Regional Transportation Models
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Board of Directors
SAN DIEGO ASSOCIATION OF GOVERNMENTS
The San Diego Association of Governments (SANDAG) is a public
agency formed voluntarily by local governments to assure overall
areawide planning and coordination for the San Diego region.
Voting members include the incorporated Cities of Carlsbad, Chula
Vista, Coronado, Del Mar, El Cajon, Encinitas, Escondido, Imperial
Beach, La Mesa, Lemon Grove, National City, Oceanside, Poway, San
Diego, San Marcos, Santee, Solana Beach, Vista, and the County of San
Diego. Advisory and Liaison members include Caltrans, U.S. Department
of Defense, San Diego Unified Port District, and Tijuana/Baja
California/Mexico.
CHAIRMAN: Hon. Mike Bixler
VICE CHAIRMAN: Hon. Elliot Parks
SECRETARY-EXECUTIVE DIRECTOR: Kenneth E. Sulzer
CITY OF CARLSBAD
Hon. Bud Lewis, Mayor
(A) Hon. Ann Kulchin, Councilmember
(A) Hon. Julianne Nygaard, Mayor Pro Tem
CITY OF CHULA VISTA
Hon. Shirley Horton, Mayor
(A) Hon. Jerry Rindone, Mayor Pro Tem
CITY OF CORONADO
Hon. Mary Herron, Mayor
(A) Hon. David Blumenthal, Councilmember
CITY OF DEL MAR
Hon. Elliot Parks, Councilmember
(A) Hon. Mark Whitehead, Councilmember
(A) Hon. Henry Abarbanel, Deputy Mayor
CITY OF EL CAJON
Hon. Richard Ramos, Councilmember
(A) Hon. Mark Lewis, Councilmember
CITY OF ENCINITAS
Hon. Gail Hano, Councilmember
(A) Vacant
CITY OF ESCONDIDO
Hon. Jerry Harmon, Councilmember
(A) Hon. Lori Holt Pfeiler, Councilmember
CITY OF IMPERIAL BEACH
Hon. Mike Bixler, Mayor
(A) Hon. Gail Benda, Councilmember
CITY OF LA MESA
Hon. Art Madrid, Mayor
(A) Hon. Barry Jantz, Councilmember
(A) Hon. Jay LaSuer, Councilmember
CITY OF LEMON GROVE
Hon. Jerome Legerton, Mayor Pro Tem
(A) Hon. Craig Lake, Councilmember
CITY OF NATIONAL CITY
Hon. Rosalie Zarate, Councilmember
(A) Vacant
CITY OF OCEANSIDE
Hon. Dick Lyon, Mayor
(A) Hon. Colleen O'Harra, Deputy Mayor
CITY OF POWAY
Hon. Don Higginson, Mayor
(A) Hon. Bob Emery, Councilmember
(A) Hon. Mickey Cafagna, Councilmember
CITY OF SAN DIEGO
Hon. Judy McCarty, Councilmember
(A) Hon. Barbara Warden, Councilmember
(A) Hon. Valerie Stallings, Councilmember
CITY OF SAN MARCOS
Hon. Lee Thibadeau, Mayor
(A) Hon. Mark Loscher, Councilmember
CITY OF SANTEE
Hon. Jack Dale, Mayor
(A) Hon. Hal Ryan, Councilmember
CITY OF SOLANA BEACH
Hon. Marion Dodson, Deputy Mayor
(A) Hon. Teri Renteria, Mayor
(A) Hon. Joe Kellejian, Councilmember
CITY OF VISTA
Hon. Gloria E. McClellan, Mayor
(A) Hon. Ed Estes, Councilmember
COUNTY OF SAN DIEGO
Vacant
(A) Hon. Pam Slater, Chair
(A) Vacant
STATE DEPT. OF TRANSPORTATION
(Advisory Member)
James van Loben Sels, Director
(A) Gary Gallegos, District 11 Director
U.S. DEPARTMENT OF DEFENSE
(Liaison Member)
CAPT. Tom Gunn, CEC, USN
Commanding Officer Southwest Division
Naval Facilities Engineering Command
SAN DIEGO UNIFIED PORT DISTRICT
(Advisory Member)
Jess Van Deventer, Commissioner
TIJUANA/BAJA CALIFORNIA/MEXICO
(Advisory Member)
Hon. Hector G. Osuna Jaime
Presidente Municipal de Tijuana
Revised January 6, 1995
ABSTRACT
TITLE: Regional Transportation Models
AUTHOR: San Diego Association of Governments
DATE: January 1995
SOURCE OF COPIES: San Diego Association of Governments
401 B Street, Suite 800
San Diego, CA 92101
(619) 595-5300
NUMBER OF PAGES: 354
ABSTRACT: This report describes transportation
modeling procedures that are
currently used by the San Diego
Association of Governments to produce
regional highway and public transit
travel demand forecasts for the years
1990 to 2015. The operation of each
step of the modeling process is de-
scribed, along with data
requirements, products produced,
calibration procedures, and data file
formats.
ACKNOWLEDGEMENTS
The following staff of the San Diego Association of Governments
contributed to the preparation of this document.
Kenneth E. Sulzer, Executive Director
Stuart R. Shaffer, Deputy Executive Director
Bob Parrott, Director of Research
Lee F. Hultgren, Director of Transportation
Bill McFarlane, Senior Transportation Planner
Michael Hix, Senior Transportation Planner
Jeff Martin, Senior Research Planner
Dan Hildebrand, Assistant Transportation Planner
Andrew Abouna, Assistant Transportation Planner
Mike Calandra, Senior Transportation Technician
Julie Jamarta, Senior Transit Technician
TABLE OF CONTENTS
CHAPTER 1 INTRODUCTION. . . . . . . . . . . . . . . . . . . . .3
Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . .3
Software. . . . . . . . . . . . . . . . . . . . . . . . . . . .4
Hardware. . . . . . . . . . . . . . . . . . . . . . . . . . . .5
Process . . . . . . . . . . . . . . . . . . . . . . . . . . . .5
New Developments. . . . . . . . . . . . . . . . . . . . . . . .11
Model Results . . . . . . . . . . . . . . . . . . . . . . . . .13
CHAPTER 2 ZONE SYSTEM . . . . . . . . . . . . . . . . . . . . .19
Data File Documentation . . . . . . . . . . . . . . . . . . . .29
CHAPTER 3 SURVEYS . . . . . . . . . . . . . . . . . . . . . . .35
Travel Behavior Survey. . . . . . . . . . . . . . . . . . . . .35
Regional Transit Survey . . . . . . . . . . . . . . . . . . . .36
External Trip Surveys . . . . . . . . . . . . . . . . . . . . .38
1991 Visitor Survey . . . . . . . . . . . . . . . . . . . . . .39
Traffic Counts. . . . . . . . . . . . . . . . . . . . . . . . .39
Transit Passenger Counts. . . . . . . . . . . . . . . . . . . .40
Data File Documentation . . . . . . . . . . . . . . . . . . . .43
CHAPTER 4 GROWTH FORECASTS. . . . . . . . . . . . . . . . . . .61
Input Assumptions . . . . . . . . . . . . . . . . . . . . . . .62
Regional Growth Control Totals. . . . . . . . . . . . . . . . .65
Sub-Regional Employment Allocation. . . . . . . . . . . . . . .65
Sub-Regional Residential Allocation . . . . . . . . . . . . . .66
MGRA Allocation . . . . . . . . . . . . . . . . . . . . . . . .68
Data File Documentation . . . . . . . . . . . . . . . . . . . .71
CHAPTER 5 TRANSPORTATION NETWORKS . . . . . . . . . . . . . . .85
Data File Documentation . . . . . . . . . . . . . . . . . . . .91
CHAPTER 6 HIGHWAY NETWORKS. . . . . . . . . . . . . . . . . . .103
Network Procedures. . . . . . . . . . . . . . . . . . . . . . .105
Capacity. . . . . . . . . . . . . . . . . . . . . . . . . . . .106
Travel Time . . . . . . . . . . . . . . . . . . . . . . . . . .111
Tranplan Input File . . . . . . . . . . . . . . . . . . . . . .112
Turn Prohibitor File. . . . . . . . . . . . . . . . . . . . . .114
Tranplan Highway Network File . . . . . . . . . . . . . . . . .114
Tranplan Zone-to-Zone Travel Time Files . . . . . . . . . . . .114
Data File Documentation . . . . . . . . . . . . . . . . . . . .119
CHAPTER 7 TRANSIT NETWORKS. . . . . . . . . . . . . . . . . . .131
Input Files . . . . . . . . . . . . . . . . . . . . . . . . . .133
Network Processing. . . . . . . . . . . . . . . . . . . . . . .135
Network Validation. . . . . . . . . . . . . . . . . . . . . . .141
Access Procedures . . . . . . . . . . . . . . . . . . . . . . .143
Data File Documentation . . . . . . . . . . . . . . . . . . . .147
CHAPTER 8 TRIP GENERATION . . . . . . . . . . . . . . . . . . .163
Model Structure . . . . . . . . . . . . . . . . . . . . . . . .164
Model Outputs . . . . . . . . . . . . . . . . . . . . . . . . .169
Model Calibration . . . . . . . . . . . . . . . . . . . . . . .172
Data File Documentation . . . . . . . . . . . . . . . . . . . .179
CHAPTER 9 TRIP DISTRIBUTION . . . . . . . . . . . . . . . . . .199
Model Structure . . . . . . . . . . . . . . . . . . . . . . . .199
Model Outputs . . . . . . . . . . . . . . . . . . . . . . . . .200
Model Calibration . . . . . . . . . . . . . . . . . . . . . . .202
Data File Documentation . . . . . . . . . . . . . . . . . . . .215
CHAPTER 10 VEHICLE TRIP FACTORING . . . . . . . . . . . . . . .229
Model Structure . . . . . . . . . . . . . . . . . . . . . . . .229
Model Outputs . . . . . . . . . . . . . . . . . . . . . . . . .232
Model Calibration . . . . . . . . . . . . . . . . . . . . . . .233
Data File Documentation . . . . . . . . . . . . . . . . . . . .237
CHAPTER 11 MODE CHOICE. . . . . . . . . . . . . . . . . . . . .241
Model Structure . . . . . . . . . . . . . . . . . . . . . . . .241
Model Outputs . . . . . . . . . . . . . . . . . . . . . . . . .259
Model Calibration . . . . . . . . . . . . . . . . . . . . . . .260
Data File Documentation . . . . . . . . . . . . . . . . . . . .273
CHAPTER 12 EXTERNAL TRIPS . . . . . . . . . . . . . . . . . . .283
Model Structure . . . . . . . . . . . . . . . . . . . . . . . .283
Data File Documentation . . . . . . . . . . . . . . . . . . . .289
CHAPTER 13 HIGHWAY ASSIGNMENT . . . . . . . . . . . . . . . . .293
Model Structure . . . . . . . . . . . . . . . . . . . . . . . .293
Post-Assignment Processing. . . . . . . . . . . . . . . . . . .297
Model Outputs . . . . . . . . . . . . . . . . . . . . . . . . .299
Calibration . . . . . . . . . . . . . . . . . . . . . . . . . .301
Data File Documentation . . . . . . . . . . . . . . . . . . . .311
CHAPTER 14 TRANSIT ASSIGNMENT . . . . . . . . . . . . . . . . .315
Model Structure . . . . . . . . . . . . . . . . . . . . . . . .315
Model Outputs . . . . . . . . . . . . . . . . . . . . . . . . .316
Model Calibration . . . . . . . . . . . . . . . . . . . . . . .317
CHAPTER 15 MOTOR VEHICLE EMISSION MODELING. . . . . . . . . . .323
Model Structure . . . . . . . . . . . . . . . . . . . . . . . .326
Data File Documentation . . . . . . . . . . . . . . . . . . . .335
LIST OF TABLES
Table 1-1 Computer Resource Requirements. . . . . . . . . . . . 7
Table 1-2 Historical Demographic and Travel Indicators. . . . .14
Table 1-3 Forecasts of Demographic and Travel Indicators. . . .15
Table 3-1 Travel Behavior Survey Expansion Factors. . . . . . .37
Table 4-1 Series 8 Employment by Major Statistical Area . . . .67
Table 4-2 Series 8 Dwelling Units by Major Statistical Area . .68
Table 6-1 Highway Network Summary . . . . . . . . . . . . . . 103
Table 6-2 Default Roadway Attributes. . . . . . . . . . . . . 106
Table 6-3 Green-to-Cycle Time Ratios. . . . . . . . . . . . . 109
Table 6-4 Turn Lane Capacities. . . . . . . . . . . . . . . . 110
Table 6-5 Default Capacities. . . . . . . . . . . . . . . . . 110
Table 6-6 Tranplan Highway Inputs . . . . . . . . . . . . . . 112
Table 6-7 Assignment Group Definitions. . . . . . . . . . . . 113
Table 7-1 Transit Network Summary . . . . . . . . . . . . . . 131
Table 7-2 Transit Modes . . . . . . . . . . . . . . . . . . . 134
Table 7-3 Transit Company Descriptions. . . . . . . . . . . . 138
Table 7-4 Transit Fares by Company. . . . . . . . . . . . . . 140
Table 7-5 Light Rail Transit Fares. . . . . . . . . . . . . . 141
