Transportation Planning and Transportation Planning and the Land Use Connectionthe Land Use Connection

Presented to the FDOT and MPOs

May 14, 2010

By Paul G. Van Buskirk, Ph. D. , AICP,PE

Copyright©2010 VanBuskirk,Ryffel and Associates, Inc.

Problems and Issues Problems and Issues Land Use ConnectionLand Use Connection

A research report for the Jacksonville, A research report for the Jacksonville, Florida Region entitled “Comparing Short Florida Region entitled “Comparing Short Term Traffic Projections With Traffic Term Traffic Projections With Traffic Counts-The JUATS 2015 Model” prepared Counts-The JUATS 2015 Model” prepared for FDOT (Florida Department of for FDOT (Florida Department of Transportation), concluded that having Transportation), concluded that having forecasted 2000 demographic and forecasted 2000 demographic and socioeconomic data from a 1990 baseline socioeconomic data from a 1990 baseline and then comparing the 2000 Census data and then comparing the 2000 Census data that:that:

Problems and IssuesProblems and IssuesLand Use Connection (con’t)Land Use Connection (con’t)

“ “ For a fast growing urban area in Florida For a fast growing urban area in Florida like Jacksonville, the transportation like Jacksonville, the transportation demand models in the past are likely to demand models in the past are likely to underestimate the overall number of trips underestimate the overall number of trips produced since socioeconomic, produced since socioeconomic, demographic and employment data may demographic and employment data may be underestimated…. The difference be underestimated…. The difference between the estimated and observed at between the estimated and observed at the zonal level were significant.”the zonal level were significant.”

Problems and IssuesProblems and IssuesLand Use Connection (con’t)Land Use Connection (con’t)

They found that those TAZs (Traffic They found that those TAZs (Traffic Analysis Zones) that were approaching Analysis Zones) that were approaching built-out were overestimated and those built-out were overestimated and those that were developing were underestimated that were developing were underestimated an average of a 25% variance for a 10-an average of a 25% variance for a 10-year projection. year projection.

Problems and IssuesProblems and IssuesLand Use Connection (con’t)Land Use Connection (con’t)

A A research report by the University of research report by the University of Washington’s Institute on public policy and Washington’s Institute on public policy and Management stated in its executive summary Management stated in its executive summary that while much has been written about the that while much has been written about the transportation/ land use relationship, the more transportation/ land use relationship, the more we learn about transportation and land use the we learn about transportation and land use the worse the traffic jams and the sprawl gets. worse the traffic jams and the sprawl gets. According to the study the major reason for this According to the study the major reason for this apparent negative learning curve is the way we apparent negative learning curve is the way we have separated authority for transportation at have separated authority for transportation at the state and regional level from the powers of the state and regional level from the powers of land use at the local level. land use at the local level.

Opportunities and SolutionsOpportunities and SolutionsLand Use ConnectionLand Use Connection

The Interactive Growth Model™ (IGM) The Interactive Growth Model™ (IGM) address these disconnects and solves address these disconnects and solves these issues that results in more accurate these issues that results in more accurate forecasted demographic and forecasted demographic and socioeconomic attributes at the zonal socioeconomic attributes at the zonal level and then for forecasting accurate trip level and then for forecasting accurate trip productions. productions.

Opportunities and SolutionsOpportunities and SolutionsLand Use Connection (con’t)Land Use Connection (con’t)

The IGM has several sub-models to The IGM has several sub-models to determine the forecasted demand for and determine the forecasted demand for and the apportionment of land uses by type the apportionment of land uses by type and intensity as well as its spatial and intensity as well as its spatial distribution to mitigate trip lengths and to distribution to mitigate trip lengths and to meet the demands of the characteristics of meet the demands of the characteristics of future populations over time. future populations over time.

