasets were linked in ArcMap 10 using the Join by Attribute function. This allowed both the associated data sets to be displayed spatially according to the Mesh Block geometry. Cross checking was undertaken between selected Meshblocks of the joined data sets and the original data for each of the study areas and was deemed to be accurate.

Given Mesh Block areas are not uniform in size, it was necessary to normalise the data by the area of each Mesh Block so that comparative assessment between each of the study areas could be undertaken. The normalisation process is undertaken using the ArcMap 10 Normalisation function. Functions (1) and (2) are shown for the Normalised Population NMB(UPR) and Normalised Dwelling Units NMB(DU) respectively. The functions take the Dwelling Units (DU) or UPR of the relevant Mesh Block and divide by the Total Area of the Mesh Block, providing a square metre value which represents the normalised value.

!

(1) 
 (2)

Symbology for UPR and DU densities were defined under eight categories to represent the range of values experienced over the study areas. The UPR densities were presented in intervals of 10 Pers / Ha with the Page 14

exception of the low values to demarcate the difference between areas with almost no population densities to those with low population densities. The DU densities were presented in intervals of 5 DU / Ha intervals to correspond with typically adopted values for very low, low, medium and high density categories of dwelling densities.

Employment Density To display Employment Densities, ABS Census data from 2011 was obtained at the Destination Zone resolution for each of the study areas using ABS Table Builder Pro. The Destination Zone resolution is the finest geographic region for which employment data is available from the ABS Census 2011. The Destination Zone data includes an aggregation of respondent numbers whose place of employment is located within the Destination Zone. Total employment, in addition to the distribution of employment across key employment sectors (such as Retail Trade, Health Care and Social Assistance, Professional, Technical and Scientific Services, Education and Training, etc.) was available as part of this dataset.

Data obtained from ABS was provided in tabular format, with categorised employment data linked to each uniquely identified Destination Zone. Given the need to display the data spatially, linking the raw data to the Destination Zone spatial data was required. Zone boundaries were obtained from the ABS Statistical Geography web portal and the datasets were linked in ArcMap 10 using the Join by Attribute function. This allowed the associated data to be displayed spatially according to the Destination Zone geometry.

Given the Destination Zone geometry represents a relatively course grain dataset in the context of the study areas, it was not possible to provide a detailed representation of employment distribution for each area. The Destination Zone data did however provide a suitable indication of overall employment that could be used in comparative assessment of the study areas.

To calculate numbers of dwellings, persons and jobs in the 800m walkable catchment areas, given the Meshblocks for Dwelling and Population were small, if the Meshblock centroid was within the 800m walkable catchment area its contents were counted. However, given the Destination Zones for Employment were large, the Intersection Tool function was used in ArcGIS to create a new polygon and the Allow Ratio Policy function was used to obtain a ratio of the new polygon so a proportional figure was counted.

Mix! Grain Mix To display grain mix information, Parcel shapefile data was obtained from VicMap Property. The data consisted of polygons representing Victoria’s land parcels. The shapefile data obtained did not include computed data for individual lots, resulting in the need to undertake area calculations based on the polygon geometry using ArcMap 10 Calculate Geometry function.

To display the range of grain sizes present in each of the study areas, a symbology was developed to depict the difference in grain sizes. Increments for the symbology were chosen on the basis of relevance to urban typologies present in each of the study areas, including small lot housing (<300m2), conventional residential (>300m2), larger residential block (>500m2) and lots capable of delivering large scale multiunit development (>1000m2).

Page 15

Functional Mix Functional mix has been mapped from Planning Zone and parcel data. Functional mix was developed using a two-step classification system. The first step involved assigning Planning Zone information obtained from VicMap Planning to individual land parcels. Given the Planning Zone information is independent of land parcels, the Spatial Join function in ArcMap 10 was used to assign the zone attributes where a parcel was located within a zone boundary. Given there are some areas of overlap between zone and parcel boundaries, a manual selection of land parcels was required to rectify anomalies in the spatial join process. A symbology was then defined for the relevant zoning as shown in Table 2.

!

Functional Mix Category

Zone Code

Commercial

B1Z, B2Z, B3Z, B4Z, ACZ, C1Z

Industrial

IN1Z, IN2Z, IN3Z

Residential

R1Z, R2Z, R3Z

Mixed Use

MUZ

Public / Institution

PUZ1, PUZ2, PUZ3, PUZ7

Government

PUZ6

Open Space

PPRZ, PCRZ

Cemetery

PUZ5

Car Parking

PUZ4

TABLE 2: FUNCTION ATTRIBUTED TO PLANNING SCHEME ZONE CODE (SOURCE: AUTHOR)!

!

The second step of the process involved detailed analysis of parcels to confirm the actual use was consistent with the functional mix category. Where actual use differed from zoned use categorisation, the actual use was utilised to display the functional mix. This second step was important to also identify areas of dis-use (e.g. south of Hoppers Crossing station) and large areas of car parking, both publicly and privately owned, and within and outside land parcels (i.e. in unbuilt space).

Access!

Public Transport Network Accessibility is a concept that is difficult to define and even more difficult to measure (Handy, 2002).

Hansen (1959) defined accessibility as “the potential for interaction.” It is commonly measured with respect to the cost of reaching potential destinations, where cost is often represented by travel distance, or as the number of destinations reachable within a specified travel distance (Handy and Niemeier, 1996).

Accessibility is therefore a function of both proximity and connectivity. Proximity is determined by land use patterns – what is located where, how close one thing is to another. Connectivity is a measure of the quantity of the connections in the network, and the directness and multiplicity of routes through the network. From a transportation standpoint, only connections to destinations are important, so connectivity in Page 16

some cases is defined with respect to the locations of potential destinations. This detailed analysis was undertaken and is included in Appendix 1, with only stop locations and routes mapped.

Public Transport Network information was obtained from spatial data obtained from Vicmap Transport.

Train Lines, Train Stops, Tram Routes, Tram Stops and Bus Routes as at June 2012 were incorporated into the GIS system.

Road Network & Parking To display Road Network information spatial data was obtained from Vicmap Transport. The data consisted of polylines representing Victoriaâ&#x20AC;&#x2122;s road network and road hierarchy information. Symbology to represent the road network was displayed on the basis of road type classification, which follows a 0-5 classification.

With 0 representing a higher speed but lower local access function and 5 representing a low speed but high local access function. The maps display this hierarchy as Motorway, Freeway or Tollway in the deepest dark red ranging down to Local Roads in the lightest red.

Classification Description 0

Motorway, Freeway or Tollway

1

Arterial / Highway

2

Sub-Arterial

3

Main Collector

4

Minor Collector

5

Local Road

TABLE 3: ROAD HIERARCHY CLASSIFICATION (SOURCE: AUTHOR)

!

Pedestrian paths (formal and informal) maintained by the relevant local Council were also included in the dataset, however not displayed in the road network mapping. These were included in the pedestrian accessibility mapping referred to earlier.

Parking information was gathered from site inspections and visual assessment on Google Street View. Large areas of parking, both public and private, were recorded and ground level car parking was differentiated from multi-deck car parking.

!

Page 17

Findings!

From the data analysis techniques outlined above, 10 station precincts have been mapped according to density, mix and access and scored based on high achievers (‘+’) and low achievers (‘-’) of each category.

The following section compares findings from the maps, data extracted from ArcGIS relating to each 800m Walkable Catchment Area (WCA) and PTV’s (2013) Station Patronage Research. Most significantly the performance of each pedestrian network as it translates into each WCA is discussed first, followed by a discussion on and scoring of the chosen measures of density, mix, and access. The WCAs allow for easy comparison between the station precincts.

Pedestrian Network & Walkable Catchment Areas As mentioned previously the primary concern of this research is 'pedestrian accessibility' or the ability of pedestrians to access their destination. Pedestrian accessibility has been mapped by constructing the pedestrian network around each of the 10 train stations (refer Figure 5), and expressed as a WCA for comparative evaluation of how easy it is to move through each area (refer Figure 6). These walkable catchment maps are the best estimates of walkability and have been used to define the train station precincts.

Table 4 shows walkable catchments expressed as a land area calculation and as a percentage of the Euclidean (or theoretical) area within 800m walking distance of each station, known as the Pedestrian Catchment Ratio (PCR). The Euclidean 800m walking distance is shown on each map as a circle with a radius of 800m drawn around the station. The higher the PCR, the better the walkability of the area. Generally a ratio of 50-60 per cent characterises an adequate pedestrian environment while a ratio of 30 per cent or less characterises areas that are inhospitable for pedestrians (Schlossberg and Brown, 2004 and Schlossberg 2006). A good target for a PCR within 800m walking distance of a train station is 60 per cent (Gallagher, 2012).

