SMART GROWTH TRIP GENERATION OF COFFEE SHOPS
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
Divya Elizabeth Jacob
SUMMER
2013
SMART GROWTH TRIP GENERATION OF COFFEE SHOPS
A Project
by
Divya Elizabeth Jacob
Approved by:
__________________________________, Committee Chair
Kevan Shafizadeh, Ph.D., P.E., PTP, PTOE
____________________________
Date
ii
Student: Divya Elizabeth Jacob
I certify that this student has met the requirements for format contained in the University format
manual, and that this project is suitable for shelving in the Library and credit is to be awarded for
the project.
__________________________, Graduate Coordinator
Matthew Salveson, Ph.D., P.E.
Department of Civil Engineering
iii
___________________
Date
Abstract
of
SMART GROWTH TRIP GENERATION OF COFFEE SHOPS
by
Divya Elizabeth Jacob
Smart growth developments are high density developments which have a mix of land
uses like residential, retail, commercial for example, on the same location thereby providing good
potential for interaction between them. Due to the proximity of these different land uses, there
exists convenience for other modes of transport like walking, biking and the use of transit. The
ITE Trip Generation manual, which has been the traditional source of trip generation data, is
mostly based on suburban dispersed study sites where there is limited potential for using other
modes of transport other than automobile. Therefore, relying on the conventional ITE Trip
Generation manual for estimating trip generation rates for smart growth areas do not produce
accurate results. Using suburban development trip rates for smart growth settings might
overestimate the trip generation rates resulting in more transportation infrastructure than required.
This project focuses on establishing the trip generation rates for smart growth settings in
California focusing on the land use coffee shops and creates a mode share database of the
different trips generated. The total vehicle trips from this study are compared with standard ITE
trips to see if there are any differences in the rates.
iv
The methodology adopted in this study utilizes the same methodology adopted by
Caltrans for the study on trip generation in urban infill areas and by UC Davis for the study on
smart growth trip generation in California. The methodology uses a combination of intercept
survey and in and out door counts to determine the trip generation rates of coffee shops. This
methodology counts the trips generated by walking, biking and transit in addition to auto trips. It
also helps in calculating the auto trip generation which uses shared parking and off-street parking
which is common in smart growth sites.
Six coffee shops in and around San Jose, California were chosen as study sites, and data
were collected in May 2013. The responses from intercept surveys were combined with door
count data to estimate the peak-hour trips along with the mode share during the peak hour.
Results reveal that on an average, ITE overestimates peak-hour trips by 34% during the AM peak
hour and by 18% during the PM peak hour at smart growth coffee shops. There is also a
noticeable contribution of trips by other modes such as walk (24%), bike (2%) and transit (2%).
As smart growth developments are becoming more and more popular, future studies with more
sites focusing on a particular land use is recommended to gather more identified trip generation
results of smart growth sites.
_______________________, Committee Chair
Dr. Kevan Shafizadeh, P.E., PTP, PTOE
_______________________
Date
v
ACKNOWLEDGEMENTS
I am thankful to God and my family including my mother Usha Jacob, my sisters and my in-laws
for their continuous prayers and support during my studies. A big thanks to my lovely daughter
Rheanna for being so adjusting and patient with me at all times and for allowing me time away
from her to complete this project. I extend my very special thanks to my husband Anit Kuruvilla,
for his tremendous and invaluable support and motivation throughout and also for his
involvement in data collection for this project. In addition, I would like to acknowledge and
appreciate my late father, K.T. Jacob, who always kept faith in me and encouraged me in all paths
of my life. I am also thankful to my uncle Mathew Jacob and my aunt Baby Jacob for their
valuable support in taking care of my daughter in my busy schedules while pursuing my project. I
would also like to thank my uncle Isaac Mathews and my cousin Sony Jacob for accompanying
me to help with the data collection.
I am extremely grateful to my advisor Dr. Kevan Shafizadeh, the department chair of the Civil
Engineering Department at California State University, Sacramento (CSUS) for his valuable
guidance and timely support and advice for this project. I am thankful to all the store managers
and property managers of Starbuck’s and Peet’s coffee for allowing me to collect data at coffee
shops in San Jose, California for this project. I would also like to acknowledge and thank all the
Civil Engineering Department faculty and staff at the California State University, Sacramento. I
appreciate the dedication and patience of the staff including Neysa Bush, whose help was
instrumental to this project. Thanks to all, those who directly or indirectly helped me complete
this project.
vi
TABLE OF CONTENTS
Page
Acknowledgements ......................................................................................................................... vi
List of Tables .................................................................................................................................. ix
List of Figures .................................................................................................................................. x
Chapter
1. INTRODUCTION ....................................................................................................................... 1
1.1 Background ........................................................................................................................... 1
1.2 Problem Description ............................................................................................................. 2
1.3 Purpose of Study ................................................................................................................... 3
2. LITERATURE REVIEW ............................................................................................................ 4
2.1 Trip Generation Definition and Uses .................................................................................... 4
2.2 Related Trip Generation Studies ........................................................................................... 5
2.2.1 Caltrans Study on Urban Infill Land Uses in California ............................................... 5
2.2.2 San Diego Association of Governments Smart Growth Trip
Generation / Parking Study .......................................................................................... 5
2.2.3 U.C. Davis Study on California Smart Growth Trip Generation Rates Study .............. 5
2.3 The Traditional ITE Method and Its Limitations .................................................................. 5
3. DATA COLLECTION ................................................................................................................ 7
3.1 Site Selection ........................................................................................................................ 7
3.2 Data Collection Methodology ............................................................................................... 8
3.2.1 Door Counts .................................................................................................................. 8
3.2.2 Intercept Surveys ........................................................................................................... 8
3.2.3 Time periods for data collection.................................................................................... 9
vii
3.3 Additional Information ......................................................................................................... 9
3.4 Overview of Survey Sites ................................................................................................... 10
4. DATA ANALYSIS .................................................................................................................... 12
4.1 Data Analysis Steps ............................................................................................................ 12
5. RESULTS .................................................................................................................................. 16
5.1 Mode Share of Peak Hour Person Trips .............................................................................. 16
5.2 Comparison of Observed Trips with ITE Trips .................................................................. 18
6. CONCLUSIONS........................................................................................................................ 21
7. LIMITATIONS .......................................................................................................................... 22
8. RECOMMENDATIONS ........................................................................................................... 23
Appendix A. Data Summaries of Coffee Shop Sites ..................................................................... 24
Appendix B. Door Count Form ..................................................................................................... 56
Appendix C. Intercept Survey Questionnaire ................................................................................ 58
References ...................................................................................................................................... 60
viii
LIST OF TABLES
Tables
Page
Table 1 The Alameda Mode Share Calculation ............................................................................ 14
Table 2 AM and PM Peak Hour Mode Share ............................................................................... 17
Table 3 Observed Peak- Hour Vehicle Trips vs. ITE Vehicle Trips............................................. 19
Table 4 Rio Robles Door Count Data ........................................................................................... 25
Table 5 Observed Peak Hour Trip Generation (Site: Rio Robles) ................................................ 29
Table 6 Civic Center Door Count Data ......................................................................................... 30
Table 7 Observed Peak Hour Trip Generation (Site: Civic Center) ............................................. 35
Table 8 The Alameda Door Count Data ....................................................................................... 36
Table 9 Observed Peak Hour Trip generation (Site: The Alameda) ............................................. 40
Table 10 Technology Drive Door Count Data .............................................................................. 41
Table 11 Observed Peak Hour Trip Generation (Site: Technology Drive) .................................. 45
Table 12 River Oaks Door Count Data ......................................................................................... 46
Table 13 Observed Peak Hour Trip Generation (Site: River Oaks).............................................. 50
Table 14 Rivermark Door Count Data .......................................................................................... 51
Table 15 Observed Peak Hour Trip Generation (Site: Rivermark)............................................... 55
Table 16 Door Count Form ........................................................................................................... 57
