July 2007 www. camsys. com
Bay Area/ California High- Speed Rail Ridership
and Revenue Forecasting Study
Statewide Model Validation
prepared for
Metropolitan Transportation Commission and the California High-
Speed Rail Authority
prepared by
Cambridge Systematics, Inc.
with
Mark Bradley Research and Consulting
final
report
final report
Bay Area/ California High- Speed
Rail Ridership and Revenue
Forecasting Study
Statewide Model Validation
prepared for
Metropolitan Transportation Commission and the California High- Speed Rail Authority
prepared by
Cambridge Systematics, Inc.
555 12th Street, Suite 1600
Oakland, California 94607
with
Mark Bradley Research and Consulting
date
July 2007
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Cambridge Systematics, Inc. i
7530.005
Table of Contents
1.0 Introduction......................................................................................................... 1- 1
1.1 Purpose of the Report ................................................................................ 1- 1
1.2 Overall Model Design................................................................................ 1- 1
1.3 Contents of the Report ............................................................................... 1- 6
2.0 Data for Model Validation................................................................................ 2- 1
2.1 Travel Surveys............................................................................................. 2- 1
American Traveler Survey ( ATS) ............................................................. 2- 1
Caltrans Household Travel Survey.......................................................... 2- 3
Census Transportation Planning Package ( CTPP)................................. 2- 5
2.2 Air Passengers............................................................................................. 2- 7
2.3 Rail Passengers............................................................................................ 2- 8
2.4 Highway Volumes...................................................................................... 2- 9
3.0 Trip Frequency Model Calibration ................................................................. 3- 1
3.1 Interregional Trips...................................................................................... 3- 1
3.2 Intraregional Trips...................................................................................... 3- 2
4.0 Destination Choice Model Calibration .......................................................... 4- 2
4.1 Interregional Trips...................................................................................... 4- 2
4.2 Intraregional Trips...................................................................................... 4- 2
5.0 Mode Choice Model Calibration ..................................................................... 5- 2
5.1 Interregional Trips...................................................................................... 5- 2
5.2 Intraregional Trips...................................................................................... 5- 2
6.0 Trip Assignment ................................................................................................. 6- 2
6.1 Trip Tables ................................................................................................... 6- 2
6.2 Air Passengers............................................................................................. 6- 2
6.3 Conventional Rail Passengers................................................................... 6- 2
6.4 Highway Assignment ................................................................................ 6- 2
7.0 2030 Forecast ........................................................................................................ 7- 2
7.1 Trip Frequency............................................................................................ 7- 2
7.2 Destination Choice...................................................................................... 7- 2
7.3 Mode Choice................................................................................................ 7- 2
Table of Contents, continued
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7.4 Trip Assignment ......................................................................................... 7- 2
Air Passengers............................................................................................. 7- 2
Rail Passengers............................................................................................ 7- 2
Auto Passengers.......................................................................................... 7- 2
8.0 Summary .............................................................................................................. 8- 2
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List of Tables
Table 2.1 Average Daily Interregional Trips in the American Traveler
Survey Over 100 Miles ( Long)............................................................... 2- 2
Table 2.2 Mode Shares in the American Traveler Survey Over 100 Miles
( Long)........................................................................................................ 2- 3
Table 2.3 Average Daily Interregional Trips in the Caltrans Household
Travel Survey Less Than 100 Miles ( Short) ......................................... 2- 4
Table 2.4 Mode Shares in the Caltrans Household Travel Survey Less
Than 100 Miles ( Short)............................................................................ 2- 4
Table 2.5 Average Daily Commute Interregional Trips in the Census
Transportation Planning Package......................................................... 2- 5
Table 2.6 Mode Shares in the in the Census Transportation Planning
Package ..................................................................................................... 2- 7
Table 2.7 Air Passenger Boardings for 2000 by Market...................................... 2- 8
Table 2.8 Rail Passengers in 2000 by Operator and Route ................................. 2- 9
Table 2.9 Average Daily Traffic Count Miles Traveled by Facility Type....... 2- 10
Table 2.10 Average Daily Traffic Count Miles Traveled by Area Type ........... 2- 10
Table 2.11 Average Daily Traffic Counts for Gateways between
California Cities..................................................................................... 2- 11
Table 3.1 Trip Frequency Model Results for Short Trips ................................... 3- 2
Table 3.2 Trip Frequency Model Results for Long Trips.................................... 3- 2
Table 3.3 Trip Frequency Model Alternative- Specific Constants...................... 3- 2
Table 3.4 Intraregional Auto Vehicle Trips .......................................................... 3- 2
Table 4.1 Destination Choice Model Results for Short and Long Trips ........... 4- 2
Table 4.2 Destination Choice Alternative- Specific Constants for Regions ...... 4- 2
Table 4.3 Destination Choice Alternative- Specific Constants for Travel
Markets ..................................................................................................... 4- 2
Table 5.1 Comparison of Observed Trips by Mode ............................................ 5- 2
Table 5.2 Observed Main Mode Shares for Calibration ..................................... 5- 2
Table 5.3 Main Mode Choice Model Results........................................................ 5- 2
Table 5.4 Main Mode Choice Model Alternative Specific Constants ............... 5- 2
List of Tables, continued
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Table 5.5 Observed Access and Egress Mode Shares by Mode and
Purpose ..................................................................................................... 5- 2
Table 5.6 Estimated Access and Egress Mode Shares by Mode and
Purpose ..................................................................................................... 5- 2
Table 5.7 Access and Egress Mode Choice Model Alternative Specific
Constants .................................................................................................. 5- 2
Table 5.8 Intraregional Trips by Mode from MTC Model.................................. 5- 2
Table 5.9 Intraregional Trips by Mode from SCAG Model................................ 5- 2
Table 5.10 Intraregional Volumes by Mode from SANDAG Model .................. 5- 2
Table 6.1 2000 Interregional Trips by Mode........................................................ 6- 2
Table 6.2 2000 Interregional Vehicle Occupancy ( Persons per Vehicle) .......... 6- 2
Table 6.3 2000 Interregional Peaking Factors....................................................... 6- 2
Table 6.4 2000 Auto Vehicle Trips by Mode and Source.................................... 6- 2
Table 6.5 2000 Air Passenger Boarding Validation ............................................. 6- 2
Table 6.6 2000 Rail Passenger Boarding Validation............................................ 6- 2
Table 6.7 2000 Highway Assignment Validation ................................................ 6- 2
Table 7.1 Socioeconomic Forecasts from 2000 to 2030 by Region ..................... 7- 2
Table 7.2 Trip Frequency Model Results for Short Trips ................................... 7- 2
Table 7.3 Trip Frequency Model Results for Long Trips.................................... 7- 2
Table 7.4 Destination Choice Model Results for Short and Long Trips ........... 7- 2
Table 7.5 2030 Main Mode Choice Model Results............................................... 7- 2
Table 7.6 2030 Interregional Trips by Mode......................................................... 7- 2
Table 7.7 2030 and 2000 Assignments by Mode .................................................. 7- 2
Table 7.8 2030 and 2000 Air Passenger Boardings .............................................. 7- 2
Table 7.9 2030 and 2000 Rail Passenger Boardings ............................................. 7- 2
Table 7.10 2030 Highway Assignment Validation ................................................ 7- 2
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List of Figures
Figure 1.1 California Urban Areas and HSR Station Locations .......................... 1- 2
Figure 1.2 Integrated Modeling Process................................................................. 1- 4
Figure 1.3 Interregional Model Structure............................................................... 1- 5
Figure 4.1 Destination Choice Model Regions ...................................................... 4- 2
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1.0 Introduction
1.1 PURPOSE OF THE REPORT
The focus of this report is on the validation of the combined interregional and
intraregional ( urban) models for the Bay Area/ California High- Speed Rail
Ridership and Revenue Forecasting Study. This statewide model was estimated
from a combination of existing and new household and intercept traveler
surveys collected in California, and combined with intraregional trips generated
from regional and statewide sources. There is a full set of new interregional
models, including trip frequency, party size, and destination and mode choice
models included in this statewide model. These models are segmented by trip
purpose, distance, and location of the interregional trip households.
This report includes information on the calibration process, data used for
observed travel behavior, and resulting calibration parameters for the
interregional trips. In addition, this report includes summaries and
reasonableness checks on the intraregional trips derived from the metropolitan
planning organizations ( MPO) trip tables. These are not separately validated or
calibrated because each MPO has provided assurances that these trip tables are
validated. The base year for the model validation process is 2000. This report
does not include a description of the model development process or integration
of the interregional and intraregional trips, because these were documented
separately ( see below).
1.2 OVERALL MODEL DESIGN
The Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting
Study includes the following components:
· Intraregional travel;
· Interregional travel;
· External travel; and
· Trip assignment.
Intraregional trips include all trips with both ends in one of the three urban
areas with more than one proposed high- speed rail station. These areas are the
San Francisco Bay Area, Greater Los Angeles, and San Diego regions.
Sacramento also is considered to ensure that this capability is available for future
purposes. The metropolitan planning organizations ( MPO) representing these
areas are the Metropolitan Transportation Commission ( MTC), the San Diego
Association of Governments ( SANDAG), the Southern California Association of
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Governments ( SCAG), and the Sacramento Area Council of Governments
( SACOG). These urban areas are presented in Figure 1.1.
Figure 1.1 California Urban Areas and HSR Station Locations
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Interregional trips include all trips with both ends in California and whose
origin and destination are in different urban areas ( or different counties outside
the urban areas) having proposed high- speed rail stations.
External trips include trips with one end outside California and one end in an
urban area with a proposed high- speed rail station.
We recognize that some urban trips may be longer than some interregional trips
by this definition and vice- versa. However, these definitions do clearly fit in
with urban and statewide planning definitions, and do identify most
interregional trips as those that begin or end outside an urban area. One
example of an anomaly is a trip from Modesto to San Jose ( defined as an
interregional trip), which is similar in distance to a trip from Palmdale to Los
Angeles ( defined as an urban trip). Even taking these anomalies into
consideration, there was consensus that the definition of urban and interregional
trips fit well with most trips in the system, and that the models proposed for
each would adequately address the behavioral nature of each trip type. In
addition, as discussed below, we have segmented the interregional trips into
short trips ( less than 100 miles) and long trips ( longer than 100 miles) to help
address this issue.
Trip assignment includes the merging of the urban, interregional, and external
trips into modal trip tables that are assigned to highway, rail, and air networks.
These assignments were validated in the base year and forecast year to evaluate
reasonableness and accuracy compared to observed data sources. The model
base year is 2000 and the forecast year is 2030. The California interregional
models explicitly model peak and offpeak travel for both intraregional and
interregional trip movements.
The integrated modeling process for the development of the statewide model is
presented in Figure 1.2. This process shows that the accessibility of the system
( represented by travel time) is included in the mode choice models and in the
interregional trip frequency and destination choice models. This feature allows
us to estimate the induced travel for the interregional travel market.
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Figure 1.2 Integrated Modeling Process
Trip Generation Trip Frequency
Trip Distribution
Mode Choice
Destination Choice
Mode Choice
Urban Models Interregional Models
Travel Times
Trip Assignment
Travel Times
The interregional models are comprised of four sets of models: trip frequency,
destination choice, main mode choice, and access/ egress mode choice. The
structure and contents of the interregional modeling system is presented in
Figure 1.3.
The trip frequency model component predicts the number of interregional trips
that individuals in a household will make based on the household’s
characteristics and location. The destination choice model component predicts
the destinations of the trips generated in the trip frequency component based on
zonal characteristics and travel impedances. The mode choice components
predict the modes that the travelers would choose based on the mode service
levels and characteristics of the travelers and trips. The mode choice models
include a main mode choice, where the primary interregional mode is selected,
and access/ egress components, where the modes of access and egress for the air
and rail trips are selected.
