July 2007 www. camsys. com
Bay Area/ California High- Speed Rail Ridership
and Revenue Forecasting Study
prepared for
Metropolitan Transportation Commission and the California High-
Speed Rail Authority
prepared by
Cambridge Systematics, Inc.
with
Corey, Canapary & Galanis
Mark Bradley Research & Consulting
HLB Decision Economics, Inc.
SYSTRA Consulting, Inc.
Citilabs
draft final
report
draft final report
Bay Area/ California High- Speed
Rail Ridership and Revenue
Forecasting Study
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
Corey, Canapary & Galanis
Mark Bradley Research & Consulting
HLB Decision Economics, Inc.
SYSTRA Consulting, Inc.
Citilabs
date
July 2007
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Cambridge Systematics, Inc. i
7530.009
Table of Contents
1.0 Introduction ......................................................................................................... 1- 1
1.1 Overview...................................................................................................... 1- 1
1.2 Contents of The Report and Related Reports ......................................... 1- 1
2.0 Model System Overview................................................................................... 2- 1
2.1 Interregional Models .................................................................................. 2- 4
2.2 Intraregional Models.................................................................................. 2- 8
3.0 Data Collection.................................................................................................... 3- 1
3.1 Travel Surveys............................................................................................. 3- 1
3.2 Socioeconomic Data.................................................................................... 3- 2
3.3 Base Year Travel Patterns .......................................................................... 3- 3
4.0 Existing Modal Services .................................................................................... 4- 1
4.1 Air Service.................................................................................................... 4- 1
4.2 Highway Supply and Traffic Counts....................................................... 4- 3
4.3 Passenger Rail Services .............................................................................. 4- 6
5.0 Ridership Model Development ....................................................................... 5- 1
5.1 Interregional Models .................................................................................. 5- 1
Trip Frequency............................................................................................ 5- 1
Destination Choice...................................................................................... 5- 3
Mode Choice................................................................................................ 5- 4
5.2 Intraregional Models.................................................................................. 5- 8
MTC Regional Mode Choice Models....................................................... 5- 8
SCAG Regional Mode Choice Models................................................... 5- 10
California Statewide Auto Trip Tables .................................................. 5- 11
5.3 Model Validation ...................................................................................... 5- 11
2000 Trip Tables ........................................................................................ 5- 12
2000 Assignments by Mode..................................................................... 5- 12
2030 Baseline Forecasts ............................................................................ 5- 13
6.0 Level of Service Assumptions .......................................................................... 6- 1
6.1 Cost ............................................................................................................... 6- 1
Auto Operating Costs ................................................................................ 6- 2
Bridge Tolls.................................................................................................. 6- 2
Table of Contents, continued
ii Cambridge Systematics, Inc.
7530.009
Line- Haul Fares........................................................................................... 6- 2
Access- Egress Costs.................................................................................... 6- 3
6.2 Travel Times ................................................................................................ 6- 4
Line- Haul Times ......................................................................................... 6- 4
Frequencies .................................................................................................. 6- 4
Access- Egress Times................................................................................... 6- 5
Wait Times................................................................................................... 6- 5
Terminal Times ........................................................................................... 6- 6
Transfer Times............................................................................................. 6- 7
Total Travel Times ...................................................................................... 6- 7
6.3 Reliability ..................................................................................................... 6- 8
6.4 Future No- Project Networks................................................................... 6- 10
7.0 Ridership and Revenue Forecasts.................................................................... 7- 1
7.1 Sensitivity Tests .......................................................................................... 7- 1
7.2 Network Alternatives................................................................................. 7- 2
7.3 Alignment Alternatives ............................................................................. 7- 6
7.4 Combined Altamont and Pacheco Alternatives..................................... 7- 9
8.0 Peer Review.......................................................................................................... 8- 1
8.1 First Peer Review ........................................................................................ 8- 2
8.2 Second Peer Review ................................................................................... 8- 4
Model Development................................................................................... 8- 4
Forecast Assumptions ................................................................................ 8- 6
8.3 Third Peer Review ...................................................................................... 8- 7
9.0 Conclusions.......................................................................................................... 9- 1
9.1 Ridership Forecasts .................................................................................... 9- 1
9.2 Potential Model Improvements................................................................ 9- 2
9.3 Acknowledgments...................................................................................... 9- 3
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List of Tables
Table 3.1 Total of All Survey Interregional Trips by Mode, Distance,
and Purpose ............................................................................................. 3- 1
Table 3.2 Socioeconomic Forecasts from 2000 to 2030 by Region ..................... 3- 3
Table 3.3 2000 Average Daily Interregional Trips Over 100 Miles ( Long)....... 3- 5
Table 3.4 2000 Average Daily Interregional Trips Under 100 Miles
( Short) ....................................................................................................... 3- 5
Table 4.1 Annual Intrastate Passengers for California Airports ....................... 4- 2
Table 4.2 California Statewide Conventional Rail Passengers .......................... 4- 8
Table 5.1 2000 Daily Interregional Trips by Mode ............................................ 5- 12
Table 5.2 2000 Daily Assignments by Mode ...................................................... 5- 13
Table 5.3 2030 Daily Interregional Trips by Mode ............................................ 5- 14
Table 5.4 2000 and 2030 Assignments by Mode ................................................ 5- 14
Table 6.1 Total Peak Travel Times by Mode for Selected City Pairs ................ 6- 8
Table 7.1 Sensitivity Tests for High- Speed Rail................................................... 7- 1
Table 7.2 Pacheco Pass Network Alternative Results......................................... 7- 3
Table 7.3 Altamont Pass Alternative Results ....................................................... 7- 4
Table 7.4 Pacheco Pass Alignment Alternative Results...................................... 7- 7
Table 7.5 Altamont Pass Alignment Alternative Results ................................... 7- 8
Table 7.6 Combined Altamont/ Pacheco Alternative Results.......................... 7- 11
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List of Figures
Figure 2.1 California Urban Areas and High- Speed Rail Station
Locations................................................................................................... 2- 2
Figure 2.2 Integrated Modeling Process................................................................. 2- 3
Figure 2.3 Interregional Model Structure............................................................... 2- 4
Figure 2.4 Market Segments in Each Model .......................................................... 2- 7
Figure 2.5 Model Component Linkages ................................................................. 2- 8
Figure 4.1 California Statewide Air Network........................................................ 4- 3
Figure 4.2 New Statewide Model Highway Network.......................................... 4- 4
Figure 4.3 Caltrans Count Stations ( Red) and Screenline Locations ( Blue)....... 4- 5
Figure 4.4 California Statewide Conventional Rail Network.............................. 4- 7
Figure 5.1 Access and Egress Mode Choice Model Structure ............................. 5- 4
Figure 5.2 Main Mode Choice Model Structure .................................................... 5- 6
Figure 5.3 MTC Updated Mode Choice Structure for Home- Based Work
Peak ........................................................................................................... 5- 9
Figure 5.4 Updated MTC Mode Choice Model Structure for Nonwork
and Off- Peak Models ............................................................................ 5- 10
1.0 Introduction
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1.0 Introduction
1.1 OVERVIEW
The California High- Speed Rail Authority ( CHSRA) and the Metropolitan
Transportation Commission ( MTC) are developing an innovative statewide
model to support evaluation of high- speed rail alternatives in the State of
California. This statewide model will also support future planning activities of
the California Department of Transportation ( Caltrans). The approach to this
statewide model explicitly recognizes the unique characteristics of intraregional
travel demand and interregional travel demand. As a result, interregional travel
models capture behavior important to longer- distance travel, such as induced
trips, business and commute decisions, recreational travel, attributes of destina-tions,
reliability of travel, party size, and access and egress modal options.
Intraregional travel models rely on local highway and transit characteristics and
behavior associated with shorter- distance trips ( such as commuting and shopping).
The project objectives were to develop a new ridership forecasting model that
would serve a variety of planning and operational purposes:
• To evaluate high- speed rail ridership and revenue on a statewide basis;
• To evaluate potential alternative alignments for high- speed rail into and out
of the San Francisco Bay Area; and
• To provide a foundation for other statewide planning purposes and for
regional agencies to better understand interregional travel.
The core model design feature is the recognition that interregional and urban
area travel is distinct and should be modeled separately to capture these distinc-tions
accurately. This led to our approach to develop separate, but integrated,
interregional and intraregional models. There are two primary reasons for
developing separate models for interregional and urban area travel: first, the trip
purposes are different and second, the interregional travel models need to
explicitly estimate induced demand. These models are applied to both peak and
off- peak conditions for an average weekday. Weekend travel demand and
annual ridership estimates are developed using annualization factors developed
from observed data on high- speed rail systems around the world.
1.2 CONTENTS OF THE REPORT AND RELATED
REPORTS
This executive summary is an overview of the full project, but the details of the
work conducted are documented in separate task reports. All relevant reports
are detailed below.
Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
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• Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Findings from the First Peer Review Panel Meeting, Cambridge Systematics, Inc.,
July 2005.
• Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Findings from the Second Peer Review Panel Meeting, Cambridge Systematics,
Inc., July 2006.
• Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Model Design, Data Collection, and Performance Measures, Cambridge
Systematics, Inc.; with Citilabs; Corey, Canapary & Galanis; HLB Decision
Economics; Mark Bradley Research and Consulting; and SYSTRA Consulting,
May 2005.
• Metropolitan Transportation Commission High- Speed Rail Study, Overview and
Documentation of Surveys ( Air/ Rail/ Auto Trips), Corey, Canapary & Galanis,
December 2005.
• 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.
• 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
Statewide Model Networks, Cambridge Systematics, Inc., July 2007.
• Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Statewide Model Validation, Cambridge Systematics, Inc., March 2007.
• 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.
• Bay Area/ California High- Speed Rail Ridership and Revenue Forecasting Study
Ridership and Revenue Forecasts, Cambridge Systematics, Inc., July 2007.
These reports are available from the MTC or the CHSRA1.
There are nine sections in this report:
1. The introduction;
2. An overview of the model system;
3. A summary of the data collection;
4. Descriptions of the modal networks;
1 http:// www. cahighspeedrail. ca. gov/ ridership/.
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5. An overview of the model development by component, along with model
validation and the 2030 no project forecasts;
6. Forecast assumptions by mode;
7. Ridership and revenue forecasts;
8. Peer review panel; and
9. A final summary of the forecasting process and potential model improve-ments,
along with acknowledgments for the work.
Data sources include travel surveys, ridership counts, and traffic volumes.
Model components include trip frequency, destination choice, mode choice, and
trip assignment models.
2.0 Model System Overview
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2.0 Model System Overview
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 14 regions in the
State, as shown in Figure 2.1. The intraregional trips for the San Francisco and
Los Angeles metropolitan regions are developed by integrating their regional
travel forecasting models with new mode choice models that identify potential
high- speed rail riders. In addition, high- speed rail riders were estimated for the
San Diego region using existing and previous forecasting data sources. The
metropolitan planning organizations ( MPO) representing these areas are the
MTC, the San Diego Association of Governments ( SANDAG), and the Southern
California Association of Governments ( SCAG). None of the other California
regions have more than one proposed high- speed rail station and do not gener-ate
intraregional high- speed rail trips, so mode choice models for these regions
were not necessary. Instead, intraregional auto trips were estimated from the
Caltrans Statewide Model2 and included in auto assignments to accurately reflect
congestion for these other regions.