Opportunities and SolutionsOpportunities and SolutionsLand Use Connection (con’t)Land Use Connection (con’t)

The IGM addresses the disconnect by The IGM addresses the disconnect by generating accurate demographic and generating accurate demographic and socioeconomic data at the local level. For socioeconomic data at the local level. For example, through an analysis of adopted future example, through an analysis of adopted future land use plans, the IGM can be used to test land use plans, the IGM can be used to test those plans to identify deficiencies in land uses those plans to identify deficiencies in land uses and lead to suggested adoptive amendments to and lead to suggested adoptive amendments to address those deficiencies and then generate address those deficiencies and then generate the data for the MPO traffic models for state the data for the MPO traffic models for state transportation authorities transportation authorities

THE POPULATION MODEL vs THE INTERACTIVE THE POPULATION MODEL vs THE INTERACTIVE GROWTH MODEL™GROWTH MODEL™

1. 1. THE POPULATION FORECAST MODEL THE POPULATION FORECAST MODEL PRODUCES THE GROSS, NON-SPATIAL BUILD-PRODUCES THE GROSS, NON-SPATIAL BUILD-OUT POPULATION OF A JURISDICTION OF ANY OUT POPULATION OF A JURISDICTION OF ANY SIZE IN 5 YEAR INCREMENTSSIZE IN 5 YEAR INCREMENTS

2.THE INTERACTIVE GROWTH MODEL™(IGM) BY 2.THE INTERACTIVE GROWTH MODEL™(IGM) BY COMPARISON, USES THE GROSS POPULATION COMPARISON, USES THE GROSS POPULATION FORECAST TO BUILD-OUT AS AN INPUT AND FORECAST TO BUILD-OUT AS AN INPUT AND DISTRIBUTES POPULATION SPATIALLY IN 5-DISTRIBUTES POPULATION SPATIALLY IN 5-YEAR INCREMENTS THOUGHOUT THE YEAR INCREMENTS THOUGHOUT THE JURISDICTION OR STUDY AREAJURISDICTION OR STUDY AREA

Accurate Population ForecastAccurate Population Forecast

Accurate forecast is a key parameterAccurate forecast is a key parameter

MethodsMethods•Cohort ComponentCohort Component•ExtrapolationExtrapolation•ExponentialExponential•Pearl-Reed (Sigmoid)Pearl-Reed (Sigmoid)

Historically, population forecasting in South Florida Historically, population forecasting in South Florida has under estimated growthhas under estimated growth

Forecast Results - Y(t)

395.00

100395.00

200395.00

300395.00

1970 1990 2010 2030 2050

BCRI tf.Innovate v4.03

st_pete_popPearl-Reed Min.SSE 95 % Conf Interval

R-Squared: 0.9382Sum-of-Squares Error: 628386968

Standard 'a' Error: 1.91813806

a = 1951.891b = 0.09620453L = 250000.00

Confedence Rangex y x y Upper Lower1900 1575 1900 1686.31108 2025.286 1403.751910 4127 1905 2716.67294 3260.047 2263.0361920 14237 1910 4365.53343 5231.727 3640.6191930 40425 1915 6986.87908 8355.498 5837.0231940 60812 1920 11111.0489 13243.5 9308.4611950 96738 1925 17494.357 20745.52 14719.991960 181298 1930 27128.4294 31924.16 22977.221970 216232 1935 41129.4324 47868.64 35173.911980 238647 1940 60398.1389 69247.86 52351.831990 238629 1945 85019.5673 95656.8 74990.492000 248232 1950 113661.082 125163.2 102349.4

1955 143555.616 154651.4 132152.61960 171426.632 181013.3 161161.61965 194805.798 202333.1 186463.11970 212740.555 218221 206503.41975 225578.197 229353.6 221199.11980 234318.7 236821.8 231377.51985 240068.721 241686.6 238151.51990 243766.413 244795 242540.81995 246109.662 246756.7 2453362000 247580.809 247985.2 247096.22005 248499.029 248750.7 2481972010 249070.041 249226.3 248882.42015 249424.328 249521.1 2493082020 249643.834 249703.8 249571.8

Observed Data Fitted Value

Forecast Results - Y(t)

