1 simaurif, the integrated land use – transportation model for the paris region andré de palma,...
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SIMAURIF, The integrated land use – transportation model for the
Paris Region André de Palma, University of Cergy-Pontoise and ENPC, FR.
Kiarash Motamedi, UCP, FR.Hakim Ouaras, UCP, FR.
Nathalie Picard, UCP and INED, FR.
ETH, Zurich, March 18th, 2008
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Sponsors PREDIT: Interministerial land transport
research and innovation program. DREIF: State department of transportation for Ile-de-
France. RFF : Railway Network of France.
Other partners Dany Nguyen-Luong, IAURIF, FR. José Moyano, adpC, BE. With kind help of Professor Paul Waddell and CUSPA team
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Paris Region KEY FIGURESKEY FIGURES (in 1999)
12 000 km2
11 millions inhabitants (2 millions in Paris)
4.5 millions households 5 millions jobs 4.9 millions housings
2000 km Highways and Freeways
1380 km Railway & Light rail network, 890 stations
35 160 000 trips/day ; +1%/Year
Modes share : 20% Public
Transport, 44% Private Cars 36% Walk and bikes
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Administrative divisions
•3 Rings
•8 Districts
•1300 Cities (Communes)
•IRIS
•Ilots
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Geographical units:Parcels (Communes, Îlots) and Cells
Ilot:
Homogenuous residential parcel modified for each census
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Data
General Census (RGP) (1990 & 1999)
Regional Transportation Survey (EGT) (1976, 1983, 1991 & 2001)
Regional Employment Survey (ERE) (1997 & 2001)
EVOLUMOS : numerical land use database (1982, 1990, 1994 & 1999) – but not cadastre, no floor space.
Family Budget Survey (2000)
Income imputation.
Other sources : notaries’ Database, UNEDIC, firms, retail, local land use plans databases, …
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Real estate prices data
Land price vs. dwelling and office prices Notaries data : average price and the number of
transactions for dwellings reported at commune level (1990-2003; Houses/appart; new/old).
Data from Cote Callon : average price for sale or rent per m² and for dwelling and offices in ~300 communes with more than 5000 inhab. (1998, 2001)
Correlation (Notaries/Callon) is from 71% to 84% for the appartments, from 53% to 74% for houses.
Correlation between sale price and rent for appartments : 58% if it is old, 82% for the new one. Respectively 59% and 50% for houses.
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Model of office prices
Hedonic model :
By property type : old (renovated / non-renovated) / new House / Appartment
Price interval : Min / Max Price for one square meter ~300 observations, R² between 75% & 81%.
1
PK
k kc c c c c
k
Ln Cte X Cte X
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Model of office prices (2)CLPBAnMin
CLPBAnMax
CLPBNeMin
CLPBNeMax CLPBNe
Constant 6,0495 6,5305 7,0950 7,5246 7,3043 (55,44) (53,29) (67,67) (75,09) (74,09)
Paris 0,2181 0,0959 0,2740 -0,0160 0,0783 (1,69) (0,66) (2,21) (-0,14) (0,67)
La Défense 0,4602 0,4773 0,4709 0,5165 0,4920 (3,95) (3,65) (4,20) (4,83) (4,67)
Employment Density 0,0071 0,0076 0,0081 0,0106 0,0094 (2,93) (2,77) (3,44) (4,74) (4,27)
Fraction of service (6) 0,5228 0,6913 0,4396 0,5905 0,5769 in employmentf (3,61) (4,25) (3,16) (4,44) (4,41)
Fraction of low income HH -0,6299 -0,5885 -0,6458 -0,7841 -0,7085 (-3,25) (-2,70) (-3,46) (-4,40) (-4,04)
Average age of dwellings 0,0076 0,0084 0,0083 0,0093 0,0092 (4,63) (4,58) (5,31) (6,20) (6,26)
Number of subway stations 0,0182 0,0190 0,0152 0,0188 0,0173 (3,04) (2,82) (2,64) (3,41) (3,18)
Travel time PT -0,0330 -0,0311 -0,0383 -0,0347 -0,0257 (-1,47) (-1,23) (-1,77) (-1,68) (-1,26)
Travel time car -0,3957 -0,2870 -0,3305 -0,3697 -0,3775 (-5,09) (-3,29) (-4,43) (-5,18) (-5,38)
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Residential location model
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The origin of movers
0%
2%
4%
6%
8%
10%
12%
14%
Out 75 92 94 93 78 91 95 77 Out 75 92 94 93 78 91 95 77 Out 75 92 94 93 78 91 95 77 Out 75 92 94 93 78 91 95 77 Out 75 92 94 93 78 91 95 77 Out 75 92 94 93 78 91 95 77 Out 75 92 94 93 78 91 95 77 Out 75 92 94 93 78 91 95 77
75 92 94 93 78 91 95 77
Paris Close Suburbs Far Suburbs
Origin countiesResidence counties
Paris / Close Suburbs / Far Suburbs
Pec
enta
ge
of
mo
vin
gs
Current District
Origin districtTotal
Outside Paris C. S. F. S.