Interregional trips include all trips with both ends in California and whose ori-gin
and destination are in different regions ( shown in Figure 2.1). These interre-gional
trips were estimated using a new set of estimated models, derived from
survey data collected for this study combined with other relevant survey data
sources. The model estimates all interregional trips by purpose and length,
identifies which region the interregional trips will be going to, and then esti-mates
which access, egress, and line- haul mode the interregional trip will use.
External trips include trips with one end outside California and one end in an
urban area with a proposed high- speed rail station. External auto trips were
included in auto assignments to accurately reflect the congestion caused by these
external trips, but air and rail trips were not included explicitly.
2 California Department of Transportation and Dowling Associates, Inc., California
Statewide Model Description, January 20, 2004.
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Figure 2.1 California Urban Areas and High- Speed Rail Station Locations
We recognize that some intraregional trips may be longer than some interre-gional
trips by this definition and vice- versa. However, these definitions do
clearly fit in with regional 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 interre-gional
trip), which is similar in distance to a trip from Palmdale to Los Angeles
( defined as an intraregional trip). Even taking these anomalies into considera-tion,
there was consensus that the definition of intraregional and interregional
trips fits 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 addi-tion,
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.
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Trip assignment includes the merging of the intraregional, 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 interre-gional
models explicitly model peak and off- peak travel for both intraregional
and interregional trip movements.
The integrated modeling process for the development of the statewide model is
presented in Figure 2.2. 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.
Figure 2.2 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
There are 14 regions established in the State that define interregional and
intraregional travel. An interregional trip is any trip that terminates in a differ-ent
region that it started in. Accordingly, an intraregional trip terminates in the
same region that it began. Interregional models estimate trip frequency, destina-tion
choice, and mode choice stratified by trip purpose ( business, commute, rec-reation,
and other), as well as by distance ( trips greater than or less than
100 miles) and by trip type ( trips made by residents of the four largest cities in
California versus other trips). The interregional trip frequency models allow
estimate induced travel based on improved accessibilities due to high- speed rail
options. Intraregional models are based on trip tables generated from the MPO
models and estimate mode choice of urban area trips. These mode choice models
reflect local urban area highway and transit systems, as well as options for high-
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speed rail within the region. Intraregional travel is stratified by trip purpose
( work, school, college, other, and nonhome- based).
The interregional and intraregional area models are based on travel survey data
collected for these purposes. These are further described below.
2.1 INTERREGIONAL MODELS
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 2.3.
Figure 2.3 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
Unpark Picked Up Rental Car
and Drive
The trip frequency model component predicts the number of interregional trips
that individuals in a household will make based on the household’s characteris-tics
and location. The destination choice model component predicts the destina-tions
of the trips generated in the trip frequency component based on zonal
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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.
Because of the way that the model components were linked, model development
occurs in the reverse order of model application:
• Access and egress mode choice models determine choice of mode to and
from airports, conventional rail stations, and high- speed rail stations. The
available modes include drive and park, picked up/ dropped off, rental car,
taxi, transit, and walk. These were based on the actual and hypothetical
access and egress modes reported in the stated- preference ( SP) surveys –
either four or six observations per respondent.
• Main mode choice models choose the main, line- haul mode, from among
car, air, conventional rail, and high- speed rail. This is based on the four
hypothetical SP responses for each respondent in the SP surveys. This model
uses information from the access and egress mode choice component for each
mode ( except car).
• Destination choice models pick the destination zone outside the region. The
model is segmented for destinations within and beyond 100 miles, and the
alternatives are all traffic analysis zones ( TAZ) applicable for the distance
segments. For the long- distance model, we use a two- stage structure of pre-dicting
“ macro- zone” and then TAZ, because that seems to be more behav-iorally
realistic. The model input data are a mix of trips from the statewide
survey and the SP survey. The models use information from the mode choice
model components, calculated for each TAZ as the key measure of imped-ance
between zones.
• Trip frequency models establish the number of interregional trips made
during a person- day ( 0, 1, or 2) for a given purpose/ distance segment. The
California Statewide survey diary- days are the data source. The models use
information from the destination choice model component calculated across
all possible TAZs as a measure of zone accessibility.
The market segmentations used for the models are:
• Purpose:
- Business;
- Commute;
- Recreation; and
- Other.
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• 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, and more than 4 people.
• Household income range – Low, medium, and high.
• Household auto- ownership – 0, 1, and 2+.
• Household number of workers – No worker, 1 worker, and 2+ workers.
• Party size: Traveling alone, and 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 applying 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.
These market segments vary by model component to take advantage of addi-tional
detail in some areas or aggregation of market segments in other areas. The
market segments in each model component are presented in Figure 2.4 and are
described further in the report, Bay Area/ California High- Speed Rail Ridership and
Revenue Forecasting Study Interregional Model System Development.
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Figure 2.4 Market Segments in Each Model
Short Trips – All Regions
Long Trips
Business
Long Trips
Commute
Long Trips
Recreation
Long Trips
Other
Short Trips – All Regions Short Trips – All Regions Short Trips – All Regions
Business/
Commute
Traveling
Alone
Business/
Commute
Traveling
with Others
Recreation/
Other
Traveling
Alone
Recreation/
Other
Traveling
with Others
Business Commute Recreation Other
Business Commute Recreation Other
Business Commute Recreation Other
Business/
Commute
Traveling
Alone
Business/
Commute
Traveling
with Others
Recreation/
Other
Traveling
Alone
Recreation/
Other
Traveling
with Others
Business/
Commute
Traveling
Alone
Business/
Commute
Traveling
with Others
Recreation/
Other
Traveling
Alone
Recreation/
Other
Traveling
with Others
Business/
Commute
Traveling
Alone
Business/
Commute
Traveling
with Others
Recreation/
Other
Traveling
Alone
Recreation/
Other
Traveling
with Others
Short Trips
MPO
Regions
Business
Short Trips
Other
Regions
Short Trips
MPO
Regions
Commute
Short Trips
Other
Regions
Short Trips
MPO
Regions
Recreation
Short Trips
Other
Regions
Short Trips
MPO
Regions
Other
Short Trips
Other
Regions
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The trip frequency, destination choice, and mode choice models all use accessi-bility
or impedance measures as inputs to the logit choice equations. For each
model component, these measures were calculated from subsequent model com-ponents
and as a result, were not available during the initial model estimation.
So, for each model component, a substitute accessibility or impedance measure
was calculated to use for initial model estimation, and then replaced with the
actual measure. These linkages are presented in Figure 2.5.
Figure 2.5 Model Component Linkages
Trip Frequency
Destination Choice
Access/ Egress
Mode Choice
Trip Assignment
Initial Estimation
Final Estimation
and Application
Main Mode Choice
Access/ Egress
Logsums
Mode Choice
Logsums from STM
Accessibility
from STM
Access/ Egress
Logsums
Mode Choice
Logsums
Dest Choice Logsums
and Accessibility
Congested Modal
Skims
Congested Modal
Skims from STM
2.2 INTRAREGIONAL MODELS
Intraregional models were used to forecast high- speed rail trips with both ends
within a region that has more than one proposed high- speed rail station. These
areas are the San Francisco Bay Area, Greater Los Angeles, and San Diego
regions. In addition, intraregional auto trips were estimated and included in
auto assignments for all 14 regions in the State.
Regional travel forecasting models for the San Francisco and Los Angeles regions
were modified to forecast intraregional high- speed rail trips for these areas. The
market segments for intraregional travel include typical trip purposes, such as
home- based work, school, university, shopping, social- recreational, and other
trips, as well as work- and nonwork- related nonhome- based trips. Due to the
small amount of potential for high- speed rail trips wholly contained within the
San Diego region, these were estimated based on expected high- speed rail trips
per person rather than by applying the local regional travel model.
To model intraregional trips, we relied on the trip generation and distribution
models in each of the urban areas and modified existing mode choice models.
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The urban mode choice models include a variety of transit modes, but not spe-cifically
a high- speed rail mode in any model. San Francisco urban mode choice
models were modified to insert a high- speed rail mode based on coefficients and
constants from the commuter rail mode. Following is a brief description of the
model implementation for each of the urban areas:
• San Francisco Bay Area – The MTC regional model was enhanced to include
transit submodes ( San Francisco Bay Area Rapid Transit District ( BART),
commuter rail, light rail, ferry, local bus, and express bus) in the mode choice
model. This allowed for easier inclusion of the high- speed rail mode in the
model. The new mode choice model was validated at the regional level to
match observed ridership numbers by mode, purpose, and time period.
• Los Angeles Region – The SCAG region was modeled using an adaptation of
the MTC mode choice model combined with SCAG networks and modes
( urban rail, commuter rail, local bus, express bus, and high- speed rail). This
new mode choice model was validated at the regional level to match
observed ridership numbers by mode, purpose, and time period.
Intraregional trip tables by mode and time period from the MTC and SCAG met-ropolitan
areas were added to the interregional trips for the assignment.
3.0 Data Collection
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3.0 Data Collection
There were three types of data compiled for the study: travel surveys, socioeco-nomic
data, and base year travel patterns.
3.1 TRAVEL SURVEYS
The travel survey data used for this project was a combination of new surveys
collected for the project and existing surveys from regional and state agencies.
There were three surveys available from MPOs around the State ( SCAG, MTC,
and Sacramento Association of Governments ( SACOG)), and there was a
Caltrans statewide survey available. The interregional models were based on
revealed- and stated- preference surveys, collected specifically for this study, of
air and rail travelers, as well as additional households in the State to capture auto
travelers. These new data were collected in 14 regions in California. These were
combined with revealed- preference surveys of households across the State col-lected
by Caltrans and interregional travel extracted from the MPO regional
travel surveys ( San Francisco, Sacramento, and Los Angeles). Intraregional
mode choice models were based on urban area travel surveys in combination
with a stated- preference survey for high- speed rail conducted in Los Angeles.
By combining the various available data sources, we were able to provide more
robust datasets for model estimation than was otherwise possible. After com-bining
these surveys, 6,882 completed surveys were available to use for model
estimation, as shown in Table 3.1. There were different estimation datasets used
for each model component, depending on the requirements for the model. This
is described in more detail in the Interregional Model System Development Report
( Cambridge Systematics, Inc., 2006).
Table 3.1 Total of All Survey Interregional Trips by Mode, Distance,
and Purpose
Drive Air Rail Bus Other Total
Long Trips
Business 314 620 27 18 17 996
Commute 263 15 9 1 74 362
Recreation 1114 228 80 3 23 1448
Other 365 85 17 8 91 566
Short Trips
Business 381 14 48 3 15 461
Commute 1136 0 168 9 108 1421
Recreation 873 2 29 3 52 959
Short Other 591 1 10 23 44 669
Total 5,037 965 388 68 424 6,882
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3.2 SOCIOECONOMIC DATA
The core drivers of demand for interregional travel in California are the socio-economic
characteristics of Californians and the State’s economic and employ-ment
picture. The relevant sources of current year data and 2030 socioeconomic
projections are:
• Decennial Census data products, specifically the Census Transportation
Planning Package ( CTPP) and the Summary Tape File ( STF) 1;
• Local agency socioeconomic estimates and projections, such as those devel-oped
and updated by the Association of Bay Area Governments ( ABAG),
SCAG, SANDAG, and SACOG; and
• State Department of Finance ( DOF) and Caltrans projections.