Station

800m WCA (Ha)

PCR (800m WCA as a percentage of 800m Euclidean Area)

Rank

Altona

113.20

56.30%

1

Hoppers Crossing

115.83

57.61%

2

Brunswick

118.32

58.85%

3

North Richmond

119.32

59.34%

4

Greensborough

119.41

59.39%

5

Oakleigh

120.30

59.83%

6

East Richmond

123.73

61.54%

7

Glenferrie

127.41

63.37%

8

Malvern

129.36

64.34%

9

St Albans

132.68

65.99%

10

TABLE 4: WALKABLE CATCHMENT AREA (WCA) & PEDESTRIAN CATCHMENT RATIO (PCR) CALCULATIONS

BY STATION (SOURCE: AUTHOR)!

From Table 4 it is evident that St Albans and Malvern are achieving a good PCR, while Altona and Hoppers Crossing are achieving a poor PCR. Scoring of these PCRs is included in the Access category in Table 9.

Page 18

! !

FIGURE 5: PEDESTRIAN ACCESS BY STATION (SOURCE: AUTHOR)!

Figure G1_Pedestrian Access around Glenferrie Station

Figure O1_Pedestrian Access around Oakleigh Station

Figure S1_Pedestrian Access around St Albans Station

Figure H1_Pedestrian Access around Hoppers Crossing Station

Figure M1_Pedestrian Access around Malvern Station

Figure Gr1_Pedestrian Access around Greensborough Station

Figure E1_Pedestrian Access around East Richmond Station

Figure N1_Pedestrian Access around North Richmond Station

Figure B1_Pedestrian Access around Brunswick Station

Legend Figure Ground Open Space Water

Figure A1_Pedestrian Access around Altona Station FIGURE 5: PEDESTRIAN ACCESS MAP BY STATION (SOURCE: AUTHOR)

Page 19

FIGURE 6: WALKABLE CATCHMENT BY STATION (SOURCE: AUTHOR)â&#x20AC;©

Figure G2_Walkable Catchment around Glenferrie Station

Figure O2_Walkable Catchment around Oakleigh Station

Figure S2_Walkable Catchment around St Albans Station

Figure H2_Walkable Catchment around Hoppers Crossing Station

Figure M2_Walkable Catchment around Malvern Station

Figure Gr2_Walkable Catchment around Greensborough Station

Figure E2_Walkable Catchment around East Richmond Station

Figure N2_Walkable Catchment around North Richmond Station

Figure B2_Walkable Catchment around Brunswick Station

Legend 400m (5 mins walk) 600m (8 mins walk) 800m (10 mins walk) Paths 800m Euclidean Distance

Figure A2_Walkable Catchment around Altona Station FIGURE 6: WALKABLE CATCHMENT MAP BY STATION (SOURCE: AUTHOR)

Page 20

Variances in these PCRs suggest there are a number of factors in the pedestrian network that influence the amount of pedestrian accessibility. The following sections describes factors recognised as influential characteristics on the WCAs.

‘Bites’ Missing!

With the smallest amount of land area accessible within 800m walking distance Altona has bites missing from both the top and bottom of the catchment. These are explained by Port Phillip Bay to the south and Cherry Lake to the north being impermeable and decreasing the WCA. Similarly, Hoppers Crossing with the second smallest WCA, has a large bite missing from the south-east of the catchment due to a very large disused parcel of land. This parcel provides no reason for pedestrians to traverse through (i.e. for functions other than low density residential separated by a highway) and is therefore impermeable. This disused land significantly decreases Hoppers Crossing’s WCA. Brunswick with the third smallest WCA, does not appear to have a bite missing from its WCA. However, pedestrian accessibility within the Brunswick station precinct is reduced on the south-west of the catchment by very large industrial areas reducing permeability. ‘Bites’ missing therefore can more broadly be described as areas of impermeability, or as a variation of general permeability.

General Permeability!

Glenferrie, East Richmond, North Richmond and Brunswick all have a dense grid (of streets and other pedestrian links) that maximise the possible walking distance. The actual area within walking distance is diamond-shaped i.e. a square rotated 45 degrees from the railway line. Almost two thirds of the Euclidean area is in walking distance in such a network and thus a diamond-shaped catchment generally offers good permeability. Key differences between these precincts are that Glenferrie and East Richmond have the railway line running east-west, and North Richmond and Brunswick have the railway line running north-south. East Richmond also has a disrupted grid due to the additional railway line west of the station, but its many short narrow blocks help to compensate this reduction in the WCA with high permeability.

By contrast, Greensborough has an organic-shaped pedestrian network and WCA, and this layout reduces the possible walking distance. Apparent reasons for this layout in Greensborough are the natural features including a very hilly topography east of the railway line and a meandering creek that crosses the railway line on the south-east side of the station. These natural features and subsequent street layout allow only a single pedestrian link across the railway line on the south-east side of the station.

The organic-shaped catchment generally offers poor permeability. In addition, because of the curvature of the line, in terms of the train line operation northbound services head south-east and southbound services head north-west. During peak-hours both platforms are used by trains travelling in either direction as just past the station the double track becomes single for the remainder of the journey to Hurstbridge. Therefore, natural features affect the pedestrian network and train line operation.

Diagonality!

Oakleigh and Malvern both have the train line that transects a north-south / east-west road network diagonally with a very regular grid street structure around each station. Oakleigh also has a slight bite missing Page 21

from the south side of the catchment due to extremely long street blocks. If these two long blocks were split the WCA in Oakleigh would be significantly increased.

Oakleigh and Malvern have traits of the diamond shape (i.e. a square rotated 45 degrees from the railway line), however due to the angle of the railway line, they also incorporate elements of radiality. Diagonality can therefore also be described as related to the direction the railway line cuts through the area, and therefore a different form of radiality.

â&#x20AC;&#x2DC;Radialityâ&#x20AC;&#x2122;!

St Albans is the largest WCA and has a relatively large grain pedestrian network. St Albans also has a lopsided centre to its WCA, which is the result of a number of pedestrian routes extending radially out immediately north-west of the station, and no pedestrian crossings on the south-east side of the station. Radiality is the major contributor to a large WCA evidenced by St Albans, Malvern and Oakleigh pedestrian networks which all incorporate paths radiating from the station and are all at the top of the PCR rankings.

The factors that affect the WCA therefore are general permeability (such as in Glenferrie and East Richmond which incorporate short blocks) and more importantly, radiality.

Density!

Density reflects how intensively land is used for housing, employment, and other purposes. Cervero and Kockelman (1997) suggest compact areas can degenerate vehicle trips and encourage non-motorised travel (i.e. walking and cycling) by bringing origins and destinations closer together. This is because compact areas typically have less car parking, better quality transit services, wider mixes of land uses, and larger shares of low-income households - all factors that reduce car usage (Cervero and Kockelman, 1997). In theory, greater density should lead to fewer car journeys. This thesis will not investigate if higher densities result in fewer car journeys. However, density has been measured to see if there is a relationship between compact areas and train travel.

Intensity Separate measures of dwelling density, population density and employment density for each station precinct have been mapped and are included as Figures 7, 8 and 9 respectively. Dwelling and population density, both a measure of persons within the station precincts, were analysed to identify variances in household occupancy in the station precincts. Areas with a high density of dwellings, population and employment are represented by intense red, blue and purple respectively, while areas of low density are represented by light red, blue and purple respectively.

Figures 7, 8 and 9 show that North Richmond, East Richmond and Glenferrie have the highest intensity of dwelling density, while Hoppers Crossing and Greensborough have the lowest intensity of dwelling density.