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Figure 1.3 Interregional Model Structure
Trio Frequency/ Day
• Household Characteristics
• Trip Purpose/ Distance Class
• Level of Service ( Logsum & Accessibility
• Region
• Party Size ( For Short Distance)
Destination Choice
• Level of Service ( Logsum & Accessibility
• Employment & Household Characteristics
• Region and Area Type
• Trip Purpose/ Distance Class
• Party Size ( For Long Distance)
Main Mode Choice
• Level of Service
• Household Characteristics
• Purpose/ Distance Class
• Party Size ( For Long Distance)
• Access & Egress ( Logsum)
Access Mode Choice
• Level of Service
• Household Characteristics
• Purpose/ Distance Class
• Party Size ( For Long Distance)
• Main Mode ( Rail/ HSR/ Air)
Egress Mode Choice
• Level of Service
• Household Characteristics
• Purpose/ Distance Class
• Party Size ( For Long Distance)
• Main Mode ( Rail/ HSR/ Air)
One Trip
Two- Plus
No Trips Trips
Zone 1 Zone 2 Zone N- 1 Zone N
Car Rail HSR Air
Drive
and Park
Drop
Off
Rental
Car
Taxi Transit Walk Taxi Transit Walk
Picked Up Rental Car
Unpark
and Drive
The market segmentations used for the models are:
· Purpose:
– Business ( peak- period);
– Commute ( peak- period);
– Recreation ( offpeak- period); and
– Other ( offpeak- period).
· Distance range/ residence area type:
– Less than 100 miles, from large MPO regions;
– Less than 100 miles, from small MPO regions; and
– More than 100 miles.
· Household size – 1 person, 2 people, 3 people, more than 4 people;
· Household income range – Low, medium, or high;
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· Household auto- ownership – 0, 1, 2+;
· Household number of workers – 1) no workers, 2) 1 worker, 3) 2+ workers;
and
· Party size: Traveling alone, traveling with others.
The distance ranges of less than or greater than 100 miles were determined by
reviewing the trip length distributions from the surveys and judgment about
behavior for short versus long trips. Party size is a segmentation variable
primarily for the Recreation and Other segments, because it has a large effect on
the travel cost of the car mode versus the other modes, and thus on the choices
throughout the model chain.
1.3 CONTENTS OF THE REPORT
There are seven sections in this report: the introduction, a discussion of data
sources, calibration of each model component, and a summary of the validation.
Data sources include travel surveys, ridership counts, and traffic volumes.
Model components include trip frequency, destination choice, mode choice, and
trip assignment models.
This report builds on several other reports developed in earlier stages of this
project:
· Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Interregional Model System Development, Cambridge Systematics, Inc., with
Mark Bradley Research & Consulting, August 2006;
· Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Levels of Service Assumptions and Forecast Alternatives, Cambridge Systematics,
Inc., with Systra Consulting, Inc. and Citilabs, August 2006; and
· Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Socioeconomic Data, Transportation Supply, and Base Year Travel Patterns Data,
Cambridge Systematics, Inc., December 2005.
These reports are available from Metropolitan Transportation Commission and
the California High- Speed Rail Authority ( CHSRA). These reports are contained
on the CHSRA web site1 as part of the Ridership and Revenue Study.
1 http:// www. cahighspeedrail. ca. gov/ ridership/
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2.0 Data for Model Validation
A variety of travel survey data sources, ridership, and traffic count data were
used for model calibration and validation of the interregional travel models.
These sources are summarized below. Data sources developed for use in model
estimation of the interregional travel models were reported in the Interregional
Model System Development report.
2.1 TRAVEL SURVEYS
Travel surveys were combined to create a comprehensive set of data for use in
calibrating the trip frequency, destination choice, and mode choice models. The
following surveys were used for each of the interregional trip purposes:
· The American Traveler Survey ( ATS) was used to validate the business,
recreation, and other long trip purposes;
· The Census Transportation Planning Package ( CTPP) was used to validate
the commute long and commute short trip purposes; and
· The California Statewide Travel Survey was used to validate the business,
recreation, and other short trip purposes.
These surveys are described below for the relevant trip purposes used for the
statewide model validation dataset. The datasets are summarized by major
market ( based on city- to- city trip movements), because this was a focus of the
model validation effort.
American Traveler Survey ( ATS)
The American Travel Survey ( ATS), developed and conducted by the Bureau of
Transportation Statistics ( BTS) in 1995, obtained information about long- distance
travel of persons living in the United States. The information was used to
identify characteristics of current use of the nation’s transportation system,
forecast future demand, analyze alternatives for investment in and development
of the system, and assess the effects of Federal legislation and Federal and state
regulations on the transportation system and its use.
We processed the ATS to extract intra- California trips that were over 100 miles in
length ( consistent with our long trip definition), and converted these trips from
1995 annual trips to 2000 daily trips using a growth factor of 6.9 percent ( based
on population growth in California during this time) and a annualization factor
of 365 days per year. The subsequent average daily trips were segmented by trip
purpose and market in Table 2.1. Commute trips were excluded from this
analysis, since they were derived from the CTPP data.
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Table 2.1 Average Daily Interregional Trips in the American
Traveler Survey Over 100 Miles ( Long)
Business Recreation Other Total
LA to Sacramento 5,169 7,127 1,467 13 , 764
LA to San Diego 10,313 61,763 13,567 85 , 642
LA to SF 17,356 44,108 6,787 68 , 251
Sacramento to SF 5,645 21,443 7,306 34 , 394
Sacramento to San
Diego
1,227 1,227 218 2 , 672
San Diego to SF 5,966 16,443 2,258 24 , 667
LA/ SF to SJV 4,396 19,777 5,690 29 , 863
Other to SJV 12,538 12,886 4,725 30 , 150
To/ from Monterey/
Central Coast
8,271 19,829 6,796 34 , 895
To/ from Far North 3,129 12,359 2,366 17 , 854
To/ from W. Sierra
Nevada
531 7,528 1,510 9 , 570
Total 74 , 540 224 , 491 52 , 691 351 , 722
Source: U. S. Department of Transportation Bureau of Transportation Statistics, 1995
American Traveler Survey, Technical Documentation,
http:// www. bts. gov/ publications/ 1995_ american_ travel_ survey/
index. html.
One problem with the ATS data is that trips are only recorded to and from
standard Metropolitan Statistical Areas ( MSAs). Trips that are not destined or
originating from an MSA in California are coded as “ not within an MSA.” These
trips were not included in the survey data summaries. Instead, trips within the
regions in the statewide model that did not correspond with a MSA were
obtained from the California Department of Transportation ( Caltrans)
Household Travel Survey, described below.
The ATS data also provided mode shares for the business, recreation, and other
long trip purposes. These are presented in Table 2.2.
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Table 2.2 Mode Shares in the American Traveler Survey Over
100 Miles ( Long)
Mode Business Recreation Other
Auto 76.13% 87.84% 87.98%
Rail 0.70% 2.32% 3.27%
Air 23.17% 9.85% 8.75%
Source: U. S. Department of Transportation Bureau of Transportation Statistics, 1995
American Traveler Survey, Technical Documentation,
http:// www. bts. gov/ publications/ 1995_ american_ travel_ survey/
index. html.
Caltrans Household Travel Survey
The California Statewide Travel Survey was conducted in 2000 to 2001 for
weekday travel. 2 This survey was an activity- based survey and included all in-home
activities and travel completed in accessing activity locations over a 24-
hour period. The survey of 17,040 households was conducted in each of the
58 counties throughout the State. The survey reported 8.6 total trips per
household.
The survey was conducted by NuStats Research and Consulting, who surveyed
randomly selected households using the telephone recruitment/ diary mail- out/
telephone trip retrieval method. These data were used in this study as
disaggregate data, so expansion and adjustment factors developed for the survey
were not utilized. This includes adjustment factors developed from Global
Positioning System ( GPS) surveys conducted to identify trip under- reporting;
and those developed to account for changes in travel behavior due to the
September 11, 2001, attacks on the World Trade Center and Pentagon, which
severely disrupted travel throughout the U. S. The survey was conducted in
waves, with the fall 2000 and spring 2001 waves completed before 9/ 11 and the
fall 2001 wave completed before and after 9/ 11.
Table 2.3 presents a summary of the Caltrans household travel survey, weighted
and summarized for interregional travel. Several markets are too long to have
any short trips ( under 100 miles), but many markets are close enough to have
both short and long trips ( such as Los Angeles to San Diego).
2 State of California, Department of Transportation, Division of Transportation System
Information, Office of Travel Forecasting and Analysis, Statewide Travel Analysis
Branch, 2000- 2001 California Statewide Travel Survey Weekday Travel Report, June 2003.
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Table 2.3 Average Daily Interregional Trips in the Caltrans
Household Travel Survey Less Than 100 Miles ( Short)
Business Other Recreation Total
LA to Sacramento – – –
LA to San Diego 19,244 42,340 27,512 89 , 095
LA to SF –
Sacramento to SF 17,805 17,383 12,394 47 , 582
Sacramento to San
Diego
– – – –
San Diego to SF – – – –
LA/ SF to SJV 11,769 16,565 25,518 53 , 852
Other to SJV 20,223 24,382 8,341 52 , 946
To/ from Monterey/
Central Coast
16,351 44,784 67,024 128 , 159
To/ from Far North 15,626 47,494 89,480 152 , 599
To/ from W. Sierra
Nevada
2,421 10,566 6,840 19 , 827
Total 103 , 439 203 , 514 237 , 108 544 , 061
Source: State of California, Department of Transportation, Division of
Transportation System Information, Office of Travel Forecasting and
Analysis, Statewide Travel Analysis Branch, 2000- 2001 California Statewide
Travel Survey Weekday Travel Report, June 2003.
The California Statewide Travel Survey data also provided mode shares for the
business, recreation, and other short trip purposes. These are presented in
Table 2.4.
Table 2.4 Mode Shares in the Caltrans Household Travel Survey
Less Than 100 Miles ( Short)
Mode Business Recreation Other
Auto 92.89% 99.28% 89.60%
Rail 0.11% 0.72% 8.35%
Air 7.00% 0.00% 2.05%
Source: State of California, Department of Transportation, Division of
Transportation System Information, Office of Travel Forecasting and
Analysis, Statewide Travel Analysis Branch, 2000- 2001 California Statewide
Travel Survey Weekday Travel Report, June 2003.
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Census Transportation Planning Package ( CTPP)
The Census Transportation Planning Package ( CTPP) is a set of special
tabulations from the decennial census designed for transportation planners. The
CTPP contains tabulations by place of residence, place of work, and for flows
between home and work. CTPP is a cooperative effort sponsored by the State
Departments of Transportation ( DOT) under a pooled funding arrangement with
the American Association of State Highway and Transportation Officials
( AASHTO). The data are tabulated from answers to the Census 2000 long form
questionnaire, mailed to one in six U. S. households. Because of the large sample
size, the data are reliable and accurate. CTPP provides comprehensive and cost-effective
data, in a standard format, across the United States.
The CTPP was collected in 2000 for the MPOs in the State of California and
summarized for use in this project for commute travel, and for both long and
short trips. Table 2.5 presents a summary of the CTPP data, weighted and
summarized for both long and short interregional commute travel.