To the extent that commercial sources and state employment data are used to
develop the local agency socioeconomic estimates and projections, they were
included, but these were not evaluated and incorporated separately for this
study because there is a desire to remain consistent with current local agency
forecasts.
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 3.2. 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.
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Table 3.2 Socioeconomic Forecasts from 2000 to 2030 by Region
Households Employment
2000 2030
Percent
Increase 2000 2030
Percent
Increase
AMBAG 226,349 395,421 75% 286,937 436,369 52%
Central Coast 227,200 401,234 77% 278,494 450,493 62%
Far North 376,965 627,175 66% 335,737 522,011 55%
Fresno / Madera 287,110 548,198 91% 365,397 678,786 86%
Kern 207,413 465,913 125% 242,283 707,966 192%
South SJ Valley 144,050 271,240 88% 170,813 336,868 97%
Merced 63,225 125,328 98% 63,403 130,516 106%
SACOG 571,978 817,389 43% 946,259 1,469,041 55%
SANDAG 988,205 1,305,990 32% 1,168,880 1,875,810 60%
San Joaquin 180,276 341,230 89% 202,498 345,819 71%
Stanislaus 143,942 311,488 116% 159,900 354,453 122%
W. Sierra Nevada 68,929 110,703 61% 55,358 99,057 79%
MTC 2,465,287 3,088,370 25% 3,753,533 5,120,598 36%
SCAG 5,631,180 7,623,778 35% 7,393,491 10,740,549 45%
Total 11,582,109 16,433,457 42% 15,422,983 23,268,336 51%
3.3 BASE YEAR TRAVEL PATTERNS
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) 3 was used to validate the business, rec-reation,
and other long- trip purposes. The 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 infor-mation
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
3 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.
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Federal legislation and Federal and state regulations on the transportation
system and its use.
• The Census Transportation Planning Package ( CTPP) 4 was used to validate
the commute for long- and short- trip purposes. CTPP is a set of special
tabulations from the decennial census designed for transportation planners.
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 California Statewide Travel Survey5 was used to validate the business,
recreation and other short trip purposes. The California Statewide Travel
Survey was conducted in 2000 to 2001 for weekday travel. This survey was
an activity- based survey and included all in- home activities and travel com-pleted
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 datasets were summarized by major market ( based on city- to- city trip
movements), because this was a focus of the model validation effort. Table 3.3
presents the validation dataset for the long- interregional trips, and Table 3.4 pre-sents
the validation dataset for the short- interregional trips.
4 U. S. Department of Transportation, Federal Highway Administration, Census
Transportation Planning Package, September 11, 2006,
http:// www. fhwa. dot. gov/ ctpp/.
5 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 3.3 2000 Average Daily Interregional Trips Over 100 Miles ( Long)
Source 􀃆 CTPP American Traveler Survey
Trip Purpose 􀃆 Commute Business Recreation Other Total
Market
LA to Sacramento 5,103 5,169 7,127 1,467 18,866
LA to San Diego 29,665 10,313 61,763 13,567 115,308
LA to SF 22,124 17,356 44,108 6,787 90,375
Sacramento to SF 16,986 5,645 21,443 7,306 51,380
Sacramento to San Diego 886 1,227 1,227 218 3,558
San Diego to SF 4,840 5,966 16,443 2,258 29,507
LA/ SF to SJV 53,741 4,396 19,777 5,690 83,604
Other to SJV 10,950 12,538 12,886 4,725 41,099
To/ from Monterey/
Central Coast
28,809 8,271 19,829 6,796 63,705
To/ from Far North 16,982 3,129 12,359 2,366 34,836
To/ from W. Sierra Nevada 9,730 531 7,528 1,510 19,299
Total 199,817 74,540 224,491 52,691 551,539
Table 3.4 2000 Average Daily Interregional Trips Under 100 Miles ( Short)
Source 􀃆 CTPP Caltrans Travel Survey
Trip Purpose 􀃆 Commute Business Recreation Other Total
Market
LA to Sacramento 0 0 0 0 0
LA to San Diego 69,728 19,244 42,340 27,512 158,824
LA to SF 0 0 0 0 0
Sacramento to SF 37,192 17,805 17,383 12,394 84,774
Sacramento to San Diego 0 0 0 0 0
San Diego to SF 0 0 0 0 0
LA/ SF to SJV 77,112 11,769 16,565 25,518 130,964
Other to SJV 128,792 20,223 24,382 8,341 181,738
To/ from Monterey/
Central Coast
96,448 16,351 44,784 67,024 224,607
To/ from Far North 36,658 15,626 47,494 89,480 189,258
To/ from W. Sierra Nevada 17,672 2,421 10,566 6,840 37,499
Total 463,603 103,439 203,514 237,108 1,007,664
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Air passenger data was acquired from the U. S. DOT Federal Aviation
Administration ( FAA) origin- destination ( O& D) 10- percent sample database.
This includes actual ticket information for 10 percent of the tickets collected by
large air carriers. While the 10- percent ticket sample data represent a robust data
of air fares and travel times, these data are subject to sampling error. In addition,
the O& D databases generally do not include tickets for passengers with itinerar-ies
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.
Rail passenger data were 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. The allo-cation
of rail boardings to interregional and intraregional for the San Francisco
Bay Area is based on estimates provided by the MTC.
Highway traffic counts were obtained primarily from the Caltrans traffic count
database and from the MTC and 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 net-works
were largely compatible with the Caltrans database rather than the MPO
databases.
4.0 Existing Modal Services
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4.0 Existing Modal Services
The base year service levels were used in model calibration/ validation, and fore-cast
year service levels were used in model application to evaluate alternative
scenarios. The primary sources of this supply information were the California
Statewide Travel Demand Model6, which includes both highway, and public
mode transportation networks ( base and forecast), the regional travel demand
models, and the base year published timetables and fare tables for public modes.
The Statewide Model and the MTC, SCAG, SANDAG, and SACOG demand
models were used to develop base year and forecast year highway networks that
reflect congested travel times by time of day. The Statewide Model is the pri-mary
source of the intercity highway network, and we retained that model’s
zone system for most of the state geography. Where the Statewide Model over-laps
with one of the large regional model systems, we added detail from the
regional models.
We also updated the Statewide Model’s public mode networks using airline
schedule and fare information from the Official Airline Guide, the airline web
sites, and the U. S. DOT’s T- 100 reports. We assembled intercity rail schedules
and fares from Amtrak and other rail operators in the corridor. We used the
regional models to develop base year and forecast year intraregional transit net-works
for the new zone system.
4.1 AIR SERVICE
Base and future year air networks included 18 airports within California that
offer significant commercial airline passenger service between California cities.
Table 4.1 lists these airports and provides estimates of their numbers of annual
passenger boardings for intrastate travel for the years 2000 and 2005. Los
Angeles International ( LAX) is the busiest airport in California with more than
2.6 million boardings in 2000; and Oakland International Airport ( OAK) is the
busiest California airport in 2005 with almost 2.6 million boardings. The Long
Beach Airport had almost no intrastate service in 2000, but JetBlue began signifi-cant
California operations at Long Beach Airport between 2000 and 2005, which
significantly increased ridership at this Airport.
6 California Department of Transportation and Dowling Associates, Inc., California
Statewide Model Description, January 20, 2004.
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Table 4.1 Annual Intrastate Passengers for California Airports
Annual Passengers
Airport Airport Code 2000 2005
Percent
Change
San Diego SAN 1,814,410 1,563,190 - 14%
Santa Ana SNA 1,259,160 1,141,630 - 9%
Long Beach LGB 130 231,380 18%
Los Angeles LAX 2,648,790 1,723,580 - 35%
Ontario ONT 962,530 874,900 - 9%
Burbank BUR 1,230,590 1,045,620 - 15%
San Jose SJC 1,930,020 1,510,660 - 22%
San Francisco SFO 1,960,230 812,650 - 59%
Oakland OAK 2,341,300 2,593,880 11%
Sacramento SMF 1,555,760 1,634,400 5%
Palm Springs PSP 87,610 88,410 1%
Oxnard OXR 5,310 2,060 - 61%
Santa Barbara SBA 84,560 22,310 - 74%
Bakersfield BFL 5,440 3,050 - 44%
Fresno FAT 25,790 22,850 - 11%
Monterey MRY 18,620 21,810 17%
Arcata/ Eureka ACV 29,440 37,000 26%
Modesto MOD 5,920 3,300 - 44%
Total 15,965,610 13,332,680 - 16%
In addition to those listed, there were 17 other airports in California that offered
scheduled air service, but did not provide significant intrastate service or pas-sengers
to warrant being included in the air network for this study. These air-ports
include Crescent City ( CEC), Chico Municipal ( CIC), Carlsbad McClennan
Palomar ( CRQ), Imperial County ( IPL), Inyokern ( IYK), Merced Municipal
( MCE), Palmdale ( PMD), Redding Municipal ( RDD), Riverside March ( RIV), San
Luis County Regional ( SBP), Stockton Metropolitan ( SCK), Santa Maria ( SMX),
Sonoma County ( STS), Lake Tahoe ( TVL), Victorville ( VCV), Visalia ( VIS), and
Van Nuys ( VNY).
Fifty- seven airport- to- airport pairs had nonstop commercial intrastate air traffic
for both 2000 and 2005. Airport- to- airport pairs that required a connecting flight
were not considered. Air level of service information, including gate- to- gate
travel time, fares, and reliability, are based on averages of the FAA data obtained
from the 10- percent ticket sample, supplemented with Internet queries in August
2006.
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Figure 4.1 California Statewide Air Network
4.2 HIGHWAY SUPPLY AND TRAFFIC COUNTS
The representation of highway network supply is primarily determined by the
level of detail in the highway network and the attributes associated with the
roadway system, such as lanes, distances, speed, and capacity. A brief summary
of these networks is provided here.
Beginning with the existing statewide highway network, detail was added using
the following regional models:
• MTC region – The entire highway network was incorporated into the model;
• SCAG region – The entire highway network was incorporated into the
model;
• SANDAG region – Highway network was incorporated only within a five-mile
radius of the three proposed high- speed rail stations;
• SACOG region – Highway network was incorporated only within a five-mile
radius of the proposed high- speed rail station; and
• Kern County region – Highway network was incorporated only within a
five- mile radius of the proposed high- speed rail station.
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Figure 4.2 shows the highway network in CUBE software. The new highway
network includes 4,667 zones; 127,600 links; and 206,150 nodes.
Figure 4.2 New Statewide Model Highway Network
Roadway and area type classifications from the various regional models have
been consolidated to eight functional classifications and three area types. Speed
and capacity definitions by functional class and area type are different for each
regional model. These values are based on local conditions in each region, and
some minor modifications were made during model validation. To take advan-tage
of the work done in each region, values from the individual models were
kept intact instead of developing a new look- up table based on area type and
functional class.