ix
LIST OF FIGURES
Figures
Page
Figure 1 Comparison between Observed Vehicle Trips and ITE Vehicle
Trips-AM Peak-Hour ..................................................................................................... 20
Figure 2 Comparison between Observed Vehicle Trips and ITE Vehicle
Trips-PM Peak-Hour ..................................................................................................... 20
Figure 3 Site Location: 31 Rio Robles East, San Jose, CA........................................................... 25
Figure 4 Rio Robles AM Peak Hour ............................................................................................. 28
Figure 5 Rio Robles PM Peak Hour ............................................................................................. 28
Figure 6 Site Location: 39370 Civic Center Drive Fremont, CA ................................................. 30
Figure 7 Civic Center AM Peak Hour .......................................................................................... 34
Figure 8 Civic Center PM Peak Hour ........................................................................................... 35
Figure 9 Site Location: 1338 The Alameda, San Jose, CA ........................................................... 36
Figure 10 Alameda AM Peak Hour .............................................................................................. 39
Figure 11 Alameda PM Peak Hour ............................................................................................... 39
Figure 12 Site Location: 1759 Technology Drive, San Jose, CA ................................................. 41
Figure 13 Technology Drive AM Peak Hour................................................................................ 44
Figure 14 Technology Drive PM Peak Hour ................................................................................ 44
Figure 15 Site Location: 670 River Oaks Parkway, San Jose CA ................................................ 46
Figure 16 River Oaks AM Peak Hour ........................................................................................... 49
Figure 17 River Oaks PM Peak Hour ........................................................................................... 49
Figure 18 Site Location: 3932 Rivermark Plaza, Santa Clara, CA ............................................... 51
Figure 19 Rivermark AM Peak Hour ............................................................................................ 54
x
Figure 20 Rivermark PM Peak Hour ............................................................................................ 54
xi
1
Chapter 1
INTRODUCTION
1.1 Background
Under CEQA (California Environmental Quality Act), as part of the approval process, all state
and local agencies are required to identify the significant environmental impacts of proposed
projects and to avoid or mitigate those impacts, if feasible. As part of this approval process,
public agencies require developers to submit Traffic Impact Analysis (TIA) reports which
evaluate the environmental impacts of proposed projects. The first step towards preparing a TIA
is to estimate the number of trips generated by the development. The primary source of such trip
generation data for planners and engineers has been the ITE Trip Generation manual. As
mentioned in the ITE Trip Generation Handbook, the ITE trip generation data is collected
primarily from conventional isolated, low density, suburban developments where there is limited
transit, bicycle or walking facilities (ITE, 2004). In the case of more dense and compact smart
growth developments, the mix of land uses like offices, residential, and retail, facilitate and
promote other modes of transport like walking, bicycling and use of public transit. Because the
different land uses lie in proximity to each other in smart growth sites, the interaction between
such land uses is more likely. Hence many trips generated in a mixed use development, might end
up internally, thus reducing trips on the adjacent streets. Due to these factors, estimating trip
generation rates of smart growth areas by just adding up the trip rates of individual uses is not
advisable. ITE has warned that the use of ITE data in such cases need not be accurate. It could
lead to an overestimation of automobile trips resulting in an over-prediction of traffic impacts,
2
which would eventually result in providing more transportation infrastructure than what may be
required.
This project focuses on estimating trip generation rates for the land use-coffee shops (ITE land
use code 936 in the Trip Generation manual, Eighth Edition) in smart growth developments (ITE
2008). This project also establishes the mode share of trips generated at such sites.
1.2 Problem Description
The ITE Trip Generation manual has been the primary source of trip generation rates for
engineers and planners. However as mentioned above, the data reflected in the ITE manual is
based on sites where there is limited pedestrian activity and transit proximity. There are also a lot
of other factors which affect trip generation in urban smart growth areas like density of the area,
diversity, walkability and development compactness. ITE Trip Generation manual does not take
into account any of these factors. The increased demand of smart growth developments in
California coupled with the limitations of ITE mentioned above, pose significant challenges when
it comes to contemporary travel demand analysis. Although recently there have been a few
number of other research studies pertaining to trip generation and internal trip capture (Kimley
Horn & Associates 2009; San Diego Association of Governments 2010; UC Davis ITS 2012)
more data points are still required to make a substantial statistical analysis. Planners and
engineers still require reliable data on trip generation rates to accurately assess the impacts of
developments in transportation systems.
3
1.3 Purpose of Study
The primary goals of this project is to develop trip generation rates for coffee shops in smart
growth settings in California and to create a mode share database of the different trips generated.
The vehicle trips from this study are compared with standard ITE trips to see if there are any
differences. The data from this study and other future trip generation studies are expected to help
engineers and planners with future trip generation analysis in coffee shops in smart growth areas
and better understand the difference between traditional ITE trip generation estimates and trip
generation estimates at sites with smart growth characteristics.
4
Chapter 2
LITERATURE REVIEW
2.1 Trip Generation Definition and Uses
Trip generation is the first step in the conventional four-step travel forecasting process. It is
defined as the number of trips that are coming in and going out of a development project. The
main uses of trip generation data includes approval of proposed developments, preparation of
traffic impact analyses and implementation of traffic impact fees.
In California, under CEQA all state and local agencies are required to analyze and disclose all
potential environmental impacts of proposed projects and mitigate the significant negative effects
of the project if any. This analysis is mandatory for the approval of all projects under CEQA. The
first step in the analyses is to calculate the trips generated from the proposed development. Hence
trip generation plays a crucial role in approval or non-approval of such projects (Bass et al. 1999).
A Traffic Impact Analysis (TIA) is a study that evaluates the impacts of a proposed development
on the transportation system in the immediate proximity of the development. It provides
information on the projected traffic expected from a proposed development. The first step in
estimating this projected traffic is by calculating the trip generation rates of the development
(Bass et al. 1999).
Traffic impact fees are fees that are being charged on new developments to pay for the new
infrastructure necessitated by new developments. Impact fees are used to account for the travel
demand added by new development. The fee shall be used to pay costs associated with the
5
reduction of traffic impacts that is caused by the development that is requiring approval. This fee
is calculated based on the number of trips generated by the development (Bass et al. 1999).
2.2 Related Trip Generation Studies
2.2.1 Caltrans Study on Urban Infill Land Uses in California: This study focused on studying
travel characteristics of infill development in California’s metropolitan areas. The specific goals
of this research were to develop trip generation rates for common infill land use categories in
urban areas of California, to build a California urban infill land use trip generation database, and
supplement ITE trip generation data (Kimley Horn & Associates 2009).
2.2.2 San Diego Association of Governments Smart Growth Trip Generation/Parking
Study: This study focused on estimating trip generation for smart growth developments. The
study designed a spreadsheet tool which applies mixed use method to determine trip generation
and parking demand for smart growth sites. The findings are intended to be a resource for local
agencies in the San Diego region as they implement smart growth (SANDAG 2010).
2.2.3 U.C. Davis Study on California Smart Growth Trip Generation Rates Study: This
study focused on establishing multimodal trip generation rates for smart growth developments.
The findings, along with trip-rates data from other studies were used to develop a method
embedded in a spreadsheet tool that can be used to adjust available trip-generation rates for
“smart growth” land use projects proposed in California (UC Davis ITS 2012).
2.3 The Traditional ITE Method and Its Limitations
The standard procedure adopted by ITE to estimate the trip rates is explained in the ITE Trip
Generation Handbook. It involves taking manual or automatic traffic counts at the access points
6
of the study site. This method is reliable for isolated stand-alone sites with dedicated parking. But
in the case of smart growth developments, people may use street parking or off-site parking and
then walk to the coffee shop. Another drawback of this method is that it does not take into
account the people who reached the site by other modes like transit, bike or walk. Due to these
limitations, applying the conventional approach to smart growth/ mixed use developments may
not derive accurate results (Kimley Horn & Associates 2009).
7
Chapter 3
DATA COLLECTION
This section explains the different criteria used for the identification and selection of sites and the
methodology adopted for data collection. The data collection approach adopted in this study uses
the same approach that has been established in previous studies on trip generation (UC Davis ITS
2012; Kimley Horn & Associates 2009) in estimating trip generation. A combination of intercept
survey responses and daily in and out door counts are used to determine the number of trips
generated and the mode choices of coffee shop users.
3.1 Site Selection
The basic criterion for site selection was that it should fulfill some qualifying characteristics of
smart growth development. There is no specific definition for smart growth. In general, smart
growth developments are compact urban areas where there is a mix of at least two or three land
uses like offices, residential, and retail. Due to the proximity of these land uses, there is
convenience for using other modes of transport like walking, bicycling and transit. Hence the
coffee shop locations selected for this study were high-density, compact developments in the
urban areas of San Jose and had good potential for interaction among at least three land uses:
residential, offices, and retail.

