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Project 1 Addressing Future Uncertain0es of Perth @3.5 Million: What-‐If Scenarios for Mass Transit
Precinct Typology as Input to Inform the LU and PT Planning
Research team: Simon Moncrieff, Gary McCarney, Tristan Reed, Yuchao Sun, Cate PaCson, BreE Smith, Sharon Biermann, and Doina Olaru
ObjecIves • Assist/provide guidance on how to keep P&[email protected] moving
(meeting growth), while being sustainable and liveable (alleviating congestion and promoting PT and active travel
• Development of a typology to Inform the LU and PT Planning (Stage 1)
• Optimise use of the (integrated) transport network and best cater for accessibility and growth
• Combination of various secondary data sources • Review of several case studies across Australia • Combination of data analysis techniques
– Clustering of Place, Node, Background Traffic Functions – Mapping all station precincts (and other locations) – Exploring associations between boardings/alightings and the three
functions – Quick calculators (using JTW) for stations with low patronage
Data and Analysis – Stage 1
Place, Node, Background
Traffic
Clusters and Factors (based on indicators)
Mapping station precincts on P, N,
BT functions
Quantify patronage = f
(P, N, BT)
Place FuncIon -‐ Unidimensional Mciver
Claisebrook
Maylands Victoria Street
Glendalough Mosman Park
East Perth Mt Lawley Victoria Park
Subiaco
Daglish North Fremantle
Meltham
Carlisle
West Leederville
Shenton Park
Clarkson Claremont Swanbourne
Leederville Oats Street Grant Street
Bayswater
Showgrounds
Currambine Queens Park
Loch Street
Canning Bridge Bull Creek Warnbro
Bassendean
City West Butler (formerly Jindalee)
Greenwood WhiYords CoEesloe
Thornlie
Cockburn Central Warwick Murdoch
Armadale
Cannington Rockingham
Sherwood
Gosnells Burswood SIrling Joondalup KarrakaEa
Fremantle Success Hill Beckenham
Ashfield Edgewater Mandurah Wellard
KelmscoE Challis Welshpool
Seaforth Maddington
Kenwick East Guildford Midland
West Midland (Woodbridge) Guildford
Kwinana 0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70
Only 46% variance Incomplete data for the 8 cases
Australian Case Studies – top of the ladder -‐ Wolli Creek, Kelvin Grove, Chatswood -‐ Footscray, Northshore, Box Hill -‐ Albion Mill, Yeerongpilly
Place Score (1.6km buffers) vs Distance from the CBD
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80
Place score (rescaled to 100
)
d from CBD (km) Armadale (1.6km) Fremantle (1.6km) Joondalup (1.6km) Midland (1.6km) Mandurah (1.6km) Linear (Armadale (1.6km)) Linear (Fremantle (1.6km)) Linear (Joondalup (1.6km)) Linear (Midland (1.6km)) Linear (Mandurah (1.6km))
Score re-‐scaled 0 to 100!