Table 7-6 Distribution of Route Time Errors . . . . . . . . . 141
Table 7-7 Assignment of Survey Transit Trips. . . . . . . . . 142
Table 8-1 Trip End Balancing Factors. . . . . . . . . . . . . 170
Table 8-2 Total Person Trips by Major Statistical Area. . . . 171
Table 8-3 Person Trips by Trip Type . . . . . . . . . . . . . 171
Table 8-4 Person Trip Rate Summary. . . . . . . . . . . . . . 172
Table 8-5 Observed and Estimated Total Person Trips . . . . . 174
Table 8-6 Observed and Estimated Home-Work Person Trips . . . 174
Table 8-7 Observed and Revised Estimated Home-Work
Person Trips. . . . . . . . . . . . . . . . . . . . 175
Table 8-8 Trip Generation Root Mean Square Error. . . . . . . 175
Table 9-1 Person Trip Lengths . . . . . . . . . . . . . . . . 201
Table 9-2 Commute Person Trips Between
Major Statistical Areas . . . . . . . . . . . . . . 203
Table 9-3 Total Person Trips Between
Major Statistical Areas . . . . . . . . . . . . . . 204
Table 9-4 Attraction Error Rates by Iteration . . . . . . . . 206
Table 9-5 Observed and Estimated Screenline Traffic Volumes . 206
Table 9-6 Observed and Estimated Intra-zonal Person Trip
Percentages . . . . . . . . . . . . . . . . . . . . 208
Table 9-7 Observed and Estimated Person Trip Lengths. . . . . 209
Table 9-8 Observed and Estimated Commute Person Trips
Between Major Statistical Areas . . . . . . . . . . 210
Table 9-9 Observed and Estimated Total Person Trips
Between Major Statistical Areas . . . . . . . . . . 211
Table 10-1 Time of Day Factors . . . . . . . . . . . . . . . . 231
Table 10-2 Directional Vehicle Trip Factors. . . . . . . . . . 232
Table 11-1 Mode Choice Coefficients. . . . . . . . . . . . . . 245
Table 11-2 Mode Constants. . . . . . . . . . . . . . . . . . . 253
Table 11-3 Income Constants. . . . . . . . . . . . . . . . . . 253
Table 11-4 Trip Length Constants . . . . . . . . . . . . . . . 254
Table 11-5 Transit Commute Major Statistical Area
Adjustment Factors. . . . . . . . . . . . . . . . . 256
Table 11-6 Transit Non-Commute Major Statistical Area
Adjustment Factors. . . . . . . . . . . . . . . . . 257
Table 11-7 Vehicle Occupancies for 3+ Person Autos . . . . . . 258
Table 11-8 Trips by Mode . . . . . . . . . . . . . . . . . . . 260
Table 11-9 Commute Transit Trips Between Major
Statistical Areas . . . . . . . . . . . . . . . . . 261
Table 11-10 Total Transit Trips Between Major
Statistical Areas . . . . . . . . . . . . . . . . . 262
Table 11-11 Observed and Estimated Trips by Mode. . . . . . . . 265
Table 11-12 Observed and Estimated Income Distribution
by Mode . . . . . . . . . . . . . . . . . . . . . . 266
Table 11-13 Observed and Estimated Trip Length Distribution
by Mode . . . . . . . . . . . . . . . . . . . . . . 267
Table 11-14 Observed and Estimated Commute Transit Trips
Between Major Statistical Areas . . . . . . . . . . 268
Table 11-15 Observed and Estimated Total Transit Trips
Between Major Statistical Areas . . . . . . . . . . 269
Table 12-1 External Vehicle Trips. . . . . . . . . . . . . . . 285
Table 13-1 Speeds by Volume/Capacity Ratio . . . . . . . . . . 294
Table 13-2 Signal Delay Times by Volume/Capacity Ratio . . . . 295
Table 13-3 Off-Peak Period Adjusted to Input Speed Ratios. . . 296
Table 13-4 Peak Period Adjusted to Input Speed Ratios. . . . . 297
Table 13-5 Vehicle Miles of Travel . . . . . . . . . . . . . . 300
Table 13-6 Average Speeds After Assignment . . . . . . . . . . 301
Table 13-7 Assignment Error by Number of Iterations. . . . . . 302
Table 13-8 Assignment Differences by Number of Iterations. . . 303
Table 13-9 Observed and Estimated Vehicle Miles of Travel. . . 304
Table 13-10 Root Mean Square Error by Functional Class. . . . . 305
Table 13-11 Root Mean Square Error by Volume Group. . . . . . . 306
Table 13-12 VMT Error by Functional Class . . . . . . . . . . . 307
Table 13-13 VMT Error by Volume Group . . . . . . . . . . . . . 308
Table 14-1 Transit Boardings . . . . . . . . . . . . . . . . . 316
Table 14-2 Access/Egress Mode Use. . . . . . . . . . . . . . . 317
Table 14-3 Observed and Estimated Transit Boardings. . . . . . 318
Table 14-4 Observed and Estimated Access/Egress
Mode Use . . . . . . . . . . . . . . . . . . . . . 319
Table 15-1 Hot Soak Emission Rates . . . . . . . . . . . . . . 325
Table 15-2 Vehicle Classification Groups . . . . . . . . . . . 327
Table 15-3 Simplified Temperature Assumptions. . . . . . . . . 327
Table 15-4 Motor Vehicle Emission Forecasts. . . . . . . . . . 330
LIST OF FIGURES
Figure 1-1 Transportation Modeling Process . . . . . . . . . . . 6
Figure 2-1 Block Split Example . . . . . . . . . . . . . . . . .21
Figure 2-2 MGRA and Zone Relationship. . . . . . . . . . . . . .21
Figure 2-3 Series 8 Zones. . . . . . . . . . . . . . . . . . . .22
Figure 2-4 External Zones. . . . . . . . . . . . . . . . . . . .23
Figure 2-5 Subregional Areas . . . . . . . . . . . . . . . . . .24
Figure 2-6 Major Statistical Areas . . . . . . . . . . . . . . .25
Figure 2-7 Average Vehicle Ridership Zones . . . . . . . . . . .26
Figure 6-1 Highway Networks. . . . . . . . . . . . . . . . . . 104
Figure 7-1 Transit Networks. . . . . . . . . . . . . . . . . . 132
Figure 8-1 Unique Generators . . . . . . . . . . . . . . . . . 167
Figure 9-1 Highway Screenlines . . . . . . . . . . . . . . . . 207
Figure 11-1 Parking Costs . . . . . . . . . . . . . . . . . . . 247
Figure 11-2 Walking Time. . . . . . . . . . . . . . . . . . . . 249
Figure 15-1 Air Quality Modeling Grids. . . . . . . . . . . . . 324
CHAPTER 1
INTRODUCTION
INTRODUCTION
The ability of San Diego's street, freeway, and public transportation
systems to accommodate travel in the face of continuing population
growth is a concern of many San Diego residents. Another issue is the
effectiveness of proposed transportation facilities in attracting new
users and reducing congestion. Other concerns deal with side effects
of large-scale transportation projects on San Diego's quality of life,
such as increased air pollution from vehicular emissions.
Transportation models have been developed to help answer these and
other questions. Models are computerized procedures for
systematically predicting travel changes in response to changes in
development patterns, transportation systems, and demographics given
certain assumptions about travel behavior based upon existing
conditions.
The many factors affecting travel make manual analysis prohibitively
time consuming for almost all applications. The complexity of the
problem can quickly exhaust computer capacity as well so that the
design of transportation models requires compromises on the level of
detail and number of factors that can be considered.
The last several years have seen increasing demands placed upon
transportation models. Part of this increased demand is the result of
recent air quality and congestion management legislation which
mandates transportation model analysis. In addition, as computers
become more ingrained in our culture, there is greater reliance placed
upon computer-generated data in any decision-making process.
Users should remember that transportation models are primarily
accounting tools and provide limited insight into the "right"
decision. The main advantage of a model is that it provides a
systematic analysis process so that alternatives can be evaluated in
an even-handed manner.
SETTING
San Diego has one transportation model that is used for all
applications in the Region. A single source of transportation data
not only reduces discrepancies, it also enables data files to be
better maintained, an important factor when many different users
utilize the data in a variety of ways. Model users include:
þ SANDAG (regional planning)
þ Caltrans (freeway planning)
þ Transit agencies (bus and rail patronage studies)
þ Local jurisdictions (circulation element studies)
þ Developers (site-specific impact reports)
þ Air Pollution Control District (vehicle emissions)
Forecasting traffic volumes in response to land use changes is
probably the most commonly requested model application. Proposed land
use changes can be quite detailed, such as a site-specific project
that a developer may need analyzed as part of an environmental impact
report. At the other extreme are generalized regionwide growth
alternatives.
Forecasting traffic volumes on proposed freeways and streets is
another common application. Typically, a study looks at several
alternative alignments for a proposed road. Models are run to
determine the differences in traffic volumes between alternatives.
Traffic impacts upon nearby roads are often a concern as well.
Models are frequently used to forecast transit patronage for light
rail extensions. These studies usually involve the evaluation of
alternative transit improvements in a corridor. For example, the
performance of improved express bus service might be compared to the
performance of new rail service. Models produce forecasts of transit
ridership on specific routes, overall transit trips, and traffic
volume impacts that assist in the selection of the best alternative.
SOFTWARE
SANDAG uses a transportation planning computer package called
Tranplan, distributed by the Urban Analysis Group. Tranplan provides
a framework for performing much of the computer processing involved
with modeling. Tranplan functions have numerous user options that
enable different urban areas to tailor the generalized software to
their specific needs.
Another software package used extensively in the modeling process is
Arc/Info, distributed by Environmental Systems Research Institute,
Inc. This geographic information system (GIS) maintains, manipulates,
and displays transportation, land use, and demographic data. The
power of Arc/Info lies in its ability to relate data contained in
different geographic files for shared uses. Arc/Info also has
enhanced computer mapping capabilities.
The two software vendors are working on developing common databases.
Until these procedures become operational, SANDAG Fortran programs
provide the linkage between Tranplan and Arc/Info. Data maintained in
Arc/Info is translated into temporary Tranplan input files. Results
from Tranplan are fed back to Arc/Info for generating plots and
reports. Other SANDAG programs manipulate data and perform some
modeling functions such as trip generation and mode choice.
SANDAG has extensive experience with both Tranplan and Arc/Info.
SANDAG has used Tranplan since 1981 for a wide range of modeling
applications. Arc/Info was first installed at SANDAG in 1985.
Tranplan and Arc/Info have been used in conjunction for transportation
modeling since 1987.