Another qualifying factor was transit proximity. The site should be within 1/4 mile of a
transit service.

There should be good potential for walking with sidewalks on both sides of the street and
bicycle accessibility to and from coffee shops to other land uses.
8

The site should not be completely new or reached its mature stage. All sites chosen for
study were at least two years old.

The area within half a mile radius of the site should be well-developed and mostly
occupied.
3.2 Data Collection Methodology
This study adopted a methodology which uses a combination of exit intercept surveys and in and
out door counts at the study sites. The intercept surveys collected travel characteristics in terms
of the modes of transport used by coffee shop customers which was then combined with the
pedestrian counts to estimate the automobile trip generation rates for peak hours of traffic. Data
were collected at six different coffee shops in smart growth sites in and around the city of San
Jose, California.
3.2.1 Door Counts: All people entering and exiting the coffee shop were counted. This count
provided the total number of person-trips generated at each study location during the morning
and afternoon peak period. The door counts were taken in five minute increments to make the
calculation easier and simpler. Recording door counts in five minute durations versus longer
intervals allows a more accurate representation of hourly peaking patterns. A sample of the door
count form developed by the UC Davis study (2012) is given in Appendix B.
3.2.2 Intercept Surveys: In person intercept surveys were collected from a sample of the coffee
shop customers. The customers were approached as they exited the coffee shops. The results of
these sample surveys were applied on the total population. Intercept surveys collected
information on the user’s primary mode of travel to and from the coffee shop and the gender of
9
the coffee shop user. A sample of the intercept survey questionnaire modified from the U.C
Davis study (2012) is included in Appendix C.
3.2.3 Time periods for data collection: The intercept survey was conducted during the peak
hours of adjacent street traffic (7:00 to 9:00 AM and 4:00 to 6:00 PM) on typical weekdays
(Tuesdays through Thursdays). Trip generation rates were computed for the “peak hour of
adjacent street traffic” which is one hour between the two hour study periods (7:00 a.m. to 9:00
a.m. and between 4:00 p.m. and 6:00 p.m.) which has the maximum people counts.
3.3 Additional Information
In addition to the above pieces of data, further information was required to determine the trip
generation which includes the total number of person trips, average vehicle occupancy, and the
size of the coffee shop (the independent variable). The total number of people entering and
exiting the site (person trips) during the study period was required. All access points need not be
surveyed as the intercept survey is done for random sampling but all persons entering or exiting
through all access points need to be counted to get the total population size. The intercept surveys
were conducted on a random sample of people accessing the site during the study period.
Questions were asked to determine what mode share was used by people accessing the shop,
whether the trip was a pass by trip or a linked/multi-purpose trip and what the average vehicle
occupancy was. The independent variable used to calculate trip rate for coffee shops is consistent
with the variable used in the ITE Trip Generation manual. ITE Trip Generation manual
recommends that gross leasable square footage area should be selected as the independent
variable for commercial and retail purposes. In this case, the square footage was provided by the
store manager or the property manager of each coffee shop.
10
3.4 Overview of Survey Sites
Starbucks at Rio Robles: This coffee shop is located at 31 Rio Robles East in San Jose,
California. The VTA Express Shuttle service is available within a distance of 220 feet, providing
15 minute headways and hence meeting the transit proximity criteria. The coffee shop is 1275
square feet. The parking is on-street and the location is surrounded by residential and office land
uses. The crosswalks are marked at the nearby intersections and there are sidewalks on both sides
of the street. The data at this site were collected on May 07, 2013.
Starbucks at Civic Center: This coffee shop is located at 39370 Civic Center Drive in Fremont,
California. The nearest transit station is 133 feet away and express bus services are available at
frequent intervals. The surrounding land uses include a mix of medical, office and residential.
The coffee shop is 1550 square feet and it has two entrance doors. The upper floors of this
building contain residential uses. The walkability of this area is high with marked crosswalks at
nearby intersections and sidewalks on both sides of the street. The data at this site were collected
on May 09, 2013.
Starbucks at The Alameda: This coffee shop is located at 1338 The Alameda in San Jose,
California and is part of The Alameda Development. The size of the coffee shop is 1568 square
feet. The nearest transit station is at 214 feet and regular and limited stop bus services are
available providing 15 minute headways. The location is surrounded by office, services and
residential land uses. The coffee shop has two entrance doors with one being street-level entry
and the other being parking lot entry, sidewalks on both sides of the street, and a signalized
crosswalk. The data at this site were collected on May 14, 2013.
11
Starbucks at Technology Drive: This coffee shop is located at 1759 Technology Drive in San
Jose, California. The coffee shop is 1496 square feet. The surrounding land uses include a mix of
office and services. The nearest transit station is at a distance of 493 feet and the service is
provided with 15 minute headways. The adjacent street has marked crosswalks at intersections
and sidewalks on both sides of the street. The data at this site were collected on May 15, 2013.
Starbucks at River Oaks Pkwy: This Starbucks building is located at 670 River Oaks Parkway
San Jose, California and is part of The Elan Development. The nearest transit station is at a
distance of 226 feet and provides limited stop bus services and ACE light rail shuttle services at
frequent intervals. The size of the coffee shop is 1000 square feet. This coffee shop has only one
entrance door and the surrounding mix of land uses include residential, office and commercial.
The upper floors of this building contain residential uses. This coffee shop is served by a free,
shared parking lot. The data at this site were collected on May 21,2013.
Peet’s at Rivermark Plaza: This coffee shop is located at 3932 Rivermark Plaza in Santa Clara,
California and is part of the Rivermark Village shopping center. The nearest transit station is
located at a distance of 500 feet, and limited stop bus services are available. The size of the coffee
shop is 2003 square feet. The store has two entrance doors. The Rivermark Village shopping
center has more than 30 stores with free, shared parking. The surrounding land uses include a mix
of services, office and residential. The data at this site were collected on May 22, 2013.
12
Chapter 4
DATA ANALYSIS
This chapter discusses the different steps involved in analyzing the door count data and the
survey data to estimate the trip generation rate for the peak hour. The peak hour is determined
through door counts and the responses from surveys are utilized to estimate the trip generation
and mode share of study sites.
4.1 Data Analysis Steps
1. Determine the total number of peak hour person trips generated by the coffee shop.
The total number of people entering and exiting the coffee shop was counted over five minute
intervals between 7 a.m. to 9 a.m. and 4 p.m. to 6 p.m. The counts thus obtained were summed up
to get the total number of person trips. From the total person trips, the peak hour and the peak
hour person trips were determined.
2. Determine the trip mode share.
Intercept surveys give information on the travel behavior of respondents. People were asked on
what mode of transport they used from their previous location to come to the coffee shop. The
mode choices were auto, walk, bike and transit. The number of people in the vehicle was also
noted, if automobile was the chosen mode. The proportion of trips by each mode was determined.
Thus survey responses provided details on the mode share, percentage of trips and the average
vehicle occupancy.
13
3. Allocate peak hour person trips by mode.
The intercept surveys give the mode share percentage of each trip. Using these data with the door
count data for the peak hour, the number of peak hour person trips by each mode can be
determined.
4. Calculate total observed automobile trips.
Total person trips in the peak hour multiplied by the percentage of auto trips gives the total
automobile person trips during the peak hour. Automobile person trips during the peak hour are
then divided by the average vehicle occupancy to get the number of automobile trips generated.
Example of Data Analysis at The Alameda
The following example is used to explain the different steps involved in calculating the trip
generation during the morning peak hour. There are two entrance doors at The Alameda which
serves the customers of the coffee shop. The surveyor randomly interchanged between the two
doors to facilitate equal proportion.
a) Calculate the total number of peak hour person trips generated by the coffee shop.
All people coming in and going out of both the doors were counted between 7:00 a.m. and 9:00
a.m. in the morning. In total, 285 inbound and outbound trips were recorded at both doors during
two hours of data collection. The total trips during each hour were summed up to get the peak
hour. The peak hour fell between 7:20 and 8:19 which recorded a total of 171 trips.
14
b) Determine the trip mode share.
