PSRC Travel Model Documentation (for Version 1.0) Updated for Congestion Relief Analysis. Final Report

Transcription

1 (for Version 1.0) Updated for Congestion Relief Analysis Final Report prepared for Washington State Department of Transportation and Puget Sound Regional Council prepared by Cambridge Systematics, Inc. with Mirai Associates RST International Mark Bradley Research revised by Puget Sound Regional Council

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3 Final Report PSRC Travel Model Documentation (for Version 1.0) Updated for Congestion Relief Analysis prepared for Washington State Department of Transportation and Puget Sound Regional Council prepared by Cambridge Systematics, Inc th Place SE Sammamish, Washington with Mirai Associates RST International Mark Bradley Research revised by Puget Sound Regional Council

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5 Table of Contents 1.0 Introduction Purpose of the Report Evolution of the Model First Generation Models (1961 to 1970) Second Generation Models (1970 to 1985) Third Generation Models (1985 to 1998) Next Generation Models (1999 and Beyond) Report Organization Modeling Process Modeling Steps Model Years Model Interactions Land Use and Economic Forecasting Models Travel Demand Forecasting Models Data Sources Economic Forecasting Model Model Assumptions Personal Income Employment and Population Land Use Forecasting Models Modifications to the DRAM/EMPAL Model System Lagged Structure Travel Impedance Employment Model (EMPAL) Modifications of EMPAL Household Model (DRAM) Modifications of DRAM Single-Family/Multifamily Share Model Residential Land Consumption Model Classification of Households and Employment Zonal Allocation Process Model Calibration Cambridge Systematics, Inc. i WSDOT and PSRC

6 Table of Contents, continued EMPAL Calibration DRAM Calibration Results Adjustment of Model Outputs Vehicle Availability Model Cross-Classification Models Regression Model Summary of Results Trip Generation Model Trip Purposes Trip Production Models Home-Based Work College Home-Based Shopping Home-Based Other Home-Based School Non-Home-Based Trips Trip Attraction Models Home-Based Work College Home-Based Shopping Home-Based Other Home-Based School Non-Home-Based Trips Group Quarters External Trips Trip Balancing Summary of Results Trip Distribution Model Trip Purposes Gravity Models Friction Factors Special Generators Implementation College Trips External Trips Summary of Results WSDOT and PSRC ii Cambridge Systematics, Inc.

7 8.0 Mode Choice Model Model Structure Value of Time Mode Choice Models by Purpose Home-Based Work Model Home-Based College (HBC) Mode Choice Model Home-Based Non-Work Mode Choice Model Home-Based School (HBS) Mode Choice Model Non-Home-Based Mode Choice Model Vanpool Model Approach Data Vanpool Travel Behavior Survey Inventory of Vanpool Demand Vanpool Model Development Vanpool Model Calibration/Validation Vanpool Model Integration Mode Choice Model Calibration Calibration of the Home-Based Work Model Calibration of the Home-Based College Model Calibration of the Home-Based School Model Calibration of the Home-Based Non-work Model Calibration of the Non-Home-Based Model Summary of Results Time-of-Day Model Time Periods Auto Access to Transit (Park-and-Ride) Time-of-Day Models for Transit, Non-Motorized and Through Travel Auto Time-of-Day Models Through Trips Summary of Results Truck Model Background and Overview Approach Overview Truck Types Cambridge Systematics, Inc. iii WSDOT and PSRC

8 Table of Contents, continued 10.3 Data Socioeconomic Data Truck Model Parameters Truck Trip Generation Special Generator Trips External Trips Trip Distribution Time of Day Truck Trip Assignment Multi-Class Assignments Passenger Car Equivalents Trip Assignment Model Time Periods Impedance Measures Highway Travel Time Travel Cost and Values of Time Transit Impedance Park-and-Ride (Auto Access to Transit) Distance Highway Assignment Classes Volume-Delay Functions Turn Penalties Transit Assignment Modes Transit Time Functions Summary of Results Model Validation Validation Data Sources PSRC Household Travel Survey Highway Traffic Counts Speeds and Travel Times Sound Transit Ridership Data Trip Behavior Validation Trip Generation Trip Distribution WSDOT and PSRC iv Cambridge Systematics, Inc.

9 Mode Choice Time-of-Day Model Assignment Validation Highway Assignment Volumes by Corridor Vehicle Miles Traveled by Facility Type, Volume Group, and Area Type Vehicle Miles Traveled by Time Period Travel Times by Time Period and Corridor Total Average Daily Volumes Transit Assignment Cross-Sound Travel Truck Travel Summary Cambridge Systematics, Inc. v WSDOT and PSRC