HARDWARE
SANDAG's transportation modeling and database maintenance is performed
on SUN workstations. SPARC-10 and SPARC-20 workstations are dedicated
exclusively for transportation modeling. Seven other SUN workstations
are shared between transportation and other agency users. Each work
station is accompanied by a two giga-byte disk drive for data storage.
Outside modelers use either workstations or PCs. Outside workstations
most often are IBM RISC-6000s that can share data with SUN
workstations. PCs are primarily used by smaller cities for running
subsets of the regional model focused on their city.
PROCESS
The figure on the next page illustrates SANDAG's transportation
modeling process. The process is broken down into four major steps of
trip generation, trip distribution, mode choice, and assignment. This
four-step process is widely used throughout the country. Additional
functions provide inputs to the transportation models.
As indicated by the figure, the models can be applied in two stages.
First-stage applications make use of simplified trip distribution and
mode choice procedures. Second-stage applications make use of peak
and off-peak period travel times from first-stage highway assignment.
These travel times are used to redistribute trips and determine mode
choice.
Processing would stop after the first stage for most applications.
Federal guidelines for modeling air quality and major investment
impacts require more elaborate procedures. These studies would use
second-stage transportation models in order to better match
transportation demand with transportation supply. While the
federally-mandated process may yield somewhat better results,
increased computer resource requirements prevent its use on a routine
basis.
A brief summary of each function follows. Table 1-1 summarizes
computer processing time and disk space requirements for each step of
the modeling process where appropriate. Computer times assume that
programs are processed on a SPARC-20 workstation. Programs require
about 50% more time on SPARC-10 workstations.
Zone System
Zones are geographic subareas that provide a method of geographically
summarizing land use, demographic and travel data. SANDAG has a 4,545
transportation zone system that is the basis for most transportation
modeling. A more detailed set of 25,929 Master Geographic Reference
Areas (MGRAs) underlies transportation zones. The MGRAs are used in
transit access procedures and special applications.
(Insert Figure 1-1 - Transportation Modeling Process)
Table 1-1
COMPUTER RESOURCE REQUIREMENTS
Function Computer Time Disk Space
(Minutes) (Kilobytes)
First Stage
Trip Generation 10 6,900
Build Highway Network 45 15,600
Highway Path-Building 55 84,000
Trip Distribution 95 83,000
Vehicle Trip Factoring 40 44,000
Highway Assignment 190 4,900
Post-Assignment Processing 10 7,000
Sub-Total 445 245,400
(7 1/2 Hours)
Second Stage
Highway Path-Building 110 186,200
Trip Distribution 280 124,000
Build Transit Network 80 7,600
Transit Path-Building 60 232,900
Mode Choice 210 68,700
Highway Assignment 390 4,900
Transit Assignment 15 4,300
Post-Assignment Processing 20 7,000
Vehicle Emissions 20 5,100
Sub-Total 1185 640,700
(20 Hours)
TOTAL 1630 886,100
(27 Hours)
Surveys
Surveys are conducted periodically to calibrate relationships used
within the transportation models, such as the number of trips per
dwelling unit. These relationships are assumed to remain constant
between surveys and over the forecast period. Other survey data and
counts are obtained to validate model results.
Growth Forecasts
Every three to five years, SANDAG produces a new "series" of
population, dwelling unit, employment, and land use forecasts for the
Region as a whole and for various geographic levels within the Region.
The most recent Interim Series 8 Growth Forecasts were approved for
use in February 1994. These forecasts cover a 1990 to 2015 time span.
Trip Generation
Household person trip generation rates by structure type are applied
to MGRA level occupied dwelling unit forecasts to calculate
residential trip ends by MGRA. Non-residential trip ends are
estimated by applying trip rates to MGRA level forecasts of non-
residential land use by 80 land use categories. MGRA trip ends are
aggregated to zones for use in transportation models. The trip
generation model estimates daily person trips by ten trip types:
home-work, home-college, home-school, home-shop, home-other, work-
other, other-other, serve passenger, visitor, and airport. Time of
day factors by trip type and land use category split daily trip ends
into peak period and off-peak period trip ends for second stage model
applications.
Transportation Networks
A master transportation database is maintained using Arc/Info
software. The database includes existing and proposed transit and
highway facilities. Attributes are coded for individual segments that
describe geometric, operational, and phasing characteristics.
Highway Networks (First Stage)
Highway facilities for an alternative are selected from the master
transportation database. Capacity and travel times are computed for
each link and a Tranplan highway network is created. Off-peak zone-
to-zone travel times and distances are estimated from the coded
highway network.
Trip Distribution (First Stage)
A gravity model form of the trip distribution model links trip
productions estimated by the trip generation model with trip
attractions in other zones to produce trip movements between zones
based upon zone-to-zone travel times from the highway network
function. SANDAG distributes daily person trips for ten trip types
using off-peak highway travel times.
Vehicle Trip Factoring (First Stage)
Person trip tables from the trip distribution model are factored to
obtain vehicle trip tables for highway assignment. Vehicle trip
factors vary by time period, location, distance, and trip type.
Transit service is represented in a general manner to speed processing
and simplify procedures. Time of day and directional factors are
applied to obtain peak and off-peak period vehicle trip tables for
assignment.
External Trips (First Stage)
Vehicle trips with an end outside of the San Diego Region are
estimated by factoring base-year trip tables from roadside surveys
conducted at cordon stations located where major roads cross boundary
of the Region. Factors are based upon the change in person trips by
subarea and trip type within the San Diego Region. Model estimates
are adjusted to control totals for major roads. External vehicle trip
tables are added to internal vehicle trip tables prior to highway
assignment.
Highway Assignment (First Stage)
Vehicle trips between zones are loaded onto specific links based upon
travel times via alternative routes and limitations imposed by roadway
carrying capacity. Four iterations of Tranplan's equilibrium highway
assignment model are performed for peak and off-peak time periods.
Assignments from the two time periods are merged to obtain daily
traffic forecasts.
Post-Assignment Processing (First Stage)
SANDAG procedures tailor Tranplan highway assignment outputs to San
Diego's specific needs. Model-estimated volumes are adjusted to
compensate for calibration error. Link speeds and times for peak and
off-peak period conditions are computed using Highway Capacity Manual
procedures. Plots, reports, and datasets are produced.
Highway Networks (Second Stage)
Tranplan networks are created with peak and off-peak period congested
link times from the post-assignment process. An additional peak
period high-occupancy vehicle (HOV) network is output reflecting the
higher operating speeds on HOV-only facilities. Zone-to-zone peak and
off-peak period mixed-flow travel times, and peak period HOV travel
times are generated.
Trip Distribution (Second Stage)
Peak and off-peak period person trip ends for the ten trip types are
distributed separately. Mixed-flow congested travel times for each
time period are produced from the first-stage post-assignment process.
Transit Networks (Second Stage)
Tranplan transit networks are generated from the master transportation
database and post-assignment highway travel times. Nodes are located
at transit access points (TAPs). Links connect transit access points
and represent the roadway or rail line over which transit vehicles
operate. Lines are coded over links and describe the frequency of
service, type of service, and path followed by transit routes. Peak
and off-peak period TAP-to-TAP transit paths and travel times are
generated from transit networks.
Transit access files are also created that specify walk and auto
connections between zones and TAPs. Walk access is based upon MGRA
level trip ends and Arc/Info transit coding. Auto access is based
upon mixed-flow peak period zone-to-zone travel times and park-and-
ride lot locations.
Mode Choice (Second Stage)
Person trips between zones are split into six forms of transportation
called modes: drive alone, 2 person autos, 3 or more person autos,
transit-walk, transit-auto, and other. The model determines mode
shares based upon the level of service provided by each mode and trip
maker characteristics. Mode use percentages are calculated separately
by time period, income level, and trip type. Highway vehicle
directional factors are applied to produce peak and off-peak vehicle
trip tables for highway assignment. Transit peak and off-peak period
trips by walk and auto access are produced for transit assignment.
Highway Assignment (Second Stage)
Second stage highway assignment procedures are the same as first stage
procedures. Vehicle trip tables from the mode choice model are input
instead of trip tables from the vehicle trip factoring process.
Post-Assignment Processing (Second Stage)
Second stage post-assignment procedures are the same as first stage
procedures.
Vehicle Emissions
Vehicle emissions are computed based upon zone level vehicle trip ends
from the mode choice model, highway link volumes and speeds from the
post-assignment process, and transit link bus volumes and speeds from
the transit network. Other data from outside of the transportation
models such as vehicle emission factors are also input. Regionwide
emission summaries by pollutant are produced. A file of emissions by
hour of the day, type of pollutant, and air quality modeling gridcell
is optionally produced for use by the Air Pollution Control District's
dispersion model.
NEW DEVELOPMENTS
Developing a new set of growth forecasts provides an opportunity to
implement enhanced modeling procedures. Transportation models
underwent a major over-haul during Series 8 development. The major
objective of these revisions was to merge SANDAG's regional and sub-
area transportation models into one system. Three factors led to this
decision.
Previously, SANDAG had a regional transportation model that was used
to produce forecasts for SANDAG's Regional Transportation Plan,
transit patronage studies, and air quality assessments. Much of the
Region was also covered by one or more special-purpose sub-area
highway models that had been developed at the request of member
agencies. Maintaining multiple databases and sorting out conflicting
forecasts grew to be a major problem as the number of sub-area models
increased.
Computer processing power has grown enormously in the last few years.
The two transportation SUN workstations have about 30 times the
capacity of SANDAG's old mainframe computer that was used for Series 7
transportation modeling. Enhanced computers coupled with Arc/Info's
ability to manage large amounts of data enable SANDAG to model the
entire region at a level of detail previously only possible for small
areas.
Finally, a growth management initiative was recently passed that
requires that transportation impacts of growth be considered more
rigorously for all circulation element roads. Regional models were
too general for this type of analysis and sub-area models covered only
part of the Region.
Compromises between procedures used under the two approaches were
necessary to establish one system applicable for all uses.
Differences between Series 7 and Series 8 are highlighted below. In
general, Series 8 procedures are more straightforward than Series 7
regional procedures although somewhat more complicated than Series 7
sub-area procedures.
Zone Structure
Series 7 regional zones were discarded. Series 8 zones are largely
built around sub-area zones that had previously been developed for
Series 7 subarea studies. There was some adjustment of sub-area zone
boundaries to match nearby Census block boundaries and accommodate
transit access considerations. New detailed zones were developed in
areas not previously covered by sub-area models. The definition of
the Region was expanded to encompass the entire County, not just the
western 40% of the County used in Series 7. The number of zones
increased from 773 zones in Series 7 to 4,545 in Series 8.
Highway Networks
Series 7 regional highway networks included freeways, prime arterials,
major arterials, and regional zone connectors in the western part of
the County. Sub-area networks contained all circulation element roads
within a study area, regional roads outside a study area, and sub-area
zone connectors.
Series 8 highway networks contain all circulation element roads
throughout the Region, as shown in each jurisdiction's adopted General
Plan. Freeways are now represented by one-way links for each freeway
direction in contrast to previous coding which used single, two-way
links. Schematic ramp representations have been replaced with ramp
coding that follows actual alignments. Direction codes have been
dropped in favor of turn prohibitors. The number of Tranplan links
has increased from 17,000 to 45,000 in a typical network.
Transit Networks
Sub-area models dealt only with vehicle trips so sub-area transit
networks were not coded. Series 8 transit networks are similar to
Series 7 transit networks. Procedures have been re-worked to make use
of the new zone system and other databases.
Trip Generation
The Series 7 regional model estimated person trip ends by five trip
types based upon dwelling units by income level by zone and employees
by five Standard Industrial Classification groups by zone. Dwelling
units and employment were phased in five-year increments between 1985
and 2010.
Sub-area models estimated vehicle trip ends by five trip types within
a study area based upon dwelling units by structure type and acres of
non-residential land use by 80 land use categories. Series 7 regional
trip ends were used outside of individual study areas. Full
development of general plans was assumed inside most study areas.
