Morning and Afternoon Peak-Hour Vehicle Trip Generation At

MORNING AND AFTERNOON PEAK-HOUR VEHICLE TRIP GENERATION AT

MID-RISE, TRANSIT-ORIENTED APARTMENTS NEAR

BAY AREA RAPID TRANSIT (BART) STATIONS

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

Civil Engineering

Gurpreet Singh Dhaliwal

FALL 2012

Gurpreet Singh Dhaliwal

MORNING AND AFTERNOON PEAK-HOUR VEHICLE TRIP GENERATION AT

MID-RISE, TRANSIT-ORIENTED APARTMENTS NEAR

BAY AREA RAPID TRANSIT (BART) STATIONS

A Project

Gurpreet Singh Dhaliwal

Approved by:

______, Committee Chair Kevan Shafizadeh, Ph.D., P.E., PTP, PTOE

______Date

iii

Student: Gurpreet Singh Dhaliwal

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.

______, Dean ______Emir José Macari, Ph.D. Date

Department of Civil Engineering

Abstract

MORNING AND AFTERNOON PEAK-HOUR VEHICLE TRIP GENERATION AT

MID-RISE, TRANSIT-ORIENTED APARTMENTS NEAR

BAY AREA RAPID TRANSIT (BART) STATIONS

Gurpreet Singh Dhaliwal

The impact of Bay Area Rapid Transit (BART) proximity on morning and afternoon peak-hour vehicle trips generated by Transit-Oriented Apartments (TOAs) was observed. BART is one of the busiest rail transit system in the U.S. located in the. It connects San Francisco and the Peninsula region to the East Bay of the San Francisco

Bay Area. Ten TOAs, both in the East Bay and Peninsula region, were selected near ten

BART stations. The morning and afternoon peak-hour volumes were observed from 6:00 a.m. to 9:30 a.m. and 4:00 p.m. to 7:30 p.m., and then compared with the peak-hour trips estimated by the Trip Generation Manual (8th Edition) published by the Institute of

Transportation Engineers (ITE).

The analysis and comparison of observed trip generation data with ITE estimates suggests that fewer peak-hour vehicle trips were generated both in the morning and afternoon, however the impact varied from site to site. Most TOAs showed a reduction in v

the morning and afternoon peak-hour volumes. In the morning, about 19% fewer vehicle

trips were produced; whereas in the afternoon, about 50% fewer vehicle trips were

produced. It is hypothesized that this reduction in peak-hour trips can be attributed, in

part, to the TOA’s proximity to BART.

______, Committee Chair Kevan Shafizadeh, Ph.D., P.E., PTP, PTOE

______Date

DEDICATION

I dedicate this project to my parents who inspired me and helped me as well to reach this point.

vii

ACKNOWLEDGEMENTS

I would like to thank my father, mother, and brother for continuously encouraging me to finish this project. I also cannot overlook the patience and support of my wife and daughter. Finally, I would like to thank Dr. Kevan Shafizadeh for his invaluable advice and direction in completing this project.

viii

TABLE OF CONTENTS

Page

Dedication ...... vii

Acknowledgements ...... viii

List of Tables ...... xi

List of Figures ...... xii

Chapter

1. INTRODUCTION ...... 1

2. BACKGROUND ...... 3

Bay Area Rapid Transit (BART) ...... 3

Transit-Oriented Apartments (TOAs) ...... 4

3. METHODOLOGY ...... 9

Trip Data Collection ...... 9

Data Analysis ...... 11

Estimation of ITE Trips ...... 11

Data Comparison ...... 12

4. INFORMATION ABOUT SELECTED TOAs ...... 13

Union City ...... 14

Hayward ...... 16

Fremont ...... 19

Concord ...... 21

Colma ...... 23

South San Francisco ...... 25

Walnut Creek ...... 27

Berkeley ...... 29

5. RESULTS AND DISCUSSION ...... 40

6. CONCLUSION ...... 53

Appendix A Observed Data ...... 55

Appendix B Trip Rate For Mid-Rise Apartments...... 65

Appendix C BART Ridership ...... 67

References ...... 70

LIST OF TABLES

Table Page

1. Distance Between Selected TOAs and the Nearest BART Stations ...... 34

2. Walk Score Scale ...... 38

3. Selected TOAs and Their City’s Walk Scores ...... 39

4. A.M. and P.M. Peak-Hour Periods for Selected TOAs ...... 40

5. Number of Observed A.M. and P.M. Peak-Hour Trips vs. ITE-Estimated Peak-

Hour Trips ...... 42

6. Observed and ITE-Estimated Peak-Hour A.M. and P.M. Trip Rate ...... 49

LIST OF FIGURES

Figure Page

1 BART System Map ...... 4

2. Projected Demand for Housing Near Transit in 10 U.S. Transit Regions ...... 7

3. Union City – Avalon Apartments ...... 15

4. Hayward – Montelena Apartment Homes ...... 17

5. Hayward – City Centre Apartment Homes ...... 18

6. Fremont – Archstone Apartment Communities ...... 20

7. Concord – Park Central Apartments ...... 22

8. Colma – La Terrazza Apartments ...... 24

9. South San Francisco – Archstone Apartment Communities ...... 26

10. Walnut Creek – Trinity House Apartment Homes ...... 28

11. North Berkeley – Acton Apartments ...... 30

12. Downtown Berkeley – Berkeleyan Apartments ...... 31

13. Size and Occupancy of Selected TOAs ...... 32

14. Selected TOA Locations ...... 35

15. BART’s Average Daily Ridership in September & October 2012 ...... 36

16. Percent Reduction in A.M. Peak-Hour Trips Compared with ITE ...... 44

17. Percent Reduction in P.M. Peak-Hour Trips Compared with ITE ...... 45

18. Comparison of Observed A.M. Peak-Hour Trips with ITE ...... 47 xii

19. Comparison of Observed P.M. Peak-Hour Trips with ITE ...... 48

20. Comparison of Observed A.M. Peak-Hour Trip Rate with ITE ...... 50

21. Comparison of Observed P.M. Peak-Hour Trip Rate with ITE ...... 51

22. A.M. & P.M. Peak-Hour Trip Rate for All TOAs Combined ...... 52

xiii 1

Chapter 1

INTRODUCTION

The purpose of this project was to analyze the vehicle trips generated by Transit -

Oriented Apartments (TOAs) located in close proximity of Bay Area Rapid Transit

(BART) stations. The focus was on morning and afternoon peak-hour trips generated by the TOAs.

BART is one of the most prominent and popular public transportation modes located in the San Francisco Bay Area. It connects East Bay cities (e.g., Fremont,

Oakland, Concord, Pittsburg, Richmond, Dublin) to San Francisco and five Peninsula cities (Daly City, Colma, South San Francisco, San Bruno, and Millbrae). Possible reasons behind BART’s popularity include connectivity to major employment hubs

(mainly, San Francisco and Oakland), quite frequent service, severe traffic congestion on

San Francisco Bay Area freeways, and scarce or expensive parking. The congestion on the freeways, the environmental concerns, and the suburban sprawl have made local, state, and federal governments recognize Transit-Oriented Developments (TODs) as a possible sustainable solution to address the above mentioned challenges.

For this project, ten multifamily housing complexes were selected near ten BART stations (out of 44 stations total). All of the selected Transit-Oriented Apartments (TOAs) were situated within short walking distance from the nearest BART station. After meeting property managers and conducting thorough online research of each of all selected TOAs, it was found that almost all of these complexes were well furnished and had very low vacancy rates (less than 5%). First, the morning (6:00 a.m. to 9:30 a.m.) and

afternoon (4:00 p.m. to 7:30 p.m.) trip data – number of vehicles entering and exiting the chosen sites – were documented. Then the peak-hour time and volume were computed, and finally the results were compared with the trip generation data estimated using the

Trip Generation Manual (8th Edition) published by the Institute of Transportation

Engineers (ITE). The percentage difference represented the impact of BART proximity

on the peak-hour trip generation.

This project is significant because most transportation improvements are capacity based, generally depending upon the peak-hour traffic. So, a minor change in the A.M. and P.M. peak-hour trip volumes can possibly lead to significant change in the cost and extent of the improvement projects. Also, TOAs are promoted by all levels of governments (U.S. GAO, 2009), but it remains uncertain to what extent TOAs really satisfy the planned objectives.

Chapter 2

BACKGROUND

Bay Area Rapid Transit (BART)

BART is the busiest heavy rail transit system in the western U.S. region and the fifth-busiest in the U.S. (American Public Transportation Association, 2012). It is located in the heart of the San Francisco Bay Area, connecting East Bay cities such as Fremont,

Oakland, Berkeley, Concord and Pittsburg to San Francisco and Peninsula cities such as

South San Francisco, Millbrae, Colma, and Daly City. It has five routes: Fremont to

Richmond, Fremont to Daly City, Richmond to Daly City, Dublin/Pleasanton to Daly

City, and Pittsburg/Bay Point to Millbrae, as shown in Figure 1.

On a typical weekday, BART carries about 400,000 riders along all 44 stations, including San Francisco International Airport station (BART, 2012a). Most of the BART stations have parking facilities available for riders to park their personal vehicles and commute on BART, but the parking prices depend on the geographical location. For example, the daily parking fee at the Union City station is $1 and the South San

Francisco station is $2, however it is free at the Richmond station. BART also has monthly reserved parking and single day reserved parking, varying from station to station.

