Bill Davidson | PB | 410-243-4601 | davidson@pbworld.comDawn McKinstry | PB | 714-973-4880 | mckinstry@pbworld.comMarie-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 ConferenceMay 6-10, 2007, Daytona Beach, Florida

Session 18: Taken for a Ride: Ridership and Transit Forecasting

Introduction

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

Calibration Process

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

Los Angeles Model

Mode Choice Model

Los Angeles Model

Study Area

Los Angeles Model

Challenges Encountered/Resolved1. Unrealistic Trip Patterns and Trip Lengths2. Uncongested Highway Speeds3. Metrolink Fares4. Drive Egress

Los Angeles Model

Validation Results

Los Angeles Model

Challenges Encountered/Resolved1. Unrealistic Trip Patterns and Trip Lengths2. Uncongested Highway Speeds3. Metrolink Fares4. Drive Egress

Los Angeles Model

Daily Boardings Summary

Mode Estimated Daily

Boardings

Observed(*) Daily

Boardings

Absolute Difference

Percent Difference

All Buses 1,282,500 1,184,700 97,800 8.3

Urban Rail 192,100 208,300 -16,200 -7.8

Commuter Rail 32,400 34,300 -1,900 -5.4

Rail Line Estimated Daily

Boardings

Observed Daily

Boardings

Absolute Difference

Riverside County 3,100 4,200 -1,100

91 Line 2,700 1,500 1,200

Riverside Line combined with 91 Line

5,800 5,700 100

Miami Model

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

Miami Model

Study Area

Miami Model

Updated Nest Structure

Miami Model

Review of Model Coefficients

Previous Model Updated 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.

Miami Model

Miami-Dade College Trip Patterns

0%

5%

10%

15%

20%

25%

30%

35%

40%

Zip Codes

Per

cen

t

Model Data (Production Trip Ends) Place of Residence

0%

5%

10%

15%

20%

25%

30%

35%

40%

1 3 5 6 7 8 9 10 12

Distance (miles)

Per

cen

t T

rip

s

Model Distribution Place of Residence Distribution

Miami Model

Validation Results

Trip Purpose Target Value Estimated Value

Ratio of Estimated over Target

Home-Based Work Total Trips 3,952,017 3,952,019 1.00 Auto Trips 3,820,728 3,820,912 1.00 Transit Trips 131,289 131,107 1.00 Home-Based Non-Work Total Trips 6,939,560 6,939,266 1.00 Auto Trips 6,812,507 6,811,512 1.00 Transit Trips 127,053 127,754 1.01 Non-Home Based Total Trips 4,325,235 4,325,239 1.00 Auto Trips 4,271,676 4,271,248 1.00 Transit Trips 53,559 53,991 1.01

Transit Mode Observed Estimated Absolute Difference

Percent Difference

Local Bus (1) 237,823 234,744 -3,079 -1% Express Bus (1) 2,202 2,169 -33 -1% Jitney 3,007 3,002 -2 0% Tri-Rail 4,985 5,110 124 2% Metrorail 54,182 52,167 -2,016 -4% Metromover 38,486 36,939 -1,548 -4% Total 340,685 334,129 -6,556 -2% (1) – Includes both Miami-Dade and Broward County lines.

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