Production Exchange Consumption Allocation System Modeling ...€¦ · Production Exchange Consumption Allocation System Modeling Review and Update Mike Alexander, ... parameters

PECASProduction Exchange Consumption Allocation System

Modeling Review and Update

Mike Alexander, AICP

Dr. JD Hunt, HBA Specto

Jim Skinner, AICP

Wei Wang, GISP

Q1: What (land use) model are COGs/MPOs

already implementing ?

• ARC is in process of implementing a PECAS model

(HBA Specto) for small-area forecast allocation of

control totals derived from a REMI model (with

Technical Advisory Group input).

• PECAS will replace the DRAM/EMPAL model used

since the early 1990s. A custom product

developed in tandem with PB, called the TAZ-

Disaggregator (TAZ-D) is being used as a ―bridge

model‖ while PECAS is under development .

Q2, 5: What stage of implementation is the ARC?

How is PECAS to be used?

• PECAS has two modules: Activity Allocation (AA) and Spatial

Development (SD) . We have run both modules, integrated

and for 20-counties (thus full model) uncalibrated through

time for a three year test period of 2005-2008. Current

status is 2nd and 3rd stage calibration of both modules, and

base data refinement

• The goal for development is, by end of 2010, an integrated

run through plan horizon year of 2040, with calibration of

parameters nearing completion, and best available base

data in place.

• Our modeling goal is to use PECAS as small-area allocation

model in conformity work for next RTP/RDP (est. 2014-15

delivery date), as well as more ongoing scenario work and

TIP modeling

Q3,4,6: What are the major issues of implementation and

how they are being overcome ?

• Data demands- , cost data, equation

parameters

– Building relationships (involving education) to collect the data from

constituents

– Close cooperation of other internal divisions (Land Use, Transportation)

in building impedance and zoning information

– Collaborative post-processing of in-house or public sector data

(Estimates, Census Data)

– Purchase of data from third-parties in private sector (Means, Epermits,

IMPLAN)

• Module calibration

– Automated procedures developed or being developed by consultants

• Output analysis (to inform calibration)

– Custom spreadsheets and charting

– CSV Data Loader

– Web Mapping and Analysis Interface

SD: Parcel Level Data County Parcels

Barrow 28,184

Bartow 42,167

Carroll 50,633

Cherokee 93,866

Clayton 88,723

Cobb 228,690

Coweta 55,348

DeKalb 230,888

Douglas 39,140

Fayette 42,808

Forsyth 77,639

Fulton 341,017

Gwinnett 260,371

Hall 77,103

Henry 72,839

Newton 44,374

Paulding 59,670

Rockdale 34,780

Spalding 29,616

Walton 36,561

Total 1,934,417

– 20-County parcel features

We tried to print it, but the machine kept crashing….

SD: Parcel Level DataWingap format tax data

So which table has the year a structure was built???

Zoning Clean-up Will It Ever End??

UHHH……. NO

AA—Transportation Cost Coefficients

Transport

Coefficients:

κHp,c

amount of

commodity

consumed per

visit

transport

money cost

coefficient

Household Obtained

Services $ / visit utils / $

Summary

calculated

inputs

calculated

inputs

Travel

Segment

Travel

Segment $ / $-util

Commodity Name

Commodity

UnitsSkim Name

Skim

Units

CG10RetailOutput $HBNW utils 0.02708 138.97 -0.5315000

CS13OtherServOutpu

t $HBNW utils 0.02569 146.48 -0.5315000

CS14HealthOutput $HBNW utils 0.00282 1,335.48 -0.5315000

CS15GSEdOutput $HBNW utils 0.01687 223.03 -0.5315000

CS16HiEdOutput $HBNW utils 0.01265 297.41 -0.5315000

AA-Price Estimations

AA-Trip Length CalibrationCommodity BuyingSelling Group

CG01AgMinDirection selling Work-Other

CG02AgMinOutput buying CV Heavy

CG03ConDirection selling Work-Other

CG04ConOutput buying CV Heavy

CG05MfgDirection selling Work-Other

CG06MfgOutput buying CV Med/Light

CS07TCUDirection selling Work-Other

CS08TCUOutput buying CV Med/Light

CS09WsOutput buying CV Med/Light

CS10RetailOutput buying HB Shop

CS11FIREOutput buying Work-Other

CS13OthServOutput buying HB Other

CS14HealthOutput buying HB Other

CS15GSEdOutput buying HB School

CS16HiEdOutput buying HB Univ

CS17GovOutput buying Work-Other

CL23WhiteCollar selling HB Work

CL24Services selling HB Work

CL25Health selling HB Work

CL26Retail selling HB Work

CL27BlueCollar selling HB Work

CL28Military selling HB Work

SD: Rents– Local effect coefficients

– Fulton County -Institutional spaces -Last sold price

exponential I (r2=0.195) INS (r2=0.199) O (r2=0.155) R (r2=0.153) RD (r2=0.194) RH (r2=0.298)

neg exponential B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile)