To meet the transfer needs or to serve destinations not near the BART stations, transit connections are available at all stations; for instance, at the Richmond station, passengers can transfer between BART, Capitol Corridor (Amtrak train that operates between San Jose and Sacramento), Amtrak San Joaquin (train service that operates

4 between Sacramento and Oakland to Bakersfield), Alameda-Contra Costa (A.C.) Transit bus service, and the Golden Gate Transit bus service that operates between Santa Rosa,

San Rafael, and San Francisco. All BART stations have at least one transit connection.

Figure 1. BART System Map (BART, 2012b)

Transit-Oriented Apartments (TOAs)

Freeway congestion, air pollution, and suburban sprawl have forced planners and responsible authorities to discover the viable solutions. One of the main reasons behind

these three challenging issues is the land use policy implemented in U.S. for decades. The relatively lower cost of land near outskirts may encourage suburban sprawl, especially the housing, although employment generation is mostly near the central business districts, forcing suburbanites to drive longer distances to work, which contributes to congestion and air pollution. The longer commute distances on personal vehicles contribute to air pollution and freeway congestion, which results in lost time and fuel wastage. According to a U.S. Department of Treasury report, “Traffic congestion on our roads results in 1.9

billion gallons of gas wasted per year, and costs drivers over $100 billion in wasted fuel and

lost time” (U.S. Department of Treasury, 2012). To address these challenges, agencies are

considering the intertwined land use and transportation planning as a potential solution.

The key focus is probably on the land available for development or development in the

vicinity of existing public transit (e.g., rail stations, bus stops).

According to the Center For Transit-Oriented Development (CTOD), a “Transit-

oriented development, or TOD, is a type of community development that includes a

mixture of housing, office, retail and/or other commercial development and amenities

integrated into a walkable neighborhood and located within a half-mile of quality public

transportation” (CTOD, 2012a). TODs are also referred as transit villages, transit-

proximate development, or walkable neighborhoods. Center For Transit-Oriented

Development (CTOD) promotes sustainability in transportation and community

development. Since significant numbers of automobile trips are employment related,

mixed-use TODs can possibly reduce the number of trips substantially. According to the

CTOD, almost any location, even suburban, with appropriate transit service can perform

well if the employment generation areas are clustered and located in relatively dense

concentrations; the parking control is required, too (Belzer et al., 2011). It also suggests

viewing concentrated versus dispersed employment nodes, not urban versus suburban,

when employment activity is addressed.

As the name suggests, the Transit-Oriented Apartments (TOAs) are the apartment

complexes located in walking distance within a half-mile radius of a public transit

facility. From compactness perspective, TOAs are generally medium- to high-density

development projects. Not only do they help in reducing freeway congestion and air

pollution (especially greenhouse gases which are primarily held responsible for global

warming), they also lead to active and healthy lifestyle, increased public transit ridership,

and improved economic opportunities for low- to mid-income families by freeing up automobile related expenses and easier access to jobs (Reconnecting America, 2004a). In comparison to the traditional suburban single family houses, TOAs can accommodate people of all age groups and incomes since they provide choices in the type of housing

(e.g., lofts, studios, apartments) (Reconnecting America, 2004a).

The demographic changes are also forcing the modification of the land use policy and planning. By 2050, singles will outnumber typical families, and older Americans will be the new majority (Reconnecting America, 2004a). So the traditional suburban single family houses may not meet the future housing needs of single households. CTOD estimates that about 25% of house seekers, or about 15 million households, in 2030 will prefer housing near transit, requiring appropriate planning and implementation now to meet the growing demand (Reconnecting America, 2008).

Before altering the land use policy and planning practices in favor of TOAs, it is

necessary to examine the future demand for them. In the ten major metropolitan areas,

where roughly one-fourth of the U.S. population resides, the demand for TOAs will grow strongly by the middle of next decade, as shown in Figure 2, which depicts the percentage of TOAs in year 2000 and estimated percentage of TOAs in year 2025

(Reconnecting America, 2004b).

60%

% Regional housholds near transit, 2000 50% Projected % regional households near transit, 2025

40%

30%

20%

10%

Figure 2. Projected Demand for Housing Near Transit in 10 U.S. Transit Regions (Reconnecting America, 2004b)

All ten metropolitan areas, shown in Figure 2, show a strong demand for TOAs by

2025. The Los Angeles metropolitan area shows the strongest increase in demand (four times) for TOAs, followed by Dallas, Miami, Portland, and Washington, D.C. The demand in New York, Chicago, Boston, San Francisco Bay Area, and Philadelphia metropolitan areas will also grow approximately by one-and-a-half times by year 2025.

Chapter 3

METHODOLOGY

The goal of this project was to compare the observed trips generated with the ITE-

estimated trips for the selected TOAs to evaluate the impact of BART proximity on trip

generation. After selecting the qualifying TOAs, this project was divided into four parts:

1. Collect vehicle trip data collection for the selected TOAs;

2. Analyze of the collected trip data to find the peak-hour periods and volumes;

3. Compute vehicle trip estimates using the ITE Trip Generation Manual; and

4. Compare the observed trip data with the ITE-estimated trip data.

The data collection was the most critical task of this project because the results relied on its accuracy. The four tasks are explained in detail below.

Trip Data Collection

This critical and laborious task of the project was done in the morning and afternoon for all selected TOAs. The morning and afternoon data collection times were from 6:00 a.m. to 9:30 p.m. and 4:00 p.m. to 7:30 p.m., respectively, to find and capture the peak-hour volumes. Generally, peak hours fall between 7:00 a.m. and 9:00 am and between 4:00 p.m. and 6:00 p.m., but they can vary. The number of vehicles entering and exiting the TOAs, and the number of TOA residents utilizing the on-street parking, was documented in 15-minute increments. The data were collected only on Tuesday,

Wednesday, or Thursday to prevent possible introduction of unusual travel behavior.

Appendix A contains the field observed data (number of vehicle trips generated or

attracted by the selected TOAs) in quarter-hour increments. The peak-hour computations for both morning and afternoon are also shown in Appendix A tables for each selected

TOA.

The number of automobile access points for all TOAs played an important role in the data collection process. The number of access points and their locations dictated the number of data collectors needed, not the size of the TOA complexes in this project. A few TOAs required only one data collector, whereas others required two or more. For instance, the Union City TOA was the largest among selected sites, but it required only one data collector since both vehicle entrances/exits were located on the same side of the building; the on-street parking was located in between the two entrances, so one data collector could easily track and document the vehicle movements. The Downtown

Berkeley, North Berkeley, and Colma TOAs had only one vehicle entrance and limited on-street metered parking, so they only required one data collector. Walnut Creek and

Concord TOA were almost like the Union City TOA, so they only required one person in the field. The South Hayward TOA had three vehicle access points – one emergency access gate and one exit only gate on the south, and one entrance/exit on the west, but it required only one data collector because one person at the southwest corner of the building could easily capture the vehicle movements. The street parking was only available on one side, so one person could simply track and document the number of observed trips. Fremont, South San Francisco, and Hayward required two data collectors because of the vehicle entrances/exits number and location.

The availability of limited resources constrained the data collection to one day per

TOA. This study utilized data collectors who were physically present at the TOA, instead

of using automatic vehicle counters (pneumatic tubes) on the automobile access points.

Data Analysis

After the data collection task was finished, the data were analyzed to find the peak-hour period and volume for the morning and afternoon. The peak-hour and associated trips were found by adding consecutive 15-minute trip volumes, as shown highlighted in red in Appendix A. After arriving at the peak hour, the trip counts were

compared with the ITE-estimated vehicle trips.

Estimation of ITE Trips

The peak-hour vehicle trips estimated by the Trip Generation Manual served as

the benchmark for comparing the observed trips. The equations provided in the Land Use

Code 223 of the Trip Generation Manual were used to estimate the A.M. and P.M. peak-

hour trips for the selected TOAs. To compute the ITE suggested trips for the selected

TOAs, the following peak-hour equations (shown in Appendix B) were used:

T = 0.46 (X) – 14.01 (Weekday, A.M. Peak Hour of Generator)

T = 0.53(X) – 11.27 (Weekday, P.M. Peak Hour of Generator)

where, X is the number of dwelling units, and

T is the average number vehicle trip ends (i.e., trips “generated”)

Since the number of occupied apartments were known, the peak-hour A.M. and P.M. volumes were relatively easier to compute than the previous two steps. After collecting and analyzing the data from both ITE and observed method, the results were compared to discover the impact, if any, of BART proximity. The data comparison procedure is shown in the next step.

Data Comparison

After finishing the previous two steps, it was necessary to compare the trips with the ITE suggested results. Since all selected TOAs are three to five stories high, their data was compared with the mid-rise apartments, provided in the “Land Use 223” of the Trip

Generation Manual (ITE). The Trip Generation Manual, which is published by the

Institute of Transportation Engineers, estimates the number of trips that a specific land use will produce. It provides charts and equations to quickly estimate the number trips for many land uses, including residential, industrial, agricultural, institutional, office, retail, medical, and recreational. Transportation/Traffic Engineers generally refer to this manual to evaluate the impact of development or redevelopment of land. Although it was compiled after studying the trip generation behavior of many sites falling in each land use, but it does not guarantee the accuracy since all sites behave differently. It is a widely-used guideline, however. The data comparison results are provided and discussed in detail in Tables 5 and 6, and in Figures 17 through 23 of Chapter 5 (Results and

Discussion).

