1. Recent Developments in OPUS/UrbanSim Center for Urban Simulation and Policy Analysis University of Washington March 18, 2008

2. Current Projects at CUSPA Integration with Activity-based Modeling – EPA Uncertainty Analysis - NSF Graphical User Interface - Arizona MPOs GIS Integration - Arizona MPOs Real Estate Development - PSRC, Arizona Modeling Travel Behavior - NSF Hierarchical Dynamic Bayesian Networks Discrete Choice

3. Significant Changes Since 2006 Implementation of Open Platform for Urban Simulation Moved from Gridcells to Parcels and Buildings Businesses: SIC to NAICS, Matched to Buildings Business models (San Francisco) Household Synthesizer, Assignment to Buildings Land Price Model replaced by Real Estate Price Model Developer Model Completely Redesigned Workplace Choice model (individual accessibility) GIS Integration Graphical User Interface Estimation of Discrete Choice Models with Availability Constraints New Calibration Method using Bayesian Melding

5. Transition Models (Economic) Current Development Handling of home-based employment, as input to: Home-based job choice model Workplace choice model (non-home-based) Future Priorities Replace with macroeconomic/hybrid model Approaches: Input/Output, Hybrid I/O-Econometric, CGE Preference for dynamic, multi-region, potential for integration with microsimulation

6. Transition Models (Demographic) Future Priorities (now in preliminary planning) Household and person demographic change Aging, births, deaths, split household (child leaves, divorce), merge households Educational attainment Labor market Enter and leave labor market, occupation, part-time, self- employed Job change

7. Relocation Models Future Development Change to binary logit (from rate-based probability) Include push factors: current location characteristics Include pull factors: alternative location characteristics Link move and location choice (nested or correlated choices) Allow ‘buyers remorse’: back out of a move if options poor

8. Location Unit in Choice Models Gridcell Efficient for spatial queries Poor for behavioral interpretation – esp. reflecting real estate Parcel More behaviorally realistic Vary in size and shape Building Can be tied to any geography Zone or other Neighborhood Geography Potentially attractive as higher – level Estimation results have generally been poorer

9. Location Unit in Choice Models Gridcell unit of choice in most applications < 2007 Some concerns about behavioral realism Some concerns about measurement error Building is unit of choice in newest applications More behaviorally transparent and realistic Variables measured with less error Can incorporate meaningful typology of buildings Improved model fit Model system runs at least as fast as with gridcells

10. Location Choice Models Current Development Refine parcel (building) level choice model Incorporate capacity constraints in estimation (dePalma, Picard, Waddell, J Urban Econ) Future Priorities Incorporate multi-level approach Incorporate endogenous prices Incorporate heterogeneous preferences Incorporate tenure choice

11. Real Estate Price Model Current Development Robust regression Spatial correlation in errors Auction approach to joint price – winning bidder type (Ellickson, Lerman and Kern) Future priorities Bayesian formulation – joint price, location, auction, submarkets

12. Developer Model Approaches: Site looking for a use Landowner’s problem; highest and best use Land cover or land use transition models Early generation UrbanSim developer model Use looking for a site Developer’s problem; site selection Later version of UrbanSim developer model Hybrid approach current UrbanSim developer model (parcel-level)

13. Developer Model (Hybrid) Parcel – based Uses Development Templates Project Templates Building Templates Can express wide variety of projects  Infill building on single lots  Single-family subdivisions  Mixed use buildings and projects  High-rise development, big-box, strip centers, etc. Could incorporate street patterns… Generates proposals: Filters templates based on land use plan designation on parcel Proposals compete within and across parcels; financing

14. Developer Model Step 1: Select Parcel for Evaluation

15. Step 2: Based on Plan Type, Evaluate Development Constraints

16. Step 3: Constraints Identify Allowed Development Templates Development templates can be defined in a number of different ways, including using the known development projects as the basis, or general plans, or a statistical analysis of recent developments. PSRC project is using the latter approach.

17. Step 4: Select Development Proposals to Build A hedonic price model predicts the price of a post-development proposal on the parcel, and subtracts the predicted cost of site acquisition, and other costs such as site preparation, demolition of existing buildings, extension of infrastructure, financing, and other costs that can be incorporated. This is used to generate an estimate of the Return on Investment (ROI) for each proposal. Proposals are selected using a probability that is proportional to their expected ROI. When projects are accepted, they result in Development Projects with status set to ‘Active’. These projects are then added by the Building Construction Model.

