landusemodel cusim m.ppt · land use and economic policies model. land use modeling frameworks from...
TRANSCRIPT
CUSIM MCUSIM-M:Competing-destinations Urban Spatial
I t ti M d l i MATLAB Interaction Model in MATLAB
A Forecasting Tool for Spatial Distribution– A Forecasting Tool for Spatial Distribution of Employment and Population –
David Jung-Hwi LEE, Senior Regional Planner
George Washington Regional CommissionOFredericksburg Area MPO
GWRC/FAMPO
DC
RichmondRichmond
Virginia: 21 Planning District CommissionsVirginia: 21 Planning District CommissionsPD16 GWRC PDC vs. FAMPO (Mid/small size MPO)
Stafford, Spotsylvania, King George, Caroline, and Fredericksburg City
9I-95
Population: 309,763 (2006)
Employment: 113,501 (2006) Employment: 113,501 (2006)
TAZ: 802 zones
2000 GWRC/FAMPO Work Trip Patterns
Work T i
Work T iTrip
67148 59.1% To FAMPO Workplace
46471 40.9% To outside Workplace
113619 100.0% From FAMPO Residence
Trip
67148 82.3% From FAMPO Residence
14477 17.7% From outside Residence
81625 100.0% To FAMPO Workplace
2000 GWRC/FAMPO Work Trip Patterns (Intra Only)
Integration of
Trip Generation Trip DistributionCapacity-
Constrained TripAssignment
Mode-SplitAnalysisLand Use
and Transportation
Future (Design)Network
Specification
Evaluation ofCharacteristics of
Loaded
TransportationModel
Transportation
Transport Services (Congested)Future Network
Integration:Forecasting Data
Integration:Initial Data Integration:
Forecasting Data
Specification(Characteristics) of Future
Transportation Systems andFacilities
INTEGRATION: Policy Feedbacks
INTEGRATION: Evaluation Feedbacks
Facilities
Description ofBase Year Spatial
Distributions ofActivities
Forecasts ofSpatial
Distributions of
Land Use (Residence andEmployment Location)Forecasting Procedure
Local / Regional Review andEvaluation of Land Use and LocationPatterns and Policy Consequences
Region-wideForecasts of
Population andEconomy
ActivitiesForecasting Procedure Patterns and Policy Consequences
Land Use
Descriptions of ProposedLand Use and Economic
Policies
Use Model
Land Use Modeling Frameworks From Waddell 2005
LOWRY:Gravity Model
Leontieff:Input-Output Model
Alonso/Mills/Muth:Urban EconomicBid-Rent Theory
Orcutt:Microsimulation
1960
Spatial InteractionLILTDRAM/EMPALHLFM II+
Spatial Input-OutputMEPLANTRANUS
McFadden:Discrete-Choice
1970
HLFM II+LUTRIM Models
EquilibriumC
DynamicC
Geographic f
1980Discrete Choice Land Use Models:METROSIMMUSSAM t
Discrete Choice Land Use Models:NBER/HUDSMASTERIRPUD
Information Systems
Spatially Detailed1990Metroscope IRPUD
Spatially DetailedDynamic Land Use
Spatially DetailedRule-based Planning Tools:INDEXPlaces2000
Hybrid Spatial I/OPECAS/ODOT 2nd
Gen
Models:DELTAILUTEUrbanSim
PlacesUPlanCUFMWhatIf?
2000
• Land Use
Integration of Land Use and Transportation• Land Use
– Macro: Spatial Interaction Models• TELUM, Metropilus, ITLUP, DRAM/EMPAL/LANCON (S.H. Putman) •• CUSIMCUSIM--M / CUSIMM / CUSIM--C++ / CUSIMC++ / CUSIM--G (David JG (David J--H. LEE)H. LEE)
– Linear Programming Models• TOPAZ
– Input-Output– Input-Output• TRANUS (De La Barra)• MEPLAN (Echenique)• PECAS (J.D. Hunt)
– Micro: Economic Based Approaches• MUSSA (Francisco Martinez C.)• METROSIM (Alex Anas)• METROSCOPE (Portland Metro)• METROSCOPE (Portland Metro)• UrbanSIM (Paul Waddell)• DELTA (D. Simmonds)
• Transportation
– Four Step Modeling/ l d d l ( )/ l d d l ( )•• CUBE/Voyager Travel Demand Model (TDM)CUBE/Voyager Travel Demand Model (TDM)
• TransCAD Travel Demand Model (TDM)– Activity Based Approach
MODEL SEARCH :Aggregate s Disaggregate Zonal S stemAggregate vs. Disaggregate: Zonal System
• TAZ: small enough?