Chapter 4

INFORMATION ABOUT SELECTED TOAs

For this project, ten TOAs near BART stations were chosen from all regions of the BART network. The chosen TOAs were located on the Peninsula, and in the northern, eastern, and southern parts of the East Bay. Only ten TOAs were studies due to limited resources. The selected ten sites represented more than 22% of the possible BART stations (44 station). The ease with which field data could be collected and retrieved from the site played a significant role in the TOA selection. For example, apartment complexes with more than four entrances and exits were not selected due to limited number of data collectors; Archstone Walnut Creek near Pleasant Hill/Contra Costa Centre BART station and Avalon Dublin Station near Dublin/Pleasanton BART station were not chosen because both had more entrances and exits than could be managed with a limited number of data collectors.

The accuracy of this project relied on the accuracy of the TOA site information, including the total number of apartments, its occupancy rate, and the parking information.

Only those TOAs whose property managers agreed to provide the critical information (or had it available on TOA’s website) were chosen. The walking distance from the respective BART station also played a major role in selecting a TOA. The TOAs falling within a half-mile radius were selected. The TOA selection criterion was discussed in

Chapter 3 (Methodology). The following sections provide detailed information about the selected TOAs, and brief information about the cities in which selected TOAs are located.

Union City

Union City is centrally located between Oakland and San Jose. It was

incorporated in 1959 and has diverse population of just over 70,000 (City of Union City,

2012). It falls in Alameda County and shares its boundaries with Hayward on the north,

Fremont on south, and Newark on west side (Google Maps, 2012). It is well connected

with the north, south and Peninsula region of the San Francisco Bay Area through

highways and freeways, mainly I-880, I-580, I-680, CA-92, CA-84, and CA-238 (Google

Maps, 2012). The median household income of Union City is about 17% higher than

California’s average ($60,883 for Union City and $51,914 for California) (U.S. Census

Bureau, 2010). Also, the mean travel to work time is 30 minutes compared to California’s average of 26.9 minutes (U.S. Census Bureau, 2010). There are two BART routes

(Fremont to Richmond and Fremont to Daly City) that serve the Union City BART station. On a typical weekday, the daily BART ridership was more than 9,300 passengers at Union City BART station (BART, 2012a). The passenger ridership information is available for selected TOAs in Appendix C.

The TOA selected for this project was Avalon Union City located at 24 Union

Square in Union City. It was a five-story apartment complex with 443 units, including studios, one-, two-, and three-bedroom apartments. In addition to the uncovered parking spaces, the facility had one seven-story parking structure and a three-story underground parking garage. The on-street metered parking was also available between the two entrances/exits. The tenants were provided at least one parking space per unit; two for select two- and three-bedroom units. The additional parking spaces could be rented for

$45 per month for uncovered parking and $60 per month for covered parking. The site’s proximity to BART station separates it from other similar sites in the city; it was located within a walk distance of three minutes from Union City BART station. It was also well connected with other public transit agencies such as AC Transit, Dumbarton Express, and

Union City Transit. The Avalon Union City had an occupancy rate of 99% at the time of field data collection. Figure 3 shows the general appearance and parking facilities (on- and off-site) of Avalon Union City.

Figure 3. Union City – Avalon Apartments

Hayward

The city of Hayward is the third most populous city in Alameda County, after

Oakland and Fremont (U.S. Census Bureau, 2010). It is situated between Oakland and

San Jose, with Castro Valley and San Leandro on its north and Union City on its south

(Google Maps, 2012). It is home to the California State University (CSU), East Bay, and

Chabot Community College. The median household income and average travel time to

work of City of Hayward is almost same as that of the California’s average (U.S. Census

Bureau, 2010). There are two BART lines (Fremont to Richmond and Fremont to Daly

City) and two BART station stops in Hayward (South Hayward and Hayward). One TOA

was selected near each station. The daily weekday ridership at South Hayward and

Hayward BART station was more than 7,000 and 9,300 passengers, respectively (BART,

2012a).

Montelena Apartment Homes (MAH) was selected near the South Hayward

station and City Centre Apartment Homes near the Hayward station. MAH was located at

655 Tennyson Road in Hayward. It was conveniently located between CA-238 and South

Hayward BART station. This TOA was a three-story apartment complex with 189 one-

and two-bedroom apartments. It had three vehicle access points, but only west side access

was used for both entering and exiting vehicles. One of the two accesses on the south side

was emergency access which was not used by residents, and the remaining access was

used only to exit the site. Although MAH was located adjacent to the BART station and

other transit facilities, but it was still classified as being “somewhat walkable,” due to lack of other facilities within walking distance (Walk Score, 2012a). The tenants of MAH

17 were provided at least one parking space from the available covered and uncovered parking spaces. Additional parking spaces were used by residents on first-come, first- served basis. There was street parking available as well which was commonly used by

BART commuters. At the time of data collection, the occupancy rate was around 97%.

Figure 4 shows the general appearance and parking facilities (on- and off-site) of

Montelena Apartment Homes.

Figure 4. Hayward – Montelena Apartment Homes

The other Hayward TOA selected was City Centre Apartment Homes located adjacent to the Hayward BART station. City Centre Apartment Homes were located at

22800 Meridian Drive in Hayward. It was few blocks from the City Hall, shopping

centers, services, and recreational facilities. This three-story apartment complex with

total 192 one-, two-, and three-bedroom apartments had occupancy rate of about 99% at

the time of field data collection. The site had personal garages, carports, and uncovered parking available for tenants and each apartment was provided at least one parking space.

Extra parking spaces were used on first-come, first-served basis. There was street parking available on the south side of the building as well, but was found unoccupied during the data collection period. Figure 5 shows the general appearance and parking facilities (on-

and off-site) of City Centre Apartment Homes.

Figure 5. Hayward – City Centre Apartment Homes

Fremont

With population of about 217,000, the city of Fremont is the second most

populated city in Alameda County, after Oakland (U.S. Census Bureau, 2010). The city’s

median household income is about 1.5 times higher than California’s average, whereas

the mean commute time is slightly higher than California’s average (U.S. Census Bureau,

2010). It shares its borders with Milpitas, Union City and Newark (Google Maps, 2012).

There are two BART routes (Fremont to Richmond and Fremont to Daly City) that serve

the Fremont station. As of now, Fremont is the end of the BART line, but construction is

in progress to extend the line from Fremont to San Jose. On a typical weekday, the

average daily ridership was more than 17,000 passengers, almost half of which have

origin or destination of Downtown San Francisco (BART, 2012a).

The TOA selected in Fremont was Archstone Fremont, part of the famous

Archstone Apartment Communities located in more than twelve east and west coast states. Archstone Fremont was situated at 39410 Civic Center Drive in Fremont. This site was categorized as being “very walkable” (Walk Score, 2012a) and was located close to

BART, hospitals, shopping centers, recreational areas, and freeway (I-880) and highway

(CA-238). This four-story TOA had 328 one-, two-, and three-bedroom apartments with garage and uncovered parking spaces. There were retail shops available at the ground floor of the building, located right on the street. Each apartment was provided at least one parking space; additional parking spaces could be taken on first-come, first-served basis.

In addition to the BART proximity, the site was also near the Santa Clara Valley

Transportation Authority (VTA) and AC Transit bus services. The occupancy rate was

20 about 99% at the time of the field data collection. Figure 6 shows the general appearance and parking facilities (on- and off-site) of Archstone Fremont.

Figure 6. Fremont – Archstone Apartment Communities

Concord

The city of Concord is the most populous city of Contra Costa County (U.S.

Census Bureau, 2010). It is located about 35 miles northeast of San Francisco, 60 miles north of San Jose, and 70 miles southwest of Sacramento (Google Maps, 2012). The city’s median household income is about 7% higher and the mean commute time is 10%

higher than California’s average (U.S. Census Bureau, 2010). There is only one BART

route (Pittsburg/Bay Point to Millbrae) that serves the Concord station. The average daily

ridership of Concord BART station was more than 5,300 passengers (BART, 2012a).

The site selected in Concord was Park Central Apartments located at 1555

Galindo Street in Concord. It was located eight minutes by foot from the station. This

complex with 259 one-, two-, and three-bedroom apartments was categorized as being

“very walkable” (Walk Score, 2012a). The site had two vehicle entrances/exits, two

parking garages, and significant on-street parking which was frequently used by tenants.

Each apartment was provided at least one dedicated parking space, whereas additional

parking spaces were used by residents on first-come, first-served basis. At the time of

data collection, the site had an occupancy rate of about 99%. Figure 7 shows the general

appearance and parking facilities (on- and off-site) of Park Central Apartments.

Figure 7. Concord – Park Central Apartments

Colma

The town of Colma is located 10 miles south of San Francisco and about 42 miles

northwest of San Jose (Google Maps, 2012). At the Colma BART station, two routes

(Richmond to Millbrae and Pittsburg/Bay Point to Millbrae) serve the station. The average daily BART ridership at Colma station was more than 9,200 passengers, almost

two-thirds of which is directed to/from Downtown San Francisco (BART, 2012a).