1686.00

51686.00

101686.00

151686.00

201686.00

1900 1920 1940 1960 1980

Linear Transform of Forecast - Z(t)

-6.00

-4.00

-2.00

0.00

2.00

4.00

1900 1920 1940 1960 1980 2000

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

Year

Popula

tion

St Pete Tampa Miami

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

Year

Popula

tion

St Pete Tampa Miami

Population Growth of Three Major Florida CitiesPopulation Growth of Three Major Florida Cities

8 Key Design Steps For an 8 Key Design Steps For an Interactive Growth ModelInteractive Growth Model

1.1. Accurate Population ForecastAccurate Population Forecast2.2. Disaggregate Community Into Disaggregate Community Into

ZonesZones3.3. Current Inventory of Current Inventory of

Development and Demographics Development and Demographics by Zonesby Zones

4.4. Build out Inventory of Build out Inventory of Development and Forecast of Development and Forecast of Demographics by ZonesDemographics by Zones

8 Key Design Steps For an 8 Key Design Steps For an Interactive Growth ModelInteractive Growth Model

5.5. Develop Sub ModelsDevelop Sub Models6.6. Criteria and Formulas For Spatial Criteria and Formulas For Spatial

Distribution of Development Over TimeDistribution of Development Over Time7.7. Data Output For 5 Year IncrementsData Output For 5 Year Increments8.8. Graphic Interpretation of ResultsGraphic Interpretation of Results

Case Study Collier CountyCase Study Collier CountyProblemProblem

Western coastal area is developed and the Western coastal area is developed and the eastern area of 1,900 sq miles is undeveloped. eastern area of 1,900 sq miles is undeveloped. The problem was to accurately determine The problem was to accurately determine when and were development by type and when and were development by type and intensity would take place, the chacteristics of intensity would take place, the chacteristics of future populations, and the data for future populations, and the data for infrastructure planning and traffic forecastinginfrastructure planning and traffic forecasting

Case Study Collier CountyCase Study Collier CountySolutionSolution

1.1. Developed a accurate forecasting Developed a accurate forecasting modelmodel

2.2. Developed a accurate forecasting Developed a accurate forecasting model for when and were model for when and were development would occur, development would occur, demographics of future populations demographics of future populations and allocation of support land usesand allocation of support land uses

Zones are Aggregated by Zones are Aggregated by ClustersClusters

• Immokalee Urbanized Area• Rural Lands Stewardship Area

(RLSA)• Golden Gate Estates• Rural Fringe Mixed Use District• Urban Residential Fringe District• Urban Coast Fringe District• Rural Settlement Area• Industrial Area• Conservation Areas

TAZ Screen 2009 Existing TAZ Screen 2009 Existing DevelopmentDevelopment

mod 1 screen.docxmod 1 screen.docx

Future Land Use Plan and Future Land Use Plan and ElementsElements

C:\Users\paul\Desktop\DCA C:\Users\paul\Desktop\DCA rule\rule\

b1_5withTAZswhole051507.pb1_5withTAZswhole051507.pdfdf

Forecasting Future Forecasting Future Development by Districts and Development by Districts and

Sub-Districts Sub-Districts at Build-Outat Build-Out

Each parcel in each district and sub-district was queried by zones to determine development yield at build- out based on district or sub-district guidelines, rules and regulations according to the FLUE of the Growth Management Plan.

Example of Development Yield at Example of Development Yield at Build-out Build-out

RLSA From Historic Data RLSA From Historic Data

028,125Agriculture

5,62528,125Baseline

3,95018,8542,633Hamlets/CRD

29,40070,21210,29514 Villages

54,000128,96118,9006 Towns

11,00028,6595,027Ave Maria

Dwelling UnitsDwelling UnitsCredits Credits SpentSpent

AcresAcresDevelopment Development

TAZ Screen Build Out TAZ Screen Build Out DevelopmentDevelopment

mod2 screen.docxmod2 screen.docx

Demographics and Demographics and Economic ModelsEconomic Models

submodel factors taz.docxsubmodel factors taz.docx

TAZ Model ParametersTAZ Model Parameters

TAZconstants scenario.docxTAZconstants scenario.docx

Housing Unit Development Housing Unit Development Schedule 2009 to Build OutSchedule 2009 to Build Out

mod6devopmentsch.xmlmod6devopmentsch.xml

Demand ModelsDemand Models

mod8submodels.xmlmod8submodels.xml

School FacilitiesSchool Facilities

modF1schools.xmlmodF1schools.xml

Formulated the Build Out Scenario for Formulated the Build Out Scenario for the RLSA and RFMUDthe RLSA and RFMUD

