"Métro du Grand Paris": application of a prototype RELU

"Métro du Grand Paris": application of a prototype RELU

Spatial effects of the “Métro du Grand
Paris”: application of a prototype RELUTRAN approach
Matthieu de Lapparent, IFSTTAR
Alex Anas, State University of New York at Buffalo
21 Mai 2012
Background
• RELU-TRAN is a computable general equilibrium model, based on
microeconomic
theory and designed for the analysis of metropolitan development scenarios
and policies. See the page on RELU-TRAN at URL:
http://sites.google.com/site/alexanashomepage/the-relu-tran-model-and-itsapplications
•
The model has been developed in two phases
RELU-TRAN1 (2000-2006) and RELU-TRAN2 (2006-2010). It however
continues a long period of development of earlier theoretical models and
applications by Alex Anas
Background --continued
• RELU-TRAN 1 was developed under a grant from the United States
National Science Foundation to Dr. Alex Anas. The NSF received 115
proposals and funded 5 of them.
• RELU-TRAN 2 was developed as an extension of RELU-TRAN 1
incorporating treatment of energy consumption in automobile travel under
an award to Dr. Alex Anas from the United States Environmental
Protection Agency’s Science to Achieve Results Program.
Background --continued
• The model has been applied to the Chicago, MSA (Metropolitan Statistical
Area), making forecasts and policy analysis scenarios over the time span
from 2000 to 2030.
• Currently under a Multi-campus Research Initiative of the University of
California which has made an award, RELU-TRAN 2 is being developed
for the Greater Los Angeles Region. The project will last until 2014 (it
began in 2010).
Background: Related references
• Anas, Alex & Ikki Kim, General Equilibrium Models of Polycentric Urban Land
Use with Endogenous Congestion and Job Agglomeration, Journal of Urban
Economics, 40, 217-232, 1996.
• Anas, Alex and Richard J. Arnott, “Taxes and Allowances in a Dynamic Equilibrium
Model of Urban Housing Market with a Size -Quality Hierarchy”, Regional Science
and Urban Economics, 27, 547-580, 1997.
• Anas, Alex & Rong Xu, Congestion, Land Use and Job Dispersion : A General
Equilibrium Analysis, Journal of Urban Economics , 45,3, 451-473, 1999.
•
Alex Anas & Hyok-Joo Rhee, Curbing excess sprawl with congestion tolls and
urban boundaries, Regional Science and Urban Economics, 36, 510-541, 2006.
• Alex Anas & Hyok-Joo Rhee, When are urban growth boundaries second-best
policies to congestion tolls ?, Journal of Urban Economics, 61, 263-286, 2007.
• Alex Anas & Yu Liu, A Regional Economy, Land Use and transportation Model
(RELU- TRAN): Formulation, algorithm design and testing, Journal of Regional
Science, vol. 47, 3, 415–455, 2007.
Recent articles on the Chicago
applications of RELU-TRAN2
• Alex Anas and Tomoru Hiramatsu, “The Effect of the Price of Gasoline on
the Urban Economy: From Route Choice to General Equilibrium” In Press
(accepted), Transportation Research A, Special Issue on Transportation
Economics.
• Alex Anas, “Decentralization and the Stability of Travel Time”,
In review, 2011.
• Alex Anas and Tomoru Hiramatsu, “The Economics of Cordon Tolling:
General Equilibrium and Welfare Analysis,” Working paper, August 19,
2011
Scope
• The model has been used to analyze the impacts of the following scenarios
and policies in the Chicago MSA.
1.
2.
3.
4.
The effects of a gasoline price increase;
Growth in population and the effects of continuing urban sprawl;
Cordon tolls around the center and Pigouvian tolling of all major roads;
Effects of installing urban growth boundaries that limit a city’s outward
expansion.
Why a SCGE?
• G.E. models help avoid oversimplification.
• G.E. models can help us study real cities.
EXAMPLES OF ISSUES G.E. MODELING CAN HELP WITH…
• Travel trends are becoming complex.
• Interdependence of producer and consumer location decisions.
• Decisions involve several dimensions that are related in complex ways.
Outline
• RELU-TRAN architecture
• Application: data and model specification/estimation/calibration
• Results
9
Structure of the model and solution algorithm
• The next pages describe the behavior of consumers, firms and real estate
developers in RELU-TRAN and how these are modeled.
• After the structure of the model the solution algorithm is described by
means of three flowcharts.
RELU-TRAN architecture: Consumer Decisions
Decisions are hierarchically linked and involve discrete as well as continuous choices
Consumer
Decision to work:
Workplace- residence
locations
• Labor supply / leisure
• Commuting mode and vehicle choices
• Housing (quantity / type)
• Vehicle ownership (type and fuel intensity)
• Discretionary travel pattern to obtain goods and services
Where to go ? Where not to go ?
How many trips per period ?
How much to spend ?
Mode choice / vehicle choice on each trip
Decision to
not work
All choices on left
apply except those in
red
A mix of discrete and
continuous choices
Discrete choice of
Working/not working
Stay out of labor market
Enter labor market
Discrete choice of triplet:
i: residence zone
j: workplace zone
k: type of housing
(i,j,k)
Discrete choice of mode
for commuting
Auto
Transit
Continuous variables
•Floor space of type k in residence zone I
•Labor hours of work supplied to place of work at j
• Number of non-work trips and their destinations and modes
• Quantity of goods purchased on non-work trips
CONSUMER
Assets
Wages
Work
Income
Consumer
Commute/Mode and Route Choice
Housing
(Floor space)
Non-work trips: Commute/Mode and Route Choice
Residence
Retail trips
and Purchases
Utility maximization problem






