1. Transportation Planning and the 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.

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

3. Problems and Issues Land Use Connection (con’t) “ For a fast growing urban area in Florida like Jacksonville, the transportation demand models in the past are likely to underestimate the overall number of trips produced since socioeconomic, demographic and employment data may be underestimated…. The difference between the estimated and observed at the zonal level were significant.”

4. Problems and Issues Land Use Connection (con’t) They found that those TAZs (Traffic Analysis Zones) that were approaching built-out were overestimated and those that were developing were underestimated an average of a 25% variance for a 10- year projection.

5. Problems and Issues Land Use Connection (con’t) A research report by the University of Washington’s Institute on public policy and Management stated in its executive summary that while much has been written about the transportation/ land use relationship, the more we learn about transportation and land use the worse the traffic jams and the sprawl gets. According to the study the major reason for this apparent negative learning curve is the way we have separated authority for transportation at the state and regional level from the powers of land use at the local level.

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

7. Opportunities and Solutions Land Use Connection (con’t) The IGM has several sub-models to determine the forecasted demand for and the apportionment of land uses by type and intensity as well as its spatial distribution to mitigate trip lengths and to meet the demands of the characteristics of future populations over time.

8. Opportunities and Solutions Land Use Connection (con’t) The IGM addresses the disconnect by generating accurate demographic and socioeconomic data at the local level. For example, through an analysis of adopted future land use plans, the IGM can be used to test those plans to identify deficiencies in land uses and lead to suggested adoptive amendments to address those deficiencies and then generate the data for the MPO traffic models for state transportation authorities

9. THE POPULATION MODEL vs THE INTERACTIVE GROWTH MODEL™ THE POPULATION MODEL vs THE INTERACTIVE GROWTH MODEL™ 1. THE POPULATION FORECAST MODEL PRODUCES THE GROSS, NON-SPATIAL BUILD- OUT POPULATION OF A JURISDICTION OF ANY SIZE IN 5 YEAR INCREMENTS 2.THE INTERACTIVE GROWTH MODEL™(IGM) BY COMPARISON, USES THE GROSS POPULATION FORECAST TO BUILD-OUT AS AN INPUT AND DISTRIBUTES POPULATION SPATIALLY IN 5- YEAR INCREMENTS THOUGHOUT THE JURISDICTION OR STUDY AREA

10. Accurate Population Forecast Accurate Population Forecast Accurate forecast is a key parameter Methods Cohort ComponentCohort Component ExtrapolationExtrapolation ExponentialExponential Pearl-Reed (Sigmoid)Pearl-Reed (Sigmoid) Historically, population forecasting in South Florida has under estimated growth

14. 8 Key Design Steps For an Interactive Growth Model 1.Accurate Population Forecast 2.Disaggregate Community Into Zones 3.Current Inventory of Development and Demographics by Zones 4.Build out Inventory of Development and Forecast of Demographics by Zones

15. 8 Key Design Steps For an Interactive Growth Model 5.Develop Sub Models 6.Criteria and Formulas For Spatial Distribution of Development Over Time 7.Data Output For 5 Year Increments 8.Graphic Interpretation of Results

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

17. Case Study Collier County Solution 1. Developed a accurate forecasting model 2. Developed a accurate forecasting model for when and were development would occur, demographics of future populations and allocation of support land uses

19. Zones are Aggregated by Clusters 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

20. TAZ Screen 2009 Existing Development mod 1 screen.docx mod 1 screen.docx

21. Future Land Use Plan and Elements C:\Users\paul\Desktop\DCA rule\b1_5withTAZswhole0515 07.pdf C:\Users\paul\Desktop\DCA rule\b1_5withTAZswhole0515 07.pdf

22. Forecasting Future Development by Districts and Sub-Districts at 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.