The TOA selected in Colma was La Terrazza Apartments situated at 7800 El

Camino Real in Colma. La Terrazza Apartments were located adjacent to the Colma

BART station. This apartment complex with garage parking and limited time street parking had 153 one-, two-, and three-bedroom apartments. It also had few service shops including a sandwich shop and tax services on the ground floor, facing the historic El

Camino Real which runs from San Jose to San Francisco. The site had one vehicle entrance/exit but had multiple pedestrian doorways. The residents were provided at least one parking space per apartment in the garage. The TOA had on-street parking as well which was quite frequently by the residents. This TOA was considered to be “very walkable” with a walk score of 72 (Walk Score, 2012a). At the time of field data collection, the occupancy rate was about 96%. Figure 8 shows the general appearance and parking facilities (on- and off-site) of La Terrazza.

Figure 8. Colma – La Terrazza Apartments

South San Francisco

With population over 64,000, this major city in San Mateo County is located on

the San Francisco Peninsula (U.S. Census Bureau, 2010), ten miles south of San

Francisco and about 40 miles northwest of San Jose (Google Maps, 2012). South San

Francisco’s median household income is about 22% higher than California’s average, whereas mean travel time to work is 14% less than California’s average, probably due to its proximity to San Francisco – a major employment generation hub (U.S. Census

Bureau, 2010). Two BART routes (Richmond to Millbrae and Pittsburg/Bay Point to

Millbrae) serve the South San Francisco BART station. The typical daily BART ridership was more than 6,700 passengers, approximately two-thirds of which traveled between

Downtown San Francisco (BART, 2012d).

The site selected in South San Francisco was Archstone South San Francisco situated at101 McLellan Drive in South San Francisco. This TOA was a 360 one- and two-bedroom apartment complex. The building was located adjacent to the South San

Francisco BART station and on the historic El Camino Real. This four-story building had two parking garages for residents, and some street parking was available near the site which was found unoccupied on the data collection day. The residents were provided one dedicated parking space, and the few extra spaces were used on the first-come, first- served basis. It also had few service shops such as a coffee shop, bank, and wireless phone store on the ground floor at street fronts. Figure 9 shows the general appearance and parking facilities (on- and off-site) of Archstone South San Francisco.

Figure 9. South San Francisco – Archstone Apartment Communities

Walnut Creek

This city of Walnut Creek with population of 65,000 is located in the heart of

Contra Costa County (U.S. Census Bureau, 2010). It is about 25 miles northeast of San

Francisco and 47 miles north of San Jose (Google Maps, 2012). With median household

income of about $81,000, it is approximately 33% higher than California’s average;

whereas the mean travel time to work is almost same as that of California’s average (U.S.

Census Bureau, 2010). There is only one BART line (Pittsburg/Bay Point to Millbrae) that serves the Walnut Creek station. The average daily ridership was about 13,500 passengers, half of which was between Downtown San Francisco (BART, 2012d).

The apartment complex selected in Walnut Creek was Trinity House Apartment

Homes, which was located at 1812 Trinity Avenue in Walnut Creek. It was situated within a nine minute walking distance from the Walnut Creek BART station. This three- story TOA had 76 one- and two-bedroom apartments. The site was located at a walkable distance from shopping, retail, service, entertainment, and schools, raising its walk score to 82, which was second highest (after Downtown Berkeley’s Berkeleyan Apartments) among selected ten sites (Walk Score, 2012a). The residents were provided at least one dedicate parking space and additional parking spaces were used on first-come, first-

served basis. The site had on-street parking as well, which was quite frequently used. At

the time of field data collection, the occupancy rate was approximately 97%. Figure 10 shows the general appearance and parking facilities (on- and off-site) of Trinity House

Apartments.

Figure 10. Walnut Creek – Trinity House Apartment Homes

Berkeley

Home to renowned U.C. Berkeley and Lawrence Berkeley National Laboratory,

this East Bay city is located in Alameda County. It shares boundaries with Albany on the

north and Oakland and Emeryville on the south (Google Maps, 2012). With population of

about 114,000, the median household income is slightly less than California’s average,

however the number of people below poverty is significantly higher (18.9% for Berkeley and 13.7% for California) (U.S. Census Bureau, 2010). From walkability perspective, the city has the fourth highest walk score of 82 in California, after West Hollywood, Albany, and San Francisco (Walk Score, 2012a).

There are two BART stations in Berkeley – North Berkeley and Downtown

Berkeley, so TOAs near both stations were selected. The selected Berkeley TOAs did not provide any free or dedicated parking. Acton Apartments – a five-story, 71 one- and two- bedroom apartments building was selected near North Berkeley BART station. It was situated at 1370 University Avenue in Berkeley. On the ground floor, it had retail shops, including a bakery/restaurant, salon, digital signs, and a self-defense training center. The

complex had limited parking and dedicated parking spaces were provided at a monthly

rate of $100 per parking space. There was on-street parking available but it was metered,

however it could be used for overnight parking after designated hours. This TOA was

considered to be “very walkable” with a walk score of 82 (Walk Score, 2012b). The site

had 100% occupancy at the time of field data collection. Figure 11 shows the general appearance and parking facilities (on- and off-site) of Acton Apartments.

Figure 11. North Berkeley – Acton Apartments

The other apartment complex selected in Berkeley was Berkeleyan Apartments, situated at 1910 Oxford Street in Berkeley. This complex was located in the heart of downtown Berkeley, close to the Downtown Berkeley BART station. This five-story, 56 one- and two-bedroom apartment building had a café and office/retail space on the ground floor. This TOA did not have any free parking, but it had limited garage parking, which could be obtained for $150 per month per parking space. The site had limited on- street parking. At the time of data collection, this TOA had 100% occupancy rate. It had the best walk score (of 94) among all selected TOAs (Walk Score, 2012b). Figure 12

shows the general appearance and parking facilities (on- and off-site) of Berkeleyan

Apartments.

Figure 12. Downtown Berkeley – Berkeleyan Apartments

The size of selected TOAs varied from 56 for Downtown Berkeley to 443 for

Union City. The variation in the size of selected TOAs helped in observing the relationship between the size of a TOA and the impact of BART proximity on trip generation. Since the charts and equations provided by the Trip Generation Manual relied on the number of occupied apartments, it was necessary to investigate the size and occupancy rate of the selected TOAs. The number of apartments and their occupancy rate for selected apartment complexes are shown in Figure 13. The occupancy rate of all selected TOAs varies from 96% for Colma to 100% for North Berkeley and Downtown

Berkeley.

450 100%

375 99%

300 98% Occupancy

225

97% 150

96% Number Number of Occupied Apartments 75

0 95%

Figure 13. Size and Occupancy of Selected TOAs

The size of these selected TOAs – based on the total number of apartments –

varied from site to site, but all of these TOAs were three to five stories high. The

Downtown Berkeley (Berkeleyan Apartments) TOA was the smallest complex with just

56 apartments, whereas Union City (Avalon Apartments) was the largest with 443

apartments. The selected apartments were categorized under mid-rise apartments land use, as suggested by the Trip Generation Manual (ITE). The mid-rise apartments (Land

Use Code 223) are those that are three to ten stories high (ITE). Using Google Maps, the shortest walking distance and walk travel time between TOAs and the nearest BART station were determined and are shown in Table 1. All selected TOAs were within one- half mile radius of a BART station; the average walk time was less than six minutes. The

BART route map shows the selected TOAs in Figure 14.

Table 1. Distance Between Selected TOAs and the Nearest BART Stations

Selected TOAs Nearest BART Approx. Approx. Station Distance Walk Time (feet) (minutes) Union City – Avalon Apartments Union City 550 3

South Hayward – Montelena Apartments South Hayward 550 3

Hayward – City Centre Apartments Hayward 1,050 4

Fremont – Archstone Apartments Fremont 2,100 7

Concord – Park Central Concord 2,150 8

Colma – La Terrazza Colma 550 3

South San Francisco – Archstone South San 1,100 5 Apartments Francisco

Walnut Creek – Trinity House Apartments Walnut Creek 2,650 9

North Berkeley – Acton Courtyard North Berkeley 1,600 7 Apartments Downtown Berkeley – Berkeleyan Downtown 2,150 8 Apartments Berkeley

Note: Walk distances and times were estimated using Google Maps.

Figure 14. Selected TOA Locations (BART, 2012b)

Before initiating this project, it was necessary to investigate the BART ridership at the selected BART stations to learn more about the nature (e.g., work, recreational) of

the BART trips. The monthly and yearly ridership data spreadsheets were reviewed and

compared for the selected BART stations to find any abnormalities in station usage. The

typical weekday ridership varied from 6,700 passengers for South San Francisco to

27,000 passengers for Downtown Berkeley (BART, 2012a). Figure 15 shows the

BART’s average daily ridership (total and to downtown San Francisco) from selected

TOAs.

30,000

Downtown San Francisco Total Ridership 25,000

20,000

15,000

Daily Ridership Daily 10,000

5,000

Figure 15. BART’s Average Daily Ridership in September & October 2012 (BART, 2012a)

The selected TOAs were located very close, adjacent in few cases, to the BART stations, so walkability was an essential feature of selected sites. Walkability, which has health, environmental, and economic aspects, is guided by the absence or presence of amenities, infrastructure, pedestrian safety laws, traffic conditions, and accessibility

(Walk Score, 2012c). To compare the walkability of the TOAs, walk score was used.

“Walk Score is a number between 0 and 100 that measures the walkability of any

address” (Walk Score, 2012c). A walk score based on many factors, including the type of

amenity (e.g., grocery store, restaurant, bank, theater), its distance from the points of interest, pedestrian friendliness which is measured by the intersection density (number of intersections per square mile) and average block length. Higher intersection density and lower average block length are considered pedestrian friendly in computing walk score.