Paris 42.9% 37.1% 10.1% 10.00% 30.68%
Close Suburbs 29.5% 10.9% 49.8% 9.7% 35.15%
Far Suburbs 24.8% 4.6% 11.9% 58.7% 34.17%
Region 32.01% 16.79% 24.66% 26.53% 100,00%
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Spatial disparity
Sub-region District Average St Dev Minimum Maximum
Paris 75 294,500 16,5241 83,939 694,375
Close Suburbs (small ring)
92 (West) 247,556 20,5038 66,966 1,198,950
93 (North) 115,709 49,055 47,876 259,163
94 (South) 144,098 74,603 53,356 373,499
Far away suburbs
(large ring)
78 (West) 135,122 65,714 38,112 373,815
91 (South) 114,826 46,740 24,719 332,338
95 (North) 104,375 41,670 25,154 241,692
77 (East) 91,539 37,220 18,028 253,827
Low income families: 26% for 78, 41% for 93
High income families: 38% for 78, 26% for 93
Single households: 51% for Paris
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Some points
Location choice model: Multinomial vs. Nested (district then city)
Use of a nested Logit model: move commune infra commune (Ilots, Cells)
Using notaries’ data of mean transaction prices.
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Location / Dwelling price model
Location choice model Individual i=1…N, alternative j=1..J Random utility model :
exp
expj
ijii i i
j jij j ij
k
j
k
iV
V PU ZV
P
Expected demand :jji
i
D P
1 2 3j j j jj X SP D • Price model :
• Price endogenous , 0 0jijCorr
j
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Dwelling price results (R²=0.53)
Parameter StandardVariable estimate error t-statistic p-valueIntercept 11.02668 0.12800 86.14 <.0001Log(Supply) -0.04791 0.02466 -1.94 0.0522Log(Demand) 0.09918 0.02244 4.42 <.0001Average travel time -0.00280 0.00085 -3.28 0.0011from j to work (minutes)
• Expected signs for Supply and demand but not exactly opposed• Negative effect of travel time to work places as expected
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Dwelling price results (ctn’d)
Parameter StandardVariable estimate Error t-student p-value% households with 1 member 5.09136 0.37884 13.44 <.0001 with 2 members 1.87960 0.34135 5.51 <.0001 with no working member 1.25241 0.30954 4.05 <.0001 with 1 working member 0.82300 0.33762 2.44 0.0149% poor households -6.63187 0.50316 -13.18 <.0001% households with medium income -4.54311 0.33102 -13.72 <.0001 with a foreign head 1.58406 0.36279 4.37 <.0001
Noticeable effect of the presence of smaller families. Negative effect of the presence of low and medium income families. Presence of foreign families increase the prices may be because of no
distinction between foreigners’ origin (OECD countries or Developing ones)
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Residential Location choice results (PseudoR²=0.22)
Parameter StandardVariable Estimate Error t Value p-valueLog(Price) Residual 0.0360 0.0336 1.07 0.2846Same district as before move 2.5461 0.009353 272.24 <.0001Paris -0.2988 0.0267 -11.19 <.0001Log(Price) -1.7285 0.1009 -17.14 <.0001Log(Price)* (Age-20)/10 -0.0653 0.004695 -13.92 <.0001Log(Price)* Log(Income) 0.1783 0.0100 17.7 <.0001
• No endogeneity problem between location choice and housings’ price• A strong preference to move in the same district• Ceteris paribus, a less preference for Paris (but Paris offers a better accessibility)• Negative effect of price but less important for younger and richer families. The young rich families may prefer more expensive locations
No price endogeneity problem
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Location choice results (ctn’d)Parameter Standard
Variable Estimate Error t Value p value___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
Number Railway stations -0.0129 0.002838 -4.56 <.0001Number Subway stations 0.007070 0.001300 5.44 <.0001Average travel time from j, 0.000561 0.000483 1.16 0.2457commuting (TC)TC*(Dummy female) -0.006842 0.000697 -9.82 <.0001Average travel time from j, -0.001391 0.000481 -2.89 0.0038by private car (VP)Distance to highway [km] -0.003392 6.273E-7 -5.41 <.0001