• Zonal Scale- Broadbent’s Rule- Sikdar (1982, 1984)
S l d H l (1996)- Steel and Holt (1996)- Norner and Murray (2002)- Gitlesen (2004)as disaggregate as possibleas disaggregate as possible
• GIS & Microsimulation: models the behavior of individuals
Di t d t DBMS- Dissagregate data; DBMS- Fotheringham & Wegener (2000)- Spiekermann & Wegener (1996)- Landis & Zhang Landis & Zhang
• GWRC data: Limited parcel layer availabilityG di TAZ l l
Vector grid zonal system example
Geocoding: TAZ level accuracy Thiessen Polygon
MODEL SEARCH :Aggregate or Disaggregate ?Fotheringham and O’Kelly (1989)
Aggregate or Disaggregate ?
Aggregate FlowsSpatial Interaction
Disaggregate FlowsIndividual DecisionsMore BehavioralGeo-psychologicalSpatial AwarenessSpatial Information Processing
Spatial Choice
Spatial Information Processing
MODEL SEARCH :S ti l i t ti d li (SIM)
(1) spatial interaction as social physics
Spatial interaction modeling (SIM)
Newtonian gravity modelLowry model
(2) spatial interaction as statistical mechanics
Extensions of Lowry model
Wilson’s entropy maximizing procedurefamily of spatial interaction modelsy pAlonso’s (1973; 1978) framework Additive Version of Family of SIM by Tobler
(3) spatial interaction as aspatial information processing
(4) spatial interaction as spatial information processingg
Fotheringham 2000
MODEL SEARCH :S ti l i t ti d li (SIM)
(1) spatial interaction as social physics
Spatial interaction modeling (SIM)
( ) p p y
(2) spatial interaction as statistical mechanics
(3) ti l i t ti ti l i f ti i(3) spatial interaction as aspatial information processing
discrete choice models by McFadden LOGIT f l ti (1974 1978 1980)LOGIT formulation (1974; 1978; 1980)Two undesirable properties - independence from irrelevant alternatives (IIA)
(4) spatial interaction as spatial information processing
- regularity
Nested LogitCompeting Destinations modelNo more IIA and RegularityNo more IIA and Regularity
Fotheringham 2000
MODEL SEARCH :Nested Logit s Competing Destinations
hierarchical processing
Nested Logit vs. Competing Destinations
search strategy
Nested Logit Competing Destinationsp g
assumes that the modeller has knowledge to individual choice
considers the likelihood of an alternative being o dg o d dua o
setsof an alternative being in the true choice set
Thi lik lih d i di t This likelihood is according to the similarity, or position, of that alternative relative to the other alternatives
Pellegrini and Fotheringham (1999)
to the other alternatives.