Intercept survey responses were used to determine the percentage of trips made by each mode. Of
the 26 trips that were made during the two hour study period, 22 (85%) were auto trips, 3 (11%)
were walk trips, and 1 (4%) were transit trips. Of these surveyed trips, 54% were made by male
and 46% were made by female. The percentage of male and female trips observed in actual door
counts was 44% and 56%, respectively. Hence while calculating the total trip mode share, to
avoid any gender bias, the male survey trips were weighted by 81% (44/54) and female trips were
weighted by 122% (56/46). The weighted male and female trips are summed up to get the total
weighted trips. The mode share is calculated based on the total weighted trips. The survey
responses also gave information on average vehicle occupancy during the two hour study period.
The average vehicle occupancy during the morning study period at The Alameda was determined
to be 1.4.
Table 1: The Alameda Mode Share Calculation
The Alameda
Surveyed Male Trips
Surveyed Female Trips
Gender Weighted Male Trips (81%)
Gender Weighted Female Trips (122%)
Total Weighted Trips
Weighted Mode Share
Auto
12
10
9.72
12.20
22
84.4%
Walk
1
2
0.81
2.44
3
12.5%
AM
Bike
0
0
0.00
0.00
0
0.0%
Transit
1
0
0.81
0.00
1
3.1%
Total
14
12
11.34
14.64
25.98
100%
15
c) Allocate peak hour person trips by mode.
The mode share calculated using the above step was applied to the peak hour person trips to get
the total number of automobile person trips by each mode. Of the total 171 trips that were
recorded, 144 (171*0.84) were auto trips, 21 (171*0.12) were walk trips and 5 (171*0.03) were
transit trips.
d) Calculate observed automobile trips.
The 144 automobile person trips obtained are then divided by the average vehicle occupancy
(1.4) to get the actual number of automobile trips generated at the coffee shops. Hence the total
number of automobile trips was calculated to be 103 (144/1.4) trips.
16
Chapter 5
RESULTS
The methodology using the intercept survey and total in and out door counts yield two particular
results worth discussing: the total observed person trips of coffee shops for the peak hour by each
mode and a comparison of the observed trips with the ITE trips.
5.1 Mode Share of Peak Hour Person Trips
The mode share of the person trips during morning and afternoon peak hours is estimated from
the intercept survey responses. The total person trips for all the six sites for the peak hours were
estimated by calculating the percentage of trips by each mode of transport from the survey
responses and applying it to the highest one hour door count rate during morning and afternoon
periods. Trips by modes other than automobile (walk, bike, transit) were observed in all the six
study sites. Even though the share of auto trips was the highest, the percentage of trips by other
modes was still noteworthy. On an average, auto trips contributed 72% of the total trips, walk
trips shared 24%, and bike and transit trips shared 2% each of the total trips. The detailed mode
split is shown in Table 2.
17
Table 2: AM and PM Peak Hour Mode Share
AM Peak-Hour Mode Share
Site
Rio Robles
Civic Center
Alameda
Technology Dr
River Oaks
Rivermark
Number of
Completed
Surveys
40
31
13
13
12
16
Site
Rio Robles
Civic Center
Alameda
Technology Dr
River Oaks
Rivermark
Number of
Completed
Surveys
25
17
12
14
12
13
Auto
Trips
122
140
144
182
167
185
Auto
Trips
197
61
76
52
90
75
Walk
Trips
71
28
21
71
53
49
Walk
Trips
71
22
5
41
64
8
Bike Transit
% Auto
Trips Trips Total Trips Trips
5
0
199
61.5%
10
5
182
76.7%
0
5
171
84.4%
0
0
253
71.8%
13
0
233
71.5%
0
0
234
79.3%
PM Peak-Hour Mode Share
Bike Transit
Trips Trips Total Trips
0
11
279
6
5
94
0
5
87
0
0
93
0
0
154
6
0
90
% Auto
Trips
70.7%
64.5%
87.9%
56.0%
58.7%
83.4%
% Walk
Trips
35.8%
15.5%
12.5%
28.2%
22.8%
20.8%
% Walk
Trips
25.5%
23.7%
6.1%
44.0%
41.3%
9.4%
% Bike
Trips
2.7%
5.3%
0.0%
0.0%
5.7%
0.0%
%
Transit
Trips
0.0%
2.5%
3.1%
0.0%
0.0%
0.0%
% Bike
Trips
0.0%
6.5%
0.0%
0.0%
0.0%
7.2%
%
Transit
Trips
3.8%
5.3%
6.1%
0.0%
0.0%
0.0%
18
5.2 Comparison of Observed Trips with ITE Trips
The observed auto trips were estimated by dividing the person trips with the average vehicle
occupancy of the location. The ITE automobile trips for the a.m. and p.m. peak hour can be
determined from ITE Trip Generation manual based on the study location characteristics like
gross floor area (square feet) for example. The ITE trips were derived from the ITE Trip
Generation manual Eighth Edition. The observed auto trips were compared with the ITE trips.
Table 3 below shows the observed vehicle trips from the surveys and the ITE vehicle trips. Five
out of six study sites had trips lower than the ITE trip estimates for the morning peak hour, and
four of the six study sites had trips lower than the ITE values in the afternoon peak hour. A
comparison of the two values is also shown in Figure 1 and in Figure 2 as scatter plots during the
AM and PM peak hour. The coefficient of determination (R2) for both AM and PM peak hours
was also determined from the graph. The positive R-squared value of 0.2 and 0.1 for the ITE trips
indicates that 20% and 10% of the variation in vehicle trips during the AM and PM peak hour can
be explained by square footage, respectively. The results observed at the smart growth sites
yielded a negative coefficient of determination (R2) for both AM and PM peak hours, which
implies that the data points plotted are showing no linear relation between the square footage and
the total trips generated in the case of smart growth developments, which leads to the conclusion
that there may be other factors which may affect trip generation in the case of smart growth
developments. This comparison also suggests that the data collected for this study fits the
dependent variable (vehicle trip ends) at least as well as the data used in practice for trip
generation estimates. It also illustrates that even though only six sites were studied for this work,
the number of sites is comparable to ITE’s use of seven.
19
Table 3: Observed Peak- Hour Vehicle Trips vs. ITE Vehicle Trips
Site
Square Footage
(1000 square
feet)
Rio Robles
Civic Center
Alameda
Technology Drive
River Oaks
Rivermark
1
1
1.275
1.55
1.568
1.496
1
2.003
AM Peak Hour2
PM Peak Hour3
ITE Vehicle
ITE
Observed Vehicle ITE Vehicle Trips/Observed Observed Vehicle ITE Vehicle Trips/Observed
Trips4
Trips5
Vehicle Trips
Trips
Trips
Trips
122
147
1.20
132
52
0.39
116
181
1.56
38
63
1.67
103
184
1.78
48
64
1.34
151
175
1.15
42
61
1.46
146
113
0.77
65
41
0.63
151
238
1.58
52
82
1.58
Average
1.34
1.18
Square footage is expressed in 1000 square feet.
AM data were collected from 7:00 p.m. to 9:00 p.m.
3
PM data were collected from 4:00 p.m. to 6:00 p.m.
4
Observed vehicle trips were calculated by dividing the person trips with the average vehicle
occupancy.
5
ITE vehicle trips are estimated from standard ITE Trip Generation manual.
2
20
Figure 1: Comparison between Observed Vehicle Trips and ITE Vehicle Trips-AM Peak-Hour
Figure 2: Comparison between Observed Vehicle Trips and ITE Vehicle Trips-PM Peak-Hour
21
Chapter 6
CONCLUSIONS
This study focused on collecting trip generation data from coffee shops at smart growth areas. It
also established a mode share database of the trips generated by coffee shops in smart growth
settings. The data collected from six coffee shops in smart growth settings showed that the
observed trips were generally lower than the ITE trips. The results revealed that on an average
ITE overestimated the peak hour trips by 34% during the AM peak hour and by 18% during the
PM peak hour. This study also revealed that the trips generated by other modes of transport like
walking, bicycling, and transit in smart growth settings are noteworthy. Auto trips contributed
more than fifty percent (72%) of the total trips. Walk trips had the second highest share in the
total mode share which contributed an average of 24% of the total trips. When compared against
a national average of 83.4% for auto trips and 10.4% for walk trips, it is consistent with the
general perception that smart growth areas generate fewer vehicle trips and higher trips by other
modes like walking (Santos et al. 2009). The newly derived multi-modal trip generation data and
lower auto trips in smart growth settings will help local agencies in better evaluation of traffic
impacts for smart growth projects under CEQA review. The lower automobile trips will lead to
lower impact levels on the transportation system thereby resulting in implementation of adequate
and identified mitigation measures. As many communities are working towards promoting smart
growth by building equitable and sustainable communities throughout California, it is advisable
to put in more money and effort on future studies based on this methodology.