1




 f  f

 MaxZ z ,b U ijk | f   f ln   z|ijf ( Z z )    f ln b  Eijk | f  eijk | f  
 z







 *


U ijk | f   subject to the budget :  ( pz  cijf giz ) Z z  bRik  dgij 

Max(i , j ,k ) 
z



 



 w jf  H  dGij   cijf Z z Giz   M f


z

 





 and H  dGij   cijf Z z Giz  0.



z
 




CONTINUOUS CHOICES
Demand for floor space
bijk | f   f
 ijf
Rik
Demand for retail goods
Z z|ijf 


1
1 f
z |ijf

1
 f 1
z |ijf
1
1 f
s s|ijf

f
 f 1
 s|ijf
 f  ijf
DISCRETE CHOICES
Indirect utility (simplest version):
Uijk | f   f ln  f   f ln  f  ln ijf   f ln Rik
f
1


 f (1   f )
1 f
 f 1

ln   z z|ijf  z|ijf   Eijk| f


f


Workplace-Residence Discrete Choice Probability (simplest
version, nested logit is also, see after in the application):
Pijk | f 


exp  f U ijk | f

exp(

U
)
f
stn
|
f
 ( s ,t , n )
FIRMS
LABOR
TYPES
BUILDING
TYPES
PRODUCTION FUNCTION
INTERMEDIATE
INPUTS FROM OTHER INDUSTRIES
OUTPUT
MIXED C.E.S.-COBB-DOUGLAS PRODUCTION FUNCTION


X rj  Arj K  r    f |rj L f r 
 f 0

F
r
r

r 

B
  k |rj k 
 k 0


r
r
LABOR INPUTS
L f |rj 
 f |rj
1
1 r
1
1 r
F

z 0
w jf
1
 r 1
w jz
z |rj
r
 r 1
 r prj X rj
BUILDING INPUTS
Bk |rj 
 k |rj
1
1 r
2

z 0
1
R jk
1
1 r
z |rj
 r 1
R jz
r
 r 1
r prj X rj
INTERMEDIATE INPUTS
Ysn|rj 
1
1
1 sr
 sr 1
sn|rj
snj
 sr
1