(Constant)2.44 0.595 4.098 1.08 0.397 2.719 1.403 0.101 13.854 3.525 0.183 19.29 2.775 0.044 63.602 3.009 0.047 63.522

Major Road-0.368 0.126 -2.913 0.5 -0.094 0.171 -0.547 0.5 -0.606 0.095 -6.4 0.5 -0.519 0.084 -6.158 0.5 -0.083 0.006 -14.56 0.5 0.056 0.01 5.58 0.5

Freeway1.504 0.335 4.483 0.25 -0.052 0.117 -0.442 3.00 -0.008 0.008 -1.037 3.00 -0.026 0.015 -1.736 3.00

Freeway Exit-0.984 0.571 -1.724 0.25 -0.525 0.424 -1.238 5 -0.431 0.176 -2.445 0.25 -0.282 0.041 -6.95 0.25 -0.027 0.033 -0.81 0.25

MARTA0.993 0.837 1.187 0.25 -0.428 0.214 -2.003 2 0.713 0.161 4.44 1 0.252 0.128 1.968 0.5 -0.116 0.011 -10.44 1 0.056 0.013 4.269 1

School0.045 0.013 3.454 0.25 0.063 0.025 2.532 0.25

Greenspace-0.788 0.236 -3.334 1.00 0.581 0.113 5.14 1.00 0.04 0.007 5.835 0.25 0.033 0.01 3.428 0.25

eAge0 0.003 -0.208 -0.003 0.002 -1.596 -0.005 0.001 -4.593 -0.001 0 -12.31 -0.005 0 -42.01

luz11.243 0.183 6.782 0.824 0.297 2.769 0.339 0.076 4.449 0.004 0.114 0.038 -0.716 0.014 -51.74

luz20.678 0.334 2.03 0.36 0.299 1.206 -0.161 0.122 -1.318 -0.012 0.007 -1.774 -0.665 0.015 -45.57

luz31.053 0.455 2.315 1.037 0.231 4.493 -0.571 0.089 -6.43 0.013 0.128 0.101 0.291 0.009 31.637 -0.589 0.01 -56.6

luz40.024 0.312 0.076 0.967 0.442 2.188 -0.667 0.109 -6.105 0.229 0.11 2.092 0.676 0.01 67.374 -0.122 0.009 -13.14

luz50.461 0.19 2.432 -0.002 0.144 -0.014 -0.735 0.104 -7.074 -0.301 0.009 -34.85 -0.792 0.013 -62.94

luz60.901 0.142 6.332 0.957 0.226 4.234 -0.494 0.155 -3.195 0.5 0.011 44.94

luz7-0.457 0.129 -3.549 0.013 0.231 0.055 -0.248 0.277 -0.898 -0.607 0.095 -6.376 -0.254 0.008 -30.52 -0.916 0.014 -65.66

luz80.299 0.358 0.836 1.278 0.535 2.388 -1.342 0.259 -5.174 -0.55 0.132 -4.162 0.862 0.326 2.646 0.004 0.019 0.212

luz9-0.27 0.126 -2.137 -0.406 0.364 -1.113 -0.76 0.1 -7.614 -0.234 0.009 -27.11 -0.84 0.014 -60.74

luz10-0.369 0.137 -2.69 0.186 0.453 0.41 0.209 0.748 0.28 -0.592 0.217 -2.732 -0.36 0.01 -37.84 -1.051 0.039 -26.74

luz11-0.518 0.159 -3.246 0.753 0.195 3.864 -0.957 0.274 -3.496 -0.588 0.097 -6.094 -0.297 0.009 -31.83 -0.902 0.018 -49.23

luz12-0.162 0.187 -0.866 -0.395 0.316 -1.252 0.017 0.241 0.07 -0.748 0.119 -6.295 -0.451 0.008 -57.56 -1.177 0.016 -72.29

