A COMPARATIVE ANALYSIS OF VEHICLE TRIP GENERATION METHODS AT
THE 65TH STREET AND FOLSOM BOULEVARD SMART GROWTH
DEVELOPMENT
A Project
Presented to the faculty of the Department of Civil Engineering
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
Civil Engineering
by
Lucas Jeffrey Fuson
SPRING
2013
© 2013
Lucas Jeffrey Fuson
ALL RIGHTS RESERVED
ii
A COMPARATIVE ANALYSIS OF VEHICLE TRIP GENERATION METHODS AT
THE 56TH STREET AND FOLSOM BOULEVARD SMART GROWTH
DEVELOPMENT
A Project
by
Lucas Jeffrey Fuson
Approved by:
__________________________________ , Committee Chair
Kevan Shafizadeh, Ph.D., P.E., PTP, PTOE
____________________________
Date
iii
Student: Lucas Jeffrey Fuson
I certify that this student has met the requirements for format contained in the University
format manual, and that this project is suitable for shelving in the Library and credit is to
be awarded for the project.
__________________________, Graduate Coordinator
Matthew Salveson, Ph.D., P.E.
Department of Civil Engineering
iv
___________________
Date
Abstract
of
A COMPARATIVE ANALYSIS OF VEHICLE TRIP GENERATION METHODS AT
THE 65TH STREET AND FOLSOM BOULEVARD SMART GROWTH
DEVELOPMENT
by
Lucas Jeffrey Fuson
The purpose of this research project is to evaluate the accuracy of industry
accepted vehicle trip generation methods for smart growth developments in the
Sacramento Region. An existing smart growth development located at the intersection of
65th Street and Folsom Boulevard in Sacramento was chosen as the subject development.
Estimates of generated vehicle trips for the daily, A.M. peak hour, and P.M. peak hour
time periods were calculated using the Institute of Transportation Engineers (ITE) MultiUse Trip Generation Method and the San Diego Association of Governments (SANDAG)
Trip Generation for Smart Growth Method. The results were compared to observed
vehicle trips at the subject development.
The observed vehicle trips at the development were counted using automatic
vehicle counters (pneumatic tubes) at each of the two driveways that provide
ingress/egress to the development over a 24-hour period. The vehicle trip generation
estimates were calculated using the direction provided by the Institute of Transportation
Engineers and the San Diego Association of Governments. The inputs required to
v
complete the calculations were obtained by contacting local government agencies and the
owners and operators of the development. The required data included Geographical
Information System (GIS) files to estimate employment and transit, which were provided
by the Sacramento Area Council of Governments (SACOG) and Sacramento Regional
Transit (RT). United States Census information was available online, and land use
characteristics were provided by the owners and operators of the development.
In the A.M. peak hour, the 516 vehicle trips estimated using the ITE Multi-Use
Method was 108% of the 479 observed vehicle trips. The 322 vehicle trips the SANDAG
Trip Generation for Smart Growth Method estimated in the A.M. peak hour was 67% of
the 479 observed vehicle trips. In the P.M. peak hour, the 361 vehicle trips the ITE
Multi-Use Method estimated was 42% of the 853 observed vehicle trips. The 472 vehicle
trips estimated using the SANDAG Trip Generation for Smart Growth Method was 55%
of the 853 observed vehicle trips. In the daily time period, the 6,250 vehicle trips the ITE
Multi-Use Method estimated was 125% of the 4,976 observed vehicle trips. The 6,189
vehicle trips estimated using the SANDAG Trip Generation for Smart Growth Method
was 124% of the 4,976 observed trips.
The SANDAG Trip Generation for Smart Growth Method requires significantly
more effort to produce vehicle trip generation results compared to the ITE Multi-Use
Method. The SANDAG Trip Generation for Smart Growth Method requires research and
analysis to identify the inputs its spreadsheet tool uses to calculate vehicle trip reductions
for smart growth developments, which include using the U.S. Census, GIS software
vi
which is not readily available to all users to perform the analysis, and detailed and
sophisticated analysis of travel analysis zones and regional transit travel times. The ITE
Multi-Use Method is based on an initial calculation of vehicle trips and two easily
obtained internal capture rate tables provided in the ITE Trip Generation Handbook.
_______________________, Committee Chair
Kevan Shafizadeh, Ph.D., P.E., PTOE
_______________________
Date
vii
ACKNOWLEDGEMENTS
I would like to thank Melissa whose support during this work is only one of many
times she’s been essential during life’s adventures and challenges.
I would like to thank Dr. Kevan Shafizadeh for his guidance and instruction
during this project and throughout my graduate school experience.
I would like to thank the Sacramento State faculty for providing an invaluable
learning experience during my time pursuing a graduate degree.
viii
TABLE OF CONTENTS
Page
Acknowledgements ................................................................................................... viii
List of Tables ................................................................................................................ x
List of Figures ............................................................................................................ xii
Chapter
1. INTRODUCTION .................................................................................................. 1
1. BACKGROUND .................................................................................................... 7
2. LITERATURE REVIEW ..................................................................................... 11
3. METHODS AND METHODOLOGY ................................................................. 15
4. DATA COLLECTION ......................................................................................... 28
5. RESULTS ............................................................................................................. 33
6. FINDINGS AND CONCLUSIONS ..................................................................... 52
7. RECOMMENDATIONS / FUTURE WORK ...................................................... 59
Appendix A. ................................................................................................................ 63
Appendix B. ................................................................................................................ 69
Appendix C. ................................................................................................................ 73
Appendix D. ................................................................................................................ 93
References ................................................................................................................... 97
ix
LIST OF TABLES
Tables
Page
Table 1: ITE Raw Trip Generation Summary................................................................... 33
Table 2: Raw Trips Separated into Land Use Groups ..................................................... 35
Table 3: Internal Capture Rates Based from ITE Trip Generation Handbook Tables 7.1
and 7.2 ................................................................................................................ 36
Table 4: ITE Trip Generation Daily Net External Trips for the Multi-Use Method Trip
Generation Reduction Results............................................................................ 38
Table 5: AM Peak Hour ITE Trip Generation Multi-Use Method Trip Generation
Reduction Results .............................................................................................. 39
Table 6: PM Peak Hour ITE Trip Generation Multi-Use Method Trip Generation
Reduction Results .............................................................................................. 40
Table 7: SANDAG Land Use Quantities Used To Calculate Raw Trips ......................... 42
Table 8: Summary of TAZs and Jobs within One Mile of the F65 Development ............ 45
Table 9: SANDAG Smart Growth Trip Generation Results for All Trips ....................... 50
Table 10: Comparison of Results –ITE Multi-Use Method, SANDAG Mixed-Use
Method, and F65 Site Driveway Counts .......................................................... 52
Table 11: Comparison of Predicted and Observed External Vehicle Counts by Feldman,
et al. .................................................................................................................. 56
Table 12: TAZs and Number of Jobs (2008) within a 30-Minute Transit Trip Beginning
at the F65 Development ................................................................................... 73
x
Table 13: Combined Driveway Counts for the North and East F65 Driveways .............. 93
xi
LIST OF FIGURES
Figures
Page
Figure 1: Project Location Map .......................................................................................... 8
Figure 2: Vicinity Map........................................................................................................ 9
Figure 3: Developed Area of the F65 Site ........................................................................ 16
Figure 4: Building Locations at the F65 Development .................................................... 18
Figure 5: ITE Trip Generation Rate for Land Use Code 820 (Shopping Center)............ 21
Figure 6: Northside Driveway ......................................................................................... 29
Figure 7: Eastside Driveway ............................................................................................. 30
Figure 8: Eastside Driveway Counts................................................................................ 31
Figure 9: Northside Driveway Counts ............................................................................. 32
Figure 10: One-Mile Radius Centered at the F65 Development ...................................... 44
Figure 11: 30-Minute Transit Trip Beginning at the F65 Development ........................... 47
Figure 12: Sacramento Regional Transit System Map ..................................................... 63
Figure 13: University Enterprises Retail Land Uses......................................................... 64
Figure 14: Fulcrum Properties Land Use Square Footage (1 of 4) ................................... 65
Figure 15: Fulcrum Properties Land Use Square Footage (2 of 4) ................................... 66
Figure 16: Fulcrum Properties Land Use Square Footage (3 of 4) ................................... 67
Figure 17: Fulcrum Properties Land Use Square Footage (4 of 4) ................................... 68
Figure 18: ITE Trip Generation Spreadsheet .................................................................... 69
Figure 19: ITE Multi-Use Method Daily Trips................................................................. 70
xii
Figure 20: ITE Multi-Use Method A.M. Peak Hour Trips ............................................... 71
Figure 21: ITE Multi-Use Method P.M. Peak Hour Trips ................................................ 72
Figure 22: 2010 American Community Survey 1-Year Estimate Produced by the
American Fact Finder Website (1 of 4) .......................................................... 80
Figure 23: 2010 American Community Survey 1-Year Estimate Produced by the
American Fact Finder Website (2 of 4) .......................................................... 81
Figure 24: 2010 American Community Survey 1-Year Estimate Produced by the
American Fact Finder Website (3 of 4) .......................................................... 82
Figure 25: 2010 American Community Survey 1-Year Estimate Produced by the
American Fact Finder Website (4 of 4) .......................................................... 83
Figure 26: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 1 .............. 84
Figure 27: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 2 .............. 85
Figure 28: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 2 (Cont.) .. 86
Figure 29: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 2 (Cont.) .. 87
Figure 30: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 2 (Cont.) .. 88
Figure 31: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 3 .............. 89
Figure 32: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 3 (Cont.) .. 90
Figure 33: SANDAG Smart-Growth Trip Generation Spreadsheet – Results ................. 91
Figure 34: SANDAG Smart-Growth Trip Generation Spreadsheet – Results (Cont.) ..... 92
xiii
1
Chapter 1
INTRODUCTION
Over recent years, planning agencies in the Sacramento region have put
significant effort into guiding documents that include smart growth as a component of
development and redevelopment. Smart growth refers to urban development that
includes multiple land uses, such as residential, retail, and office on the same site, while
also aiming to take advantage of compact building design (Smart Growth Network,
International City/County Management Association, 2002). Smart growth attempts to
reduce vehicle activity by incorporating features that accommodate multiple modes of
transportation such as transit, bicycle, and walking (Smart Growth Network, International
City/County Management Association, 2002). Research has shown that by providing
enhanced options for alternative modes of transportation associated with smart growth
makes it easier for those who want to drive less to do so (Handy, 2002).
The Sacramento Area Council of Governments (SACOG) adopted the
Metropolitan Transportation Plan/Sustainable Communities Strategy for 2035 (MTP/SCS
2035) in April 2012 (Sacramento Area Council of Governments, 2008). The MTP/SCS
2035 is a long range plan for transportation in the Sacramento Region that is built on a
preceding SACOG document, the Sacramento Region Blueprint (Sacramento Area
Council of Governments, 2004). The Sacramento Region Blueprint presents a vision for
growth that promotes compact, mixed-use development and additional transit choices as
an alternative to low density development. The MTP/SCS 2035 uses the blueprint as a
basis for the land use on which transportation investments will be made. The MTP/SCS
2
2035 identifies guiding principles for transportation planning. Smart land use is one of
the principles. The smart land use guiding principle aims to design a transportation
system to support good growth patterns, including increased housing and transportation
options, focusing more growth inward and improving the economic viability of rural
areas (Sacramento Area Council of Governments, 2008). Sacramento is cited as a leader
promoting redevelopment and growth management and is among medium sized cities
where the shift inward has been most dramatic (Thomas, 2009).