North Richmond, East Richmond, Glenferrie and Brunswick have the highest intensity of population density, while Hoppers Crossing and Greensborough have the lowest intensity of population density. North Richmond and East Richmond have the highest intensity of employment density, while St Albans and Altona have the lowest intensity of employment density. Scoring of these intensities of density are included in Table 6.â&#x20AC;Š Page 22

FIGURE 7: DWELLING DENSITY BY STATION PRECINCT (SOURCE: AUTHOR)â&#x20AC;©

Figure G3_Dwelling Density around Glenferrie Station

Figure O3_Dwelling Density around Oakleigh Station

Figure S3_Dwelling Density around St Albans Station

Figure H3_Dwelling Density around Hoppers Crossing Station

Figure M3_Dwelling Density around Malvern Station

Figure Gr3_Dwelling Density around Greensborough Station

Figure E3_Dwelling Density around East Richmond Station

Figure N3_Dwelling Density around North Richmond Station

Figure B3_Dwelling Density around Brunswick Station

Legend 0 - 5 DU/Ha 5 - 10 DU/Ha 10 - 15 DU/Ha 15 - 20 DU/Ha 20 - 30 DU/Ha 30 - 35 DU/Ha 35 - 40 DU/Ha Figure A3_Dwelling Density around Altona Station

> 40 DU/Ha

FIGURE 7: DWELLING DENSITY MAP BY STATION PRECINCT (SOURCE: AUTHOR)

Page 23

!

FIGURE 8: POPULATION DENSITY BY STATION PRECINCT (SOURCE: AUTHOR)!

Figure G4_Population Density around Glenferrie Station

Figure O4_Population Density around Oakleigh Station

Figure S4_Population Density around St Albans Station

Figure H4_Population Density around Hoppers Crossing Station

Figure M4_Population Density around Malvern Station

Figure Gr4_Population Density around Greensborough Station

Figure E4_Population Density around East Richmond Station

Figure N4_Population Density around North Richmond Station

Figure B4_Population Density around Brunswick Station

Legend 0 - 5 Pers/Ha 5 - 10 Pers/Ha 10 - 20 Pers/Ha 20 - 30 Pers/Ha 30 - 40 Pers/Ha 40 - 50 Pers/Ha 50 - 60 Pers/Ha Figure A4_Population Density around Altona Station

> 60 Pers/Ha

FIGURE 8: POPULATION DENSITY MAP BY STATION PRECINCT (SOURCE: AUTHOR)

Page 24

!

FIGURE 9: EMPLOYMENT DENSITY BY STATION PRECINCT (SOURCE: AUTHOR)!

Figure G5_Employment Density around Glenferrie Station

Figure O5_Employment Density around Oakleigh Station

Figure S5_Employment Density around St Albans Station

Figure H5_Employment Density around Hoppers Crossing Station

Figure M5_Employment Density around Malvern Station

Figure Gr5_Employment Density around Greensborough Station

Figure E5_Employment Density around East Richmond Station

Figure N5_Employment Density around North Richmond Station

Figure B5_Employment Density around Brunswick Station

Legend 0 - 5 Jobs/Ha 5 - 10 Jobs/Ha 10 - 20 Jobs/Ha 20 - 30 Jobs/Ha 30 - 40 Jobs/Ha 40 - 50 Jobs/Ha 50 - 60 Jobs/Ha Figure A5_Employment Density around Altona Station

> 60 Jobs/Ha

FIGURE 9: EMPLOYMENT DENSITY MAP BY STATION PRECINCT (SOURCE: AUTHOR)

Page 25

People & Jobs Actual numbers of dwellings, people and jobs within each WCA was also obtained using GIS and are included as Table 5 below. The importance of pedestrian accessibility is noted as although some precincts may have areas high intensity of dwelling, population and employment density, the actual numbers of each can be lower than expected due to the size of the WCA.

Station

Dwellings

Persons

Jobs

Glenferrie

2368

4715

5847

Oakleigh

1155

2874

1932

St Albans

1120

2603

982

Hoppers Crossing

507

1206

1209

Malvern

2121

4643

2533

Greensborough

707

1635

2646

East Richmond

2291

4335

6906

North Richmond

4020

8032

7616

Brunswick

1489

3138

3441

Altona

1024

2062

706

TABLE 5: COUNT OF DWELLINGS, PERSONS & JOBS BY STATION PRECINCT (SOURCE: AUTHOR)!

!

Table 5 suggests that North Richmond, Glenferrie, East Richmond and Malvern have the most dwellings, while Hoppers Crossing and Greensborough have the least dwellings. North Richmond, Glenferrie, Malvern and East Richmond have the most people, while Hoppers Crossing, Greensborough, and Altona have the least people. North Richmond, East Richmond and Glenferrie have the most jobs, while Altona, St Albans and Hoppers Crossing have the least jobs. Scoring of these numbers of dwellings, persons and jobs are included in Table 6.

Comparing Figures 7, 8 and 9 and Table 5 it is evident that although Malvern has very few intense dwelling and population density areas, it has relatively higher numbers of dwellings and people. This is because Malvern has more areas of higher than average density and a comparatively large WCA (refer Table 4).

Surprisingly St Albans, with the largest WCA, lacks areas of intense dwelling, population and employment density and numbers of dwellings, people and jobs. This represents a missed opportunity to accommodate more people and jobs close to rail infrastructure.

!

Page 26

DENSITY MEASURES

Gl Oak St A HC Mal Gr ER NR Br

DU/Ha Intensity

+

-

-

+

+

UPR/Ha Intensity

+

-

-

+

+

+

+

Employment/Ha Intensity

-

SUBTOTAL - INTENSITY

2

# of Dwellings

-1

+ -

-2

0

-2

3

3

+

-

+

-

+

+

# of People

+

-

+

-

+

+

-

# of Jobs

+

+

+

-

SUBTOTAL - NO. OF PEOPLE & JOBS

3 5

TOTAL SCORE FOR DENSITY

0

Al

1

-1

-

-

0

-1

-3

2

-2

3

3

0

-2

0

-2

-5

2

-4

6

6

1

-3

TABLE 6: SUMMARY OF DENSITY SCORING BY STATION PRECINCT (SOURCE: AUTHOR)!

!

By scoring density measures in Table 6 it is evident that East Richmond and North Richmond both have the most amount of intense dwelling, population and employment density, while Hoppers Crossing and Greensborough overall have the least intense density. North Richmond, East Richmond and Glenferrie have the most dwellings, people and jobs in the WCA, while Hoppers Crossing has the least, followed by Greensborough (in numbers of dwellings and people) and Altona (in numbers of people and jobs).

Generally from the density maps there appears to be little overlap between population and employment densities. That is, where there is low intensity purple (i.e. low employment density) areas, there is intense red and blue (i.e. high dwelling and population density) areas. Where there exists slight overlap such as in North Richmond, this can be explained by the size of the Destination Zone polygon spreading over high and low population density areas.

The maps also highlight that there is congruence between intensities of red and blue, indicating that dwelling density is related to population density. However there are variations evident where Meshblocks have more intense red than blue (i.e.more dwelling density than population density) or to a lesser extent vice versa, such as halls of residence, aged care facilities or higher dwelling occupants.

A shortfall of the data available for density analysis was the difference in collection areas (or resolutions of data). Population and dwelling density appear opposite to employment density in all maps, however if Mesh Block resolution was collected for employment destinations rather than Destination Zone resolution, there might be more overlap allowing for more in-depth analysis.

!

Page 27

Mix! Mix has been mapped by grain sizes and functional categories within each of the 10 station precincts.

Firstly grain mix has been analysed in terms of amount of fine grain (i.e. diversity found in small format development) and mixture of grain sizes (i.e. proportional distribution of each grain size within each station precinct) overall. Secondly, functional mix has been analysed in terms of number of functions and spread across each station precinct. Finally, grain mix has been analysed within commercial and residential areas for comparative evaluation of development formats. Commercial and residential are the predominant function and only categories common to all station precincts (other than open space), and were therefore considered in more detail for their mix of development formats (or built form).

Grain Mix Figure 11 show the grain mix by station precinct. In these maps the amount of dark green (i.e. parcels under 300 square metres) and yellow (i.e. parcels between 500 and 1000 square metres) and red (i.e. parcels over 5000 square metres) stand out. Generally, dark green represents fine grain, inner-urban development, yellow-orange represents suburban residential development and red represents open space, industry, public/iInstitution (i.e. education or health), cemetery, or commercial (i.e. large format retail).

Figure 10 shows the distribution of grain sizes within each of the station precincts to aid visual assessment for grain mix. The data was collected from GIS and graphed using Microsoft Excel for comparative analysis between number and proportions of grain size within each of the WCA.

3,000

2,250 > 5000 sqm 1001 - 5000 sqm 501 - 1000 sqm 401 - 500 sqm 301 - 400 sqm 0 - 300 sqm

1,500

Altona

Brunswick

North Richmond

East Richmond

Greensborough

Malvern

Hoppers Crossing

St Albans

Oakleigh

Glenferrie

750

FIGURE 10: PROPORTIONS OF GRAIN SIZES BY STATION PRECINCT (SOURCE: AUTHOR)!

