the lyon experience with urbansim (version 4.1.0) speaker: mark kryvobokov [email protected]...

THE LYON EXPERIENCE WITH URBANSIM

(version 4.1.0)

speaker: Mark Kryvobokov

[email protected]

contributors: Fabrice Marchal

Jean-Pierre Nicolas

Philippe Zuccarello

European URBANSIM User Group meeting

at Swiss Federal Institute of Technology, Zurich

17-18 March 2008

European UrbanSim User Group, IVT, Zürich, 17-18 March 2008

LET

Contents

0. Two projects with UrbanSim at LET

1. Our way from Eugene to Lyon

2. Tables and relations

3. Estimation and simulation

4. Preliminary results

5. Some obstacles with the software

6. Questions


LET

SMA-LOCMOB (resp. Fabrice Marchal)

– Systems of Multi-Agents for LOCation and MOBility

– Investigatation of some issues in TLUMs:

• spatial and temporal resolution

• modeling features

SIMBAD (resp. Jean-Pierre Nicolas)

– A more macroeconomic model of the Lyon Urban Area

– Purpose: assessing the sustainability of transport policies by crossing environmental, economic and social indicators

0. Two projects with UrbanSim at LET


LET


The Lyon Urban Area:

1,576,000 inhabitants

more than 660,000 households

about 700,000 jobs


LET



LET

HLCM

ELCM

DPLCM

LPM

RLSM

Transportation model

Car ownership model

+

UrbanSim models

model using only Lyon data

external model

future developments

models using Lyon data + ad hoc assumptions


• 777 zones covering the Lyon Urban Area

• geographic data: polygons, centroïds

• origin-destination matrix of accessibilities

• number of residential units

• environmental data: percent water, open space, residential land, distance to highway and arterial etc.

• average apartment price per square meter

• number of jobs for 4 sectors in each zone

• household characteristics: zone, number of persons, workers, cars, income, age of head, ownership status, job status of head

zones and gridcells

tables

jobs table

households table

Overview of available data


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Spatial resolution

of the Lyon prototype:

each zone includes only one gridcell:

777 zones = 777 gridcells

(but zone_id and grid_id are different)

zones roughly correspond to municipalities


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Example: data sources for the gridcells table in the Lyon prototype

Lyon data Assumptions Eugene datagrid_idzone_idresidential_unitsdistance_to_highwaydistance_to_arterialrelative_xrelative_ypercent_waterpercent_open_spacefraction_residential_land

Additional data:percent_industrial_spaceaverage_apartment_price

residential_improvement_valueindustrial_sqftindustrial_improvement_valuecommercial_sqftcommercial_improvement_valuegovernmental_sqftgovernmental_improvement_valueresidential_land_valuenonresidential_land_valueis_outside_urban_growth_boundarydevelopment_type_idplan_type_id

city_idcounty_idpercent_wetlandpercent_roadspercent_public_spacepercent_slopepercent_stream_bufferpercent_floodplainyear_builtgovernmental_sqft_per_jobcommercial_sqft_per_jobindustrial_sqft_per_job


LET


Examples of assumptions:

relative_X = (X - minX)/100 + 1

relative_Y = (Y - minY)/100 + 1

residential_improvement_value =

= average_apartment_price • 80 • residential_units

residential_land_value = residential_improvement_value • 0.2


LET


Examples of assumptions:

development_type_id = 1 (R1) if 1<= residential_units <= 100

development_type_id = 2 (R2) if 100 < residential_units <= 400

…

plan_type_id = 8 (High Density Residential) if

1 <= development_type_id <= 7 (R1-R7)

plan_type_id = 9 (Light-Medium Industrial) if

development_type_id = 20 (I1)

...