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11 List of Tables Table 2.1 Table 3.1 Table 3.2 Table 4.1 List of Data Sources Used in Land Use and Travel Demand Models Features of PSEF 2006 Model Sectoring Plan for PSEF 2006 Model EMPAL91 Calibration Results Table 4.2 DRAM91 Calibration Results Table 5.1 Table 5.2 Table 5.3 Table 6.1 Table 6.2 Utility Equations for Accessibility Effect on Vehicle Availability Area Reached for Each Accessibility Measure Households by Size, Workers, Income, and Vehicle Availability Categories in Home-Based Work Trip Productions Per Household College Trip Productions Per Household Table 6.3 Home-Based Shopping Trip Productions Per Household Table 6.4 Home-Based Other Trip Productions Per Household Table 6.5 Table 6.6 Table 6.7 Table 6.8 Table 6.9 Table 6.10 Table 6.11 Table 6.12 Table 6.13 Table 6.14 Table 6.15 Home-Based School Trip Productions Per Household Non-Home-Based Work Trip Ends Per Household Non-Home-Based Other Trip Ends Per Household Home-Based Work Trip Attractions Per Employee and Household Home-Based College Trip Attractions Per Full-time Equivalent Enrollment Home-Based Shopping Trip Attractions Per Employee Home-Based Other Trip Attractions Per Employee and Household Home-Based School Trip Attractions Per Employee and Household Non-Home-Based Work Trip Attractions Per Employee Non-Home-Based Other Trip Attractions Per Employee Non-Institutional Group Quarters Trip Rates (Per Person) Cambridge Systematics, Inc. vii WSDOT and PSRC

12 List of Tables, continued Table 6.16 Table 6.17 Table 6.18 Non-Institutional Group Quarters Trips by County Internal-External and External-Internal Trips by Purpose Trip Balancing by Purpose Table 6.19 Subarea Zones Used for Trip Balancing of School Trips Table 6.20 Person Trip Ends by Purpose Table 6.21 Trip Rates by Purpose Table 7.1 Coefficients Used to Estimate Friction Factors Table 7.2 Table 7.3 Table 7.4 Table 8.1 Table 8.2 Table 8.3 Special Generators Summary of Auto External Trips Summary of Trip Distribution Results Summary of Recommended Values of Time Household Classification System for Home-Based Work Mode Choice Model Home-Based Work Market Segmentation Parameters by Car- Worker Class and Income Table 8.4 Home-Based Work Mode Choice Model Parameters Table 8.5 Table 8.6 Table 8.7 Table 8.8 Home-Based College Mode Choice Model Parameters Household Classification System for the Home-Based Non- Work Mode Choice Home-Based Non-Work Market Segmentation Parameters by Car-Worker Class and Income Home-Based Non-Work Mode Choice Model Table 8.9 Home-Based School Mode Choice Model Table 8.10 Table 8.11 Home-Based School Vehicle Trip Factors Non-Home-Based Mode Choice Model Table 8.12 Summary of Vanpool Survey Prior Modes Table 8.13 Vanpool Inventory Data by County Table 8.14 Vanpool Users by County Table 8.15 Summary of Vanpool Model Results Table 8.16 Table 8.17 Summary of Vanpool Model Statistics Comparison of Mode Shares Between Vanpool Survey and Household Survey for King County WSDOT and PSRC viii Cambridge Systematics, Inc.

13 Table 8.18 Table 8.19 Distribution of Modes for PSRC Aggregate Vanpool Demand Records List of Person Trip Tables by Mode Choice Table 8.20 Regional Auto Occupancies (2000) Table 9.1 Time-of-Day Factors by Purpose and Direction and Mode Table 9.2 Auto Time-of-Day Model Time Periods Table 9.3 Table 9.4 Table 9.5 Home-Based Work Time-of-Day Choice Model Home-Based Shop Time-of-Day Choice Model Home-Based Other Time-of-Day Choice Model Table 9.6 Summary of Through Trips Table 9.7 Trips by Mode and Time Period Table 10.1 Table 10.2 Table 10.3 Table 10.4 Passenger and Truck Model Employment Categories Benchmarking and Adjustment Factors for Truck Model Employment Passenger and Truck Model Employment by Category Truck Trip Production Rates by Truck Type Table 10.5 Truck Trip Consumption Rates by Truck Type Table 10.6 Port Truck Trips Table 10.7 Table 10.8 Table 10.9 Warehouse and Distribution Center Truck Trips External Truck Trips by Industry Summary of 2000 External Trucks Table Average Truck Trip Lengths Table Truck Time-of-Day Factors Table 11.1 Table 11.2 Table 11.3 Table 11.4 Terminal Times (Minutes) Summary of Recommended Values of Time Transit Travel Time Parameters Volume-Delay Functions Table 11.5 Turn Penalty Functions Table 11.6 Transit-Time Functions Table 11.7 Summary of Highway Assignment Inputs and Outputs Table 11.8 Summary of Transit Assignment Inputs and Outputs Cambridge Systematics, Inc. ix WSDOT and PSRC

14 List of Tables, continued Table 12.1 Count Locations by Functional Class Table 12.2 Regional Travel Time Data Table 12.3 Trip Rates Per Household by Trip Purpose Table 12.4 Table 12.5 Average Trip Duration and Length by Purpose Intrazonal Trips and Travel Times by Trip Purpose Table 12.6 Mode Choice Calibration Target Matrix by Mode and Purpose Table 12.7 Overall Percent Mode Share Table 12.8 Table 12.9 Mode Share by Trip Purpose (Work and College Trips) Mode Share by Trip Purpose (Non-Work Trips) Table Distribution of Transit Trips by Trip Purpose Table Stage 1 Time-of-Day Choice Model Validation by Trip Purpose, Mode, and Direction Table Estimated and Observed Screenline Volumes Table Screenline Volumes Aggregated by County Table Average Daily Observed and Estimated VMT by Facility Type Table Average Daily Observed and Estimated VMT by Volume Group Table Average Daily Observed and Estimated VMT by Area Type Table Average Daily Observed and Estimated VMT by Time Period Table Comparison of AM Peak Travel Times by Corridor Table Comparison of Midday Travel Times by Corridor Table Estimated and Observed Transit Boardings by Operator Table Transit Observed and Estimated Screenline Volumes Table Cross-Sound Observed and Estimated Travel by Mode Table Estimated and Observed Truck Volumes by Functional Classification Table Regional Statistics Comparing Observed and Estimated Highway Volumes WSDOT and PSRC x Cambridge Systematics, Inc.