• Preference for more subway stations and, ceteris paribus, less railway stations (may be because of noise, pollution or congestion).• The men indifferent to transit travel time but the female headed famillies are sensitive.• Preference for a less average travel time to jobs by car.• Places farther than highways are more appreciated (with the same accessibility)
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Location choice results (ctn’d)Parameter Standard
Variable Estimate Error t Value p value___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
% households with 1 member * 1 member in h * 2.6327 0.0851 30.95 <.0001 2 members* 2 members in h * 0.9366 0.3060 3.06 0.0022 3+ members* 3+ member in h * 3.2437 0.0810 40.03 <.0001 no working member * no working member in h * 6.1790 0.2287 27.02 <.0001 1 working member * 1 working member in h * 0.3384 0.1455 2.33 0.0201 2+ working member * 2+ working member in h * 0.7132 0.1078 6.61 <.0001%
• A global preference to live with the people in the same category which is more strong for smaller families. Families with no worker (retired or unemployed persons prefer respectively the same categories)._________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
* : % hh in relevant category in the alternative * Dummy hh in relevant category
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Location choice results (ctn’d)
Parameter StandardVariable estimate error t student p value% households with a young head* -0.0147 0.1335 -0.11 0.9122 young head * young head in h* 4.7947 0.1351 35.50 <.0001% poor households* 0.3853 0.1706 2.26 0.0240% households with a foreign head * foreign head in h* 6.2094 0.1622 38.28 <.0001 foreign head * French head in h* -2.7905 0.1007 -27.70 <.0001
The young households move prefer to live with the young. The coefficient for the percentage of poor households is positive. It may present that poorer places are more populated. The foreign people live mostly together and the french families show less interest to live where there is more foreigners.
______________________________________________________________________________________________________________________________________________________________________________________________________________________________________
* : % hh in relevant category in the alternative * Dummy hh in relevant category
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Capacity constrained location choice
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Employment location choice
Employment sectors1. Agriculture2. Industry3. Energy, construction and commerce4. Transportation5. Financial and Real Estate services6. Service7. Education8. Administration
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Data source: ERE
Exhaustive business establishments database (1997,2001) Activity sector Employments number by gender Located at commune level Location of some establishments at cell level for 2001:
located fraction depends on size and location of establishment.
Problems Errors or modifications in establishments coding Difficult identification of complex establishments particularly in
public sector (eg : hospitals, schools) Difference between employer address and real job location
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Data preparation
Three data bases: ERE 1997, ERE 2001 & Geolocalized ERE have been joined
ERE 2001 is used as pivot and an official establishment identifier (SIRET) was the matching variable. If there was any problem in SIRET, we used address and number of employees to match establishments
SIRENE database was not available for us. This database provides creation and closure dates.