Entropy
Lower entropy for Armadale and staIons on the Mandurah line
Cluster 1
Cluster 4
Cluster3
Cluster2
Place vs Node
Bassendean Bayswater
KelmscoE
Oats Street Cannington
Mandurah
Claremont
Clarkson
Edgewater
City West Claisebrook
Warnbro
Cockburn Central
Kwinana
Subiaco
Maylands
Midland Murdoch
Bull Creek
Glendalough
SIrling
Greenwood
Wellard
Leederville
Currambine
McIver
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Place
Node >=1,000/day <1,000/day
Node ‘dominant’
Place ‘dominant’
5/11/17
Example Output Regression
R2-‐adj =0.759 DV = AM boardings Generator stations
b Std. Error Beta t Sig. Tolerance VIF (Constant) -‐275.303 573.573 -‐0.48 0.637
IRSAD percenIle 3.517 3.745 0.138 0.939 0.36 0.336 2.973
% Access to Jobs 45min by PT 23.724 7.695 0.433 3.083 0.006 0.371 2.698
Bus Services 4.034 1.335 0.323 3.022 0.007 0.641 1.561
PnR Supply 0.748 0.263 0.469 2.847 0.011 0.269 3.722
Commercial -‐185.543 115.313 -‐0.237 -‐1.609 0.125 0.337 2.969
EducaIon 101.798 134.785 0.168 0.755 0.46 0.147 6.808
Health -‐191.503 180.462 -‐0.27 -‐1.061 0.303 0.113 8.889
ResidenIal -‐815.288 752.822 -‐0.237 -‐1.083 0.293 0.153 6.527
Retail Outlets 249.651 199.107 0.137 1.254 0.226 0.611 1.637
Natural Elements -‐401.162 642.52 -‐0.082 -‐0.624 0.54 0.426 2.347
Walking score -‐201.641 611.69 -‐0.051 -‐0.33 0.745 0.302 3.315
Bike route (km) 34.763 41.25 0.1 0.843 0.41 0.515 1.943 Pop. Density (‘000 people/km2)
37.705 149.225 0.048 0.253 0.803 0.202 4.955
MulIcollinearity Effects
Unstandardised Coefficients
Standardised Coefficients
Sig.
Unstandardised Coefficients
Standardised Coefficients Sig.
B Beta B Beta (Constant) -‐207.541 0.404 -‐468.14 0.113 IRSAD percenIle -‐0.103 -‐0.004 0.963 5.513 0.239 0.019 Bus Services 2.197 0.194 0.027 3.427 0.302 0.004 PnR 0.809 0.535 0 0.78 0.516 0 Walk score -‐128.233 -‐0.039 0.676 -‐412.614 -‐0.125 0.258 Bikeroute (km) -‐0.686 -‐0.002 0.974 15.389 0.055 0.543 Entropy Place -‐245.463 -‐0.064 0.445 251.686 0.066 0.491 PopulaIon 1.6km radius -‐0.005 -‐0.035 0.711
0.034
0.224
0.018
% Access to Jobs 45min by PT 34.721 0.469 0 R2-‐adj=0.781 R2-‐adj=0.679
CauIon when fiCng models!
5/11/17
Another Example Output Regression
R2-‐adj =0.690 DV = AM boardings Attractor stations
b Std. Error Beta t Sig. Tolerance VIF
(Constant) -‐682.473 1167.756 -‐0.584 0.59 IRSAD percenIle -‐27.509 17.606 -‐0.364 -‐1.562 0.193 0.44 2.273
Bus Services 4.542 1.669 0.881 2.722 0.053 0.227 4.399
PnR Supply -‐6.711 2.691 -‐0.789 -‐2.493 0.067 0.238 4.205
ResidenIal 2,250.827 2,384.005 0.242 0.944 0.399 0.362 2.762
Retail Outlets 654.687 289.032 0.698 2.265 0.086 0.251 3.988 Walk score 3,322.618 2,622.356 0.426 1.267 0.274 0.211 4.743 Bikeroute (km) -‐118.606 190.605 -‐0.188 -‐0.622 0.567 0.262 3.819 Popula2on 1.6km radius 0.097 0.269 0.122 0.362 0.736 0.211 4.734 Employment 1.6km radius -‐0.122 0.039 -‐0.997 -‐3.139 0.035 0.236 4.237
Simple Excel Calculator
StaUon Name
Corridor
Daily patronage weekdays
Daily boardings
AM boardings
Current populaUon 1.6km radius
Current total workers
JTW_train Gen.