Page 28

!

Figure G7_Grain Mix (Lot Sizes) around Glenferrie Station

Figure O7_Grain Mix (Lot Sizes) around Oakleigh Station

Figure S7_Grain Mix (Lot Sizes) around St Albans Station

Figure H7_Grain Mix (Lot Sizes) around Hoppers Crossing Station

Figure M7_Grain Mix (Lot Sizes) around Malvern Station

Figure Gr7_Grain Mix (Lot Sizes) around Greensborough Station

Figure E7_Grain Mix (Lot Sizes) around East Richmond Station

Figure N7_Grain Mix (Lot Sizes) around North Richmond Station

Figure B7_Grain Mix (Lot Sizes) around Brunswick Station

Legend 0 - 300 sqm 301 - 400 sqm 401 - 500 sqm 501 - 1000 sqm 1001 - 5000 sqm > 5000 sqm

Figure A7_Grain Mix (Lot Sizes) around Altona Station FIGURE 11: GRAIN MIX MAP BY STATION PRECINCT (SOURCE: AUTHOR)

Page 29

From Figure 10 East Richmond, North Richmond, Brunswick, Glenferrie and Malvern can be classified as ‘inner-urban’, because they have a large amount of dark green (i.e. parcels under 300 square metres).

Hoppers Crossing, St Albans, Greensborough, Altona and Oakleigh can be classified as ‘suburban residential’, because they have a large amount of yellow-orange (i.e. parcels between 500 and 1000 square metres).

The analysis found that 74% of East Richmond, 72% of North Richmond, and 58% of Brunswick station precincts are made up of 300 to 400 square metre fine grain parcels. These fine grains offer additional mix because of the number of small scale functions and diversity at pedestrian scale. By contrast, 16% of Hoppers Crossing station precinct is made up of 300 to 400 square metre fine grain parcels, and nearly 70% of its WCA is greater than 500 square metres. In Hoppers Crossing this contributes to a less permeable pedestrian network and ultimately smaller WCA. Oakleigh has the most even distribution of grain sizes (i.e. diversity of parcel sizes), while Hoppers Crossing, East Richmond and North Richmond have the least grain mix (i.e. uniform property sizes).

Scoring of these amounts of fine urban grain and grain mix are included in Table 7.

Functional Mix Figure 12 illustrates functional mix within the station precincts. From the maps the number and spread of different functions people can perform within each WCA can be analysed. These maps are the best indications of connectivity within the train station precincts.

Figure 12 shows that Oakleigh, Greensborough, Brunswick and Altona have the most number of functions, while Malvern has the least number of functions. Glenferrie, North Richmond and Brunswick have the most dispersed functions, while Oakleigh, Hoppers Crossing, Malvern and Altona have the least dispersed (i.e. most clustered) functions. Scoring of these numbers and spread of functions are included in Table 7.

Residential & Commercial Mix Figure 12 shows that commercial and residential functions are common to all station precincts and are the predominant functions within all of the station precincts. Due to lack of further built environment information such as building heights across all areas, the mix of commercial and residential typologies have been further analysed.

Using the polygons created in GIS for the employment count data, the amount of jobs by industry of employment in each train station precinct was calculated and graphed using Excel. These proportions of industry of employment are shown in Figure 13. Dwelling typology proportions for each station precinct were also collected at the State Suburb (SSC) level from 2011 Census Data (and where required averaged across two or more SSC) and graphed by station precinct (see Figure 14).

!

Page 30

FIGURE 11: GRAIN MIX BY STATION PRECINCT (SOURCE: AUTHOR)!

FIGURE 12: FUNCTIONAL MIX BY STATION PRECINCT (SOURCE: AUTHOR)â&#x20AC;©

Figure G6_Functional Mix (Land Use) around Glenferrie Station

Figure O6_Functional Mix (Land Use) around Oakleigh Station

Figure S6_Functional Mix (Land Use) around St Albans Station

Figure H6_Functional Mix (Land Use) around Hoppers Crossing Station

Figure M6_Functional Mix (Land Use) around Malvern Station

Figure Gr6_Functional Mix (Land Use) around Greensborough Station

Figure E6_Functional Mix (Land Use) around East Richmond Station

Figure N6_Functional Mix (Land Use) around North Richmond Station

Figure B6_Functional Mix (Land Use) around Brunswick Station

Legend Residential Mixed Use (Com + Res) Commercial Public / Institution Government Open Space Cemetery Figure A6_Functional Mix (Land Use) around Altona Station

Industry

FIGURE 12: FUNCTIONAL MIX MAP BY STATION PRECINCT (SOURCE: AUTHOR)

Page 31

100% Other Health Care & Social Assistance Education & Training Public Adminstration & Safety Administrative & Support Services Professional, Technical & Scientific Services Rental, Hiring & Real Estate Services Financial & Insurance Services Transport, Postal & Warehousing Accommodation & Food Services Retail Trade Wholesale Trade Construction Manufacturing

75%

50%

Altona

Brunswick

North Richmond

East Richmond

Greensborough

Malvern

Hoppers Crossing

St Albans

Oakleigh

Glenferrie

25%

FIGURE 13: PROPORTIONS OF INDUSTRY OF EMPLOYMENT BY STATION PRECINCT (SOURCE: AUTHOR)!

100%

75% House or flat attached to a house Flat, unit or apartment Semi-detached row/terrace/townhouse Separate house

50%

Altona

Brunswick

North Richmond

East Richmond

Greensborough

Malvern

Hoppers Crossing

St Albans

Oakleigh

Glenferrie

25%

FIGURE 14: PROPORTIONS OF RESIDENTIAL TYPOLOGIES BY STATION PRECINCT (SOURCE: AUTHOR)!

!

Figure 14 shows that Glenferrie, Malvern, East Richmond and North Richmond all have relatively even distributions of housing typologies. Brunswick SSC also appears to have a relatively even distribution of housing typologies, however it is evident from Figure 12 (Figure B6) that the small residential area within the WCA is not very mixed. This suggests the mix of housing typologies in Brunswick fall outside the WCA. Page 32

From Figure 11 and 12 it is evident that East Richmond, North Richmond and Brunswick have the most fine grain residential. Comparing station precincts in Figure 14, for East Richmond, North Richmond and Brunswick the residential typology is likely semi-detached row or terrace house, townhouse, etc., as these station precincts are uniquely abundant in this typology. By contrast, St Albans, Hoppers Crossing and Greensborough have the least amount of fine grain (and most course grain) residential, and from comparisons in Figure 14 the residential typology is likely separate housing, as these station precincts are uniquely abundant in this typology. Glenferrie, Malvern, Altona have the most grain mix in Residential (i.e. most mix of residential building typologies), while Hoppers Crossing, East Richmond, North Richmond and Brunswick have the least grain mix in residential (i.e. least mix of residential building typologies).

Figures 11 and 12 also suggest that Glenferrie, East Richmond, North Richmond and Brunswick have the most fine grain commercial. By contrast, Hoppers Crossing and Altona have the least amount of fine grain (and most course grain) commercial. For Hoppers Crossing and Altona these are likely in Retail, Health Care & Social Assistance or Accommodation & Food Services (see Figure 13). Glenferrie, East Richmond, North Richmond and Brunswick have the most grain mix in commercial areas (i.e. most mix of commercial building typologies), while Hoppers Crossing and Altona have the least grain mix in commercial areas (i.e. least mix of commercial building typologies).

Scoring of these of residential and commercial mix is included in Table 7.

MIX MEASURES

Gl

Oak

St A

Amount of Fine Grain - Overall Mix of Grain Sizes - Overall SUBTOTAL - GRAIN MIX

+ 0

# of Functions

1

0

HC

ER

NR

Br

-

+

+

+

-

-

-

0

0

-2

+

Spread of Functions

+

-

SUBTOTAL - FUNCTIONAL MIX

1

0

Amount of Fine Grain - Residential Mix of Grain Sizes - Residential

0

-

+

-

0

-1

-2

-

-

+

-2

1

Al

1

0

+

+

+

+

0

1

0

1

2

-

+

+

+

-

-

-

+

0

0

0

1

SUBTOTAL - RESIDENTIAL MIX

0

Amount of Fine Grain - Commercial

+

-

+

+

+

-

Mix of Grain Sizes - Commercial

+

-

+

+

+

-

SUBTOTAL - COMMERCIAL MIX

2

0

0

-2

0

0

2

2

2

-2

3

2

-1

-7

-1

0

2

3

5

-1

TOTAL SCORE FOR MIX

-1

Gr

0

-

+ 1

Mal

-1

TABLE 7: SUMMARY OF MIX SCORING BY STATION PRECINCT (SOURCE: AUTHOR)!