Points are assigned for each specific feature, total of which gives the walk score (Walk

Score, 2012d). The walkable neighborhoods reduce the commuting stress and increase

the community development. Also, people in walkable places are healthier, probably due

to the presence of amenities that facilitate or encourage walking (Walk Score, 2012c).

Walk score is divided into five categories, shown in Table 2, depending upon the number of amenities present in the walking distance. New York City, which probably has one of the highest population densities, ranks first with a walk score of 85.3 among the 50 largest U.S. cities. San Francisco and Boston rank second and third with walk scores of

84.9 and 79.2, respectively.

Walk score may be an important parameter that could potentially impact the results of this project. Since employment related and shopping trips almost make up 50%

of the total trips produced by an average household (National Household Travel Survey,

2009), BART proximity and walkability to basic amenities could affect the number of

vehicle trips produced by selected TOAs. As a result, it was necessary to compare the

walk score of a TOA with the reduction, if any, of vehicle trips generated by each TOA.

Table 2. Walk Score Scale (Walk Score, 2012c)

Walk Score Description 90-100 Walker's Paradise Daily errands do not require a car. 70-89 Very Walkable Most errands can be accomplished on foot. 50-69 Somewhat Walkable Some amenities within walking distance 25-49 Car-Dependent A few amenities within walking distance 0-24 Car-Dependent Almost all errands require a car.

The walk scores of the TOAs and their cities are shown in Table 3. The

Downtown Berkeley TOA had the highest walk score of 94, whereas South San

Francisco had the lowest walk score of 57. Table 3 also shows the average walk score for the city in which selected TOAs fall. The average walk scores of the TOA cities were in the fifties or low sixties, with the exception of Berkeley which was in the eighties.

Table 3. Selected TOAs and Their City’s Walk Scores (Walk Score, 2012a; Walk Score, 2012b)

Location TOA's Walk Score City's Average Walk Score

Union City 66 52

South Hayward 60 58

Hayward 80 58

Fremont 75 52

Concord 88 53

Colma 72 Not Available

South San Francisco 57 62

Walnut Creek 88 51

North Berkeley 82 82

Downtown Berkeley 94 82

Chapter 5

RESULTS AND DISCUSSION

After analyzing the collected trip generation data and comparing it with the data suggested in the ITE Trip Generation Manual (8th Edition), the impacts of BART proximity on selected TOAs were clear. The results did vary, however, but the overall assessment appeared to reduce the A.M. and P.M. peak-hour trips generated.

Table 4 shows the observed peak A.M. and P.M. peak-hour periods for all selected TOAs. The A.M. peak hours varied from 7:00 a.m. to 9:00 a.m., as anticipated,

however the P.M. peak-hour varied from 5:00 p.m. to 7:15 p.m. for most TOAs, except

North Berkeley.

Table 4. A.M. and P.M. Peak-Hour Periods for Selected TOAs

TOAs Peak A.M. Hour Peak P.M. Hour Union City 7:30 - 8:30 6:15 - 7:15

South Hayward 7:15 - 8:15 6:00 - 7:00 Hayward 7:00 - 8:00 6:00 - 7:00

Fremont 8:00 - 9:00 6:15 - 7:15 Concord 8:00 - 9:00 5:00 - 6:00

Colma 7:45 - 8:45 5:15 - 6:15 South San Francisco 7:30 - 8:30 5:45 - 6:45 Walnut Creek 7:00 - 8:00 5:45 - 6:45

North Berkeley 7:00 - 8:00 4:00 - 5:00 Downtown Berkeley 7:15 - 8:15 5:15 - 6:15

The first data analysis step was to find the peak A.M. and P.M. trip rate for all

selected sites and compare it with the Trip Generation Manual estimated trip generation rate. Table 5 shows the A.M. and P.M. peak rates and percent difference between the observed trip generation data and ITE-estimated trip data. Table 5 shows the A.M. and

P.M. observed and ITE-estimated number of trips generated by the specific TOA. It also compares the observed and ITE trips for both the A.M. and P.M. The comparison shown

by a positive value represented the reduction in trip generation due to BART proximity

whereas a negative number represented the increase in trip generation. For instance,

Union City showed a 22% reduction in A.M. peak-hour trip generation, whereas Fremont

showed a 5% increase in P.M. peak-hour trip generation even though it is located very

close to BART station. The grahical representation of Table 5 for both A.M. and P.M.

periods are illustrated in Figures 16 and 17.

Table 5. Number of Observed A.M. and P.M. Peak-Hour Trips vs. ITE-Estimated Peak-

Hour Trips

TOAs Number of A.M. A.M. A.M. % P.M. P.M. P.M. % Occupied Peak- Peak- Difference Peak- Peak- Difference Dwelling Hour Hour Hour Hour Units (DUs) Observed ITE Observed ITE Trips Trips Trips Trips Union City 439 146 188 22% 153 221 31%

Fremont 324 182 135 -35% 169 160 -5% South 184 71 71 -1% 67 86 22% Hayward Hayward 190 57 73 22% 79 89 12%

Concord 257 62 104 41% 72 125 42%

Colma 147 48 54 10% 45 67 32% South San 354 86 149 42% 71 176 60% Francisco Walnut 76 25 21 -19% 24 29 17% Creek North 71 7 19 62% 6 26 77% Berkeley Downtown 56 9 12 23% 7 18 62% Berkeley

Figures 16 and 17 depict the reduction in peak-hour trips for both A.M. and P.M.

for most of the TOAs. The size of the facilities did not influence the trip rate, as North

Berkeley (Acton Apartments), South San Francisco (Archstone Apartments), and

Concord (Park Central Apartments) showed maximum reduction, even though South San

Francisco and Concord are the second and fourth biggest TOAs, respectively, of the

project, whereas North Berkeley (Acton) was the smallest selected TOA. The Fremont

and Walnut Creek TOA showed higher number of trips, which was not anticipated, in the

A.M. peak-hour, however, Walnut Creek data showed reduction in P.M. peak-hour trips.

The South Hayward TOA showed neglible benefits of BART proximity in the A.M. time, but improved in the P.M. time. Both Berkeley TOAs (Acton Apartments and Berkeleyan

Apartments) were the only two sites in the project which had no dedicated free parking for the residents – only limited, paid parking was available, but North Berkeley TOA performed better than the Berkeleyan Apartments even though it was located near less walkable amenities, as shown by the Walk Score, compared to Berkeleyan Apartments.

80%

60%

40% Hour Trips -

20%

0% Reduction in A.M.Peak -20%

-40%

Figure 16. Percent Reduction in A.M. Peak-Hour Trips Compared with ITE

80%

60% Hour Trips - 40%

20%

Reduction in P.M.Peak 0%

-20%

Figure 17. Percent Reduction in P.M. Peak-Hour Trips Compared with ITE

After comparing the impact results shown in Figures 16 and 17 with the TOAs walk scores, it was found that higher walk scores did not necessarily lead to higher reduction in trips. For instance, the Fremont and Walnut Creek TOA had higher walk score than

Union City and South San Francisco TOA, but they generated higher number of trips.

The results did not conclusively suggest that a shorter distance between TOA and

San Francisco, which is a major employment generation hub and a BART ridership

attractor, lead to higher reduction in trip generation rate. However, the significantly

higher reduction in trip rate for Berkeley (North Berkeley and Downtown Berkeley) and

South San Francsico indicated higher reduction intrip generation rate, but Colma, which

to more closer to San Francisco than South San Francisco, did not strongly support it.

Also, Concord, which is quite farther than Berkeley and South San Francisco, showed

lower trip generation rate than Hayward or Union City, which are almost same distance

away from San Francisco.

The Peninsula cities – South San Francsico and Colma – performed well, but the

South San Francisco TOA resulted in significant reduction in peak-hour trips in both

A.M. and P.M. as compared to its neighbor Colma.

In the previous two graphs, the individual TOA results were discussed, but the combined effect (as shown in Figures 18 and 19) of all TOAs clearly showed that the

BART proximity did help in reducing the trip generation rate in both A.M. and P.M. hours. The solid line represents the observed trip generation data whereas the dashed line stands for the ITE-estimated trip generation data. The lower slope of solid line evidently demonstrates that the TOAs regardless of their size produce less number of peak-hour trips than ordinary, away from transit apartment complexes. Also, the trendline equation can be utilized to estimate the peak-hour trips for similar TOAs by simply inputting the

number of apartments in the equation. Also both solid and dashed line diverge, indicating

larger TOAs result in higher reduction in A.M. and P.M. peak-hour trips as compared to smaller TOAs.

200

Observed

ITE y = 0.46x - 14.01

150

y = 0.37x - 9.36

100 Peak Peak A.M. Trips

0 0 100 200 300 400 500 Number of Dwelling Units (DUs) Figure 18. Comparison of Observed A.M. Peak-Hour Trips with ITE

250

Observed ITE 200 y = 0.53x - 11.27

150

y = 0.37x - 7.82

Peak P.M.Peak Trips 100

0 0 50 100 150 200 250 300 350 400 450 500 Number of Dwelling Units (DUs)

Figure 19. Comparison of Observed P.M. Peak-Hour Trips with ITE

The graphs in Figures 18 and 19 show the impact of BART proximity on individual project TOAs, without eliminating their size factor. But since all selected

TOAs do not have identical size, it is difficult to compare the results without graphing them. The assignment of peak-hour trip rates for the number of dwelling units provides a uniform platform to compare the results. Table 6 shows the observed and ITE-estimated

A.M. and P.M. peak-hour trip rate (or trips per dwelling unit).