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Sources for employment number variation
1. Creation of new establishments given by classes of: Activity sector Size Location
2. Destruction of establishments
3. Relocation of existing establishments: Destruction then creation
Sector and size of the initial establishment Location choice
4. Variation of the number of employees in a non moving establishment
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Created and destructed establishments
2001
1997
Absent Present Total
Absent ND Creation Creation
Present Destruction Stable Total97
Total Destruction Total01 Total
Creation rate = 120 428 / 292 863 = 41.12% Destruction rate = 112 177 / 284 612 = 39.48%
120 428
112 177 172 435
120 428
284 612
112 177 292 863 405 040
41.1%
39.4%
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Size of created and destructed establishments
0%
10%
20%
30%
40%
50%
60%
Tot=1 1<Tot<=3 3<Tot<=9 9<Tot<=49 49<TotClasses d'effectif
Fré
qu
ance
Etablissements créés
Etablissements détruits
Emplois dans établissements créés
Emplois dans établissements détruits
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Establishments' evolution by size class
Tot=1 1<Tot<=3 3<Tot<=9 9<Tot<=49 50<Tot Somme
Disapeared 27% 30% 27% 13% 3% 100%
Created 26% 29% 27% 15% 4% 100%
Not displaced Size en 1997
Size 2001↓ Tot=1 1<Tot<=3 3<Tot<=9 9<Tot<=49 50<Tot Sum
Tot=1 11,4% 3,7% 0,6% 0,1% 0,0% 15,8%
1<Tot<=3 3,8% 16,8% 4,8% 0,2% 0,0% 25,6%
3<Tot<=9 0,7% 5,7% 24,4% 2,3% 0,1% 33,1%
9<Tot<=49 0,1% 0,3% 3,5% 14,8% 0,5% 19,2%
49<Tot 0,0% 0,0% 0,1% 1,0% 5,2% 6,3%
Sum 16,0% 26,5% 33,3% 18,4% 5,8% 100,0%
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Variation of establishments' size
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Jobs, establishments & firms
Estimation of establishments' location choice: 1 model per sector Discret predictors for establishment size Weighting in location model at job level
All the employments of an establishment are located at a same place Better geographical distribution of employments Better model for agglomeration of empoyments
Implementation in UrbanSim : Building types can be used to limit the alternatives for
very grand firms
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Establishment disappearance model
Binary Probit to compute the probability to disappear
Establishment age is an important missing data All sectors together, R²=75,9%
Sect. 1 2 3 4 5 6 7 8
R2 69,4% 87,8% 22,8% 99,8% 80,2% 45,1% 85,5% 98,8%
Disparu
2240 10140 38854 3790 6681 39517 6065
3750
Restant
3624
14465
53694 4955
11785
55196
18942
9592
2001
, (0,1)
Pr( ) Pr( 0) Pr( ) ( )
e s c s e e e
e e s c s e s c s e
DU X Y N
e E DU X Y F X Y
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Sector 2 3 6 All togetherConstant -7,854 0,031 0,708 -2,585 (-22,66) (0,12) (2,86) (-20,24)Workforce = 2 -0,382 -0,364 -0,374 -0,393 (-8,11) (-19,32) (-20,53) (-35,67)Workforce between 3 & 5 -0,698 -0,669 -0,616 -0,671 (-15,56) (-36,92) (-35,04) (-63,35)Workforce between 3 & 5 -0,075 -0,126 -0,080 -0,104Gradient (-4,48) (-15,93) (-9,80) (-21,41)Workforce between 6 & 9 -0,863 -0,912 -0,773 -0,887 (-19,67) (-49,63) (-43,22) (-83,18)Workforce between 6 & 9 -0,008 -0,013 -0,068 -0,027Gradient (-0,78) (-2,30) (-11,68) (-8,05)Workforce between 10 & 19 -1,001 -0,849 -0,816 -0,913 (-23,20) (-45,09) (-44,84) (-85,06)Workforce between 10 & 19 0,008 -0,020 -0,005 -0,003Gradient (2,25) (-8,86) (-2,05) (-2,64)Workforce between 20 & 49 -0,958 -0,944 -0,764 -0,888 (-23,10) (-52,70) (-45,39) (-88,46)Workforce between 20 & 49 -0,010 -0,003 0,001 -0,005Gradient (-12,07) (-4,38) (2,58) (-15,00)