Series 8 combines both procedures. Person trip ends are estimated
based upon dwelling units by structure type and acres of non-
residential land use by 80 land use categories. Dwelling units and
acres are phased by five-year increments between 1990 and 2015. The
number of trip types has been expanded to ten. Trip ends by peak and
off-peak time periods have been added. Trip rates are generally
reduced from previous sub-area model levels.
Trip Distribution
The Series 7 regional model distributed person trips using weighted
peak and off-peak period highway times. Sub-area models distributed
vehicle trips based upon off-peak highway times.
Series 8 distributes daily person trips based upon off-peak period
highway times for most applications. A second stage gravity model is
available which distributes peak period person trips using peak period
highway times and off-peak trips using off-peak period highway times.
Friction factors have been re-calibrated for the new zone system.
Mode Choice
Series 7 sub-area models avoided the need for a mode choice model by
modeling only vehicle trips. Series 8 has a simplified person trip to
vehicle trip factoring process that is available for highway-oriented
applications. This procedure is not based upon transit network times,
so processing time is much less than incorporating the full mode
choice procedures.
For more detailed transit analysis, Series 8 uses a mode choice model
that is similar in structure to the Series 7 regional model. Model
parameters have been re-calibrated, and some procedures have been
modified.
Highway Assignment
Highway assignment procedures were the same between Series 7 regional
and sub-area models. Series 8 makes use of Tranplan's equilibrium
assignment instead of the capacity restraint procedures used
previously. Minor modifications have been made to input parameters.
Transit Assignment
Series 7 and Series 8 transit assignment procedures are similar.
MODEL RESULTS
Table 1-2 presents historical demographic and travel measures for the
years 1980, 1990, and 1993. The 1980-1990 time period was an era of
high growth, while the years 1991-1993 represent a time of poor
economic conditions. These historical measures provide a context for
evaluating Series 8 forecasts summarized in Table 1-3 for the Series 8
base year of 1990, an intermediate year of 2000, and the Series 8
horizon year of 2015.
As indicated in Table 1-2, the 1980's saw strong employment growth in
San Diego, averaging 4.7% per year, fueled by high defense-related
expenditures. San Diego's economy has been in a prolonged recession
since 1990, causing a regional employment loss through 1993. Table 1-
3 shows moderate employment growth through the forecast period after
bottoming out in 1994. These economic trends translate into fewer
employees per household in 2000 and 2015 than in 1990.
Relatively high population growth is expected to continue throughout
the forecast period, although the 3.4% annual average population
growth rate experienced during the 1980's slows considerably to 1.9%.
The slower growth rate is largely due to a drop in the rate of in-
migration linked to fewer job opportunities.
Average household size increased slightly from 1980 to 1990, but is
expected to decrease by a small amount over the forecast period. This
leads to a dwelling unit growth rate that is somewhat higher than
population growth rate.
Travel changes tend to mirror economic conditions. The annual average
growth in vehicle miles of travel (VMT) drops from 7.0% during the
1980s to 1.4% during the 1990's. VMT grows more rapidly after 2000,
averaging 2.3% per year. VMT increased dramatically during the
1980's, growing at twice the rate of population growth. However, VMT
has remained virtually unchanged since 1990, while population has
increased about 2% per year. The high VMT per capita growth rate in
the 1980s was due to unusually strong economic growth and population
growth in high travel age categories.
Historical trends show transit ridership increasing at the same rate
as VMT during the 1980's. Since 1990, transit ridership has dropped,
while overall travel has remained constant. Transit ridership is
expected to grow by 3.1% per year during the 1990's and 4.5% after
2000. Ridership forecasts are produced by SANDAG's mode choice model
and reflect a significant expansion of San Diego's light rail system
and the effects of a proposed Travel Demand Management Ordinance.
Table 1-2
HISTORICAL DEMOGRAPHIC TRAVEL INDICATORS
Indicator 1980 1990 1980-1990 1993 1990-1993
Population 1,873,000 2,520,000 +3.4% 2,668,000 +2.0%
Dwelling Units 674,000 892,000 +3.2% 923,000 +1.1%
Employment 764,000 1,121,000 +4.7% 1,081,000 -1.2%
Vehicle Miles 36,636,000 62,043,000 +6.9% 62,456,000 +0.2%
Miles per Capita 19.6 24.6 +2.6% 23.4 -1.6%
Transit Trips 85,000 146,000 +7.1% 143,000 -0.7%
% = Annual average percentage change
Table 1-3
FORECASTS OF DEMOGRAPHIC TRAVEL INDICATORS
Indicator 1990 2000 1990-2000 2015 2000-2015
Population 2,520,000 3,002,000 +1.9% 3,816,000 +1.8%
Dwelling Units 892,000 1,030,000 +1.6% 1,385,000 +2.3%
Employment 1,121,000 1,132,000 +0.1% 1,472,000 +2.0%
Vehicle Miles 62,043,000 70,778,000 +1.4% 95,589,000 +2.3%
Miles per Capita 24.6 23.6 -0.4% 25.0 +0.3%
Vehicle Trips 8,415,000 9,601,000 +1.4% 12,502,000 +2.0%
Trips per Capita 3.3 3.2 -0.3% 3.3 +0.2%
Average Trip Miles 7.4 7.4 +0.0% 7.6 +0.2%
Vehicle Hours 1,927,000 2,198,000 +1.4% 3,045,000 +2.6%
Average Trip Minutes 13.7 13.7 +0.0% 14.6 +0.4%
Transit Trips 146,000 191,000 +3.1% 320,000 +4.5%
% = Annual average percentage change
CHAPTER 2
ZONE SYSTEM
ZONE SYSTEM
The entire 4,200 square mile County of San Diego is defined as the San
Diego Region and is covered by SANDAG's modeling system. SANDAG
breaks down the Region into various sub-areas, depending upon the type
of application. Transportation models primarily make use of
transportation zones, although SANDAG's other levels of geography also
come into play.
SANDAG's smallest unit of geography is called a master geographic
reference area (MGRA), of which there are 25,929. The concept behind
MGRAs is to produce forecasts for very small areas that can later be
aggregated into higher levels of geography as needed.
MGRAs are based upon blocks as defined by the Census Bureau for use in
the 1990 Census. Blocks are polygons bounded by streets that existed
in 1989 and other Census Bureau features such as Census Tract
boundaries. There are 20,317 blocks averaging 600 feet on a side in
size, but ranging from 100 feet to over 10 miles on a side. The main
problem with using blocks directly for planning purposes is the size
of blocks in rural areas slated for development. Since blocks only
reflect on-the-ground features, they are not well suited for analyzing
proposed development sites. Another problem with blocks is that they
meander along ridge lines and other areas that lack regular street
patterns.
In order to overcome these problems, blocks were split by the
following features to create MGRAs:
þ Community plan boundaries
þ Zip code boundaries
þ City sphere of influence boundaries
þ Planned roadways
þ 1/2 mile buffer around transit routes
þ Transportation zone boundaries
Information is most often requested by zip code, community plan area,
city, and city spheres of influence. These boundaries were added
where they differed from census block boundaries.
Planned freeways and local circulation element roads were added to
further subdivide blocks. These facilities provide convenient
breakpoints and were brought into the MGRA boundary file from
transportation network files.
Specifying the amount of activity within walking distance of transit
is important when estimating transit patronage. SANDAG assumes 1/2
mile is the maximum distance people will walk to transit. Blocks were
manually split as necessary to delineate walk areas around existing
and proposed transit routes and rail stations. Ridge lines and other
topographic features that prevent walk access to transit were also
added.
MGRAs were further subdivided for highway modeling purposes. This
part of the process was done in conjunction with developing a
transportation zone system, which is the next highest level of
geography. One of the objectives of upgrading transportation models
was to allow analysis of traffic volumes for smaller streets and local
transit routes. This dictated having a large number of zones. Much
of the Region had previously been modeled at a detailed level in
various sub-area traffic studies. Zone boundaries from these studies
were added to the MGRA file where there were no nearby features
already in the file. MGRAs in the rest of the Region not covered by
past studies were split as necessary to separate dissimilar land uses
and specify access opportunities.
Figure 2-1 shows how blocks were split to form MGRA's in the Chula
Vista area. The western part of the mapped area is a developed, older
area and has few block splits. The eastern part of the mapped area
was only partially developed in 1990 and, therefore, has more block
splits to accommodate future development.
Once all additional features had been added to the block file,
creating transportation zones was simply a matter of aggregating MGRAs
to zones. A total of 4,545 transportation zones were created. Of
these, 4,536 zones are internal to the Region and nine are external
zones located where major roads cross the County line. Figure 2-2
illustrates how MGRAs are aggregated to form zones in the Chula Vista
area. Figure 2-3 depicts boundaries of the 4,545 zones. External
zones are shown in more detail in Figure 2-4.
Transportation models also make use of three higher levels of
geography which are shown in maps at the end of the chapter.
Transportation zones are nested into 45 sub-regional areas (SRAs), 8
major statistical areas (MSAs), and 3 average vehicle ridership (AVR)
zones. It should be noted that additional SRAs and MSAs have been
added for transportation modeling purposes that are not in SANDAG's
standard definitions.
All geographic files are maintained as Arc/Info coverages. This
enables other aggregations of forecasted data to be easily produced as
requested. Cross-reference files between different levels of
geography can be readily produced. Arc/Info also has sophisticated
mapping capabilities which are useful for error checking and display
purposes.
(Insert Figure 2-1 - Block Split Example
and
Insert Figure 2-2 - MGRA and Zone Relationship)
(Insert Figure 2-3 - Series 8 Zones)
(Insert Map/Figure 2-4 - External Zones)
(Insert Map/Figure 2-5 - Subregional Areas)
(Insert Map/Figure 2-6 - Major Statistical Areas)
(Insert Map/Figure 2-7 - Average Vehicle Ridership Zones)
DATA FILE
DOCUMENTATION
ZONES
The transportation zone coverage is called "zones" and is located
under the /max7/data/covs directory. The following is a list of
polygon attributes coded in the zones coverage.
AREA Arc/Info computed polygon area in square feet.
PERIMETER Arc/Info computed polygon perimeter in feet.
ZONES# Arc/Info assigned unique, sequential ID number for
polygon.
ZONES-ID User assigned unique, fixed ID number for polygon.
Zone number used in transportation modeling.
ZONE Same as ZONES-ID.
COLOR Number indicating color that would be used to shade
zone when producing plot.
TEMP Temporary variable.
SUB-REGIONAL AREAS
The sub-regional area (SRA) coverage used in transportation modeling
is called "pseudosra" and is located under the /max7/data/covs
directory. This coverage has additional SRAs delineating Centre City,
Mission Valley, and Otay Mesa needed for transportation modeling that
are not in SANDAG's standard SRA coverage. External zones have also
been added. The following is a list of polygon attributes coded in
the SRA coverage.
AREA Arc/Info computed polygon area in square feet.
PERIMETER Arc/Info computed polygon perimeter in feet.
PSEUDOSRA# Arc/Info assigned unique, sequential ID number for
polygon.
PSEUDOSRA-ID User assigned unique, fixed ID number for polygon.
SRA SANDAG's two-digit SRA number.
MSA SANDAG's standard Major Statistical Area in which SRA
is located.
NAME Name of SRA.
SEQSRA Sequential SRA number.
MAJOR STATISTICAL AREAS
The major statistical area (MSA) coverage used in transportation
modeling is called "pseudomsa" and is located under the
/max7/data/covs directory. This coverage has an additional MSA
delineating Centre City San Diego that is not in SANDAG's standard MSA
coverage. The following is a list of polygon attributes coded in the
MSA coverage.
AREA Arc/Info computed polygon area in square feet.
PERIMETER Arc/Info computed polygon perimeter in feet.
PSEUDOMSA# Arc/Info assigned unique, sequential ID number for
polygon.
PSEUDOMSA-ID User assigned unique, fixed ID number for polygon.
MSA SANDAG's one-digit MSA number, where:
1 = North City
2 = South Suburban
3 = East Suburban
4 = North County East
5 = North County West
6 = East County
8 = Centre City
9 = Central Area
NAME Name of MSA.