Table 6. Observed and ITE-Estimated Peak-Hour A.M. and P.M. Trip Rate TOA A.M. A.M. ITE P.M. P.M. ITE Observed Trip Rate Observed Trip Rate Trip Rate Trip Rate Union City 0.33 0.43 0.35 0.50

Fremont 0.56 0.42 0.52 0.50

South Hayward 0.39 0.38 0.36 0.47

Hayward 0.30 0.39 0.42 0.47

Concord 0.24 0.41 0.28 0.49

Colma 0.33 0.36 0.31 0.45 South San 0.24 0.42 0.20 0.50 Francisco Walnut Creek 0.33 0.28 0.32 0.38

North Berkeley 0.10 0.26 0.08 0.37 Downtown 0.16 0.21 0.13 0.33 Berkeley Mean of All TOAs 0.30 0.36 0.30 0.45

Standard Deviation 0.13 0.08 0.13 0.06

The observed mean trip rates for TOAs for both A.M. and P.M. were less than the

mean trip rate estimated by ITE. The percent reduction in trip rate seems to be higher in the P.M.; however the standard deviation, which represents variation of data from the average, is also slightly higher in the P.M. compared to ITE, suggesting more data variability. After conducting a t-test (95% confidence level) on the A.M. and P.M. peak-

hour trip rate to learn whether the difference between the observed and the ITE data were

statistically significant or not, it was found that the difference was not significant in the

morning (t-value = 1.222). However it was statistically significant in the afternoon (t-

value = 3.245). The two graphs (Figures 20 and 21) below show the graphical representation of Table 6.

Downtow n… A.M. ITE

North A.M. Observed Berkeley

Walnut Creek

South San …

Colma

Concord

Hayward

South Hayward

Fremont

Union City

0.00 0.10 0.20 0.30 0.40 0.50 0.60 Trip Rate

Figure 20. Comparison of Observed A.M. Peak-Hour Trip Rate with ITE

Downtown Berkeley P.M. ITE

North P.M. Observed Berkeley

Walnut Creek

South San Francisco

Colma

Concord

Hayward

South Hayward

Fremont

Union City

0.00 0.10 0.20 0.30 0.40 0.50 0.60 Trip Rate

Figure 21. Comparison of Observed P.M. Peak-Hour Trip Rate with ITE

Figure 22 shows the A.M. and P.M. peak-hour rate per dwelling unit for all TOAs combined. The lower observed peak-hour trip both in A.M. and P.M. precisely shows the positive impact of BART proximity by reducing the trip rates in comparison to the trip rate provided by ITE manual.

P.M. Peak

A.M. Peak

0.00 0.10 0.20 0.30 0.40 0.50 Trip Rate ITE Trip Rate Actual Trip Rate

Figure 22. A.M. & P.M. Peak-Hour Trip Rate for All TOAs Combined

Chapter 6

CONCLUSION

In this project, the role of Transit-Oriented Apartments (TOAs) in reducing the peak-hour A.M. and P.M. trip was observed. The selected ten TOAs were located near five BART routes. The trip generation field data were collected and compared with the

Trip Generation Manual published by the Institute of Transportation Engineers (ITE) , which is used for traffic forecasting, to compute the benefits of BART proximity on reducing personal vehicle trips.

After finishing the challenging task of selecting qualifying TOAs, the A.M. and

P.M. trip generation field data were noted, and analyzed to assess the trip reduction impact, if any. Not only were the individual TOAs compared with the ITE manual’s estimated results, the combined impact of all ten TOAs combined was compared.

The analysis suggested that the TOAs result in lower vehicle trip generation. The two TOAs in Berkeley showed different performance, although both were almost same in size and had no free or dedicated parking available, the North Berkeley TOA produced fewer trips both in the morning and afternoon compared to Downtown Berkeley. The collective results of all TOAs indicated the reduction in A.M. trips by 19% and P.M. trips by 51%.

So the TOAs appear to be serving the purpose for which they were planned. They may be reducing the congestion on the adjacent streets, possibly reducing the infrastructure needs for peak vehicle traffic demand near BART stations. They may be

54 helping in reducing air pollution and greenhouse gases emissions, by providing people opportunity to reside near BART system.

APPENDIX A

OBSERVED DATA

Field Data for Union City Data collected on: Wednesday, 12 September 2012

Entering Exiting

Time 1st 2nd 3rd 4th Total 1st 2nd 3rd 4th Total Total Interval Qtr. Qtr. Qtr. Qtr. Qtr. Qtr. Qtr. Qtr. Entering + Hour Hour Hour Hour Hour Hour Hour Hour Exiting A.M. a b c d A = e f g h B = A+B a+b+c+d e+f+g+h 6:00 - 7:00 1 1 3 1 6 10 7 15 10 42 48

6:15 - 7:15 1 3 1 4 9 7 15 10 17 49 58

6:30 - 7:30 3 1 4 3 11 15 10 17 25 67 78

6:45 - 7:45 1 4 3 7 15 10 17 25 31 83 98

7:00 - 8:00 4 3 7 4 18 17 25 31 26 99 117

7:15 - 8:15 3 7 4 4 18 25 31 26 36 118 136

7:30 - 8:30 7 4 4 14 29 31 26 36 24 117 146

7:45 - 8:45 4 4 14 7 29 26 36 24 22 108 137

8:00 - 9:00 4 14 7 3 28 36 24 22 20 102 130

8:15 - 9:15 14 7 3 9 33 24 22 20 16 82 115

8:30 - 9:30 7 3 9 0 19 22 20 16 0 58 77

P.M.

4:00 - 5:00 11 18 17 22 68 7 9 9 8 33 101

4:15 - 5:15 18 17 22 19 76 9 9 8 5 31 107

4:30 - 5:30 17 22 19 22 80 9 8 5 8 30 110

4:45 - 5:45 22 19 22 18 81 8 5 8 17 38 119

5:00 - 6:00 19 22 18 16 75 5 8 17 12 42 117

5:15 - 6:15 22 18 16 21 77 8 17 12 12 49 126

5:30 - 6:30 18 16 21 28 83 17 12 12 15 56 139

5:45 - 6:45 16 21 28 20 85 12 12 15 15 54 139

6:00 - 7:00 21 28 20 21 90 12 15 15 18 60 150

6:15 - 7:15 28 20 21 24 93 15 15 18 12 60 153

6:30 - 7:30 20 21 24 23 88 15 18 12 13 58 146

Field Data for South Hayward Data collected on: Wednesday, 19 September 2012

Entering Exiting

Time 1st 2nd 3rd 4th Total 1st 2nd 3rd 4th Total Total Interval Qtr. Qtr. Qtr. Qtr. Qtr. Qtr. Qtr. Qtr. Entering Hour Hour Hour Hour Hour Hour Hour Hour + Exiting A.M. a b c d A = e f g h B = A+B a+b+c+d e+f+g+h 6:00 - 7:00 0 2 2 3 7 2 5 4 6 17 24

6:15 - 7:15 2 2 3 1 8 5 4 6 7 22 30

6:30 - 7:30 2 3 1 2 8 4 6 7 11 28 36

6:45 - 7:45 3 1 2 3 9 6 7 11 21 45 54

7:00 - 8:00 1 2 3 8 14 7 11 21 11 50 64

7:15 - 8:15 2 3 8 3 16 11 21 11 12 55 71

7:30 - 8:30 3 8 3 3 17 21 11 12 5 49 66

7:45 - 8:45 8 3 3 6 20 11 12 5 7 35 55

8:00 - 9:00 3 3 6 1 13 12 5 7 3 27 40

8:15 - 9:15 3 6 1 1 11 5 7 3 1 16 27

8:30 - 9:30 6 1 1 0 8 7 3 1 0 11 19

P.M.

4:00 - 5:00 6 5 5 4 20 2 7 2 4 15 35

4:15 - 5:15 5 5 4 14 28 7 2 4 4 17 45

4:30 - 5:30 5 4 14 8 31 2 4 4 6 16 47

4:45 - 5:45 4 14 8 9 35 4 4 6 6 20 55

5:00 - 6:00 14 8 9 6 37 4 6 6 3 19 56

5:15 - 6:15 8 9 6 13 36 6 6 3 6 21 57

5:30 - 6:30 9 6 13 13 41 6 3 6 6 21 62

5:45 - 6:45 6 13 13 8 40 3 6 6 6 21 61

6:00 - 7:00 13 13 8 11 45 6 6 6 4 22 67

6:15 - 7:15 13 8 11 11 43 6 6 4 3 19 62

6:30 - 7:30 8 11 11 12 42 6 4 3 2 15 57

Field Data for Hayward Data collected on: Tuesday, 25 September 2012

Entering Exiting

6:15 - 7:15 0 0 1 3 4 1 2 4 17 24 28

6:30 - 7:30 0 1 3 1 5 2 4 17 11 34 39

6:45 - 7:45 1 3 1 0 5 4 17 11 9 41 46

7:00 - 8:00 3 1 0 3 7 17 11 9 13 50 57

7:15 - 8:15 1 0 3 4 8 11 9 13 5 38 46

7:30 - 8:30 0 3 4 5 12 9 13 5 3 30 42

7:45 - 8:45 3 4 5 3 15 13 5 3 8 29 44

8:00 - 9:00 4 5 3 1 13 5 3 8 15 31 44

8:15 - 9:15 5 3 1 4 13 3 8 15 9 35 48

8:30 - 9:30 3 1 4 0 8 8 15 9 0 32 40

P.M.