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Establishment workforce evolution
Low R2 for prediction of evolution, but the explanatory power is not less than the prediction of final workforce that gives better R2
Extreme cases are ignored: establishments with a very grand workforce (Workforce >1000) or a very grand evolutions (Rel. Var. > 20 or Rel. Var. < 0,99)
Sector 1 2 3 4 5 6 7 8All
together
R2 8,03% 6,08% 6,84% 7,53% 4,81% 4,95% 2,45% 5,74% 5,22%
N Obs 3 62214 427
53 663
4 921 11 740
55 012
18 846
9 469 171 700
01
97
97 inf, ,( ) * * *( ) *e
es s t t s t t e t s c
t T t T
Ln Cte I I X
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Sector 3 5 7 All togetherConstant 0,25868 0,23728 0,07576 0,20578 (6,84) (2,25) (1,30) (8,99)Workforce = 2 -0,23903 -0,18787 -0,15593 -0,21347 (-32,39) (-11,28) (-14,27) (-49,61)Workforce between 3 & 5 -0,30761 -0,21164 -0,15648 -0,27100 (-41,32) (-13,00) (-11,93) (-61,32)Workforce between 3 & 5 -0,02466 -0,03300 0,01906 -0,01704Gradient (-5,50) (-3,29) (1,84) (-6,07)Workforce between 6 & 9 -0,36392 -0,26458 -0,10667 -0,30946 (-40,72) (-14,13) (-6,16) (-57,99)Workforce between 6 & 9 -0,00335 -0,01517 -0,02436 -0,00560Gradient (-0,79) (-1,66) (-2,72) (-2,19)Workforce between 10 & 19 -0,37583 -0,29910 -0,16103 -0,31840 (-44,37) (-17,55) (-13,14) (-66,89)Workforce between 20 & 49 -0,37630 -0,31849 -0,13976 -0,30662 (-27,11) (-10,70) (-7,76) (-40,81)Workforce between 20 & 49 0,00124 -0,00068 -0,00177 -0,00099Gradient (1,30) (-0,33) (-1,56) (-2,02)
Establishment workforce evolution
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Sector 3 5 7 All togetherWorkforce between 50 & 99 -0,36444 -0,29414 -0,17020 -0,31904 (-22,29) (-9,70) (-11,48) (-40,98)
Workforce more than 100 -0,46207 -0,30054 -0,14200 -0,35091 (-20,22) (-8,55) (-7,57) (-35,98)
Workforce more than 100 -0,00015 -0,00023 -0,00015 -0,00016Gradient (-1,32) (-1,48) (-1,85) (-3,93)
Number of railway stations -0,00450 -0,00225 -0,00086 -0,00165SNCF et RER (-3,55) (-0,83) (-0,42) (-2,27)
Average travel time -0,00991 0,00083 0,00277 -0,00659PT (Hour) DREIF (-2,35) (0,08) (0,47) (-2,59)
Average travel time -0,03845 -0,05972 -0,02271 -0,01044PV (Hour) DREIF (-1,57) (-0,93) (-0,54) (-0,70)Employment zone density -7,63059 -9,98476 -5,28097 -5,933701997 (Emp/m2) (-3,49) (-1,78) (-1,45) (-4,37)
Number of employments in 0,00498 0,00581 -0,00037 0,00248commune sector 3 (thousands) (4,85) (2,82) (-0,21) (4,25)
Number of employments in -0,00136 -0,00295 -0,00095 -0,00141commune sector 5 (thousands) (-2,01) (-2,34) (-0,78) (-3,81)
Establishment workforce evolution
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Location choice model: method and size effects
Multinomial Logit to choose a job place where all the job places in a commune have the same utility. The present number of employments used as proxy for number of job places.
'' 1
exp,
hih
i I hii
V
V
P
' ' '' 1 ' in ' ' 1
exp exp log.
exp log
h hk k k kh h
k k i K Kh hi k kk i k k
G V V GG
V V G
P P
Sector 1 2 3 4 5 6 7 8Nb Observations 1 945 8 343 41 465 41 465 7 150 46 666 6 083 4 395 Adjusted Estrella 32,4% 28,7% 20,4% 20,4% 24,1% 14,0% 21,0% 15,7%Aldrich-Nelson 29,0% 25,1% 18,3% 18,3% 21,7% 13,1% 19,4% 15,3%McFadden's LRI 8,9% 7,3% 4,9% 4,9% 6,0% 3,3% 5,2% 3,9%Veall-Zimmermann 35,3% 30,6% 22,3% 22,3% 26,5% 15,9% 23,6% 18,6%