As with any development project, the impact that the development will have on
the surrounding community and the natural environment, including the impact that the
traffic generated by the development will have on the transportation network must be
considered. The impacts to the operation of the transportation network and to air quality
that new development will cause is of significant importance and must be addressed
through environment documentation in accordance with the California Environmental
Quality Act (CEQA) (Bass, et al.,1999). CEQA requires potentially significant
environmental impacts to be studied and formally identified in an environmental
document that is circulated through all responsible agencies with jurisdiction over the
development and surrounding area, and to the public to give an opportunity for comments
and concerns to be addressed (Bass, et al.,1999). Examples of impacts that may cause a
project to have significant environmental impacts related to transportation and traffic
include substantial traffic increases relative to existing load and capacity, the project
resulting in inadequate parking capacity, or causes the transportation network to exceed a
3
service standard predetermined by the local agency or department of transportation (Bass,
et al.,1999).
Air quality is a required component of the CEQA environmental documentation
process. Examples of potentially significant air quality impacts related to transportation
include conflicts with the implementation of adopted air quality plans and net increases
of pollutants in non-attainment areas (Bass, et al., 1999). In 2010, transportation
contributed approximately 27 percent of total U.S. greenhouse gas emissions
(Environmental Protection Agency, 2012). The MTP/SCS 2035 includes a plan for the
Sacramento region to reduce vehicle miles traveled in an effort to reduce greenhouse gas
emissions and to conform to air quality conformance standards. In 2008, shortly after the
MTP/SCS 2035 was adopted, the state law Senate Bill 375 was passed, which
significantly influenced the preparation of the MTP/SCS 2035. This bill requires the
California Air Resources Board to set performance targets for passenger vehicle
emissions and requires Metropolitan Planning Organizations (MPOs) to include a
sustainable community strategy to integrate land use and transportation (California State
Legislature, 2008). The bill also amends CEQA to provide incentives for residential and
retail mixed use projects to meet the goals of the bill (Sacramento Area Council of
Governments, 2008). The MTP/SCS 2035 aims to better integrate future land use
patterns, transportation investments, and air quality impacts, including higher levels of
development near current and future transit corridors and CEQA incentives for smart
growth development to produce transportation and air quality benefits (Sacramento Area
Council of Governments, 2008).
4
Traffic impact studies and traffic analysis are used to determine the traffic impact
a development will have in the project area, including emission estimates due to
transportation. This analysis is an important piece of project development and is included
in the environmental documentation process for traffic congestion and air quality
analysis, and is vital to planning the appropriate infrastructure that will serve the
development and surrounding transportation network. The traffic analysis is used to
evaluate the required improvements to the existing or new transportation system that are
required due to the impacts that a new development may have to the system. Traffic
impact studies and traffic analysis are used to plan the scope of roads, bridges, transit,
pedestrian facilities, and any other component of transportation facilities. The studies are
used to determine developer fees that are paid to the government agency, typically a
department of transportation or department of public works, which will own and maintain
the transportation facilities. For example, if during the environmental documentation
process it is determined that a new retail development impacts the adjacent roads such
that significant impacts are caused due to the additional traffic, the traffic impact analysis
may determine appropriate mitigation strategies to return the traffic to an acceptable
level. Geometric improvements may be identified, such as adding new lanes or
extending the length of turning bays, which would provide congestion relief. Should
mitigation be required, the developer may be required to compensate the agency for the
costly improvements to public infrastructure that are required to mitigate the significant
environmental impact caused by the development.
5
Trip generation is an important component of the planning of urban development
and is used to study the traffic impacts that will be incurred on the transportation network
due to a new development or a redevelopment project. An initial step of traffic analysis
is to determine the new vehicle trips that a development project would generate. The
estimated numbers of vehicle trips the project would generate are used to evaluate the
affects the development would have on the existing transportation network. The new
trips are combined with the existing or forecasted trips that would use the transportation
facilities in the area influenced by the new development to determine if any
improvements are required.
Because trip generation is an initial step in the planning process of transportation
facilities, it is important that the calculated estimates of trips are accurate and defendable.
Public agencies, developers, and the general public depend on the recommendations of
traffic impact analyses and use them to plan future infrastructure and to assess
environmental impacts in accordance with CEQA. The standardized and widely accepted
method of estimating trips is based on the Institute of Transportation Engineer’s (ITE)
Trip Generation Handbook (ITE, 2004). ITE has compiled vehicle trip data of numerous
existing developments of various land uses from around the United States and published
trip generation rates for each land use studied. The ITE method is used throughout the
United States, including the Sacramento region. As part of its Trip Generation
Handbook, ITE has developed a modified method to estimate trip generation for a multiuse development. The ITE method was not developed specifically for smart growth
development. Rather it was developed for multi-use development, which accounts for
6
different land uses on a single development, but doesn’t account for smart growth
features such as pedestrian and bicycle accessibility, access to transit, and compact
building design.
The San Diego Association of Governments (SANDAG) has developed a method
to estimate vehicle trips for smart growth developments. This method is named “Trip
Generation for Smart Growth” and was published in June 2010. This method was
developed by collecting vehicle trip data for existing smart growth developments in the
San Diego region.
This project uses the Institute of Transportation Engineer’s Multi-Use Method and
the SANDAG Trip Generation for Smart Growth Method to calculate vehicle trips for an
existing Sacramento smart growth development by applying reduction factors to raw trips
consistent with the methodologies set forth. This project then compares the results of the
ITE and SANDAG methods to actual vehicle trip counts that were collected at an existing
smart growth development over a 24-hour period. The existing smart growth
development that was chosen was the 65th Street and Folsom Boulevard (F65) mixed-use
development in Sacramento, California. This development is located in the northwest
quadrant of the 65th Street / Folsom Boulevard intersection and meets the criteria for a
smart growth development. Finally, this project evaluates the accuracy of the two
methods when they are applied in the Sacramento region by comparing them to realworld vehicle trip data collected at the site.
7
Chapter 2
BACKGROUND
The F65 development is located within the City of Sacramento. The 2010 United
States Census reports that the population in the Sacramento Census County Division is
1,072,790 (U.S. Census Bureau, 2012). The site is located in close proximity with
Interstate 80 with the I-80/65th Street interchange located immediately south of the site.
65th Street is a four-lane arterial roadway that runs south to north in the project vicinity.
65th Street terminates to the north of the project at Elvas Avenue and runs southward
through Sacramento City and Sacramento County and terminates at Florin Road. Folsom
Boulevard is a major east-west roadway that runs throughout Sacramento County and the
surrounding communities and is a four-lane arterial in the project vicinity. The area
surrounding the development is an established area of Sacramento consisting primarily of
residential neighborhoods and commercial/retail land uses.
The site is approximately 0.25 miles southwest of California State University,
Sacramento and approximately three miles east of the downtown Sacramento central
business district. The close proximity of Sacramento State to the F65 development
provides an excellent opportunity for alternative modes of transportation to be used
between the university and the site. Bicycle trips are convenient for students living onsite. The residential buildings provide a large area for bicycle parking. Folsom
Boulevard and 65th Street accommodate bicycles in Class II bicycle lanes, and there is a
bicycle / pedestrian entrance located on Elvas Boulevard that provides access to the
8
university. Figure 1 shows a project vicinity map and the location of the F65
development.
Project location
Figure 1: Project Location Map
(Source: Google Maps)
The American River runs east to west through Sacramento and is located
approximately 0.5 miles north of the site. The American River presents a physical
boundary for transportation between the site and the area to the north. There are two
bridges near the F65 development that cross the American River, one bridge at J Street
and one bridge at Howe Avenue. There is also a pedestrian / bicycle bridge located
within California State University, Sacramento that provides access via University
Avenue on the east side of the river and the American River Parkway Class I pathway
9
system. Figure 2 provides an overview of the project area, including the location of the
F65 development and its proximity to the California State University, Sacramento and the
American River.
Project location
CSUS
Figure 2: Vicinity Map
(Source: Google Maps)
The site is conveniently located near several transit stops. A major Sacramento
Regional Transit transfer station is located across 65th Street. This transfer station serves
the light rail “Gold Line” and the numbers 26, 34, 38, 81, 82, and 87 busses. There is
also a bus stop located at the intersection of Folsom Boulevard and 65th Street for the
10
number 210 and number 211 busses. The availability of numerous transit routes from the
65th Street / Q Street station provide patrons and residents of the F65 development with
the option to travel to areas throughout Sacramento and the surrounding region. A map
of the Sacramento Regional Transit system is provided in Appendix A in Figure 12.
11
Chapter 3
LITERATURE REVIEW
The literature review focused on recent studies related to smart growth in the
Sacramento Region. SACOG and the City of Sacramento have adopted plans for smart
growth and sustainable development. SACOG has developed the Sacramento Region
Blueprint which is a plan for growth that promotes compact, mixed-use development and
more transit choices (Sacramento Area Council of Governments, 2004). The Blueprint
was a result of extensive workshops and meetings involving local staff and elected
officials. It emphasizes seven growth principles, which are all related to smart growth: 1)
transportation choices, 2) mixed-use development, 3) compact development, 4) housing
choice and diversity, 5) use of existing assets, 6) quality design, and 7) natural resource
conservation.
Under the transportation choices principle, the utilization of alternative modes of
transportation is emphasized. For example, the principle states that development should
encourage walking, bicycling, carpooling and transit while using Blueprint growth
concepts for land use and right-of-way. The Blueprint’s conclusion is this combination
will promote higher transit ridership and on average, shorter trips for the remaining
automobile trips. Under the mixed-use development and compact development
principles, the Blueprint aims to build homes, shops, entertainment, office, and industrial
land uses near each other or in the same building, which would promote a sense of
community and would be conducive to walking and biking. Similarly, compact
12
development can create an environment that is conducive to more walking, biking, and
transit trips and shorter auto trips.
Subsequent to the Sacramento Region Blueprint, the SACOG Board adopted the
MTP/SCS 2035 (Sacramento Area Council of Governments, 2008). This plan describes
strategies for the region to plan and implement smart growth policies. It supports
education to the public regarding smart growth principles and calls for support for road,
transit, and bridge investments that are supportive of the Metropolitan Transportation
Plan for 2035.
A 2012 study by Todd Litman prepared for the Victoria Transport Policy
Institute, titled “Land Use Impacts on Transport – How Land Use Factors Affect Travel
Behavior,” concluded that integrated smart growth programs can reduce vehicle
ownership and travel 20-40%, and can significantly increase walking, cycling, and use of
public transit. Litman also stated that the effects can be even greater if policy changes
such as increased investment in alternative modes of transportation are integrated.
Litman defines the type of smart growth that results in this magnitude of vehicle
ownership and travel reductions as comparable to development that occurred prior to
1950. Development prior to 1950 was more dense and was designed for multi-modal
access with sidewalks, connected streets, local shops, transit services, limited parking,
and regional accessibility. Whereas, development between 1950 and 2000 prioritized
vehicle access and promoted regional shopping, abundant parking, and less opportunity
for alternative modes of transportation and pedestrians (Litman, 2012).
13
The California Department of Transportation (Caltrans) has been pursuing a
spreadsheet tool to quantify trip generation rates for smart growth. Caltrans has
published fact sheets that explain this effort. In the fact sheet published by Caltrans
titled, “Methodology for Estimating Trip-Generation Rates of Smart Growth Land Use
Projects,” Caltrans states that there is currently no methodology, tool, or data available in
the U.S. that can adequately estimate travel associated with smart growth land use
projects (California Department of Transportation, 2011). The lack of vehicle trip
generation data related to smart growth development presents a significant challenge in
the development of a dependable spreadsheet tool. However, the importance of a
standardized, acceptable methodology is important to practitioners who are tasked to
prepare transportation impact studies for smart growth land use development because the
impacts estimated in the studies are used to quantify mitigation that the smart growth
development is ultimately responsible for. Caltrans has teamed with UC Davis to work
towards a solution for the smart growth trip generation issue with the goal of developing
an acceptable methodology and tool.