Traffic counts were obtained from the Caltrans traffic count database. It
included detailed daily and hourly traffic counts from approximately 1,100 per-manent
count census station locations. Two- way total daily traffic volumes were
also input from the 2000 Caltrans Traffic Volumes for 75 locations on screenlines.
These are displayed in Figure 4.3. This traffic count data was also supplemented
from the individual regional models. These include the Los Angeles, Sacramento,
San Francisco, San Diego and Kern county regions.
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Figure 4.3 Caltrans Count Stations ( Red) and Screenline Locations ( Blue)
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4.3 PASSENGER RAIL SERVICES
Year 2000 passenger rail services consist of a variety of intraregional and interre-gional
services. Passenger rail services were also subdivided by mode – metro
rail ( i. e., BART), conventional rail ( both intercity and commuter services), and
light rail. These rail services for interregional travel are as follows.
• The San Diego Region has two rail operators – San Diego Trolley ( light rail)
and the Coaster ( conventional rail).
• The SCAG region has metro, conventional, and light- rail services. The Los
Angeles Metropolitan Transportation Authority ( MTA) operates metro and
light- rail services. The Southern California Regional Rail Authority ( SCCRA)
operates Metrolink conventional commuter rail services. The MTA Rail sys-tem
is comprised of the Metro Blue, Green, Red, and Gold Lines. The Metro
Red Line subway operates between Union Station, the Mid- Wilshire area,
Hollywood, and the San Fernando Valley. The remaining light- rail lines are
the Blue Line ( Long Beach to Los Angeles), the Green Line ( Norwalk to
Redondo Beach), and the Gold Line ( Los Angeles Union Station ( LAUS) to
Pasadena).
• Within the MTC region, metro, convention and light- rail services are pro-vided.
Services include BART, Caltrain, Muni Metro, and Santa Clara Valley
Transportation Authority ( VTA) light- rail systems. In 2000, the BART system
consisted of 39 stations serving four East Bay lines ( Fremont, Dublin/
Pleasanton, Pittsburg/ Bay Point, and Richmond), as well as the Daly City/
Colma line through San Francisco and the West Bay. In 2002, BART service
was extended south of Colma to San Francisco Airport and to Millbrae, and
four new stations were added. Caltrain currently operates 86 daily trains
between San Jose and San Francisco, including three daily peak- period, peak
direction round trips to Gilroy. There are five light- rail ( metro) lines that
operate in the Market Street subway, three cable car routes, and the historic
trolley line operating on Market Street. Santa Clara light- rail lines were
extended in 2000 to East San Jose ( Alum Rock) and to Winchester ( Vasona
line).
• The SACOG region’s rail services are limited to the Sacramento RT light- rail
system. Since 2000, two RT extensions have come on- line: in 2003, the South
Line extension was implemented. This new extension resulted in RT running
two lines for the first time. More recently, the Folsom extension became
operational. The Folsom Line is an extension of the existing line that operates
along the U. S. 50 corridor.
Interregional rail services are all conventional rail systems. These include the
Capitol Corridor, Altamont Commuter Express ( ACE), Surfliner, and the San
Joaquin systems. The intraregional and interregional rail services are shown in
Figure 4.4
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Figure 4.4 California Statewide Conventional Rail Network
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Table 4.2 presents the conventional rail passenger boardings for the year 2000 by
operator and route for both intraregional and interregional travel. These data
were developed from daily ridership estimates and annualized using 260 days
per year for ACE ( which only has weekday services), 300 days per year for all
remaining intraregional services, and 335 days per year for all interregional
services.
Table 4.2 California Statewide Conventional Rail Passengers
2000 Annual Passengers
Operator/ Route Market Served
Total
Boardings Intraregional Interregional
Amtrak Capital Corridor Sacramento to San
Francisco
1,070,500 300,000 770,500
Amtrak Surfliner Santa Barbara to San
Diego
1,610,500 840,000 770,500
Amtrak San Joaquin San Joaquin Valley to
San Francisco
703,350 30,000 673,350
ACE Stockton to San Jose 806,000 182,000 624,000
Coaster, San Diego
Trolley
San Diego region 29,220,000 29,220,000 0
Metrolink, Metro Rail Los Angeles region 70,971,000 70,770,000 201,000
BART, Caltrain, SF
Muni, Santa Clara VTA
San Francisco region 166,770,000 166,770,000 0
Regional Transit LRT Sacramento region 11,280,000 11,280,000 0
Total 282,431,350 279,392,000 3,039,350
5.0 Ridership Model
Development
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5.0 Ridership Model
Development
5.1 INTERREGIONAL MODELS
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 are presented in
Figure 2.3. The trip frequency model component predicts the number of interre-gional
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 pre-dict
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. These are described in more detail below.
Trip Frequency
We used a simple multinomial logit ( MNL) model to predict interregional trip
frequency. Eight trip frequency models predict interregional person- trips per
day, segmented by trip purpose ( business, commute, recreation, and other) and
length ( over or under 100 miles). The MNL formulation allows important
explanatory variables, such as accessibility measures, to affect the propensity to
make interregional trips. In this case, the composite logsums from the destina-tion
choice model are fed back to the trip frequency model to account for travel
that is induced due to the presence of high- speed rail ( or any other new services).
The trip frequency models are segmented by length to allow different model
specifications and parameters for short and long trips. For each model, the
choice set for each person is zero, one, or two or more interregional trips per day.
The final model specification constrains the variable coefficients of one- trip and
two- trip choices to be equal, while allowing the alternative- specific constants for
one- and two- trip choices to be estimated individually. This overcomes some
illogical individual variable coefficients for each market segment, but allows us
to retain separate choices for interregional travel.
Three types of variables were tested in the trip frequency models: socioeco-nomic,
accessibility, and geographic region of residence. Even though the trip
frequency models are estimated at the person level, estimation variables were
constrained to be at the household level to be consistent with existing future year
socioeconomic predictions. Socioeconomic variables that were tested in model
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specifications include household size; household size greater than two dummy
variable; number of household workers; zero- worker household dummy vari-able;
number of household vehicles; number of household vehicles is less than
the number of household workers dummy variable; zero- vehicle household
dummy variable; high household income ( greater than $ 75,000); medium house-hold
income ( between $ 35,0000 and $ 75,000); low household income ( less than or
equal to $ 35,000); and a missing income dummy variable for survey records with
no income collected. The missing income dummy variable is used during model
estimation, but is not included in the final model specification for application.
The estimation results follow an intuitive pattern. More household workers
increase one’s propensity to make interregional business and commute trips, but
decrease one’s propensity to make interregional recreation and other trips. The
income coefficients indicate that as income increases, more interregional trips are
taken. Households with fewer cars than workers are less likely to have the
resources to undertake interregional travel. Three- person households are less
likely to undertake interregional recreation and other trips, perhaps substituting
this type activity closer to home.
As discussed above, the trip frequency models include measures that capture the
accessibility of all relevant travel opportunities from travelers’ home zones. For
each residence, we calculated three peak/ work and three off- peak/ nonwork
accessibility measures for destinations in 1) their home region; 2) outside their
region, within 100 miles of home; and 3) over 100 miles from home. The final
model specifications rely on synthesized accessibility measures ( a weighted
travel time) for the within home region destinations and on logsums calculated
from the destination choice models for the remaining accessibility measures. The
synthesized accessibility measure is necessary within the home region since the
urban area models are not destination choice models ( they are gravity models),
and are therefore not able to produce logsums for the destination choices within
the region. Logsums are a means to produce a weighted average of all potential
destinations.
A high calculated “ regional accessibility” to jobs, goods, and services within
one’s region of residence indicates less need to travel outside of the region.
Therefore, as expected, this variable has a negative effect on all interregional
travel. Separate short ( within 100 miles of residence and outside the residence
region) and long ( outside 100 miles of residence and outside the residence
region) logsums were calculated to represent accessibility to goods and services
outside of one’s home region. A higher logsum outside a home region increases
the likelihood that an interregional trip will be undertaken.
Regional dummy variables for the MTC, SANDAG, SACOG, and SCAG regions
are included to account for the different interregional trip- making patterns
observed for residents of large, metropolitan areas compared to residents in the
rest of California. These were calibrated to match observed trips in these regions.
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Destination Choice
The destination choice models were estimated with a simple multinomial logit
model structure using ALOGIT software. The destination choice estimation
dataset used the trip frequency dataset combined with the SP survey ( used in the
mode choice models) to increase the number of “ long” ( more than 100 miles)
trips in the dataset ( By nature, the household surveys are generally better at
capturing the more typical “ short” trips.). Since the trip frequency models
already differentiate between the two, we can use this information as a valuable
input to the destination choice models. This not only constrains an individual’s
choice set based on destinations being greater or less than 100 miles, but it recog-nizes
that an individual may value different trip characteristics for different
distance- categories of travel.
The short- trip destination choice models used all four trip purposes modeled in
the trip frequency step: business, commute, recreation, and other. Due to sam-ple
size considerations, only two aggregate trip purposes were estimated for the
long- trip destination choice models: business/ commute and recreation/ other.
The models use multimodal composite logsums from the mode choice models.
This variable measures the combined utility of all available modal choices and
level of service characteristics. All the destination choice models use a distance
power series, including distance, distance- squared, and distance- cubed. An area
type is assigned to each destination zone: rural, suburban, or urban. The models
use several interaction terms to capture whether travelers were starting and
ending in the same area type: rural to rural, suburban to suburban, and urban to
urban.
Similar to the area type interaction variables, the location type interaction vari-ables
relate where you want to go, to where you currently are, based on the loca-tion
of the origin and destination. We tested four origin- destination location
type interaction variables for all the “ long” destination choice models: Los
Angeles to/ from San Francisco, Sacramento to/ from San Francisco, San
Francisco to/ from San Diego, and Sacramento to/ from Los Angeles. These were
adjusted during model calibration to match observed travel. Size functions
measure the amount of activity that occurs at each destination zone, and incorpo-rate
this into the utility of alternative variables. This variable is used in the des-tination
choice models to account for differences in zone sizes and employment
levels. Four size variables are used in these models: retail employment, service
employment, other employment, and households. Other employment is used as
the base size variable for business and commute trips and is constrained to 1.0,
while retail and service are further segmented by household income levels – low,
medium, high, and missing. Households are used as the base size variable for
recreation and other trips. Income is used as a per person variable as an interac-tion
between employment and income to show that different income levels of the
destination choices will affect the attractiveness of the zone for particular travel-ers.
For commute trips, short and long, as income increases, retail employment
has a bigger impact on destination choice than service employment.
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The model estimation results of the destination choice models were reasonable.
The distance power series of coefficients for these models are both decreasing
functions as expected. All other variables have the sign and size we expect,
except for the coefficient of rural to rural for recreation/ other trips, which is
positive when we expect it to be negative, but it is not significantly different than
zero.