4:00 - 5:00 1 12 12 14 39 9 4 6 4 23 62

4:15 - 5:15 12 12 14 10 48 4 6 4 6 20 68

4:30 - 5:30 12 14 10 9 45 6 4 6 4 20 65

4:45 - 5:45 14 10 9 9 42 4 6 4 8 22 64

5:00 - 6:00 10 9 9 9 37 6 4 8 7 25 62

5:15 - 6:15 9 9 9 12 39 4 8 7 8 27 66

5:30 - 6:30 9 9 12 12 42 8 7 8 8 31 73

5:45 - 6:45 9 12 12 14 47 7 8 8 4 27 74

6:00 - 7:00 12 12 14 8 46 8 8 4 13 33 79

6:15 - 7:15 12 14 8 12 46 8 4 13 8 33 79

6:30 - 7:30 14 8 12 0 34 4 13 8 0 25 59

Field Data for Fremont Data collected on: Wednesday, 26 September 2012

Entering Exiting

6:15 - 7:15 2 5 3 3 13 3 8 16 13 40 53

6:30 - 7:30 5 3 3 7 18 8 16 13 19 56 74

6:45 - 7:45 3 3 7 5 18 16 13 19 14 62 80

7:00 - 8:00 3 7 5 9 24 13 19 14 19 65 89

7:15 - 8:15 7 5 9 13 34 19 14 19 31 83 117

7:30 - 8:30 5 9 13 17 44 14 19 31 32 96 140

7:45 - 8:45 9 13 17 28 67 19 31 32 25 107 174

8:00 - 9:00 13 17 28 20 78 31 32 25 16 104 182

8:15 - 9:15 17 28 20 15 80 32 25 16 17 90 170

8:30 - 9:30 28 20 15 0 63 25 16 17 0 58 121

P.M.

4:00 - 5:00 17 15 11 15 58 28 16 15 20 79 137

4:15 - 5:15 15 11 15 10 51 16 15 20 8 59 110

4:30 - 5:30 11 15 10 19 55 15 20 8 14 57 112

4:45 - 5:45 15 10 19 18 62 20 8 14 17 59 121

5:00 - 6:00 10 19 18 22 69 8 14 17 18 57 126

5:15 - 6:15 19 18 22 18 77 14 17 18 18 67 144

5:30 - 6:30 18 22 18 23 81 17 18 18 19 72 153

5:45 - 6:45 22 18 23 23 86 18 18 19 16 71 157

6:00 - 7:00 18 23 23 22 86 18 19 16 24 77 163

6:15 - 7:15 23 23 22 26 94 19 16 24 16 75 169

6:30 - 7:30 23 22 26 11 82 16 24 16 8 64 146

Field Data for Concord Data collected on: Wednesday, 3 October 2012

Entering Exiting

6:15 - 7:15 1 0 1 1 3 5 16 14 11 46 49

6:30 - 7:30 0 1 1 0 2 16 14 11 13 54 56

6:45 - 7:45 1 1 0 1 3 14 11 13 11 49 52

7:00 - 8:00 1 0 1 3 5 11 13 11 7 42 47

7:15 - 8:15 0 1 3 4 8 13 11 7 15 46 54

7:30 - 8:30 1 3 4 3 11 11 7 15 12 45 56

7:45 - 8:45 3 4 3 3 13 7 15 12 14 48 61

8:00 - 9:00 4 3 3 3 13 15 12 14 8 49 62

8:15 - 9:15 3 3 3 3 12 12 14 8 9 43 55

8:30 - 9:30 3 3 3 0 9 14 8 9 31 40

P.M.

4:00 - 5:00 6 5 10 7 28 2 3 9 2 16 44

4:15 - 5:15 5 10 7 15 37 3 9 2 7 21 58

4:30 - 5:30 10 7 15 9 41 9 2 7 7 25 66

4:45 - 5:45 7 15 9 8 39 2 7 7 5 21 60

5:00 - 6:00 15 9 8 14 46 7 7 5 7 26 72

5:15 - 6:15 9 8 14 10 41 7 5 7 6 25 66

5:30 - 6:30 8 14 10 15 47 5 7 6 5 23 70

5:45 - 6:45 14 10 15 7 46 7 6 5 6 24 70

6:00 - 7:00 10 15 7 8 40 6 5 6 4 21 61

6:15 - 7:15 15 7 8 9 39 5 6 4 5 20 59

6:30 - 7:30 7 8 9 0 24 6 4 5 0 15 39

Field Data for Colma Data collected on: Wednesday, 10 October 2012

Entering Exiting

6:15 - 7:15 0 0 2 1 3 3 9 3 8 23 26

6:30 - 7:30 0 2 1 1 4 9 3 8 4 24 28

6:45 - 7:45 2 1 1 2 6 3 8 4 6 21 27

7:00 - 8:00 1 1 2 0 4 8 4 6 9 27 31

7:15 - 8:15 1 2 0 2 5 4 6 9 18 37 42

7:30 - 8:30 2 0 2 4 8 6 9 18 5 38 46

7:45 - 8:45 0 2 4 6 12 9 18 5 4 36 48

8:00 - 9:00 2 4 6 2 14 18 5 4 4 31 45

8:15 - 9:15 4 6 2 1 13 5 4 4 3 16 29

8:30 - 9:30 6 2 1 0 9 4 4 3 0 11 20

P.M.

4:00 - 5:00 3 4 6 7 20 2 3 6 3 14 34

4:15 - 5:15 4 6 7 2 19 3 6 3 4 16 35

4:30 - 5:30 6 7 2 8 23 6 3 4 7 20 43

4:45 - 5:45 7 2 8 5 22 3 4 7 3 17 39

5:00 - 6:00 2 8 5 4 19 4 7 3 6 20 39

5:15 - 6:15 8 5 4 8 25 7 3 6 4 20 45

5:30 - 6:30 5 4 8 8 25 3 6 4 2 15 40

5:45 - 6:45 4 8 8 4 24 6 4 2 2 14 38

6:00 - 7:00 8 8 4 10 30 4 2 2 3 11 41

6:15 - 7:15 8 4 10 5 27 2 2 3 6 13 40

6:30 - 7:30 4 10 5 0 19 2 3 6 0 11 30

Field Data for South San Francisco Data collected on: Wednesday, 17 October 2012

Entering Exiting

6:15 - 7:15 3 2 2 3 10 15 11 15 8 49 59

6:30 - 7:30 2 2 3 2 9 11 15 8 15 49 58

6:45 - 7:45 2 3 2 4 11 15 8 15 15 53 64

7:00 - 8:00 3 2 4 5 14 8 15 15 16 54 68

7:15 - 8:15 2 4 5 5 16 15 15 16 21 67 83

7:30 - 8:30 4 5 5 4 18 15 16 21 16 68 86

7:45 - 8:45 5 5 4 2 16 16 21 16 10 63 79

8:00 - 9:00 5 4 2 7 18 21 16 10 11 58 76

8:15 - 9:15 4 2 7 3 16 16 10 11 8 45 61

8:30 - 9:30 2 7 3 0 12 10 11 8 29 41

P.M.

4:00 - 5:00 10 20 8 6 44 7 6 5 9 27 71

4:15 - 5:15 20 8 6 4 38 6 5 9 12 32 70

4:30 - 5:30 8 6 4 7 25 5 9 12 8 34 59

4:45 - 5:45 6 4 7 5 22 9 12 8 12 41 63

5:00 - 6:00 4 7 5 7 23 12 8 12 9 41 64

5:15 - 6:15 7 5 7 6 25 8 12 9 13 42 67

5:30 - 6:30 5 7 6 9 27 12 9 13 7 41 68

5:45 - 6:45 7 6 9 12 34 9 13 7 8 37 71

6:00 - 7:00 6 9 12 9 36 13 7 8 5 33 69

6:15 - 7:15 9 12 9 7 37 7 8 5 7 27 64

6:30 - 7:30 12 9 7 0 28 8 5 7 20 48

Field A.M. Data for Walnut Creek Data collected on: Thursday, 18 October 2012

Entering Exiting

6:15 - 7:15 1 1 0 2 4 1 2 3 3 9 13

6:30 - 7:30 1 0 2 2 5 2 3 3 2 10 15

6:45 - 7:45 0 2 2 4 8 3 3 2 4 12 20

7:00 - 8:00 2 2 4 2 10 3 2 4 6 15 25

7:15 - 8:15 2 4 2 0 8 2 4 6 4 16 24

7:30 - 8:30 4 2 0 1 7 4 6 4 3 17 24

7:45 - 8:45 2 0 1 1 4 6 4 3 2 15 19

8:00 - 9:00 0 1 1 1 3 4 3 2 1 10 13

8:15 - 9:15 1 1 1 1 4 3 2 1 2 8 12

8:30 - 9:30 1 1 1 0 3 2 1 2 5 8

P.M.

