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Microsimulationof Land Use andTransport inCities
Michael Wegener
Advanced Modelling in Integrated Land-Use and Transport Systems(AMOLT) M.Sc. Transportation Systems TU München, 14 July 2009 2
Model levels
Dortmund
Regions
Dortmund
Zones
Dortmund City
Raster cells
Multi-level
Multi-scale
3
Model levels
Dortmund
Regions
Dortmund
Zones
Dortmund City
Raster cells4
Microsimulation
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Microsimulation
Theory
Microsimulation is the reproduction of a macro process bymany micro events.
Events:
The basic building block of microsimulation is the event.
No deterministic assertions (that are valid with certainty)can be made about events, only probabilistic assertions(that are valid with probability) are possible.
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Microsimulation
There are two types of events:
- Transitions are changes of an individual or object fromone state to another subject to transition probabilities(e.g. ageing of persons or buildings) .
- Choices are selections between alternatives by anindividual as a function of their perceived utility (e.g.modal choice, residential location).
If the causal chain behind choices is of no interest, alsochoices are modelled as transitions (e.g. birth, marriage,divorce).
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Microsimulation
The method of microsimulation consists of generating asequence of events as a function of their probability ofoccurrence.
To generate an event, a lottery is drawn the result ofwhich is one of the possible outcomes of the event andsubject to their probability. This is why microsimulation isalso called Monte-Carlo simulation.
Another term for microsimulation models is agent-basedmodels (ABM). Cellular automata (CA) models are raster-based models of transitions of raster cells based on neigh-bourhood properties.
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Microsimulation
The lottery consists of the mapping of a random numberbetween 0 and 1 to the vector of cumulated probabilities ofpossible outcomes of the event.
Example:An event has three possible outcomes with probabilities 0.3, 0.5 and0.2. If the random number 0.74 is drawn, Outcome 2 is the event.
Random number 0.74
Outcome1
Outcome2
Outcome3
0 0.3 0.8 1.0
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Random number generators
Random number generator Pseudo random number generator 10
Micro database
Microsimulation models require micro data.
In reality micro data are rarely available or where availablecannot be used because of privacy concerns.
Synthetic micro data are micro data generated fromavailable spatially aggregate data with which they areconsistent in all known attribute distributions.
Microsimulation models can be operated with syntheticmicro data instead of real micro data.
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Micro database
BuildingsResidential buildings (micro location)- Dwellings (type, size, quality)
Nonresidential buildings (micro location)- floorspace (industrial, retail, offices)
Households/BusinessesHouseholds (micro location)- Households (size, income,cars)- Persons (age, sex, education, job)
Businesses (micro location)- Firms (industry, size, vehicles)- Employment (skill)
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Spatial disaggregation
For the synthetic micro database zone data are allocated toraster cells. Two steps are performed:
(1) Conversion of polygons to raster cellsThe polygons of a land-use map are converted to rastercells and each raster cell is assigned a land-use category.
Land-use categories
Residential high-density
Residential low densityIndustrial
Open Space
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Spatial disaggregation
(2) Allocation of zone data to raster cellsThe data elements are allocated to raster cells by Monte-Carlo simulation according to their density.
Probabilities Number ranges n Weight of all zones
10050 data elements10
Weights
1,487 dataelements
10
5
1
1
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Spatial disaggregation
100 x 100 m raster cells
1 km2
Spatial disaggregation
100 x 100 m raster cells
1 km2
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15 16
Synthetic population for Dortmund
Population density in raster cells of 100 x 100 m
0 - 2020 - 4040 - 6060 - 8080 - 100
100 - 120120 - 140140 - 160160 - 180180 - 200200 -
Pop/ha
0
km
2
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Dwellings in DortmundWorkplaces in Dortmund
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Suburb-to-suburb commuter flows in Dortmund
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Commuter flows in Dortmund
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The ILUMASS Model
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The ILUMASS Project (2001-2006)
The project ILUMASS (Integrated Land-Use Modelling andTransport Systems Simulation) embedded a microscopicdynamic simulation model of urban traffic flows into acomprehensive model system incorporating both changesof land use and the resulting changes in transport demandas well as their environmental impacts.
For testing the land use submodels, the transport andenvironmental submodels were replaced by the aggregatetransport model of the IRPUD model and simpler envir-onmental impact models (= reduced ILUMASS model).
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Internal zones
External zones
Dortmund
Hamm
Hagen
Bochum
Study area
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Study area
Hamm
0 10 km5
Dortmund
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Syntheticpopulation- housing- persons- cars
Synthetic firms- floorspace- firms- jobs- vehicles
Base year data
Transportnetworks- roads- public transport
Firms- floorspace- firms- jobs
Person traveldemand- activities- week plans- trips
Goodstransportdemand- activities- trips
Environmental impacts
Emissions- air pollution,- traffic noiseat sources
Population- housing- households- persons- cars
Dynamic trafficassignment- network flows
Impacts- air quality,- traffic noiseat work
Impacts- air quality- traffic noise
at housing
Accessibility- of jobs- of shops- of population- of facilities
Land use
Transport
ILU
MA
SSm
odel
ILUMASS model
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Dortmundmodel
Forecast ofSASI model
Microsimulation
ReducedILUMASSmodel
Forecast ofSASI model
Microsimulation
23 24
Modules
Travel
Environmentalimpacts
Firms
Households
Dwellings
Moves
Firms
Households
Dwellings
Moves
Firms
Households
Dwellings
Moves
Travel
Environmentalimpacts
Firms
Households
Dwellings
Moves
t=0 t=1 t=2 t=3
Start
meso
micro
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Firms and householdsStart
End
Anotherhousehold?