The SANDAG Mixed-Use Development Model and the ITE Multi-Use
methodology that were used as a basis for this project were studied extensively. The
results of the study and the implementation of the methods will be discussed throughout
this project. However, a paper titled “Evidence on Mixed-Use Trip Generation – Local
Validation of the National Survey” by Feldman, et al., who were instrumental in the
development of the SANDAG Mixed-Use Model, discussed the validation of the model
and deserves discussion. There were 239 mixed-use developments in Seattle, Portland,
14
Sacramento, Houston, Atlanta, and Boston used to validate the model. Of the 239
developments, 25 were in Sacramento. Additionally, data were collected for all model
variables at 22 existing sites. The results of the validation showed that the SANDAG
mixed-use method consistently outperformed the ITE multi-use method. The validation
effort and its results are discussed in Chapter 7 of this project. Additionally, the paper
discussed some perceived weaknesses of the ITE Multi-Use Method. Specifically, that
paper reiterates that the published values that capture unconstrained internal capture rates
for trip origins and destination within a multi-use development were based on data
collected at only three sites in Florida. The internal capture rates quantify vehicle trip
reduction due to people utilizing multiple land uses once at the site. The accuracy of the
results of the method after applying the internal capture rates may be dependent on how
closely the site being analyzed matches the sites used in the table’s creation (Feldman, et
al., 2010). The paper also illustrates that only three land uses: residential, office, and
retail are available for the user in the analysis and that the scale of the development is
disregarded. Also, land use context and mode shifts for well-integrated and transit served
sites are not considered. These are important observations given the complexity of smart
growth, which can encompass a variety of specialized land uses and transit opportunities
in diverse settings. This paper concludes by reinforcing that unless developers are
rewarded for trip-reducing impacts of mixed-use developments, the market incentive to
build smart-growth projects is substantially removed (Feldman, et al., 2010).
15
Chapter 4
METHODS AND METHODOLOGY
The initial effort of this project was to estimate vehicle trips based on the widely
accepted methodology published in the Institute of Transportation Engineer’s (ITE) Trip
Generation – An Informational Report, 8th Edition (Institute of Transportation Engineers,
2008). ITE was first used to estimate the number of new vehicle trips that the F65
development is expected to generate based on published trip generation rates for the
appropriate land uses in the development. The initial estimation of vehicle trips does not
account for internal capture at the smart growth development that is due to interaction
between multiple land uses once the vehicle is parked at the site. Throughout this
research project these initial vehicle trip estimates that have not been reduced due to site
specific internalization are referred to as “raw trips.” Secondly, the raw trips were
reduced according to the Multi-Use Method, provided in Chapter 7 of the ITE Trip
Generation Handbook, to account for interaction between multiple land uses at the site
(Institute of Transportation Engineers, 2004).
To estimate the number of raw trips that can be expected according to the land
uses at the site, the area of each of the retail/commercial sites was calculated and the
number of dwelling units was counted for the residential land uses. The F65
development site is on a 7.43 acre parcel that consists of residential and retail land uses.
The F65 study area is shown in Figure 3.
16
Figure 3: Developed Area of the F65 Site
The site is comprised of properties owned by two entities. University Enterprises,
Inc. owns and operates a residential land use named the Upper Eastside Lofts and the
commercial building in the northwest quadrant of the property. The Upper Eastside Lofts
includes three residential buildings. In the residential building located in the southeast
corner of the project site, there are a total of six townhouses that are three story units with
three bedrooms and three bathrooms. The remaining two residential buildings that
University Enterprises owns and operates are located along the south property boundary
and have a total of 134 units with a total of 348 beds. University Enterprises provided the
number of dwelling units in the Upper East Side Lofts via a phone conversation in
February 2011. All of the University Enterprises residential buildings are primarily
17
occupied by Sacramento State students and the occupancy rates vary by time of year and
student demand. In February 2011, there were 12 vacant units and seven units under
construction in the two large residential buildings. For the ITE Trip Generation analysis,
the units that were vacant or under construction were subtracted from the total number of
residential units, which resulted in 121 dwelling units.
University Enterprises, Inc. (UEI) provided the square footage of each
retail/commercial property it owns and operates on the project site. The University
Enterprises retail/commercial center is located in the northwest quadrant of the project
site and includes the following retail uses; Bikram Yoga, Mr. Pickle’s, Inc. (sandwich
shop), Gunther’s Ice Cream, and Anytime Fitness. University Enterprises provided the
approved construction documents for the development which were used to estimate the
square foot area of each space. The construction document showing the square footage
of the University Enterprises Retail Land Uses is provided in Appendix A, Figure 13.
The remainder of the land uses on the project site are owned by Fulcrum
Properties and leased by Voit Real Estate Services. The Fulcrum Properties owned land
uses are located along the easterly project boundary adjacent to 65th Street and along the
northerly property boundary adjacent to Folsom Boulevard. Voit Real Estate Services
provided the square footage of each retail/commercial space owned by Fulcrum
Properties and are shown in Appendix A in Figures 14-17. The building along the north
project boundary and adjacent to Folsom Boulevard includes Office Depot, Dos Coyote’s
restaurant, and Dolce Nails and Spa. At the time the land use information was collected,
there was one vacant retail space in this building that was excluded in the trip generation
18
calculations. The building along the east project boundary and adjacent to 65th Street
includes Bento Box (restaurant), Supercuts (hair salon), Pita Pit (sandwich shop), Jamba
Juice, and Starbucks. There are residential units above the ground floor retail in this
building. A phone conversation with Loftworks in March 2011, which is responsible for
leasing the residential units that are owned by Fulcrum Properties, confirmed that there
are eight dwelling units on the second floor of this building that are approximately 1,000
square feet each. The location of the buildings at F65 is provided in Figure 4.
Figure 4: Building Locations at the F65 Development
The development was designed with the buildings along the outer perimeter of the
site with a parking lot in the center that is shared with designated parking for the
residential and retail land uses. There are contiguous pedestrian walkways that connect
19
the two buildings owned by Fulcrum Properties and there is a large plaza area in the
northeast corner, between Dos Coyotes and Starbucks, where people can gather and dine
outside. There are two driveways that access the site. One driveway (North Driveway) is
located opposite the Folsom Boulevard and 64th Street intersection. This driveway
accommodates left turns in, right turns in, and right turns out. This driveway provides
separation between the commercial land use buildings along Folsom Boulevard that
pedestrians must cross to walk between the buildings. The second driveway (East
Driveway) is located on 65th Street approximately 300 feet south of the Folsom
Boulevard / 65th Street intersection. This driveway accommodates right turns in and right
turns out. The East Driveway provides separation between the commercial/residential
building and the residential building along 65th Street that has eight dwelling units.
A trip generation spreadsheet was developed for this study using the ITE Trip
Generation – An Informational Report, 8th Edition to calculate raw trips and is shown in
Appendix B, Figure 18. ITE has published trip generation rates for a variety of land uses.
The data are presented in a chart form as shown in Figure 5 below. A separate chart is
available for each land use for various time periods including the daily time period, A.M.
peak hour, and P.M. peak hour. Figure 5 shows the trip generation data for land use code
820 (shopping center) in the A.M. peak hour. Each data point on the chart represents
actual vehicle trip generation data collected at an existing development. Each chart
shows a land use characteristic, such as leasable square feet of the development, on the
abscissa (X axis) and the number of trip ends on the ordinate (Y axis). A regression
equation is shown near the bottom of the trip generation chart. The regression equation
20
represents a line that best fits the data (Institute of Transportation Engineers, 2004). ITE
also provides an average trip generation rate that can be used to estimate vehicle trips
instead of the regression equation. Selection of the average rate or the equation may be
dictated by local ordinance or agency policy (Institute of Transportation Engineers,
2004). For the purposes of this study the regression equation was used when given. If
the quantity of data points was limited and a regression equation was not provided, this
study used the average vehicle trip generation rate.
The name of the establishment, the establishment’s ITE land use code and the
quantity of units were entered. The residential land use quantities were entered as
“dwelling units,” and all other land uses were entered as “KSF” (thousand square feet of
leasable area). ITE trip generation rates for daily trips, the AM peak hour of adjacent
street traffic, and the PM peak hour of adjacent street traffic were entered based on the
land use tables and charts provided by the ITE Trip Generation-An Informational Report,
8th Edition. The percentage of vehicles entering and exiting the land use during the AM
and PM peak hour were also entered.
21
Figure 5: ITE Trip Generation Rate for Land Use Code 820 (Shopping Center)
(Source: ITE Trip Generation-An Informational Report, 8th Edition)
Two specific businesses, “Dolce Nail and Spa” and “Supercuts,” were entered as
land use code 918 “shopping center,” because the ITE Trip Generation Land Use for
“hair salon” does not provide data for daily trip data. The AM and PM peak hour rates
for Starbucks were entered based on ITE Trip Generation land use code 936. The land
22
use associated with code 936 is a “coffee/donut shop without a drive-through window.”
No daily trip generation rates are available for land use 936. The daily trip generation
rate used for Starbucks was based on land use code 933, which is a “fast-food restaurant
without a drive-through window.” Land use 933 was chosen for the daily time period to
provide consistency with the results of the SANDAG raw trip generation calculations.
The SANDAG method does not provide a “coffee shop” land use classification and fastfood restaurant without a drive-through window seems to be a logical choice for a coffee
shop. The total trips that were calculated in the trip generation table were summed into
two land use groups, either retail or residential. The output of this spreadsheet is
discussed in the results section of this report.
The Institute of Transportation Engineers Trip Generation Handbook, Second
Edition, includes a Multi-Use Method that provides reductions to net trip generation
calculations (Institute of Transportation Engineers, 2004). The process described in
Chapter 7 of that document, calculates an internal capture rate that represents the amount
of “internalization,” or more specifically, the process quantifies the number of trips that
are made internal to the site, without a vehicle trip ever entering or exiting the
development. An internal capture rate is defined as a percentage reduction that can be
applied to the trip generation estimates for individual land uses to account for trips
internal to the site (Institute of Transportation Engineers, 2004). Examples of this
internalization may include a person living on-site that visits retail or restaurants on-site
before returning to their residence, or a trip that enters the development from an off-site
location and visits multiple on-site retail, residential or office land uses.
23
The ITE Multi-Use Method defines a “multi-use development” as typically a
single real-estate project that consists of two or more ITE land use classifications
between which trips can be made without using the off-site road system (Institute of
Transportation Engineers, 2004). A Central Business District, a shopping center with
multiple land uses, and developments that can be considered an “office park” (Land use
code 750), or a “general office building” (Land use code 710) is not considered to be a
“multi-use development”.
According to the ITE Trip Generation Handbook, the methodology for estimating
internal capture rates and trip generation at a multi-use site is based on two assumptions.
The proportions of trips between interaction land use types are assumed to be stable, and,
if sufficient data were available, these internal capture percentages could be predicted
with adequate confidence (Institute of Transportation Engineers, 2004). The ITE
methodology simplifies the trip-making dynamics within a multi-use development by
reducing the number of key variables used in the analysis. For example, variables such
as proximity of on-site land uses, the presence of pedestrian connections, and the location
of the multi-use development in the surrounding urban/suburban area are not considered.
Tables 7.1 and 7.2 of the ITE Trip Generation Handbook give recommended
unconstrained internal capture rates for trip origins and destinations within a multi-use
site. ITE recognizes that the estimated typical internal capture rates presented in the
tables rely directly on data collected at a limited number of multi-use sites in Florida
(Institute of Transportation Engineers, 2004). Although a relatively small sample size,
these are the only rates available and published for use. ITE recommends that local data
24
be used if internal capture rates by paired land uses can be obtained. Tables 7.1 and 7.2
provide rates for the midday peak hour, the PM peak hour of adjacent street traffic, and
daily (Institute of Transportation Engineers, 2004).