Table 2.5 Average Daily Commute Interregional Trips in the
Census Transportation Planning Package
Short Commute Long Commute Total
LA to Sacramento – 5,103 5 , 103
LA to San Diego 69,728 29,665 99 , 393
LA to SF – 22,124 22 , 124
Sacramento to SF 37,192 16,986 54 , 178
Sacramento to San Diego – 886 886
San Diego to SF – 4,840 4 , 840
LA/ SF to SJV 77,112 53,741 130 , 853
Other to SJV 128,792 10,950 139 , 743
To/ from Monterey/ Central
Coast
96,448 28,809 125 , 257
To/ from Far North 36,658 16,982 53 , 640
To/ from W. Sierra Nevada 17,672 9,730 27 , 402
Total 463 , 603 199 , 817 663 , 420
Source: U. S. Department of Transportation, Federal Highway Administration,
Census Transportation Planning Package, September 11, 2006,
http:// www. fhwa. dot. gov/ ctpp/.
The CTPP data also provided mode shares for the commute trip purposes ( long
and short). These are presented in Table 2.6. The CTPP included air, walk, bike,
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school bus, and other modes in an “ other” mode category, which we assumed to
be primarily air for interregional trips.
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Table 2.6 Mode Shares in the in the Census Transportation
Planning Package
Mode Commute Long Commute Short
Auto 99.29% 99.52%
Rail 0.71% 0.48%
Air 0.00% 0.00%
Source: State of California, Department of Transportation, Division of
Transportation System Information, Office of Travel Forecasting and
Analysis, Statewide Travel Analysis Branch, 2000- 2001 California Statewide
Travel Survey Weekday Travel Report, June 2003.
2.2 AIR PASSENGERS
The U. S. DOT Federal Aviation Administration ( FAA) origin- destination ( O& D)
10- percent sample database includes actual ticket information for 10 percent of
the tickets collected by large air carriers. While the 10- percent ticket sample data
represents a robust data of airfares and travel times, these data are subject to
sampling error. In addition, the O& D databases generally will not include tickets
for passengers with itineraries that begin on airlines classified by the FAA as
“ Small Certificated Air Carriers,” those airlines who do not fly any planes with
more than 60 seats.
Despite the limitations of the data, the O& D database is probably the most
accurate single source for defining intrastate air markets. These data are more
accurate for larger air markets, where there are few, if any, Small Certificated Air
Carriers. During model validation, we uncovered a discrepancy between the air
demand data in the ATS data and the air demand data in the FAA data for
California. The ATS data for air travel in California reported 62,069 air trips and
the FAA data reported only 48,246 for year 2000, as shown in Table 2.7. In
addition, the FAA data for 2005 shows a significant decline in the observed
volumes; these also are reported in Table 2.7. In an effort to accommodate the
difference in observed data sources, a new validation target of 55,158 air trips
was chosen and these additional air trips were allocated proportionally to each
market that increased from 2000 to 2005. Markets that decreased from 2000 to
2005 were held constant in the new validation targets. Flights per day are also
estimated for the FAA data, based on the amount of service reported in the FAA
10- percent ticket sample data.
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Table 2.7 Air Passenger Boardings for 2000 by Market
Observed Average
Daily Volumes
Passengers
per Flight
2000 2005
2000
Adjusted
Flights
Per Day
2000
Adjusted
LA to Sacramento 7,182 7,410 12,308 123 100
LA to San Diego 387 113 387 47 8
LA to SF 29,329 22,990 29,329 455 64
Sacramento to SF 5 8 8 15 1
Sacramento to San
Diego
2,246
2,507 3,848
39
99
San Diego to SF 8,096 6,697 8,096 120 68
LA/ SF to SJV 82 163 140 81 2
Other to SJV 64 54 64 32 2
To/ from Monterey/
Central Coast
596
265 596
162
4
To/ from Far North 170 221 292 56 5
To/ from W. Sierra
Nevada
- -
–
Intraregion 88 21 88 23 4
Total 48 , 246 40 , 449 55 , 158 1 , 152 48
Source: U. S. Department of Transportation O& D Market Database obtained from
the Bureau of Transportation Statistics web site, accessed October 2005.
2.3 RAIL PASSENGERS
Rail passenger data was obtained from interregional rail operators in California
and from MPOs in the State for intraregional area rail travel. The data have been
aggregated for each urban area and for each interregional rail market. These
data were compiled for all rail operators in California, as shown in Table 2.8. The
allocation of rail boardings to interregional and intraregional for the San
Francisco Bay Area is based on estimates provided by the MTC. The
interregional rail line in the Los Angeles region is the Metrolink Orange County
line ( from Los Angeles Union Station to Oceanside in San Diego County), and
was estimated based on local knowledge at 600 boardings out of a total of 5,600
boardings for the line.
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Cambridge Systematics, Inc. 2- 9
Table 2.8 Rail Passengers in 2000 by Operator and Route
Operator/ Route Market Served Boardings Intraregional Interregional
Amtrak Capital
Corridor
Sacramento to
San Francisco
3,300 1,000 2,300
Amtrak Surfliner Santa Barbara to
San Diego
5,100 2,800 2,300
Amtrak San
Joaquin
San Joaquin
Valley to San
Francisco
2,110 100 2,010
Altamont
Commuter Express
( ACE)
Stockton to San
Jose
3,100 700 2,400
Coaster, San
Diego Trolley
San Diego region 97,400 97,400
Metrolink, Metro
Rail
Los Angeles region 236,500 235,900 600
BART, Caltrain,
SF Muni, SCVTA
San Francisco
region
555,900 555,900
Regional Transit
LRT
Sacramento
region
37,600 37,600
Total 941 , 010 931 , 400 9 , 610
Source: Individual rail operator and Metropolitan Planning Organization data
sources reported in Cambridge Systematics, Bay Area/ California High-
Speed Rail Ridership and Revenue Forecasting Study, Socioeconomic
Data, Transportation Supply, and Base Year Travel Patterns Data,
December 2005.
The observed rail data showed a similar discrepancy between the ATS demand
for rail travel and the aggregated rail boardings by operator for interregional
travel. The ATS rail demand data resulted in 13,275 passenger trips and the
summation of the rail passenger boardings by operator resulted in 7,560
passenger trips. This represents only 57 percent of total rail demand reported in
the ATS data. This would indicate a much higher percent of interregional
boardings on interregional rail routes than is assumed in the current estimates.
2.4 HIGHWAY VOLUMES
Highway traffic counts were obtained primarily from the Caltrans traffic count
database and from the MTC and the Southern California Association of
Governments ( SCAG) traffic count databases. Sacramento and San Diego urban
area traffic count databases were not required since the Caltrans traffic count
data has sufficient locations in these regions, and because the networks were
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2- 10 Cambridge Systematics, Inc.
largely compatible with the Caltrans database rather than the MPO databases.
At the time of this report, the SCAG traffic count database was not available and
was, therefore, not included in these summaries. Table 2.9 summarizes the
highway traffic counts by facility type. Table 2.10 presents the same information
by area type.
Table 2.9 Average Daily Traffic Count Miles Traveled by Facility
Type
Facility Type Number of Count Locations Count Miles Traveled
Freeway 517 41,344,381
Expressway 638 14,322,157
Major Arterial 179 3,764,260
Minor Arterial 17 120,794
Collector 8 28,199
Total 1 , 359 59 , 579 , 791
Source: Caltrans Traffic Count Database – CA_ ValVol( statewide model 2000
counts). dbf with 1,191 locations; Metropolitan Transportation Commission
2000 model validation counts with 175 locations; and Sacramento Area
Council of G overnments 2000 model validation counts with 4 locations.
Table 2.10 Average Daily Traffic Count Miles Traveled by Area
Type
Facility Type Number of Count Locations Count Miles Traveled
Rural 836 28,096,076
Suburban 133 4,784,532
Urban 390 26,699,182
Total 1,359 59,579,791
Source: Caltrans Traffic Count Database – CA_ ValVol( statewide model 2000
counts). dbf with 1,191 locations; Metropolitan Transportation Commission
2000 model validation counts with 175 locations; and Sacramento Area
Council of G overnments 2000 model validation counts with 4 locations.
The primary highway validation test is the comparison of traffic counts and
modeled volumes at critical gateways in the system. The gateways correspond
to the air and rail markets of consideration. Table 2.11 presents a list of these
gateways and the average daily traffic counts available for validation.
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Table 2.11 Average Daily Traffic Counts for Gateways
between California Cities
Gateway
Routes
Included
Average Daily
Traffic Count
Sacramento to San Francisco I- 80 115,536
Sacramento to San Joaquin Valley I- 5
SR 99
109,365
San Joaquin Valley to San Francisco
( Altamont Pass)
I- 580
SR 205
111,500
San Joaquin Valley to San Francisco
( Pacheco Pass)
SR 152 20,728
San Joaquin Valley to Los Angeles
( The G rapevine or Tejon Pass)
I- 5
SR 14
78,927
Los Angeles to San Diego I- 5
I- 15
442,951
Total 879,007
Source: Caltrans Traffic Count Database – CA_ Screens. dbf with 76 locations.
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3.0 Trip Frequency Model
Calibration
3.1 INTERREGIONAL TRIPS
Interregional trips are calibrated by trip purpose ( business, commute, recreation,
and other) and by distance class ( short and long); and by major metropolitan
areas ( Sacramento Area Council of Governments ( SACOG), MTC, SCAG, and
San Diego Association of Governments ( SANDAG)). These provide the detail
needed by the subsequent models for trip purpose and distance class, and some
assurance that the four major metropolitan areas are accurately producing
interregional trips. The observed trips for the trip frequency model are derived
from a combination of the three surveys described in Section 2.0: 1) ATS, 2) the
Caltrans Household Travel Survey, and 3) CTPP.
Table 3.1 presents the results of the trip frequency model calibration effort for
short trips ( less than 100 miles), and Table 3.2 presents the results of the trip
frequency model calibration effort for long trips ( more than 100 miles). The
majority of short interregional trips are generated outside the four largest
regions; whereas, the majority of long interregional trips are generated within
the four largest regions. This is largely due to the fact that the majority of short
interregional trips are destined for the four largest regions, and the majority of
long interregional trips are traveling between major metropolitan regions.
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Table 3.1 Trip Frequency Model Results for Short Trips
Short
Region Commute Business Recreation Other
Total Daily
Model Trips
Total Daily
Observed
Trips
Percent
Difference
Sacramento Region ( SACO G ) 43,450 11,108 11,124 17,864 83,546 83,075 1%
San Diego Region ( SANDA G ) 28,945 13,763 8,148 8,304 59,160 58,796 1%
San Francisco Region ( MTC) 38,142 20,641 25,214 15,620 99,617 98,872 1%
Los Angeles Region ( SCA G ) 54,908 9,420 36,691 40,338 141,357 140,431 1%
Remainder of CA 298,252 48,577 122,876 154,689 624,394 627,536 - 1%
Total 463 , 697 103 , 509 204 , 053 236 , 815 1 , 008 , 074 1 , 008 , 710 0%
Table 3.2 Trip Frequency Model Results for Long Trips
Long
Region Commute Business Recreation Other
Total Daily
Model Trips
Total Daily
Observed
Trips
Percent
Difference
Sacramento Region ( SACO G ) 18,192 6,204 15,784 5,050 45,230 44,271 2%
San Diego Region ( SANDA G ) 21,738 6,264 21,533 6,976 56,511 55,671 2%
San Francisco Region ( MTC) 15,800 8,359 96,235 16,269 136,663 132,131 3%
Los Angeles Region ( SCA G ) 48,715 23,008 54,771 15,644 142,138 140,818 1%
Remainder of CA 82,925 19,530 15,217 1,202 118,874 131,937 - 10%
Total 187 , 370 63 , 365 203 , 540 45 , 141 499 , 416 504 , 828 - 1%
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Table 3.3 presents the alternative- specific constants estimated during the model
calibration process by trip purpose, distance class, and metropolitan area.