SaleYear00-1.098 0.148 -7.419 -0.748 0.219 -3.421 0.858 0.107 8.042 -0.881 0.093 -9.488 -0.446 0.007 -64.27 -0.211 0.01 -20.2

SaleYear01-0.752 0.155 -4.862 -0.593 0.224 -2.649 0.803 0.111 7.238 -0.82 0.092 -8.89 -0.392 0.007 -55.54 -0.074 0.011 -6.908

SaleYear02-0.632 0.149 -4.227 -0.222 0.231 -0.962 0.771 0.12 6.423 -0.599 0.093 -6.413 -0.325 0.007 -47.23 -0.112 0.011 -10.33

SaleYear03-0.248 0.152 -1.64 -0.115 0.199 -0.577 1.035 0.114 9.077 -0.347 0.087 -3.972 -0.276 0.007 -40.97 -0.122 0.011 -11.51

SaleYear04-0.844 0.139 -6.07 -0.138 0.207 -0.667 0.802 0.097 8.301 -0.324 0.084 -3.855 -0.134 0.006 -20.94 -0.069 0.01 -6.663

SaleYear050.054 0.137 0.397 0.333 0.222 1.498 0.793 0.091 8.744 -0.108 0.085 -1.275 -0.041 0.006 -6.716 0.056 0.01 5.734

SaleYear06

SaleYear070.054 0.15 0.357 0.356 0.201 1.767 1.122 0.099 11.36 0.207 0.082 2.517 -0.074 0.006 -11.74 -0.023 0.01 -2.227

SaleYear08-0.24 0.376 -0.638 -0.134 0.359 -0.373

SD: New SD Database

– Migrated SD databases to PostgreSQL

– New database schema

– 20-County SD database are loaded– 2 million records in the parcel table

– 10 million records in the local effect table

– More pseudo parcels

– AND these numbers are for one year of data….

Web Interface /Query

SQL Table Views for Output instead of .CSVs

Open Source GIS link

Where It Has Taken Us…Today

–An integrated model run through time, with scenario evaluation •AA> SD> AA, 2005 through 2006

•A scenario of increased transportation costs

•AA running Region wide, but physical development only in Fulton County

– Maps of results to follow

•Sets the stage for further calibration (Stage 3)

–Capability for Full 20-county Run

Scenario Tests-AA Results





Comparison Labor Types by Travel Distance

White Collar Blue Collar

Travel Distance in Miles Travel Distance in Miles

Total Value of Labor

Scenario Tests-SD Results

Forecast Area (Superdistricts =building blocks)

END OF PRESENTATION

The LU Allocation Model (PECAS)

– PECAS (Production, Exchange and Consumption Allocation System)

•Developed by Drs Doug Hunt and John Abraham of University of Calgary

•Based on sound economic theory, incorporating I-O modeling approach; achieves equilibrium

•Two Modules, run Sequentially and Annually –

– Activity Allocation (AA) Module: equilibrium exchange and consumption prices are established by larger zone (LUZ)

– Space Development (SD) Module: based on pricing (rents) from AA and development costs, rational ―developer‖ makes decision or non-decision to develop space in given smaller zones (TAZ) until the market ‗clears‘

•Work Reviewed by the REMI/PECAS Technical Advisory Group (TAG)

PECAS:

Activity Allocation (AA)

$

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AA Concept

Activity Allocation (AA)

joint discrete utility

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121

ppNpnppp ,...,,..., 21

ppNpnppp ccccc ,...,,...,, 21Utility of exchanging and

shipping one unit of

Commodity between l and e

Quantity of commodity

produced or consumed under

production option p

Additional utility

associated with

production option p

Additional utility

associated with

location l for activity a

Stochastic

error terms

PECAS:

Space Development (SD)

more

the sa

me

no c

hange

mid

density

resid

entia

l

com

merc

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industria

l

dere

lict

quantity

zoning dictates set

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parcel-by-parcel

microsimulation

Space Development:

Simulation of Transitions

SD Module: joint discrete-continuous utility

qshjphjphjp lllTrjTRU

Rent less

amortized

construction

cost per unit

space

Additional Rent

less

development

costs per unit

land

Stochastic

error

terms

Space quantity

(building size) Land

quantity

(parcel size)