22
Chapter 7
LIMITATIONS
Although the study was successful in achieving its goal of estimating trip generation rates of
smart growth sites, there were a few limitations in regard to the data collection. The main
drawbacks of this study were limited sample size and inadequate number of surveys. The results
were based on studies done on six sites in San Jose, California. Even though it meets the ITE
recommendation for a minimum of three sites for trip generation studies, a larger sample size of
twenty sites would have better represented the trip generation characteristics of smart growth sites
(ITE Trip Generation Handbook 2004). Another drawback was the inadequate number of surveys
obtained. The goal was to get a minimum of 20 completed surveys at each site. However it was
difficult to get coffee shop patrons to respond during commute hours and hence, the final trips
generated for a few sites were estimated based on less than 20 surveys.
23
Chapter 8
RECOMMENDATIONS
This methodology has been successful in estimating the multimodal trip generation data and in
estimating automobile trip generation rates. But the key to success in this methodology is by
enhancing participation from survey respondents. The most challenging part in this entire project
was to get store managers permissions and to get survey participation from coffee shop patrons.
The managers and store owners were reluctant to give permission for conducting surveys as they
feared complaints from patrons. The support of the property managers of the land uses plays a
crucial role in obtaining good survey responses. Educating store managers and coffee shop
patrons on the purpose of the survey and its significance may help in better survey participation.
Another factor that may have an influence on the trip rates is paid parking. Even though the trips
were lower than the ITE trips in all six study sites, there was only slight difference in values
especially during PM peak hour. All the six sites selected for this study had free parking. In
future, studies may be done on sites with paid parking to see if it influences the trip rates. Sites
with paid parking may put more pressure on the users of the site to use modes other than
automobile.
Earlier studies (SANDAG 2010) indicate that the trip generation may also be affected by other
factors like density of the area, diversity of the area etc. Hence incorporating these additional
factors to future trip generation studies would also be advisable to validate any correlation that
exists between trip generation and these factors.
24
Appendix A
Data Summaries of Coffee Shop Sites
25
Figure 3: Site Location: 31 Rio Robles East, San Jose, CA
Building Square Footage: 1275
Survey Date: May 07, 2013;
Table 4: Rio Robles Door Count Data
Rio Robles Door Count Data
Time
Direction Male Female
7:00 - 7:04 In
0
0
7:00 - 7:04 Out
0
0
7:05 - 7:09 In
6
1
7:05 - 7:09 Out
4
1
7:10 - 7:14 In
8
2
7:10 - 7:14 Out
9
2
7:15 - 7:19 In
8
3
7:15 - 7:19 Out
12
1
7:20 - 7:24 In
2
1
7:20 - 7:24 Out
3
3
7:25 - 7:29 In
6
2
7:25 - 7:29 Out
2
0
7:30 - 7:34 In
5
0
7:30 - 7:34 Out
9
1
7:35 - 7:39 In
8
3
7:35 - 7:39 Out
6
1
7:40 - 7:44 In
5
3
7:40 - 7:44 Out
5
3
7:45 - 7:49 In
2
1
7:45 - 7:49 Out
4
7
26
7:50 - 7:54
7:50 - 7:54
7:55 - 7:59
7:55 - 7:59
8:00 - 8:04
8:00 - 8:04
8:05 - 8:09
8:05 - 8:09
8:10 - 8:14
8:10 - 8:14
8:15 - 8:19
8:15 - 8:19
8:20 - 8:24
8:20 - 8:24
8:25 - 8:29
8:25 - 8:29
8:30 - 8:34
8:30 - 8:34
8:35 - 8:39
8:35 - 8:39
8:40 - 8:44
8:40 - 8:44
8:45 - 8:49
8:45 - 8:49
8:50 - 8:54
8:50 - 8:54
8:55 - 8:59
8:55 - 8:59
4:00 - 4:04
4:00 - 4:04
4:05 - 4:09
4:05 - 4:09
4:10 - 4:14
4:10 - 4:14
4:15 - 4:19
4:15 - 4:19
4:20 - 4:24
4:20 - 4:24
4:25 - 4:29
4:25 - 4:29
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
14
5
4
9
8
6
7
4
5
3
3
6
5
4
8
4
9
7
4
7
8
8
7
5
4
11
9
3
6
2
4
0
4
4
3
3
1
1
1
1
6
2
4
4
4
2
2
4
6
2
4
8
8
4
1
4
3
2
3
4
1
2
3
1
5
3
2
1
1
0
0
5
0
4
2
2
3
2
1
1
27
4:30 - 4:34
4:30 - 4:34
4:35 - 4:39
4:35 - 4:39
4:40 - 4:44
4:40 - 4:44
4:45 - 4:49
4:45 - 4:49
4:50 - 4:54
4:50 - 4:54
4:55 - 4:59
4:55 - 4:59
5:00 - 5:04
5:00 - 5:04
5:05 - 5:09
5:05 - 5:09
5:10 - 5:14
5:10 - 5:14
5:15 - 5:19
5:15 - 5:19
5:20 - 5:24
5:20 - 5:24
5:25 - 5:29
5:25 - 5:29
5:30 - 5:34
5:30 - 5:34
5:35 - 5:39
5:35 - 5:39
5:40 - 5:44
5:40 - 5:44
5:45 - 5:49
5:45 - 5:49
5:50 - 5:54
5:50 - 5:54
5:55 - 5:59
5:55 - 5:59
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
2
1
4
2
2
2
3
1
2
2
6
1
6
3
1
2
2
1
1
1
3
2
0
1
2
1
2
2
5
4
2
4
1
3
2
2
0
1
1
0
0
0
2
0
0
0
0
2
4
1
0
3
1
0
0
1
0
0
0
0
0
0
0
0
3
0
0
0
0
3
0
0
28
Figure 4: Rio Robles AM Peak Hour
Figure 5: Rio Robles PM Peak Hour
29
Table 5: Observed Peak Hour Trip Generation (Site: Rio Robles)
Observed Peak
Hour Person Trip
Generation
AM
PM
Auto
122
197
Walk
71
71
Bike
5
Transit
0
Observed
Peak Hour
Vehicle Trip
Generation
Observed
(Collected)
AM
PM
Vehicle
Occupancy
1
1.5
0
Vehicle Trips
122
131
11
Vehicle Trips
(/1000 GSF)
96
103
30
Figure 6: Site Location: 39370 Civic Center Drive Fremont, CA
Building Square Footage: 1550
Survey Date: May 09, 2013;
Table 6: Civic Center Door Count Data