The appropriate values from Tables 7.1 and 7.2 were entered into the ITE
worksheet for daily, AM peak hour, and PM peak hour analysis. The ITE workbook uses
the internal capture rates to balance and quantify the number of trips that remain internal
to the site, and the number of trips that are generated external to the site. For each land
use, the workbook also quantifies the number of trips that enter and exit from the external
roadway network. Finally, at the bottom of each multi-use spreadsheet, a table
summarizes the reduced trips entering and exiting each land use, and produces an overall
internal capture rate.
SANDAG Trip Generation
SANDAG published a methodology for estimating trip generation for smart
growth developments in June 2010. The SANDAG method uses information that the
United States Environmental Protection Agency (EPA), under review by ITE, collected
as part of a national study of the trip generation characteristics of multi-use sites (San
Diego Association of Governments, 2010). This study collected travel survey data from
239 mixed-use developments (MXDs) in six major metropolitan regions, correlated with
the characteristics of the sites and their surroundings, and validated through traffic counts
at 16 additional sites. The findings indicated that the amount of external traffic generated
is affected by a wide variety of factors, each pertaining to one or more of what are known
as “D” characteristics. The “D” characteristics are density, diversity, design, destination
25
accessibility, development scale, demographics, and distance to transit (San Diego
Association of Governments, 2010). The “D” characteristics are a simplified manner in
which to describe a more complex set of data that explains MXD trip generation. For
example, the inter-relationship between transit frequency/level of service and the amount
of employment within a 30-minute transit trip can be described as Destination
Accessibility. To quantify the vehicle trip reduction related to destination accessibility,
the SANDAG method requires the user to input the number of jobs with a 30-minute
transit trip from the site.
The “D” characteristics were related statistically to the vehicle trip reductions
observed at the MXDs. The statistical relationship between the “D” characteristics and
the trip reductions observed in the surveys produced equations which are known as the
mixed-use method. The mixed-use method allows the user to predict the vehicle trip
reduction as a function of the “D” characteristics.
The SANDAG Mixed-Use method explains four steps that are required to achieve an
estimate of daily trips on external roadways generated by a mixed-use development. The
four steps are:
1. Compute daily trip estimates using standard rates or equations from an external
source (raw trips). These estimates do not assume any internalization, and only
minimal trips made by walking and/or transit modes.
2. Compute the probability of a trip staying internal to the mixed-use development
(Pinternal).
26
3. Compute the probability an external trip will be made by walking or bicycling
(Pwalkbike).
4. Compute the probability an external trip will be made by transit (Ptransit).
The three probabilities are calculated by inputting characteristics of the MXD into the
spreadsheet tool developed for the SANDAG Mixed-Use Method. The characteristics
provide a means to quantify the “D” characteristics. These variables are listed in the
SANDAG methodology and summarized below.
The probability of a trip staying internal to the site (Pinternal) is a function of
employment, land area, jobs/population diversity, number of intersections per square
mile, average household size, and vehicles owned per capita. The probability of a trip
being made by walking or riding a bicycle (Pwalkbike) is a function of land area,
jobs/population diversity, retail jobs/population diversity, employment within one mile,
the sum of population and employment per square mile, the number of intersections per
square mile, average household size, and vehicles owned per capita. The probability an
external trip will be made by transit (Ptransit) is a function of employment, number of
intersections per square mile, employment within a 30-minute transit trip, average
household size, and vehicles owned per capita.
Together, the SANDAG method uses these probabilities to calculate the number
of external vehicle trips generated by mixed-use developments using the following
equation:
27
External Vehicle Trips Generated by Mixed-Use Developments
= Raw Trips * (1-Pinternal) * (1-Pwalkbike-Ptransit)
The SANDAG method was initially validated by collecting survey data from 16 existing
mixed-used developments that were not included in creating the model. These
developments ranged from five acres to over 1,000 acres in size and were located in
diverse regions in the United States including Florida, Northern and Southern California,
Georgia and Texas (San Diego Association of Governments, 2010). This study will also
serve as a verification of the SANDAG model by producing data at an existing
Sacramento smart growth development.
28
Chapter 5
DATA COLLECTION
Data were collected at the F65 site on Tuesday, February 8, 2010. This date was
chosen because it is midweek, Sacramento State was in session, and it was not during
holiday season. The date was picked to give an accurate average representation of
vehicle traffic and consumer activity.
Driveway vehicle counts using automatic vehicle counters (pneumatic tubes) were
collected at both driveways that access the site, and a survey was conducted to gather
information about the people visiting the site. Images of the two driveways, one on the
north side of the property and the other on the east side on the property are shown below
in Figures 6 and 7. The automatic counters were placed across the driveways so vehicles
traveling in both directions would be counted. The tubes were fastened to the pavement
to ensure they would remain in place until the count was completed. The tubes were
placed at approximately 7:30 A.M. at the north and east driveways. The equipment was
left in place for 24 hours. It is understood that data collected in one day can be variable.
To get a more typical representation of trip behavior, more than one-day counts and
surveys would be preferred. Resources were not available for collect multi-day traffic
counts for this academic project.
29
Figure 6: Northside Driveway
30
Figure 7: Eastside Driveway
The driveway counts provide the vehicle trips entering and exiting the site in 15minute increments. The driveway counts were entered into a spreadsheet format that
consisted of the 15-minute interval vehicle counts for each driveway, and a combined
vehicle count spreadsheet that summed the two driveway vehicle counts. The combined
driveway count spreadsheet was used to calculate actual daily vehicle trips entering and
exiting the site by summing the 15-minute trips counts for the 24-hour period. The total
daily trips observed are the sum of the entering and exiting trips for the 24-hour period.
The A.M. peak hour and P.M. peak hour trips were determined using the 15-minute
interval driveway counts shown in Figures 8 and 9 below. To maintain consistency with
31
the ITE Trip Generation methodology, the A.M. peak hour used was 7:00 A.M. to 9:00
A.M. and the P.M. peak hour used was 4:00 P.M. to 6:00 P.M.
Figure 8: Eastside Driveway Counts
32
Figure 9: Northside Driveway Counts
On Tuesday, February 8, the driveway count tubes did not begin collecting data
until approximately 7:45 A.M. Therefore, for February 8th there was not data available
for the beginning portion of the A.M. peak hour. However, the driveway counts were
taken for a full 24 hours. To calculate the A.M. peak hour, a portion of the driveway
counts were taken from the February 9th data and the driveway counts for February 8th
was used to approximate the complete two-hour period. The P.M. peak hour trips used
data from February 8th only. The complete driveway vehicle count spreadsheet with the
vehicle counts from both driveways is provided in Appendix D, Table 13.
33
Chapter 6
RESULTS
The results of ITE multi-use method analysis, the SANDAG mixed-use method
analysis, and the F65 site data collection are presented in three separate sections within
this chapter. The results are compared by the number of daily, A.M. peak-hour, and P.M.
peak-hour trips.
ITE Multi-Use Method
For the ITE Trip Generation analysis, the number of raw trips that were calculated
using the ITE method as explained in the Methods and Methodology section of this study
are shown in Appendix B, Figure 18 and in Table 1.
Table 1: ITE Raw Trip Generation Summary
Daily
Trips
8,432
AM
Peak
Hour
Trips
632
AM Peak
Hour Trips
Entering
AM Peak
Hour Trips
Exiting
331 (52%)
301 (48%)
P.M.
Peak
Hour
Trips
477
P.M. Peak
Hour Trips
Entering
P.M. Peak
Hour Trips
Exiting
257 (54%)
220 (46%)
The raw trips were then used as a basis for the ITE Multi-Use Method. To use the
ITE Multi-Use method, ITE has developed a step-by-step procedure that is available in a
worksheet format in the Trip Generation Handbook, Second Edition (Institute of
Transportation Engineers, 2004). The ITE worksheets provided in the Trip Generation
Handbook were entered into Microsoft Excel spreadsheets for this study. The worksheet,
provided by ITE, requires the user to enter the net trips into one of three separate
categories, retail, residential, or office. There are no offices in the F65 development so
34
the land uses were separated into either residential or retail groups. The residential group
includes land use 220 (apartments) and the retail group includes land uses 492 (health
club / fitness), 820 (shopping center), 918 (hair salon), 932 (high-turnover (sit down)
restaurant), 933 (fast-food restaurant without drive-through window), and 936 (coffee /
donut shop). The initial step, Step 1, as described in Chapter 7 of the ITE Trip
Generation Handbook (Institute of Transportation Engineers, 2004), states “if the site has
two or more buildings containing the same land use, combine the sizes of the multiple
buildings if they are situated within reasonable and convenient walking distance of each
other. If the buildings are not close to each other, treat them as separate land uses on the
worksheet (for example Office A and Office B)” (p. 89). The F65 development has a
7.43 acre project area and there are three buildings with only retail land uses. The
furthest distance between the two retail land uses as measured using a straight line
between Bento Box restaurant and Anytime Fitness, is 430 feet. The remaining retail
uses are encompassed within that distance.
In this analysis, with the intent of capturing the retail-to-retail internalization, the
retail land uses have been analyzed as two land groups, Land Group A and Land Group
B. Land Group B consists of Anytime Fitness, Mr. Pickles sandwich shop, Bikram Yoga,
and Gunther’s Ice Cream, which are all located in the westernmost building adjacent to
Folsom Boulevard. The remaining retail land uses are included in Land Group A.
Although Office Max, Dos Coyotes restaurant, and Dolce Nail and Spa are in the center
building and not connected to the eastern building adjacent to 65th Street, they are in
close proximity to each other and connected by appropriate pedestrian walkways. Land
35
Group B is separated from other retail establishments by the north driveway so it is
appropriate to include those retail land uses in the Land Use A.
In each worksheet, the trips shown in Table 2 were entered as “Retail Land Use
A,” Retail Land Use B,” and “Residential” for the respective time periods, either daily,
A.M. peak hour, or P.M. peak hour. The following table summarizes the land use groups
and the raw trips associated with each.
Table 2: Raw Trips Separated into Land Use Groups
Land Use
Group
Quantity
Daily
Trips
A.M.
Peak
Hour
Trips
Residential
129
D.U.
858
66
Retail A
32 KSF
5,562
448
Retail B
11 KSF
2,012
118
A.M.
Peak
Hour
Trips
Entering
14
(21%)
248
(55%)
69
(58%)
A.M.
Peak
Hour
Trips
Exiting
52
(79%)
200
(45%)
49
(42%)
P.M.
Peak
Hour
Trips
80
304
92
P.M.
Peak
Hour
Trips
Entering
51
(64%)
157
(53%)
49
(53%)
P.M.
Peak
Hour
Trips
Exiting
29
(36%)
147
(48%)
43
(47%)
When using the ITE worksheets, a separate worksheet was set up for each time
period. In this analysis, worksheets have been completed for the daily, AM peak hour,
and PM peak hour time periods. The unconstrained internal capture rates based on
Tables 7.1 and 7.2 of the ITE Trip Generation Handbook where then entered. Tables 7.1
of the ITE Trip Generation Handbook gives a percentage of the total trip origins that
could be destined for another on-site land use for a given time period. For example,
Table 3 shows that 11% of daily vehicle trip origins beginning at a retail land use can be
expected to visit an on-site residential land use without exiting the site. Table 7.2 of the
36
ITE Trip Generation Handbook presents estimated unconstrained capture rates for trip
destinations. For example, Table 3 shows that for all vehicle trips entering an on-site
retail use, 9 percent of the trips can originate at an onsite residential land use in the daily
time period. The required values from the tables are “from retail to residential,” “from
residential to retail,” “from retail to retail,” “to retail from residential,” “to residential
from retail,” and “to retail from retail.” The ITE Handbook gives a different rate for the
midday peak hour trips, the P.M. peak hour trips of adjacent street traffic, and daily trips.
Table 3 shows the values for each that were used in the multi-use spreadsheets.