1 sr
 sr 1
sy|rj
syj
y 0


ˆ
p
ˆ
p
 sr prj X rj


 sr 

Y

  sn|rj sn 
s 1  n 0


 sr
 sr
.
INTER-INDUSTRY STRUCTURE
AGRICULTURE
BUSINESS
SERVICES
MANUFACTURING
RETAIL
TRADE
CONSUMER
PHYSICAL I-O COEFFICIENTS
asn rj 
Ysn|rj
X rj

1
1
1 sr
 sr 1
sn|rj
snj
 sr
1

1 sr
 sr 1
sy|rj
syj
y 0

ˆ
p

 sr prj
ˆ
p
VALUE-BASED I-O COEFFICIENTS
asn rj 
ˆ snjYsn|rj
p
prj X rj
FLOW CONSERVATION

 asn rj
ˆ snj
p
prj

 a
s 1 n  0
sn rj

 sr
1
1 sr
 sr 1
sn|rj
snj
 sr
1

1 sr
 sr 1
sy|rj
syj
y 0



ˆ
p
 sr  1
ˆ
p
   sr  1.
s 1
ZERO ECONOMIC PROFIT CONDITION FOR FIRMS
PRICE=UNIT COST UNDER C.R.T.S.
 F
prj 
 

 sr  f  0
r
r  r
Arj r r  r (  sr ) 

r
1
1 r
f |rj
r
 r 1
w jf




 r ( r 1)
r
 

 k 0

1
1 r
k |rj
s 1
  11  sr1 
    sn|rjsr pˆ snsr|rj 


s 1  n  0


 sr (  sr 1)
 sr
r
 r 1
R jk




r ( r 1)
r
DEVELOPERS
CONSTRUCTION PROBABILITIES (OWNERS OF LAND)
 1

exp  i 0 
Vik  pk ,i  mk  Ci 0k 

1  


Qiok (Vi 0 ,Vi1 ,...,Vi )
 1

 1

exp 
 i 0Vi 0    s1... exp  i 0 
V

p
m

C
 is s,i  k i0s 
ZERO-PROFIT
1  

1  

E  max  i 00 , i 0 k ; k  1...  Ri 0 
1
 i 0Vi 0  0
1 
DEMOLITION PROB. (OWNERS OF BUILDINGS)
Qik 0 (Vi 0 ,Vik )
ZERO-PROFIT
 1 


1
exp  ik 
 pk ,i   Cik 0 
Vi 0

1    mk


 1 


 1

1
exp  ik 
 pk ,i   Cik 0   exp  ik 
Vik  Cikk 
Vi 0
1  


1    mk

E  max   kk ,  k 0    ik ( Rik ) 
1
 ikVik  0
1 
BLDG 1
BLDG 2
LAND
TRAN module
• Probabilistic mode choice model to split OD matrix derived from RELU
module
• Stochastic user equilibrium traffic assignment procedure
23
TRAN module
24
TRAN module
25
TRAN module
26
Interdependence of producers and consumers
 Location of 


 Production 
 Location of 


 Residences 
• We cannot explain the location of residences (producers) if we do not know the
location of producers (residences).
Labor market, Housing market  wages, residential rents
Goods market, Housing market  goods prices, business rents
HOUSING & LABOR MARKETS
HOUSING MARKET :
f
N
  Pijk| f bijk| f
f