Generally, the size and sign of the constants are reasonable. The large negative
constants on interregional trips indicate that, all things being equal, people
would prefer to travel within their own region. Commute trips are the least
negative, indicating that people are more likely to commute outside their region
than to travel for other purposes. Other trips have the largest negative constants
for long trips, indicating that people are least likely to travel outside their region
for other trips compared to other trip purposes. The positive constants on long
recreation and other trips for metropolitan areas indicate that more long
recreation and other trips are generated in major metropolitan areas than for
other parts of the State.
Table 3.3 Trip Frequency Model Alternative- Specific Constants
Commute Business Recreation Other
Long Trips
Sacramento
Region ( SACO G ) 0.0034 0.2268 1.8168 4.0777
San Diego Region
( SANDA G ) - 0.4265 - 0.2669 0.9692 3.3428
San Francisco
Region ( MTC) - 1.4598 - 0.7273 2.9772 4.6439
Los Angeles
Region ( SCA G ) - 1.0001 - 0.3207 1.3726 3.6461
1 trip per day - 2.6718 - 4.6121 - 4.4763 - 8.4643
2 trips per day - 4.1080 - 5.2482 - 6.0397 - 9.7942
Short Trips
Sacramento
Region ( SACO G ) - 0.8145 - 0.6594 - 2.3856 - 3.2270
San Diego Region
( SANDA G ) - 1.6807 - 0.1952 - 1.6738 - 1.0711
San Francisco
Region ( MTC) - 2.2370 - 0.9736 - 1.8703 - 3.3796
Los Angeles
Region ( SCA G ) - 2.4393 - 2.0460 - 0.8903 - 0.4989
1 trip per day - 3.0659 - 4.5928 - 2.9514 - 3.7812
2 trips per day - 3.8932 - 5.1604 - 3.8573 - 4.5585
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3.2 INTRAREGIONAL TRIPS
The California Statewide High- Speed Rail Model does not model intraregional
trips from urban areas explicitly, rather it relies on existing MPO models in the
four major metropolitan areas to provide intraregional trips directly. These trips
are included in the model during trip assignment as either auto vehicle or transit
person trips. As a result, we do not maintain tabulations of total person trips
from the MPO models. Nonetheless, it is useful to compare trip generation
parameters from these MPO models and check for reasonableness. In addition,
we have derived intraregional trips from the Caltrans Statewide Model to
represent all other regions in the State beyond the four largest MPO regions.
This allows the intraregional trip table to be more comprehensive statewide.
Table 3.4 presents the auto vehicle trips ( as the best proxy for total trips) from
each of the four MPO models, and the resulting trips per person and trips per
employee statistics from these. In general, these trip rates are quite consistent
across the MPO regions, with one exception. SANDAG reports significantly
higher trips per person and trips per employee than other regions. Based on
conversations with SANDAG staff, this is because they are accounting for
significant under- reporting evidenced on their household travel survey upon
which the trip generation model was based. Overall, there are 65 million
intraregional auto vehicle trips included in the California Statewide High- Speed
Rail model.
Table 3.4 Intraregional Auto Vehicle Trips
Region
Daily Auto
Vehicle
Trips Population
Trips Per
Person Employment
Trips Per
Employee
SCA G 34,673,468 15,101,248 1.98 7,406,280 4.69
SANDA G 5,875,971 2,585,247 2.05 1,168,880 5.03
MTC 14,460,747 6,376,956 2.05 3,753,533 3.85
Remaining 13,045,337 6,717,328 1.75 3,107,079 4.20
Total 68 , 055 , 523 30 , 780 , 779 1.95 15 , 435 , 772 4.41
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4.0 Destination Choice Model
Calibration
4.1 INTERREGIONAL TRIPS
Destination choice models were calibrated to both regions and to significant
travel markets in the State. The observed dataset was developed from the three
observed travel surveys presented in the previous section. There were
alternative- specific constants for each region in the State, but additional
constants on significant travel markets were only included for the largest travel
markets. There were 14 regions included in the calibration and six major travel
markets. The regions identified in the model estimation of destination choice are
shown in Figure 4.1.
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Figure 4.1 Destination Choice Model Regions
The major travel markets were included by direction representing 12 additional
constants:
· Los Angeles ( SCAG) region to Sacramento ( SACOG) region;
· Los Angeles ( SCAG) region to San Diego ( SANDAG) region;
· Los Angeles ( SCAG) region to San Francisco ( MTC) region;
· Sacramento ( SACOG) region to San Francisco ( MTC) region;
· Sacramento ( SACOG) region to San Diego ( SANDAG) region; and
· San Diego ( SANDAG) region to San Francisco ( MTC) region.
In addition to the six major travel markets, the model calibration results are
reported for the following five travel markets:
· Los Angeles ( SCAG) region and San Francisco ( MTC) region to the San
Joaquin Valley;
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· All other regions to the San Joaquin Valley;
· To/ from the Monterey ( AMBAG) region and the Central Coast;
· To/ from the Far North region; and
· To/ from the W. Sierra Nevada region.
The first six travel markets in this list represent the primary travel markets of
interest to the high- speed rail study. The additional travel markets are included
to ensure that other regions in the State are attracting approximately the right
number of trips. The San Francisco ( MTC) region includes the nine counties:
Napa, Sonoma, Marin, Solano, Contra Costa, Alameda, San Francisco, San
Mateo, and Santa Clara. The Los Angeles ( SCAG) region includes six counties:
Ventura, Los Angeles, San Bernadino, Riverside, Orange, and Imperial.
The results of the destination choice model calibration are provided in Table 4.1.
The destination choice model results in modeled trips in each market within
+/- 10 percent of observed, except for the Sacramento to San Diego market, which
has a very small total number of observed trips per day( 2,082).
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Table 4.1 Destination Choice Model Results for Short and Long Trips
Short Long
Region
Commut
e Business
Recreati
on Other
Commut
e Business
Recreati
on Other
Total
Daily
Model
Trips
Total
Daily
Observe
d Trips
LA to Sacramento 0 0 0 0 4,987 2,093 4,063 1,271 12,414 11,568
LA to San Diego 60,682 16,518 37,229 22,594 29,009 10,660 66,529 19,715 262,936 271,100
LA to SF 0 0 0 0 16,231 7,865 26,210 4,592 54,898 50,070
Sacramento to SF 34,908 18,494 14,734 9,990 16,299 6,775 31,373 7,007 139,580 143,563
Sacramento to San
Diego 0 0 0 0 1,041 307 1,280 405 3,033 2,082
San Diego to SF 0 0 0 0 4,456 1,351 7,794 1,338 14,939 15,180
LA/ SF to SJV 78,538 14,383 15,133 23,847 38,124 12,186 23,967 3,346 209,524 217,987
Other to SJV 119,756 21,268 55,760 69,307 12,860 3,290 57 39 282,337 228,384
To/ From
Monterey/ Central
Coast 101,108 16,204 38,816 45,565 35,188 10,739 27,953 4,858 280,431 295,294
To/ From Far North 45,520 12,941 33,172 56,011 22,659 6,143 9,289 1,792 187,527 222,350
To/ From W. Sierra
Nevada 23,185 3,701 9,209 9,501 6,516 1,956 5,025 778 59,871 55,962
Total 463 , 697 103 , 509 204 , 053 236 , 815 187 , 370 63 , 365 203 , 540 45 , 141 1 , 507 , 490 1 , 513 , 540
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The destination choice model was calibrated first to regions, and then to major
travel markets. The alternative- specific constants for these regions are presented
in Table 4.2 for each destination choice model. These constants are generally of
the right sign and size for each region, based on judgment about a region’s
attractiveness for a particular trip type. For example, the Los Angeles ( SCAG)
region has very high negative constants for short commute trips, because the
SCAG region is so large that commuting within the region is much more likely.
Both the San Francisco ( MTC) and Los Angeles ( SCAG) regions have a large
positive constant for long recreation and other trips, indicating that these regions
are more likely to be tourist and other destinations for interregional travel than
other regions. These were constrained to 5.0 during model validation because
the model overpredicted long recreation and other trips to the MTC and SCAG
regions in future years.
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Table 4.2 Destination Choice Alternative- Specific Constants for Regions
Short Trips Long Trips
Business Commute Recreation Other
Business/
Commute
Recreation
/ Other
AMBA G - 0.2445 - 5.7298 5.3663 6.9090 - 0.2418 0.1833
Central Coast - 2.5528 - 11.1363 - 4.1681 - 0.4686 - 0.2546 1.3342
Far North 4.2944 0.8053 11.1214 15.8674 - 1.7279 - 0.8390
Fresno/ Modesto - 0.4407 - 7.2717 2.2259 4.7980 - 0.6854 - 0.1504
Kern 0.2741 - 12.2410 - 5.4572 - 0.5856 0.4764 0.5223
Merced - 1.4348 - 7.2677 2.3322 2.3068 - 0.8552 - 0.0942
South San Joaquin - 0.0078 - 2.1527 3.9379 3.9476 - 0.1435 0.5465
SACO G 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
SANDA G - 3.1823 - 13.2300 - 3.5181 - 2.1712 - 5.0724 - 4.3954
San Joaquin 0.5557 0.4741 4.4123 4.9147 - 0.1083 - 0.3754
Stanislaus 0.2438 - 0.3516 4.8938 4.1515 - 1.0433 - 1.4260
West Sierra
Nevada
1.6340 0.3857 5.2839 4.6007 - 0.1343 0.4070
Alameda County - 0.2746 0.8163 1.6012 2.1743 - 0.6781 5.0000
Contra Costa
County
0.2653 1.2544 2.2944 2.3108 0.2262 5.0000
Marin, Napa,
Solano Counties
0.1175 1.1294 2.8305 1.1660 0.1486 5.0000
San Francisco
County
- 0.1086 0.4466 0.8779 1.1404 - 0.8474 5.0000
San Mateo County - 0.0096 0.9610 1.2878 1.5877 - 0.6874 5.0000
Santa Clara
County
- 0.2444 0.3245 2.2959 2.0104 - 0.7104 5.0000
Solano County - 0.2181 1.4534 1.5247 2.3977 0.8002 5.0000
Imperial County - 2.2261 - 9.2739 4.2654 4.5493 - 1.8101 5.0000
Los Angeles
County
- 3.6169 - 10.9905 2.9308 2.6648 - 2.9451 5.0000
Orange County - 3.1387 - 1.8747 - 1.2074 - 2.2575 0.0963 5.0000
Riverside County - 3.7639 - 9.9196 2.4380 2.4556 - 4.4162 5.0000
San Bernardino
County
- 2.2261 - 9.2739 3.2743 4.4368 - 3.8305 5.0000
Ventura County - 3.0721 - 9.4051 3.6632 3.7485 - 3.0011 5.0000
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The destination choice model also includes alternative- specific constants for
major travel markets. Two of these markets are dominated by short trips and the
remaining four markets are for long trips, as listed below.
· San Francisco ( MTC) to Los Angeles ( SCAG) – long trips;
· San Francisco ( MTC) to San Diego ( SANDAG) – long trips;
· Sacramento ( SACOG) to Los Angeles ( SCAG) – long trips;
· Sacramento ( SACOG) to San Diego ( SANDAG) – long trips;
· Sacramento ( SACOG) to San Francisco ( MTC) – short and long trips; and
· Los Angeles ( SCAG) to San Diego ( SANDAG) – short and long trips.
The two short trip markets do contain both short and long trips, because there
are parts of each region that are more than 100 miles apart. Table 4.3 presents the
alternative- specific constants for the six major travel markets by trip purpose and
distance class. Of the four long distance travel markets, the Los Angeles ( SCAG)
region to San Francisco ( MTC) region is by far the largest market, as expected.
The large negative constant for long recreation and other trips in the model is
necessary to counteract the tendency of the model to attract more trips to this
market than is observed, based solely on the size and attractiveness of these
markets. This was constrained during model validation. The large positive
constant for the Sacramento ( SACOG) region to San Diego ( SANDAG) region is
needed to increase the small numbers of trips in this market to match observed.