!

Scoring the mix measures it is evident that Brunswick has the most function and grain mix overall within the WCAs, while Hoppers Crossing has the least. Oakleigh and Brunswick have the most grain mix and fine grain overall in the WCAs resulting in diversity at the pedestrian scale, and Hoppers Crossing has the least.

Brunswick has the most amount of functional mix, while Malvern has the least amount of functional mix Page 33

(and most clustered functions). The inner-urban station precincts (i.e. Glenferrie, East Richmond, North Richmond and Brunswick) have the most mix and most fine grain residential and commercial functions, while Hoppers Crossing has the least mix and least fine grain residential and commercial functions. In Hoppers Crossing this finding reinforces the car dominance and dependence observable in the area.

Also noteworthy is that Oakleigh, St Albans, Hoppers Crossing, Malvern and Greensborough all have large grain commercial areas (i.e. large format commercial) which act as a barrier (or hinder pedestrian accessibility) to the stations. Oakleighâ&#x20AC;&#x2122;s large grain commercial physical barrier significantly limits the number of railway line crossings and ultimately reduces the WCA. Public 24-hour access through these large format commercial areas are a missed opportunity to increase the WCA. In addition, St Albans and Brunswick have large grain Industry areas that are impermeable, and this is results in a reduced WCA.

Access!

Access has been mapped in terms of pedestrian network, WCA, public transport network, road network and parking. Findings on the pedestrian network and WCA has been discussed above, and the following section describes findings on the public transport, road network and parking. Public transport access has been mapped by mode, and road access has been mapped by hierarchy (i.e. function in the larger road network).

Public Transport Figure 15 shows the public transport network surrounding each station. From these maps it is evident that Glenferrie, Malvern and East Richmond have the most complementary inter-modal access. Glenferrie and Malvern have regular tram and bus access and East Richmond has tram access (refer Table 1). By contrast, St Albans, Greensborough and Altona have the lowest level interchange, with only bus stops to collect patrons en-route (refer Table 1).

Distances between modes was also used to measure the public transport interchange as a measure of intermodal connectivity. Distances were measured in Google Maps to each bus and tram stop in the immediate vicinity of each station. An average of distances to all interchange locations at each station was calculated using Excel. Connected routes were also counted to measure intermodal connectivity and these were cross checked with PTVâ&#x20AC;&#x2122;s (2014) Journey Planner to ensure all connected destinations were counted.

Scoring of these connections are included in Table 7.

! !

Page 34

KANGAR OO RD

MILL RD

BURLING TON ST

Figure O8_Public Transport Network around Oakleigh Station

O

L EE

DG

PR

INC

ES

EK RD WATTLET RE

E RD

HW

Y

HENRYS ST

RD HAWTHOR N

F

ST

F

G RD

SWAN ST

Figure M8_Public Transport Network around Malvern Station

Figure E8_Public Transport Network around East Richmond Station

BLYTH ST

BLYTH ST

PDE

VICTORIA

ST

Figure N8_Public Transport Network around North Richmond Station

DAWSON ST

GLENLYON

ST

Figure B8_Public Transport Network around Brunswick Station

Legend MILLERS RD

PIER ST

CIVIC PDE

Figure Gr8_Public Transport Network around Greensborough Station

VICTORIA ST

HODDLE ST

CHURCH ST

PUNT RD

VICTORIA

CHURCH ST

Figure H8_Public Transport Network around Hoppers Crossing Station

SARGOOD ST

F TO

LIN

RD RA PA

ST

MA

ON

HOPPERS LANE

EN

ND

E

INC PR

DA

Y

W SH

ST

IN

GRIMSHAW

ST HELENA RD

SYDNEY RD

OL

DIAMO ND CR E

GLENFERR

D

R NG

ST

Figure S8_Public Transport Network around St Albans Station

IE RD

MORRIS RD

HEATHS RD

ST

Figure G8_Public Transport Network around Glenferrie Station

ING OD GO

WARRIGAL RD

D RD

MAIN RD E

ST

MCKECHNIE ST

RD AUBURN

BURWOO

MAIN RD W

ALFRIED A ST

AR TH UR

ST ATKINSON

IE RD

GLENFERR

ATHERTON RD PORTMAN

E NAD P LA T ES DE EAS ANA SPL ST E WE

!

FIGURE 15: PUBLIC TRANSPORT NETWORK BY STATION PRECINCT (SOURCE: AUTHOR)!

Tram Route Tram Stop Metro Bus Route

BAY TRAIL W

Figure A8_Public Transport Network around Altona Station FIGURE 15: PUBLIC TRANSPORT NETWORK MAP BY STATION PRECINCT (SOURCE: AUTHOR)

Page 35

Average Transport Links Distance to Interchange (m) Glenferrie

211

• Tram Route 16 (Both Directions)!

Number of Routes Connected 4

• Bus Route NightRider (Both Directions)

Oakleigh

115

• Bus Routes 624, 693, 704, 742, 800, 802, 804, 862, 802/804/862 combined & 900

10

St Albans

200

• Bus Routes 419 & 422

2

Hoppers Crossing

90

• Bus Routes 436, 437, 442, 444, 445, 446, 448 & 493

8

Malvern

130

• Tram Routes 5 (Both Directions), 16 (Both Directions) & 64 (Both Directions)!

8

• Bus Route NightRider (Stops at Glenferrie Rd/ Princes Hwy & Glenferrie Rd/Princes Hwy) Greensborough

350

• Bus Routes 517, 518, 520 & 513

4

East Richmond

106

• Tram Routes 70 (Both Directions), 78 (Both Directions) & 79 (Both Directions)

6

North Richmond

129

• Tram Routes 24 (Both Directions), 31 (Both Directions), 48 (Both Directions) & 109 (Both Directions)!

10

• Bus Routes 246 & 246/250 combined Brunswick

205

• Tram Route 19 (Both Directions)!

4

• Bus Route 508 (Both Directions) Altona

113

• Bus Route 411, 412, 411/412 combined, NightRider, 415 & 903

6

TABLE 8: PUBLIC TRANSPORT CONNECTIONS (SOURCE: AUTHOR)!

!

From Table 8 it is evident that Hoppers Crossing, East Richmond, Altona and East Richmond are all connected to other modes within close proximity, while Greensborough, Glenferrie, Brunswick and St Albans do not have public transport within close proximity for intermodal connectivity. Oakleigh and North Richmond both have 10 different routes connected with North Richmond having modal choice between tram and bus, while Altona only has 2 public transport routes connected to the station.

!

Page 36

Parking Given the size of the study areas and lack of available precinct parking data, parking has been mapped according to observed significant car parking near each station, not based on parking time limits or cost.

Designated station parking numbers are included as Table 9 for reference, however the mapping exercise showed these figures to misrepresent actual station parking.

Station Car Parks Station Bike Parks Glenferrie

0

10 Racks, 8 Lockers

Oakleigh

339

5 Racks, 6 Lockers

St Albans

540

5 Racks, 12 Lockers

Hoppers Crossing

566

10 Racks, 14 Lockers

0

0

Greensborough

212

5 Racks, 8 Lockers

East Richmond

147

0

North Richmond

0

0

Brunswick

0

5 Racks

Altona

0

5 Racks, 8 Lockers

Malvern

TABLE 9: DESIGNATED PARKING FACILITIES BY STATION (SOURCE: PTV, 2013)!

!

Figure 16 shows road hierarchy and parking locations. Analysing the maps car parking areas fall under four typologies; compact car parking (e.g. Glenferrie, Malvern and Brunswick), street car parking (e.g. East Richmond and North Richmond), scattered car parking (e.g. Oakleigh, St Albans and Altona), and centre car parking (e.g. Hoppers Crossing and Greensborough). Inner-urban station precincts (i.e. East Richmond, North Richmond, Brunswick, Glenferrie and Malvern) have compact or street car parking, while suburban residential station precincts (i.e. Hoppers Crossing, St Albans, Greensborough, Altona and Oakleigh) have scattered or centre parking.