LET


Changes made to adjust to the Lyon data:

• development_constraints: min, max

• development_types: max

• development_event_history: sqft and value is 10% of the corresponding values in the gridcells table

• annual_households_control_totals: total_number_of_households

• annual_employment_control_totals: total, home_based and non_home_based employment

• urbansim_constraints: cell_size = 100

walking_distance_circle_radius = 1200


LET



LET

HLCM

ELCM

DPLCM

LPM

RLSM


Car ownership model

+

UrbanSim models

model using only Lyon data

external model

future developments

models using Lyon data + ad hoc assumptions

2. Tables and relations


LET


Estimation with logit models:


LET

HLCM

ELCM

DPLCM

LPM

RLSM


Car ownership model

+

UrbanSim models


Estimation with logit models:

Model Number of variablesAdjusted likelihood

ratio indexHLCM 6 0.392ELCM-industrialsubmodel 1 (industrial empl.)submodel 5 (construction empl.)submodel 11 (service empl.)submodel 16 (agricultural empl.)

2222

0.0230.0230.0300.018

ELCM-commercialsubmodel 1 (industrial empl.)submodel 5 (construction empl.)submodel 11 (service empl.)submodel 16 (agricultural empl.)

3432

0.0140.0030.0050.002

ELCM-home_based 6 0.047DPLCM-industrial 2 - 13.600DPLCM-commercial 5 0.226DPLCM-residential 3 0.008


LET



LET

HLCM

ELCM

DPLCM

LPM

RLSM


Car ownership model

+

UrbanSim models

Estimation with regression models:


Estimation with regression models:

Model Number of variables Adjusted R-squaredLPM 6 0.812RLCM 6 0.282


LET


Simulation:

Technical performance:

Baseyear cache: 71 MB

Simulation for 14 years (1981-1994) takes 1 hour 20 min.

Output cache: 931 MB

Limitations:

In DPLCM, there is only residential and industrial development.

In the households table, after the first year simulation some households are located in a gridcell with grid_id=0...


LET


Output from make_indicators.py

(fragment of population)


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LET


Map_Pop

POP91_90

-153 - -51

-50 - -1

0 - 50

51 - 100

101 - 336

Population change 1990-1991

Map_Pop

POP81_80

-3566 - -1001

-1000 - -51

-50 - -1

0 - 50

51 - 100

101 - 1119


Map_Pop

POP94_93

-135 - -51

-50 - -1

0 - 50

51 - 100

101 - 198



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LET

Map_Jobs

Job91_90

-4 - 0

1 - 20

21 - 50

51 - 100

101 - 223

Commercial employment change 1990-1991

Map_Jobs

Job94_93

-7 - 0

1 - 20

21 - 50

51 - 100

101 - 255


Map_Jobs

Job81_80

-993 - -201

-200 - -11

-10 - 0

1 - 20

21 - 46



• Not everything is described and updated in the documentation. Comments for each attribute are absent in the MySQL tables (suggestion: distribute Eugene also as a MySQL dump?)

Thank you so much for the MAILING LIST!

• Our attempt to work with 9-digit zone_id values failed

• The map representation of indicators is mirrored vertically (as if the ordinate is multiplied by -1)


LET


• Handling of gridcells and polygons

• Ambiguity in the Eugene package:– the identifiers group_id for employment and group_id for development are

unrelated


LET


• Ambiguity in the Eugene package:– the same identifier has 3 different

names in 3 tables:

id in job_building_types;

building_type in jobs;

building_type_id in building_types


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6. Questions

1. Can we have long values and big differences between values of zone_id (and grid_id)?

2. How improvement value and land value were calculated for each gridcell for Eugene?

3. Should the development_type_id in the jobs table correspond to the development_type_id from the gridcells table?

4. Where the table households_characteristics_for_ht is used?


LET

6. Questions

5. If we do not have data about buildings, should we set corresponding values for employment sector_id and building_types :

- if sector_id is services then

building_types are commercial and governmental;

- if sector_id is construction then

building_type is industrial

etc.?


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6. Questions

6. What is number_of_developer_model_history_years and developer_model_estimation_threshold_count in the table urbansim_constants?

7. In RLSM, the number of observations for estimation is less than the number of gridcells with fraction_residential_land>0, but we do not see any threshold in residential_land_share_model_configuration_creator.py

8. How to create indicators for population change and employment change?

9. How to create our own REPM for gridcells using the San Francisco model?


LET

the lyon experience with urbansim (version 4.1.0) speaker: mark kryvobokov [email protected]...

Documents

lyon experience

urbansim version

lyon examples of assumptions

lyon spatial resolution

lyon urban area purpose

units residential

residential land

macroeconomic model