A COMPARATIVE ANALYSIS OF VEHICLE TRIP GENERATION METHODS AT THE 65TH STREET AND FOLSOM BOULEVARD SMART GROWTH DEVELOPMENT.

Transcription

1 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

2 2013 Lucas Jeffrey Fuson ALL RIGHTS RESERVED ii

3 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

4 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. Date Department of Civil Engineering iv

5 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) Multi- Use 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

6 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

7 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

8 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

9 TABLE OF CONTENTS Page Acknowledgements... viii List of Tables... x List of Figures... xii Chapter 1. INTRODUCTION BACKGROUND LITERATURE REVIEW METHODS AND METHODOLOGY DATA COLLECTION RESULTS FINDINGS AND CONCLUSIONS RECOMMENDATIONS / FUTURE WORK Appendix A Appendix B Appendix C Appendix D References ix

10 LIST OF TABLES Tables Page Table 1: ITE Raw Trip Generation Summary Table 2: Raw Trips Separated into Land Use Groups Table 3: Internal Capture Rates Based from ITE Trip Generation Handbook Tables 7.1 and Table 4: ITE Trip Generation Daily Net External Trips for the Multi-Use Method Trip Generation Reduction Results Table 5: AM Peak Hour ITE Trip Generation Multi-Use Method Trip Generation Reduction Results Table 6: PM Peak Hour ITE Trip Generation Multi-Use Method Trip Generation Reduction Results Table 7: SANDAG Land Use Quantities Used To Calculate Raw Trips Table 8: Summary of TAZs and Jobs within One Mile of the F65 Development Table 9: SANDAG Smart Growth Trip Generation Results for All Trips Table 10: Comparison of Results ITE Multi-Use Method, SANDAG Mixed-Use Method, and F65 Site Driveway Counts Table 11: Comparison of Predicted and Observed External Vehicle Counts by Feldman, et al Table 12: TAZs and Number of Jobs (2008) within a 30-Minute Transit Trip Beginning at the F65 Development x

11 Table 13: Combined Driveway Counts for the North and East F65 Driveways xi

12 LIST OF FIGURES Figures Page Figure 1: Project Location Map... 8 Figure 2: Vicinity Map... 9 Figure 3: Developed Area of the F65 Site Figure 4: Building Locations at the F65 Development Figure 5: ITE Trip Generation Rate for Land Use Code 820 (Shopping Center) Figure 6: Northside Driveway Figure 7: Eastside Driveway Figure 8: Eastside Driveway Counts Figure 9: Northside Driveway Counts Figure 10: One-Mile Radius Centered at the F65 Development Figure 11: 30-Minute Transit Trip Beginning at the F65 Development Figure 12: Sacramento Regional Transit System Map Figure 13: University Enterprises Retail Land Uses Figure 14: Fulcrum Properties Land Use Square Footage (1 of 4) Figure 15: Fulcrum Properties Land Use Square Footage (2 of 4) Figure 16: Fulcrum Properties Land Use Square Footage (3 of 4) Figure 17: Fulcrum Properties Land Use Square Footage (4 of 4) Figure 18: ITE Trip Generation Spreadsheet Figure 19: ITE Multi-Use Method Daily Trips xii

13 Figure 20: ITE Multi-Use Method A.M. Peak Hour Trips Figure 21: ITE Multi-Use Method P.M. Peak Hour Trips Figure 22: 2010 American Community Survey 1-Year Estimate Produced by the American Fact Finder Website (1 of 4) Figure 23: 2010 American Community Survey 1-Year Estimate Produced by the American Fact Finder Website (2 of 4) Figure 24: 2010 American Community Survey 1-Year Estimate Produced by the American Fact Finder Website (3 of 4) Figure 25: 2010 American Community Survey 1-Year Estimate Produced by the American Fact Finder Website (4 of 4) Figure 26: SANDAG Smart-Growth Trip Generation Spreadsheet Section Figure 27: SANDAG Smart-Growth Trip Generation Spreadsheet Section 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 Figure 32: SANDAG Smart-Growth Trip Generation Spreadsheet Section 3 (Cont.).. 90 Figure 33: SANDAG Smart-Growth Trip Generation Spreadsheet Results Figure 34: SANDAG Smart-Growth Trip Generation Spreadsheet Results (Cont.) xiii

14 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

15 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

16 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 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).