Mode Choice
There were two types of mode choice models developed for this study: access
and egress models and main mode choice models. Models were estimated to
predict the access and egress modes to and from airports and rail stations. The
models were based on actual reported and hypothetical- stated data. For people
who were intercepted making actual air or rail journeys, the access and egress
mode choices are the actual reported ones. For people whose actual journey was
by car, the air and conventional rail access/ egress mode choices are hypothetical.
Obviously, the high- speed rail access and egress mode choices are hypothetical
for all respondents.
For access, the majority of respondents reported either driving or parking at the
station/ airport or else getting dropped off. For egress, the reported mode shares
varied more by purpose and distance, with transit more popular for short trips,
and rental car and taxi more popular for long trips and business trips. In all
there were six modes considered for each. A nested structure was adopted, as
shown in Figure 5.1. The auto modes – drive and ( un) park, pick up/ drop off,
and rental car – are all in separate nests, while taxi, transit ( bus or light rail), and
walk are nested together. This nesting structure gave the most reasonable results
for all purposes.
Figure 5.1 Access and Egress Mode Choice Model Structure
Drive/ Park Drop Off Rental Car
Taxi Transit Walk/ Bike
Access/ Egress Mode
Didn’t Drive
The results of the access/ egress mode choice models were within expectations.
A reasonable value of time was asserted for each segment based upon a review
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of other research. As the survey was not designed primarily to estimate access
and egress choice models, and the zone size is in a statewide model is quite large
for this type of local choice, the fact that access and egress time and cost
parameters had to be constrained is perhaps not surprising. Also note that the
costs of options, such as taxi and rental car and airport/ station parking, are not
readily obtained from network data. Other results of note are:
• The out- of- vehicle time coefficients were estimated for most segments, and
result in ratios of out- of- vehicle time to in- vehicle time that are in the range of
2.0 to 2.9.
• The drop off and pick up alternatives have an additional negative in- vehicle
time effect, capturing the disutility of the driver that has to make the round
trip to the airport.
• We did not include taxi cost explicitly, but did include an additional distance
coefficient for taxi, which is significant and negative for most segments, typi-cally
with an equivalent value of over $ 1.00 per mile.
• For most segments, transit is less likely to be chosen if there is no reasonable
walk access to transit, meaning that a drive to transit path was included
instead.
• For most segments, transit, which can include rail and/ or bus, is more likely
to be chosen if rail is included in the best transit path.
• For the long segments, taxi, parking, and rental cars are generally less desir-able
to rail stations than to airports, while transit is more desirable from rail
stations. Walking is very rare to or from airports, capturing accessibility
effects that are not captured well in the zone system.
• Drive- and- park access is less likely at the busiest airports – SFO, LAX, and
SAN – and somewhat at SJC as well. This may capture both cost and incon-venience
effects at those airports.
• For most segments, those in larger households are more likely to be dropped
off.
• In general, high income favors rental car, taxi, and drive and park; and low
income slightly favors transit in some segments.
• There is a logsum coefficient less than 1.0 on the nest that includes transit,
walk, and taxi. Each of the other three alternatives is in its own “ nest,” and
scaled by the same logsum parameter to preserve equal scaling at the ele-mental
level.
• The scale ( the inverse of the residual error variance) for the hypothetical
choices relative to the actual choices was significantly lower than 1.0 for most
of the egress model segments. This result indicates that many respondents
have difficulty making an accurate assessment of mode choice options in less
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familiar surroundings at the nonhome end of their trip, so that hypothetical
choices should be weighted less in estimation than actual ones.
The main mode choice models produce probabilities that each trip will choose
one of the main modes ( auto, air, conventional rail, and high- speed rail). Several
nesting structures were tested for the main mode choice models, and the final
nesting structure chosen is shown in Figure 52 with all the nonauto modes in a
single nest. This structure provided the most logical and statistically sound
nesting structure for the mode choice models.
Figure 5.2 Main Mode Choice Model Structure
Auto
Air Conventional
Rail
High- Speed
Rail
Main Mode
Non- Auto
The main mode choice models were based on SP survey data. The overall choice
shares in the SP data were around 50 percent for high- speed rail with most of the
other choices for the respondents’ actual chosen modes. The high- speed rail
choice share was highest for business trips and long trips, giving a first indica-tion
that high- speed rail substitutes more closely with air than with car.
To prepare the data for estimation, the access and egress mode choice models
were first applied to calculate access and egress mode logsums for each alterna-tive.
Then, a nested logit model was estimated across the four main modes for
each of the segments ( only three alternatives for the short segments, as air was
not available for those segments).
Some of the results from the mode choice model estimation include the following:
• The residual mode- specific constants for high- speed rail are generally not
very much higher than for the other modes. This result indicates that the
high choice shares found for high- speed rail are mainly due to the attractive-ness
of the time and cost by the mode, rather than to SP- related survey effects
or biases.
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• The cost and in- vehicle time parameters were estimated nonconstrained and
give very reasonable values of time ( VOT). In general, VOT for the longer,
more expensive trips is higher than for the shorter, more frequent trips. This
is a typical result.
• The value of frequency ( headway) is significant for all segments, but is only
about 20 percent as large as the in- vehicle time coefficient. If wait time were
half the headway and valued twice as highly as in- vehicle time, then we
would expect the same coefficient on headway and in- vehicle time. For these
modes, and particularly air, headway is less related to wait time than it is to
scheduling convenience. Because none of the levels used in the SP had
headways higher than a few hours, the implications for scheduling may not
have been large enough to greatly influence mode choice.
• The value of reliability is fairly low for all segments, although with the cor-rect
sign. It is very difficult to measure the effect of reliability in a large- scale
mailout SP survey, so we decided to use a somewhat higher effect of reliabil-ity
in application, based on any evidence from elsewhere.
• Those traveling with others are more likely to use car and less likely to use
air. This effect was also tested on the cost coefficients and not found to be
significant, so this relative mode preference appears to be related to more
than just cost – such as the fact that people can share driving for long trips.
Party size models were estimated to generate these data, but are not included
here for brevity.
• People in larger households are more likely to use car. Even though we
already have the group/ alone segmentation, people in larger households are
likely to be in larger groups.
• Higher income generally favors air and high- speed rail versus auto.
• Low auto availability within the household is related to less chance of
choosing the auto.
• A nest with air, rail, and high- speed rail ( with car in its own “ nest”) produced
a logsum coefficient below 1.0 for all segments, indicating that this was a rea-sonable
nesting structure for interregional trips.
• The access mode choice logsums were estimated with positive coefficients in
the range of 0.11 to 0.46 for all segments.
For the long trips, the egress mode accessibility seems to have somewhat more
influence on mode choice than does the access mode. Travelers may be less con-strained
at the home end, where they know the options and can use their own
auto, than they are at the destination end.
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5.2 INTRAREGIONAL MODELS
The intraregional models were developed to be integrated with existing MPO
regional models and the Caltrans Statewide Model. To that end, the intrare-gional
models rely on existing model trip tables as much as possible to provide a
more streamlined modeling process. For both the San Francisco Bay Area and
the greater Los Angeles region, mode choice models were adapted from existing
models to include the high- speed rail mode and applied to the MPO trip tables
for each region. San Diego is the only other region that contains the possibility of
intraregional high- speed rail trips, but the estimate of these riders is very low
relative to the other regions; and the level of effort to develop, calibrate, and
apply the regional mode choice model is very high, so we decided to develop
intraregional ridership for San Diego using a population- based estimate rather
than a traditional mode choice model.
It was also necessary to supplement the three regions with multiple high- speed
rail stations with auto trip tables for all other regions. Although there was no
need for mode choice models in these regions, it was necessary to accurately rep-resent
congestion in these areas to present realistic travel times for auto trips
across the State. These auto trip tables were derived from the Caltrans Statewide
Model, but could be replaced with local or regional trip tables for statewide cor-ridor
or regional planning studies in the future.
MTC Regional Mode Choice Models
Mode choice models for the high- speed rail study were developed using the
Transbay Mode Choice Models as a starting point. These mode choice models
used a detailed submode version of the MTC mode choice model, and were then
calibrated for work and nonwork purposes during peak and off- peak periods.
School trips were included as trip tables for auto trips, but were not included in
the mode choice models, because they were not likely to produce many high-speed
rail trips7. The following trip purposes were modeled:
• Home- based work in four income quartiles;
• Home- based shop/ other;
• Home- based social/ recreation; and
• Non- home- based.
The four income groups for the MTC are households with less than $ 25,000;
$ 25,000 to $ 50,000; $ 50,000 to $ 75,000; and more than $ 75,000. The home- based
work peak models have walk and drive access for each transit mode: BART,
7 Cambridge Systematics, Bay Area/ California High- Speed Rail Ridership and Revenue
Forecasting Study: Model Design, Data Collection and Performance Measures, prepared for
the Metropolitan Transportation Commission, May 2005.
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commuter rail, light rail, express bus, local bus, and ferry. The updated MTC
home- based work mode choice model structure is shown in Figure 5.3. The
home- based off- peak and nonwork ( both peak and off- peak) models have walk
access for each transit mode, but only one drive access mode, which is the best
path to drive to any transit mode. The updated MTC home- based work off- peak
and nonwork mode choice model structure is shown in Figure 5.4. Modal con-stants
for each mode, purpose, and time period were calibrated to match
observed values in year 2000.
Figure 5.3 MTC Updated Mode Choice Structure for Home- Based Work Peak
Main Mode
Motorized Non- Motorized
Auto Transit Walk Bike
Drive
Alone
Shared
Ride 2
Shared
Ride 3+
Walk
Access
Drive
Access
BART
Light Rail
Local Bus
Commuter Rail
Express Bus
Ferry
High- Speed Rail
BART
Light Rail
Local Bus
Commuter Rail
Express Bus
Ferry
High- Speed Rail
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Figure 5.4 Updated MTC Mode Choice Model Structure for Nonwork
and Off- Peak Models
Main Mode
Motorized Non- Motorized
Auto Transit Walk Bike
Drive
Alone
Shared
Ride 2
Shared
Ride 3+
Walk
Access
Drive
Access
BART Light
Rail
Local
Bus
Commuter
Rail
Express
Bus Ferry High- Speed
Rail
The coefficients and utility equations for all modes are the same as the original
MTC mode choice models8. The high- speed rail mode was established to emu-late
the commuter rail mode, with the same coefficients and constants for each
purpose and time period. The constants were calibrated the same for all geo-graphic
areas within the Bay Area, even though the MTC model has the capabil-ity
to incorporate different constants for different areas.
SCAG Regional Mode Choice Models
The SCAG regional mode choice models were adapted from the MTC regional
model choice models for the same purposes and time periods, except that the
home- based work off- peak and nonwork purposes retained the full nested model
structure with separate submodes for drive access. This procedure was used to
meet the schedule for high- speed rail forecasts required for environmental
documentation, and is a more simplified mode choice model than is used by
SCAG. It was calibrated to match SCAG’s validation dataset by mode, purpose,
and time period. The high- speed rail forecasting capability in the SCAG model is
still under development. SCAG’s own regional mode choice model is being used
8 Metropolitan Transportation Commission, Travel Demand Models for the San
Francisco Bay Area ( BAYCAST- 90) Technical Summary, June 1997.
http:// www. mtc. ca. gov/ maps_ and_ data/ datamart/ forecast/ BAYCAST% 20Travel% 2
0Models% 20Tech% 20Summary. pdf.