Table 3: Internal Capture Rates Based from ITE Trip Generation Handbook Tables 7.1
and 7.2
From Retail to
Residential
From Residential to
Retail
From Retail to Retail
To Retail from
Residential
To Residential from
Retail
To Retail from Retail
Midday Peak
Hour
P.M. Peak Hour of
Adjacent Street Traffic
Daily
7%
12%
11%
34%
53%
38%
29%
20%
30%
5%
9%
9%
37%
31%
33%
31%
20%
28%
(Source: ITE Trip Generation Handbook, Tables 7.1 and 7.2)
At this stage of the analysis, all of the user identified input has been entered into
the spreadsheets, and the results have been calculated. The spreadsheet reports an
37
internal capture rate which is applied to the raw trips and used to reduce the raw trips to
net external trip for the multi-use development. The following tables summarize the
results by illustrating the raw trips and the reduced trips and the percentage reduction for
each in the A.M. peak hour, P.M. peak hour, and the daily estimate. The internal capture
percentage is the overall reduction in trips the multi-use method calculates and applies to
the raw trips. The tables show the reductions for each land use for trips entering, exiting,
and the total number of trips. The number of reduced trips can be compared to the raw
trips by reviewing the complete ITE Multi-Use Method worksheets shown in Appendix
B, Figures 19 - 21.
As shown in Table 4, a 26% percent reduction in vehicle trips was calculated in
the daily time period. The reduction percentage was calculated by applying the internal
capture rates shown in Table 3 to the number of raw trips entering and exiting each land
use, and then the reduced trips were balanced by selecting the minimum number of
internal trips that occurred between any two land uses. For example, as shown in
Appendix B in Figure 20, Table 7.1 of the ITE Trip Generation Handbook gives a 37%
reduction for trips that travel “to residential land uses from retail land uses, and a 7%
reduction for trips that travel “from retail land uses to residential land uses.” These
percentages were multiplied by the number of trips exiting the retail land use and entering
the residential land use to give the number of trips that remain internal to the site. The
minimum value calculated in this step was chosen, which is referred to as the number of
“balanced internal trips.” This process is completed for all combinations of trips between
land uses and the balanced trips are summed, which results in the total number of internal
38
trips. The numbers of internal trips were then subtracted from the raw trips and the
percentage of reduced trips to raw trips was calculated. The internal capture rate is this
percentage. Applying the 26% percent reduction factor to the 8,432 raw vehicle trips
estimated results in 6,250 vehicle trips when accounting for internalization of the multiuse development.
Table 4: ITE Trip Generation Daily Net External Trips for the Multi-Use Method Trip
Generation Reduction Results
Daily Net External Trips for Multi-Use Development
Land Use A
Land Use B
Land Use C
Total
Retail A
Retail B
Residential
Enter
2,316
633
176
3,125
Exit
2,357
593
175
3,125
Total
4,673
1,226
351
6,250
Internal
Capture
Single-Use
Trip Gen.
Estimates
5,562
2,012
858
8,432
26%
39
As shown in Table 5, an 18% percent reduction in vehicle trips was calculated in the
A.M. peak hour time period. Applying the 18% percent reduction factor to the 632 raw
vehicle trips estimated results in 516 vehicle trips when accounting for internalization of
the multi-use development.
Table 5: AM Peak Hour ITE Trip Generation Multi-Use Method Trip Generation
Reduction Results
A.M Peak Hour Net External Trips for Multi-Use Development
Land Use A
Land Use B
Land Use C
Total
Retail A
Retail B
Residential
Enter
222
45
6
273
Exit
174
32
37
243
Total
Single-Use
Trip Gen.
Estimates
396
77
43
516
Internal
Capture
448
118
66
632
18%
As shown in Table 6, a 24% percent reduction in vehicle trips was calculated in the P.M.
peak hour time period. Applying the 24% percent reduction factor to the 477 raw vehicle
trips estimated results in 361 vehicle trips when accounting for internalization of the
multi-use development.
40
Table 6: PM Peak Hour ITE Trip Generation Multi-Use Method Trip Generation
Reduction Results
P.M. Peak Hour Net External Trips for Multi-Use Development
Land Use A
Land Use B
Land Use C
Total
Retail A
Retail B
Residential
Enter
134
35
30
199
Exit
121
29
11
162
Total
Single-Use
Trip Gen.
Estimates
255
64
41
361
Internal
Capture
304
92
80
477
24%
SANDAG Mixed-Use Method
The SANDAG Mixed-Use method is available on the SANDAG webpage as a
Microsoft Excel Spreadsheet tool (San Diego Association of Governments, 2010).
The
spreadsheet generally requires the user to input the quantities for land uses and the site
characteristics that are required to calculate the variables for the proportion of internal
trips (Pinternal), the proportion of walk/bike trips (Pwalkbike), and the proportion of transit
trips (Ptransit). The cells in the spreadsheet that do not require direct input specific to the
project site are locked such that the user cannot modify the values or equations. The
locked cells include the trip generation rates and equations, the peak hour percentages,
production and attraction calculations for vehicle miles traveled, and all calculations
required to produce the results.
The tool initially requires the raw trips to be calculated. SANDAG has published
trip generation rates for the San Diego region and these values are used in the SANDAG
Mixed-Use Method. The trip generation rates for daily trips and the corresponding A.M.
41
and P.M. peak hour percentages published by SANDAG are included in the SANDAG
Trip Generation Spreadsheet Tool, and the cells are locked so the user cannot modify
them. Therefore, it is only necessary to calculate the number of dwelling units for the
residential properties and the area, expressed in KSF for all other land uses. These values
are entered into the spreadsheet, and the spreadsheet automatically calculates the raw
trips based on the SANDAG trip rates that cannot be modified. The quantities for each
land use that were input into the spreadsheet are shown in Table 7.
42
Table 7: SANDAG Land Use Quantities Used To Calculate Raw Trips
Name of Establishment
Quantity
Units
Upper East Side Lofts - Building 2
Upper East Side Lofts - Building 3
Upper East Side Lofts - Building 4
Fulcrum Property Lofts
Bikram Yoga
Mr. Pickles
Gunther’s Ice Cream
Anytime Fitness
Office Depot
Dolce Nails and Spa
Dos Coyotes
Starbucks
Jamba Juice
Pita Pit
Supercuts
Bento Box
Game Stop
The Sandwich Spot
Available Property
Available Property
Totals
Apartments
Specialty Retail/Strip Commercial
High-Turnover (Sit-Down) Restaurant
Fast-Food Restaurant without DriveThrough Window
58
57
6
8
4.080
1.171
1.268
4.000
16.841
1.066
2.800
1.600
1.351
1.269
1.068
3.171
1.273
1.273
1.327
0.081
DU
DU
DU
DU
KSF
KSF
KSF
KSF
KSF
KSF
KSF
KSF
KSF
KSF
KSF
KSF
KSF
KSF
KSF
KSF
129
28
6
DU
KSF
KSF
8
KSF
The land use quantities were entered into Section 3 of the SANDAG spreadsheet tool
which then calculated the raw trips based on the published SANDAG trips generation
rates. The number of raw vehicle trips calculated was 8,454 daily trips, 452 trips in the
A.M. peak hour, and 639 trips in the P.M. peak hour. Section 3 of the SANDAG MixedUse Trip Generation Spreadsheet is provided in Appendix C in Figures 31 and 32.
43
Next, attention was given to Section 1 of the spreadsheet which requires inputs
related to general site information. The F65 development is located on a site that is 7.43
acres as shown in Figure 3. This value includes internal streets, right of way, parking
lots, and all land uses. The SANDAG method requires that the site be between five and
2,000 acres, which the area of the F65 development falls within. There are five
intersections adjacent to the site including Folsom Boulevard / 64th Street, Folsom
Boulevard / 65th Street, 65th Street / Q Street, and the two driveway entrances to the site.
The “Land Use – Surrounding Area” statistics that are required in Section 1 of the
spreadsheet are “employment within one mile of the mixed use development” and
“employment within a 30-minute transit trip (door-to-door).” The employment within
one mile of the site has been calculated using Travel Analysis Zone (TAZ) information
provided by the Sacramento Area Council of Governments (Sacramento Area Council of
Governments). SACOG provided a GIS shape file in October 2012 that included all
TAZs in the regional travel demand model for the Sacramento Region. The TAZ
information included the population and number of jobs for each TAZ in 2008.
Using GIS, a model of the region was built to show a map of the area and the
roadway network with the TAZs overlaid. A one-mile-radius circle was drawn in GIS
with its center at the F65 development to illustrate which TAZs are located within one
mile of the site. Since some TAZs fall entirely within the one-mile circle while others
intersect the circle with varying amounts of the TAZ inside the one-mile radius, a GIS
command was used to include any TAZ that was entirely within in the circle and any
TAZ with its centroid within the circle. This command was chosen to attempt to obtain
44
an accurate representation of which TAZs should be included. Figure 10 shows the onemile radius buffer center at the site with the TAZs shown in the background.
Figure 10: One-Mile Radius Centered at the F65 Development
An output file was created for the selected TAZs that shows the total number of
jobs to be included. The output file was exported to Microsoft Excel and the number of
jobs for each selected TAZ was summed to get the total number of jobs within one mile
of the site. The results indicate that there are approximately 13,536 jobs within one mile
of the development. This number was input into the SANDAG Spreadsheet Tool. The
45
TAZs and the associated number of jobs within one mile of the development are shown
in Table 8.
Table 8: Summary of TAZs and Jobs within One Mile of the F65 Development
TAZ
471
514
1177
1178
515
472
1176
475
518
476
519
1175
483
484
Total
Number of Jobs in 2008
738
2,159
949
1,174
560
1,587
46
2,040
331
699
1,453
723
69
1,008
13,536
Similarly, GIS and TAZ information was used to calculate the employment within
a 30-minute transit trip with its origin at the F65 development. The GIS model created to
calculate the employment within one mile of the site was used as a base file. Sacramento
Regional Transit (RT) provided a GIS shape file in October 2012 that included all transit
lines in the region, including bus, light rail, and Amtrak. This shape file was added to the
model. The Online Trip Planner on Sacramento’s Regional Transit website
(www.sacrt.com) was used to calculate the distance that can be traveled in 30 minutes
using the F65 development as the origin. The online trip planner asks the user for a
starting point and a trip destination point. The planner produces an output that includes
46
schedule, transfers, travel time per link, overall travel time, and wait times at intermediate
stops. The intention of the exercise was to build a shape in GIS that encompasses all
areas a transit user can access within 30 minutes. To accomplish this task, using the F65
development as a starting point, various areas in the Sacramento region were input as a
destination. Examples of the areas chosen included downtown Sacramento, south
Sacramento, Watt Avenue, Howe Avenue, Citrus Heights, Rancho Cordova, Folsom,
Sunrise Boulevard, and Natomas. Each attempt provided a travel time to each destination
and using each attempt a location was determined with a coinciding 30-minute trip and
documented on a map. To account for the door-to-door time it could take a transit user to
get to and from the transit stop, a few minutes were subtracted from the distance traveled
while on the bus or train. Eventually, enough data points were collected to create a shape
in GIS. Figure 11 shows the shape created in GIS that was used to estimate the distance a
passenger could travel in 30 minutes with the trip beginning at the site. The TAZs are
outlined and numbered.
47
Figure 11: 30-Minute Transit Trip Beginning at the F65 Development
Using this shape, and similar to the method used for the one mile analysis, a GIS
command was used to find all TAZs within the shape. An output file was created and the
total number of jobs was summed and was calculated to be 326,770. The TAZs and the
number of jobs within a 30-minute transit trip are shown in Appendix C, Table 12.
Section 1 of the SANDAG spreadsheet requires the user to input the average
vehicles owned per dwelling unit. To accomplish this task, the United States Census
webpage was used. Specifically, the American Fact Finder search tool (U.S. Census
Bureau, 2012) produced housing statistics for Sacramento County and Sacramento City.