23. Example of Development Yield at Build-out 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 Units Credits Spent AcresDevelopment

24. TAZ Screen Build Out Development mod2 screen.docx mod2 screen.docx

25. Demographics and Economic Models Demographics and Economic Models submodel factors taz.docx submodel factors taz.docx

26. TAZ Model Parameters TAZconstants scenario.docx TAZconstants scenario.docx

27. Housing Unit Development Schedule 2009 to Build Out mod6devopmentsch.xml

28. Demand Models mod8submodels.xml

29. School Facilities modF1schools.xml

30. Formulated the Build Out Scenario for the RLSA and RFMUD CIGM originally estimated the location of future towns and villages in the RLSA Within a month the coalition of property owners after reviewing the CIGM data, provided to the county the 22 locations where they envision the towns to be located This helped to recalibrate the CIGM and brought clarity to where infrastructure development would be required as the RLSA built out as well to generate ZDATA1 and ZDAT2 for transportation planning

31. District Nodes with Development Data C:\Users\paul\Desktop\DCA rule\00075_EasternCollierLongRangeTransportation_2 0081212_v11_2208.pdf C:\Users\paul\Desktop\DCA rule\00075_EasternCollierLongRangeTransportation_2 0081212_v11_2208.pdf

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

33. Population of the Study Area for the CIGM 22,539 378 32,867 2,052 21,732 79,568 0 50000 100000 150000 200000 250000 300000 350000 400000 450000 ImmokaleeRLSAGGERFMUDOtherTotal 2007 59,127 210,632 79,614 34,837 58,327 442,537 Build-Out

34. CIGM Results for TAZ Data 2025 Copy of Collier taz 2025.xls Copy of Collier taz 2025.xls

35. Addition Benefits CIGM Provides the land use modeling and data in the development of the Master Mobility Plan (MMP)

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

37. Summary The results were a scientifically accurate connection between future land use forecasting at the local government level and the transportation planning at the regional level. The data does not result in a linear extrapolation that over estimates demographic data as a TAZ approaches build out or underestimate data as TAZ’s are undergoing expansion in their early states of development.

38. Case Study-City of Cape Coral the Problem  pre platted city, land use was 95% residential and little supporting land uses to meet needs of current and future population  long trip lengths-many traveled to Ft Myers for work and shopping.  underestimated population forecast and infrastructure falling behind

39. Case Study-City of Cape Coral the Solution  Accurate population forecasting  Accurate forecasting of the timing and distribution of development and supported land uses and their facilities to reduce trip lengths, balance the tax base and provide employment opportunities.

40. Cape Coral Future Land Use Plan FLU Map.pdf FLU Map.pdf

42. Neighborhood Shopping By Year

43. Fire Stations By Year & Location

44. Comp Plan Amendments Needs Analysis LU amendnorthcapeindustrilinstitl esswetlands (2ind50).xlsx LU amendnorthcapeindustrilinstitl esswetlands (2ind50).xlsx

45. Year2020 Single Family TAZUNITS % NON- PERM & VACANCY % VACANT OCCUPIEDSEASONALVACANTPOP HH SIZE TOTAL AUTO NO AUTO ONE AUTO 2> AUTOS 22629615926917277292.7126910106154 23722218920220204142.05202779115 2426591910593596613112.2159319233339 247204251317724273982.24177670101 587542005420017563.2454220213309Year2020Multi-FamilyTAZUNITS % NON- PERM & VACANCY % VACANT OCCUPIED SEASONA L VACANTPOP HH SIZE TOTAL AUTO NO AUTO ONE AUTO 2> AUTOS 226290181225517354791.652550 100146 23725020823030205202.082300 90131 2428033186612141181.476622637 247197201217316243801.931730 6899 587283221224928346002.122490 98142 Year2020 TAZ Socio-Economic Data TAZINDUSTRIALEMPLOYEES COMMERCIAL EMPLOYEES SERVICE EMPLOYEES TOTALEMPLOYEES SCHOOL ENROLLMENT HOTEL / MOTEL UNITS 22601091062150 0 237010142400 24202580105860 24707525100 00 5870208170378 00

46. Questions????