(assumed AM)
JtoW by train (%)
JTW train/AM
boardings
AddiUonal boardings required
Assumed mode share by train for new workers
AddiUonal people required
Armadale Armadale 1,825 917 322.43 10,583 2,991.2 225.1 7.5% 0.70 83 7.5% 1,103 Beckenham Armadale 1,010 566 251.23 7,461 1,528.2 279.0 18.3% 1.11 434 18.3% 2,377 Carlisle Armadale 733 432 146.3 10,339 1,983.0 299.1 15.1% 2.04 568 15.1% 3,766 Challis Armadale 344 193 71.28 5,505 1,643.5 154.4 9.4% 2.17 807 9.4% 8,591 CoEesloe Fremantle 1,096 592 163.74 4,413 1,729.5 160.7 9.3% 0.98 408 9.3% 4,390 Daglish Fremantle 944 536 162.29 10,477 5,308.9 244.6 4.6% 1.51 464 4.6% 10,070 East Guildford Midland 530 296 108.38 2,491 741.1 79.8 10.8% 0.74 704 10.8% 6,535 East Perth Midland 994 660 80.69 5,725 3,636.1 141.6 3.9% 1.75 340 3.9% 8,731 Gosnells Armadale 1,760 995 359.61 10,584 1,296.6 252.4 19.5% 0.70 5 19.5% 26 Grant Street Fremantle 426 232 113.76 5,682 1,532.1 193.1 12.6% 1.70 768 12.6% 6,093 Guildford Midland 835 484 187.36 1,221 1,342.7 44.6 3.3% 0.24 516 3.3% 15,528 KarrakaEa Fremantle 428 247 74.29 2,672 2,113.0 41.8 2.0% 0.56 753 2.0% 38,104 Kenwick Armadale 806 459 213.69 4,991 1,083.7 153.5 14.2% 0.72 541 14.2% 3,820 Loch Street Fremantle 417 217 79.06 4,357 1,465.6 112.3 7.7% 1.42 783 7.7% 10,215 Maddington Armadale 1,486 841 323.25 7,569 4,426.5 159.3 3.6% 0.49 359 3.6% 9,974 Meltham Midland 799 498 204.02 9,870 1,378.5 402.1 29.2% 1.97 502 29.2% 1,721 Mosman Park Fremantle 780 427 156.06 5,259 1,202.3 275.1 22.9% 1.76 573 22.9% 2,504 Mt Lawley Midland 494 307 91.5 9,937 3,574.2 260.0 7.3% 2.84 693 7.3% 9,525 North Fremantle Fremantle 828 457 212.25 4,897 2,414.9 173.8 7.2% 0.82 543 7.2% 7,547 Queens Park Armadale 1,146 647 196.19 8,608 4,511.4 333.1 7.4% 1.70 353 7.4% 4,781 Seaforth Armadale 202 115 40.78 4,535 1,167.4 111.5 9.6% 2.74 885 9.6% 9,263 Sherwood Armadale 554 319 117.37 9,606 1,352.2 216.5 16.0% 1.84 681 16.0% 4,254 Success Hill Midland 221 132 55.27 4,152 805.4 131.1 16.3% 2.37 868 16.3% 5,331 Victoria Park Armadale 1,256 709 207.69 14,385 5,177.3 312.0 6.0% 1.50 291 6.0% 4,829 Victoria Street Fremantle 713 398 181.7 4,360 592.4 219.5 37.0% 1.21 602 37.0% 1,625
12% (av.) of the labour force living within the 0.8 km staIon buffers travel to work by train!
Conclusions -‐ Regression • Good Place (7D, including ì entropy) enhance amenity &
liveability, enable creaIon/development of good acIvity centres (thus good job accessibility for the local residents), this may not lead to substanIal increase in transit ridership!