48
On average, there are 0.92 vehicles owned per dwelling unit. The 2010 American
Community Survey One-Year Estimate produced by the American Fact Finder website
(U.S. Census Bureau, 2012) is provided in Appendix C in Figures 22-24. Section 1 of the
completed spreadsheet is provided in Appendix C in Figure 27.
Section 2 of the SANDAG spreadsheet tool requires the user to input “Variable
Modeling Parameters.” These parameters consist of “average household size,” “jobs per
thousand square feet (KSF)” for various job types, and “jobs from ITE rates per other
unit.” The United States Census reports that the overall household size in Sacramento is
2.57 (U.S. Census Bureau, 2012). The SANDAG Mixed-Use Method requires average
household size for five types of residences; estate, urban, or rural, single family detached,
or condominium. The average household size per housing type is not available in
Sacramento and because the average for each housing type is unknown, the average value
of 2.57 people was used for all types. The F65 development has only apartments and
condominiums, and it is assumed that those dwelling units have an average size. It
should be noted that much of the housing on the F65 site is occupied by California State
University, Sacramento students and further research would be necessary to evaluate
whether the average household size matches the averages for household size in
Sacramento.
The remaining input in Section 2 requires the user to enter the number of “jobs
per KSF” of employment land uses including retail, office, light industrial,
manufacturing, warehousing, and miscellaneous uses. Section 2 also requires the number
of “jobs per hotel room,” “number of jobs per movie screen,” “and number of jobs per
49
student” for land uses associated with those types of jobs. The jobs per KSF section and
the jobs from ITE rates per other unit of the spreadsheet have been left unchanged from
the values SANDAG provides, which are based on ITE nationally published data, and no
improvement can be made to the values as part of this study.
Since the intention of this study is to quantify vehicle trip generation only, all
vehicle miles traveled related data available for input in Section 2 is not applicable and
was left unchanged from the information provided in the SANDAG spreadsheet.
The next portion of Section 2 of the spreadsheet addresses “Site Specific
Internalization.” If site specific internalization is used, it must be calculated and the
method must be explained. Site specific internalization is used when there are specific
trips the user wants to exclude from the mixed-use process. These trips are counted as
internal, and subtracted from the raw trips before applying the model. The overall trip
reduction percentage will still take these trips into account, and thus be a higher reduction
than if the model works on raw trips alone (San Diego Association of Governments,
2010). For the purposes of this study, Site Specific Internalization was not utilized.
The final area of Section 2 of the SANDAG spreadsheet addresses local serving
retail parameters. This section of the spreadsheet is part of the site-specific
internalization calculation. Since site specific internalization was not used, this section of
the spreadsheet was left unmodified from the default version available from SANDAG.
The completed Section 2 of the spreadsheet is provided in Appendix C, Figures 27 – 30.
At this stage of the analysis, all user inputs have been identified and entered into
the spreadsheet. The results are shown in the “Results” tab of the spreadsheet. All cells
50
on this worksheet are locked and cannot be changed by the user. Table 9 shows a
summary of the results. The complete results worksheet produced using the SANDAG
Mixed-Use Trip Generation spreadsheet is available in Appendix C in Figures 33 and 34.
As shown in Table 9, a 27% reduction in vehicle trips was calculated for the daily
time period. Applying the 27% reduction factor to the 8,454 raw vehicle trips estimated
results in 6,189 reduced vehicle trips when accounting for the smart growth variables.
The 27% reduction factor is similar to the reduction factor calculated using the ITE
method for the daily time period, which was 26%. In the A.M. peak hour time period, a
reduction factor of 29% was calculated resulting in 322 reduced vehicle trips. The ITE
method calculated an 18% reduction for this time period. In the P.M. peak hour, a
reduction factor of 26% was calculated resulting in 472 reduced vehicle trips. A
reduction factor of 24% was calculated using the ITE method for this time period.
Table 9: SANDAG Smart Growth Trip Generation Results for All Trips
Results
Raw Trips
Net Trips
Daily
AM Peak Hour
PM Peak Hour
8,454
452
639
6,189
322
472
Reduction
Percentage
27%
29%
26%
The SANDAG final results do not report the number of trips separated into trips
entering and exiting the site for the Daily, A.M. Peak Hour, or the P.M. Peak Hour,
which makes these results difficult to compare.
51
Collected F65 Site Data
The combined driveway counts observed February 8th and 9th for the north and
east driveways at the F65 development are shown in Appendix D, Table 13. At the north
driveway, the counts are shown in 15-minute intervals beginning and ending at 8:00 A.M.
for vehicle entering and exiting the site. The total number of vehicles that entered the site
via the north driveway over the 24-hour period was 2,195 and the total number of
vehicles that exited the site via the north driveway was 1,300. Similarly, the counts are
shown in 15-minute intervals beginning and ending at 7:45 A.M. for vehicle entering and
exiting the site at the east driveway. The total number of vehicles that entered the site via
the east driveway over the 24-hour period was 355 and the total number of vehicles that
exited the site was 1,126. In the A.M. peak hour, there were a total of 263 vehicles that
entered the site and 216 vehicles that exiting the site, yielding 479 total vehicle trips. In
the P.M. peak hour, there were 463 vehicles that entered the site and 390 vehicles that
exited the site, yielding 853 total vehicle trips.
52
Chapter 7
FINDINGS AND CONCLUSIONS
In this chapter, the F65 vehicle count data are compared with the ITE Multi-Use
Method results and the SANDAG Mixed-Use Method results.
Table 10 shows the results for daily trips, A.M. peak hour trips, and P.M. peak
hour trips for the data that were collected at the F65 development, the reduced trip results
for the ITE Multi-Use Method, and the reduced trip results for the SANDAG Mixed-Use
Method.
Table 10: Comparison of Results –ITE Multi-Use Method, SANDAG Mixed-Use
Method, and F65 Site Driveway Counts
Trip
Generation
Method
Daily
Trips
AM Peak
Hour
F65 Site
4,976
479
Counts
ITE Multi6,250
516
Use
(125% of (108% of
Method
observed) observed)
SANDAG
Smart
6,189
322
Growth
(124% of (67% of
Trip
observed) observed)
Generation
AM
Peak
Hour
Entering
263
(55%)
AM
Peak
Hour
Exiting
216
(45%)
273
(53%)
243
(47%)
361
(42% of
observed)
199
(55%)
162
(45%)
N/A
472
(55% of
observed)
N/A
N/A
N/A
PM Peak
Hour
853
PM
PM
Peak
Peak
Hour
Hour
Entering Exiting
463
390
(54%)
(46%)
Daily Time Period
By comparison, the number of daily trips calculated using the ITE method (6,250)
and the SANDAG method (6,189) were within 3% of each other. As shown in Table 10,
the number of daily trips estimated using the ITE Multi-Use Method and the SANDAG
53
Mixed-Use Method were 125% and 124% of the 4,976 observed vehicle trips,
respectively.
A.M. Peak Hour
In the A.M. peak hour, the ITE Multi-Use Method estimated 516 vehicle trips, or
108% of the 479 trips observed at the F65 site. The ITE estimate is slightly higher than
the number of observed trips, which for planning purposes is desirable compared to an
underestimation. The SANDAG Mixed-Use Method estimated 322 vehicle trips, or 67%
of the 479 trips observed at the F65 site. The ITE Multi-Use Method seemed to provide
more accurate results compared to the one-day data collection while requiring
significantly less effort.
P.M. Peak Hour
In the P.M. peak hour, the number of trips observed at the site was substantially
higher than the results estimated using either of the trip generation methods. There were
853 trips observed at the site between the hours of 4 P.M. and 6 P.M. compared to 361
trips estimated using the ITE Multi-Use Method and 472 trips using the SANDAG
Mixed-Use Method. Although the estimated number of trips was only 55% of the
observed trips, the SANDAG Mixed-Use provided a closer approximation of trip for this
time period. The ITE Multi-Use Method estimated only 42% of the observed trips.
Potentially, the high number of trips observed at the site could be attributable to
the students who live in the on-site residences returning from school. However, due to a
fairly even split of 55% percent entering the site and 45% exiting the site it seems that the
P.M. peak hour time period is simply a busy time of day for the development. The land
54
uses on-site may tend to attract evening users. Fitness Systems and Bikram Yoga attracts
students and professionals in the evenings, Starbucks is a good place for students to
study, and the on-site restaurants can be busy, which combined with the residential peak
hour traffic may produce high volumes that are not accounted for in the mixed-use
methods.
When comparing the raw trips estimated by the two methods without accounting
for trip reductions, it can be observed that both the ITE Method, which estimated 477
P.M. peak hour trips, and the SANDAG Method, which estimated 639 P.M. peak hour
trips, were still below the observed number of trips. This result is unexpected and may be
explained if the F65 development generates more trips than average. More likely, the
variation in the actual trips observed and the estimated trips is a function of data
collection.
Because the data were collected over only one day, it is possible that an accurate
representation of average trip behavior was not observed. Additional data collection may
help obtain a more accurate representation of average vehicle trips. Because the daily
trip counts match the estimated trips fairly accurately and only the P.M. peak hour is
significantly different lends to this theory. The P.M. peak hour is a short time duration to
only have a one-day count, and thus, it is difficult to have a high degree of confidence
that the driveway counts represent the average, which the ITE and SANDAG methods
utilize.
A comparison of the daily raw trips calculated prior to incorporating reductions
using the ITE method and SANDAG method were nearly identical, with the ITE method
55
estimating 8,432 trips and the SANDAG method estimating 8,454 trips, a 0.1%
difference. The small variation in results for the daily time period in both the raw and
reduced trip generation calculations using each method is of interest. The SANDAG
spreadsheet tool is fairly complex and requires significantly more data collection than
that required for the ITE Multi-Use Method. The SANDAG method required work/effort
to identify the user inputs as compared to the ITE method. The ITE method is based on
raw trips and two easily obtained internal capture rate tables (Table 7.1 and Table 7.2)
provided in the ITE Trip Generation Handbook, whereas the SANDAG method required
extensive research using the U.S. Census, GIS software which is not readily available to
all users to perform the analysis, and detailed and sophisticated analysis of travel analysis
zones and regional transit travel times to quantify the amount of employment in the
project area.
Comparison with Other Studies
The greater accuracy of the ITE Multi-Use Method in the A.M. peak hour
compared to the SANDAG Mixed-Use Method and the similarity of the results during the
daily time period results between the two methods in this analysis is in contrast to the
validation that was conducted and published for the SANDAG method (Feldman, et al.,
2010). 239 mixed-use developments were selected for a validation study from six major
U.S. metropolitan areas that included developments in Sacramento (Feldman, et al.,
2010). In that study, and comparable to the analysis in this project, in-field traffic counts
were obtained and compared to the mixed-use trip generation results. Traffic counts were
obtained for 22 mixed-use developments with 12 located in California. The Percent Root
56
Mean Square Error (%RMSE) and the R2 were calculated and used to compare the
percent difference between predicted and observed external vehicle counts. Root Mean
Squared Error is used in transportation fields to evaluate model accuracy. A %RMSE of
less than 40% is generally considered good (Feldman, et al., 2010). The R2 value is a
measurement of the squared difference between the observed and predicted vehicle
counts as a percentage of the squared variation of the observed vehicle counts about the
mean over all development sites (Feldman, et al., 2010). A high R2 value indicates the
model results are an accurate representation of the observed conditions. Observation of
both statistics in Table 11 shows the mixed-use method outperformed the raw trip
analysis and the ITE Mixed-Use Method. The validation showed that the mixed-use
models (SANDAG Mixed-Use Method) were consistently more accurate than the ITE or
San Diego Raw Trip calculations and the ITE Multi-Use method. Table 11 is a summary
of the results of the validation study by Feldman, et al.
Table 11: Comparison of Predicted and Observed External Vehicle Counts by Feldman,
et al.