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to estimate high- speed rail trips for a local planning study, and once validated
could be used for further intraregional trip forecasting.
California Statewide Auto Trip Tables
The Caltrans Statewide Model was used to develop auto trip tables for the
11 other regions in the State beyond San Francisco and Los Angeles regions:
• Sacramento region;
• San Joaquin County;
• Stanislaus County;
• Merced County;
• Fresno/ Madera Counties;
• South San Joaquin Valley region;
• Kern County;
• Monterey Bay Area region;
• Central Coast region;
• West Sierra Nevada region; and
• Far North region.
The Caltrans Statewide Model does not distinguish between drive alone and
shared ride, so these are all assumed to be drive alone trips. Since the majority of
the high- occupancy vehicle ( HOV) lanes are contained within the San Francisco
and Los Angeles regions in the State, this assumption is reasonable given the
available data and resources. It may be preferable in the future to consider
incorporating drive alone and shared ride trips from the Sacramento region,
since there are additional HOV lanes in this region.
5.3 MODEL VALIDATION
The validation of the combined interregional and intraregional ( urban) models
was completed for the year 2000, because the available observed data for 2000
was more robust than for any other year. This statewide model was estimated
from a combination of existing and new household and intercept traveler sur-veys
collected in California and combined with intraregional trips generated
from regional and statewide sources.
The validation work included the calibration process, development of data used
for observed travel behavior, and documentation of the resulting calibration
parameters for the interregional trips. In addition, this work included summa-ries
and reasonableness checks on the intraregional trips derived from the MPO
trip tables. These were not separately validated or calibrated, because each MPO
has provided assurances that these trip tables are validated.
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2000 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 5.1 presents
a summary of the 2000 interregional trips by mode and market.
Table 5.1 2000 Daily 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
Intraregion – – – –
Total 1,441,942 55,935 9,613 1,507,490
Source: California Statewide High- Speed Rail Forecasting Model run for 2000 “ base year” conditions.
Highway trips are converted from person trips to vehicle trips using vehicle
occupancy factors derived from the Caltrans Statewide Travel Survey. In addi-tion,
highway trips are separated into peak and off- peak time periods, so that
peak and off- peak 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.
Following the development of peak and off- peak auto vehicle interregional trips,
these were combined with the auto vehicle intraregional trips. These intrare-gional
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.
2000 Assignments by Mode
Validation of the base year assignments by mode involved detailed review of
observed and modeled volumes. For air, these reviews focused on assignments
for the major markets. For rail, these reviews focused on assignments by operator.
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For highway, these reviews focused on assignments by gateway and by region.
A summary of the assignments by mode is provided in Table 5.2.
Table 5.2 2000 Daily Assignments by Mode
Mode Units Observed Model Difference
Percent
Difference
Air Boardings 54,271* 54,876 605 1%
Rail Boardings 16,710** 17,743 1,033 6%
Auto Vehicle Counts 27,145,300*** 25,206,373 ( 1,938,927) - 7%
* Source: U. S. Department of Transportation FAA O& D 10- percent sample database.
** Source: Interregional rail operators and the MTC.
*** Source: Caltrans, MTC, and SCAG traffic count databases.
Even though the air and rail assignments were very small compared to auto,
these were critical to the evaluation of high- speed rail, so a great attention to the
validation of these modes was important. For the major markets and operators,
these compared very well with observed numbers. Auto assignments were pri-marily
validated based on gateways along the high- speed rail corridors. These
compared very well to observed traffic counts. Additional validation effort to
refine and improve the highway assignments is recommended if this model were
to be used for highway planning purposes.
2030 Baseline Forecasts
Comparison of the 2030 forecast to a No- Project scenario was completed for vali-dation
to ensure that the 2030 forecasts are reasonable for each model compo-nent.
Overall, there is a 42 percent increase in households and a 51 percent
increase in employment ( see Table 3.2), and there is a 62 percent increase in inter-regional
trips. The 2030 interregional trip table is presented in Table 5.3.
The higher percent of interregional trips compared to statewide household and
employment growth is a reflection of the expansion of the regions beyond their
regional borders, causing more travelers to make interregional travel instead of
intraregional travel. The auto assignments ( represented by total vehicle miles
traveled ( VMT)) increase by 73 percent from 2000 to 2030, which is also caused
by travelers having to go further to reach their destinations. These are presented
in Table 5.4. Rail boardings increase at a higher rate than auto, indicating that as
congestion increases, more travelers are taking rail as expected. Air boardings
do not increase as fast as rail or auto, because the air fares increased and frequen-cies
decreased between 2000 and 2005, making air a less attractive option. The
2005 observed air level of service was kept constant through 2030. The primary
reason for significant changes in air service from 2000 to 2005 was the
September 11 terrorist attacks in 2001, which affected air travel more than other
modes.
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Table 5.3 2030 Daily 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
Source: California Statewide High- Speed Rail Forecasting Model run for 2030 “ no- project” conditions.
Table 5.4 2000 and 2030 Assignments by Mode
Mode Units 2000 Model 2030 Model Difference
Percent
Difference
Air Boardings 54,876 80,643 25,767 47%
Rail Boardings 16,430 30,653 14,222 87%
Auto VMT 748,606,510 1,297,116,168 548,509,657 73%
Source: California Statewide High- Speed Rail Forecasting Model run for 2000 “ base year” and 2030
“ no project” conditions.
6.0 Level of Service Assumptions
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6.0 Level of Service Assumptions
Level of service ( LOS) assumptions include costs ( i. e., operating costs and fare
prices); service frequencies; travel and access/ egress times; terminal times; and
reliability measures for each of the interregional travel modes under considera-tion
– auto, air, conventional rail ( CR), and high- speed rail. Reliability is a newly
developed measure for the new statewide model system. Reliability was
included in the SP survey choice experiment options, along with the more tradi-tional
time and cost variables.
These data come from a variety of sources. Much of the information has been
predetermined from earlier bodies of work. For example, assumptions about the
future background highway and transit networks generally come from existing
regional and metropolitan transportation plans. As appropriate, this report
identifies data sources for each assumption. Some other data were newly
researched. The consultant team has compiled data on air travel times and fares
between California airport pairs. Three sets of data for comparison: observed
travel data for the year 2000 base year, year 2005 existing conditions, and previ-ously
developed CHSRA network assumptions. All costs and incomes were
developed in year 2005 dollars.
This study also included an extensive new data collection effort of interregional
revealed- and stated- preference travel patterns. New data collection comprises
3,172 revealed- and stated- preference surveys of California interregional air,
auto, and rail passengers. These surveys provide a rich source of data on areas,
such as access/ egress times and costs, and airport terminal times.
The travel skims have been developed using the new Cube program Public
Transport ( PT), which varies from previous transit network/ assignment mod-ules
in development of paths. PT is a significant enhancement over past transit
path- building and assignment modules, because the transit path- finding algo-rithm
finds all possible transit paths for the zone pairs with the specified
parameters ( maximum travel time, access time, number of transfers, etc.); and
assigns them to each route based on probability. PT reports average skims;
whereas, earlier modules used an “ all- or- nothing” process to assign all trips to
the best path.
6.1 COST
Cost assumptions include auto operating costs, as well as fares for conventional
and high- speed rail and air travel. Cost assumptions also include access and
egress costs, such as parking charges at airports. All cost assumptions are in
2005 constant dollars, unless otherwise specified.
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Auto Operating Costs
The consultant team prepared the auto operating costs with data that the MTC
has compiled on an ongoing basis ( up to April 2006). The auto operating costs
are comprised of gasoline and nongasoline operating costs. Gasoline operating
costs are calculated on a per- mile basis from the price of average retail gasoline
divided by the average fuel economy. The MTC obtains monthly retail gasoline
costs from the California Energy Commission ( CEC). A constant average fuel
economy of 21.9 miles per gallon has been assumed.
Nongas operating costs include maintenance and repair, motor oil, parts, and
accessories. The California Department of Energy used to track the nongas oper-ating
costs, but more recently MTC has assumed that nongas operating costs are
fixed to 60 percent that of gasoline operating costs.
The year 2000 model system uses year 2000 automobile operating costs of
16 cents per mile, while the 2005 model runs uses the 2005 value of 20 cents per
mile. An important assumption will be future gas prices for the purposes of
alternatives evaluation for 2030 forecasts. Gasoline prices are notoriously vola-tile,
and we assume a constant cost of gasoline ( with respect to inflation), rather
than a real, annual increase in auto operating costs. In addition, we tested the
sensitivity of ridership forecasts to changes in gas prices by increasing the cost of
gasoline.
Bridge Tolls
Tolls are charged on seven California bridges – all of them in the San Francisco
Bay Area. Current tolls are $ 3.00 on all seven bridges, except the Golden Gate,
which is $ 5.00 in year 2000 and $ 4.00 on all seven bridges beginning in 2007. The
other six bridges include the Dumbarton, San Mateo- Hayward, San Francisco
Bay, Carquinez, Benicia- Martinez, and Antioch. There are two bridge facilities
that no longer charge tolls. These are the Gerald Desmond Bridge ( serving the
Ports of Long Beach and Los Angeles) and the Coronado Bridge ( serving
Coronado Island in San Diego).
Line- Haul Fares
Line- haul air fares were obtained from the FAA and supplemented with data
from several web sites over several months to obtain data on air fares for origin-destination
pairs in California. The fares were obtained directly for year 2000
and 2005 from the 10- percent ticket sample maintained by the FAA. Business
and nonbusiness fares were queried and summarized separately, but there was
no significant difference overall in these markets between business and nonbusi-ness
fares, so they were averaged for the purposes of this study. Average air
fares typically increased from 2000 to 2005; for example, between Bay Area air-ports
and Los Angeles airports, the air fares increased from $ 82 to $ 106 between
2000 and 2005, or a 29 percent increase.
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An important part of this project was to evaluate different high- speed rail fare
policies in order to maximize benefits. As such, the study team and peer review
panel has agreed that, as a starting point, fare assumptions similar to those
developed by Charles Rivers Associates ( CRA) for the previous high- speed rail
model would be employed here. CRA’s base fare structure for interregional trips
was based on 50 percent of the average Los Angeles- Bay Area airfare. Using the
average airfare of $ 106 ( in 2005 dollars) in our current model, the high- speed rail
fare equates to a boarding charge of $ 15 and a distance charge of 0.9 cents per
mile. The station- to- station high- speed rail fares are used both as an input to the
models and to calculate high- speed rail revenue. The revenue is calculated by
summing the product of the station- to- station, high- speed rail ridership matrix
and the station- to- station, high- speed rail fares.
For intraregional commuter travel, CRA assumed that intraregional high- speed
rail fares would be 50 percent higher than commuter rail fares, on average.
Using this assumption in our current model, the high- speed rail fare equates to a
boarding charge of $ 7.00 and a distance charge of 0.6 cents per mile. Both the
interregional and intraregional per- mile, high- speed rail charges were applied to
the driving distance between stations in order to avoid different fare structures
for Altamont and Pacheco high- speed rail routings.