15 List of Figures Figure 2.1 Land Use and Travel Demand Forecasting Process Figure 5.1 Vehicle Availability Modeling Figure 5.2 Cross-Classification of Households from PUMS Data Figure 5.3 Impact of Accessibility Measures on Utility Equations Figure 6.1 Trip Generation Modeling Process Figure 7.1 Trip Distribution Modeling Process Figure 7.2 Friction Factors for Home-Based Work by Income Group Figure 7.3 Figure 8.1 Figure 8.2 Figure 9.1 Figure 9.2 Figure 9.3 Figure 9.4 Friction Factors for Non-Work Purposes Current Mode Choice Modeling Process Nesting Structure of the Home-Based Work Mode Choice Model Time-of-Day Modeling Process Person-Miles of Travel by Time Period and Trip Purpose Home to Work Distribution as a Function of A.M. Peak Work to Home Distribution as a Function of P.M. Peak Delay Figure 9.5 Revised Delay Values Figure 10.1 Friction Factors by Truck Type and Length Figure 11.1 Trip Assignment Modeling Process Figure 11.2 Example of Park-and-Ride Lot Choice for Auto Access to Transit Trips Figure 11.3 Time Period Factors for A.M. and P.M. Peak Periods Figure 11.4 Volume-Delay Functions for Roadways by Facility Type Figure 11.5 Volume-Delay Function for Ferries Figure 12.1 Screenlines for the 2000 PSRC Model Figure 12.2 Transit Screenlines from the Sound Transit Model Figure 12.3 Regional Map of PSRC Districts Figure 12.4 Percent of Home-Based Work Trip Productions within Each Super-District Cambridge Systematics, Inc. xi WSDOT and PSRC

16 List of Figures, continued Figure 12.5 Percent of Home-Based Work Trip Attractions within Each Super-District Figure 12.6 Home-Based Other Trip Productions to Each Super-District Figure 12.7 Home-Based Other Trip Attractions From Each Super-District Figure 12.8 Non-Home-Based Trip Productions to Each Super-District Figure 12.9 Non-Home-Based Trip Attractions From Each Super-District Figure Maximum Desirable Deviation in Screenline Volumes Figure Observed and Estimated Highway Volumes by Time Period and Screenline Figure Estimated and Observed Average Daily Traffic Cambridge Systematics, Inc. xii WSDOT and PSRC

17 1.0 Introduction This document is a complete report of the Puget Sound Regional Council s (PSRC) travel demand forecasting model in. The document combines work completed to update the PSRC travel forecasting model for the Congestion Relief Analysis Phase II project for the Washington State Department of Transportation (WSDOT) with previous work completed by Cambridge Systematics for the PSRC and the WSDOT, as follows: PSRC Documentation of the Current Models (June 2001); PSRC Current Model Validation (June 2001); PSRC New Model Documentation (June 2001); PSRC New Model Validation (June 2001); PSRC Travel Model Improvements (March 2003); PSRC Model Calibration and Validation (March 2003); and WSDOT Congestion Relief Analysis Model Improvement Methodologies (April 2005); and PSRC Model Users s Guide (January 2007). The primary objectives of the current round of model improvements to the PSRC model were to adapt the models for use in pricing studies, and to modify specific aspects of the models to improve overall credibility for use in transit and highway planning studies. This report documents the model parameters, structure, and results of the current PSRC travel demand forecasting model. It does not identify specific model improvements made for the Congestion Relief Analysis project separate from previous model improvements made for other studies. 1.1 PURPOSE OF THE REPORT The purpose of the PSRC Travel Model Documentation report is designed to provide planners and modelers within PSRC and the WSDOT, and those member agencies or consultants that use the PSRC model, a detailed description of the modeling process and of individual components. Each model component is described as follows: Overview of the modeling process; Description of model parameters and assumptions; and Summary of model results for Cambridge Systematics, Inc. 1-1 WSDOT and PSRC

18 This report refers to previous sources of information and to the other companion documents, where necessary, to provide background or further information. Data used in the modeling process, such as networks and zonal data, are described in the PSRC Travel Model User s Guide. 1.2 EVOLUTION OF THE MODEL Land use and travel modeling in the Central Puget Sound Region began in the 1960s with the Puget Sound Regional Transportation Study (PSRTS). This study was conducted by an independent governmental organization, which subsequently merged with the Puget Sound Governmental Conference. The conference was the predecessor agency to the Puget Sound Council of Governments (PSCOG), which in turn became the Puget Sound Regional Council (PSRC). The current land use and travel demand forecasting models at PSRC are a culmination of many years of model development and application. The following summarizes some of the earlier generations of modeling in Central Puget Sound. First Generation Models (1961 to 1970) In 1961, a significant amount of travel data was collected to support the development of travel models: Home-interview survey of travel behavior at 34,000 households; Roadside cordon survey of 105,000 vehicles; Vehicle and passenger ferry survey of 5,200 persons; and Truck-taxi survey of 8,800 vehicles. The travel model was developed using these data sources and application data on networks and traffic zones with the Bureau of Public Roads (BPR) software package. This initial model followed the four-step planning process for travel demand, but did not include a formal land use model. Land use forecasts were completed at a 60-district level and disaggregated to zones based on consensus. The model contained nine trip purposes. There were refinements in land use forecasts and mode choice models in the latter years of this period. Trip generation and distribution model updates were documented in a series of reports. 1,2 Second Generation Models (1970 to 1985) From 1970 through 1971, there was a smaller sample home-interview survey to collect data on travel behavior that was used to update the first generation 1 Puget Sound Governmental Conference, Trip Generation Update, Puget Sound Governmental Conference, Forecasting Travel Patterns, Cambridge Systematics, Inc. 1-2 WSDOT and PSRC