RMSE
R2
ITE or San Diego
Raw Trips
ITE Multi-Use
Method
44%
0.66
32%
0.73
Mixed-Use
Development
Models used in
Validation Study
26%
0.88
Variation of the results of the F65 development analysis and the validation study
performed by Feldman, et al. suggest that additional data collection at the site would be
57
beneficial. Additional data collection would reduce uncertainty that is inherent in a one
day data collection and would assist in validating the results observed at the F65
development.
The basic premise behind the data in the ITE Trip Generation – An Informational
Report, 8th Edition is that the data were collected at single-use free-standing sites. The
ITE Multi-Use Method was developed to quantify the interaction among users between
land uses within a multi-use site (ITE, 2004). A multi-use site, as defined by ITE, is a
development that is typically a single real-estate project that consists of two or more ITE
land use classifications between which trips can be made without using the off-site road
system (ITE, 2004, Pg. 85). ITE does not differentiate between “multi-use” and “smart
growth.” Features of smart growth such as compact building design and accessibility for
pedestrians, bicycles, and transit are not accounted for in the ITE Multi-Use Method.
The ITE Multi-Use Method captures the interaction among users once the users are onsite, but does not apply reductions to vehicles due to these smart growth features, such as
the number of users that used transit to arrive at the site.
The SANDAG Mixed-Use Method was developed specifically for smart growth.
The spreadsheet tool quantifies reductions in vehicle trips due to smart growth by
requiring inputs that were designed to capture the “D” characteristics, or the
characteristics that were determined to be significant for smart growth trip reduction.
Variables such as distance to transit, proximity to employment, and census data are
utilized to estimate vehicle trip reductions due to smart growth. As a result, the
SANDAG method accounts for the vehicle trip reductions due to interaction among users
58
between land uses and quantifies vehicle trip reductions due to smart growth features,
such as such as the availability of alternative modes of transportation.
Since the SANDAG Mixed-Use Method was developed for smart growth and the
ITE Multi-Use method was developed for multi-use development, it is expected that the
SANDAG method would produce lower and more accurate estimates of vehicle trips
generated at the F65 development compared to the ITE Multi-Use Method, which would
be in agreement with the results of the validation study performed by Feldman, et al.
However, the results of the analysis performed in this project show that the SANDAG
Mixed-Use Method generally did not provide more accurate estimate of vehicle trips
generated. In the daily time period, the two methods performed nearly identically when
comparing the number of vehicle trips generated and reduction factor percentages that
were calculated. Additionally, although the SANDAG Mixed-Use Method provided a
higher underestimation of 29% compared to 18%, and a lower estimate of vehicle trips in
the A.M peak hour, the ITE Multi-Use Method provided results that were more
representative of the actual vehicle trips observed. In the P.M. peak hour, the reduction
factors were similar, with SANDAG estimating a 26% reduction in trips and ITE
estimating a 24% reduction in trips. However, the ITE Multi-Use Method provided the
lower estimate of vehicle trips, with both methods underestimated the actual vehicle trip
observed.
59
Chapter 8
RECOMMENDATIONS / FUTURE WORK
To more accurately represent the average trip generation and traffic patterns of the
F65 development significantly more data collection is required. Additional data could be
acquired by maintaining the automatic driveway counters at the two driveways for an
extended period of time. This study collected driveway counts for only one day, which
gives a generally idea of vehicle trips but not enough to have confidence that a
representation of the average operations were observed. Also, additional surveys could
be conducted to gather data related to how people use the site. Factors such as how often
users access the site, travel distance, vehicle occupancy, and mode of travel are important
to understand the dynamics of smart growth development.
The results show that although the SANDAG Mixed-Use Method was developed
specifically for smart growth, it did not outperform the ITE Multi-Use Method when
compared to the actual vehicle trips observed at the F65 development. Additional effort
in the Sacramento Region to validate and calibrate a trip generation method for smart
growth would be valuable. In general, the SANDAG methodology requires and
incorporates a significant amount of data in an effort to find variables that correlate smart
growth development with reductions in vehicle trip generation. Moving forward,
additional similar data should be collected at smart growth sites in the Sacramento. If
enough sites were used to contribute data, a stand-alone analysis could be conducted and
the correlation between transportation and population variables and trip reduction at
smart growth sites could be developed. This analysis would have the benefit of
60
calibrating the San Diego SANDAG method to meet the needs of the Sacramento
SACOG region. A side-by-side comparison could be used between the SANDAG results
and the newly developed SACOG results and a recommendation could be made for a
formal methodology to be used in the SACOG region. Hypothetically, the “D”
characteristics could be adjusted, omitted, or new significant variables could be
identified.
The SANDAG spreadsheet tool requires the user to use the SANDAG trip
generation rates that are specific to the SANDAG region, with the trip generation rates
and equations provided in locked cells in the spreadsheet. ITE trip generation rates could
be used in the analysis, which would provide consistency with the majority of planning
documents that are completed outside of San Diego. Additionally, the ITE trip
generation rates could be used to develop the method which could provide additional
model validation for the region.
The SANDAG method requires a significant amount of data collection to be
entered into the spreadsheet. It practice, this collection requires effort and time, which
equates to cost incurred by the project proponent. Census data were used, which is fairly
thorough but is updated periodically and perhaps does not capture the changes in
development and transportation patterns. The census data can be fairly general and may
not provide exact matches to the data required by the SANDAG method, which was
evident in the SANDAG method’s requirement to enter the specific dwelling type as
either estate, urban or rural, single family detached condominium, apartment, or mobile
home. These data were not readily available by practitioners. The SANDAG method
61
requires the user to research transit patterns and employment. To sufficiently gather
these data, sophisticated knowledge of transit times, schedules, wait times, and proximity
to transit stops are required. To estimate the employment within a thirty-minute transit
trip using a documented defensible method, the user is required to have a travel demand
model, in GIS or a travel demand software, which includes the required information, or
the user must research routes schedules and times and use trial and error to develop a
zone that encompasses the distance and areas accessible.
The SANDAG instructions also state that the user should include home-to-transit
and transit-to-work travel times which vary depending on the location of the passenger’s
home and nearest transit stop. An average approximation is required to provide this
information and is subject to the user’s judgment. The distance of travel available is one
part of the analysis required for this single input into the spreadsheet. The user must still
estimate the number of jobs within the transit travel zone. As SANDAG suggests, this
information can be difficult to obtain. To gather a defensible estimate of employment,
the SANDAG method suggests use of a travel demand model with Travel Analysis
Zones. The SACOG travel demand model is robust and a great tool, however, it requires
the software to run the model, the current version of the model, and an analysis with the
knowledge to run the model and produce the required data, all of which require an
investment. Since this study is an academic project with limited resources, significant
effort was required to gather the transit and employment data. A simpler method that
requires less time and effort while producing similar results could be evaluated.
62
The amounts of effort the City of Sacramento, SACOG, and the State of
California have completed demonstrate that smart-growth principles will be a component
of urban planning moving forward. As smart growth development increases, traditional
methods of estimating anticipated vehicle traffic due to these developments should be
evaluated for accuracy. Additional vehicle trip data can be expected as more smartgrowth developments are constructed. Moving forward, these data should be collected,
analyzed and used to refine the estimating procedures specific to smart growth in the
Sacramento Region.
63
APPENDIX A
Figure 12: Sacramento Regional Transit System Map
64
Figure 13: University Enterprises Retail Land Uses
65
Figure 14: Fulcrum Properties Land Use Square Footage (1 of 4)
66
Figure 15: Fulcrum Properties Land Use Square Footage (2 of 4)
67
Figure 16: Fulcrum Properties Land Use Square Footage (3 of 4)
68
Figure 17: Fulcrum Properties Land Use Square Footage (4 of 4)
69
APPENDIX B
Figure 18: ITE Trip Generation Spreadsheet
70
Figure 19: ITE Multi-Use Method Daily Trips
71
Figure 20: ITE Multi-Use Method A.M. Peak Hour Trips
72
Figure 21: ITE Multi-Use Method P.M. Peak Hour Trips
73
APPENDIX C
Table 12: TAZs and Number of Jobs (2008) within a 30-Minute Transit Trip Beginning
at the F65 Development
TAZ
330
385
329
885
1238
560
562
1233
1232
376
1236
374
573
559
571
378
373
1521
381
1043
375
380
561
884
575
1230
576
379
377
372
371
899
Neighborhood
North Highlands
North Sacramento
North Sacramento
Arden Arcade
Rancho Cordova
Rancho Cordova
Rancho Cordova
Rancho Cordova
Rancho Cordova
Arden Arcade
Rancho Cordova
Arden Arcade
Rancho Cordova
Rancho Cordova
Rancho Cordova
Arden Arcade
Arden Arcade
Rancho Cordova
North Sacramento
Rancho Cordova