Nested logit structure

No change Demolish DerelictAdd spaceNew space type

QuantityQuantity

multi-level nested

discrete-continuous

logit

Design Diagram-Atlanta (Sorry about size)

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PRODUCING ACTIVITIES LAND / PARCEL ZONING

AI01AgMinMan f d agricultural or mining zoned land

AI02AgMinProd f very low density residential zoned land

AI03ConMan f d low density residential zoned land

AI04ConProd f d medium density residential zoned land

AI05MfgMan f high density residential zoned land

AI06MfgProd f high-rise residential

AI07TCUMan f a a mixed-use high rise residential

AI08TCUProd f d d majority use mixed use commercial

AI09Whole f d school zoned land

AI10Retail f institutional land

AI11FIRE f passive park zoned land

AI12PTSci active park zoned land (ballparks)

AI13ManServ f manufacturing (heavy) industrial zoned land

AI14PBSOff a d other (light) industrial zoned land

AI15PBSRet d office park industrial zoned land

AI16PSInd d office zoned land

AI17Religion d d low density commercial retail zoned land (office in retail)

AI18BSOnsite d d high density commercial retail zoned land (office in retail)

AI19PSOnsite d a transportation communications utilities zoned land

AI20FedGov f d military base land

AI21StLocGov f

AI22Military

AI23GSEdu f

AI24HiEdu f

AI25Health f

AA26FedGovAccounts

AA27StLocGovAccounts

AA28CapitalAccounts

AH29HHlt20_12 e e e e e e

AH30HHlt20_3p e e e e e e

AH31HH2050_12 e e e e e e

AH32HH2050_3p e e e e e e

AH33HH50100_12 e e e e e e

AH34HH50100_3p e e e e e e

AH35HHge100_12 e e e e e e

AH36HHge100_3p e e e e e e

IMPORTS x x x x x x x x x x x x x x x x x x x x x x x x x x x

EXCHANGE LOCATIONS c p c p c p c p p p p p p p p p s s s s s c c c c c c

EXPORTS x x x x x x x x x x x x x x x x x x x x x x x

CONSUMING ACTIVITIES LEGEND

AI01AgMinMan f f f f f f e f = fixed make or use technical coefficient

AI02AgMinProd f f f f f f e e= exchange

AI03ConMan f f f f f f e x = export or import available

AI04ConProd f f f f f f c = exchange occurs at consumption zone

AI05MfgMan f f f f f f e p = exchange occurs at production zone

AI06MfgProd f f f f f f e d = space development possible

AI07TCUMan f f f f f f e g = time and distance for transport disutilities

AI08TCUProd f f f f f f e h = accessibility or related log sum for transport disutilities

AI09Whole f f f f f f e d = space development permitted

AI10Retail f f f f f f f f f f e a = space development allowed with special application

AI11FIRE f f f f f f e e at higher costs

AI12PTSci f f f f f f e

AI13ManServ f f f f f f e

AI14PBSOff f f f f f f e

AI15PBSRet f f f f f f e

AI16PSInd f f f f f f e

AI17Religion f f f f f f e

AI18BSOnsite f f f f f f

AI19PSOnsite f f f f f f

AI20FedGov f f f f f f e e

AI21StLocGov f f f f f f e e

AI22Military f f f f f f e

AI23GSEdu f f f f f f e

AI24HiEdu f f f f f f e

AI25Health f f f f f f e e

AA26FedGovAccounts f f f f f f

AA27StLocGovAccounts f f f f f f

AA28CapitalAccounts f f f f f f

AH29HHlt20_12 f e e

AH30HHlt20_3p f e e

AH31HH2050_12

AH32HH2050_3p

AH33HH50100_12

AH34HH50100_3p

AH35HHge100_12 f e e

AH36HHge100_3p f e e

TRANSPORT FLOWS

home-based work g g g g g g

home-based other g g g g g g

non-home-based g g g g g g g

light truck g g g g

medium truck g g g g

heavy truck g g g g

Figure 1: Diagram Summarizing the Structure of the Atlanta PECAS Model

Generalized Development Process

•Gather behavioral data

•Establish targets

•3-stage calibration

•Agile development

task 1 SD Development

01: Establish parcel-level space rent equations

02: Establish space transition costs calculation system

03: Establish space maintenance costs equations

04: Establish base-year parcel database

05: Establish base-year quantities of space by type in each zone

06: Establish all-year parcel-level inputs for calibration period

07: Identify space transition constants

task 2 Transport Utility Equation Establishment

08: Establish transport utility equations

task 3 Technology Option Point Development

09: Identify household technology option points:

10: Identify industrial technology option points:

11: Identify accounts categories technology option points:

task 4 AA Target Development

12: Develop labor production zonal-level targets:

13: Develop labor consumption zonal-level targets:

14: Develop labor spatial flow targets:

15: Develop commodity production zonal-level targets:

16: Develop commodity consumption zonal-level targets:

17: Develop commodity spatial flow targets:

18: Develop import and exports targets:

task 5 Import Export Calibration

19: Establish imports and exports functions equation parameters:

task 6 AA Inputs Development

20: Develop skim matrices from transport model:

21: Develop X-Vector Attribute values

task 7 Stage 2 Calibration

22: Establish buying and selling utility equation parameters

23: Establish size terms for imports and exports in external zones

24: Establish technology allocation utility equation parameters

25: Establish location allocation utility equation sensitivity parameters

26: Establish location allocation utility equation zonal constants

task 8 Stage 3 Calibration

27: Set-up semi-automated calibration process

PECAS General Task List

2008 Progress: AA Summary

– IMPLAN data = core of input data, along with PUMS and baseline employment data (NAICS)

– Initial integration with transport model achieved (skims)

–Unconstrained module (20-co) run in June ‗08, LUZ level

– Initial constrained module run in December 2008

IMPLAN Data

PUMS Data: Occ by Income

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

lt35 35-75 75plus

Retail

Blue Collar

White Collar

PUMS Data: Occ by Industry

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Education Gvmt/Military Industrial/Manuf. Medical Natl resource Office/Prof. Retail

Retail

Blue Collar

White Collar

Travel Demand Model Skims

2008 Progress: SD Summary

– Parcel spatial data and assessor data = core of input data

– Module (Fulton County) run in May 08, parcel level, with zoning

– Inconsistency between three data sources (assessor space, NAICS employment, LandPro acreage)>> reevaluation

– Used LandPro and NAICS employment to develop TAZ level targets for space (key: consistency across 20 counties)

– Then, disaggregated to parcel using Abraham Floorspace Synthesizer

– Initial synthesizer runs for 15 of the 20 counties

2009 Progress

– Focus of work: June-December 2009

– Model Integration

• AA>SD>AA RUN through time

• Transportation Model Output Incorporated and Used.

– Data Development

– Database Development

– 2nd stage Calibration (size terms, dispersion parameters, transition constants)

– Continued Consultant & Client Collaboration

• Gotomeetings semi-weekly

• Three workshops (March, September, November 2009)

• Gotomypc logins – Allow for maintenance of Directory Structure

– Facilitate coding

AA-Trip Length CalibrationCommodity BuyingSelling Group

CG01AgMinDirection selling Work-Other

CG02AgMinOutput buying CV Heavy

CG03ConDirection selling Work-Other

CG04ConOutput buying CV Heavy

CG05MfgDirection selling Work-Other

CG06MfgOutput buying CV Med/Light

CS07TCUDirection selling Work-Other

CS08TCUOutput buying CV Med/Light

CS09WsOutput buying CV Med/Light

CS10RetailOutput buying HB Shop

CS11FIREOutput buying Work-Other

CS13OthServOutput buying HB Other

CS14HealthOutput buying HB Other

CS15GSEdOutput buying HB School

CS16HiEdOutput buying HB Univ

CS17GovOutput buying Work-Other

CL23WhiteCollar selling HB Work

CL24Services selling HB Work

CL25Health selling HB Work

CL26Retail selling HB Work

CL27BlueCollar selling HB Work

CL28Military selling HB Work

AA—Transportation Cost Coefficients

Transport

Coefficients:

κHp,c

amount of

commodity

consumed per

visit

transport

money cost

coefficient

Household Obtained

Services $ / visit utils / $

Summary

calculated

inputs

calculated

inputs

Travel

Segment

Travel

Segment $ / $-util

Commodity Name

Commodity

UnitsSkim Name

Skim

Units

CG10RetailOutput $HBNW utils 0.02708 138.97 -0.5315000

CS13OtherServOutpu

t $HBNW utils 0.02569 146.48 -0.5315000

CS14HealthOutput $HBNW utils 0.00282 1,335.48 -0.5315000

CS15GSEdOutput $HBNW utils 0.01687 223.03 -0.5315000

CS16HiEdOutput $HBNW utils 0.01265 297.41 -0.5315000

AA-Price Estimations

SD: Space Development Module

– Disaggregate process to the parcel level

– Represent developers‘ action

– Connection with AA

– From AA: current year space price at LUZ level

– To AA: quantity of the spaces for next year AA

– Output

– Space is a commodity consumed by the activities in the AA model

– 8 PECAS space types (A/D/S/M/O/R/L/H)