Civic Center Door Count Data
Time
Direction Male Female
7:00 - 7:04
In
3
4
7:00 - 7:04
Out
5
2
7:05 - 7:09
In
3
3
7:05 - 7:09
Out
3
3
7:10 - 7:14
In
3
3
7:10 - 7:14
Out
1
3
7:15 - 7:19
In
4
2
7:15 - 7:19
Out
3
3
7:20 - 7:24
In
1
2
7:20 - 7:24
Out
4
2
7:25 - 7:29
In
1
5
7:25 - 7:29
Out
1
3
7:30 - 7:34
In
4
2
7:30 - 7:34
Out
4
3
7:35 - 7:39
In
5
4
31
7:35 - 7:39
7:40 - 7:44
7:40 - 7:44
7:45 - 7:49
7:45 - 7:49
7:50 - 7:54
7:50 - 7:54
7:55 - 7:59
7:55 - 7:59
8:00 - 8:04
8:00 - 8:04
8:05 - 8:09
8:05 - 8:09
8:10 - 8:14
8:10 - 8:14
8:15 - 8:19
8:15 - 8:19
8:20 - 8:24
8:20 - 8:24
8:25 - 8:29
8:25 - 8:29
8:30 - 8:34
8:30 - 8:34
8:35 - 8:39
8:35 - 8:39
8:40 - 8:44
8:40 - 8:44
8:45 - 8:49
8:45 - 8:49
8:50 - 8:54
8:50 - 8:54
8:55 - 8:59
8:55 - 8:59
7:00 - 7:04
7:00 - 7:04
7:05 - 7:09
7:05 - 7:09
7:10 - 7:14
7:10 - 7:14
7:15 - 7:19
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
3
8
4
1
5
2
0
4
3
3
4
1
1
1
3
3
1
5
0
4
5
0
4
4
3
1
2
3
0
5
2
5
2
3
5
3
3
3
1
4
4
4
3
3
3
5
4
8
3
3
3
5
5
10
5
4
11
3
3
5
5
4
4
2
2
4
1
6
4
3
3
2
3
4
2
3
3
3
3
2
32
7:15 - 7:19
7:20 - 7:24
7:20 - 7:24
7:25 - 7:29
7:25 - 7:29
7:30 - 7:34
7:30 - 7:34
7:35 - 7:39
7:35 - 7:39
7:40 - 7:44
7:40 - 7:44
7:45 - 7:49
7:45 - 7:49
7:50 - 7:54
7:50 - 7:54
7:55 - 7:59
7:55 - 7:59
8:00 - 8:04
8:00 - 8:04
8:05 - 8:09
8:05 - 8:09
8:10 - 8:14
8:10 - 8:14
8:15 - 8:19
8:15 - 8:19
8:20 - 8:24
8:20 - 8:24
8:25 - 8:29
8:25 - 8:29
8:30 - 8:34
8:30 - 8:34
8:35 - 8:39
8:35 - 8:39
8:40 - 8:44
8:40 - 8:44
8:45 - 8:49
8:45 - 8:49
8:50 - 8:54
8:50 - 8:54
8:55 - 8:59
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
3
1
4
1
1
4
4
5
3
8
4
1
5
2
0
4
3
3
4
1
1
1
3
3
1
5
0
4
5
0
4
4
3
1
2
3
0
5
2
5
3
2
2
5
3
2
3
4
4
4
3
3
3
5
4
8
3
3
3
5
5
10
5
4
11
3
3
5
5
4
4
2
2
4
1
6
4
3
3
2
33
8:55 - 8:59
4:00 - 4:04
4:00 - 4:04
4:05 - 4:09
4:05 - 4:09
4:10 - 4:14
4:10 - 4:14
4:15 - 4:19
4:15 - 4:19
4:20 - 4:24
4:20 - 4:24
4:25 - 4:29
4:25 - 4:29
4:30 - 4:34
4:30 - 4:34
4:35 - 4:39
4:35 - 4:39
4:40 - 4:44
4:40 - 4:44
4:45 - 4:49
4:45 - 4:49
4:50 - 4:54
4:50 - 4:54
4:55 - 4:59
4:55 - 4:59
5:00 - 5:04
5:00 - 5:04
5:05 - 5:09
5:05 - 5:09
5:10 - 5:14
5:10 - 5:14
5:15 - 5:19
5:15 - 5:19
5:20 - 5:24
5:20 - 5:24
5:25 - 5:29
5:25 - 5:29
5:30 - 5:34
5:30 - 5:34
5:35 - 5:39
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
2
2
3
3
4
2
5
3
2
1
1
1
0
3
1
0
0
2
2
1
2
2
2
1
2
2
1
2
3
2
1
2
2
3
3
2
1
1
2
1
3
1
3
1
5
2
4
2
3
1
0
2
2
3
1
1
2
3
1
3
1
0
4
1
3
1
1
1
3
1
4
2
1
1
2
1
1
0
3
0
34
5:35 - 5:39
5:40 - 5:44
5:40 - 5:44
5:45 - 5:49
5:45 - 5:49
5:50 - 5:54
5:50 - 5:54
5:55 - 5:59
5:55 - 5:59
Out
In
Out
In
Out
In
Out
In
Out
2
4
3
1
2
1
3
0
1
Figure 7: Civic Center AM Peak Hour
1
1
0
1
0
2
3
2
3
35
Figure 8: Civic Center PM Peak Hour
Table 7: Observed Peak Hour Trip Generation (Site: Civic Center)
Observed Peak Hour
Person Trip Generation
AM
PM
Auto
140
61
Walk
28
22
Bike
10
6
Transit
5
5
Observed Peak Hour Vehicle Trip
Generation
Observed
(Collected)
AM
PM
Vehicle
1.2
1.6
Occupancy
Vehicle Trips
116
38
Vehicle Trips
75
24
(/1000 GSF)
36
Figure 9: Site Location: 1338 The Alameda, San Jose, CA
Building Square Footage: 1568
Survey Date: May 14, 2013;
Table 8: The Alameda Door Count Data
Alameda Door Count Data
Time
Direction Male Female
7:00 - 7:04
In
2
4
7:00 - 7:04
Out
2
4
7:05 - 7:09
In
1
2
7:05 - 7:09
Out
3
3
7:10 - 7:14
In
2
4
7:10 - 7:14
Out
2
2
7:15 - 7:19
In
3
2
7:15 - 7:19
Out
1
3
7:20 - 7:24
In
5
1
7:20 - 7:24
Out
1
0
7:25 - 7:29
In
2
2
7:25 - 7:29
Out
4
1
7:30 - 7:34
In
6
7
7:30 - 7:34
Out
1
1
7:35 - 7:39
In
3
6
7:35 - 7:39
Out
3
5
7:40 - 7:44
In
7
4
37
7:40 - 7:44
7:45 - 7:49
7:45 - 7:49
7:50 - 7:54
7:50 - 7:54
7:55 - 7:59
7:55 - 7:59
8:00 - 8:04
8:00 - 8:04
8:05 - 8:09
8:05 - 8:09
8:10 - 8:14
8:10 - 8:14
8:15 - 8:19
8:15 - 8:19
8:20 - 8:24
8:20 - 8:24
8:25 - 8:29
8:25 - 8:29
8:30 - 8:34
8:30 - 8:34
8:35 - 8:39
8:35 - 8:39
8:40 - 8:44
8:40 - 8:44
8:45 - 8:49
8:45 - 8:49
8:50 - 8:54
8:50 - 8:54
8:55 - 8:59
8:55 - 8:59
4:00 - 4:04
4:00 - 4:04
4:05 - 4:09
4:05 - 4:09
4:10 - 4:14
4:10 - 4:14
4:15 - 4:19
4:15 - 4:19
4:20 - 4:24
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
3
9
3
1
4
1
6
2
2
1
2
3
2
2
2
0
1
3
1
2
3
6
3
2
0
1
2
3
2
3
2
2
2
2
0
1
0
4
2
4
2
5
4
3
4
4
6
6
5
5
9
4
6
3
3
2
2
5
1
2
4
2
1
2
4
3
1
2
3
4
2
1
3
1
2
4
1
1
3
2
38
4:20 - 4:24
4:25 - 4:29
4:25 - 4:29
4:30 - 4:34
4:30 - 4:34
4:35 - 4:39
4:35 - 4:39
4:40 - 4:44
4:40 - 4:44
4:45 - 4:49
4:45 - 4:49
4:50 - 4:54
4:50 - 4:54
4:55 - 4:59
4:55 - 4:59
5:00 - 5:04
5:00 - 5:04
5:05 - 5:09
5:05 - 5:09
5:10 - 5:14
5:10 - 5:14
5:15 - 5:19
5:15 - 5:19
5:20 - 5:24
5:20 - 5:24
5:25 - 5:29
5:25 - 5:29
5:30 - 5:34
5:30 - 5:34
5:35 - 5:39
5:35 - 5:39
5:40 - 5:44
5:40 - 5:44
5:45 - 5:49
5:45 - 5:49
5:50 - 5:54
5:50 - 5:54
5:55 - 5:59
5:55 - 5:59
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
1
3
2
2
1
3
0
2
2
0
1
3
1
2
4
3
3
0
3
2
2
3
0
1
1
3
1
5
3
2
0
1
0
6
3
5
4
0
3
0
1
1
1
3
3
0
2
1
0
2
2
0
0
2
3
2
0
1
0
0
0
0
0
1
1
0
2
3
1
2
3
2
3
1
1
2
1
0
39
Figure 10: Alameda AM Peak Hour
Figure 11: Alameda PM Peak Hour
40
Table 9: Observed Peak Hour Trip generation (Site: The Alameda)
Observed Peak Hour