j
Sik qik
SUPPLY OF FLOOR SPACE
DEMAND FOR FLOOR SPACE
LABOR MARKET :
r L
f |rj
DEMAND FORTYPE f LABOR
 
i
k

 f
 H  DGij  cijf Z z|ijf Gij  N Pijk| f
z


SUPPLY OFTYPE f LABOR

SOLUTION ALGORITHM 1
STARTING POINT
p, w, R,V,S G, g
RELU-TRAN CYCLE
RELU
RELU LOOPS CONVERGED
Update
G and g
for next
cycle
RELU TRIPS
TRAN
TRAN ITERATIONS CONVERGED
G and g converged?
p, w, R, V converged?
Excess demands, profits
converged?
YES
RELU-TRAN CYCLES
CONVERGED
Cyclical linking of the RELU and TRAN algorithms in
RELU-TRAN
START POINT
p, w, R, V, S, G, g
RELU LOOP
PRICES, p
( w, R ) p
OUTPUTS, X
( p, w, R, S,V ) X
WAGES, w
( p, X, R,S,V )w
Update
p, w, R, V
for next loop
RENTS, R
(p, X, w, S,V) R
VALUES, V
RV
STOCKS, S
VS
NO
YES
p, w, R, V converged?
Excess demands converged?
Economic profits converged?
RELU loops converged
The RELUALGORITHM
algorithm
SOLUTION
2
SOLUTION ALGORITHM 3
RELU TRIPS
AUTO MODE CHOICE
PROBABILITIES
ROUTE CHOICE & NETWORK
EQUILIBRIUM FLOW
ITERATIONS CONVERGED
CONGESTED HIGHWAY LINK
TRAVEL TIMES
ZONE-TO-ZONE
EXPECTED TIMES & COSTS
G and g
TRAN
The TRAN Algorithm
Application
•
•
•
•
•
•
Exploit flexibility of RELU-TRAN model to adapt to case study
Spatial resolution: medium level application
Data collection and preparation
Estimation and calibration
Definition of scenarios
Results
32
Prototyped RELU-TRAN model
• Tight schedule:
– Data access
– Implementation of the model
– Choice has been made to not consider different industrial sectors and different types of
workers: only total employment
– The same for workers and households: there is no difference
– No adequate data about construction or demolition
33
Spatial resolution
• Zone system: 50 internal zones + 4 outside zones
– Starting at 1300 “communes” resolution
– Based on definition of June 2011 “contrats de développement
territoriaux – CDT”
– Then using the 40 administrative “arrondissements” to allocate the
other “communes”
– Isolated “communes” finally reassigned to neighbour “arrondissement”
of the same “département”
– When not possible, isolated “communes” were assigned to neighbour
CDT of the same “département”
– The 4 outside zones caracterize the surrounding rest of the world
34
35
36
Data collection for base year
• Data spanning from years 2005 to 2010 were collected to calibrate the
model:
– 2005 DRIEA/IAU base year data on population and employment
– 2006 Census: persons, households, workers, housing, employment at
workplace, housing stocks (occupied and vacant) by type
– 2007 Côtes Callon (2006 prices): average market prices and rents of
apartments, houses, shops, and offices, in euros per square metres (only about
250 observations)
– INSEE: 2005 median net wages, inflation rate, median net incomes
– DIREM: 2006 fuel prices
– DGIFP: 2006 median fiscal incomes
– DRIEA: 2009 road network, OD flows, travel times and distances for road and
public transport (inputs & outputs from MODUS model, peak hours, all
purposes)
– IAU: 2008 land use data (MOS/EVOLUMOS)
– IGN & SGP shapefiles
• For the sake of clarity, “2005” reference year
37
Data Preparation
• For RELU module:
– Figures about population, workers, households, employment at workplace,
establishments, land use, are aggregated from 1300 “communes” to the 50 inner zones