19 models. This survey was used in combination with the 1970 U.S. Census data and a separate employment survey. A land use model called EMPIRIC was added to the process in The land use allocation model was based on economic forecasts from the Economic Base Model, first developed in the 1960s. Trip generation and distribution models were updated 3,4, but mode choice models were not recalibrated due to the smaller sample size of the survey. From 1973 through 1974, the Urban Transportation Planning System (UTPS) was first applied to produce travel demand forecasts in the region. The updated models were validated to 1975 data, and highway impedances were fed back to the trip distribution model. In 1977 through 1982, the mode choice model was updated using an on-board transit survey and model coefficients from the Minneapolis-St. Paul model choice model. Trip generation and distribution models were updated again in 1982 using the 1980 U.S. Census data. In 1980, the original Synchronized Translator for Econometric Projections (STEP) model was developed to produce regional economic forecasts. In 1981, the Disaggregated Residential Allocation Model (DRAM) and the Employment Allocation Model (EMPAL) were imported from Kansas City, adapted and calibrated to Puget Sound data, and applied to forecasting. Both of these models were substantially restructured and re-estimated in In 1983, the inclusion of a composite travel cost measure for cross-sound ferry links allowed for the inclusion of Kitsap County into the regional modeling system. Third Generation Models (1985 to 1998) In the late 1980s, another series of data collection activities provided new data for travel model development. This included a series of six household travel surveys in 1985 through 1988 and on-board transit surveys in all four counties. Between 1989 and 1999, a series of panel surveys was conducted in eight waves to collect data on longitudinal travel behavior (all other household surveys conducted prior to this collected data on cross-sectional travel behavior). In 1990, the forecast and traffic analysis zones were redefined, along with the highway and transit networks, to provide more detail for land use and travel forecasts. In the early 1990s, the models were transferred to the EMME/2 software package and refined to take advantage of advanced computing capabilities. In 1994, the trip generation model was updated using the new household travel survey (1985 through 1988). 5 In 1995, a new vehicle availability model was 3 Puget Sound Council of Governments, Travel Demand Analysis Trip Generation Analysis 1976 Update, October Puget Sound Council of Governments, Travel Demand Analysis Calibration of the Trip Distribution Model and Model Validation Report, October DKS Associates, Inc., Travel Model Development and Refinement Trip Generation Final Report, prepared for the Puget Sound Regional Council, June Cambridge Systematics, Inc. 1-3 WSDOT and PSRC

20 developed to support mode choice models and future model enhancements using the 1990 U.S. Census data. 6 In 1997, the trip distribution and mode choice models were updated using the household travel survey 7, but these models were not adopted into the current regional modeling process until These models were validated and documented as part of the Model Documentation and Validation of the New Models (June 2001) reports. The new models, referred to as part of the Third Generation Models, were developed in the mid-1990s and have not yet been adopted into practice. The current models also were updated during the Third Generation Models and continued to be used for planning applications during this period. The new models were updated as part of this project during 2005 to support pricingrelated studies for the WSDOT and PSRC. The current and new models should not be confused with the Next Generation Models, which have not yet been developed. Next Generation Models (1999 and Beyond) In 1999, PSRC conducted another household travel survey of 6,000 households in the 4-county region to support future modeling needs and completed the 8 th wave of the transportation panel survey. Additional data on traffic counts also was collected to support the evaluation of screenlines in model validation. The 1999 survey was used to revise, update, calibrate and validate the most recent round of model improvements, the results of which are detailed throughout this report. In addition, there have been previous studies to identify a process to evaluate the data requirements and design framework for the next generation models. This model design process was documented in three separate reports developed by Cambridge Systematics and the University of Washington for the PSRC, as follows: Assessment of Model Requirements, June 2001; Review of Literature and Operational Models, June 2001; and Model Design Recommendations, June In 2006, PSRC conducted a household activity survey of 4,746 households in the 4-county region 8. There were three major component data collection activities: 6 KJS Associates, Inc. with Texas Transportation Institute, Vehicle Availability Model Final Report, prepared for the Puget Sound Regional Council, June DKS Associates, Inc., Travel Model Development and Refinement Phase Two: Trip Distribution and Mode Choice Final Report, prepared for the Puget Sound Regional Council, December Cambridge Systematics, with Mark Bradley, ECO Northwest, Morpace International, PSRC 2006 Household Activity Survey Analysis Report, prepared for the Puget Sound Regional Council and the Washington State Department of Transportation, April Cambridge Systematics, Inc. 1-4 WSDOT and PSRC