This constant also was constrained during model validation. The large positive
constant for long recreation/ other trips from the Los Angeles ( SCAG) region to
the San Diego ( SANDAG) region is primarily to reflect the fact that there are
more long distance recreation trips in this market than short distance trips.
Recreation trips are often not based on shortest time and distance parameters,
since they are destined for a particular destination regardless of distance.
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Table 4.3 Destination Choice Alternative- Specific Constants for
Travel Markets
Short Long
Travel Market
Commut
e Business
Recreati
on Other
Commut
e/
Business
Recreati
on/
Other
MTC- SCA G 0.00 0.00 0.00 0.00 - 1.12 - 6.40
MTC- SANDA G 0.00 0.00 0.00 0.00 1.14 3.19
SACO G - SCA G 0.00 0.00 0.00 0.00 - 1.74 - 1.57
SACO G -
SANDA G 0.00 0.00 0.00 0.00 0.37 8.00
SCA G - MTC 0.00 0.00 0.00 0.00 - 1.12 - 6.40
SCA G - SACO G 0.00 0.00 0.00 0.00 - 1.74 - 1.57
SANDA G - MTC 0.00 0.00 0.00 0.00 1.14 3.19
SANDA G -
SACO G 0.00 0.00 0.00 0.00 0.37 8.00
MTC- SACO G - 0.47 2.70 7.14 10.368 0.77 0.75
SACO G - MTC - 0.47 2.70 7.14 10.37 0.77 0.75
SCA G -
SANDA G 0.10 - 1.08 0.75 - 2.36 5.40 7.73
SANDA G -
SCA G 0.10 - 1.08 0.75 - 2.36 5.40 7.73
4.2 INTRAREGIONAL TRIPS
Since the California Statewide High- Speed Rail Model does not explicitly model
intraregional distribution of trips, there are no validation comparisons made for
the distribution models. Since each of the MPO models and the California
Statewide Models is validated for trip distribution, these validations are assumed
to suffice for the purposes of this project. The following are reference reports for
these validations:
· Metropolitan Transportation Commission, Travel Demand Models for the San
Francisco Bay Area ( BAYCAST- 90), Technical Summary, June 1997;
· Cambridge Systematics, SCAG Travel Model Improvement Program Model
Update Documentation, prepared for the Southern California Association of
Governments, July 2005; and
· California Department of Transportation and Dowling Associates, California
Statewide Travel Model Description, January 20, 2004.
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The Sacramento and San Diego urban model files were obtained from these
agencies, but model documentation was not available to review, so discussions
with their modeling staff ensured that the trip tables were the official, adopted
versions as of spring 2006.
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5.0 Mode Choice Model
Calibration
5.1 INTERREGIONAL TRIPS
The mode choice models were a little more complicated to calibrate, since there
was conflicting observed data on boardings, highway volumes, and mode shares.
The observed mode shares were derived from the same three observed data
sources used for trip frequency and destination choice. These observed mode
shares were translated into trips by mode and compared to observed boardings
by mode for air and rail. The observed mode shares resulted in higher estimates
of trips by mode than boardings for both air and rail. Table 5.1 presents a
comparison of the observed datasets. In the case of air boardings, an adjusted
observed value was derived to account for the under- representation in the FAA
dataset for smaller markets. The mode choice calibration targets were then
adjusted to match the observed adjusted boardings for air and the observed
boardings for rail. The final calibration targets for mode shares are reported in
Table 5.2.
Table 5.1 Comparison of Observed Trips by Mode
Air Rail
Observed Trips from Travel Survey Data 61,327 16,006
Observed Boardings from Transit
Operators
48,246 9,610
Difference 13,081 6,396
Adjusted Observed Boardings 55,156
Source of Observed Boardings FAA Amtrak, ACE,
Metrolink
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Table 5.2 Observed Main Mode Shares for Calibration
Short Trips Long Trips
Mode Business Commute
Recreatio
n/
Other
Business/
Commute
Recreatio
n/
Other Total
Trips by Mode
Auto 102,086 461,293 441,190 223,786 220,419 1,448,774
Air - - - 26,139 29,017 55,156
Rail 1,589 2,310 242 932 4,537 9,610
Total 103 , 675 463 , 603 441 , 432 250 , 857 253 , 973 1 , 513 , 540
Mode Shares
Car 98.5% 99.5% 99.9% 89.2% 86.8% 95.7%
Air 0.0% 0.0% 0.0% 10.4% 11.4% 3.6%
Rail 1.5% 0.5% 0.1% 0.4% 1.8% 0.6%
Mode shares were calibrated to match these observed mode shares by mode and
trip purpose. Table 5.3 presents the results of the mode choice model calibration.
Calibration was completed to match mode shares; trips are reported to provide
information on these results. The final results are almost exact in total and quite
close by mode and purpose.
Table 5.3 Main Mode Choice Model Results
Short Trips Long Trips
Mode Business Commute
Recreatio
n/
Other
Business/
Commute
Recreatio
n/
Other Total
Trips by Mode
Auto 102,430 459,160 440,563 221,120 218,669 1,441,942
Air 28,754 27,181 55,935
Rail 1,079 4,537 305 861 2,831 9,613
Total 103 , 509 463 , 697 440 , 868 250 , 735 248 , 681 1 , 507 , 490
Mode Shares
Car 99.0% 99.0% 99.9% 88.2% 87.9% 95.7%
Air 0.0% 0.0% 0.0% 11.5% 10.9% 3.7%
Rail 1.0% 1.0% 0.1% 0.3% 1.1% 0.6%
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The main mode choice model alternative specific constants are presented in
Table 5.4. These constants include the wait time and terminal time, which were
determined to be the same for each mode based on the evaluation of the level- of-service
assumptions. 3 The table includes the actual constant for each mode after
accounting for the effects of the wait time and terminal time components. The
high- speed rail constants were set based on an analysis of the original high- speed
rail constants in the model estimation and the relationship to the air and rail
constants by mode and purpose from the calibrated models. For short trips, the
high- speed rail constant is similar to the rail constant and for long trips, the high-speed
rail constant is between the air and rail constants. The small discrepancy
in the high- speed rail constants for short trips ( i. e., that they do not match exactly
with conventional rail constants) is because the conventional rail constants were
revised after the high- speed rail constants were set and the difference was not
significant enough to revise the high- speed rail constants. For example, the
biggest difference in the high- speed rail constants compared to conventional rail
constants was for short business trips, which account for approximately 8
percent of total high- speed rail trips in the future base conditions and so
adjustments in the high- speed rail constant to make them more consistent would
account for less than a one percent change in overall number of high- speed rail
trips, thus the change was not necessary.
3 Cambridge Systematics, Inc., with Systra Consulting, Inc. and Citilabs, Bay
Area/ California High- Speed Rail Ridership and Revenue Forecasting Study Levels of Service
Assumptions and Forecast Alternatives, prepared for the Metropolitan Transportation
Commission and the California High- Speed Rail Authority, August 2006.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
5- 14 Cambridge Systematics, Inc.
Table 5.4 Main Mode Choice Model Alternative Specific
Constants
Short Trips Long Trips
Business Commute
Recreation/
Other
Business/
Commute
Recreation/
Other
Air Constants
Calibrated
Constant
0.0000 0.0000 0.0000 - 10.2689 - 4.6833
Wait Time
Constant
0.0000 0.0000 0.0000 - 1.9734 - 1.1715
Terminal
Time
Constant
0.0000 0.0000 0.0000 - 0.7894 - 0.4260
Actual
Constant
0.0000 0.0000 0.0000 - 7.5062 - 3.0858
Conventional Rail Constants
Calibrated
Constant
- 6.2316 - 7.1260 - 5.5412 - 4.6197 1.2723
Wait Time
Constant
- 1.5000 - 0.7500 - 0.4305 - 0.5382 - 0.3195
Terminal
Time
Constant
- 0.3000 - 0.1500 - 0.0861 - 0.1076 - 0.0639
Actual
Constant
- 4.4316 - 6.2260 - 5.0246 - 3.9738 1.6557
High- Speed Rail Constants
Calibrated
Constant
- 7.5296 - 6.9635 - 5.6853 - 6.7570 - 0.7132
Wait Time
Constant
- 1.5000 - 0.7500 - 0.4305 - 0.5382 - 0.3195
Terminal
Time
Constant
- 1.0000 - 0.5000 - 0.2870 - 0.3588 - 0.2130
Actual
Constant
- 5.0296 - 5.7135 - 4.9678 - 5.8600 - 0.1807
Auto Constant
Calibrated
Constant
0.0000 0.0000 0.0000 0.0000 0.0000
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Cambridge Systematics, Inc. 5- 15
The access and egress models are calibrated separately from the main mode
choice models. The observed access and egress trips by mode are presented in
Table 5.5. The access and egress mode choice models are calibrated based on
mode shares. The access and egress trips were derived from the model
estimation dataset and are, therefore, not as accurate in the aggregate as an
independent validation data source of trips would be. Nonetheless, this is the
only data source available for access and egress trips.
The accuracy of the access and egress models are not as critical to the resulting
ridership, because the access and egress models are used solely to provide
logsums for access and egress to the main model choice models. As a result, the
tolerance levels of accuracy are looser than they are for the main mode choice
models. In addition, there are certain levels of detail in the statewide model,
such as walk times for larger zones or transit access times, that are not as
accurate as would be needed to adequately capture walk access and egress
modes. Table 5.6 presented the model results for the access and egress models.
The aggregated auto and non- auto access and egress modes are all within +/- 14
percent of the observed mode shares. The final calibration was reasonable based
on these aggregated comparisons.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
5- 16 Cambridge Systematics, Inc.
Table 5.5 Observed Access and Egress Mode Shares by Mode and
Purpose
Short Trips Long Trips
Business Commute
Recreation
/
Other
Business/
Commute
Recreation
/
Other
Drive and park Access 80.7% 81.8% 52.0% 59.7% 24.1%
Egress 14.6% 25.9% 33.8% 12.6% 2.3%
Rental car Access 0.0% 0.0% 0.0% 2.6% 1.3%
Egress 11.6% 3.2% 33.8% 47.6% 34.4%
Drop off Access 12.1% 14.8% 38.5% 20.2% 57.4%
Egress 22.1% 36.8% 0.8% 22.4% 33.1%
Taxi Access 3.0% 1.8% 5.3% 6.8% 7.9%
Egress 48.8% 26.4% 26.4% 16.6% 26.3%
Subtotal Auto Access 95.9% 98.4% 95.9% 89.3% 90.7%
Egress 4.1% 1.6% 4.1% 10.7% 9.3%
Transit Access 3.4% 1.3% 2.9% 8.2% 5.6%
Egress 2.9% 7.3% 5.2% 0.8% 3.6%
Walk/ bike Access 0.8% 0.3% 1.2% 2.5% 3.7%
Egress 0.1% 0.5% 0.0% 0.0% 0.3%
Subtotal Non- Auto Access 97.1% 92.3% 94.8% 99.2% 96.1%
Egress 2.9% 7.7% 5.2% 0.8% 3.9%
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Cambridge Systematics, Inc. 5- 17
Table 5.6 Estimated Access and Egress Mode Shares by Mode and
Purpose
Short Trips Long Trips
Business Commute
Recreation
/
Other
Business/
Commute
Recreation
Other
Drive and park Access 80.3% 60.6% 68.6% 59.5% 52.6%
Egress 14.6% 25.9% 33.8% 12.6% 2.3%
Rental car Access 0.0% 0.0% 0.0% 3.4% 3.0%
Egress 11.6% 3.2% 33.8% 47.6% 34.4%
Drop off Access 9.0% 22.4% 9.0% 20.0% 28.9%
Egress 22.1% 36.8% 0.8% 22.4% 33.1%
Taxi Access 1.8% 1.7% 8.9% 11.2% 7.2%
Egress 48.8% 26.4% 26.4% 16.6% 26.3%
Subtotal Auto Access 91.1% 84.7% 86.5% 94.1% 91.6%
Egress 8.9% 15.3% 13.5% 5.9% 8.4%
Transit Access 8.4% 12.9% 13.4% 5.8% 7.4%
Egress 2.9% 7.3% 5.2% 0.8% 3.6%
Walk/ bike Access 0.5% 2.4% 0.1% 0.0% 1.0%
Egress 0.1% 0.5% 0.0% 0.0% 0.3%
Subtotal Non- Auto Access 97.1% 92.3% 94.8% 99.2% 96.1%
Egress 2.9% 7.7% 5.2% 0.8% 3.9%
Table 5.7 presents the access and egress mode choice model alternative specific
constants. In some cases, these constants are quite large, resulting from small
sample sizes. These were constrained to 5.0 so that forecasts would not be
unrealistic because of the high constants. We do not envision that constraining
these constants is problematic because of the smaller sample sizes for these trip
purpose and access and egress mode combinations.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
5- 18 Cambridge Systematics, Inc.