• For TRAN module:
– Road network: 3004 road links and 335 nodes
– OD flows (total and by mode) aggregated from the MODUS zone system to the 50+4
system
– Transit travel times computed as “weighted” average times
38
39
40
Model calibration
41
Model calibration
42
Model calibration
43
Model calibration
44
Model calibration: joint residential-workplace location
45
Model calibration: joint residential-workplace location
46
Model calibration: business establishment location
47
Model calibration: occupancy/vacancy of housing units
48
Model calibration: construction
49
Model calibration: TRAN, road
50
Definition of scenarios
• Spatial distribution of population and employment over the 50+4 zones at
2025 and 2035 horizons:
– Three assumptions about total population and employment: 1 do-nothing
scenario + 2 scenarios for project implementation (one “Low”, one “High”)
– When project is implemented:
• 2025: partial implementation of the new infrastructure
• 2035: full implementation of the new infrastructure
Base year
Reference
Project « low »
Project « high »
2005
2025
2035
2025
2035
2025
2035
Population
11433302
12512268
13051748
12558372
13120875
12633299
13233316
Jobs
5360447
5907272
6180692
6000443
6320442
6160451
6560455
51
Definition of scenarios
• Population and jobs in difference with respect to base year under the
different scenarios assumptions
Population
Jobs
Reference
2025
2035
1078966
1618446
546825
820245
Project « low »
2025
2035
1125070
1687573
639996
959995
Project « high »
2025
2035
1199997
1800014
800004
1200008
52
53
54
Definition of scenarios
• Further working assumptions:
– Annual fuel price increases at 2% over inflation rate up to 2025 and real price
doubles at horizon 2035
– No construction is allowed in the city of Paris:
• Only vacancy rates and (perfect) substitution between types of real estate are used to
manage demand and supply
• No information on vacancy for shops and offices: presumed to be similar to this of
housing
• No house in Paris
– 2.5 members per household in 2025, 2.2 in 2035
– Transportation LOS:
• Levels of services for road network are endogenous
• Levels of services for public transportation are computed using DRIEA OD matrices
55
Results: population
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the region
External zones
pop2005
2 162 810
POP2025R
2 155 980
POP2025L
2 167 398
POP2025H
2 188 817
POP2035R
2 208 916
POP2035L
2 226 645
POP2035H
2 256 921
237 729
250 790
251 691
252 579
263 060
264 330
265 687
177 813
189 196
190 097
190 973
197 374
198 621
199 959
721 233
765 344
768 761
772 091
803 380
807 833
812 973
340 392
360 231
545 585
341 369
126 261
670 782
379 697
387 787
594 843
375 624
132 477
721 246
380 193
389 928
597 021
377 168
132 662
722 429
381 469
391 862
599 593
378 940
133 097
725 373
392 208
417 198
638 811
392 582
135 434
773 105
392 919
420 574
642 085
394 862
135 590
774 947
394 816
423 668
646 335
397 580
136 129
779 751
162 929
186 488
186 936
187 637
193 574
194 182
195 230
5 586 172
6 301 912
70 883
6 322 111
71 978
6 354 079
76 790
6 578 982
57 123
6 609 251
59 036
6 658 979
65 288
56
Results: population
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the region
External zones
DELTA 2025R2005
-6 830