21 an activity and travel survey of a representative sample of households in the Puget Sound region to collect basic demographics, activities, and tour and travel characteristics; a GPS tracking of 220 households participating in the diary portion of the study; and a stated-preference (SP) survey of 916 adult (age 16+) respondents participating in the diary portion of the survey, whose revealed trips fit criteria of interest for possible public transit and highway toll alternatives. This survey will be used to support development of activity-based models and transit market models. 1.3 REPORT ORGANIZATION This report is organized to present an overview of the modeling process in Section 2.0, and each remaining section covers a specific model component, as follows: Section 3.0 Economic Forecasting Model; Section 4.0 Land Use Forecasting Model; Section 5.0 Vehicle Availability Model; Section 6.0 Passenger Trip Generation Model; Section 7.0 Passenger Trip Distribution Model; Section 8.0 Passenger Mode Choice Model; Section 9.0 Passenger Time-of-Day Model; Section 10.0 Truck Model; Section 11.0 Trip Assignment Model; and Section 12.0 Model Validation. The focus of these sections is to describe the models, including inputs and outputs to each model and model parameters and assumptions applied. The source of the model parameters and assumptions has been identified wherever possible. The description of data used in these models is contained primarily in the PSRC Model User s Guide. Cambridge Systematics, Inc. 1-5 WSDOT and PSRC

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23 2.0 Modeling Process An overview of the modeling process to apply land use and travel demand forecasting models in the central Puget Sound region (four counties) is presented in Figure 2.1. This figure highlights the level of detail for different model components (a single four-county region, 219 forecast analysis zones, and 938 traffic analysis zones). This figure also presents an overview of the input and output data provided for each step of the modeling process. The modeling steps and model interactions are described in further detail below. 2.1 MODELING STEPS There are eight modeling steps in the current land use and travel demand forecasting model: 1. Economic forecasting; 2. Land use forecasting; 3. Vehicle availability; 4. Trip generation; 5. Trip distribution; 6. Mode choice; 7. Time of day; and 8. Trip assignment. Each of these modeling steps is developed and applied to serve an individual purpose in the modeling process, and to provide outputs that are used by subsequent steps in the process, as well as to serve other planning applications. Cambridge Systematics, Inc. 2-1 WSDOT and PSRC

24 Figure 2.1 Land Use and Travel Demand Forecasting Process 4-County Region U.S. and Washington Economies Economic Forecasting Model Output, Jobs, and Personal Income by 30 Industrial Sectors Forecast Analysis Zones Prior Years Households and Employment Location Land Use Allocation Models Households by 4 Income Groups and Employment by 5 Categories Traffic Analysis Zones Zonal Data Vehicle Availability Model Trip Generation Model Households by Workers, Income, Household Size, and Vehicles Available Trips by 7 Purposes (HBW by 4 income groups) Highway Networks Trip Distribution Model Trip Tables by 7 Trip Purposes (HBW by 4 income groups) Additional Zonal and Cost Data Mode Choice Model Trip Tables by 5 Trip Purposes and 7 modes Transit Networks Time of Day Model Trip Tables for 5 Time Periods and 4 Purposes (HBW by 4 income groups Truck Model Trip Assignment Model Highway and Transit Volumes and Travel Times Legend: Input Files Models/Processes Data Output Files Cambridge Systematics, Inc. 2-2 WSDOT and PSRC

25 2.2 MODEL YEARS The current modeling process establishes a particular base year for specific planning purposes and associated forecast years for this base year. The Metropolitan Transportation Planning (MTP) update process typically drives this system. A list of the base years established recently is provided below is the first year that the entire four-county region was modeled; 1990 base year was used to support the 1995 MTP; 1997 base year was used to support the 1998 MTP refinement; 1998 base year was used to support the 2001 MTP update; and 2000 is the base year used to support this project. The current forecast years are 2010, 2020, 2030 and MODEL INTERACTIONS There are a number of model interactions in the modeling process. These are described individually in each of the model documentation sections, but an overview of these interactions is provided here for clarity. Land Use and Economic Forecasting Models The initial application of the land use and economic forecasting models for any particular year is based on currently available economic and land use projections and highway impedance measures from the most recent year. In theory, the land use and economic models could be rerun each time a new highway network alternative is generated, but this has not shown significant differences in the land use allocation process. So in practice, the land use forecasts are not updated using different highway networks, unless there is a significant change in these networks or a significant change in the base year land uses. A new land use forecasting model based on the URBANSIM land use forecasting platform is being developed and tested at PSRC at this time. This land use forecasting model is not documented here as it is not being used for current planning applications. Travel Demand Forecasting Models There are four feedback loops between the trip assignment model and the trip distribution model to equilibrate travel times between these models. The generalized cost (a function of travel time and cost) is the variable that is fed back into the trip distribution model following each of four iterations of the trip assignment model. There is no separate feedback to the mode choice or time-ofday models, but these models are run during each of the four iterations of the feedback to trip distribution and will, therefore, provide different results based on these updated highway travel times. Cambridge Systematics, Inc. 2-3 WSDOT and PSRC