Arden Arcade
North Sacramento
Rancho Cordova
Arden Arcade
Rancho Cordova
Rancho Cordova
Rancho Cordova
Arden Arcade
Arden Arcade
Arden Arcade
Arden Arcade
Rancho Cordova
Number of Jobs in
2008
1912
130
322
934
1911
1764
952
1055
202
1110
675
483
329
324
335
2120
330
547
1258
4200
765
647
579
918
739
72
643
802
1977
1814
1068
4640
74
900
343
344
879
347
350
351
1231
358
356
361
572
574
342
903
567
569
357
346
352
1520
1109
584
362
902
1073
781
1068
1528
1063
1531
1074
338
244
880
881
1067
1229
1110
1070
1072
577
Rancho Cordova
North Sacramento
North Sacramento
North Sacramento
Arden Arcade
Arden Arcade
Arden Arcade
Rancho Cordova
Arden Arcade
Arden Arcade
Arden Arcade
Rancho Cordova
Rancho Cordova
North Sacramento
Rancho Cordova
Rancho Cordova
Rancho Cordova
Arden Arcade
Arden Arcade
Arden Arcade
Rancho Cordova
North Sacramento
Rancho Cordova
Arden Arcade
Rancho Cordova
Downtown
Downtown
Downtown
Downtown
Downtown
North Sacramento
Downtown
North Sacramento
North Sacramento
Arden Arcade
Arden Arcade
Downtown
Rancho Cordova
Arden Arcade
Downtown
Downtown
Rancho Cordova
743
2821
1008
1246
846
1511
1411
34
719
783
552
302
1190
3808
3383
612
0
332
744
4815
0
3046
7430
476
1559
999
352
828
697
622
2616
151
1936
3060
2903
1423
259
1120
1579
249
86
341
75
339
1071
345
355
882
1062
360
365
883
1066
366
1064
1065
568
340
1069
1527
901
1076
779
563
1123
1122
783
782
348
363
353
570
359
1228
805
804
466
1075
764
765
585
583
1077
784
780
North Sacramento
Downtown
Arden Arcade
Arden Arcade
Arden Arcade
Downtown
Arden Arcade
Arden Arcade
Arden Arcade
Downtown
Arden Arcade
Downtown
Downtown
Rancho Cordova
Arden Arcade
Downtown
Downtown
Rancho Cordova
Downtown
Downtown
Rancho Cordova
Rancho Cordova
Rancho Cordova
Downtown
Downtown
Arden Arcade
Arden Arcade
Arden Arcade
Rancho Cordova
Arden Arcade
Rancho Cordova
Downtown
Downtown
East Sacramento
Downtown
Downtown
Downtown
Rancho Cordova
Rancho Cordova
Downtown
Downtown
Downtown
910
300
1669
1007
958
326
200
363
148
243
46
136
331
351
2620
810
0
183
0
843
3994
1776
3978
0
0
521
267
947
288
313
3613
724
209
351
364
137
2841
1799
764
36
2853
0
76
763
578
803
766
468
364
802
769
801
582
341
349
354
799
893
771
785
786
770
806
591
800
467
808
337
767
590
797
579
807
774
812
798
768
787
581
809
470
565
878
335
336
Downtown
Rancho Cordova
Downtown
Downtown
East Sacramento
Arden Arcade
Downtown
Downtown
Downtown
Rancho Cordova
Arden Arcade
Arden Arcade
Arden Arcade
Downtown
East Sacramento
Downtown
Downtown
Downtown
Downtown
Downtown
Rancho Cordova
Downtown
East Sacramento
Downtown
Arden Arcade
Downtown
Rancho Cordova
Downtown
Rancho Cordova
Downtown
Downtown
Downtown
Downtown
Downtown
Downtown
Rancho Cordova
Downtown
East Sacramento
Rancho Cordova
Arden Arcade
Arden Arcade
Arden Arcade
240
2022
726
4284
1161
106
474
9884
356
3146
1991
2878
270
28
101
4510
3887
478
2193
7159
358
420
2702
7659
1350
5645
1
2317
310
5120
1933
1305
4446
13009
89
950
2228
933
4492
3008
387
567
77
777
334
772
906
904
795
810
894
778
773
474
469
811
796
789
788
471
905
514
1162
776
657
517
792
790
594
473
775
791
656
589
793
479
516
1177
658
794
477
907
Downtown
Arden Arcade
Downtown
Rancho Cordova
East Sacramento
Downtown
Downtown
East Sacramento
Downtown
Downtown
East Sacramento
East Sacramento
Downtown
Downtown
Downtown
Downtown
East Sacramento
Rancho Cordova
East Sacramento
East Sacramento
Downtown
Land Park - Pocket Meadowview
East Sacramento
Downtown
Downtown
Rancho Cordova
East Sacramento
Downtown
Downtown
Land Park - Pocket Meadowview
East Sacramento
Downtown
East Sacramento
East Sacramento
East Sacramento
Land Park - Pocket Meadowview
Downtown
East Sacramento
East Sacramento
1094
2978
774
1782
952
1941
372
2373
580
305
293
2455
257
2080
3056
327
738
3224
2159
214
626
770
4
340
783
1123
362
1075
528
637
256
504
685
4
949
696
697
116
7
78
1178
1214
592
515
472
663
915
522
580
916
1042
521
1176
480
593
478
595
475
1161
518
523
476
519
1174
1175
520
1509
596
1180
482
483
485
1179
1212
484
1213
527
487
East Sacramento
Rancho Cordova
East Sacramento
East Sacramento
East Sacramento
Land Park - Pocket Meadowview
Land Park - Pocket Meadowview
East Sacramento
Rancho Cordova
Land Park - Pocket Meadowview
East Sacramento
East Sacramento
East Sacramento
East Sacramento
East Sacramento
East Sacramento
Rancho Cordova
East Sacramento
Land Park - Pocket Meadowview
East Sacramento
East Sacramento
East Sacramento
East Sacramento
East Sacramento
East Sacramento
East Sacramento
Rancho Cordova
East Sacramento
East Sacramento
East Sacramento
East Sacramento
East Sacramento
East Sacramento
Rancho Cordova
East Sacramento
Rancho Cordova
East Sacramento
East Sacramento
1174
590
2124
560
1587
1889
180
1090
1957
1908
3207
701
46
1236
294
4917
2144
2040
123
331
1914
699
1453
1482
723
1127
116
722
95
561
69
675
498
379
1008
218
1603
402
79
486
493
488
489
490
491
495
1163
895
896
500
497
498
923
499
501
East Sacramento
South Sacramento
East Sacramento
East Sacramento
South Sacramento
South Sacramento
South Sacramento
South Sacramento
South Sacramento
South Sacramento
South Sacramento
South Sacramento
South Sacramento
South Sacramento
South Sacramento
South Sacramento
178
1234
526
499
492
896
749
54
778
67
299
652
1393
2051
63
390
Total = 326,770
80
Figure 22: 2010 American Community Survey 1-Year Estimate Produced by the
American Fact Finder Website (1 of 4)
81
Figure 23: 2010 American Community Survey 1-Year Estimate Produced by the
American Fact Finder Website (2 of 4)
82
Figure 24: 2010 American Community Survey 1-Year Estimate Produced by the
American Fact Finder Website (3 of 4)
83
Figure 25: 2010 American Community Survey 1-Year Estimate Produced by the
American Fact Finder Website (4 of 4)
84
Figure 26: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 1
85
Figure 27: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 2
86
Figure 28: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 2 (Cont.)
87
Figure 29: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 2 (Cont.)
88
Figure 30: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 2 (Cont.)
89
Figure 31: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 3
90
Figure 32: SANDAG Smart-Growth Trip Generation Spreadsheet – Section 3 (Cont.)
91
Figure 33: SANDAG Smart-Growth Trip Generation Spreadsheet – Results
92
Figure 34: SANDAG Smart-Growth Trip Generation Spreadsheet – Results (Cont.)
93
APPENDIX D
Table 13: Combined Driveway Counts for the North and East F65 Driveways
Number
Date
0
2/8/2011
1
2/8/2012
2
2/8/2012
3
2/8/2012
4
2/8/2012
5
2/8/2012
6
2/8/2012
7
2/8/2012
8
2/8/2012
9
2/8/2012
10
2/8/2012
11
2/8/2012
12
2/8/2012
13
2/8/2012
14
2/8/2012
15
2/8/2012
16
2/8/2012
17
2/8/2012
18
2/8/2012
19
2/8/2012
20
2/8/2012
21
2/8/2012
Time
07:45
AM
08:00
AM
08:15
AM
08:30
AM
08:45
AM
09:00
AM
09:15
AM
09:30
AM
09:45
AM
10:00
AM
10:15
AM
10:30
AM
10:45
AM
11:00
AM
11:15
AM
11:30
AM
11:45
AM
12:00
PM
12:15
PM
12:30
PM
12:45
PM
01:00
PM
Entering
Exiting
Entering
+ Exiting
Cumulative
Trips
Parked
Accumulation
4
15
19
19
-11
-11
29
23
52
71
6
156
39
27
66
137
12
168
44
28
72
209
16
184
32
27
59
268
5
189
33
25
58
326
8
197
25
31
56
382
-6
191
21
31
52
434
-10
181
30
19
49
483
11
192
23
25
48
531
-2
190
28
23
51
582
5
195
29
26
55
637
3
198
35
31
66
703
4
202
45
35
80
783
10
212
48
32
80
863
16
228
50
46
96
959
4
232
58
33
91
1050
25
257
69
41
110
1160
28
285
69
49
118
1278
20
305
62
65
127
1405
-3
302
47
69
116
1521
-22
280
41
68
109
1630
-27
253
94
22
2/8/2012
23
2/8/2012
24
2/8/2012
25
2/8/2012
26
2/8/2012
27
2/8/2012
28
2/8/2012
29
2/8/2012
30
2/8/2012
31
2/8/2012
32
2/8/2012
33
2/8/2012
34
2/8/2012
35
2/8/2012
36
2/8/2012
37
2/8/2012
38
2/8/2012
39
2/8/2012
40
2/8/2012
41
2/8/2012
42
2/8/2012
43
2/8/2012
44
2/8/2012
45
2/8/2012
46
2/8/2012
47
2/8/2012
01:15
PM
01:30
PM
01:45
PM
02:00
PM
02:15
PM
02:30
PM
02:45
PM
03:00
PM
03:15
PM
03:30
PM
03:45
PM
04:00
PM
04:15
PM
04:30
PM
04:45
PM
05:00
PM
05:15
PM
05:30
PM
05:45
PM
06:00
PM
06:15
PM
06:30
PM
06:45
PM
07:00
PM
07:15
PM
07:30
PM
38
41
79
1709
-3
250
52
41
93
1802
11
261
65
34
99
1901
31
292
38
47
85
1986
-9
283
35
37
72
2058
-2
281
37
52
89
2147
-15
266
41
31
72
2219
10
276
42
42
84
2303
0
276
56
40
96
2399
16
292
49
55
104
2503
-6
286
62
36
98
2601
26
312
49
49
98
2699
0
312
53
43
96
2795
10
322
46
35
81
2876
11
333
42
33
75
2951
9
342
50
42
92
3043
8
350
54
39
93
3136
15
365
46
48
94
3230
-2
363
65
43
108
3338
22
385
58
58
116
3454
0
385
58
55
113
3567
3
388
46
54
100
3667
-8
380
50
47
97
3764
3
383
32
41
73
3837
-9
374
37
45
82
3919
-8
366
27
44
71
3990
-17
349
95
48
2/8/2012
49
2/8/2012
50
2/8/2012
51
2/8/2012
52
2/8/2012
53
2/8/2012
54
2/8/2012
55
2/8/2012
56
2/8/2012
57
2/8/2012
58
2/8/2012
59
2/8/2012
60
2/8/2012
61
2/8/2012
62
2/8/2012
63
2/8/2012
64
2/8/2012
65
2/8/2012
66
2/9/2012
67
2/9/2012
68
2/9/2012
69
2/9/2012
70
2/9/2012
71
2/9/2012
72
2/9/2012
73
2/9/2012
07:45
PM
08:00
PM
08:15
PM
08:30
PM
08:45
PM
09:00
PM
09:15
PM
09:30
PM
09:45
PM
10:00
PM
10:15
PM
10:30
PM
10:45
PM
11:00
PM
11:15
PM
11:30
PM
11:45
PM
12:00
AM
12:15
AM
12:30
AM
12:45
AM
01:00
AM
01:15
AM
01:30
AM
01:45
AM
02:00
AM
41
36
77
4067
5
354
28
56
84
4151
-28
326
32
44
76
4227
-12
314
16
25
41
4268
-9
305
21
28
49
4317
-7
298
18
34
52
4369
-16
282
13
19
32
4401
-6
276
12
16
28
4429
-4
272
12
24
36
4465
-12
260
15
19
34
4499
-4
256
8
21
29
4528
-13
243
1
1
2
4530
0
243
7
10
17
4547
-3
240
4
5
9
4556
-1
239
3
2
5
4561
1
240
5
4
9
4570
1
241
5
3
8
4578
2
243
2
2
4
4582
0
243
2
1
3
4585
1
244
3
3
6
4591
0
244
0
1
1
4592
-1
243
1
4
5
4597
-3
240
4
3
7
4604
1
241
1
3
4
4608
-2
239
3
1
4
4612
2
241
1
1
2
4614
0
241
96
74
2/9/2012
75
2/9/2012
76
2/9/2012
77
2/9/2012
78
2/9/2012
79
2/9/2012
80
2/9/2012
81
2/9/2012
82
2/9/2012
83
2/9/2012
84
2/9/2012
85
2/9/2012
86
2/9/2012
87
2/9/2012
88
2/9/2012
89
2/9/2012
90
2/9/2012
91
2/9/2012
92
2/9/2012
93
2/9/2012
94
2/9/2012
95
2/9/2012
96
2/9/2012
02:15
AM
02:30
AM
02:45
AM
03:00
AM
03:15
AM
03:30
AM
03:45
AM
04:00
AM
04:15
AM
04:30
AM
04:45
AM
05:00
AM
05:15
AM
05:30
AM
05:45
AM
06:00
AM
06:15
AM
06:30
AM
06:45
AM
07:00
AM
07:15
AM
07:30
AM
07:45
AM
Total
1
0
1
4615
1
242
0
0
0
4615
0
242
2
1
3
4618
1
243
0
0
0
4618
0
243
1
4
5
4623
-3
240
0
0
0
4623
0
240
1
1
2
4625
0
240
1
1
2
4627
0
240
2
0
2
4629
2
242
2
0
2
4631
2
244
0
0
0
4631
0
244
2
1
3
4634
1
245
11
6
17
4651
5
250
14
6
20
4671
8
258
24
9
33
4704
15
273
9
12
21
4725
-3
270
8
6
14
4739
2
272
16
16
32
4771
0
272
23
10
33
4804
13
285
15
26
41
4845
-11
274
29
22
51
4896
7
281
23
30
53
4949
-7
274
19
8
27
4976
11
285
2550
2426
4976
4976
97
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