Interregional conventional rail ( CVR) fares for the San Joaquin, ACE, Capitol
Corridor, Pacific Surfliner, and Metrolink ( Oceanside) lines were developed from
the operators for 2000 and 2005 and assumed to be constant ( relative to inflation)
from 2005 to 2030.
Access- Egress Costs
Airport hourly and daily on- and off- site parking charges were collected by the
MTC staff for San Francisco and Oakland, and by Cambridge Systematics staff
for Los Angeles and Ontario airports as part of a recent study. Parking rates for
all other airports were collected from an Internet search. Parking costs at SFO
and OAK were highest at $ 26 per day.
Conventional rail parking charges are typically free with some exceptions.
Parking charges apply at the Sacramento depot ( serving Capitol Corridor and
selected San Joaquin line trains), and at Oakland’s Jack London Square ( served
by Capitol Corridor and San Joaquin lines); however, the lot only contains
75 parking spaces and is generally half- filled each day. In Southern California,
parking at Los Angeles Union Station is $ 6.00 per day ( served by Metrolink and
Surfliner Routes).
High- speed rail is assumed to have ample market rate parking at all stations. For
initial forecasts, interregional parking charges at high- speed rail stations will be
set to a minimum rate of $ 3.00 per day, except for areas where parking is already
charged, such as San Francisco ($ 25 per day), Oakland, Los Angeles, Sacramento
($ 6.00 per day), and San Diego ($ 12 per day).
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6.2 TRAVEL TIMES
Travel times for interregional travel modes are broken down into detailed com-ponents:
line- haul times ( the time spent in an airplane, high- speed, or conven-tional
train or automobile); access and egress times; terminal times; wait times;
and transfer times.
Line- Haul Times
Auto travel times are derived by summing the travel time ( based on distance and
speed) in the highway network. These are available for peak and off- peak or
free- flow conditions.
Intra- California airport to airport line- haul times are developed from the FAA
data in the 10- percent ticket sample and updated with current schedules in some
markets where the FAA data were too low. Airport pairs without direct ( non-stop)
service show line haul times with transfer times included, since the air
network represents all direct service. Travel times were estimated for both 2000
and 2005, and there were small differences in these travel times, but they were
within the margin of error and there were many unexplainable anomalies, so
travel times for both 2000 and 2005 were set equal. Line- haul times for outbound
and return flights have been averaged to produce a single run time for both
directions of travel. This includes direct and connecting service for intrastate
flights, where demand in 2005 is greater than one trip per day ( 400 annual trips).
High- speed rail line- haul times were developed for both Pacheco Pass and
Altamont Pass alternatives. The high- speed rail times have been developed by
the CHSRA’s rail operations consultant, Parsons Brinckerhoff.
Conventional rail times include ACE, Capitol Corridor, San Joaquin, Pacific
Surfliner, and Metrolink- Orange County Route. These were developed from cur-rent
schedules for 2005 and were the same for 2000 and 2030.
Frequencies
Observed air travel frequencies were obtained from the FAA reports. These fre-quencies
represent only direct service within California. They were developed
for both peak and off- peak conditions.
Generalized peak- period high- speed rail frequencies were developed for the ini-tial
northern ( Altamont) and southern ( Pacheco) alignment alternatives. These
frequencies are assumed as an initial starting point for forecasting purposes.
Testing of alternative service scenarios was conducted during sensitivity testing.
High- speed rail schedules are a fairly complex mix of local, express, regional,
semi- express, and suburban express trains.
Conventional rail frequencies are not as complex as air or high- speed rail ser-vices.
These were derived from current conventional rail schedules.
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Access- Egress Times
Access and egress times are compiled for all mass transportation modes – air
travel, and conventional and high- speed rail. There are no access- egress times
for auto modes; out- of- vehicle time for auto is identified as terminal time and
this is covered in a separate section below. Access- egress times cover the time
required to travel from home ( or activity location, such as from a workplace) to
the curb of the train station/ airport terminal. Times inside the stations/ termi-nals
include both terminal and wait times, and are covered in the next two
subsections.
The choice of mode to and from airports, conventional rail stations, and high-speed
rail stations includes drive and park, picked up/ dropped off, rental car,
taxi, transit, and walk. The auto- based modes ( drive and park/ picked up/
dropped off, rental car, and taxi) will all use highway network travel times for
peak or off- peak travel. The walk network is based on the highway network,
with freeways and expressways removed, and walk speeds are set to 3 miles per
hour on all remaining arterial and collector links.
Wait Times
Wait time refers to the time between arriving at the airline gate or train platform,
and closing of the airplane or train door after everyone has boarded. The time
spent prior to arriving at the airline gate or train platform is the terminal time,
and is discussed further below.
For air travel, the wait time includes both the time spent waiting at the gate for
the plane to arrive; the actual boarding time; and the time up until the plane,
loaded with passengers, leaves the gate area. Once the plane leaves the gate,
line- haul time begins. An initial review of wait times for air travelers in the sur-veys
collected for this project revealed no significant difference between wait
times for business and nonbusiness travelers. In addition, we believe that air
traveler wait times are not a function of the air service frequencies, as recom-mended
by the peer review panel. The rationale for using set wait times is each
seat must be reserved in advance, so the presence of more or less frequent service
between airport pairs does not influence the wait times. As a result, air wait
times for air passengers were based on a review of the surveys’ reported wait
times at 55 minutes. The air wait times was derived from self- reported data on
arrival time before departure in the air passenger travel surveys collected for this
study, which include both wait and terminal times.
For rail travel, the wait times are lower than air for a number of reasons. First,
trains will have numerous doors, making boarding a train a much faster propo-sition
than boarding an airplane. In addition, the hassle and time variance of
getting a boarding pass, checking luggage, and getting through security requires
arrival at the airport earlier than at a train station without security checkpoints.
It is explicitly assumed that high- speed rail will not have the elaborate security
check- in procedures, boarding passes will not be required to wait for a train,
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seats are not assigned, and that luggage is typically self- carried on the train. The
rail wait time was set at 15 minutes for both high- speed and conventional rail
travelers.
Terminal Times
Terminal time is the amount of time it takes someone to travel between their
access mode and the airport boarding area or train platform. It also includes the
time it takes an auto traveler to walk from their car to their destination. Terminal
times are defined for both access and egress ends. At the origin/ access end of a
trip, terminal time includes the following:
• Time to walk ( or ride a shuttle) between the parking area and terminal;
• Time to receive a ticket or boarding pass;
• Time to check luggage;
• Time to clear security; and
• Time to walk from security to the boarding area or platform.
Destination/ egress end of a trip, terminal time includes:
• Time to deboard the airplane or train;
• Time to walk from the plane/ train to baggage claim;
• Time to pick up baggage; and
• Time to walk ( or ride a shuttle) between the terminal and parking area, or to
other ground transportation modes.
Terminal times for public modes were determined from a combination of peer
review recommendations and subsequent refinements made by Cambridge
Systematics. The following terminal times were used:
• Ten minutes for high- speed rail stations;
• Twenty minutes for nonbusiness/ commute trips at airports;
• Twenty- two minutes for business/ commute trips at airports; and
• Three minutes for conventional rail stations.
Terminal times for auto were added to represent the average time to access one’s
vehicle at each end of the trip. The Caltrans Statewide Model assumes an aver-age
terminal time at the production ( home) end of trips and at the trip attraction
based on the area type of the zone, ranging from one to five minutes, depending
on the location of the trip ( urban, suburban, or rural). Longer terminal times in
central urban areas are assumed, because of the extra time involved in finding
parking and walking between a parking space and the final destination.
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Transfer Times
Transfer times apply when connecting from one mass transportation mode to
another. In typical urban travel models, transfer wait times are defined as half
the headway of the connecting modes. For interregional travel, transfer times are
somewhat more complicated because local transit access/ egress to/ from the
high- speed rail modes is part of the access/ egress time.
Because the interregional travel mode will be the primary mode of travel, it is
assumed the traveler will know the schedule of the interregional mode, and will
plan their trip accordingly. As a result, no time will be assessed for trips that
include using local transit to access the interregional mode.
For example, consider a traveler living in San Francisco and traveling to Southern
California. This traveler will take BART to SFO, followed by a flight to a Southern
California airport. The notion of assessing a transfer time of half the airline
headway ( or some similar such measure) does not make sense since the traveler
will obviously take a BART train that gets him/ her to the airport on time for his/
her flight. In this case, all of the relevant access travel time components are
applied – a walk to the BART station, a wait for the BART train to arrive, and the
actual BART ride. From there, the traveler will walk from the BART platform to
the SFO entrance. The times, in total, comprise the access time. This traveler will
have the airport terminal and wait times, as well as the airline flight time, for
their trip, so an assessment of a transfer time for this trip would be redundant
and unrealistic.
Nevertheless, the egress mode for the return trip would assess the typical trans-fer
time – for the airline to BART connection. In this case, the traveler will have
flown back to SFO and will need to transfer to BART. Coming off a relatively
long flight and egress terminal time, the traveler will likely have to wait half the
BART headway. The peer review panel suggested that the transfer egress time
be capped at 15 minutes, and that recommendation has been implemented.
Total Travel Times
To compare travel times across modes, selected city pairs have been identified
and compared across modes and between the base year ( 2000) and the forecast
year ( 2030) in Table 6.1. The forecast year travel times reflect one of the baseline
build scenarios, so that the high- speed rail mode can be compared to competing
modes in these markets.
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Table 6.1 Total Peak Travel Times by Mode for Selected City Pairs
Auto Air
High- Speed
Rail
Conventional
Rail
City to City Pair 2000 2030 2000 2030 2030 2000/ 2030
Los Angeles downtown to
San Francisco downtown
6: 28 6: 32 3: 30 3: 38 3: 23 No service
Fresno downtown to
Los Angeles downtown
3: 32 3: 38 3: 17 3: 24 2: 14 No service
Los Angeles downtown to
San Diego downtown
2: 37 2: 39 2: 51 3: 01 2: 13 3: 26
Burbank ( airport) to San Jose
downtown
5: 31 5: 40 2: 46 2: 43 3: 07 No service
Sacramento downtown to
San Jose downtown
2: 29 2: 24 2: 41 2: 41 2: 15 4: 06
High- speed rail total travel times compete with air favorably in many markets,
because of the recognition that the terminal and wait times are lower for high-speed
rail than air. In many cases, the access and egress times are also shorter,
because in many areas there are more high- speed rail stations than airports.
High- speed rail also competes well with auto in these longer- distance markets
( over 100 miles) because it is faster. Conventional rail is longer than high- speed
rail in all competing markets.
6.3 RELIABILITY
Reliability is a new measure that was included directly into the interregional
mode choice models currently under development. Information collected was
from correspondences with conventional rail system planners, the FAA data, and
previous high- speed rail environmental documentation ( 2003).
The SP surveys, collected for this study, included the following reliability options
across modes as part of the overall choice experiments. The reliability question
was posed for each of four modes as the percent variations in the frequency of
encountered delays.
