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SIMAURIF, The integrated land use – transportation model for the Paris Region

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 18 th , 2008. Sponsors.

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SIMAURIF, The integrated land use – transportation model for the Paris Region

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  1. 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

  2. Sponsors • PREDIT: Interministerial land transportresearch 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

  3. 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 Paris Region KEY FIGURES (in 1999)

  4. Administrative divisions • 3 Rings • 8 Districts • 1300 Cities (Communes) • IRIS • Ilots

  5. Geographical units:Parcels (Communes, Îlots) and Cells Ilot: Homogenuous residential parcel modified for each census

  6. 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, …

  7. 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.

  8. 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%.

  9. Model of office prices (2)

  10. Residential location model

  11. The origin of movers

  12. Spatial disparity Low income families: 26% for 78, 41% for 93 High income families: 38% for 78, 26% for 93 Single households: 51% for Paris

  13. 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.

  14. Location / Dwelling price model • Location choice model • Individual i=1…N, alternative j=1..J • Random utility model : • Expected demand : • Price model : • Price endogenous

  15. Dwelling price results (R²=0.53) • Parameter Standard • Variable estimate error t-statistic p-value • Intercept 11.02668 0.12800 86.14 <.0001 • Log(Supply) -0.04791 0.02466 -1.94 0.0522 • Log(Demand) 0.09918 0.02244 4.42 <.0001 • Average travel time -0.00280 0.00085 -3.28 0.0011 • from j to work (minutes) • Expected signs for Supply and demand but not exactly opposed • Negative effect of travel time to work places as expected

  16. Dwelling price results (ctn’d) Parameter Standard Variable 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)

  17. Residential Location choice results(PseudoR²=0.22) No price endogeneity problem • Parameter Standard • Variable Estimate Error t Value p-value • Log(Price) Residual 0.0360 0.0336 1.07 0.2846 • Same district as before move 2.5461 0.009353 272.24 <.0001 • Paris -0.2988 0.0267 -11.19 <.0001 • Log(Price) -1.7285 0.1009 -17.14 <.0001 • Log(Price)* (Age-20)/10 -0.0653 0.004695 -13.92 <.0001 • Log(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

  18. Location choice results (ctn’d) • Parameter Standard • Variable Estimate Error t Value p value • ___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ • Number Railway stations -0.0129 0.002838 -4.56 <.0001 • Number Subway stations 0.007070 0.001300 5.44 <.0001 • Average travel time from j, 0.000561 0.000483 1.16 0.2457 • commuting (TC) • TC*(Dummy female) -0.006842 0.000697 -9.82 <.0001 • Average travel time from j, -0.001391 0.000481 -2.89 0.0038 • by 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)

  19. 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

  20. Location choice results (ctn’d) Parameter Standard Variable 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

  21. Capacity constrained location choice

  22. Employment location choice Employment sectors • Agriculture • Industry • Energy, construction and commerce • Transportation • Financial and Real Estate services • Service • Education • Administration

  23. 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

  24. 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.

  25. Sources for employment number variation • Creation of new establishments given by classes of: • Activity sector • Size • Location • Destruction of establishments • Relocation of existing establishments: Destruction then creation • Sector and size of the initial establishment • Location choice • Variation of the number of employees in a non moving establishment

  26. Created and destructed establishments • Creation rate = 120 428 / 292 863 = 41.12% • Destruction rate = 112 177 / 284 612 = 39.48% 120 428 120 428 41.1% 172 435 284 612 112 177 39.4% 112 177 292 863 405 040

  27. Size of created and destructed establishments

  28. Establishments' evolution by size class

  29. Variation of establishments' size

  30. 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

  31. Establishment disappearance model • Binary Probit to compute the probability to disappear • Establishment age is an important missing data • All sectors together, R²=75,9%

  32. 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)

  33. Establishment workforce evolution

  34. Establishment workforce evolution

  35. 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.

  36. 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.

  37. Land development model Project location vs. transition

  38. Trad. Static Model Travel Decision Destination Choice Traveler Dynamic Model Mode Choice Transit Priv. car Route & Departure time Choices (R,T)1 (R,T)I (R,T)1 (R,T)I Accessibility

  39. Integration of UrbanSim and METROPOLIS: an automatic process l : cell’s number Demographics Model Mi(t) Number of households of type i Mil(t) UrbanSim Macro-economics Model Es(t) Number of employments in sector s Esl(t) t -> t+1 ttOD,Accessibility (O, D) Origin-Destination Matrix 3 steps model SIMAURIF METROPOLIS

  40. Simultaneous destination & mode choice 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)

  41. Technical issues and simulations

  42. 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

  43. UrbanSim • We used activity location model instead the commercial and industrial location models • Development types: • residential • activity • vacant

  44. Calibration • Estimation by an external tool (e.g. SAS) • Iterative adjustment process Calibration Projection Prevision year: 2026 Base Year: 1990 Calibration Year: 1999

  45. 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',

  46. UrbanSim household location choice model coefficients

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