18. Developer Model Current Development Testing and refinement of new algorithms/code Develop inventory of templates Develop visual selection approach for users to identify which kinds of development are consistent with plan Improve cost calculations: infrastructure, financing, fees Infill and redevelopment refinements Future priorities Parcel subdivision/consolidation Development risk (real options, e.g. Cunningham)

19. Workplace Choice Model Replace HBW Trip Distribution Model Move behavior to long-term choice (land use) Discrete choice models Home-based Job Choice Worker chooses to be a home-based job (binary) Aggregate # home-based jobs allocated to HB workers Workplace Choice Model Non-home based workers choose a vacant job Interaction of worker/household and job characteristics

20. Model System Calibration/Validation Approaches Cross-sectional (as in TRANUS, Dram/Empal, Travel Models) K-Factors internalize errors to match base year Longitudinal calibration/validation (preferred) 1 period to calibrate 2 nd period to validate Bayesian melding approach (Sevcikova, Raftery, Waddell)  Will the 90% confidence band on predictions really cover approximately 90% of the observations? Sensitivity Analysis

21. Bayesian Melding for Calibrating Uncertainty in Model System Method# Cases Missed by 90% Confidence Interval Percent Covered Cases Bayesian Melding 3188% Multiple Runs 16338% Results from Eugene-springfield - in Transportation Research B, 2007

22. Open Platform for Urban Simulation OPUS

23. Implementation of OPUS Core implemented in Python Numpy for Fast Array Processing Modular Model Configuration Choice Models Regression Models Integrated Model Estimation Bayesian Melding for Model Calibration Full Implementation of UrbanSim - Generalized Geography GUI Under Development GIS Integration ArcGIS PostGIS/QGIS

24. Integrated Visualization Parcel Size Seattle Area

35. Storyboard: Models Once the database is ready to use in modeling, a user would: Open an existing model system configuration Or create one from an existing template Change configurations as desired Specify models – what variables to use Estimate parameters Create new models (e.g. submodels) Create interfaces to external models (travel models) Run and diagnose models

41. Storyboard: Indicators Can specify a set of indicators that should be computed As a separate, reusable configuration For a particular model run Requirements: Configure indicators and reports Types: maps, charts, tables Purposes:  For diagnosing the models  For evaluating outcomes Generate while model is running or after it has completed Generate a batch report, adding charts, maps, and tables into a document

43. Storyboard: Running the Model Managing runs of the model: Creating or editing a scenario Starting a run Monitoring progress Examining indicators while the model runs Pausing or stopping a run if needed Browsing through previously run results

46. Software Architecture for the GUI GUI is implemented in Python using the PyQt UI toolkit The basic chunk of information for configurations in the GUI is a “project” A project is represented for storage and sharing using xml Can be saved in a file, put in an svn repository, etc. Compatibility issue The existing model code uses Python dictionaries for configurations GUI uses an adaptor class XMLConfiguration that converts the xml to the old-style dictionary representation for use with estimation and simulation runs

47. Inheritance (the implementation of inheritance for xml projects is currently being changed -- this is the new design that should be implemented soon) A project can inherit from one or more parent projects Can override any parts of the xml; others inherited from the parents without change For programming language fans: this is prototype-based rather than class-based semantics User’s view in the GUI: Contents of parent, grandparent, … projects will be shown in the GUI, but greyed out (read-only) Can click on any node in the xml tree and ask that it be copied down to the child project; it can then be edited

48. Priorities for Development Meeting of MPOs in the US using UrbanSim – March 2008

49. Priorities from Meeting 1. Population dynamics/ HH transition 2. Base Data synthesis and evaluation tools - Household, Employment synthesizer - Including characteristics (occupation, wage, education, race/ethnicity) 3. Neighborhood dynamics/gentrifications - Relocation of households and jobs, change in neighborhood conditions

50. Priorities from Meeting 4. Calibration and validation tools, including back casting, Easy to use interface to new calibration tools (BM) 5. Multi-tier modeling (sub-markets)- demand/supply balance, internal feedback loops - with switches 6. Dedicated support - tech. support, training (boot camp), maintenance 7. Project templates for help start new project. Documentation-tutorials-user interface

51. Priorities from Meeting (Lower) Business Evolution Macroeconomic modeling (multi-region) Parallelization