DELTA 2035R2005
46 106
DELTA 2025L2005
4 588
DELTA 2035L2005
63 835
DELTA 2025H2005
26 007
DELTA 2035H2005
94 111
13 061
25 331
13 962
26 601
14 850
27 958
11 383
19 561
12 284
20 808
13 160
22 146
44 111
82 147
47 528
86 600
50 858
91 740
39 305
27 556
49 258
34 255
6 216
50 464
51 816
56 967
93 226
51 213
9 173
102 323
39 801
29 697
51 436
35 799
6 401
51 647
52 527
60 343
96 500
53 493
9 329
104 165
41 077
31 631
54 008
37 571
6 836
54 591
54 424
63 437
100 750
56 211
9 868
108 969
23 559
30 645
24 007
31 253
24 708
32 301
715 740
70 883
992 810
57 123
735 939
71 978
1 023 079
59 036
767 907
76 790
1 072 807
65 288
57
Results: population
58
Results: population
59
Results: population
60
Results: population
52 936
59 247
DELTA
2035H2025H
68 104
21 419
30 276
11 418
17 729
DELTA
2025H2025R
21 419
12 271
12 639
13 107
888
1 357
902
1 269
888
2 626
8 178
8 524
8 986
877
1 338
900
1 247
877
2 585
38 036
39 072
40 882
3 331
5 141
3 417
4 453
3 331
9 593
12 511
29 411
43 968
16 958
2 956
51 858
12 727
30 645
45 064
17 693
2 928
52 518
13 347
31 807
46 742
18 640
3 032
54 378
1 276
1 933
2 572
1 771
435
2 944
1 897
3 095
4 250
2 718
538
4 804
496
2 141
2 178
1 544
185
1 182
711
3 375
3 274
2 280
157
1 842
1 276
1 933
2 572
1 771
435
2 944
2 608
6 470
7 524
4 998
695
6 646
7 086
7 246
7 593
701
1 048
448
609
701
1 657
277 071
287 140
304 901
31 968
49 728
20 199
30 269
31 968
79 997
-13 760
-12 942
-11 502
4 812
6 251
1 095
1 913
4 812
8 165
DELTA
DELTA
2035R-2025R 2035L-2025L
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the
region
External zones
DELTA
DELTA
DELTA
DELTA
2025H-2025L 2035H-2035L 2025L-2025R 2035L-2035R
DELTA
2035H2035R
48 005
61
Results: jobs
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the
region
External zones
job2005
1 646 905
JOB2025R
1 487 688
JOB2025L
1 503 838
JOB2025H
1 554 632
JOB2035R
1 546 911
JOB2035L
1 580 393
JOB2035H
1 659 154
61 922
77 780
80 296
82 253
81 111
84 458
87 162
44 679
53 572
54 844
56 219
56 925
58 382
60 276
296 013
367 464
389 069
399 167
397 345
420 387
436 158
149 956
322 928
172 199
147 721
141 719
380 434
189 654
480 060
232 736
186 959
174 600
482 567
187 831
498 635
238 157
193 945
175 795
481 275
195 016
509 868
241 324
199 212
180 934
493 575
204 487
517 208
241 429
200 998
186 809
501 927
203 559
541 636
246 236
211 056
188 187
501 623
215 382
558 105
249 910
219 211
195 438
519 930
47 097
63 973
63 426
64 914
68 657
68 136
70 517
1 948 875
2 110 219
2 133 332
2 183 337
2 176 885
2 216 391
2 289 212
62
Results: jobs
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the region
DELTA 2025R2005
-159 217
DELTA 2035R2005
-99 994
DELTA 2025L2005
-143 067
DELTA 2035L2005
-66 512
DELTA 2025H2005
-92 273
DELTA 2035H2005
12 249
15 858
19 189
18 374
22 536
20 331
25 240
8 893
12 246
10 165
13 703
11 540
15 597
71 451
101 332
93 056
124 374
103 154
140 145
39 698
157 132
60 537
39 238
32 881
102 133
54 531
194 280
69 230
53 277
45 090
121 493
37 875
175 707
65 958
46 224
34 076
100 841
53 603
218 708
74 037
63 335
46 468
121 189
45 060
186 940
69 125
51 491
39 215
113 141
65 426
235 177
77 711
71 490
53 719
139 496
16 876
21 560
16 329
21 039
17 817
23 420
161 344
228 010
184 457
267 516
234 462
340 337
63
Results: jobs
64