26 2.4 DATA SOURCES There are many data sources used in the development and application of the land use and travel demand forecasting models. Data sources related to the development of the models are documented in the remaining sections of this report, as they apply to each individual model component. Data sources related to application of the models are documented in more detail in the PSRC Model User s Guide (January 2007). Both development and application data are summarized in Table 2.1. Data sources that could be used in future model development efforts were evaluated and documented in the PSRC Data Sources for Land Use and Travel Demand Forecasting (June 2001). Table 2.1 List of Data Sources Used in Land Use and Travel Demand Models Type of Data Data Source Use in Model Census Land use Real Estate Market Conditions Census Summary Files U.S. Census Public Use Microdata (PUMS) U.S. Census Standard File (SF) 3 Census Transportation Planning Package (CTPP) Buildable Lands Survey Compilation of Comprehensive Plans County Subdivision and Platting Data Digital Ortho Quad (DOQ) Digital Orthophotographs LandSat TM images Parcel data Dupre & Scott Surveys and Reports Seattle Everett Real Estate Commission Washington Center for Real Estate Research Household data Vehicle availability model Household data Travel data for home-based work trips Land use data Land use data Land use data Land use data Land use data Land use data Land use data Land use data Land use data Land use data Cambridge Systematics, Inc. 2-4 WSDOT and PSRC

27 Table 2.1 List of Data Sources Used in Land Use and Travel Demand Models (continued) Type of Data Data Source Use in Model Employment Transportation Employment Services Department Government and Education employment survey Household Travel Surveys (1960, 1971, 1985, and 1999) Puget Sound Transportation Panel Parking Survey Washington State Ferry Ridership On-Board Transit Surveys Automated Passenger Counts Traffic Counts Park-and-Ride Lot Utilization Survey HOV Lane Evaluation and Monitoring Reebie TRANSEARCH data for 1997 and 2015 Employment data Employment data Trip generation, trip distribution, mode choice, and time-of-day models Validation data Validation data Validation data Validation data Validation data Validation data Validation data Validation data Truck model Cambridge Systematics, Inc. 2-5 WSDOT and PSRC

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29 3.0 Economic Forecasting Model From 1980 to 2002, the Regional Council relied on the STEP (Synchronized Translator of Econometric Projections) model to produce forecasts of employment by sector and population. However, in 2005, the agency decided to develop a new regional forecasting model, as key time series data inputs to the STEP model were disrupted by the national change in the classification of economic data from the Standard Industrial Classification (SIC) to the new North American Industrial Classification System (NAICS). In 2006, the Regional Council used a variation of a regional model called the Puget Sound Economic Forecaster (PSEF) model that incorporates the NAICS scheme. The PSEF was developed by Dick Conway and Associates and adapted to the agency s needs under contract. Figure 3.1 provides an overview of the PSEF model. It is comprised of two submodels: one projecting the regional economy, and one forecasting the individual county economies. Although the model produces county-level forecasts, much of the employment and population forecast detail is only available at the regional level, due to database limitations. PSRC uses only the regional forecasts from PSEF as inputs to the DRAM and EMPAL models, given both the data limitations, and the consensus that local land use trends, patterns, and plans need to be considered in developing a final county-level forecast. Figure 3.1 Puget Sound Economic Forecaster (PSEF) Model U.S. Economy Boeing Microsoft Puget Sound Economy County Economies In structure, the PSEF model resembles the STEP model in that it is a simultaneous equations econometric model, reflecting economic base theory. Under this concept, growth is directly tied to the growth in sectors that export goods or services outside the region, thereby bringing income and jobs into the region. The PSEF model uses linked equations to forecast 103 endogenous Cambridge Systematics, Inc. 3-1 WSDOT and PSRC

30 variables, with the equations estimated using with quarterly data from 1970 to Like the STEP model before it, the PSEF model makes annual economic and demographic forecasts for the Puget Sound region (see Table 3.1 for model features). It forecasts aggregate regional (four-county) economic data for 21 industrial sectors in the region. Other forecast variables include population by age group, single-family and multifamily households, civilian labor force, and regional unemployment rates. The forecasts predict a wide variety of economic and demographic variables on an annual basis for a 35-year forecast horizon, from which the aggregate employment groupings for the subarea forecasts are developed. These aggregated forecasts become control totals for the allocation of households and employment using DRAM and EMPAL. A detailed description of the PSEF model is contained in Appendix B of Puget Sound Economic and Demographic Forecast: Detailed Forecasts and Methodology (February 2006), the report prepared by Dick Conway & Associates, available from PSRC. 3.1 MODEL ASSUMPTIONS The PSEF model adopts the conceptual framework of the economic base theory of regional growth. This theory distinguishes between the export (basic) and local (non-basic) demands placed upon the Puget Sound economy. The theory postulates that general economic growth, whether measured in terms of output, employment, or income, is related to growth of the basic sector. Thus, an expansion (decline) of exports is expected to trigger a responding process in the regional economy that leads to increased (decreased) production, jobs, and income in the non-basic sector. PSEF also recognizes that most industries are not entirely basic or non-basic in nature, since they produce goods and services for both external and internal markets. The model links to exogenous forecasts of the economic growth of the United States. For the 2006 model, national forecasts were purchased from Global Insight, Inc. Model estimation is based on the principal that the amount of data needed to develop the model equations should be at least as long as the period being forecast, which is the case with the 2006 build of the PSEF model. Quarterly data from 1970 to 2005 were used to forecast out 35 years, to The 62 behavioral equations function as a fully-integrated time-series model. New techniques have been introduced in the PSEF model where often the model is predicting the year to year percent change of a forecast variable, as opposed to what the actual value will be. More detail on the model structure, including example equations, can be found in the full report 9. 9 Dick Conway & Associates, Puget Sound Economic and Demographic Forecast: Detailed Forecasts and Methodology: Appendix B, prepared for the Puget Sound Regional Council, February Cambridge Systematics, Inc. 3-2 WSDOT and PSRC