• Travel by auto – Percent of the time there are no extra delays of more than
15 minutes;
• Travel by air – Percent of flights that arrive within 15 minutes of schedule;
• Travel by conventional rail – Percent of trains that arrive within 15 minutes
of schedule; and
• Travel by high- speed rail – Percent of trains that arrive within 5 minutes of
schedule.
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These data did not result in a significant parameter in the mode choice models.
In conjunction with the peer review panel, we hypothesized that this was
because the survey questions on reliability were too narrow ( i. e., percent of
flights or trains that arrive within 15 minutes), making it difficult for travelers to
distinguish between the modes for longer interregional travel decisions. As a
result, Cambridge Systematics modified the definition of the reliability measure
to reflect the percent of flights or trains that arrive within 60 minutes, which
increased the impact this reliability has on a person’s modal choice. In turn, the
consultant team, in consultation with the MTC and other study participants, has
constrained the reliability measure in the mode choice models to reflect this
change.
Highways tend to be the least reliable of the four modes on a day- in, day- out
basis. Reliability on highways is highly susceptible to incidents, weather, vol-ume
variation, and inadequate base capacity. On two of these factors ( construc-tion
and special events), auto is more susceptible than the other modes. It is only
when considering the influence of vehicle availability and routing that highways
have a lower susceptibility than all other modes.
The measure of reliability that has been used on a series of studies by Cambridge
Systematics is the freeway vehicle hours of delay. This measure indicates that, as
delay on the freeway increases, the overall reliability of the system would tend to
decrease. The probability, expressed in decimal terms, of an auto traveler arriving
within 60 minutes of the congested travel time can be found with the following
function:
( )
⎟ ⎟ ⎟ ⎟ ⎟
⎠
⎞
⎜ ⎜ ⎜ ⎜ ⎜
⎝
⎛
⎥⎦
⎤
⎢⎣
⎡ −
+
+
=
TC TC TO TO
P TC
* 60 *
0.18
0.0073* ( / 1)
60
0.117647 5.2695
Where:
TO = Free- flow travel time in minutes; and
TC = Congested travel time in minutes.
The prior equation uses the concept of “ travel time index,” and essentially looks
at the likelihood that someone’s trip will be delayed by 60 minutes or more by
nonrecurring incident delay. The probability is referenced against congested
travel time, since auto travelers presumably already account for the effects of
recurring congestion in their mode choice decisions. The portion of the equation
shown in bold represents the estimate of incident delay, measured in minutes.
This auto reliability measure relies on existing research to define the function for
determining auto reliability, but is applied on an origin- destination basis, rather
than a link basis for the purposes of this study. The resulting percent reliability
estimates for a trip from Los Angeles to San Francisco are in the range of 67 to
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92 percent, depending on the specific details of a trip. Trips with no congestion
will have 100 percent reliability.
Airline reliability data for 2000 and 2005, as well as forecasts for 2025, were com-piled
from the FAA data. This reflects an average reliability for air of 91 percent
in 2000, 95 percent in 2005, and 94 percent in 2030. Airline travel shows reliabil-ity
improvements since 2000, probably due to the airline practice of increasing
scheduled air times to allow for better on- time performance.
There was no available on- time performance data for conventional rail services
arriving within 60 minutes of the scheduled time. The proposed measurement
takes into account the same relationship that air performance has between 5 and
60 minutes, and assesses individual performance for each service. The following
reliability measures were obtained and estimated: ACE on- time performance
within 60 minutes was estimated at 97 percent; Metrolink on- time performance
within 60 minutes was estimated at 98 percent; San Joaquin’s on- time perform-ance
within 60 minutes was estimated at 89 percent; Capitol Corridor on- time
performance within 60 minutes was estimated at 94 percent; and Surfliner’s on-time
performance within 60 minutes was estimated at 94 percent.
Typical high- speed rail reliability for European and Japanese systems was ana-lyzed
by SYSTRA staff. On dedicated high- speed rail track, even with express
and local trains, both the French and Japanese have reported average delays of 29
to 40 seconds per train ( including weather and earthquake delays), which is more
than 99 percent on time ( within 10 minutes of schedule in European practice). In
California, there will be origin- destination pairs that will have 100 percent dedi-cated
right of ways ( ROW), where a very high on- time performance ( OTP) could
be expected. This translates to 99 percent reliability for the defined criteria of
OTP within 60 minutes.
6.4 FUTURE NO- PROJECT NETWORKS
The future baseline networks were developed for 2030, with assumptions about
transportation infrastructure improvements. The 2030 horizon year presents the
best source of information, since this year is close to the horizon year for regional
and metropolitan transportation plans ( RTPs and MTPs, respectively). RTPs/
MTPs for the four major urban areas have been identified and coded into the
baseline transit and highway networks. The consultant team used the statewide
travel model ( STM) for other areas of the State – particularly the Central Valley.
Assumptions about network improvements were identified by comparing the
base and future networks.
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The details of these transportation infrastructure investments are documented in
detail in the level of service report9.
9 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 Metropolitan Transportation
Commission and the California High- Speed Rail Authority, August 2006.
7.0 Ridership and Revenue
Forecasts
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7.0 Ridership and Revenue
Forecasts
This section outlines aggregate high- speed rail ridership and revenue forecasts
for sensitivity tests, network, and alignment alternatives. These results are
detailed and discussed further in Bay Area/ California High- Speed Rail Ridership and
Revenue Forecasting Study Ridership and Revenue Forecasts.
7.1 SENSITIVITY TESTS
A series of sensitivity tests were conducted to test the impacts of changes in level
of service on high- speed rail ridership and revenue. These tests were designed to
assist in developing an improved operating plan and optimum fares, and to
understand the impacts of potential changes in assumptions to the air and auto
modes. The results of the sensitivity tests are provided in Table 7.1.
Table 7.1 Sensitivity Tests for High- Speed Rail
Percent Change from Base
Sensitivity Test Change in Level of Service Boardings Revenues
High- speed rail level of service tests
Higher high- speed rail fares 25% increase - 13% 2%
Average daily headways High- speed rail headways* - 15% - 14%
Higher high- speed rail freq 100% increase 15% 16%
Express service SF/ LA Double freq SF/ LA to SJV, SD/ SF
to SAC
22% 24%
Air and auto level of service tests
Higher air/ auto times 6% increase** 6% 6%
Higher air/ auto costs 50% increase 46% 53%
Combined level of service tests
Higher high- speed rail fares and
higher air/ auto costs
25% increase in fares, 50%
increase in costs
13% 19%
Higher high- speed rail fares and
higher air/ auto costs
50% increase in both 31% 40%
Higher high- speed rail fares and
higher air/ auto costs
100% increase in fares, 50%
increase in costs
- 6% 1%
* Average daily headways assume that the headways in the peak and off- peak periods are equal. This
effectively increases peak headways and decreases off- peak headways.
** The 6- percent increase in travel time was based on a 30- minute increase in travel time from San
Francisco to Los Angeles by car.
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The results show that improvements in high- speed rail frequencies can support
much higher high- speed rail ridership; increased high- speed rail frequencies in
the major corridors ( San Francisco to Los Angeles, Los Angeles to San Joaquin
Valley, San Diego to Sacramento, and San Francisco to Sacramento) were then
retained for the alternatives analysis. These results also show that raising high-speed
rail fares will not significantly increase revenues, unless this is combined
with different assumptions of air and auto costs. Assumptions regarding air and
auto cost increases remain a difficult issue, given the volatility in these costs in
the past 5 years alone. The sensitivity tests do show that high- speed rail rider-ship
is highly sensitive to the assumptions of air and auto costs, and can increase
as much as 46 percent with a 50- percent increase in air and auto costs, which
seems quite reasonable compared to current trends in these costs.
7.2 NETWORK ALTERNATIVES
There are 6 network alternatives for the Pacheco Pass ( southern alignment into
the Bay Area) alternative and 11 network alternatives for the Altamont Pass
( northern alignment alternative) alternative. These network alternatives are
described in detail in the Environmental Impact Statement ( EIS) report10. The
interregional and intraregional models were run for the 2030 forecast year for
each alternative and ridership, and revenues were summarized and compared
for each.
The Pacheco Pass alternative results are summarized in Table 7.2. For each alter-native,
the amount of service is held constant in order to better compare the net-work
changes. In the case of the combined San Francisco and Oakland alterna-tive
( P3), service from San Jose is split proportionally between the two cities,
which causes overall level of service in each destination to be lower than in the
base. So even though this alternative reaches more travelers directly in terms of
station location, the lesser level of service causes lower ridership and revenues.
The Transbay alternatives ( P5 and P6) both have higher ridership and revenue
than the base because service is not split and every train serves all three destina-tions
( San Francisco, San Jose, and Oakland), but are not as likely to be cost
effective, given the expense of constructing an additional Transbay tube.
The Altamont Pass alternative results are summarized in Table 7.3. The
Altamont Pass alternatives generally do not compare favorably to the Pacheco
Pass alternatives; only because many of these alternatives have split service to
multiple destinations, rather than a single line, as is the case in most of the
Pacheco alternatives. Some of the Altamont alternatives go to single destinations
and compare well with similar Pacheco alternatives, such as the alternatives to
San Francisco ( A5), to Oakland ( A6), and to San Jose ( A4). In addition, the
Transbay tube alternative ( A10) compares reasonably well with the same
Pacheco Pass alternative ( P5).
10 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.
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Table 7.2 Pacheco Pass Network Alternative Results
Network Alternative Name and Description Annual Ridership Annual Revenues
P1 Pacheco to San Jose and San Francisco 93,890,000 $ 3,098,000,000
From San Francisco to San Jose, this network alternative would use the existing Caltrain rail ROW. The Pacheco and Henry
Miller ( to the UPRR) alternatives would be used between San Jose and the Central Valley. The BNSF N/ S ( north of Merced) and
UPRR N/ S ( south of Merced) alignments would be used in the Central Valley.
P2 Pacheco to San Jose and Oakland
From Oakland to San Jose, this network alternative would use the Niles/ I- 880 alignment. The Pacheco and Henry Miller ( to the 91,720,000 $ 3,083,000,000
UPRR) alternatives would be used between San Jose and the Central Valley. The BNSF N/ S ( north of Merced) and UPRR N/ S
( south of Merced) alignments would be used in the Central Valley.
- 2.3%* - 0.5%*
P3 Pacheco to San Jose, San Francisco, and Oakland
From San Francisco to San Jose, this Network Alternative would use the existing Caltrain ROW. From Oakland to San Jose, the 86,080,000 $ 2,790,000,000
Niles/ I- 880 alignment would be used. The Pacheco and Henry Miller ( to the UPRR) alternatives would be used between San
Jose and the Central Valley, and the BNSF N/ S ( north of Merced) and UPRR N/ S ( south of Merced) alignments would be used in
the Central Valley.
- 8.3%* - 9.9%*
P4 Pacheco to San Jose 80,040,000 $ 2,678,000,000
The Pacheco and Henry Miller ( to the UPRR) alternatives would be used between San Jose and the Central Valley, and the
BNSF N/ S ( north of Merced) and UPRR N/ S ( south of Merced) alignments would b