Person Trip Generation
AM
PM
Auto
144
76
Walk
21
5
Bike
0
0
Transit
5
5
Observed Peak Hour Vehicle Trip
Generation
Observed
(Collected)
AM
PM
Vehicle
1.4
1.6
Occupancy
Vehicle Trips
103
48
Vehicle Trips
66
30
(/1000 GSF)
41
Figure 12: Site Location: 1759 Technology Drive, San Jose, CA
Building Square Footage: 1496
Survey Date: May 15, 2013;
Table 10: Technology Drive Door Count Data
Technology Door Count Data
Time
7:00 - 7:04
7:00 - 7:04
7:05 - 7:09
7:05 - 7:09
7:10 - 7:14
7:10 - 7:14
7:15 - 7:19
7:15 - 7:19
7:20 - 7:24
7:20 - 7:24
7:25 - 7:29
7:25 - 7:29
7:30 - 7:34
7:30 - 7:34
7:35 - 7:39
7:35 - 7:39
7:40 - 7:44
7:40 - 7:44
7:45 - 7:49
7:45 - 7:49
Direction Male Female
In
3
3
Out
0
0
In
2
1
Out
5
2
In
1
2
Out
2
3
In
3
3
Out
0
2
In
5
5
Out
2
3
In
14
4
Out
9
2
In
9
3
Out
6
3
In
9
7
Out
6
4
In
5
6
Out
3
3
In
7
3
Out
7
5
42
7:50 - 7:54
7:50 - 7:54
7:55 - 7:59
7:55 - 7:59
8:00 - 8:04
8:00 - 8:04
8:05 - 8:09
8:05 - 8:09
8:10 - 8:14
8:10 - 8:14
8:15 - 8:19
8:15 - 8:19
8:20 - 8:24
8:20 - 8:24
8:25 - 8:29
8:25 - 8:29
8:30 - 8:34
8:30 - 8:34
8:35 - 8:39
8:35 - 8:39
8:40 - 8:44
8:40 - 8:44
8:45 - 8:49
8:45 - 8:49
8:50 - 8:54
8:50 - 8:54
8:55 - 8:59
8:55 - 8:59
4:00 - 4:04
4:00 - 4:04
4:05 - 4:09
4:05 - 4:09
4:10 - 4:14
4:10 - 4:14
4:15 - 4:19
4:15 - 4:19
4:20 - 4:24
4:20 - 4:24
4:25 - 4:29
4:25 - 4:29
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
4
4
4
7
6
3
6
7
9
8
7
7
6
4
4
7
8
2
3
6
4
5
2
2
9
1
4
1
0
2
4
1
6
5
5
6
3
2
6
1
3
5
1
5
5
4
2
3
3
1
6
1
7
10
6
7
3
4
4
4
4
3
4
5
1
2
4
1
0
4
0
1
2
1
0
1
5
0
0
0
43
4:30 - 4:34
4:30 - 4:34
4:35 - 4:39
4:35 - 4:39
4:40 - 4:44
4:40 - 4:44
4:45 - 4:49
4:45 - 4:49
4:50 - 4:54
4:50 - 4:54
4:55 - 4:59
4:55 - 4:59
5:00 - 5:04
5:00 - 5:04
5:05 - 5:09
5:05 - 5:09
5:10 - 5:14
5:10 - 5:14
5:15 - 5:19
5:15 - 5:19
5:20 - 5:24
5:20 - 5:24
5:25 - 5:29
5:25 - 5:29
5:30 - 5:34
5:30 - 5:34
5:35 - 5:39
5:35 - 5:39
5:40 - 5:44
5:40 - 5:44
5:45 - 5:49
5:45 - 5:49
5:50 - 5:54
5:50 - 5:54
5:55 - 5:59
5:55 - 5:59
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
5
4
0
4
0
2
2
0
3
1
3
4
3
1
3
0
0
1
1
1
0
0
4
0
4
5
3
1
0
1
2
1
0
1
2
5
3
1
1
0
0
2
2
0
0
0
0
1
0
1
0
1
0
1
1
0
0
1
1
3
1
1
0
1
0
0
4
1
1
0
0
1
44
Figure 13: Technology Drive AM Peak Hour
Figure 14: Technology Drive PM Peak Hour
45
Table 11: Observed Peak Hour Trip Generation (Site: Technology Drive)
Observed Peak Hour
Person Trip Generation
AM
PM
Auto
182
52
Walk
71
41
Bike
0
64
Transit
0
0
Observed Peak Hour Vehicle Trip
Generation
Observed
(Collected)
AM
PM
Vehicle
1.2
1.25
Occupancy
Vehicle Trips
152
42
Vehicle Trips
101
28
(/1000 GSF)
46
Figure 15: Site Location: 670 River Oaks Parkway, San Jose CA
Building Square footage: 1000
Survey Date: May 21,2013;
Table 12: River Oaks Door Count Data
River Oaks Door Count Data
Time
Direction Male Female
7:00 - 7:04 In
3
1
7:00 - 7:04 Out
3
0
7:05 - 7:09 In
4
3
7:05 - 7:09 Out
5
3
7:10 - 7:14 In
0
1
7:10 - 7:14 Out
4
4
7:15 - 7:19 In
6
3
7:15 - 7:19 Out
1
2
7:20 - 7:24 In
2
2
7:20 - 7:24 Out
7
1
7:25 - 7:29 In
7
2
7:25 - 7:29 Out
0
3
7:30 - 7:34 In
12
2
7:30 - 7:34 Out
6
2
7:35 - 7:39 In
4
5
7:35 - 7:39 Out
9
3
7:40 - 7:44 In
7
2
7:40 - 7:44 Out
6
3
7:45 - 7:49 In
7
6
47
7:45 - 7:49
7:50 - 7:54
7:50 - 7:54
7:55 - 7:59
7:55 - 7:59
8:00 - 8:04
8:00 - 8:04
8:05 - 8:09
8:05 - 8:09
8:10 - 8:14
8:10 - 8:14
8:15 - 8:19
8:15 - 8:19
8:20 - 8:24
8:20 - 8:24
8:25 - 8:29
8:25 - 8:29
8:30 - 8:34
8:30 - 8:34
8:35 - 8:39
8:35 - 8:39
8:40 - 8:44
8:40 - 8:44
8:45 - 8:49
8:45 - 8:49
8:50 - 8:54
8:50 - 8:54
8:55 - 8:59
8:55 - 8:59
4:00 - 4:04
4:00 - 4:04
4:05 - 4:09
4:05 - 4:09
4:10 - 4:14
4:10 - 4:14
4:15 - 4:19
4:15 - 4:19
4:20 - 4:24
4:20 - 4:24
4:25 - 4:29
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
6
7
8
5
7
3
4
5
3
8
4
7
7
6
7
4
5
0
3
7
3
2
7
2
2
10
3
5
7
9
5
6
7
7
11
6
6
6
3
3
4
1
2
1
5
7
2
4
4
3
2
6
7
4
4
4
3
3
1
3
6
2
2
0
2
2
0
1
3
6
2
2
3
1
5
2
0
1
1
3
48
4:25 - 4:29
4:30 - 4:34
4:30 - 4:34
4:35 - 4:39
4:35 - 4:39
4:40 - 4:44
4:40 - 4:44
4:45 - 4:49
4:45 - 4:49
4:50 - 4:54
4:50 - 4:54
4:55 - 4:59
4:55 - 4:59
5:00 - 5:04
5:00 - 5:04
5:05 - 5:09
5:05 - 5:09
5:10 - 5:14
5:10 - 5:14
5:15 - 5:19
5:15 - 5:19
5:20 - 5:24
5:20 - 5:24
5:25 - 5:29
5:25 - 5:29
5:30 - 5:34
5:30 - 5:34
5:35 - 5:39
5:35 - 5:39
5:40 - 5:44
5:40 - 5:44
5:45 - 5:49
5:45 - 5:49
5:50 - 5:54
5:50 - 5:54
5:55 - 5:59
5:55 - 5:59
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
9
5
5
3
1
1
1
3
1
3
1
6
1
4
5
4
3
0
4
2
2
0
1
1
0
2
2
1
1
1
1
1
2
1
1
4
3
2
0
4
2
0
0
1
2
2
2
3
1
0
1
1
1
2
0
1
2
2
1
1
0
0
0
0
0
1
0
0
0
0
1
0
0
1
49
Figure 16: River Oaks AM Peak Hour
Figure 17: River Oaks PM Peak Hour
50
Table 13: Observed Peak Hour Trip Generation (Site: River Oaks)
Observed Peak Hour
Person Trip Generation
AM
PM
Auto
167
90
Walk
53
64
Bike
13
0
Transit
0
0
Observed Peak Hour Vehicle Trip
Generation
Observed
(Collected)
AM
PM
Vehicle
1.14
1.4
Occupancy
Vehicle Trips
146
64
Vehicle Trips
146
64
(/1000 GSF)
51
Figure 18: Site Location: 3932 Rivermark Plaza, Santa Clara, CA
Building Square Footage: 2003
Survey Date: May 22, 2013;
Table 14: Rivermark Door Count Data
Rivermark Door Count Data
Time