AVERAGE VEHICLE RIDERSHIP ZONES
The average vehicle ridership (AVR) zone coverage used in
transportation modeling is called "avrzone" and is located under the
/max7/data/covs directory. The following is a list of polygon
attributes coded in the AVR zone coverage.
AREA Arc/Info computed polygon area in square feet.
PERIMETER Arc/Info computed polygon perimeter in feet.
AVRZONE# Arc/Info assigned unique, sequential ID number for
polygon.
AVRZONE-ID User assigned unique, fixed ID number for polygon.
AVRZONE Average Vehicle Ridership zone number, where:
1 = Centre City
2 = Suburban
3 = Rural, unincorporated
CHAPTER 3
SURVEYS
SURVEYS
Surveys are needed at many points in the modeling process to establish
relationships between input variables and model-estimated variables.
At other points, model estimates need to be verified against
independent data. Data collection is costly and time consuming so
surveys are conducted relatively infrequently. This normally does not
create a problem since underlying model relationships are relatively
stable over time.
Four surveys listed below provide most of the data used to calibrate
transportation models.
þ 1986 Travel Behavior Survey
þ 1990 San Diego Regional Transit Survey
þ 1986 and 1991 External Trip Surveys
þ 1991 San Diego Visitor Survey
The 1990 Census Transportation Planning Package (CTPP) will provide
additional data for work trips, once it has been tabulated and
checked. A 500 household survey of San Diego residents was also
conducted as part of a 1990 California State-wide Survey. This survey
was not used for model calibration because it lacked some of the data
needed for SANDAG's models and produced trip totals that differed
significantly from local surveys.
Major sources of validation data are traffic counts from Caltrans and
local jurisdictions and transit passenger counts from SANDAG's Transit
Passenger Counting Program.
TRAVEL BEHAVIOR SURVEY
SANDAG's 1986 Travel Behavior Survey (TBS) is the primary source of
model calibration data. Survey procedures and results are documented
in 1986 Travel Behavior Surveys, Volumes 1 and 2 (SANDAG, 1987).
In this survey, 2,754 San Diego households were interviewed to obtain
household, household member, and household trip characteristics on a
survey day. Surveying was conducted on weekdays between February and
June 1986. A two-stage interviewing process was used. First,
households were telephoned using a directory-based digit dialing
method. The survey was explained and general information was
collected. Households agreeing to participate in the survey were
mailed a travel diary in which household members recorded all trips
made on a pre-determined survey day. Surveyors subsequently called
back survey households to record travel diary entries. Of the 8,000
households contacted, 34% agreed to participate in the survey and
completed travel diaries.
Once survey data had been collected, coders manually assigned Series 7
zone numbers at the start and end of each trip based upon addresses,
cross-street names, or place names from survey respondents. Survey
responses were entered into a hierarchical data file with a record for
each household containing household characteristics, followed by a
record for the first person in the household containing person
characteristics, followed by a record for each trip made by the person
on the survey day containing trip characteristics. Alternating person
and trip records follow for each person in the household.
Several modifications were made to the TBS dataset before use in
calibrating Series 8 models. Model-estimated travel times and
distances between start and end zones were added to survey records.
Another modification was to link trip records having a start or end
purpose of "change mode" with other trips. For example, the survey
would record a trip from home to work by driving to a park-and-ride
lot and then getting on a bus as two trips: a home-to-change mode
trip by auto and a change mode-to work trip by bus. The two trips
would be combined into one home-to-work trip by bus through the
linking process.
Another modification was to compute new survey expansion factors to
represent 1990 conditions. This was done by dividing 1990 Census
occupied households by Major Statistical Area, structure type, and
automobile availability by survey households in each category. Table
3-1 summarizes expansion factor calculations. Expansion factors by
structure type, auto availability, and MSA were averaged to compute
expansion factors in cells that had fewer than ten observations.
Finally, Series 7 zone numbers were replaced with Series 8 zone
numbers. The fact that Series 7 zones are often split into several
Series 8 zones complicated the task. Furthermore, Series 8 zones do
not nest into Series 7 zones. First, the fraction of trip ends of
each Series 7 zone in Series 8 zones was computed. TBS trip records
were read and multiple output records were created for each Series 7
and 8 zone combination at the start and end of the trip. Expansion
factors were modified to reflect the fraction of trip ends that the
new record represents.
The Travel Behavior Survey was tabulated to develop the following
calibration data:
þ Trip generation rates for the trip generation model
þ Time-of-day factors for the trip generation model
þ Trip length frequency distributions for the trip distribution
model
þ Vehicle trip factors for the person trip to vehicle trip factoring
process
þ Non-transit mode use percentages for the mode choice model
þ Time of day factors for the emissions model
REGIONAL TRANSIT SURVEY
Every five years SANDAG, in cooperation with transit operators,
conducts an on-board transit survey to obtain transit trip and transit
user characteristics. The most recent survey, conducted in fall of
1990, provides data used to calibrate the transit portion of the mode
choice model. Survey procedures and results are presented in 1990 San
Diego Regional Transit Survey, Volumes 1 and 2 (SANDAG, October 1991).
Table 3-1
TRAVEL BEHAVIOR SURVEY EXPANSION FACTORS
Click HERE for graphic.
Surveyors stationed on-board buses and trolleys distributed
questionnaires to passengers over 12 years of age as they boarded the
vehicle. Passengers filled out forms while they completed their trip
and dropped off forms as they got off vehicles. A total of 57,000
survey forms were distributed over a three month time span from
September to November 1990. This represents a sampling rate of about
27 percent of all passengers. About 41,000 surveys were returned with
useable information.
Survey responses were entered into a dataset. Coders added bus stop
numbers based upon boarding and alighting bus stop locations.
SANDAG's bus stop inventory was used to add state plane coordinates
based upon bus stop numbers. An automated geo-coding process was used
to assign state plane coordinates to the starting and ending address
of each trip as provided by survey respondents. Zone numbers were
attached by overlaying survey coordinates on zones using Arc/Info
procedures. Transit travel times were appended to trip records based
upon transit network estimates.
The survey sample was selected to provide a representative sample by
route configuration, geographic area, and time of day. An expansion
factor was calculated for each survey record to match Passenger
Counting Program boardings by routes, time period and 50 geographic
areas.
Transit survey records were tabulated to determine the following
calibration and validation data:
þ Transit trip shares by income level, trip type, and trip length
for mode choice calibration
þ MSA-to-MSA transit trips for mode choice calibration
þ Park-and-ride locations for coding transit network park-and-ride
nodes
þ Walk access distance distribution to set maximum walk access
distances
þ External transit trip table for external trip modeling
þ Relationship of total boardings to linked trips for transit
assignment validation
þ Access mode percentages for transit assignment validation
þ Zone-to-route trips for transit network validation
þ Zone-to-zone trip tables for transit network calibration
EXTERNAL TRIP SURVEYS
Roadside interview surveys are conducted periodically to determine the
travel characteristics of trips coming into or passing through the San
Diego Region from outside San Diego. Roadside interviews were
conducted in 1986 at five locations to obtain external trip data
consistent with 1986 Travel Behavior Survey data. Four new external
zones were added for Series 8 forecasts when the Region was expanded
to cover the entire County. These new stations were surveyed in 1991.
In addition, two zones on the Mexican border were re-surveyed in 1991
to account for new conditions. Figure 2-4 summarizes the location and
survey year of external zones.
Roadside surveys were conducted by stopping some vehicles as they were
leaving the Region. Surveyors noted vehicle characteristics and
questioned drivers about trip characteristics. Survey responses were
coded and expanded to match daily traffic counts by vehicle type for
each station.
External trip surveys are used to obtain base-year external vehicle
trip tables by trip type. These external trip tables are factored to
represent future year trips and added to internal trip tables from the
rest of the modeling process.
1991 VISITOR SURVEY
San Diego is a major convention and vacation destination. While
travel by business and vacation visitors to San Diego is significant,
other surveys pick up only partial visitor data. Travel behavior
surveys collect information about visitors staying with San Diego
residents. Transit surveys collect information about visitor transit
trips. External surveys collect information about the first San Diego
stop of visitors who drive to San Diego. A small-scale visitor survey
was conducted during the months of July, August and September 1991 to
obtain a more complete picture of visitor travel patterns.
The approach used was to tack on a travel survey to a periodic visitor
survey conducted for the San Diego Convention and Visitors Bureau
(CONVIS). Surveyors stationed outside selected hotels and tourist
attractions questioned passers-by about their trips made on the
previous day. Visitors who were on the first day of their stay in San
Diego were questioned about that day's trips. Approximately 1,400
visitors were surveyed, resulting in 3,200 useable survey trip
records. Survey responses were coded and expanded to the estimated
average daily visitors supplied by CONVIS.
The Visitor Survey was used to obtain visitor trip generation rates
and visitor trip length frequency distributions for gravity model
calibration.
TRAFFIC COUNTS
Traffic counts, used in model validation, are obtained in a variety of
ways, depending upon who has jurisdiction over a facility. Caltrans
counts traffic at relatively few freeway locations each year, but
produces annual count estimates for every freeway segment. There was
some question as to whether model validation should be based upon
actual freeway counts or upon the complete set of estimated counts.
After trying both approaches, it was decided that the more extensive
coverage of estimated counts provided a better basis for validation.
The City and County of San Diego conduct comprehensive traffic count
programs, together collecting approximately 3,500 counts each year.
SANDAG converts traffic count stations into an Arc/Info point coverage
and subsequently matches counts to network links.
Most of the other cities in the region also have good count programs.
For the last 15 years SANDAG, in cooperation with local jurisdictions,
has produced an annual Traffic Flow Map. Each year, cities provide
SANDAG with an updated listing of traffic counts on Traffic Flow Map
links within their jurisdiction. Counts from this program were used
for calibration purposes on surface streets in incorporated areas
outside the City of San Diego. A shortcoming with using Traffic Flow
Map counts is that one Traffic Flow Map link may cover several
Tranplan links. It is not apparent which Tranplan link to assign the
count to. Since traffic volumes usually vary by only small amounts
over a Traffic Flow Map link, this is not believed to be a serious
problem.
TRANSIT PASSENGER COUNTS
SANDAG has operated a Passenger Counting Program since 1979 in
response to Federal requirements and a need for passenger data at the
local level. The program is documented in Transit Passenger Counting
Program Technical Documentation, SANDAG, March 1994. Under the
program, every bus route is counted once a year. Routes are scheduled
for counting using a random selection process. A bus route is a
collection of individual bus trips. All regularly scheduled bus trips
on a route are counted. Light rail trips are counted using a
different sampling technique, where a random sample of five trips
every three days is selected for counting.
Trips are counted by stationing surveyors on-board transit vehicles.
Surveyors record the number of passengers boarding and alighting at
each transit stop. The number of passengers on-board vehicles between
stops is computed from the boarding and alighting data. Surveyors
also record arrival and departure times at selected time points along
a route. An up-to-date transit route and stop inventory is maintained
as part of the Passenger Counting Program.
Bus stop inventories from the Passenger Counting Program are used to
determine bus stop locations in transit network coding. The following
results of the program are used to validate transit assignment
estimates:
þ Ons and offs at stops
þ Screenline counts
þ Boardings by route and mode
DATA FILE
DOCUMENTATION
TRAVEL BEHAVIOR SURVEY
Travel Behavior Survey responses are contained in a file called
"survey" under "/max7/data/tbs." Other files that are derived from
the original survey responses are too numerous to document here. The
survey file is an ASCII file with one record for each surveyed
household, one record for each person, and one record for each trip.