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Sector 2 3 4 5 6Paris 0,1442 0,1442 (2,41) (2,41) Commune neighbouring Paris -0,1041 -0,1429 -0,1429 -0,0111 0,0162 (-1,81) (-5,91) (-5,91) (-0,17) (0,56)La défense -0,2045 -0,2045 -0,0157 -0,2604 (-4,28) (-4,28) (-0,15) (-5,83)New city -0,1498 -0,1412 -0,1412 -0,0294 -0,1244 (-2,39) (-5,02) (-5,02) (-0,37) (-4,03)Population of commune in 99 0,0036 0,0036 0,0040 0,0035(thausands) (14,15) (14,15) (7,06) (12,19)Pop Density of commune in 99 16,0993 0,8235(Pers./m2) (5,68) (0,60)% high income households 1,7098 1,7098 3,5395 3,1164 (7,66) (7,66) (5,77) (13,61)% low income households 0,7297 3,1969 3,1969 2,6617 2,7578 (2,29) (11,43) (11,43) (3,18) (9,09)
38
Sector 2 3 4 5 6
% hh with age of head 5,5359 0,6003 0,6003 -3,0029 -0,1676between 35 & 65 (5,24) (2,99) (2,99) (-5,01) (-0,82)
% hh with age of head 2,9331 0,5987 0,5987 1,4412 1,6658Less than 35 (3,86) (3,28) (3,28) (3,17) (8,78)
Travel time PT -0,0340 -0,0287(hour) (-1,17) (-2,16)
Travel time PC -0,6766 -0,6766 -0,3889 -0,3537(hour) (-10,04) (-10,04) (-2,02) (-4,55)
Number of subway stations 0,0243 0,0107 -0,0038
(5,04) (1,91) (-1,59)
Number of train stations 0,0104 0,0104 0,0212 0,0145
(2,94) (2,94) (2,31) (4,32)
Logarithm of offices’ price 0,2678 0,2116 0,3382
(3,03) (2,22) (9,44)
% socio-professional cat. 3 -1,5292 -1,5292 -0,9018Cadres (-11,93) (-11,93) (-7,64)
39
Sector 2 3 4 5 6Log of the total employment -1,0496 -1,0321 -1,0321 -1,0252 -0,9684number (-9,44) (-26,92) (-26,92) (-9,01) (-25,70)Log of the employment number 0,0656 0,0400 0,0400 0,0731 0,0706secteur 1 (2,97) (4,08) (4,08) (2,49) (6,78)Log of the employment number 0,2102 0,0638 0,0638 0,0613 0,0487secteur 2 (7,81) (7,82) (7,82) (2,76) (4,96)Log of the employment number 0,3985 0,4587 0,4587 0,0758 0,1864secteur 3 (8,54) (27,19) (27,19) (1,45) (9,72)Log of the employment number -0,0353 -0,0127 -0,0127secteur 4 (-2,10) (-1,78) (-1,78)Log of the employment number 0,0150 0,0150 0,2472 0,0780secteur 5 (1,65) (1,65) (9,56) (8,04)Log of the employment number 0,1454 0,1303 0,1303 0,1105 0,2120secteur 6 (3,24) (7,65) (7,65) (2,13) (11,74)Log of the employment number 0,0594 0,0854 0,0854 0,2288 0,0682secteur 7 (2,18) (7,49) (7,49) (6,68) (5,88)
40
Estimation of number of employments at cell level We consider the number of employments at each alternative as
a proxy for its capacity to receive new employments (establishments)
The geo localized establishments are located at cell level The communes’ non geo localized workforces are distributed
over the cells. A linear model of the number of non geo-localized
employments in function of: Number of geo localized employments and
establishments Number of geo localized employments by
establishment size class Crossing these variables with indicators for Paris,
neighbouring communes and near suburbs.
41
Land development model
Project location vs. transition
42
Accessibility
Traveler
Transit Priv. car
(R,T)1 (R,T)I (R,T)1 (R,T)I
Trad. Static Model Dynamic
Model
,
1
0
.ln
exp( ( ) / )
aTaij
k VP TC
T k aTaijT
L
C u du
Travel Decision
Destination Choice
Mode Choice
Route & Departure time Choices
43
Integration of UrbanSim and METROPOLIS: an automatic process
Demographics Model
Macro-economics Model
Mi(t) Number of households of type i
Es(t) Number of employments in sector s
UrbanSim
Mil(t)
Esl(t)
3 steps model
METROPOLIS
Origin-Destination
Matrix
ttOD,
Accessibility (O, D)
l : cell’s number
t -> t+1
SIMAURIF
44
Simultaneous destination & mode choice
where .l l l l lijm ijm ijm ijm ijmU V V X
exp( )
exp( )
lijm
lijm l
ikn
k n
V
PV
Random Utility Model (Logit) for destination and mode choice:
However, the number of trips allocated to a destination by RUM is not necessarily equal to its trip attraction.