• DisIncIon between primary and secondary benefits of balanced Place-‐Node precincts/TODs is necessary
• Node dominant -‐ may benefit more from densificaIon, if aligned with greater city-‐wide access to employment – High % train/workers è propensity to commute by PT
• Place dominant -‐ in general low patronage and low raIos of train use for commuIng, as well as of train ridership relaIve to jobs
• Mixed (not necessarily Balanced?) Place and Node – would benefit from increased densiIes (jobs) combined with PnR access
Conclusions – Cont’d
• Measures targeIng populaIon and economic growth vs increasing accessibility (city-‐wide and local)
• Analysis should be at the corridor/city level, rather than staIon precinct – Accessibility is key, especially PT more aEracIve than car
• Places with good PT access could deliver maximum value for patronage
• Same story for the good TODs selected as cases – Yeerongpilly (1,500/day), Wolli Creek (5,600) – Chatswood (43,000/day) the highest, followed by Footscray (14,800/day)
PotenIal LU&T SoluIons
Cluster 1 ’Low Access, Node, PnR, SE of the city’ • Bus services (+), Offices (-‐) è BnR or Flexible access
soluIons DensificaIon
• Cluster 2 ‘Best Place & Node, close to CBD’
• Workers access in 45 min by PT (+), Bike route (-‐)
è Do nothing Add jobs if populaIon ì
Cluster 3 ’Interchange, Feeder buses, Generators’ • PnR (+), Bus services (+),
Workers access in 45 min by PT
è PnR (transiIon) and separate traffic
è BnR
• Cluster 4 ‘Moderate performance all func2ons’
• Workers access in 45 min by PT (+), Retail (+), Bus services (+)
è Mix of soluIons – BnR/Flexible access soluIons è DensificaIon outside the 800m buffer?
PotenIal Next Steps – Stage 2 • Compare findings with project 4!
– Also, larger buffers are expected to have disInct entropy, catchment, etc.
• Corridor analysis would be useful, in addiIon to individual case studies
• Suggested cases – TBD (outliers corridors, new staIons) ExisIng -‐ Byford, Challis, Carlisle, Cannington, Rockingham, Meltham, Mt Lawley, Seaforth, Shenton Park, SIrling, Thornlie, Warnbro, Wellard, Welshpool new -‐ Aubin Grove, Yanchep/Alkimos, Ellenbrook, Karnup, Morley, Nicholson Rd
• Use models for predicIon 2051 (Place indicators not available)
• SIll missing the capacity to assess performance
Thank you!
Q & A
Mix of Types by Corridor
0 1 2 3 4
0 5 10 15 20 25 30 35
Cluster
d from CBD
Joondalup
0 1 2 3 4
0 10 20 30 40 50 60 70 80
Cluster d from CBD
Mandurah
Joondalup
Kwinana and Wellard
Rockingham Warnbro
Mix of Types by Corridor
0 1 2 3 4
0 5 10 15 20 25 30 35
Cluster d from CBD
Armadale Burswood
0 1 2 3 4
0 2 4 6 8 10 12 14 16 18 20
Cluster
d from CBD
Fremantle
0 1 2 3 4
0 2 4 6 8 10 12 14 16 18
Cluster d from CBD
Midland
Welshpool
Thornlie
KarrakaEa Mosman Park and Victoria St.
Bassendean
5/11/17
Cluster Analysis (CA) • Cluster analysis groups/classifies objects/variables on the
basis of the similarity of the characteristics they possess • Technique used to classify entities into relatively
homogeneous groups • Interdependence technique - numerical taxonomy using a
hierarchical clustering, followed by refined k-means clustering
• Various types of data (binary to continuous) • Euclidean distance as dissimilarity • Various agglomeration approaches, algorithms, but
repeated at various spatial resolutions to check for consistency
Entropy • Entropy measures the spread in the distribution of LU and hence,
mixed LU has higher entropy, whereas very homogeneous (one use only patches) approach 0 in entropy
• Shannon’s entropy (SE)
• Where p(j) is the proportion of use j in the mix at a specific location
!" = − ! ! !"#$(!)!
!
28
Super-Clusters Variable 3 - Background
Variable 1 - Place
Variable 2 - Node
0
Station X (X1, X2, X3)
Station Y (Y1, Y2, Y3)
Euclidean d between items X and Y
Y2
X2
Y3
Y1
X3
X1