and Fotheringham (1999)
OPERATIONALIZATION[C++: CUSIM C++ 2005 10 Model][C++: CUSIM-C++-2005-10 Model]
OPERATIONALIZATION[MATLAB CUSIM M 2007 09 M d l][MATLAB: CUSIM-M-2007-09 Model]
Better Capability for Matrix Calculation
Faster Model Run TimeFaster Model Run Time
Simple Modular Programming
OPERATIONALIZATION[GAMS CUSIM G 2007 09 M d l][GAMS: CUSIM-G-2007-09 Model]
Good Optimization Tool
Various Solvers
Good Optimization Tool
COMPONENT :Economic Economic Base TheoryTheory
COMPONENT :Deterrence Function [Travel Time Matrix] : [ ]From FAMPO Travel Demand Model
Network
4-Step Model
Visualized Travel Time Matrix
Travel Time Matrix
INPUT :Control TotalsControl Totals
2000 2005 2006 2010 2015 2020 2025 2030 2035
BAS 51,199 68,417 73,042 82,891 94,658 107,137 119,616 131,766 143,917
NBAS 33,414 32,566 37,742 42,831 48,911 55,359 61,808 68,086 74,364
Virginia Employment Commission
Virginia’s Electronic Labor Market Access
EMP 84,613 100,983 110,784 125,722 143,569 162,496 181,424 199,852 218,281
POP 243 505 301 355 309 483 351 213 401 070 453 945 506 819 558 302 609 784
US Census Bureau Estimates Annual Population Data
State Demographer Projections Population Data
Activity Rate a=TTP/TTE2004 2005 2006
a 2 878 2 984 2 794
POP 243,505 301,355 309,483 351,213 401,070 453,945 506,819 558,302 609,784 Population Data
Activity Rate a TTP/TTE
Population Serving Ratio b=TSE/TTPa 2.878 2.984 2.794
b 0.137 0.108 0.122
INPUT :Control Totals (Pop lation)Control Totals (Population)
INPUT :Basic s Non Basic (Location Q otients)
• e = employment in some
Basic vs Non-Basic (Location Quotients)
• eir = employment in some industry (i) in some region (r)
• Σeir = total employment in the region
eir
ei∑ region eir∑Ei
• Ei = national employment in some industry (i)
i
Ei∑• ΣEi = total national employmenti p y
INPUT :Basic s Non Basic (Location Q otients)Basic vs Non-Basic (Location Quotients)
• Location quotient = 1 – Local production can just satisfy local consumption.
• Location quotient > 1– Local production can satisfy local consumption, and p y p ,
the excess is exported. This is a “basic” industry.• Location quotient < 1q
– Local production can not satisfy local consumption, and the difference must be imported.
INPUT :Basic s Non Basic (Location Q otients)Basic vs Non-Basic (Location Quotients)
• Not all of a basic industry is “basic.”• Only that part of the industry that servesOnly that part of the industry that serves
the export market is considered basic.It i th t t f th i d t th t i th• It is that part of the industry that raises the location quotient above 1.0
INPUT :Basic s Non Basic (Location Q otients)
Calculating Basic Employment Using Location Quotients
Basic vs Non-Basic (Location Quotients)
PD16 United States
NAICS 236xx 2,050 1,721,661Total Private Sector Employment 105,167 131,640,225
Employment Ratio 019492 013078Employment Ratio .019492 .013078
Location Quotient = ( 019492 / 013078) = 1 49Location Quotient = (.019492 / .013078) = 1.49
Non-basic Employment = (.013078) * 105,167 = 1,375
Basic Employment = 2,050 – 1,375 = 675
INPUT :Zonal Basic Emplo ment (e ogeno s)Zonal Basic Employment (exogenous)
APPLICATIONS & FILE FORMAT
CUBE/Voyager: TDM MATRIX: ** . MAT
MATLAB: CUSIM-M ** . CSV
TransCAD MATRIX: ** . CSV ** . DBF
GAMS: CUSIM-G MATRIX: ** . DBF ** . GDX
** . DBF
Visualization: ArcGISCUBE/Vo ager
. DBF
Visualization: ArcGISCUBE/Voyager
CUSIM-M-2007-09 Model : Forecasting2035
Forecasting for Year 2009 2035 with “CUSIM M 2007 09 Model”Forecasting for Year 2009~2035 with CUSIM-M-2007-09 Model
Density C t i t Constraint
Density Constraint Wt_POP:
weighting factorA vector of the ratio of the zonal maximum constrained population A vector of the ratio of the zonal maximum constrained population over the zonal estimated population
Maximum PopulationCapacity
(Const_POP): 500
Maximum PopulationCapacityCapacity
(Const_POP): 2,000
Density Constraint
U Uneven capacity
ByJurisdiction
Maximum Maximum PopulationCapacity
(Const_POP): 12,00012,000
Density Constraint w/ Land Use
Current Land Use for George Washington Region, 2006
Density Constraint Zonal Specific Population Capacity Constraints
Zonal Population Capacity vs. Estimated Population
Hybrid Modeling Approach
MATLABMATLABSimulation (Descriptive) Model
characterizing the general operations and mechanics of land use changes and development patterns
Model
GAMS prescribing Optimization (Prescriptive) Model
p goptimal urban development patternswith minimal concerns associated
Land Use and Transportationin LRTP
Thank you very much!!Thank you very much!!
David J-H. Lee
GWRC/FAMPO540-373-2890 Ext.235 0 3 3 890 t [email protected]