Glenferrie and Malvern have car parking back off the main pedestrian routes in high-rise/ multi-deck parking, hidden behind other uses from the street. By contrast, Oakleigh, Hoppers Crossing and Greensborough car parking is a visual and a physical barrier from the station to surrounding areas.

Road Network Looking at the road network in conjunction with the parking locations in Glenferrie, East Richmond and North Richmond it is evident that car parking is well integrated into the road and pedestrian network (i.e. not a physical or visual barrier). Glenferrie and East Richmond particularly have a road network that is ‘semi-lattice’ in structure, creating road environs from the station that are highly accessible and permeable (Alexander 1965). By contrast, Oakleigh, St Albans, Hoppers Crossing and Greensborough have road networks that are ‘tree-like’ in structure with only 1 or 2 railway line crossings, making them vulnerable to network breakdowns (Alexander 1965).

Page 37

FIGURE 16: ROAD NETWORK & PARKING BY STATION (SOURCE: AUTHOR)â&#x20AC;©

Figure G9_Road Network & Parking around Glenferrie Station

Figure O9_Road Network & Parking around Oakleigh Station

Figure S9_Road Network & Parking around St Albans Station

Figure H9_Road Network & Parking around Hoppers Crossing Station

Figure M9_Road Network & Parking around Malvern Station

Figure Gr9_Road Network & Parking around Greensborough Station

Figure E9_Road Network & Parking around East Richmond Station

Figure N9_Road Network & Parking around North Richmond Station

Figure B9_Road Network & Parking around Brunswick Station

Legend

Motorway/Freeway/Tollway Arterial/Highway Sub-Arterial Main Collector Minor Collector Local Road

Figure A9_Road Network & Parking around Altona Station

P

Car Parking

M

Multi-Deck Carpark

FIGURE 16: ROAD NETWORK & PARKING MAP BY STATION (SOURCE: AUTHOR)

Page 38

Stations surrounded by local roads were considered to have a more pedestrian friendly road environment due to the associated traffic loads and speeds of the roads. Glenferrie, East Richmond, North Richmond and Altona were considered to have the best road environs immediately surrounding the station. By contrast, Oakleigh, Hoppers Crossing, Malvern and Greensborough were considered to have the least pedestrian friendly road environs immediately surrounding the station because of the higher order roads and faster moving vehicles. Scoring of the network complexity and road environs surrounding the stations are included in Table 10.

ACCESS

Gl

Oak

Pedestrian Catchment Ratio SUBTOTAL - PEDESTRIAN ACCESS

0

Tram - Complementary to Train

++

0

St A

HC

Mal

+

-

+

1

-1

1

Gr

0

++

0

++ +

+

SUBTOTAL - PT ACCESS

3

2

1

2

Distance to Interchange

-

+

-

+

+

-2

SUBTOTAL - PT CONNECTIVITY

-1

2

Car Parking - Visual Prominence

+

Car Parking - Physical Barrier

Al

0

0

-1

+

+

+

+

+

2

2

1

-

+

-1

1

0

0

++

Bus - Stops

# of Routes Connected

Br

++ +

Bus - Interchange

NR

-

Tram - Competes with Train

+

+

3

1

3

-

+ +

1

0

-1

-

-

+

-

+

-

-

SUBTOTAL - PARKING

2

-2

0

-2

Road Network - Complexity

+

-

-

-

Road Environs Surrounding Station

+

-

SUBTOTAL - ROAD NETWORK

2

-2

6

0

TOTAL SCORE FOR ACCESS

ER

1

1

1

-

+

+

-2

1

1

-

+

-

-

-

-

+

+

+

-1

-2

-1

-2

2

1

0

0

-1

-2

4

-4

7

5

1

1

TABLE 10: SUMMARY OF ACCESS SCORING BY STATION PRECINCT (SOURCE: AUTHOR)!

!

Scoring the access for the train station precincts it is evident that St Albans and Malvern have the largest WCA and are the most pedestrian accessible precincts, while Glenferrie, Hoppers Crossing and Altona have the smallest walkable catchments and are the least pedestrian accessible precincts. Glenferrie, Malvern and East Richmond have the highest order public transport interchanges, while St Albans, Greensborough and Altona have the lowest order public transport interchanges. Oakleigh has the most connected interchange and St Albans has the least connected interchange. Glenferrie has the most visually and physically integrated car parking, while Oakleigh, Hoppers Crossing and Greensborough have the most visually and physically obtrusive car parking. Glenferrie and East Richmond have the most straight forward, connected and walking conducive road network, while Oakleigh, Hoppers Crossing and Greensborough have the most complex (i.e. vulnerable to severing or road closure) road network and unpleasant road environs for pedestrians.

Page 39

Assemblages of Density, Mix & Access!

Overall scores for density, mix and access at the 10 station precincts are included in Table 11. Station patronage has been included to shed light on the sensitivity of mode choice to changes in the built environment. The scores have been ranked to provide a scale of 1 to 10 for each measure (see Figure 17).

Gl

Oak

St A

HC

Mal

Gr

ER

NR

Br

Al

Overall Density Score

5

0

-2

-5

2

-4

6

6

1

-3

1-10 Density Rank

9

5

4

1

7

2

10

10

6

3

Overall Mix Score

3

2

-1

-7

-1

0

2

3

5

-1

1-10 Mix Rank

9

6

2

1

2

5

6

8

10

2

Overall Access Score

6

0

-1

-2

4

-4

7

5

1

1

1-10 Access Rank

9

4

3

2

7

1

10

8

5

5

Overall Patronage Score

48872 34247 33196 28481 26029 18932 14412 11683 5675 5597

1-10 Patronage Rank

10

9

8

7

6

5

4

3

2

1

TABLE 11: RANKING DENSITY, MIX, ACCESS & PATRONAGE SCORES (SOURCE: AUTHOR)!

10

8

6

Density Mix Access Patronage

4

Altona

Brunswick

North Richmond

East Richmond

Greensborough

Malvern

Hoppers Crossing

St Albans

Oakleigh

0

Glenferrie

2

FIGURE 17: DENSITY, MIX, ACCESS & PATRONAGE RANKS (SOURCE: AUTHOR)!

Page 40

!

Glenferrie!

Figure 17 suggests that in Glenferrie station precinct there are synergies between density, mix, access and patronage. Glenferrie is evidently experiencing the ‘multiplier’ effect that Jacobs (1961) describes in ‘The Death and Life of Great American Cities’ in her chapter on ‘The need for mixed primary uses’. Jacobs (1961) suggests a mixed set of primary uses, like residential and major employment or service functions (i.e. any land use that generates a large number of people moving through an area), will bring in users with demands for secondary uses, like shops, restaurants, bars and other small-scale local facilities which serve the primary uses. According to Jacobs (1961) these secondary uses need to be highly varied and intensely used to create a multiplicity of uses, attractions, and routes that enable the personal interactions that make up the vibrancy of a community.

Glenferrie’s mixed set of primary uses includes a masterplanned Swinburne University campus and college, major retailers on Glenferrie and Burwood Road, major sport and recreation facilities and a constellation of primary schools in the broader area. Secondary uses including smaller retailers, gathering spaces, cafes and other amenities serve these primary uses. In addition, movement in and around these primary and secondary uses occur at different times, forming ‘tidal’ ebbs and flows. The consideration of the activity schedules of people engaging in different activities is also fundamental to Jacobs’ (1961) argument.

This is an important lesson for any urban design approach to mixed use development. If a public space is surrounded by one type of land use it will be used only at certain periods during the day. Combining the primary activities of living and working implies a better distribution of demand over the day, and will support a greater variety of secondary facilities, all of which add to local diversity and an even spread of activity in the public realm throughout the day and evening (Jacobs, 1961).

North Richmond!

Similarly to Glenferrie, North Richmond is also ranking highly in density, mix and access. However, this assemblage is not resulting in a high station patronage. From this, it is evident that there are other factors contributing to station patronage other than density, mix and access of the built environment. For instance, North Richmond is the closest of the station precincts to the city centre, and given the demand for travel is increasing for employment trips into the city centre, proximity to the centre is a factor. With greater non-motorised travel modes becoming more feasible closer to the centre in Melbourne, less train travel is expected. In addition to competition with non-motorised transport modes, North Richmond station also competes with other public transport modes (including 10 tram and bus routes) for access in and around the city centre.

Interestingly, and although not a factor related to station patronage, North Richmond was the only other station to have an elevated structure type like Glenferrie. Given Glenferrie and North Richmond perform the highest in density, mix and access, elevated rail may be a factor in achieving these outcomes.