31 Table 3.1 Features of PSEF 2006 Model Project Horizon 1-35 years, from 2005 to 2040 Model Size 103 endogenous variables 24 exogenous variables 62 behavioral equations 41 accounting identities Industry Detail 30 industry groupings based on NAICS classifications Other Selected Endogenous Variables Employment Population Households Unemployment rate Personal income Per capita income Consumer price index Selected Exogenous Variables Source: U.S. Gross Domestic Product U.S. housing starts U.S. unemployment rate U.S. personal income U.S. personal consumption deflator U.S. mortgage rate Boeing employment Microsoft employment Stock option income Conway, D., 2006, Puget Sound Economic and Demographic Forecast: Detailed Forecasts and Methodology.Dick Conway and Associates, Seattle, Washington. Cambridge Systematics, Inc. 3-3 WSDOT and PSRC

32 Recognizing the impact that The Boeing Company and Microsoft have on the region s economy, PSEF is designed to give special consideration to forecasts of employment and income directly produced by these two employers. User judgement is central to developing future year Boeing and Microsoft projections. 3.2 PERSONAL INCOME Along with employment, the group of equations concerning personal income form the most important blocks of the Puget Sound Economic Forecaster model. Personal income is the income for county residents in the form of wages and salaries; proprietors income; other labor income; property income (interest, dividends, and rent); transfer payments (e.g., unemployment compensation); and personal contributions to social insurance (e.g., payments to the social security retirement fund) which are deducted from the other categories of personal income. Personal income is measured by place of residence, which necessitates a resident adjustment, since labor income is measured by place of work. Income data is used in both current dollars, and constant year 2000 dollars. Estimates of real income are arrived at by applying the US implicit price deflator for consumption expenditures to the current dollar estimates of income. While the income data source, the US Bureau of Economic Analysis, only publishes annual estimates of personal income, quarterly data points are interpolated to use as modeling inputs. 3.3 EMPLOYMENT AND POPULATION The employment table contains data on 30 different job categories, as shown in Table 3.2. Reflecting the importance of the Puget Sound region as a major commercial center for the Pacific Northwest, most of the industry detail is found in the Service-Producing sectors. Quarterly observations of wage and salary employment run from 1970 to In addition, the model forecasts unemployment rate, which is measured as a percentage. Jobs are defined to include full- and part-time wage and salary workers and proprietors. Jobs differ from persons employed in three major respects: 1) jobs are measured by place of work; 2) a person may hold more than one job; and 3) jobs include military personnel. The purpose of using the job concept is to have a more comprehensive measure of employment than that given by nonagricultural wage and salary employment, which is the most commonly used alternative. Wage and salary estimates substantially understate employment in certain industries, such as trade, services, and construction, where up to onefourth of the jobs are held by self-employed workers. However, unlike the STEP model, estimates of total employment are not included in the PSEF model database. Instead, PSRC adjusts the wage and salary forecasts from the PSEF model to account for self-employed workers, using a set of factors derived from national sector ratios of self-employed to wage and salaried workers. Cambridge Systematics, Inc. 3-4 WSDOT and PSRC

33 Table 3.2 Sectoring Plan for PSEF 2006 Model Industry Grouping / Sector NAICS* Code Goods producing 11,21,23,31-33 Natural resources and mining 11,21 Construction 23 Manufacturing Aerospace 3364 Other durable goods 321,327, other 33 Nondurable goods 31, Service producing 22,42-81 Wholesale and retail trade 42,44-45 Wholesale trade 42 Retail trade Transportation, warehousing, and utilities 22,48-49 Transportation and warehousing Utilities 22 Information 51 Telecommunications 517 Other information Other 51 Financial activities Professional and business services Other services 61-62,71,72,81 Food services and drinking places 722 Educational services 61 Health services 62 Other 71,721,81 Government 92 *North American Industrial Classification System. Source: Conway, D., 2006, Puget Sound Economic and Demographic Forecast: Detailed Forecasts and Methodology.Dick Conway and Associates, Seattle, Washington. Cambridge Systematics, Inc. 3-5 WSDOT and PSRC

34 For the population database, the PSEF model uses US Census Bureau estimates of resident population as of July 1 st of each year. Like income data, an interpolation procedure is applied to develop quarterly population estimates. The population dataset includes estimates for four separate age cohorts: 0 to 4 years, 5 to 19 years, 20 to 64 years, and 65 and older. In addition, the model includes detail on household population, group quarters population, households, and average household size. The results of the PSEF model (employment, population, and households) are used as the control totals for the modified DRAM/EMPAL allocation models, described in Section 4: Land Use Forecasting Models. Note that under the current version of the EMPAL model, Resource and Construction sector jobs are not forecast, due to their tendancy to have very mobile, off-site employment. As a result, total employment forecasts from PSEF and DRAM/EMPAL will not be directly comparable. Cambridge Systematics, Inc. 3-6 WSDOT and PSRC