– Rents are space prices

– Zoning rules

SD: Development Events

– Year-by-year step

– Possible development events– E0: no change

– En: new space type and quantity

– Er: alter or renovate

– Ed: derelict

– Two step process for each parcel– Selection of development events and update space type

– Update space amount

– Data needs– Epermits

– Parcel level data

– Rents

SD: Parcel Level Data

– For each parcel:– Area of the parcel

– Existing space type

– Existing space quantity (building floorspace)

– Structure year

– Zoning rules (allowable uses and density range)

– Cost and fees (associated with development of each permitted space type and quantity)

– Challenges (20 Counties: every dataset is different)

– Parcel features and ID

– Parcel attributes (building floorspace, space type…)

– Geocoded points for Clayton…

– Combine parcel with tax assessors’ data

– Updates

– 20-county parcels are cleaned and loaded– About 2 million parcels are cleaned

– Benefit other planning projects

SD: Parcel Level DataWingap format tax data

So which table has the year a structure was built???

SD: Parcel Level Data County Parcels

Barrow 28,184

Bartow 42,167

Carroll 50,633

Cherokee 93,866

Clayton 88,723

Cobb 228,690

Coweta 55,348

DeKalb 230,888

Douglas 39,140

Fayette 42,808

Forsyth 77,639

Fulton 341,017

Gwinnett 260,371

Hall 77,103

Henry 72,839

Newton 44,374

Paulding 59,670

Rockdale 34,780

Spalding 29,616

Walton 36,561

Total 1,934,417

– 20-County parcel features

We tried to print it, but the machine kept crashing….

SD: Derived Space

– Why do we need the derived space?– The quality of the parcel space data: very inconsistent

– The (in) consistency between employment and space

– Mixed use issues

– Derived using NAICS employment and Landpro

– New space totals at LUZ and disaggregate to TAZ

– Then, evaluation…

SD: Synthesizing to Parcels

– FloorSpace Synthesizer Tool

– Based on existing space type, quantity and zoning…

– Calibration


– Calibration tasks:

– Evolution of initial synthesized results

– Directing synthesized development to actual built-on parcels

– Directing the correct space type to the parcel

– Directing the synthesized built space to developed parcel in amount resembling actual quantities


–Synthesized results

SD: Rents

– Space prices are rents for the use of space

– Per unit of space per unit of time

– Rent equation:– Space price at LUZ level in AA (done by AA & SD integration)

– Local-level effects due to the density of development around the parcel

– Age of the structure

– Local Effects: distance from (or proximity to) local-level influences

– Expressway

– Interstate exit

– Major road

– School

– Marta

– Green space

SD: Rents– Local effect coefficients

– Fulton County -Institutional spaces -Last sold price

exponential I (r2=0.195) INS (r2=0.199) O (r2=0.155) R (r2=0.153) RD (r2=0.194) RH (r2=0.298)

neg exponential B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile) B

Std.

Error t-ratio

Max

(mile)

(Constant)2.44 0.595 4.098 1.08 0.397 2.719 1.403 0.101 13.854 3.525 0.183 19.29 2.775 0.044 63.602 3.009 0.047 63.522

Major Road-0.368 0.126 -2.913 0.5 -0.094 0.171 -0.547 0.5 -0.606 0.095 -6.4 0.5 -0.519 0.084 -6.158 0.5 -0.083 0.006 -14.56 0.5 0.056 0.01 5.58 0.5

Freeway1.504 0.335 4.483 0.25 -0.052 0.117 -0.442 3.00 -0.008 0.008 -1.037 3.00 -0.026 0.015 -1.736 3.00

Freeway Exit-0.984 0.571 -1.724 0.25 -0.525 0.424 -1.238 5 -0.431 0.176 -2.445 0.25 -0.282 0.041 -6.95 0.25 -0.027 0.033 -0.81 0.25