Yes
No
Select a household
Demographic events- Ageing- Death- Birth- Marriage/cohabitation- Divorce/separation- Persons leave household- Persons move together
Economic events- Education- Place of work- Employment status- Income- Mobility budget of household
Update lists- Persons- Households- Dwellings- Firms
Start
End
Anotherfirm?
Yes
No
Select a firm
Select alternative location
Accept?No No
1-10 11
Yes
Update lists- Firms- Nonresidential floorspace- Employed persons
Check satisfaction with presentlocation
Satisfied?
No
Yes
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Dwellings
Anotherproject?
EndNo
Update lists- Persons- Households- Dwellings- Construction
Yes
Select zone andmicrolocation
Select type and num-ber of dwellings
Upgrading
Select zone andmicrolocation
Select type and num-ber of dwellings
Demolition and result-ing moves
Select zone andmicrolocation
Select type and num-ber of dwellings
Construction
Start
Expected demand- Demolition- Upgrading- Construction
Select project- Demolition- Upgrading- Construction
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MovesStart
End
Anotherdwelling?
Yes
No
Select dwelling- Area- Type
Select household- Immigration- New household- Forced move- Move
Accept?No No
Update lists- Households:
- with dwelling- without dwelling
- Dwellings- occupied- vacant
1-5
Housing supply
Yes
Start
End
Anotherhousehold?
Yes
No
Select household- Outmigration- Immigration- New household- Move
Select dwelling- Area- Type
Accept?No No1-5 6
Yes
Housing demand
Update lists- Households:
- with dwelling- without dwelling
- Dwellings- occupied- vacant
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Environmental Impacts
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ReducedILUMASSmodel
Forecast ofSASI model
Microsimulation
ReducedILUMASSmodelEnvironmentalimpacts
Forecast ofSASI model
Microsimulation
Environmental impacts
29 30Air quality
31Traffic noise
32Change in traffic noise 2001-2021
33Change in traffic noise relative to reference scenario in 2021
34Quality of open space
35Access to open space
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Typical Model Run
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Model dimensions
1.2 million households2.6 million persons1.2 million dwellings
80,000 firms92,000 industrial sites
8,400 public transport links848 public transport lines
13,000 road links
246/54 internal/external zones209,000 raster cells
30 simulation periods (years)
90 minutes computing time
Con
clus
ions
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Model parametersMicro dataMicro data
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Travel flowsPublic transportflowsTravel flowsTravel flowsTravel flows
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Link loadsLink loadsLink loads
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Link loadsLink loadsLink loadsPublic transportspeed
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Link loadsLink loadsLink loadsPublic transportspeed
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Air qualityNO2
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Traffic noisedB(A)
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FirmsFirmsFirmsFirmsFirmsFirms
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HouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholdsHouseholds
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HouseholdsDwellingsHouseholdsDwellingsHouseholdsDwellingsHouseholdsDwellings
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Vacant dwellingsHouseholdsMovesMovesMovesMovesMoves
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Aggregationto zonesMicro dataMicro dataSimulationcompleted
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Conclusions
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Microsimulation
New activity-based microsimulation models improveurban simulation models:
- Lifestyles can be represented, i.e. households andindividuals can be disaggregated to the agent level.
- Environmental impacts and feedback can be modelledwith the required spatial resolution.
- Population and employment can be represented by theirdecision making units, i.e. households and firms.
- Microlocations can be represented. Households affectedby environmental impacts can be localised.
PR
OP
OLI
S&
ILU
MA
SS
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However ...
To date, no full-scale microsimulation model of urban landuse, transport and environment has become operational.There are still unresolved problems regarding the inter-faces between the submodels.The feedback between transport and environmental qualityand location has not yet been implemented.Serious problems of calibration, instability and randomfluctuations have not yet been solved.The computing time for existing models is calculated interms of weeks or days, not hours.
Con
clus
ions
53
Limits of microsimulation
There are ultimate limits to increasing the substantive,spatial and temporal resolution of behavioural models:
- There are theoretical limits when the number of pro-cesses simulated is too small to yield reliable results.
- There are empirical limits when the marginal costs ofobtaining micro data are larger than their added value.
- There are practical limits when the computing time of themodels exceeds the duration of the modelled processes.
- There are ethical limits to the collection of data aboutprivate lives for purposes of research.
Con
clus
ions
54
How much micro is enough?
There seems to be little consideration of the benefits andcosts of microsimulation:- Where is microsimulation really needed?- What is the price for microsimulation?- Would a more aggregate model do?
For spatial planning models, the answer to these questionsdepends on the planning task at hand.
Con
clus
ions
55
Conclusion
These considerations lead to a reassessment of the hypo-thesis that in the future all spatial modelling will be micro-scopic and agent-based.
Under constraints of data collection and of computingtime, there is for each planning problem an optimum levelof conceptual, spatial and temporal resolution.
This suggests to work towards a theory of balanced multi-level models which are as complex as necessary for theplanning task at hand and – to quote Albert Einstein – "assimple as possible but no simpler".
Con
clus
ions
56
More informationMoeckel, R., Schwarze, B., Spiekermann, K., Wegener, M. (2007):Simulating interactions between land use, transport and environment.Proceedings of the 11th World Conference on Transport Research.Berkeley, CA: University of California at Berkeley.
Wagner, P., Wegener, M. (2007): Urban land use, transport andenvironmental models: Experiences with an integrated microscopicapproach. disP 43(170):45–56.
More information on the development of the ILUMASS model can befound at http://www.spiekermann-wegener.de/mod/ilumassmod_e.htm.
Author:Prof. Dr.-Ing. Michael Wegener, Spiekermann & Wegener, Urban andRegional Research, Lindemannstrasse 10, 44137 Dortmund, Germany.mw@spiekermann-wegener.de. http://www.spiekermann-wegener.de.
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