!

Page 41

East Richmond!

Similarly to North Richmond, East Richmond also ranked highly in density, access and (to a lesser extent) mix, and again this assemblage is not resulting in high station patronage. East Richmond is similarly within close proximity to the city centre and greater mode choice is available which is a factor.

Another factor contributing to East Richmondâ&#x20AC;&#x2122;s low station patronage is the limited train services from the station. Due to its location at the city end of Melbourne's busiest group of railway lines, a large number of services pass through East Richmond although only a limited number stop. This is largely because of East Richmondâ&#x20AC;&#x2122;s proximity to Richmond station, a few hundred metres down Swan Street. Services to and from Lilydale and Belgrave do not normally stop at East Richmond. Glen Waverley trains stop in off-peak, but trains to and from Glen Waverley in peak hours do not stop at the station. Most services to and from Alamein, Blackburn and Ringwood stop at the station. This means train services are a factor because reduced train service means users will opt for alternative modes of transport.

Interestingly, and although not a factor related to station patronage, the analysis found that East Richmond station precinct performed the best in terms of access measures and had the highest proportion of patrons who walked all the way to the station (see Table 1). What is surprising about this is that East Richmond has 147 designated station car parks in street parking format, and with such high walk-up rates this indicates car parking can be well-integrated at a station and not detract from the pedestrian friendliness of an area.

Brunswick!

As the fourth inner-urban station precinct, Brunswick unsurprisingly also performs better than average in measures of density, mix and access of the built environment. Brunswick station has the second highest proportion of users who walked all the way to the station and one of the lowest proportions work or education journeys (see Table 1). As a result, Brunswick may have performed the best in terms of functional mix but this did not translate into job density. Brunswick stood out as the precinct with greater population density than dwelling density in the residential areas (i.e. more people living within the dwellings). If some of the industrial areas were transformed into residential and commercial areas, with more pedestrian connections though the blocks, Brunswick would not only achieve a greater WCA and improved access, but also increased dwelling, population and potentially employment density.

The other factor influencing Brunswickâ&#x20AC;&#x2122;s low station patronage is that the train is competing with the tram network for access to the city centre. Trams are stopping more frequently and are easily accessible from the commercial core along Sydney Road. Parallel train and tram lines to the centre is not a efficient use of rail infrastructure.

Oakleigh!

After the inner-urban station precincts, Oakleigh is the next highest assemblage of density, mix and access. Oakleigh station precinct has the second highest station patronage, and attracts nearly 70% work and education journeys. It also ranks similarly across its density, mix and access measures. However, it lacks most in access scores because pedestrians can only cross the railway line via a pedestrian underpass at the station, over the sub-arterial overpass (Warrigal Road) and ramps, or via the local road (Richardson Street) Page 42

further north-east. In addition, there are only two north-south vehicular connections (Warrigal Road overpass and Hanover Street) across the railway line 800m either side of the station.

Oakleigh is ranked sixth in terms of WCA and it is performing average in terms of density, mix and access measures. Oakleigh’s high station patronage can only be explained by its highly successful bus interchange. Oakleigh’s bus interchange sees nearly 29.7% of its 34,247 station patrons per week arrive and leave via one of the 10 connected bus routes. Oakleigh is also at the edge of Zone 1 of PTV’s ticketing system and is a premium station with a customer service centre staffed from the first to last train, seven days a week. Costs associated with travelling to Zone 2 and safety considerations around stations may also therefore be a factor in its high station patronage.

Malvern!

Like East Richmond, Malvern achieves reasonably well in terms of density and access indicators, but is let down by its functional mix. 70.8% of trips made via the station are for work journeys. This is a significant missed opportunity because Malvern already has a large WCA, a high proportion of patrons who walk all the way to the station and mixed urban grain. If Malvern increased its number of functions and spread of functions one might expect similar outcomes to that of Glenferrie.

Other factors that might also contribute to Malvern’s station patronage could be cultural (i.e. perceptions about going down from street level to public transport), visual connectivity to the station or socio-economic factors. Malvern uniquely has a rail under structure with a sub-arterial and main connector road crossing the railway line above at either end of the station. Further work should be conducted to understand the influence of culture and visual connection on station patronage.

St Albans!

St Albans has the largest WCA of all of the station precincts and the third highest station patronage. Over 85% of its usage comes from education and work journeys (refer Table 1), however there are very few jobs in the WCA (refer Table 5). These journeys are most likely from Victoria University’s St Albans campus (which lies 800m from the station) and Sunshine Hospital (which lies 2.3km from the station), as both of these major facilities are undergoing significant expansion and growth.

Despite its large WCA (resulting from the radial street network) and having only one railway crossing in the entire WCA, St Albans has the second highest number of designated station car parks. PTV’s (2013) Station Patronage Research suggests 29.9% of the 33,196 station patrons per week in FY2011/12 drove to the station, yet there are 540 designated station car parks. A metro train in Melbourne has 268 seats, meaning there is only enough parking for 2 morning peak hour trainloads before station parking has reached capacity and the St Albans station precinct is dominated by parked vehicles all day.

St Albans represents a significant underdevelopment of land surrounding rail infrastructure. It already has key trip generators in the broader area, high station patronage and is highly pedestrian accessible. It also has a high proportion of medium grain parcels (i.e. 501 - 1000 square metre parcels) and above containing separate houses that represent opportunity for redevelopment and infill housing.

!

Page 43

Hoppers Crossing!

Hoppers Crossing station precinct has the lowest assemblage of density, mix and access. In fact, Hoppers Crossing would be the lowest achieving in all three core dimensions of the built environment had it not been for Greensboroughâ&#x20AC;&#x2122;s very poor public transport connections. Given this, the other factors that influence the station patronage are more significant in Hoppers Crossing than in most other station precincts.

At 27.7km from the centre, Hoppers Crossing has 566 designated station car parks, 10 bike racks, 14 bike lockers, and a bus interchange with 8 connected bus routes. Similarly to St Albans, 49.5% of the 28,481 station patrons drove to the station per week in FY2011/12, leaving the Hoppers Crossing station precinct dominated by parked vehicles all day. This in conjunction with the large car parking areas for the large format retail and bulky goods to the north-east of the station, means the area is car-dominated.

In Hoppers Crossing local employment generators are limited and there are only 1209 jobs within the WCA (see Table 5). With over 80% of journeys through the station being for work and education, the station is providing access to the CBD or other employment centres on the Werribee line.

The bus interchange is also a factor affecting the station patronage, as 31.1% of station users are coming from the bus interchange.

Greensborough!

Not unlike Hoppers Crossing, Greensborough presents an assemblage that performs poorly in the rankings.

Similar to Hoppers Crossing, it provides a high proportion of work and education journeys, and it is second only to Hoppers Crossing in terms of least people housed in the area. 44.8% of the 18,932 station patrons per week in FY2011/12 drove to the station and parked in either one of the 212 designated station car parks or surrounding at-grade or multi-deck private car parks. In addition, 22.4% of the station patrons accessed the station via one of the substandard bus connections on average about 350m from the station.

Other than density, mix and access of the built environment culture is potentially another factor that contributes to the low station patronage. East of the activity centre the ground falls away, with a large drop on the alignment of the railway line. Access to the station is therefore either via the extremely hilly topography across the reserve or across the main connector road above the station. Like in the case of Malvern, potentially visual connectivity to the station below the road may also be a factor.

Altona!

Similarly to Oakleigh, Altona is a station at the edge of PTVâ&#x20AC;&#x2122;s Zone 1 ticketing system. Unlike Oakleigh however, Altona has the lowest station patronage. Altona station precinct has the smallest WCA due to Port Phillip Bay to the south and Lake Cherry to the north being impermeable. Yet despite this constraint, pedestrian access is quite good due to local roads running parallel to the station and a main collector road running perpendicular from the station. It is not surprising that 62.5% of station patrons walk to the station and that the commercial area runs perpendicular to the railway line given the regular grid street network.

Altona surprisingly though is not used like the other stations with large amounts of designated station car parks such as Hoppers Crossing, St Albans or Oakleigh. It has no designated station car parks, though there Page 44

are 70 privately owned car parks available for station users in the area for the 23.9% of 5,597 station patrons who drive to the station. Like these stations and Greensborough though, the area is made up of primarily 300-400 and 500-1000 square metre residential parcels with separate house typologies. Given the proximity to natural landscape features including the waterfront and its public transport access, Altona has the opportunity to achieve higher residential densities.