Table 5.7 Access and Egress Mode Choice Model Alternative
Specific Constants
Short Trips Long Trips
Business Commute
Recreatio
n/
Other
Business/
Commute
Recreatio
n/
Other
Drive and
park
Access 4.1656 5.0000 3.2323 4.9231 4.3564
Egress - 0.6350 - 0.7228 5.0000 1.7505 - 5.4182
Rental car Access 0.0000 0.0000 0.0000 - 5.5471 - 5.0000
Egress - 0.9882 - 5.0000 5.0000 5.9786 1.8267
Drop off Access 0.0000 0.0000 0.0000 0.0000 0.0000
Egress 0.0000 0.0000 0.0000 0.0000 0.0000
Taxi Access - 1.6820 - 4.1039 - 0.0244 1.7710 - 2.1553
Egress 4.6531 - 1.4252 5.0000 5.0000 1.0547
Transit Access 5.0000 5.0000 1.0523 4.3900 - 1.9075
Egress 5.0000 5.0000 5.0000 5.0000 - 3.6551
Walk/ bike Access 5.0000 5.7962 1.3905 5.0000 4.6959
Egress 5.0000 5.0000 5.0000 5.0000 3.0764
5.2 INTRAREGIONAL TRIPS
There are three intraregional models that provide mode choice inputs to the
statewide model – MTC, SCAG, and SANDAG. The MTC model has recently
undergone additional detailed mode choice model validation as part of the
TransBay Study and refinements to the transit and highway assignment
validation were completed in the spring of 2007. Results of the MTC mode
choice model validation are presented in Table 5.8. This shows a close fit to
observed trips by mode overall.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Cambridge Systematics, Inc. 5- 19
Table 5.8 Intraregional Trips by Mode from MTC Model
Mode
Observed
Mode Share Observed Trips
2000 Model
Mode Share Model Trips
Drive Alone 52.6% 9,158,155 52.7% 9,173,350
Shared Ride 2 16.0% 2,791,131 16.1% 2,799,465
Shared Ride
3+ 14.3% 2,481,227 14.3% 2,487,932
BART 1.9% 338,618 2.0% 356,547
Commuter
Rail 0.5% 79,081 0.5% 80,449
LRT 0.5% 85,113 0.5% 91,266
Express Bus 0.5% 83,027 0.3% 56,345
Local Bus 2.4% 410,690 2.4% 418,297
Ferry 0.1% 20,968 0.1% 14,259
Walk/ Bike 11.2% 1,952,600 11.1% 1,937,434
Total 100.0% 17 , 400 , 610 100.0% 17 , 415 , 344
A SCAG mode choice model was developed for this study to include in the
statewide model. This SCAG mode choice model uses SCAG trip tables and
skims and a recalibrated version of the MTC mode choice model to produce peak
and offpeak trips by mode and purpose for the SCAG region. This model was
calibrated to match observed SCAG trips by mode and purpose. The results of
this calibration is provided in Table 5.9. This shows a close fit to observed trips
by mode overall, but an underestimation of the shared ride 2 trips and an
overestimation of drive- alone trips. The transit modes are well validated and so
this discrepancy in the auto vehicle trips is not as much of a concern.
Table 5.9 Intraregional Trips by Mode from SCAG Model
Mode
Observed
Mode Share Observed Trips
2000 Model
Mode Share Model Trips
Drive Alone 46.2% 18,039,255 54.9% 21,466,448
Shared Ride 2 21.6% 8,423,944 11.8% 4,593,150
Shared Ride
3+ 21.3% 8,332,239 22.5% 8,792,319
Urban Rail 0.3% 104,394 0.3% 104,201
Commuter
Rail 0.1% 34,227 0.1% 34,819
Express Bus 0.2% 95,496 0.2% 96,266
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Local Bus 1.6% 634,142 1.7% 664,577
Walk/ Bike 8.8% 3,422,911 8.5% 3,335,080
Total 100.0% 39 , 086 , 607 100.0% 39 , 086 , 859
The SANDAG trips by mode were not available from existing sources, but the
highway and transit assignment validations were available from the Addendum
to the Transportation Model Documentation ( June 2005). These are presented in
Table 5.10.
Table 5.10 Intraregional Volumes by Mode from SANDAG
Model
Volume Mode Observed
2000
Model
Difference
Percent
Difference
Vehicle Miles
Traveled Highway 70,789,214 70,266,732 ( 522,482) - 1%
Boardings Rail 99,906 102,052 2,146 2%
Boardings Bus 229,369 224,161 ( 5,208) - 2%
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Cambridge Systematics, Inc. 6- 1
6.0 Trip Assignment
There are three individual trip assignments by mode to complete the statewide
model validation effort for year 2000. Each assignment is compared to observed
data sources, described in Section 2. The highway and rail assignments include
interregional and intraregional trips; the air assignment includes only
interregional trips because there are no intraregional air trips.
6.1 TRIP TABLES
Trips by mode from the interregional models are combined with intraregional
trips by mode to assign to the highway, air, and rail networks. Table 6.1 presents
a summary of the 2000 interregional trips by mode and market.
Table 6.1 2000 Interregional Trips by Mode
Market Auto Air Rail Total
LA to Sacramento 7,479 4,935 - 12,414
LA to San Diego 257,441 100 5,395 262,936
LA to SF 28,031 26,867 - 54,898
Sacramento to SF 137,739 25 1,816 139,580
Sacramento to San
Diego 175 2,858 - 3,033
San Diego to SF 4,630 10,309 - 14,939
LA/ SF to SJV 205,205 3,393 926 209,524
Other to SJV 281,750 243 344 282,337
To/ From Monterey/
Central Coast 275,794 3,532 1,105 280,431
To/ From Far North 184,506 3,005 16 187,527
To/ From W. Sierra
Nevada 59,192 668 11 59,871
Total 1 , 441 , 942 55 , 935 9 , 613 1 , 507 , 490
The air trips in this summary are assigned to direct flights across the State of
California. It is assumed that transferring to travel within the State is negligible,
so the total boardings on air are equal to the total air trips. For rail, there is the
option to transfer from one rail line to another and the resulting boardings reflect
the number of transfers ( 1.3 boardings per transfer).
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6- 2 Cambridge Systematics, Inc.
Highway trips are converted from person trips to vehicle trips using vehicle
occupancy factors derived from the Caltrans Statewide Travel Survey. These are
presented in Table 6.2.
Table 6.2 2000 Interregional Vehicle Occupancy ( Persons per
Vehicle)
Trip Type Business Commute Recreation Other
Long 1.1872 1.1118 1.7304 1.3107
Short 1.1807 1.1872 1.4946 1.536
In addition, highway trips are separated into peak and offpeak time periods so
that peak and offpeak trip tables can be assigned separately to the highway
network. This ensures that peak- period travel times will more accurately reflect
congestion that occurs in the peak- period. Table 6.3 presents the time period
factors applied by trip purpose.
Table 6.3 2000 Interregional Peaking Factors
Trip Type Business Commute Recreation Other
Peak from Home 46% 49% 39% 43%
Peak to Home 34% 34% 39% 39%
Offpeak from Home 4% 1% 12% 7%
Offpeak to Home 16% 17% 11% 12%
Following the development of peak and offpeak auto vehicle interregional trips,
these are combined with the auto vehicle intraregional trips. These intraregional
trips come from four sources: MTC, SANDAG, SCAG, and Caltrans. The
Caltrans Statewide Model is used to estimate intraregional trips for all the other
regions ( except MTC, SANDAG, and SCAG) so that the auto trip table will be
representing all statewide travel. This ensures that congestion within each
smaller urban area is adequately represented. Table 6.4 summarizes the auto
vehicle trips from each source and provides the resulting total peak and offpeak
auto vehicle trips that are assigned to the highway network.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Cambridge Systematics, Inc. 6- 3
Table 6.4 2000 Auto Vehicle Trips by Mode and Source
Region and Mode Vehicle Trips
MTC Drive Alone 9,173,350
MTC Shared Ride 2 2,799,465
MTC Shared Ride 3 2,487,932
MTC Trucks 252,577
SANDA G Peak 2,852,350
SANDA G Offpeak 3,023,621
SCA G Drive Alone Peak 12,568,822
SCA G Shared Ride 2 Peak 3,118,167
SCA G Shared Ride 3 Peak 1,922,152
SCA G Drive Alone Offpeak 11,399,239
SCA G Shared Ride 2 Offpeak 2,971,802
SCA G Shared Ride 3 Offpeak 1,509,108
SCA G Trucks 1,184,178
Caltrans Statewide ( Remaining Urban Areas) 13,045,337
Interregional 1,049,247
Total Daily 69,357,348
6.2 AIR PASSENGERS
The air passenger boarding validation, presented in Table 6.5, shows a
reasonable comparison of observed to estimated air passengers in every market
except two. The Sacramento to San Diego market is overestimated and the other
market is underestimated, but all other markets match observed boardings quite
closely. The three largest markets match boardings with observed boardings
within +/- 2 percent and the overall total air trips match observed boardings
within +/- 1 percent.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
6- 4 Cambridge Systematics, Inc.
Table 6.5 2000 Air Passenger Boarding Validation
Market
Observed
Adjusted Model Difference
LA to Sacramento 12,308 12,170 ( 138)
LA to San Diego 387 70 ( 317)
LA to SF 29,329 28,890 ( 439)
Sacramento to SF 8 22 14
Sacramento to San Diego 3,848 5,030 1,182
San Diego to SF 8,096 8,263 167
LA/ SF to SJV 140 137 ( 3)
Other 1,040 294 ( 746)
Total 55 , 156 54 , 876 ( 280)
6.3 CONVENTIONAL RAIL PASSENGERS
The rail passenger boarding validation, presented in Table 6.6, shows a
comparison of observed to estimated rail passengers by operator. These include
all conventional rail operators that serve interregional passengers except the
Metrolink Orange line, which travels from Los Angeles Union Station to Sierra
Madre Villa in the San Diego region. The Metrolink Orange line was modeled as
an interregional service, but not validated separately since the majority of the
service was intraregional. The Altamont Commuter Express market is slightly
underestimated and the Amtrak Surfliner is slightly overestimated. The other
rail markets are reasonable. The overall conventional rail assignments are within
+/- 11 percent of observed.