Classic solution: Furness method to equalize the trip number at destinations
We propose a more efficient method by adding a destination-specific constant term in utility function (representing unobserved heterogeneity at destination)
45
Technical issues and simulations
46
UrbanSim for Paris area
49 236 Cells (22 572 populated), dimension : 500 x 500 m
8 counties,1300 cities, 572 zones Run US every year Update travel data every 3 years Calibration
Baseyear: 1990 Run from 1990 to 1999
Simulation Baseyear: 1999 Run from 1999 to 2026
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UrbanSim We used activity location model instead the commercial and
industrial location models
Development types: residential activity vacant
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Calibration
Calibration Projection
Base Year:1990
Calibration Year:1999
Prevision year: 2026
Estimation by an external tool (e.g. SAS) Iterative adjustment process
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UrbanSim Models implemented:
'transport_model', # Update the variables output of the traffic model like travel time, number of stations, accessibility, …
'prescheduled_events', 'events_coordinator', 'land_price_model', 'development_project_transition_model', 'residential_development_project_location_choice_model', 'activity_development_project_location_choice_model', 'events_coordinator', 'household_transition_model', 'employment_transition_model', 'household_relocation_model', 'household_location_choice_model', 'employment_relocation_model', {'employment_location_choice_model':{'group_members': '_all_'}}, 'distribute_unplaced_jobs_model',
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UrbanSim household location choice model coefficients
Estimate coefficient_name
2,4991 same_county
-0,0222 distance_to_arterial
0,0117 distance_to_Chatelet
0,0209 subway_stations_within_walking_distance
0,0301 railway_stations
0,0139 railway_stations_within_walking_distance
-0,0000058 residential_units_within_walking_distance_if_high_income
-0,0000091 workers_t_residential_units_within_walking_distance
0,0000102 activity_sqft
-0,0000020 activity_sqft_within_walking_distance
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UrbanSim
Variable “Same county” Households: last_county_id Update:
hh_ds.modify_attribute("last_county_id",gc_ds.get_attribute("county_id")
[gc_ds.get_id_index(hh_ds.get_attribute("grid_id"))])
Machine P4 CPU 3.4 Ghz RAM 2 GB
Run time 25 minutes per year ~ 7 hours for 27 years (1999-2026)
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UrbanSim Calibration 90-99
Household location choice
County US output(Households)
Difference absolute
Difference relative %
Paris 75 1186344 75432 7%
Seine-et-Marne 77 428322 -4029 -1%
Yvelines 78 507664 4568 1%
Essonne 91 416195 -4408 -1%
Hauts-de-Seine 92 614232 -10694 -2%
Seine-St-Denis 93 535230 10843 2%
Val-de-Marne 94 492072 -7332 -1%
Val-d’Oise 95 384327 -10363 -3%
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UrbanSim
Population
County US output
(population)Difference
absoluteDifference relative %
Paris 75 2578042 500236 24%
Seine-et-Marne 77 1076807 -98447 -8%
Yvelines 78 1265698 -62758 -5%
Essonne 91 1034714 -71414 -6%
Hauts-de-Seine 92 1420589 20917 1%
Seine-St-Denis 93 1321222 -34588 -3%
Val-de-Marne 94 1173592 -25025 -2%
Val-d’Oise 95 976711 -106294 -10%
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UrbanSimJobs
CountyUS output
JobsDifference
absoluteDifference relative %
Paris 75 1815350 214535 13%
Seine-et-Marne 77 329243 -59704 -15%
Yvelines 78 447315 -57 154 -11%
Essonne 91 328502 -72 895 -18%
Hauts-de-Seine 92 786497 -28 974 -4%
Seine-St-Denis 93 490550 6 551 1%
Val-de-Marne 94 474999 2 552 1%
Val-d’Oise 95 369539 -4 911 -1%
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Application to the railway north tangential
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Application to the railway north tangential
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Some characteristics First Bypass in Paris suburbs 2 lines of 28 km 14 stations, 8 existing ones and 6 news will created Connection with 5 RER lines Full starting in 2016 Frequency 5 mn in the ruch hours and 10 mn
otherwise, speed of 50 km/h Capacity of trains is 500 passengers
Application to the railway north tangential
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Some results: Two scenarios, with north tangential and
without tangential. For the 853 cells close to the NT:
Difference in population : + 2000 Difference in jobs: +10 000
Application to the railway north tangential
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Population
Application to the railway north tangential
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Jobs
Application to the railway north tangential
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Conclusions
- Having explored the interaction of residential location choice and transportation, with particular emphasis on issues of dynamics, endogeneity.
- Estimating a semi-hedonic model for housing prices in the Paris region.
- New developments in UrbanSim related to Paris particularly adaptation to available data structure.
- Some exhaustive data in Paris region but still some missing (or non accessible) data.
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Thanks for your attention