17 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.

18 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

19 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 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 65 th Street and Folsom Boulevard (F65) mixed-use development in Sacramento, California. This development is located in the northwest quadrant of the 65 th 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.

20 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-80/65 th Street interchange located immediately south of the site. 65 th Street is a four-lane arterial roadway that runs south to north in the project vicinity. 65 th 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 65 th Street accommodate bicycles in Class II bicycle lanes, and there is a bicycle / pedestrian entrance located on Elvas Boulevard that provides access to the

21 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

22 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 65 th 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 65 th Street for the

23 10 number 210 and number 211 busses. The availability of numerous transit routes from the 65 th 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.

24 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

25 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 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 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).

26 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,

27 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).

28 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, 8 th 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.

29 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 All of the University Enterprises residential buildings are primarily

30 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 65 th 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 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

31 18 calculations. The building along the east project boundary and adjacent to 65 th 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

32 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 64 th 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 65 th Street approximately 300 feet south of the Folsom Boulevard / 65 th 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 65 th Street that has eight dwelling units. A trip generation spreadsheet was developed for this study using the ITE Trip Generation An Informational Report, 8 th 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

33 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, 8 th Edition. The percentage of vehicles entering and exiting the land use during the AM and PM peak hour were also entered.

34 21 Figure 5: ITE Trip Generation Rate for Land Use Code 820 (Shopping Center) (Source: ITE Trip Generation-An Informational Report, 8 th 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

35 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.

36 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

37 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 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

38 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 (P internal ).

39 26 3. Compute the probability an external trip will be made by walking or bicycling (P walkbike ). 4. Compute the probability an external trip will be made by transit (P transit ). 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 (P internal ) 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 (P walkbike ) 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 (P transit ) 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:

40 27 External Vehicle Trips Generated by Mixed-Use Developments = Raw Trips * (1-P internal ) * (1-P walkbike -P transit ) 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.

41 28 Chapter 5 DATA COLLECTION Data were collected at the F65 site on Tuesday, February 8, 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.

42 Figure 6: Northside Driveway 29

43 30 Figure 7: Eastside Driveway The driveway counts provide the vehicle trips entering and exiting the site in 15- minute 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

44 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

45 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.

46 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 AM Peak Hour Trips AM Peak Hour Trips Entering AM Peak Hour Trips Exiting P.M. Peak Hour Trips P.M. Peak Hour Trips Entering P.M. Peak Hour Trips Exiting 8, (52%) 301 (48%) (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

47 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 65 th Street, they are in close proximity to each other and connected by appropriate pedestrian walkways. Land

48 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 Residential Quantity 129 D.U. Daily Trips A.M. Peak Hour Trips Retail A 32 KSF 5, Retail B 11 KSF 2, 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 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

49 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

50 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 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

51 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, ,125 Exit 2, ,125 Total 4,673 1, ,250 Internal Capture Single-Use Trip Gen. Estimates 5,562 2, ,432 26%

52 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 Exit Internal Total Capture Single-Use Trip Gen. Estimates % 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.

53 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 Exit Internal Total Capture Single-Use Trip Gen. Estimates % 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 (P internal ), the proportion of walk/bike trips (P walkbike ), and the proportion of transit trips (P transit ). 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.

54 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.

55 42 Table 7: SANDAG Land Use Quantities Used To Calculate Raw Trips Name of Establishment Quantity Units Upper East Side Lofts - Building 2 58 DU Upper East Side Lofts - Building 3 57 DU Upper East Side Lofts - Building 4 6 DU Fulcrum Property Lofts 8 DU Bikram Yoga KSF Mr. Pickles KSF Gunther s Ice Cream KSF Anytime Fitness KSF Office Depot KSF Dolce Nails and Spa KSF Dos Coyotes KSF Starbucks KSF Jamba Juice KSF Pita Pit KSF Supercuts KSF Bento Box KSF Game Stop KSF The Sandwich Spot KSF Available Property KSF Available Property KSF Totals Apartments 129 DU Specialty Retail/Strip Commercial 28 KSF High-Turnover (Sit-Down) Restaurant 6 KSF Fast-Food Restaurant without Drive- Through Window 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 Mixed- Use Trip Generation Spreadsheet is provided in Appendix C in Figures 31 and 32.