CIGM originally estimated the location of future CIGM originally estimated the location of future towns and villages in the RLSAtowns and villages in the RLSA

Within a month the coalition of property owners Within a month the coalition of property owners after reviewing the CIGM data, provided to the after reviewing the CIGM data, provided to the county the 22 locations where they envision the county the 22 locations where they envision the towns to be locatedtowns to be located

This helped to recalibrate the CIGM and brought This helped to recalibrate the CIGM and brought clarity to where infrastructure development would clarity to where infrastructure development would be required as the RLSA built out as well to be required as the RLSA built out as well to generate ZDATA1 and ZDAT2 for transportation generate ZDATA1 and ZDAT2 for transportation planningplanning

District Nodes with Development DataDistrict Nodes with Development Data

C:\Users\paul\Desktop\DCA C:\Users\paul\Desktop\DCA rule\rule\

00075_EasternCollierLongRangeTransportation_200800075_EasternCollierLongRangeTransportation_20081212_v11_2208.pdf1212_v11_2208.pdf

Results for the RLSAResults for the RLSA•45,00 acres of compact land 45,00 acres of compact land forecasted to be developedforecasted to be developed•100,000 acres of flow ways, natural 100,000 acres of flow ways, natural habitat and water resources forecasted habitat and water resources forecasted to be preserved at no cost to the to be preserved at no cost to the countycounty•Forecasted build out population Forecasted build out population 210,632210,632

Population of the Study Population of the Study Area for the CIGMArea for the CIGM

22,539378

32,867

2,05221,732

79,568

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

Immokalee RLSA GGE RFMUD Other Total

2007

59,127

210,632

79,614

34,837

58,327

442,537

Build-Out

CIGM Results for TAZ Data CIGM Results for TAZ Data 20252025

Copy of Collier taz 2025.xlsCopy of Collier taz 2025.xls

Addition BenefitsAddition Benefits

CIGM Provides the land use CIGM Provides the land use modeling and data in the modeling and data in the

development of the Master development of the Master Mobility Plan (MMP)Mobility Plan (MMP)

SummarySummaryThe COLLIER experience does address The COLLIER experience does address the concerns of the FDOT’s Jacksonville the concerns of the FDOT’s Jacksonville and the University of Washington's and the University of Washington's research. The Model with Collier’s future research. The Model with Collier’s future land uses (FLUP) and its guidelines, land uses (FLUP) and its guidelines, forecasted future development over time forecasted future development over time which was integrated with demographic which was integrated with demographic and economic models to produce the and economic models to produce the demand for facilities for goods and demand for facilities for goods and services. The facilities were located to services. The facilities were located to reduce trip lengths and reduce carbon reduce trip lengths and reduce carbon emissions.emissions.

SummarySummaryThe results were a scientifically accurate The results were a scientifically accurate connection between future land use connection between future land use forecasting at the local government level and forecasting at the local government level and the transportation planning at the regional the transportation planning at the regional level.level.

The data does not result in a linear The data does not result in a linear extrapolation that over estimates extrapolation that over estimates demographic data as a TAZ approaches build demographic data as a TAZ approaches build out or underestimate data as TAZ’s are out or underestimate data as TAZ’s are undergoing expansion in their early states of undergoing expansion in their early states of development. development.