Direction Male Female
7:00 - 7:04 In
0
0
7:00 - 7:04 Out
0
0
7:05 - 7:09 In
6
1
7:05 - 7:09 Out
4
1
7:10 - 7:14 In
8
2
7:10 - 7:14 Out
9
2
7:15 - 7:19 In
8
3
7:15 - 7:19 Out
12
1
7:20 - 7:24 In
2
1
7:20 - 7:24 Out
3
3
7:25 - 7:29 In
6
2
7:25 - 7:29 Out
2
0
7:30 - 7:34 In
5
0
7:30 - 7:34 Out
9
1
7:35 - 7:39 In
8
3
7:35 - 7:39 Out
6
1
7:40 - 7:44 In
5
3
7:40 - 7:44 Out
5
3
52
7:45 - 7:49
7:45 - 7:49
7:50 - 7:54
7:50 - 7:54
7:55 - 7:59
7:55 - 7:59
8:00 - 8:04
8:00 - 8:04
8:05 - 8:09
8:05 - 8:09
8:10 - 8:14
8:10 - 8:14
8:15 - 8:19
8:15 - 8:19
8:20 - 8:24
8:20 - 8:24
8:25 - 8:29
8:25 - 8:29
8:30 - 8:34
8:30 - 8:34
8:35 - 8:39
8:35 - 8:39
8:40 - 8:44
8:40 - 8:44
8:45 - 8:49
8:45 - 8:49
8:50 - 8:54
8:50 - 8:54
8:55 - 8:59
8:55 - 8:59
4:00 - 4:04
4:00 - 4:04
4:05 - 4:09
4:05 - 4:09
4:10 - 4:14
4:10 - 4:14
4:15 - 4:19
4:15 - 4:19
4:20 - 4:24
4:20 - 4:24
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
2
4
14
5
4
9
8
6
7
4
5
3
3
6
5
4
8
4
9
7
4
7
8
8
7
5
4
11
9
3
6
2
4
0
4
4
3
3
1
1
1
7
6
2
4
4
4
2
2
4
6
2
4
8
8
4
1
4
3
2
3
4
1
2
3
1
5
3
2
1
1
0
0
5
0
4
2
2
3
2
53
4:25 - 4:29
4:25 - 4:29
4:30 - 4:34
4:30 - 4:34
4:35 - 4:39
4:35 - 4:39
4:40 - 4:44
4:40 - 4:44
4:45 - 4:49
4:45 - 4:49
4:50 - 4:54
4:50 - 4:54
4:55 - 4:59
4:55 - 4:59
5:00 - 5:04
5:00 - 5:04
5:05 - 5:09
5:05 - 5:09
5:10 - 5:14
5:10 - 5:14
5:15 - 5:19
5:15 - 5:19
5:20 - 5:24
5:20 - 5:24
5:25 - 5:29
5:25 - 5:29
5:30 - 5:34
5:30 - 5:34
5:35 - 5:39
5:35 - 5:39
5:40 - 5:44
5:40 - 5:44
5:45 - 5:49
5:45 - 5:49
5:50 - 5:54
5:50 - 5:54
5:55 - 5:59
5:55 - 5:59
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
1
1
2
1
4
2
2
2
3
1
2
2
6
1
6
3
1
2
2
1
1
1
3
2
0
1
2
1
2
2
5
4
2
4
1
3
2
2
1
1
0
1
1
0
0
0
2
0
0
0
0
2
4
1
0
3
1
0
0
1
0
0
0
0
0
0
0
0
3
0
0
0
0
3
0
0
54
Figure 19: Rivermark AM Peak Hour
Figure 20: Rivermark PM Peak Hour
55
Table 15: Observed Peak Hour Trip Generation (Site: Rivermark)
Observed Peak Hour
Person Trip Generation
AM
PM
Auto
185
75
Walk
49
8
Bike
0
6
Transit
0
0
Observed Peak Hour Vehicle Trip
Generation
Observed
(Collected)
AM
PM
Vehicle
1.23
1.45
Occupancy
Vehicle Trips
150
52
Vehicle Trips
75
26
(/1000 GSF)
56
Appendix B
Door Count Form
57
Table 16: Door Count Form
Time
[Start_:__am/pm]
:00 to :04
:05 to :09
:10 to :14
:15 to :19
:20 to :24
:25 to :29
:30 to :34
:35 to :39
:40 to :44
:45 to :49
:50 to :54
:55 to :59
Direction
Location:_____
Male
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
In
Out
Female
Location:____
Location:___
Male
Male
Female
Female
58
Appendix C
Intercept Survey Questionnaire
59
Coffee Shop Trip Generation Study
This survey is part of a Sacramento State research project on coffee shop trip
generation. Your responses are completely confidential. Thank you for your time.
Building:
Date:
Time of survey: __:__ AM/PM
M/F
What primary mode of transportation did you use from your previous location to get
here?
 Auto
 Bike
Walk
Transit (Bus, Light Rail etc)
If Auto, how many people were in the vehicle?
What primary mode of transportation will you use to leave here?
 Auto
 Bike
Walk
Transit (Bus, Light Rail etc)
If Auto, how many people will be in the vehicle?
Was this trip made strictly to come to the coffee shop or did you stop here on the way
to another destination?
 Strictly to the coffee shop  Stopped here on the way to another destination
Have you or will you be stopping at other locations within the shopping center?
Yes
No
60
REFERENCES
Bass, R.E., Herson, A.I., & Bogdan, K.M. (1999). CEQA Deskbook Point Arena, CA: Solano
Press Books.
Institute of Transportation Engineers. Trip Generation Handbook, Second Edition, Washington
D.C.: Institute of Transportation Engineers, 2004.
Institute of Transportation Engineers. Trip Generation, 8th Edition, Washington D.C.: Institute of
Transportation Engineers, 2008.
Kimley Horn & Associates, Inc. in association with Economic & Planning Systems and Gene
Bregman & Associates. Trip-Generation Rates for Urban Infill Land Uses in California, Phase 2:
Data Collection, Prepared for California Department of Transportation, Available online,
http://www.dot.ca.gov/research/researchreports/reports/2009/final_summary_reportcalif._infill_trip-generation_rates_study_july_2009.pdf, 2009.
San Diego Association of Governments (SANDAG). Trip Generation and Parking Strategies for
Smart Growth Planning: Tools for the San Diego Region, Prepared by Fehr & Peers in
association with VRPA Technologies and KTU+A, Available online,
http://www.sandag.org/uploads/publicationid/publicationid_1500_11604.pdf, June 2010
Santos, A., McGuckin. N., Nakamoto. H.Y., Gray.D., & Liss.S. Summary of Travel Trends: 2009
National Household Travel Survey, Prepared for USDOT (FHWA), Available online,
http://nhts.ornl.gov/2009/pub/stt.pdf, 2009.
61
University of California Institute of Transportation Studies (UC Davis ITS). Data Collection
Methodology and Results: California Smart Growth Trip Generation Rates Study, Prepared for
California Department of Transportation, Available online,
http://downloads.ice.ucdavis.edu/ultrans/smartgrowthtripgen/Appendix_E_Data_Collection_Met
hod.pdf, 2012.