56 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 / 64 th Street, Folsom Boulevard / 65 th Street, 65 th 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 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

57 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

58 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 Number of Jobs in , , , , , ,008 Total 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 ( 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

59 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.

60 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.

61 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 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

62 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 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

63 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 Reduction Percentage Daily 8,454 6,189 27% AM Peak Hour % PM Peak Hour % 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.

64 51 Collected F65 Site Data The combined driveway counts observed February 8 th and 9 th 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.

65 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 F65 Site Counts ITE Multi- Use Method SANDAG Smart Growth Trip Generation Daily Trips AM Peak Hour 4, ,250 (125% of observed) 6,189 (124% of observed) 516 (108% of observed) 322 (67% of observed) AM Peak Hour Entering 263 (55%) 273 (53%) N/A AM Peak Hour Exiting 216 (45%) 243 (47%) N/A PM Peak Hour (42% of observed) 472 (55% of observed) PM Peak Hour Entering 463 (54%) 199 (55%) N/A PM Peak Hour Exiting 390 (46%) 162 (45%) N/A 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

66 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

67 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

68 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

69 56 Mean Square Error (%RMSE) and the R 2 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 R 2 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 R 2 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. ITE or San Diego Raw Trips ITE Multi-Use Method Mixed-Use Development Models used in Validation Study RMSE 44% 32% 26% R 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

70 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

71 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.

72 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

73 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

74 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.

75 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.

76 63 APPENDIX A Figure 12: Sacramento Regional Transit System Map

77 Figure 13: University Enterprises Retail Land Uses 64

78 Figure 14: Fulcrum Properties Land Use Square Footage (1 of 4) 65

79 Figure 15: Fulcrum Properties Land Use Square Footage (2 of 4) 66

80 Figure 16: Fulcrum Properties Land Use Square Footage (3 of 4) 67

81 Figure 17: Fulcrum Properties Land Use Square Footage (4 of 4) 68

82 69 APPENDIX B Figure 18: ITE Trip Generation Spreadsheet

83 Figure 19: ITE Multi-Use Method Daily Trips 70

84 Figure 20: ITE Multi-Use Method A.M. Peak Hour Trips 71

85 Figure 21: ITE Multi-Use Method P.M. Peak Hour Trips 72

86 73 APPENDIX C Table 12: TAZs and Number of Jobs (2008) within a 30-Minute Transit Trip Beginning at the F65 Development TAZ Neighborhood Number of Jobs in 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 4640

87 900 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

88 339 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 0 75

89 763 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

90 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 626 Land Park - Pocket - Meadowview East Sacramento Downtown Downtown Rancho Cordova East Sacramento Downtown Downtown 528 Land Park - Pocket - Meadowview East Sacramento Downtown East Sacramento East Sacramento East Sacramento 949 Land Park - Pocket - Meadowview Downtown East Sacramento East Sacramento 7

91 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 2040 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 402

92 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 390 Total = 326,770

93 Figure 22: 2010 American Community Survey 1-Year Estimate Produced by the American Fact Finder Website (1 of 4) 80

94 Figure 23: 2010 American Community Survey 1-Year Estimate Produced by the American Fact Finder Website (2 of 4) 81

95 Figure 24: 2010 American Community Survey 1-Year Estimate Produced by the American Fact Finder Website (3 of 4) 82

96 Figure 25: 2010 American Community Survey 1-Year Estimate Produced by the American Fact Finder Website (4 of 4) 83

97 Figure 26: SANDAG Smart-Growth Trip Generation Spreadsheet Section 1 84

98 Figure 27: SANDAG Smart-Growth Trip Generation Spreadsheet Section 2 85

99 Figure 28: SANDAG Smart-Growth Trip Generation Spreadsheet Section 2 (Cont.) 86

100 Figure 29: SANDAG Smart-Growth Trip Generation Spreadsheet Section 2 (Cont.) 87

101 Figure 30: SANDAG Smart-Growth Trip Generation Spreadsheet Section 2 (Cont.) 88

102 Figure 31: SANDAG Smart-Growth Trip Generation Spreadsheet Section 3 89

103 Figure 32: SANDAG Smart-Growth Trip Generation Spreadsheet Section 3 (Cont.) 90