Results: jobs
65
Results: jobs
66
Results: jobs
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the
region
DELTA
2035R2025R
59 223
DELTA
DELTA
DELTA
DELTA
DELTA
DELTA
DELTA
DELTA
2035L-2025L 2035H-2025H 2025H-2025L 2035H-2035L 2025L-2025R 2035L-2035R 2025H-2025R 2035H-2035R
76 555
104 522
50 794
78 761
16 150
33 482
66 944
112 243
3 332
4 162
4 908
1 957
2 704
2 516
3 346
4 474
6 050
3 353
3 538
4 057
1 374
1 894
1 273
1 457
2 647
3 351
29 881
31 319
36 992
10 098
15 771
21 604
23 042
31 702
38 813
14 833
37 148
8 693
14 038
12 209
19 360
15 728
43 001
8 079
17 111
12 391
20 348
20 366
48 237
8 587
19 999
14 504
26 355
7 185
11 233
3 167
5 267
5 139
12 300
11 823
16 469
3 675
8 156
7 252
18 307
-1 823
18 575
5 420
6 985
1 195
-1 292
-928
24 428
4 806
10 058
1 377
-303
5 362
29 808
8 587
12 253
6 334
11 009
10 895
40 897
8 481
18 214
8 629
18 003
4 683
4 709
5 602
1 488
2 381
-547
-521
941
1 860
66 666
83 058
105 875
50 005
72 821
23 113
39 506
73 118
112 327
67
Results: housing rents, houses
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the
region
2005R
2025R
2035R
2025L
2035L
2025H
2035H
98.924
123.95
122.87
129.897
130.56
135.908
139.32
127.12
158.496
157.56
165.902
166.97
173.357
177.88
133.886
184.383
187.43
191.526
195.75
198.374
205.75
101.546
165.709
121.121
150.825
95.569
108.988
128.07
284.156
154.67
237.909
126.661
137.503
130.01
298.06
155.24
243.36
134.42
137.23
130.023
294.283
160.013
244.126
128.96
139.967
132.88
311.73
162.17
251.90
136.32
140.69
136.393
302.701
166.558
250.805
135.364
147.391
142.49
323.80
171.78
261.71
144.89
151.74
104.296
131.147
132.91
134.627
137.44
140.586
146.25
107.292
133.74
139.27
138.9568
139.27
145.8507
149.41
68
Results: housing rents, houses
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the region
DELTA 2025R2005
DELTA 2035R2005
DELTA 2025L2005
DELTA 2035L2005
DELTA 2025H2005
DELTA 2035H2005
25.026
23.95
30.973
31.63
36.984
40.40
31.376
30.44
38.782
39.85
46.237
50.76
50.497
53.55
57.64
61.86
64.488
71.87
26.524
118.447
33.549
87.084
31.092
28.515
28.46
132.36
34.12
92.53
38.85
28.24
28.477
128.574
38.892
93.301
33.391
30.979
31.33
146.02
41.05
101.07
40.75
31.70
34.847
136.992
45.437
99.98
39.795
38.403
40.95
158.09
50.66
110.89
49.33
42.75
26.851
28.61
30.331
33.14
36.29
41.95
26.4455
31.98
31.6648
31.98
38.5587
42.12
69
Results: housing rents, houses
DELTA
DELTA
2035R-2025R 2035L-2025L
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologi
e Seine
Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France
- Gonesse
Rest of the
region
DELTA
2035H2025H
DELTA
DELTA
DELTA
DELTA
DELTA
DELTA
2025H-2025L 2035H-2035L 2025L-2025R 2035L-2035R 2025H-2025R 2035H-2035R
-1.08
0.66
3.41
6.011
8.76
6.61
7.68
13.03
16.45
-0.94
1.07
4.53
7.455
10.91
8.48
9.41
15.80
20.33
3.05
4.22
7.38
6.848
10.00
11.37
8.32
10.94
18.32
1.94
13.91
0.57
5.45
7.76
-0.28
2.85
17.45
2.16
7.77
7.36
0.72
6.10
21.10
5.22
10.91
9.53
4.35
6.37
8.418
6.545
6.679
6.404
7.424
9.62
12.07
9.61
9.82
8.57
11.05
4.81
27.57
7.50
13.99
9.66
3.19
2.87
13.66
6.93
8.54
1.90
3.47
6.38
4.64
11.32
7.45
0.94
10.17
12.48
25.73
16.54
18.36
10.47
14.51
1.76
2.81
5.66
5.959
8.81
6.29
4.53
7.68
13.34
5.53
0.31
3.56
6.8939
10.14
5.53
0.00
6.58
10.14
70
Results: housing rents, apartments