Table 6.6 2000 Rail Passenger Boarding Validation
Market Observed
Intraregio
nal
Models
Interregio
nal Model
20000
Model
Total Difference
Altamont
Commuter Express
( ACE)
3,100 836 451 1,287 ( 1,813)
Amtrak Surfliner 5,100 2,966 5,122 8,088 2,988
Amtrak San
Joaquin
2,110 452 2,350 2,802 692
Amtrak Capital
Corridor
3,300 1,094 1,872 2,966 ( 334)
Total 13 , 610 5 , 348 9 , 795 15 , 143 1 , 533
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Cambridge Systematics, Inc. 6- 5
6.4 HIGHWAY ASSIGNMENT
Table 6.7 presents the highway assignment in four classifications of roadways:
facility type, area type, region, and gateway. There are five facility types; these
are grouped into three categories for this report. The freeways and expressways
reflect the vast majority of vehicle miles traveled on statewide facilities ( 95
percent) and these facilities are within two percent of observed volumes. The
arterials are overestimated but are not the focus of the study given their limited
use for interregional travel. Additional network review and highway validation
could improve these results. The highway assignment compares well to
observed volumes by area type. All categories are within +/- 14 percent of
observed.
The highway assignment summarized by region shows that the regions of
significance to the high- speed rail study are all within +/- 20 percent of observed
volumes, except for the SCAG region, which does not reflect the full set of counts
in the region. These will be included in the final report consistent with the Final
EIS. The Central Coast and Far North regions are outside this target, but are well
outside the proposed high- speed rail corridor so this is not a concern. In
addition, these regions are not congested, so this underestimation of volumes
does not significantly affect travel times across the State.
The gateways established for this study are located in key corridors for high-speed
rail and are consistent with the previous set of travel markets evaluated for
the trip tables. There are six gateways established. All gateways are within +/-
15 percent of observed. Although both the Altamont and Pacheco passes are
underestimated slightly, they are well balanced so that there is not a bias
towards one pass over the other for the highway validation.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
6- 6 Cambridge Systematics, Inc.
Table 6.7 2000 Highway Assignment Validation
Classification Locations Observed Model Difference
Percent
Difference
Vehicle Miles Traveled By Facility Type
Freeways/ Expressways 1,155 54,807,094 55,666,538 859,443 2%
Major Arterials 179 2,760,912 3,764,260 1,003,348 36%
Minor Arterials/ Collectors 25 144,513 148,993 4,422 3%
Total 1 , 359 57 , 712 , 519 59 , 579 , 791 1 , 867 , 213 3%
Vehicle Miles Traveled By Area Type
Rural 836 29,959,583 28,096,076 ( 1,863,506) - 6%
Suburban 133 4,321,742 4,784,532 462,790 11%
Urban 390 23,431,194 26,699,182 3,267,987 14%
Total 1 , 359 57 , 712 , 519 59 , 579 , 791 1 , 867 , 271 3%
Vehicle Miles Traveled By Region
AMBA G 39 2,166,435 1,572,883 ( 593,552) - 27%
Central Coast 70 1,756,734 3,054,418 1,297,684 74%
Far North 258 4,684,264 6,763,302 2,079,038 44%
Fresno 46 2,470,711 2,150,050 ( 320,661) - 13%
Kern 83 3,731,189 3,342,222 ( 388,967) - 10%
Merced 64 2,092,094 1,717,837 ( 374,257) - 18%
MTC 176 7,975,231 7,653,524 ( 321,707) - 4%
SACO G 150 8,416,323 8,495,630 79,308 1%
San Joaquin 90 3,328,091 3,997,801 669,710 20%
SANDA G 141 15,417,924 15,186,348 ( 231,576) - 2%
SCA G 16 638,858 466,960 ( 171,898) - 27%
South San Joaquin 20 778,733 697,951 ( 80,782) - 10%
Stanislaus 44 1,423,711 1,690,356 266,645 19%
W. Sierra Nevada 162 2,832,222 2,790,509 ( 41,713) - 1%
Total 1 , 359 57 , 712 , 519 59 , 579 , 791 1 , 867 , 271 3%
Volumes By Gateway
SAC to SF on I- 80 4 115,536 127,788 12,252 11%
SAC to SJV on I- 5 and SR- 99 4 109,365 112,105 2,740 3%
SJV to SF on I- 580 ( Altamont Pass) 4 111,500 95,831 ( 15,669) - 14%
SJV to SF on SR- 152 ( Pacheco
Pass) 2 20,728 17,705 ( 3,023) - 15%
SJV to LA on I- 5 and SR- 14 4 78,927 86,910 7,983 10%
LA to SD on I- 5 and I- 15 4 442,951 451,154 8,203 2%
Total 22 897,651 891,491 ( 6,160) - 1%
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Cambridge Systematics, Inc. 7- 1
7.0 2030 Forecast
Comparison of the 2030 forecast to a No- Build scenario is completed for
validation to ensure that the 2030 forecasts are reasonable for each model
component. This 2030 forecast uses a no- build future scenario, based on
highway, air, and conventional rail networks developed from state and regional
transportation plans. These are described in more detail in the level- of- service
assumptions report. 4 The summaries of the 2030 forecasts contained herein focus
on the interregional models.
At the heart of any travel forecast is the growth in population and employment.
Since the California statewide model is based on households, we present growth
based on households and employment in Table 7.1. This table shows that the
three largest urban areas ( SANDAG, MTC, and SCAG) are growing slower than
the average, which is intuitive since these areas are more saturated than other
parts of the State. The jobs/ housing balance also is presented in this table as it is
an indicator of higher numbers of interregional commuting trips.
7.1 TRIP FREQUENCY
Trip frequency models for the 2030 No- Build are presented in Table 7.2 for short
and Table 7.3 for long trips by trip purpose. The trip frequency models are
sensitive to changes in level of service and demographics over time. The three
largest metropolitan areas are growing slower than the average because of
growing congestion in these areas and slower than average growth in households
and employment. The highest growth for interregional travel is beyond the three
largest metropolitan areas and is consistent with growth in households and
employment for these areas.
On average, the short interregional trips are growing faster than the long
interregional trips. As people move further away from the metropolitan regions to
find affordable housing, the short interregional travel will increase due to people
continuing to work, shop, and recreate within the metropolitan region where they
moved from.
The San Francisco region is growing slower than other regions for long
interregional trips. This is primarily due to the slower growth in population in this
region, but it also may be due to increasing congestion in this area. The Los
4 Cambridge Systematics, Inc., with Systra Consulting, Inc. and Citilabs, Bay
Area/ California High- Speed Rail Ridership and Revenue Forecasting Study Levels of Service
Assumptions and Forecast Alternatives, prepared for the Metropolitan Transportation
Commission and the California High- Speed Rail Authority, August 2006.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
7- 2 Cambridge Systematics, Inc.
Angeles region also is growing slightly slower than the average, and has
significant congestion.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
7- 0 Cambridge Systematics, Inc.
Table 7.1 Socioeconomic Forecasts from 2000 to 2030 by Region
Households Employment Jobs/ Housing Balance
2000 2030
Percent
Increase 2000 2030
Percent
Increase 2000 2030
Percent
Increase
AMBAG 226,349 395,441 75% 286,937 436,375 52% 1.27 1.10 - 13%
Central Coast 227,200 401,239 77% 278,494 450,495 62% 1.23 1.12 - 8%
Far North 376,965 627,223 66% 335,737 522,003 55% 0.89 0.83 - 7%
Fresno / Madera 287,110 548,238 91% 365,397 678,779 86% 1.27 1.24 - 3%
Kern 207,413 466,354 125% 242,283 707,973 192% 1.17 1.52 30%
South SJ Valley 144,050 271,292 88% 170,813 336,862 97% 1.19 1.24 5%
Merced 63,225 125,340 98% 63,403 130,513 106% 1.00 1.04 4%
SACOG
571,978 916,754 60% 946,259 1,469,05
2
55% 1.65 1.60 - 3%
SANDAG
988,205 1,308,48
5
32% 1,168,88
0
1,875,81
4
60% 1.18 1.43 21%
San Joaquin 180,276 341,264 89% 202,498 345,824 71% 1.12 1.01 - 10%
Stanislaus 143,942 311,502 116% 159,900 354,452 122% 1.11 1.14 2%
W. Sierra Nevada 68,929 110,728 61% 55,358 99,057 79% 0.80 0.89 11%
MTC
2,465,28
7
3,090,73
9
25% 3,753,53
3
5,120,59
8
36% 1.52 1.66 9%
SCAG 5,538,290 7,739,062 40% 7,406,280 10,089,794 36% 1.34 1.30 - 3%
Total 11,491,219 16,655,692 45% 15,437,772 22,619,621 47% 1.34 1.36 1%
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Table 7.2 Trip Frequency Model Results for Short Trips
Short
Region Commute Business Recreation Other 2030 Trips 2000 Trips
Percent
Increase
Sacramento Region ( SACO G ) 62,787 19,410 28,954 45,493 156,644 83,546 87%
San Diego Region ( SANDA G ) 37,914 18,767 10,105 10,426 77,212 59,160 31%
San Francisco Region ( MTC) 49,692 28,620 37,298 24,287 139,897 99,617 40%
Los Angeles Region ( SCA G ) 49,704 13,553 48,419 72,508 184,184 141,357 30%
Remainder of CA 544,643 88,295 230,834 288,977 1,152,749 624,394 85%
Total 744 , 740 168 , 645 355 , 610 441 , 691 1 , 710 , 686 1 , 008 , 074 70%
Table 7.3 Trip Frequency Model Results for Long Trips
Long
Region Commute Business Recreation Other 2030 Trips 2000 Trips
Percent
Increase
Sacramento Region ( SACO G ) 22,421 7,730 29,024 8,531 67,706 45,230 50%
San Diego Region ( SANDA G ) 30,554 8,837 36,441 12,409 88,241 56,511 56%
San Francisco Region ( MTC) 20,520 11,158 122,119 21,130 174,927 136,663 28%
Los Angeles Region ( SCA G ) 49,588 26,168 97,525 34,039 207,320 142,138 46%
Remainder of CA 145,539 33,996 24,075 1,844 205,454 118,874 73%
Total 268 , 622 87 , 889 309 , 184 77 , 953 743 , 648 499 , 416 49%
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7.2 DESTINATION CHOICE
The primary contributors to growth in destination choice are the growth in
employment ( and to a lesser degree households), and changes in level of service
for auto and in certain markets air. Table 7.4 presents the 2030 destination choice
model results for 2030 by market and trip purpose.
The long distance markets are more affected by changes in air level of service,
because they have higher shares of air travel overall. The San Francisco and Los
Angeles regions have the lowest percent growth in employment and the lowest
percent growth market overall. This market also has higher air headways
between 2000 and 2030, which would tend to lower demand for travel in this
market. The Sacramento to San Diego market and markets into and out of the
San Joaquin Valley have the highest percent growth overall with higher than
average growth in employment. The Sacramento to Los Angeles market has
equal headways from 2000 to 2030 for several key airports and as a result, higher
growth in overall travel for this market.
The short distance markets are more affected by increasing congestion for autos
and growth in employment. The two slowest growing markets are Los Angeles
to San Diego and San Francisco to Sacramento. Both are affected by slower
growth in employment and by growing congestion in Los Angeles and San
Francisco. The fastest growing market is into and out of the San Joaquin Valley,
primarily because this area has higher growth in employment than anywhere
else.