HOUSEHOLD RECORD:
Columns Variable Type Description
1-1 I1 Record Type, where:
1 = Household Record
2 = Person Record
3 = Trip Record
3-9 I7 Survey Number
16-17 I2 Month of Survey
18-19 I2 Day of Survey
21-21 I1 Household Type, where:
1 = Single Family
2 = Multiple Family
3 = Condominium or Townhouse
4 = Mobile Home
5 = Group Quarters
6 = Other
22-22 I1 Tenure, where:
1 = Rent
2 = Own
23-24 I2 Household Size
25-26 I2 Vehicle Available
27-28 I2 Motorcycles Available
29-30 I2 Bicycles Available
31-31 I1 Income Group (1986$s), where:
1 = Less the $5,000
2 = $5,000 to $10,000
3 = $10,000 to $20,000
4 = $20,000 to $40,000
5 = $40,000 to $75,000
6 = More than $75,000
PERSON RECORD:
Columns Variable Type Description
1-1 I1 Record Type, where:
1 = Household Record
2 = Person Record
3 = Trip Record
3-9 I7 Survey Number
11-12 I2 Person Number
16-16 I1 Relationship, where:
1 = Related
2 = Not Related
3 = Visitor
17-17 I1 Gender, where:
1 = Male
2 = Female
19-20 I2 Age
21-21 I1 Drivers License, where:
1 = Licensed
2 = Not Licensed
22-22 I1 Employment Status, where:
1 = Employed Full-Time
2 = Employed Part-Time
3 = Employed with Multiple Jobs
4 = Unemployed
5 = Student
6 = Retired
7 = Not in Labor Market
9 = Student, Employed Part-Time
23-24 I2 Employment Industry
1 = Agriculture/Forestry/Fishing
2 = Mining
3 = Construction
4 = Manufacturing
5 = Transportation/Communications/Utilitie
s
6 = Wholesale Trade
7 = Retail Trade
8 = FIRE
9 = Service
10 = Government
11 = Military
25-26 I2 Employment Industry of Second Job
TRIP RECORD:
Columns Variable Type Description
1-1 I1 Record Type, where:
1 = Household Record
2 = Person Record
3 = Trip Record
3-9 I7 Survey Number
11-12 I2 Person Number
13-14 I2 Trip Number
16-16 I1 Purpose at Destination, where:
1 = Home
2 = Work
3 = Education
4 = Shop
5 = Work Related
6 = Social or Recreation
7 = Change Mode
8 = Serve Passenger
9 = Other
18-20 I3 Series 7 Zone at Destination
21-22 I2 Land Use at Destination, where:
10 = Residential
11 = Hotel/Motel
19 = Other Residential
20 = Regional Shopping Center
21 = Community Shopping Center
25 = Other Retail
26 = Gas Station
27 = Bank
28 = Restaurant/Bar
29 = Other Services
30 = Heavy Industry
31 = Light Industry
32 = High Rise Office
33 = Government Office
34 = Medical Office
39 = Other Office
40 = Nursery/Day Care
41 = Elementary School
42 = Junior High School
43 = Senior High School
44 = Junior College
45 = College/University
49 = Other Educational
Columns Variable Type Description
50 = Hospital, Nursing Home
51 = Church
52 = Cultural Center
53 = Military Base
54 = Transportation Terminal
59 = Other Institutional
60 = Beach/Bay
61 = Park
62 = Tourist Attraction
63 = Outdoor Recreation
64 = Theater/Movie
65 = Indoor Recreation
69 = Open Space
70 = Other
24-27 I4 Time (Hour/Minute) at Origin
28-28 A1 AM/PM at Origin
30-33 I4 Time at Destination
34-34 A1 AM/PM at Destination
36-37 I2 Mode, where:
1 = Automobile
2 = Pick-up/Light Truck
3 = Van
4 = Truck
5 = Motorcycle
6 = Bicycle
7 = Walk
8 = Taxicab/Limousine
9 = Public Transit
10 = School Bus
11 = Railroad
12 = Airplane
13 = Other
38-38 I1 Driver, where:
1 = Yes
2 = No
39-40 I2 Vehicle Occupancy
41-41 I1 Paid Parking at Destination, where:
1 = Yes
2 = No
42-42 I1 Primary Work Trip, where:
1 = Yes
2 = No
REGIONAL TRANSIT SURVEY
Transit survey responses are contained in a file called "survey" under
/max8/data/trs90. Other files that are derived from the original
survey responses are too numerous to document here. The survey file
is an ASCII file with one record for each unlinked transit trip that
was surveyed.
Columns Variable TypeDescription
1-6 I6 Survey Number
7-12 I6 Survey Date
13-15 I3 Route
16-19 I4 Terminal Time
20-20 I1 Direction Number
21-22 I2 Surveyor ID
23-23 I1 Language, where:
0 = English
1 = Spanish
24-24 I1 Purpose at Origin, where:
1 = Home
2 = Work
3 = School
4 = Shopping
5 = Social or Recreation
6 = Other
7 = Medical
25-25 I1 Address Status at Origin, where:
0 = No Information
1 = Good Information
26-83 A58 Address at Origin
84-85 A2 City at Origin
86-89 I4 Bus Stop Number at Origin
90-90 I1 Access Mode at Origin, where:
1 = Transferred from Bus
2 = Transferred from Rail
3 = Walked
4 = Drove Alone,
5 = Carpooled
6 = Dropped Off
7 = Other
8 = Transferred from Dial-a Ride
Columns Variable TypeDescription
91-93 I3 Route Transferred from at Origin
94-94 I1 Direction of Transfer Route
95-96 I2 Blocks Walked to Bus Stop at Origin
97-97 I1 Purpose at Destination
98-98 I1 Address Status at Destination
99-156 A58 Address at Destination
157-158 A2 City at Destination
159-162 I4 Bus Stop Number at Destination
163-163 I1 Egress Mode at Destination
164-166 I3 Route Transferred from at Destination
167-167 I1 Direction of Transfer Route
168-169 I2 Blocks Walked from Bus Stop at Destination
170-171 I2 Fare, where:
1 = Cash
2 = Monthly Pass
3 = Transfer Slip
4 = Ten Pack
5 = Other
172-172 I1 Auto Availability, where:
1 = Auto Available
2 = Auto Not Available
173-173 I1 Transit Use Frequency, where:
1-7 =
Days Used per Week
9 = Less Than Once a Week
174-174 I1 Gender, where:
1 = Male
2 = Female
175-175 I1 Visitor, where:
1 = Visitor
2 = Resident
176-176 I1 Military, where:
1 = Military
2 = Civilian
177-177 I1 Household Size, where:
1-4 =
Number in Household
5 = Five or more
178-178 I1 Ethnicity, where:
1 = Hispanic
2 = White/Non-Hispanic
3 = Black
4 = Asian
5 = Other
6 = American Indian
Columns Variable TypeDescription
179-179 I1 Quality of Transit Service, where:
1 = Good
2 = Average
3 = Poor
180-181 I2 Age
182-182 I1 Income (1990 $s), where:
1 = Less Than $10,000
2 = $10,000 to $20,000
3 = $20,000 to $30,000
4 = $30,000 to $40,000
5 = $40,000 to $50,000
6 = $50,000 to $60000
7 = More Than $60,000
183-183 I1 Knowledge of Route Schedules, where:
1 = Knowledgeable
2 = Not Knowledgeable
184-184 I1 Rail Border Crossing, where:
1 = Trip Crosses U.S. Border
2 = Trip Stays in U.S.
185-185 I1 Prior Mode Use of Rail Passengers, where:
1 = Drove Alone
2 = Carpooled
3 = Rode Bus
4 = Trip Not Made
5 = Other
186-188 I3 Prior Bus Route of Rail Passengers
189-191 I3 First Comment
192-194 I3 Second Comment
195-201 I7 X-Coordinate of Origin Bus Stop
202-208 I7 Y-Coordinate of Origin Bus Stop
209-209 A1 Location Code of Origin Bus Stop
210-211 I2 Expansion Zone at Origin Bus Stop
212-218 I7 X-Coordinate of Destination Bus Stop
219-225 I7 Y-Coordinate of Destination Bus Stop
226-226 A1 Location Code of Destination Bus Stop
227-228 I2 Expansion Zone at Destination Bus Stop
229-235 I7 X-Coordinate of Origin
236-242 I7 Y-Coordinate of Origin
243-244 I2 Expansion Zone at Origin
245-251 I7 X-Coordinate of Destination
252-258 I7 Y-Coordinate of Destination
259-260 I2 Expansion Zone at Destination
261-267 F7.3 Unlinked Trip Expansion Factor
268-269 I2 Number of Links (Transfers)
Columns Variable TypeDescription
270-270 I1 Transit District at Origin
271-271 I1 Transit District at Destination
272-277 I6 Census Tract at Origin
278-281 A4 Census Block at Origin
282-285 I4 Census Place at Origin
286-291 I6 Census Tract at Destination
292-295 A4 Census Block at Destination
296-299 I4 Census Place at Destination
300-305 I6 Census Tract at Origin Bus Stop
306-309 A4 Census Block at Origin Bus Stop
310-313 I4 Census Place at Origin Bus Stop
314-319 I6 Census Tract at Destination Bus Stop
320-323 A4 Census Block at Destination Bus Stop
324-327 I4 Census Place at Destination Bus Stop
1986 EXTERNAL TRAVEL SURVEY
Travel Behavior Survey responses are contained in a file called
"survey" under "/max7/data/ext86." Other files that are derived from
the original survey responses are too numerous to document here. The
survey file is an ASCII file with one record for each surveyed trip.
Columns Variable TypeDescription
1-5 I5 Survey Number
6-10 I5 Survey Station
11-11 I1 Direction of Travel, where:
1 = North
2 = South
3 = East
4 = West
12-13 I2 Beginning Hour of Interview
14-14 I1 Vehicle Occupancy
15-16 I2 Vehicle Type
17-20 A4 Residence, where:
X000 =
External Place Code
0000 =
External Place Code
M000 =
Mexican Zone
000 = Series 7 Zone
21-23 I3 Entry Cordon Station
24-25 I2 Length of Stay (Days)
26-29 I4 Place Code of External Last Stop
30-32 I3 Series 7 Zone of Internal Last Stop
33-34 I2 Purpose of Last Stop
35-36 I2 Purpose of Next Stop
37-40 I4 Location of Next Stop (See 17-20)
41-41 I1 Final Destination, where:
1 =
Yes
2 =
No
42-45 I4 Location of Final Destination (See 17-20)
46-47 I2 Frequency of Trip
48-53 F6.2 Expansion Factor
54-55 I2 Trip Type
1 =
Home-Work
2 =
Home-Shop
3 =
Home-Other
4 =
Work-Other
5 =
Other-Other
1991 EXTERNAL TRAVEL SURVEY
Travel Behavior Survey responses are contained in a file called
"survey" under "/max7/data/ext91." Other files that are derived from
the original survey responses are too numerous to document here. The
survey file is an ASCII file with one record for each surveyed trip.
Columns Variable TypeDescription
1-5 I5 Survey Number
6-8 I2 Survey Station, where:
1 = SR-79
2 = SR-78
3 = I-8
4 = SR-188
5 = Otay
4 = West
9-10 I2 Beginning Hour of Interview
11-11 I1 Vehicle Occupancy
12-12 I2 Vehicle Type
13-13 I1 Residence
14-67 A54 Address at Origin
68-72 I5 Zipcode at Origin
73-74 I2 Purpose at Origin
75-76 I2 Purpose at Destination
77-80 I4 Place Code at Destination
81-84 F4.1 Expansion Factor
CALTRANS TRAFFIC COUNTS
Caltrans counts are contained in a coverage called "ststa" that is
located under the /max7/data/covs directory. The coverage contains
two points for each freeway count location; one for each direction of
travel. Count stations on surface streets are represented by one
point. Additional stations have been placed on future Caltrans
facilities where posted volumes are to be produced. These future
stations have no count data. The following is a list of point
attributes coded in the count station coverage.
AREA Blank.
PERIMETER Blank.
STSTA# Arc/Info assigned unique, sequential ID number for point.
STSTA-ID User assigned unique, fixed ID number for point.
STA Station number at Caltrans count stations. Otherwise
SANDAG assigned station number.
DIR Direction of travel.
VOL Directional 1990 average weekday traffic count.
AMVOL Directional 1990 morning peak hour traffic count.
PMVOL Directional 1990 afternoon peak hour traffic count.
DISTANCE Arc/Info assigned distance in feet to nearest highway
arc.