MARTA0.993 0.837 1.187 0.25 -0.428 0.214 -2.003 2 0.713 0.161 4.44 1 0.252 0.128 1.968 0.5 -0.116 0.011 -10.44 1 0.056 0.013 4.269 1

School0.045 0.013 3.454 0.25 0.063 0.025 2.532 0.25

GreenSpace-0.788 0.236 -3.334 1.00 0.581 0.113 5.14 1.00 0.04 0.007 5.835 0.25 0.033 0.01 3.428 0.25

eAge0 0.003 -0.208 -0.003 0.002 -1.596 -0.005 0.001 -4.593 -0.001 0 -12.31 -0.005 0 -42.01

luz11.243 0.183 6.782 0.824 0.297 2.769 0.339 0.076 4.449 0.004 0.114 0.038 -0.716 0.014 -51.74

luz20.678 0.334 2.03 0.36 0.299 1.206 -0.161 0.122 -1.318 -0.012 0.007 -1.774 -0.665 0.015 -45.57

luz31.053 0.455 2.315 1.037 0.231 4.493 -0.571 0.089 -6.43 0.013 0.128 0.101 0.291 0.009 31.637 -0.589 0.01 -56.6

luz40.024 0.312 0.076 0.967 0.442 2.188 -0.667 0.109 -6.105 0.229 0.11 2.092 0.676 0.01 67.374 -0.122 0.009 -13.14

luz50.461 0.19 2.432 -0.002 0.144 -0.014 -0.735 0.104 -7.074 -0.301 0.009 -34.85 -0.792 0.013 -62.94

luz60.901 0.142 6.332 0.957 0.226 4.234 -0.494 0.155 -3.195 0.5 0.011 44.94

luz7-0.457 0.129 -3.549 0.013 0.231 0.055 -0.248 0.277 -0.898 -0.607 0.095 -6.376 -0.254 0.008 -30.52 -0.916 0.014 -65.66

luz80.299 0.358 0.836 1.278 0.535 2.388 -1.342 0.259 -5.174 -0.55 0.132 -4.162 0.862 0.326 2.646 0.004 0.019 0.212

luz9-0.27 0.126 -2.137 -0.406 0.364 -1.113 -0.76 0.1 -7.614 -0.234 0.009 -27.11 -0.84 0.014 -60.74

luz10-0.369 0.137 -2.69 0.186 0.453 0.41 0.209 0.748 0.28 -0.592 0.217 -2.732 -0.36 0.01 -37.84 -1.051 0.039 -26.74

luz11-0.518 0.159 -3.246 0.753 0.195 3.864 -0.957 0.274 -3.496 -0.588 0.097 -6.094 -0.297 0.009 -31.83 -0.902 0.018 -49.23

luz12-0.162 0.187 -0.866 -0.395 0.316 -1.252 0.017 0.241 0.07 -0.748 0.119 -6.295 -0.451 0.008 -57.56 -1.177 0.016 -72.29

SaleYear00-1.098 0.148 -7.419 -0.748 0.219 -3.421 0.858 0.107 8.042 -0.881 0.093 -9.488 -0.446 0.007 -64.27 -0.211 0.01 -20.2

SaleYear01-0.752 0.155 -4.862 -0.593 0.224 -2.649 0.803 0.111 7.238 -0.82 0.092 -8.89 -0.392 0.007 -55.54 -0.074 0.011 -6.908

SaleYear02-0.632 0.149 -4.227 -0.222 0.231 -0.962 0.771 0.12 6.423 -0.599 0.093 -6.413 -0.325 0.007 -47.23 -0.112 0.011 -10.33

SaleYear03-0.248 0.152 -1.64 -0.115 0.199 -0.577 1.035 0.114 9.077 -0.347 0.087 -3.972 -0.276 0.007 -40.97 -0.122 0.011 -11.51

SaleYear04-0.844 0.139 -6.07 -0.138 0.207 -0.667 0.802 0.097 8.301 -0.324 0.084 -3.855 -0.134 0.006 -20.94 -0.069 0.01 -6.663

SaleYear050.054 0.137 0.397 0.333 0.222 1.498 0.793 0.091 8.744 -0.108 0.085 -1.275 -0.041 0.006 -6.716 0.056 0.01 5.734

SaleYear06

SaleYear070.054 0.15 0.357 0.356 0.201 1.767 1.122 0.099 11.36 0.207 0.082 2.517 -0.074 0.006 -11.74 -0.023 0.01 -2.227

SaleYear08-0.24 0.376 -0.638 -0.134 0.359 -0.373

SD: New SD Database

– Migrated SD databases to PostgreSQL

– New database schema

– 20-County SD database are loaded– 2 million records in the parcel table

– 10 million records in the local effect table

– More pseudo parcels

– AND these numbers are for one year of data….