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Table 7.4 Destination Choice Model Results for Short and Long Trips
Short Long
Region
Commut
e Business
Recreati
on Other
Commut
e Business
Recreati
on Other
2030
Model
Trips
2000
Model
Trips
LA to Sacramento 0 0 0 0 4,933 2,281 10,403 3,124 20,741 12,414
LA to San Diego 64,036 21,548 48,656 31,283 33,020 12,194 116,182 39,937 366,856 262,936
LA to SF 0 0 0 0 14,010 7,402 28,819 5,373 55,604 54,898
Sacramento to SF 46,747 27,070 24,827 17,435 18,435 7,832 36,002 8,320 186,668 139,580
Sacramento to San
Diego
0 0 0 0 1,833 539 2,353 697 5,422 3,033
San Diego to SF 0 0 0 0 6,462 1,948 12,653 2,234 23,297 14,939
LA/ SF to SJV 134,393 23,892 30,182 55,096 61,510 20,673 43,376 6,901 376,023 209,524
Other to SJV 228,722 41,916 112,204 137,696 31,452 8,015 132 65 560,202 282,337
To/ From
Monterey/ Central
Coast
155,143 25,851 61,126 75,784 53,576 15,508 39,538 7,493 434,019 280,431
To/ From Far North 75,652 22,145 62,876 108,384 34,108 8,824 12,919 2,678 327,586 187,527
To/ From W. Sierra
Nevada
40,047 6,223 15,739 16,013 9,283 2,673 6,807 1,131 97,916 59,871
Total 744 , 740 168 , 645 355 , 610 441 , 691 268 , 622 87 , 889 309 , 184 77 , 953 2 , 454 , 33
4
1 , 507 , 49
0
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7.3 MODE CHOICE
Table 7.5 presents the 2030 mode choice model trips by mode and mode shares.
Conventional rail and air trips increase from 2000 and 2030 due to overall growth
and increasing congestion on the highway system. Air travel would have
increased at a greater rate than shown, but the air headways in many air markets
increase from 2000 to 2005 ( and beyond to 2030). This results in a decrease in air
mode shares as a percent of total trips from 2000 to 2030. Both short and long
business and commute trips have greater increases in mode shares for air and
rail due to increasing highway congestion in the peak- periods. Table 7.6 presents
the full 2030 interregional trips by mode for each travel market.
Table 7.5 2030 Main Mode Choice Model Results
Short Trips Long Trips
Mode Business Commute
Recreatio
n/
Other
Business/
Commute
Recreatio
n/
Other 2030 Total
2000
Model
Trips by Mode
Auto 154,370 729,742 795,597 300,994 339,864 2,320,567 1,441,942
Air - - 44,317 37,351 81,668 55,935
Rail 14,275 14,998 1,704 11,200 9,922 52,099 9,613
Total 168 , 645 744 , 740 797 , 301 356 , 511 387 , 137 2 , 454 , 334 1 , 507 , 490
Mode Shares
Car 91.5% 98.0% 99.8% 84.4% 87.8% 94.5% 95.7%
Air 0.0% 0.0% 0.0% 12.4% 9.6% 3.4% 3.7%
Rail 8.5% 2.0% 0.2% 3.1% 2.6% 2.1% 0.6%
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Table 7.6 2030 Interregional Trips by Mode
Market Auto Air Rail Total
LA to Sacramento 12,636 8,105 - 20,741
LA to San Diego 340,862 96 25,898 366,856
LA to SF 30,253 25,351 - 55,604
Sacramento to SF 174,844 26 11,798 186,668
Sacramento to San
Diego 164 5,258 - 5,422
San Diego to SF 5,038 18,259 - 23,297
LA/ SF to SJV 360,177 9,609 6,237 376,023
Other to SJV 553,466 1,944 4,792 560,202
To/ From Monterey/
Central Coast 426,056 5,886 2,077 434,019
To/ From Far North 320,667 5,957 962 327,586
To/ From W. Sierra
Nevada 96,404 1,177 335 97,916
Total 2 , 320 , 567 81 , 668 52 , 099 2 , 454 , 334
7.4 TRIP ASSIGNMENT
Overall, the highway vehicle miles traveled increase at a faster rate than air and
rail boardings because the highway volumes include the fastest growing portions
of the State, which are not the predominant air and rail markets. These are faster
growing by percent growth, but the majority of the growth still resides in the
four major metropolitan areas ( San Diego, San Francisco, Los Angeles, and
Sacramento). The summary of 2030 no- build trip assignments is provided in
Table 7.7, compared to 2000.
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Table 7.7 2030 and 2000 Assignments by Mode
Mode and Volume 2000 Model 2030 Model
Growth Percent
Growth
Air Boardings 54,876 80,643 25,767 47%
Rail Boardings 30,287 37,421 7,134 24%
Auto Vehicle Miles
Traveled 748,606,510 1,297,116,168 548,509,657 73%
Note: The Auto vehicle miles traveled in this table do not match the remaining
auto assignment summaries, because these include all vehicle miles
traveled, when the remaining tables include only selected highway
segments.
Air Passengers
Table 7.8 presents the summary of air passenger boardings for the 2030 no- build
scenario compared to year 2000. Ninety percent of the overall increase in air
passenger boardings is contained in three markets – Los Angeles ( SCAG) region
to Sacramento ( SACOG) region, Los Angeles ( SCAG) region to San Francisco
( MTC) region, and San Diego ( SANDAG) region to San Francisco ( MTC) region.
The decrease in the air boardings from the Los Angeles ( SCAG) and San
Francisco ( MTC) regions into and out of the San Joaquin Valley is due to reduced
air headways in this market. The other market includes airports at Monterey,
Santa Barbara, and Eureka.
Table 7.8 2030 and 2000 Air Passenger Boardings
Market 2030 Model 2000 Model Difference
Percent
Difference
LA to Sacramento 19,629 12,170 7,459 61%
LA to San Diego 134 70 64 91%
LA to SF 35,491 28,890 6,601 23%
Sacramento to SF 24 22 2 9%
Sacramento to San
Diego 6,636 5,030 1,606 32%
San Diego to SF 17,449 8,263 9,186 111%
LA/ SF to SJV 102 137 ( 35) - 26%
Other 1,178 294 884 301%
Total 80 , 643 54 , 876 25 , 767 47%
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Rail Passengers
Table 7.8 presents the rail passenger boardings for 2030 and 2000 models. The
Amtrak San Joaquin grows by more than any other operator, probably because
this is not a viable air market ( too short) and it serves a growing travel demand.
The combination of the Metrolink Orange line and the Amtrak Surfliner is
reasonable growth in rail traffic for the Los Angeles to San Diego corridor.
Table 7.9 2030 and 2000 Rail Passenger Boardings
Operator 2030 Model 2000 Model Difference
Percent
Difference
Altamont Commuter
Express ( ACE) 1,888 1,287 601 47%
Amtrak Capital
Corridor 4,854 2,966 1,888 64%
Amtrak San Joaquin 6,538 2,802 3,736 133%
Metrolink Orange 8,125 5,613 2,512 45%
Amtrak Surfliner 13,594 8,088 5,505 68%
Total 34 , 999 20 , 756 14 , 242 69%
Auto Passengers
Table 7.9 presents a summary of the highway assignments for the 2030 no- build
and 2000 model runs by facility type, area type, region, and gateway. These
include selected highway segments only, based on the locations where counts
were used in auto assignment validation. The percentage growth in traffic is
focused on arterials, rural areas, and fast growing parts of the State, but the
absolute growth is still focused in the major metropolitan areas and major
markets ( as defined for the air and rail assignment summaries).
The arterials and collectors are growing at a faster rate than the freeways, but
still contribute only a small portion of overall traffic. Urban streets are growing
at a slower rate than rural streets. The San Diego ( SANDAG) and Los Angeles
( SCAG) regions have the largest growth in highway volumes; San Diego does
have high growth in employment and the highest increase in jobs/ housing
balance of all regions. Both will contribute to higher travel demand for
interregional travel. The Southern California gateways are growing at a faster
rate than the Northern California gateways. This is due, in part, to the higher
growth rates for auto trips in these regions.
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Table 7.10 2030 Highway Assignment Validation
Classification
Number of
Locations 2000 Model 2030 Model Growth
Percent
Growth
Vehicle Miles Traveled By Facility Type
Freeways/ Expressways 1,091 54,807,094 121,566,187 66,759,093 122%
Major Arterials 241 2,760,912 10,715,137 7,954,225 288%
Minor Arterials/
Collectors 27 144,513 559,012 414,499 287%
Total 1,359 57,712,519 132,840,336 75,127,817 130%
Vehicle Miles Traveled By Area Type
Rural 836 29,959,583 71,861,363 41,901,781 140%
Suburban 133 4,321,742 8,415,013 4,093,270 95%
Urban 390 23,431,194 52,563,960 29,132,766 124%
Total 1,359 57,712,519 132,840,336 75,127,817 130%
Vehicle Miles Traveled By Region
AMBA G 39 2,166,435 3,713,826 1,547,391 71%
Central Coast 70 1,756,734 2,898,109 1,141,375 65%
Far North 258 4,684,264 9,485,713 4,801,449 103%
Fresno 46 2,470,711 4,728,370 2,257,659 91%
Kern 83 3,731,189 8,199,171 4,467,982 120%
Merced 64 2,092,094 4,391,265 2,299,171 110%
MTC 174 7,975,231 9,914,790 1,939,560 24%
SACO G 152 8,416,323 17,686,025 9,269,703 110%
San Joaquin 110 3,328,091 6,560,230 3,232,140 97%
SANDA G 141 15,417,924 53,976,946 38,559,022 250%
SCA G 16 638,858 1,837,889 1,199,031 188%
South San Joaquin 20 778,733 1,316,790 538,056 69%
Stanislaus 44 1,423,711 2,563,491 1,139,780 80%
W. Sierra Nevada 162 2,832,222 5,567,720 2,735,498 97%
Total 1,359 57,712,519 132,840,336 75,127,817 130%
Volumes By Gateway
SAC to SF on I- 80 4 127,788 209,540 81,752 64%
SAC to SJV on I- 5 and
SR- 99 4 112,105 223,089 110,985 99%
SJV to SF on I- 580
( Altamont Pass) 4 95,831 167,576 71,745 75%
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SJV to SF on SR- 152
( Pacheco Pass) 2 17,705 35,330 17,625 100%
SJV to LA on I- 5 and SR-
14 4 86,910 234,238 147,328 170%
LA to SD on I- 5 and I- 15 4 451,154 1,083,777 632,623 140%
Total 22 891,491 1,953,550 1,062,058 119%
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8.0 Summary
The 2000 and 2030 no- build forecasting model for the State of California
described in this report provide reasonable and logical estimates of trips by
mode and highway, air, and conventional rail assignments. These estimates
have been compared to observed values for the following model components:
· Trip frequency by purpose and distance class for the four major metropolitan
areas and the remainder of the State;
· Origin and destination patterns for 14 regions in California and 11 major
travel markets ( origin and destination pairs for major metropolitan areas) by
purpose and distance class;
· Mode shares for air, conventional rail, and auto trips by purpose and
distance class;
· Access and egress mode shares ( drive and drop, rental car, taxi, drive and
park, transit, and walk) for air and conventional rail trips by purpose and
distance class;
· Air boardings for seven major travel markets in California;
· Conventional rail boardings for interregional rail operators in California; and
· Auto vehicle assignments by facility type, area type, region, and gateway
compared to traffic counts.
This report also contains details on the model calibration process and the
resulting alternative specific constants used in each model component.
These models were calibrated and validated for use in forecasting high- speed rail
ridership for the Draft Environmental Impact Statement for the Bay Area to
Central Valley High- Speed Train Program. 5 This Draft Report and other
supporting technical documentation is provided on the CHSRA web site. 6 While
no calibration and validation process is ever perfect, the process used herein has
focused on the most important characteristics and geographies that will impact
high- speed rail ridership, to ensure the reliability of these forecasts. The results
were reviewed extensively by the consultant team and members of the MTC and
CHSRA staff.
5 California High- Speed Rail Authority, Draft Bay Area to Central Valley High- Speed Train
( HST) Program Environmental Impact Report/ Environmental Impact Statement ( EIR/ EIS),
June 2007.
6 http:// www. cahighspeedrail. ca. gov/ ridership/