There are limitations on the frequency of train services that are provided to Altona however due to infrastructure constraints, and similar to East Richmond this may be a factor limiting station patronage.

Conclusion!

This thesis neither proved nor disproved whether urban morphology determines travel demand. However, this does not mean that urban morphology does not have an important role to play in achieving more sustainable travel patterns in Melbourne. Urban morphology measures (namely density, mix and access) primarily decide the outcome of the travel supply-demand interaction by determining the spatial pattern and concentration of travel flows and hence the suitability of transit for serving these flows. Furthermore, assemblages of density, mix and access can have significant effects on travel by creating synergies between measures. This ultimately means that urban design is well-placed to co-ordinate the variety of factors which individually and collectively are able to influence more sustainable travel patterns.

This thesis has ultimately contributed to the current knowledge by gaining additional insights into the linkages of density, mix and access and station patronage for a better understanding of the complex relationships of urban morphology and travel demand. Several density, mix and access measurements gathered from multiple external sources and scored provided a powerful analytic framework. Most significantly the analysis demonstrated the impact that the pedestrian network can have on pedestrian accessibility and the impact that pedestrian accessibility has on density, mix and access.

Further work should examine assemblages of density, mix and access at station precincts identified for urban renewal in Plan Melbourne as identified in Figure 18. Measuring dimensions of density, mix and access and assessing each assemblage to see what other factors might influence station patronage has proven to be crucial for understanding how to increase train mode choice.

!

Page 45

FIGURE 18: FURTHER WORK (ADAPTED FROM PLAN MELBOURNE, 2014, PP. 24 & 48)!

!

Page 46

Bibliography!

1. Cervero, R. (2002). Built Environments and mode Choice: Toward a Normative Framework" Transportation Research Part D 7(4):265-284.

2. Crane, R. (2000). The influence of urban form on travel: an interpretative review. Journal of Planning Literature 15 (1), 3–23.

3. Cervero, R. and Kockelman, K. (1997). Travel demand and the 3 Ds: density, diversity, and design.

Transportation Research D 2 (3), 119–219.

4. Levinson, H. and Wynn, F. (1963). Effects of density on urban transportation requirements. Highway Research Record 2, 38-64.

5. Boarnet, M. & Crane, R. (2001). Travel by Design: The Influence of Urban Form on Travel. Oxford University Press, New York.

6. Public Transport Victoria (PTV). (2012). Metropolitan public transport demand forecast report - August 2012. Available at: https://s3-ap-southeast-2.amazonaws.com/ptvic/Metropolitan+Patronage+Demand+Forecast+Report+2012+-+updated.pdf. Last accessed: 6 June, 2014.

7. Public Transport Victoria (PTV). (2013). Station Patronage Research - June 2013. Available at: http:// ptv.vic.gov.au/about-ptv/ptv-data-and-reports/research-and-statistics/. Last accessed: 6 June, 2014.

8. Kenworthy, J.R. (2006) The eco-city: Ten key transport and planning dimensions for sustainable city development. Environment and Urbanization, 18 (1). pp. 67-85.

9. Gallagher, P. (2012). Creating a Pedestrian Level-of-Service Index for Transit Stops: Evidence from Denver’s Light Rail System. Masters Thesis, Paper 268. University of Connecticut Graduate School.

10. Victorian Government. (2002). Melbourne 2030: planning for sustainable growth. Melbourne, VIC Government.

11. Victorian Government. (2014). Plan Melbourne. Melbourne, VIC Government. Available at: http:// www.planmelbourne.vic.gov.au/Plan-Melbourne. Last accessed: 6 June, 2014.

12. East Japan Railway Company (JR). (2012). Japanese Train Station Patronage. Available at: https:// translate.google.com/translate?sl=ja&tl=en&js=y&prev=_t&hl=en&ie=UTF-8&u=http%3A%2F %2Fwww.jreast.co.jp%2Fpassenger%2Findex.html&edit-text=&act=url. Last accessed: 16 May, 2014.

13. Canepa, Brian, (2007). Bursting the Bubble: Determining Transit-Oriented Development's Walkable Limits. Transportation Research Record: Journal of the Transportation Research Board, 1992, pp 28-34.

14. Leyden, K. M. (2003). Social capital and the built environment: the importance of walkable neighborhoods. American journal of public health, 93(9), 1546.

15. Al-Hagla, K. S. (2009). Evaluating new urbanisms walkability performance: A comprehensive approach to assessment in Saifi Village, Beirut, Lebanon. Urban Design International, 14(3), 139-151.

Page 47

16. Carnoske, C., Hoehner, C., Ruthmann, N., Frank, L., Handy, S., Hill, J., . . . Brownson, R. (2010). Developer and realtor perspectives on factors that influence development, sale, and perceived demand for activity-friendly communities. Journal of physical activity & health, 7, S48.

17. Moudon, A. V., Lee, C., Cheadle, A. D., Garvin, C., Schmid, T. L., & Weathers, R. D. (2007). Attributes of environments supporting walking. American journal of health promotion: AJHP, 21(5), 448.

18. Frank, L., & Kavage, S. (2009). A national plan for physical activity: the enabling role of the built environment. Journal of physical activity & health, 6, S186.

19. Gebel, K., Bauman, A., & Owen, N. (2009). Correlates of non-concordance between perceived and objective measures of walkability. Annals of behavioral medicine, 37(2), 228-238.

20. McCormack, G., Spence, J., Berry, T., & Doyle-Baker, P. (2009). Does perceived behavioral control mediate the association between perceptions of neighborhood walkability and moderate-and vigorousintensity leisure-time physical activity? Journal of physical activity & health, 6(5), 657.

21. Boer, R., Zheng, Y., Overton, A., Ridgeway, G. K., & Cohen, D. A. (2007). Neighborhood design and walking trips in ten US metropolitan areas. American journal of preventive medicine, 32(4), 298-304.

22. Frank, L., Sallis, J., Conway, T., Chapman, J., Saelens, B., & Bachman, W. (2006). Multiple pathways from land use to health: walkability associations with active transportation, body mass index, and air quality. Journal of the American Planning Association, 72(1), 75-87.

23. Cerin E, Leslie E, du Toit L, Owen N, et al. (2007). Destinations that matter: associations with walking for transport. Health and Place 13:713-24.

24. Australian Bureau of Statistics (ABS). (2011). Census data. Available at: http://www.abs.gov.au/census. Last accessed: 30 May, 2014.

25. Department of Sustainability and Environment. (2014). Vicmap Planning. Available at: http:// www.dse.vic.gov.au/property-titles-and-maps/maps-imagery-and-data/data/vicmap/vicmap-products/ vicmap-planning. Last accessed: 15 July, 2014.

26. Department of Sustainability and Environment. (2013). Vicmap Transport. Available at: http:// www.dse.vic.gov.au/property-titles-and-maps/maps-imagery-and-data/data/vicmap/vicmap-products/ vicmap-transport. Last accessed: 15 July, 2014.

27. Handy S, (2002), Accessibility- Vs. Mobility-Enhancing Strategies For Addressing Automobile Dependence In The U.S. Prepared For The European Conference Of Ministers Of Transport May 2002 28. Hansen, W G. (1959). How Accessibility Shapes Land Use. Journal Of The American Planning Association, 25(2), 73-85 29. Handy, S.L. & D.A. Niemeier. (1997). Measuring Accessibility: An Exploration of Issues and Alternatives, Environment and Planning A 29:1175â&#x20AC;&#x201C;94.

Page 48

30. Schlossberg, M. A., & Brown, N. (2004). Comparing Transit-oriented Developments Based on Walkability Indicators. Transportation Research Record, Journal of the Transportation Research Board, No. 1887,Transportation Research Board of the National Academies, Washington, D.C., pp. 34-42.

31. Schlossberg, M. (2006). From TIGER to Audit Instruments: Measuring Neighborhood Walkability with Street Data Based on Geographic Information Systems. Transportation Research Record, Journal of the Transportation Research Board, No. 1982 Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 48-56.

32. Jacobs, J. (1961). The Death and Life of Great American Cities. New York: Random House.

33. Alexander, C. (1965). A city is not a tree. Architectural Forum, 122(1), 58-62 (Part I) and 122(2), 58-62 (Part II).

34. Victorian Government Data Directory (Data Vic). (2014). Victorian Raw Spatial Data. Available at: http://www.data.vic.gov.au. Last accessed: 30 May, 2014.

Page 49