Case Study-City of Cape Coral Case Study-City of Cape Coral the Problemthe Problem

pre platted city, land use was 95% pre platted city, land use was 95% residential and little supporting land residential and little supporting land uses to meet needs of current and uses to meet needs of current and future populationfuture population long trip lengths-many traveled to long trip lengths-many traveled to Ft Myers for work and shopping.Ft Myers for work and shopping. underestimated population forecast underestimated population forecast and infrastructure falling behind and infrastructure falling behind

Case Study-City of Cape Case Study-City of Cape Coral Coral the Solutionthe Solution

Accurate population forecastingAccurate population forecastingAccurate forecasting of the timing Accurate forecasting of the timing and distribution of development and and distribution of development and supported land uses and their supported land uses and their facilities to reduce trip lengths, balance facilities to reduce trip lengths, balance the tax base and provide employment the tax base and provide employment opportunities.opportunities.

Cape Coral Future Land Use Cape Coral Future Land Use PlanPlan

FLU Map.pdfFLU Map.pdf

Neighborhood Shopping By

Year

Fire Stations

By Year & Location

Comp Plan Amendments Comp Plan Amendments Needs AnalysisNeeds Analysis

LU LU amendnorthcapeindustrilinstitlamendnorthcapeindustrilinstitl

esswetlands (2ind50).xlsxesswetlands (2ind50).xlsx

YearYear20202020

  Single FamilySingle Family

TAZTAZ UNITSUNITS

% NON- % NON- PERM & PERM &

VACANCYVACANCY % VACANT% VACANT OCCUPIEDOCCUPIED SEASONALSEASONAL VACANTVACANT POPPOP HH SIZEHH SIZE TOTAL AUTOTOTAL AUTO NO AUTONO AUTO ONE AUTOONE AUTO 2> AUTOS2> AUTOS

226226 296296 1515 99 269269 1717 2727 729729 2.712.71 269269 1010 106106 154154

237237 222222 1818 99 202202 2020 2020 414414 2.052.05 202202 77 7979 115115

242242 659659 1919 1010 593593 5959 6666 13111311 2.212.21 593593 1919 233233 339339

247247 204204 2525 1313 177177 2424 2727 398398 2.242.24 177177 66 7070 101101

587587 542542 00 00 542542 00 00 17561756 3.243.24 542542 2020 213213 309309

YearYear20202020 Multi-FamilyMulti-Family

TAZTAZ UNITSUNITS

% NON-% NON-PERM & PERM &

VACANCYVACANCY % VACANT% VACANT OCCUPIEDOCCUPIEDSEASONASEASONA

LL VACANTVACANT POPPOP HH SIZEHH SIZETOTAL TOTAL AUTOAUTO NO AUTONO AUTO ONE AUTOONE AUTO 2> AUTOS2> AUTOS

226226 290290 1818 1212 255255 1717 3535 479479 1.651.65 255255 0 0  100100 146146

237237 250250 2020 88 230230 3030 2020 520520 2.082.08 230230 0 0  9090 131131

242242 8080 3333 1818 6666 1212 1414 118118 1.471.47 6666 22 2626 3737

247247 197197 2020 1212 173173 1616 2424 380380 1.931.93 173173 0 0  6868 9999

587587 283283 2222 1212 249249 2828 3434 600600 2.122.12 249249 0 0  9898 142142

YearYear20202020 TAZ Socio-Economic DataTAZ Socio-Economic Data

TAZTAZINDUSTRIAL INDUSTRIAL EMPLOYEESEMPLOYEES

COMMERCIAL COMMERCIAL EMPLOYEESEMPLOYEES

SERVICE SERVICE EMPLOYEESEMPLOYEES

TOTALTOTALEMPLOYEESEMPLOYEES

SCHOOL SCHOOL ENROLLMENTENROLLMENT

HOTEL / MOTELHOTEL / MOTELUNITSUNITS

226226 00 109109 106106 215215 0 0  0 0 

237237 00 1010 1414 2424 00 0 0 

242242 00 2525 8080 105105 8686 0 0 

247247 00 7575 2525 100100   00 0 0 

587587 00 208208 170170 378378   00 0 0 

Questions????Questions????