4:00 - 5:00 1 2 1 2 6 1 1 2 2 6 12

4:15 - 5:15 2 1 2 4 9 1 2 2 3 8 17

4:30 - 5:30 1 2 4 4 11 2 2 3 2 9 20

4:45 - 5:45 2 4 4 3 13 2 3 2 1 8 21

5:00 - 6:00 4 4 3 3 14 3 2 1 3 9 23

5:15 - 6:15 4 3 3 5 15 2 1 3 2 8 23

5:30 - 6:30 3 3 5 3 14 1 3 2 3 9 23

5:45 - 6:45 3 5 3 3 14 3 2 3 2 10 24

6:00 - 7:00 5 3 3 3 14 2 3 2 2 9 23

6:15 - 7:15 3 3 3 2 11 3 2 2 1 8 19

6:30 - 7:30 3 3 2 1 9 2 2 1 1 6 15

Field Data for North Berkeley Data collected on: Tuesday, 30 October 2012

Entering Exiting

6:15 - 7:15 0 0 0 1 1 0 0 0 1 1 2

6:30 - 7:30 0 0 1 0 1 0 0 1 0 1 2

6:45 - 7:45 0 1 0 0 1 0 1 0 2 3 4

7:00 - 8:00 1 0 0 0 1 1 0 2 3 6 7

7:15 - 8:15 0 0 0 0 0 0 2 3 1 6 6

7:30 - 8:30 0 0 0 0 0 2 3 1 1 7 7

7:45 - 8:45 0 0 0 1 1 3 1 1 0 5 6

8:00 - 9:00 0 0 1 0 1 1 1 0 0 2 3

8:15 - 9:15 0 1 0 0 1 1 0 0 1 2 3

8:30 - 9:30 1 0 0 0 1 0 0 1 0 1 2

P.M.

4:00 - 5:00 0 2 1 0 3 1 2 0 0 3 6

4:15 - 5:15 2 1 0 0 3 2 0 0 0 2 5

4:30 - 5:30 1 0 0 1 2 0 0 0 0 0 2

4:45 - 5:45 0 0 1 1 2 0 0 0 0 0 2

5:00 - 6:00 0 1 1 1 3 0 0 0 0 0 3

5:15 - 6:15 1 1 1 0 3 0 0 0 0 0 3

5:30 - 6:30 1 1 0 0 2 0 0 0 1 1 3

5:45 - 6:45 1 0 0 1 2 0 0 1 1 2 4

6:00 - 7:00 0 0 1 1 2 0 1 1 0 2 4

6:15 - 7:15 0 1 1 0 2 1 1 0 0 2 4

6:30 - 7:30 1 1 0 0 2 1 0 0 0 1 3

Field Data for Downtown Berkeley Data collected on: Tuesday, 30 October 2012

Entering Exiting

6:15 - 7:15 1 0 1 0 2 0 1 0 0 1 3

6:30 - 7:30 0 1 0 2 3 1 0 0 2 3 6

6:45 - 7:45 1 0 2 2 5 0 0 2 1 3 8

7:00 - 8:00 0 2 2 0 4 0 2 1 1 4 8

7:15 - 8:15 2 2 0 0 4 2 1 1 1 5 9

7:30 - 8:30 2 0 0 0 2 1 1 1 0 3 5

7:45 - 8:45 0 0 0 2 2 1 1 0 0 2 4

8:00 - 9:00 0 0 2 1 3 1 0 0 0 1 4

8:15 - 9:15 0 2 1 0 3 0 0 0 0 0 3

8:30 - 9:30 2 1 0 0 3 0 0 0 0 0 3

P.M.

4:00 - 5:00 0 1 0 0 1 0 0 1 0 1 2

4:15 - 5:15 1 0 0 1 2 0 1 0 0 1 3

4:30 - 5:30 0 0 1 2 3 1 0 0 2 3 6

4:45 - 5:45 0 1 2 0 3 0 0 2 1 3 6

5:00 - 6:00 1 2 0 0 3 0 2 1 0 3 6

5:15 - 6:15 2 0 0 2 4 2 1 0 0 3 7

5:30 - 6:30 0 0 2 0 2 1 0 0 1 2 4

5:45 - 6:45 0 2 0 1 3 0 0 1 0 1 4

6:00 - 7:00 2 0 1 0 3 0 1 0 1 2 5

6:15 - 7:15 0 1 0 1 2 1 0 1 0 2 4

6:30 - 7:30 1 0 1 0 2 0 1 0 0 1 3

APPENDIX B

TRIP RATE FOR MID-RISE APARTMENTS

Average A.M. Peak Hour Vehicle Trips (ITE)

Average P.M. Peak Hour Vehicle Trips (ITE)

APPENDIX C

BART RIDERSHIP

Weekday Ridership in September 2012 (BART, 2012a)

BART Station Ridership between Total Ridership Downtown SF % Station & of Total Ridership Downtown San Francisco

North Berkeley 4,301 8,977 48%

Berkeley 7,428 27,112 27%

Hayward 3,035 10,234 30%

South Hayward 2,691 7,001 38%

Union City 4,129 9,122 45%

Fremont 7,906 16,752 47%

Concord 5,185 12,115 43%

Walnut Creek 6,864 13,360 51%

Colma 5,915 9,052 65%

South San 4,252 6,566 65% Francisco

Total 51,706 120,292 43%

Weekday Ridership in October 2012 (BART, 2012a)

BART Station Ridership between Total Ridership Downtown SF % Station & of Total Ridership Downtown San Francisco

North Berkeley 4,495 9,193 49%

Berkeley 7,511 27,020 28%

Hayward 3,230 11,402 28%

South Hayward 2,766 7,143 39%

Union City 4,356 9,532 46%

Fremont 8,399 17,475 48%

Concord 5,504 12,502 44%

Walnut Creek 7,126 13,657 52%

Colma 6,278 9,398 67%

South San 4,552 6,926 66% Francisco

Total 54,218 124,250 44%

Average Weekday Ridership in September 2012 and October 2012 (BART, 2012a)

BART Station Ridership between Total Ridership Downtown SF % Station & of Total Ridership Downtown San Francisco

North Berkeley 4,398 9,085 48%

Berkeley 7,469 27,066 28%

Hayward 3,133 10,818 29%

South Hayward 2,728 7,072 39%

Union City 4,242 9,327 45%

Fremont 8,152 17,114 48%

Concord 5,344 12,309 43%

Walnut Creek 6,995 13,509 52%

Colma 6,097 9,225 66%

South San 4,402 6,746 65% Francisco

Total 52,962 122,271 43%

REFERENCES

1. American Public Transportation Association (2012). Ridership Report (Second

Quarter 2012). (2012, August 14). Accessed 16 November 2012 at

http://www.apta.com/resources/statistics/Documents/Ridership/2012-q2-ridership-

APTA.pdf

2. BART (2012a). Monthly Ridership Reports. September 2012 & October 2012.

Accessed 8 November 2012 at http://bart.gov/about/reports/ridership.aspx

3. BART (2012b). Station List. Accessed 8 November 2012 at

http://bart.gov/stations/index.aspx

4. Belzer, D., Srivastava, S., Wood, J., & Greenberg, E. (2011, May). Transit-

Oriented Development (TOD) and Employment. Accessed 3 November 2012 at

http://ctod.org/pdfs/2011TOD-Employment.pdf

5. City of Union City (2012). About Union City: Facts & Figures. Accessed 24

October 2012 at http://www.ci.union-city.ca.us/about_uc.html

6. CTOD (Center For Transit-Oriented Development) 2012a. Frequently Asked

Questions. Accessed 4 November 2012 at http://ctod.org/faqs.php

7. Google Maps (2012). Accessed 5 November 2012 at https://maps.google.com/

8. Inflation Calculator (2012). US Inflation Calculator. Accessed 5 November 2012

at http://www.usinflationcalculator.com/

9. ITE (Institute of Transportation Engineers). Trip Generation Manual (8th Edition)

(2008).

10. National Household Travel Survey (2009). Summary of Travel Trends. Accessed

26 November 2012 at http://nhts.ornl.gov/2009/pub/stt.pdf

11. Reconnecting America (2004a). Why Transit-Oriented Development And Why

Now. (2007, March 28). Accessed 1 November 2012 at

http://ctod.org/pdfs/tod101.pdf

12. Reconnecting America (2004b). Hidden In Plain Sight - Capturing The Demand

For Housing Near Transit. (2004, September). Accessed 1 November 2012 at

http://www.reconnectingamerica.org/assets/Uploads/2004Ctodreport.pdf

13. Reconnecting America (2008). Center for TOD Demand Estimate Update. (2008,

February 13). Accessed 4 November 2012 at

http://www.reconnectingamerica.org/resource-center/books-and-

reports/2008/center-for-tod-demand-estimate-update/

14. U.S. Census Bureau (2010). State & County QuickFacts. Accessed 1 November

2012 at http://quickfacts.census.gov/qfd/states/06000.html

15. U.S. GAO (United States Government Accountability Office) (2009). Affordable

Housing In Transit-Oriented Development. Accessed 26 November 2012 at

http://www.gao.gov/new.items/d09871.pdf

16. Walk Score (2012a). Get Your Score. Accessed 1 November 2012 at

http://www.walkscore.com/

17. Walk Score (2012b). Cities in California. Accessed 1 November 2012 at

http://www.walkscore.com/CA

18. Walk Score (2012c). What is walkability? Accessed 1 November 2012 at

http://www.walkscore.com/live-more/

19. Walk Score (2012d). Walk Score Methodology? Accessed 1 November 2012 at

http://www.walkscore.com/methodology.shtml