Where It Has Taken Us…Today

–An integrated model run through time, with scenario evaluation •AA> SD> AA, 2005 through 2006

•A scenario of increased transportation costs

•AA running Region wide, but physical development only in Fulton County

– Maps of results to follow

•Sets the stage for further calibration (Stage 3)

–Capability for Full 20-county Run



















Labor Travel ChangeWhite Collar Travel Distance by Total Value of Labor

Travel Distance in Miles


Labor Travel ChangeBlue Collar Travel Distance by Total Value of Labor

Travel Distance in Miles


Comparison

White Collar Blue Collar

Travel Distance in Miles Travel Distance in Miles


Scenario Tests-SD Results

PECAS Task List--Progress

Task group 1 SD Development

01: Establish parcel-level space rent equations

02: Establish space transition costs calculation system

03: Establish space maintenance costs equations

04: Maintain base-year parcel database

05: Establish base-year quantities of space by type in each zone

06: Establish all-year parcel-level inputs for calibration period

07: Identify space transition constants

Task group 2 Transport Utility Equation Establishment

08: Establish transport utility equations

Task 3 Technology Option Point Development

09: Identify household technology option points:

10: Identify industrial technology option points:

11: Identify accounts categories technology option points:

Black= Annual Update Grey= Completed Pre-09 Blue= Completed in 2009 Green = In process/continual

PECAS Task List--ProgressBlack= Annual Update Grey= Completed pre-09 Blue= Complete in 2009 Green = In Process

Task group 4 AA Target Development

12: Develop labor production zonal-level targets:

13: Develop labor consumption zonal-level targets:

14: Develop labor spatial flow targets (CTPP)

15: Develop commodity production zonal-level targets:

16: Develop commodity consumption zonal-level targets:

17: Develop commodity spatial flow targets: (CFS)

18: Develop import and exports targets: (NEEDS REFINING)

Task group 5 Import Export Calibration

19: Establish imports and exports functions equation parameters:

Task group 6 AA Inputs Development

20: Develop skim matrices from transport model: (NEEDS INTG THR TIME)

21: Develop X-Vector Attribute values (e.g. schools, air quality)

Task group 7 Stage 2&3 Calibration

22: Establish buying and selling utility equation parameters

23: Establish size terms for imports and exports in external zones

24: Establish technology allocation utility equation parameters

25: Establish location allocation utility equation sensitivity parameters

26: Establish location allocation utility equation zonal constants

Task group 8 Stage 3 Calibration

27: Set-up semi-automated calibration process

PECAS Tasks-Document

What‘s Next?: 2010+ PECAS Work Plan

• Zoning Data Development

• Evaluation of Model Output over a thirty-

year planning horizon

– Sensitivity testing for the parameters developed

in Stage II (during 2009)

– Calibration of floorspace synthesizer, space

transition costs and constants, and dispersion

parameters as needed

• Limited testing with varied TDM networks

2010+ PECAS Work Plan

• Review/Analysis of New or Updated Data to

modify parameters as needed

– IMPLAN, CoStar, Means, ACS PUMS, Other

• Directory Structure Improvements/Rsync

– Refinement of coded scripts >> enhanced

integration of the AA and SD modules through

time (year by year), full integration of the ARC

transport model (ultimately, tour-based)

– GUI Interface work (possibly)/ GIS linkages

On the Shoulders of\In Tandem with…

• Portland and Oregon

• Baltimore

• Montgomery

• California

– San Diego (SANDAG)

– LA (SCAG)

– SF (ABAG)

– Statewide (CALTRANS)

• Others?

The Goals of 2010+ Work?…

• Use PECAS directly as ARC‘s small area

allocation model in the NEXT conformity

modeling process (next RTP/RDP)

• Use PECAS scenario modeling ability to

shape and inform policy development,

rather than just to test ‗existing‘, already

formulated policy

Current and Future Reference…

Documents

Production Exchange Consumption Allocation System Modeling ...€¦ · Production Exchange Consumption Allocation System Modeling Review and Update Mike Alexander, ... parameters