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the region
2005R
358.32
2025R
429.3829
2035R
427.61
2025L
435.80795
2035L
437.75
2025H
452.49725
2035H
462.86
77.40
76.442
71.87
81.20
78.11
86.361
85.73
95.71
95.691
91.27
101.31
98.49
107.384
107.46
101.87
103.387
99.89
108.70
106.30
114.366
114.67
76.57
128.19
89.17
114.22
74.48
82.87
77.868
133.935
88.777
117.593
77.692
84.654
76.02
128.78
82.45
113.75
80.52
79.26
79.69
140.34
92.90
122.40
79.75
86.83
78.66
137.63
87.88
120.33
82.47
82.35
84.783
146.714
98.194
128.422
84.889
92.777
86.35
146.97
95.81
129.22
89.46
91.31
79.59
80.862
78.49
83.78
82.35
88.727
89.72
91.14
84.90615
79.97
88.11
84.44
93.62645
92.62
71
Results: housing rents, apartments
71.06
-0.95
-0.02
69.30
-5.53
-4.44
77.49
3.81
5.60
79.44
0.71
2.78
DELTA 2025H2005
94.18
8.97
11.68
1.52
-1.98
6.83
4.43
12.50
12.80
1.30
5.74
-0.39
3.37
3.21
1.79
-0.55
0.59
-6.72
-0.47
6.04
-3.60
3.12
12.15
3.73
8.18
5.27
3.97
2.09
9.44
-1.29
6.11
7.99
-0.51
8.21
18.52
9.03
14.20
10.41
9.91
9.78
18.78
6.65
15.00
14.98
8.45
1.27
-1.10
4.19
2.76
9.14
10.13
-6.24
-11.17
-3.03
-6.70
2.48
1.48
DELTA 2025R-2005 DELTA 2035R-2005 DELTA 2025L-2005 DELTA 2035L-2005
Paris
Aulnay Montfermeil
Le Bourget
Biotechnologie
Seine Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France Gonesse
Rest of the region
DELTA 2035H2005
104.54
8.33
11.75
72
Results: housing rents, apartments
73
Results: housing rents, apartments
74
Results: housing rents, apartments
75
Results: housing rents, apartments
Paris
Aulnay
Montfermeil
Le Bourget
Biotechnologi
e Seine
Amont
Confluence
La Défense
Descartes
Pleyel
Roissy-pôle
Saclay
Val de France
- Gonesse
Rest of the
region
DELTA
DELTA
DELTA
DELTA
DELTA
DELTA
DELTA
DELTA
DELTA
2035R-2025R 2035L-2025L 2035H-2025H 2025H-2025L 2035H-2035L 2025L-2025R 2035L-2035R 2025H-2025R 2035H-2035R
-1.77
1.95
10.36
16.6893
27.05
6.42505
10.14
23.11435
35.25
-4.57
-3.10
-0.63
5.158
4.53
4.761
6.24
9.919
13.86
-4.43
-2.82
0.08
6.079
6.16
5.614
7.22
11.693
16.20
-3.50
-2.40
0.30
5.669
5.97
5.31
6.40
10.979
14.78
-1.85
-5.15
-6.33
-3.84
2.82
-5.39
-1.04
-2.71
-5.02
-2.07
2.72
-4.48
1.56
0.26
-2.38
0.80
4.57
-1.47
5.091
6.371
5.297
6.018
5.14
5.944
6.65
6.63
2.91
6.82
9.71
4.48
1.824
6.408
4.12
4.811
2.057
2.179
2.64
8.85
5.43
6.58
1.95
3.09
6.915
12.779
9.417
10.829
7.197
8.123
10.33
18.19
13.37
15.47
8.94
12.05
-2.37
-1.43
0.99
4.945
5.94
2.92
3.86
7.865
11.23
-4.94
-3.67
-1.00
5.51175
4.51
3.20855
4.47
8.7203
12.66
76
Conclusions
• Yet to be presented:
– Equilibrium rents for offices and shops
– Population of resident workers
– Housing constructions
• Architecture consistent with economic theory
• Prototype of the model shows convincing results with respect to theory
• Further develop the model in several aspects:
–
–
–
–
Higher spatial resolution: “communes” would be OK as pertains to data availability
Representation of population may be more disaggregate
The same for representation of activity sectors
Calibration and estimation may rely on higher quality data, e.g. disaggregate data for
some of the components of the model
– Introduce a transit assignment module
•
All of these items except the last rely mostly on data availability and preparation within a given schedule
77