THURSTON REGION MULTIMODAL TRAVEL DEMAND FORECASTING MODEL IMPLEMENTATION IN EMME/2

1. THURSTON REGION MULTIMODAL TRAVEL DEMAND FORECASTING MODEL IMPLEMENTATION IN EMME/2 - Presentation at the 15th International EMME/2 Users’ Group Conference Oct. 18, 2000 Jin Ren, PE, Transportation Engineer Thurston Regional Planning Council Olympia, WA (www.trpc.org)

2. TRPC Technical Modeling Process

3. Travel “Skims” Data Preparation • EMME/2 Multimodal Network Building • Travel Time and Distance by Modes (Walking/Biking/Auto/Transit) • Intrazonal Travel Time and Distance • Distance-Based Housing/Employment Density by Traffic Analysis Zones (TAZ) • Travel Time-Based Transit Accessibility • Mix Use Index (Area-Based Densities) • Area-based Local Intersection Density

4. Household Sub-Models(Multinomial Logit Choice Modeling) • Household Worker (0, 1, 2, 3+) • Household K-12 Schoolchild (0, 1, 2, 3+) • Household Auto-Ownership (0, 1, 2, 3+)

5. Trip Generation Models • Cross-classified Household Trip Rates: 1998/1999 Household Travel Survey • Truck Freight Trip Model: 1997 Riebee Freight Survey Data • External Trip Generation Model: 1997 I-5/SR-101 O-D Surveys and Vehicle Classification Counts

6. Household Cross-Classification Schemes for Trip Production

7. 1998 Daily Trip Production Calibration

8. Daily Destination Choice Model(Multinomial Logit Models with Size Variables) • O-D Travel Time from Auto Assignments • 1998 Households, Employees by Retail, Office, Service, Government and Other • The standard formula for utilities is: Utilij= exp(*timeij+*timeij2+*timeij3+ln(1…k*Employmentj1...jk + j*Householdsj)) Where , , ,  and  are parameters or estimated coefficients 1…k stand for different employment sectors i represents a ‘production’ TAZ j represents an ‘attraction’ TAZ

9. Daily Mode Choice Modeling • Drive-Alone Vehicle or Person Trips • Drive-with-Passenger Vehicle or Person Trips • Passenger-Only Person Trips • Transit Person Trips • Walk Person Trips • Bike Person Trips

10. Variables Impacting Mode Choices(Multinomial Logit Choice Modeling) • Land Use Variables (Xi): Employment Density, Transit Accessibility, Mixed-Use, & Parking Cost • Household Variables (Yj): Household Size, Auto-Ownership, Worker Size and Income Status • Network Skims Variables (Zk): Local Intersection Density and Point-to-Point Travel Time • The standard logit utility function: Utilij= exp( +i*Xi+j*Yj+k*Zk) Where , , , and  are parameters or estimated coefficients

11. Time-of-Day Models • Production-Attraction and Attraction-Production Peaking Factors (Time-of-Day Factors) • 1998 AM Peak Hour Trip Tables by Modes • 1998 Mid-Day Hour Trip Tables by Modes • 1998 PM Peak Hour Trip Tables by Modes • Add 1998 Inbound/Outbound/ Through Vehicle Trips for AM, Mid-day and PM Hours

12. Trip Assignments • 1998 Multi-Class Auto Assignments by Time Periods • 1998 Transit Person Trip Multi-Path Assignments by Time Periods • Feedback and Looping Process to Reach Ideal Equilibrium

13. Model Calibration Process • Goodness-of-Fit Statistical Testings • Control Total or Percentage Checks: - Household Numbers - Trip Productions - Mode Splits - Average Vehicle Occupancies • Screenline Analysis by 18 Screenlines • Transit Ridership Calibration to 1998 Transit Ridership Surveys

14. In Conclusion • For the first time, our region is developing a multimodal travel demand forecasting model • For the first time, we are using local survey data to develop a regional model • Model estimation and application hand in hand • Peer review groups and documentation • Effective integration of software tools for data preparation and analysis in house • Robust EMME/2 Matrix Manipulation and Macros