NM Count station description.
SEQSTA SANDAG assigned sequential station number.
HWYCOV# Arc/Info ID number of nearest highway arc.
CITY OF SAN DIEGO TRAFFIC COUNTS
City of San Diego counts are contained in a coverage called "sdsta"
that is located under the /max7/data/covs directory. The coverage
contains one point for each count station location that is on a street
contained in the master transportation coverage (TCOV). The following
is a list of point attributes coded in the count station coverage.
AREA Blank.
PERIMETER Blank.
SDSTA# Arc/Info assigned unique, sequential ID number for point.
SDSTA-ID User assigned unique, fixed ID number for point.
STA City assigned count station number.
VOL Directional 1990 weekday traffic count.
HWYCOV# Arc/Info ID number of nearest highway arc.
DISTANCE Arc/Info assigned distance in feet to nearest highway
arc.
NM Street name of count station.
LIM Limits covered by station.
SEQSTA SANDAG assigned sequential station number.
COUNTY OF SAN DIEGO TRAFFIC COUNTS
County of San Diego counts are contained in a coverage called "ctsta"
that is located under the /max7/data/covs directory. The coverage
contains one point for each count station location that is on a street
contained in the master transportation coverage (TCOV). The following
is a list of point attributes coded in the count station coverage.
AREA Blank.
PERIMETER Blank.
CTSTA# Arc/Info assigned unique, sequential ID number for point.
CTSTA-ID User assigned unique, fixed ID number for point.
DIR Direction of travel.
YR Year of count.
VOL Directional 1990 weekday traffic count.
WAY One/Two Way
STA County assigned count station number.
NM Street name of count station.
XM Cross street name at count station.
HWYCOV# Arc/Info ID number of nearest highway arc.
DISTANCE Arc/Info assigned distance in feet to nearest highway
arc.
SEQSTA SANDAG assigned sequential station number.
TRAFFIC FLOW MAP COUNTS
Traffic Flow Map counts are contained in two history files called
"hist1" and "hist2." Count files are updated annually. Files are
located in a directory called "/max7/proj/vmt00," where 00 indicates
the count file year. The files are in ASCII format and contain one
record for Traffic Flow Map Link.
The first history file (hist1) covers the years 1978-1989 with the
following format.
Columns Variable TypeDescription
1-7 I7 Link Number
7-14 I7 1977 Weekday Traffic Count
15-16 A2 1977 Count Status, where:
Blank =
Link Counted
"N" =
Link Not Counted
"E" =
Estimated Count
"V" =
Vacated Link
17-23 I7 1978 Count
24-25 A2 1978 Status
26-32 I7 1979 Count
33-34 A2 1979 Status
35-41 I7 1980 Count
42-43 A2 1980 Status
44-50 I7 1981 Count
51-52 A2 1981 Status
53-59 I7 1982 Count
60-61 A2 1982 Status
62-68 I7 1983 Count
69-70 A2 1983 Status
71-77 I7 1984 Count
78-79 A2 1984 Status
80-86 I7 1985 Count
87-88 A2 1985 Status
89-95 I7 1986 Count
96-97 A2 1986 Status
98-104 I7 1987 Count
105-106 A2 1987 Status
107-113 I7 1988 Count
114-115 A2 1988 Status
116-122 I7 1989 Count
123-125 A2 1989 Status
The second history file (hist2) is set-up to cover the years 1990-2002
with the following format.
Columns Variable TypeDescription
1-7 I7 Link Number
7-14 I7 1990 Count
15-16 A2 1990 Status
17-23 I7 1991 Count
24-25 A2 1991 Status
26-32 I7 1992 Count
33-34 A2 1992 Status
35-41 I7 1993 Count
42-43 A2 1993 Status
44-50 I7 1994 Count
51-52 A2 1994 Status
53-59 I7 1995 Count
60-61 A2 1995 Status
62-68 I7 1996 Count
69-70 A2 1996 Status
71-77 I7 1997 Count
78-79 A2 1997 Status
80-86 I7 1998 Count
87-88 A2 1998 Status
89-95 I7 1999 Count
96-97 A2 1999 Status
98-104 I7 2000 Count
105-106 A2 2000 Status
107-113 I7 2001 Count
114-115 A2 2001 Status
116-122 I7 2002 Count
123-125 A2 2002 Status
CHAPTER 4
GROWTH FORECASTS
GROWTH FORECASTS
The Region's growth rate and location of new development within the
Region largely determine future travel patterns. Thus, it is
important to understand the growth forecasting process before
discussing transportation models.
Every three to five years, SANDAG produces a new set of regional
growth forecasts that account for updated existing development,
regional growth trends, and local general plans. Work began on the
current Series 8 forecasts in 1990. SANDAG's Board approved the use
of Interim Series 8 forecasts in February 1994.
A brief overview of the growth forecasting process is provided in this
report. The process is made up of five major steps:
þ Collecting input assumptions
þ Forecasting regionwide growth control totals
þ Allocating employment to 224 subareas called zones for urban
modeling (ZUMs)
þ Allocating dwelling units to ZUMs
þ Allocating growth to 25,929 Master Geographic Reference Areas
(MGRAs)
Annual regional-level forecasts are produced for the years 1990
through 2015. Sub-regional allocation models are run only for
selected years. Standard Series 8 forecast years are 1990, 2000,
2010, and 2015. Sub-regional allocations for other years are produced
as requested.
There is extensive local involvement throughout the growth forecasting
process. SANDAG's Board of Directors represents local jurisdictions
within the region and provides policy direction. At a technical
level, local planners provide input assumptions and work with SANDAG
staff to resolve forecasting issues.
Every effort is made to ensure that regional forecasts agree with
local development plans. MGRA, ZUM and regional holding capacities
are carefully computed based upon adopted general plans. The task of
the growth forecasting models is to phase development between existing
and ultimate conditions.
Most of the data files involved with growth forecasting are maintained
in Arc/Info coverages. Arc/Info simplifies file editing, enables data
from different files to be related, and has the flexibility to produce
almost any kind of map for checking and display purposes. Arc/Info
enables SANDAG to forecast growth at the detailed MGRA level without a
loss of precision.
Forecasts were prepared for two major alternative development
scenarios under Series 8. One scenario termed "Existing Policies" is
based upon adopted local general plans and regional transportation
plans. The other "Quality of Life" alternative modifies general plans
to put more growth in existing urban areas located near transit
facilities.
Additional growth forecasts were developed for evaluating Regional
Transportation Plan (RTP) alternatives. There is assumed to be a
relationship between development and accessibility. RTP growth
forecasts reflect the effects upon land use of alternative assumptions
about transportation facilities that might exist with different
funding levels or with shifts of highway funding to transit.
INPUT ASSUMPTIONS
Developing the databases that feed into the growth models is the most
time consuming part of the process. This effort consists of
collecting data on base-year conditions, local general plans, site-
specific projects, development constraints, and transportation
facilities.
Base-Year Conditions
An accurate description of existing conditions is important because
roughly 3/4 of the urban land that will ultimately be developed in the
Region is already developed. One of the basic assumptions of the
growth forecasting process is that land now developed will retain
existing characteristics unless identified as part of a redevelopment
or in-fill project. Three base-year data files are created:
þ 1990 Regional Land Use Inventory
þ 1990 Census block statistics
þ 1990 Employment Inventory
The Regional Land Use Inventory is an Arc/Info coverage that
delineates 1990 land uses in 80 land use categories described at the
end of the chapter. The inventory is somewhat generalized. The
smallest features identified are usually 2.5 acres. The level of
detail is meant to be commensurate with proposed land use in general
plans.
The 1990 inventory is an update of a 1986 inventory used in Series 7
forecasts. Satellite imagery for 1986 and 1990 was analyzed to detect
areas where land use changes had occurred. Other sources of
information include aerial photography, cross-checks with other SANDAG
files, ground inspection, and local planner review.
Census data is used to obtain residential dwelling unit
characteristics such as structure type, household size, income, and
ethnic composition. In most cases, block data is input directly.
About 10% of blocks are split into smaller areas to form MGRAs. In
these instances, block data is apportioned to each MGRA based upon the
MGRA's proportion of total block area. Most split blocks are in rural
areas with little existing development so this is not a major problem.
The employment inventory is derived from a file that was initially
purchased from Dun and Bradstreet. The file had names, addresses,
employee count, and Standard Industrial Code for every private
employer in the Region. A considerable effort was necessary to make
the file useful for planning purposes. An address matching process
was used to assign coordinates to individual employer records. An
Arc/Info coverage of employer locations was developed that could be
related to other parts of the forecasting process. Address-based
coordinates were cross-checked with land use files and adjusted where
necessary. Other processing included adding in government employers,
factoring in self-employment estimates, and cross-checking with
Commuter Computer's large employer listing.
General Plans
Local general plans are assumed to represent ultimate conditions.
These plans are periodically updated, so that actual development may
differ significantly from what is now depicted in plans. Nonetheless,
general plans provide the only effective method of evaluating land use
policies, implementing policy changes, and forecasting land use for
small areas.
Discrepancies in planned land use can occur in areas that are now
unincorporated but fall into an incorporated city's sphere of
influence. Both plans are digitized so that either plan can be
selected for use. Normally, city plans supersede unincorporated plans
where there is a contradiction.
General plan land use designations differ between jurisdictions.
Proposed land uses are assigned a 1990 land use inventory category
that most closely matches the general plan category. General plans
also have a range of residential densities associated with each
residential land use category. Individual jurisdictions specify where
in the range they expect their area to develop.
Site-Specific Projects
Projects that are in the "pipeline" are important for accurate near-
term growth forecasts. While most projects conform to general plan
land use, more detailed site plans may be available for projects
nearing implementation. Local jurisdictions submit as much
information as possible about these projects in terms of physical
layout, land use make-up, dwelling unit counts, and employment levels.
A site-specific coverage is created that maps each project.
Development Constraints
Environmental constraints are becoming more important in dictating
where growth can occur. SANDAG maintains Arc/Info coverages of steep
slopes, public lands, floodways, and riparian habitat that could
preclude development. Local jurisdictions review maps of these areas
and identify which constraints should be included in the forecasting
process.
Transportation Facilities
Correctly accounting for the relationship between land use and
transportation facilities is important. Transportation conditions are
represented by highway and transit times between ZUMs from the
transportation models. Another input file describes the percent of
work trips by transit and is used to weight transit and auto times.
Assumptions about specific transit and highway facilities vary by
alternative. In all cases, travel times reflect the effects of
congestion as estimated by transportation models. ZUM-to-ZUM travel
times are computed by weighting zone-to-zone travel times by zone-to-
zone trips for zones comprising each ZUM.
Travel times are input in a cyclical manner. Base-year travel times
are input to the first growth forecast increment. Resulting growth
forecasts are input to transportation models, along with corresponding
transportation facility assumptions. Travel times from the
transportation models are then input to the next growth forecast
increment. This process is repeated until the final 2015 growth
forecast increment is complete.
Capacity File
An MGRA level capacity file, described at the end of the chapter,
summarizes data for input to the growth models. Arc/Info is used to
overlay the base-year land use coverage, general plan coverage, site-
specific coverage, and development-constraint coverage. The following
rules are applied to guide creation of the capacity file:
þ Constrained areas and developed areas with matching base-year and
general plan land use designations are assumed to retain their
base-year land use.
þ Vacant, unconstrained areas within site-specific projects are
assumed to develop according to site-specific plans.
þ Vacant, unconstrained areas outside of site-specific projects are
assumed to develop according to general plans.
þ Developed areas with differing base-year and general plan land use
designations are brought to the attention of local planners. Some
areas are put into a redevelopment category.
þ Developed residential areas with densities below allowable general
plan densities are brought to the attention of local planners.
Some areas are put into a residential in-fill category.
