1. Bill Davidson | PB | 410-243-4601 | davidson@pbworld.com Dawn McKinstry | PB | 714-973-4880 | mckinstry@pbworld.com Marie-Elsie Dowell | PB | 305-514-3125 | dowell@pbworld.com Calibration of the Regional Mode Choice Models for Los Angeles and Miami for New Starts Forecasting 11th National Transportation Planning Applications Conference May 6-10, 2007, Daytona Beach, Florida Session 18: Taken for a Ride: Ridership and Transit Forecasting

2. Introduction Experience in Los Angeles and Miami Calibration Process Lessons Learned from New Starts

3. Calibration Process Shift in how Mode Choice Models are Calibrated Detailed Review of Inputs Use of On-Board Transit Survey – A NECESSITY

4. Los Angeles Model Mode Choice Model

5. Los Angeles Model Study Area

6. Los Angeles Model Challenges Encountered/Resolved 1. Unrealistic Trip Patterns and Trip Lengths 2. Uncongested Highway Speeds 3. Metrolink Fares 4. Drive Egress

7. Los Angeles Model Validation Results

8. Los Angeles Model Challenges Encountered/Resolved 1. Unrealistic Trip Patterns and Trip Lengths 2. Uncongested Highway Speeds 3. Metrolink Fares 4. Drive Egress

9. Los Angeles Model Daily Boardings Summary

10. Miami Model Challenges Encountered/Resolved Cross County Trips Not Represented Nest Structure Model Coefficients College Trip Patterns Same as Other School Trips

11. Miami Model Study Area

12. Miami Model Updated Nest Structure

13. Miami Model Review of Model Coefficients Previous ModelUpdated Model Drive access weight across all the trip purposes is identical to the in-vehicle time. In most models these coefficients should reflect values that are more onerous than in-vehicle time. Single wait time.Wait time split into two parts, first and second wait.

14. Miami Model Miami-Dade College Trip Patterns

15. Miami Model Validation Results

16. Lessons Learned Review Key Model Inputs Person Trip Patterns Los Angeles – Too few trips to downtown Miami – College trips Highway and Transit Travel Time Los Angeles – Highway Speeds Up to Date Surveys Calibration Target Values Observed Trip Patterns Path Building Process Verification Observed Trip Matrices