THE FUTURE OF FORECASTING AT METROPOLITAN COUNCIL. CTS Research Conference May 23, 2012

Transcription

1 THE FUTURE OF FORECASTING AT METROPOLITAN COUNCIL CTS Research Conference May 23, 2012

2 Metropolitan Council forecasts Regional planning agency and MPO for Twin Cities metropolitan area Operates regional transit system and regional wastewater system Forecasts of population, households and employment provide a reasonable basis and yardstick for planning

3 Previous forecast model Regional Population Cohort-component model Extrapolated migration from previous decade Regional Employment Applied labor participation rates, pegging employment to population forecasts Local allocation Historic trends Policy assumptions and land use capacity Expert judgment

4 Previous forecast model REGIONAL Jobs Households Population LOCAL trip generation Land use, current and planned??? accessibility Transportation System Demand distribution Mode choice Network assignment Previous model does not consider spatial interactions No feedback between transportation and land use dynamics 4 6/5/12

5 Identified need to conceptually represent urban economic dynamics REGIONAL Jobs Population Households LOCAL production & consumption development & occupancy REGIONAL Economy and labor market LOCAL Spatial interaction trip generation price signals accessibility Land and floorspace Social & environmental outcomes Acknowledgment: Modified from JD Hunt, et al. (2005) Transportation System Demand distribution Mode choice Network assignment 5 6/5/12

6 Goals for new forecast models Provide a platform for future scenario testing Growth assumptions scenarios Policy scenarios Coordinated modeling to reflect interactions of transportation networks with land use patterns Land economics and geographic science validity Achievable with existing timeline and resources

7 Model Workflow Hi REMI PI+ Regional Employment Regional Population Inter-regional Migration Cube Land Local Pop, HHs, Emp Travel Demand Model Accessibility Profamy Regional Households by Type (and validation of Regional Population)

8 Regional Economic Model: REMI REMI is a structural economic macro-simulation model Designed to represent the components and moving parts of the economic system Minnesota implementation of REMI is spatially summarized: outputs are regional totals, regional averages, or rates not local

9 REMI model conceptual sketch

10 Regional demographic model: ProFamy Extended cohort-component macro-simulation State transitions projected for cohorts, informed by demographic schedules for age-gender-race groups Takes population and migration from REMI converts to households by age, race and household size for Cube Land 10 6/5/12

11 How we will use Cube Land Take regional totals from REMI and Profamy Allocate totals to TAZs, cities Link to travel demand model Provide socioeconomic forecasts to travel demand model Use travel demand model accessibility measures to allocate growth Platform for testing local and regional land use policies

12 6/5/12 12

13 Cube Land A Bid Auction Model Economic real estate model Based on two primary ideas: Real estate occupied by firms & households willing to pay the most Developers seek to maximize profits when deciding what & where to build Subject to a wide range of scenarios (e.g. policies, constraints, subsidies) Graphics: Citilabs 6/5/12 13

14 Cube Land Location choices Household and Employment Types Real estate types Zonal Characteristics Surrounding land uses Zonal demographics Density, lot sizes Land use policies Planned, prohibited land uses Min/max densities 6/5/12 14

15 Cube Land Accessibility From Travel Demand Model Population accessible within 20 minutes by auto Retail/Nonretail employment accessible within 20 minutes by auto Employment accessible within 20 minutes by high frequency transit From GIS Percent of TAZ within ½-mile buffer of LRT station Number of high frequency transit stops per acres within TAZ 6/5/12 15

16 Challenges Although not as data-hungry as some approaches still requires much data Assembling data required combining multiple datasets Some inputs require data we have not traditionally not collected (e.g. rents, development costs) Land use policies must be translated into model inputs 6/5/12 16

17 Forecast model results depend on What system dynamics Example: real estate market and location decisions? responses to LRT service and other factors? What assumptions and constraints Example: allowed land uses? min/ max densities? And what data are in the model Issues: currency, completeness Metropolitan Council 17 June 5, 2012

18 QUESTIONS?