A capacity file record is created for each base-year/general plan land
use combination within an MGRA. The resulting capacity file contains
about 58,000 records with 46 fields of data. The following items are
used in transportation modeling:
þ MGRA number
þ Percent of capacity used
þ Area (acres)
þ Base-year land use
þ Employment by 15 SIC groups
þ General Plan Land Use
REGIONAL GROWTH CONTROL TOTALS
SANDAG's Demographic and Economic Forecasting Model (DEFM) produces
year-by-year regional-level forecasts of some 700 demographic and
economic variables out to the year 2015. The model is documented in
DEFM Forecast 1993 to 2015, Volumes 1-5 (SANDAG, 1993). Forecasts are
based upon a history, starting in 1950, of variables at the regional,
state-wide, and national level and some assumptions about future
conditions at the same three levels. It is assumed that regional
growth is independent of local land use assumptions. SANDAG's Board
of Directories reviews regional DEFM forecasts before proceeding with
the rest of the forecast.
San Diego's historical population growth rate has fluctuated
dramatically. High growth years occurred during WWII, the Korean
Conflict, the Vietnam War and the Cold War build-up during the 1980's.
Population growth has averaged 40,000 new residents a year over the
last 20 years. During the mid-1980's, San Diego experienced its
highest recent growth years, with an 80,000 population increase during
the peak year of 1989.
Even though San Diego's economy has been in recession since mid-1989,
population has continued to increase. Population growth has averaged
40,000 over the last three recession years. Population growth is
expected to increase by 51% over the 25 year Series 8 forecast period,
averaging about 50,000 new residents a year.
Employment growth has been even more variable, although it tracks
population growth fairly closely. The San Diego Region added 36,000
new jobs a year during the 1980's. Job growth of only 13,000 jobs a
year is expected over the 1990-2015 forecast period, as poor economic
conditions are expected to persist for some time. There were 2.4
persons per employee in 1980 and 2.2 persons per employee in 1990.
This rate is expected to increase to 2.6 persons per employee in 2015.
Part of the increased unemployment is due to a higher proportion of
the population in both retirement and school age groups, although
somewhat higher unemployment is forecasted for the working age
population of 20-60.
SUB-REGIONAL EMPLOYMENT ALLOCATION
The EMPAL model distributes employment growth from the regional level
to 224 Zones for Urban Modeling (ZUMs) for seven employment categories
and three time intervals of 1990 to 2000, 2000 to 2010, and 2010 to
2015. Employment categories are based upon single-digit Standard
Industrial Classes of:
(1) Manufacturing
(2) Transportation, communication and utilities (TCU)
(3) Wholesale trade
(4) Retail trade
(5) Finance, insurance, and real estate (FIRE)
(6) Services
(7) Government
Inputs to the EMPAL model include:
þ Regional employment growth totals from the DEFM model by category
þ Employees by ZUM and employment category from the previous time
interval
þ Total occupied housing units by ZUM from the previous time
interval
þ Employment holding capacity of each ZUM
þ Peak period highway ZUM-ZUM travel times
þ Site-specific employment
The model allocates employment for each category outside of site-
specific projects to ZUMs as a function of travel times to residential
areas, travel times to other employment areas, and the incremental
employment capacity of the ZUM. SANDAG constrains employment
forecasts to be equal or less than the employment capacity by ZUM and
category computed from general plans. Site-specific employment is
added after the EMPAL allocation is complete.
Table 4-1 summarizes the results of the employment allocation by Major
Statistical Areas (Figure 2-6). As indicated, the highest employment
growth rate occurs in the South Suburban MSA, which has large areas of
vacant, industrial land. The largest absolute employment growth is
expected to occur in the North City area, where the largest share of
the Region's employment is currently located. The Central Area MSA is
largely built-out and thus has the lowest growth rate.
SUB-REGIONAL RESIDENTIAL ALLOCATION
After the EMPAL model has been run to distribute employment growth to
ZUMs for a particular time interval, the Projective Land Use Model
(PLUM) is run. PLUM's primary function is to allocate residential
growth to ZUMs, although other miscellaneous functions are performed.
Inputs to the PLUM model include:
þ Single and multi-family housing stock capacity of each ZUM
þ Peak period highway and transit ZUM-ZUM travel times
þ Fraction of work trips by transit by ZUM
þ Site-specific dwelling units
þ Regional control totals from DEFM
Table 4-1
SERIES 8 EMPLOYMENT BY MAJOR STATISTICAL AREA
Major Statistical Area 1990 2015 Change
Centre City 65,000 90,200 +39%
Central Area 299,700 303,200 + 1%
North City 396,200 493,500 +26%
South Suburban 76,800 131,700 +72%
East Suburban 139,700 181,500 +30%
Northwest County 146,000 196,500 +35%
Northeast County 119,900 180,800 +51%
East County 3,500 5,600 +60%
Region 1,246,800 1,582,100 +27%
PLUM's residential allocation works by first allocating employees from
employment ZUMs to residence ZUMs based upon the travel times between
ZUMS and the dwelling unit capacity in residence ZUMs. Separate
allocations are performed for employees living in single-family
dwelling units and those living in multiple-family units. Employees
are further disaggregated into those that commute by automobile and
those that commute by transit so that highway and transit travel times
can be weighted appropriately.
Once employees have been allocated, housing stock requirements are
estimated by applying employed resident per household rates that vary
by ZUM and vacancy rates that vary by ZUM and structure type. Housing
stock requirements by ZUM and structure type are checked against
housing stock capacity. Dwelling units are reallocated from ZUMs with
too little capacity to the closest ZUM with sufficient capacity to
meet demand. Site-specific dwelling units, base-year "other" dwelling
units, and factored base-year mobile homes are added to single and
multiple-family dwellings from the allocation process to arrive at
total dwelling units for each ZUM.
Total housing stock is converted to occupied units by applying a
vacancy rate that varies by ZUM and structure type. Population
estimates are derived by applying household size factors that vary by
ZUM and forecast year to occupied units and then adding in group
quarters population. Occupied dwelling units are apportioned to seven
income ranges using modified log normal curves that vary by ZUM and
forecast year. PLUM estimates are adjusted to match regional control
totals from DEFM.
Table 4-2 summarizes PLUM results. It should be noted that by 2015
all urban, residential land identified in existing general plans is
used-up so that 2015 dwelling unit forecasts are a function of
available capacity, not the PLUM allocation process.
Table 4-2
SERIES 8 DWELLING UNITS BY MAJOR STATISTICAL AREA
Major Statistical Area 1990 2015 Change
Centre City 5,800 35,300 +509%
Central Area 199,900 260,200 +30%
North City 219,600 315,300 +44%
South Suburban 83,100 160,300 +93%
East Suburban 154,000 225,200 +46%
Northwest County 107,800 170,000 +58%
Northeast County 110,400 199,900 +81%
East County 6,800 18,900 +176%
Region 887,400 1,385,100 +56%
MGRA ALLOCATION
A SANDAG Fortran program (SOAP) distributes dwelling units and
employment produced by PLUM and EMPAL from 224 ZUMs to 25,929 MGRAs.
In addition, SOAP performs land use accounting. SOAP uses "capacity"
files described above that are derived from existing development and
planned land use. PLUM produces "soapbase" files that list ZUM level
residential and employment estimates. Finally, "access weight" files
are input that indicate the order in which MGRAs will be developed.
Access weights are determined through the use of Arc/Info procedures.
Weights reflect the number of dwelling units and employment within 1/2
mile of each MGRA from the previous time interval.
SOAP produces updated capacity files and "soapbase" files, described
at the end of the chapter, that summarize MGRA level dwelling unit,
employment, and land use estimates for a forecast year. These
"capacity" and "soapbase" files are the files used by the
transportation models to generate trips and determine transit access.
DATA FILE
DOCUMENTATION
SERIES 8 LAND USE CODE DEFINITIONS
100 SPACED RURAL RESIDENTIAL - Single-family homes located in rural
areas with lot sizes of approximately 1 to 10 acres. Homes in
areas of lower densities are coded as agricultural or vacant,
not residential. Rural residential estates may have small
orchards, fields or small storage buildings associated with the
residential dwelling unit.
110 SINGLE-FAMILY RESIDENTIAL - Single-family detached housing
units, on lots smaller than 1 acre. This category may also
include smaller mixed uses such as churches, schools, post
offices, libraries, gas stations, small commercial
establishments (7-11, Circle K), etc. Newer developments may
include clubhouses, recreation areas, pools, tennis, etc.,
located within and associated with the residential development.
120 MULTIPLE-FAMILY RESIDENTIAL - Attached housing units, two or
more units per structure - includes duplexes, townhouses,
condominiums, apartments, and SRO's in Centre City. Newer
developments may include clubhouses, recreation areas, pools,
tennis, etc., located within and associated with the residential
development.
130 MOBILE HOME PARKS - Includes mobile home parks with 10 or more
spaces that are primarily for residential use. (RV type parks
are included within the commercial recreation category.)
1401 JAILS/PRISONS
1402 DORMITORIES
1403 MILITARY BARRACKS
1404 MONASTERIES
1409 OTHER GROUP QUARTERS - Convalescent or retirement homes not
associated with or within a health-care facility, rooming
houses, half-way houses, California Conservation Corps, Honor
Camps and other correctional facilities.
1501 HOTEL/MOTEL/RESORTS - Hotels, motels, resorts, and other
transient accommodations. Commonly found along freeways and
prime commercial areas, in downtown areas, and near tourist
attractions. Examples of resorts would be La Costa Health Spa,
Olympic Resort in Carlsbad near the airport, and Lawrence Welk.
2001 HEAVY INDUSTRY - Shipbuilding, airframe, and aircraft
manufacturing. Usually located close to transportation
facilities and commercial areas. Parcels are typically large,
20-50 acres. SANDAG has a list of heavy industrial sites in San
Diego.
2101 INDUSTRIAL PARKS - Office/industrial uses clustered into a
center. The primary uses are industrial but may include high
percentages of other uses in service or retail activities.
SANDAG's 1990 Employment file was used to assess the types of
employment within industrial areas and to classify industrial
areas by type. (See attachment regarding use of 1990 Employment
file to classify industrial by type.)
2103 GENERAL LIGHT INDUSTRY - All other industrial uses and
manufacturing not included in the categories above. These are
not located inside of parks, but are usually along major streets
or clustered in certain areas. Includes manufacturing uses such
as lumber, furniture, paper, rubber, stone, clay, and glass; as
well as light industrial uses as auto repair services and
recycling centers. Mixed commercial and office uses (if not
large enough to be identified separately) are also included.
General industrial areas are comprised of 75 percent or more of
industrial uses (manufacturing, warehousing, and wholesale
trade). SANDAG's 1990 Employment file was used to assess the
types of employment within industrial areas and to classify
industrial areas by type. (See attachment regarding use of 1990
Employment file to classify industrial by type.)
2104 WAREHOUSING/PUBLIC STORAGE - Usually large buildings located
near freeways, industrial or strip commercial areas. Public
self-storage buildings are typically long, rectangular and
closely spaced.
2201 EXTRACTIVE INDUSTRY - Mining, sand and gravel extraction, salt
evaporation.
2301 JUNKYARD/DUMPS/LANDFILLS - Active or in current use. The
landscape should show visible signs of the activity.
4101 COMMERCIAL AIRPORTS - Lindbergh Field only.
4102 MILITARY AIRPORTS - Airports owned and operated by the military.
Found on Military bases.
4103 GENERAL AVIATION AIRPORTS - All general aviation airports.
4104 AIRSTRIPS
4110 OTHER TRANSPORTATION GENERAL
4111 RAIL STATIONS/TRANSIT CENTERS/SEAPORTS - Major transit centers
(i.e., Oceanside Transit Center, El Cajon Transit Center), rail
stations (i.e., Santa Fe Depot, Del Mar Train Station, and major
trolley stations), and seaport terminals (Port of SD). Parking
areas associated with these uses are included. Transit centers
within shopping centers are included within the shopping center
category.
4112 FREEWAYS - Divided roadways with 4 or more lanes, restricted
access, grade separations, and rights-of-way with a width of 200
feet or more. Includes all right-of-way and interchange areas,
but not frontage roads.
4113 COMMUNICATIONS AND UTILITIES - TV and radio broadcasting
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