35 4.0 Land Use Forecasting Models The DRAM/EMPAL land use model system was originally implemented by PSRC in 1981, using a public domain version of the software 10. PSRC made significant structural changes to the model and software, and recalibrated it in Subsequent recalibrations were completed in 1991, and again in 2006, using updated census data. PSRC originally operated the models on a mainframe, and has since ported the models to microcomputer. The original model system interacted with the standard UTPS travel demand model system and has since been linked to EMME/2, the current travel demand modeling platform at PSRC. The model system was originally adopted for the following purposes: long-range, small-area population and employment forecasts; land use inputs to trip generation and mode split models; impact analysis of transit development and public facility siting alternatives; simulation analysis in iteration with the standard UTPS travel demand models (Sicko and Watterson, 1991). DRAM and EMPAL are both singly constrained, spatial interaction models derived from the original Lowry Gravity Model. Both are cross-sectional models, meaning that they predict the levels of households and employment by zone for a particular point in time, rather than predicting changes over time. DRAM predicts household locations based on the locations of employment locations, the impedance (or time/cost of travel from zone to zone), and the attractiveness to the households of potential residential zones. EMPAL predicts employment locations based on prior employment locations, zone size, travel impedance, and the prior location of households. 4.1 MODIFICATIONS TO THE DRAM/EMPAL MODEL SYSTEM Over the years, PSRC has made several changes to the original DRAM/EMPAL structure in order to improve the models forecasting and simulation capabilities. These changes include the following and are described below (see Watterson, W.T., 1986, The DRAM85/EMPAL85 Activity Model System: 10 The original software has been modified and brought into the private sector as a proprietary commercial product by Putman and Associates, Inc. It is now available through standard commercial software licensing. Cambridge Systematics, Inc. 4-1 WSDOT and PSRC

36 Development, Structure, and Application in the Puget Sound Region, for more detail on these modifications): using more lagged variables to approximate a quasi-dynamic approach; eliminating and consolidating variables because of problems in estimating certain parameters; adding changes in zonal attractiveness variables to improve simulation and analysis capacity; developing a composite cost measure of impedance to travel between zones; introducing single-family/multifamily household and residential land consumption submodels into the DRAM model structure. Lagged Structure The use of two time periods for parameter estimation, rather than strictly crosssectional estimation, was intended to produce a recursive, quasi-dynamic model system. In practical terms, this meant re-specifying the attractiveness terms to reflect lagged values, so that the location of activities in a particular time period was made a function of prior values of the attractiveness terms. This is consistent with the application of the model in time steps of 5 or 10 years, though it does not fundamentally change the cross-sectional nature of the models. Travel Impedance Two changes in the travel impedance specifications affect both DRAM and EMPAL. The first concerns the functional form of the travel impedance term, and the second concerns the measure used. In Putman s specification (Putman, Stephen, 1983, Integrated Urban Models: Policy Analysis of Transportation and Land Use), travel impedance is specified as a modified gamma function, which is the product of a power function of travel impedance with a negative exponential function of travel impedance, in order to more closely approximate the shape of observed work trip length frequency profiles. PSRC observed significant colinearity when attempting to estimate the two parameters of this combined function, and ultimately retained only the negative exponential function to represent travel impedance. Prior versions of DRAM and EMPAL had all used zone-to-zone peak highway times as the measure of travel impedance from home to work, or vice versa. While the dominance of the auto mode and the correlation of this measure with auto travel costs are reasonably high, PSRC had concerns about the use of a travel impedance term that did not incorporate transit effects. The former specification also precluded any policy analysis of transportation policies, such as transit fares and times, and parking costs. The mode choice model predicts the probability of traveling between zones on each mode, and uses information about the time and cost associated with each mode. The impedance used in DRAM and EMPAL is the a.m. peak auto time, Cambridge Systematics, Inc. 4-2 WSDOT and PSRC

37 aggregated to the level of Forecast Analysis Zones (FAZ) used in the DRAM/EMPAL model system. 4.2 EMPLOYMENT MODEL (EMPAL) Modifications of EMPAL The conversion to a two-period specification and calibration was accompanied by changes in the attractiveness terms. For example, EMPAL previously used only one industry sector in its attractiveness term; whereas, the revised structure used the lagged values of employment in each sector as additional attractiveness terms, thereby, adding interaction among sectors. This is consistent with a form of agglomeration economies generally referred to as inter-industry linkages. To move beyond the treatment of zones as independent islands, a simple gravity index of proximity to employment in nearby zones for an inter-zonal employment interaction was added. This approach adds information that could help reflect localization and inter-industry agglomeration effects that spill over zone boundaries. In other words, zones are fairly artificial boundaries, and employment centers can and do spill across the boundaries of zones. Without measures, such as this, there is no direct way for the attractiveness of a zone to be influenced by a growing employment center in an adjacent zone. The gravity index was specified as: Where: P i = gravity index; E j = employment in zone j; P E C 3 i j ij j C ij = travel impedance from zone i to zone j. In addition, the only land use variable in the EMPAL attractiveness term, total land area, had proven problematic in the sense that it showed no correlation with employment location, and was replaced by a variable measuring employment density. This variable has the advantage of serving as a loose proxy for land and rental prices, though the model does not incorporate land market information. 4.3 HOUSEHOLD MODEL (DRAM) Modifications of DRAM Consistent with the lagged specification of EMPAL, all of the DRAM attractiveness terms were re-specified using prior period or lagged values to approximate a recursive dynamic structure over time. The DRAM specification Cambridge Systematics, Inc. 4-3 WSDOT and PSRC