Mineta
Transportation
Institute
Created by
Congress in 1991
How Far, By Which
Route, and Why? A
Spatial Analysis of
Pedestrian Preference
MTI Report 06- 06
MINETA TRANSPORTATION INSTITUTE
The Norman Y. Mineta International Institute for Surface Transportation Policy Studies ( MTI) was established by
Congress as part of the Intermodal Surface Transportation Efficiency Act of 1991. Reauthorized in 1998, MTI was
selected by the U. S. Department of Transportation through a competitive process in 2002 as a national “ Center of
Excellence.” The Institute is funded by Congress through the United States Department of Transportation’s Research
and Innovative Technology Administration, the California Legislature through the Department of Transportation
( Caltrans), and by private grants and donations.
The Institute receives oversight from an internationally respected Board of Trustees whose members represent all major
surface transportation modes. MTI’s focus on policy and management resulted from a Board assessment of the industry’s
unmet needs and led directly to the choice of the San José State University College of Business as the Institute’s home.
The Board provides policy direction, assists with needs assessment, and connects the Institute and its programs with
the international transportation community.
MTI’s transportation policy work is centered on three primary responsibilities:
Research
MTI works to provide policy- oriented research for all levels of government and the private sector to foster the
development of optimum surface transportation systems. Research areas include: transportation security; planning and
policy development; interrelationships among transportation, land use, and the environment; transportation finance;
and collaborative labor- management relations. Certified Research Associates conduct the research. Certification
requires an advanced degree, generally a Ph. D., a record of academic publications, and professional references. Research
projects culminate in a peer- reviewed publication, available both in hardcopy and on TransWeb, the MTI website
( http:// transweb. sjsu. edu).
Education
The educational goal of the Institute is to provide graduate- level education to students seeking a career in the
development and operation of surface transportation programs. MTI, through San José State University, offers an
AACSB- accredited Master of Science in Transportation Management and a graduate Certificate in Transportation
Management that serve to prepare the nation’s transportation managers for the 21st century. The master’s degree is the
highest conferred by the California State University system. With the active assistance of the California Department
of Transportation, MTI delivers its classes over a state- of- the- art videoconference network throughout the state of
California and via webcasting beyond, allowing working transportation professionals to pursue an advanced degree
regardless of their location. To meet the needs of employers seeking a diverse workforce, MTI’s education program
promotes enrollment to under- represented groups.
Information and Technology Transfer
MTI promotes the availability of completed research to professional organizations and journals and works to integrate
the research findings into the graduate education program. In addition to publishing the studies, the Institute also
sponsors symposia to disseminate research results to transportation professionals and encourages Research Associates
to present their findings at conferences. The World in Motion, MTI’s quarterly newsletter, covers innovation in the
Institute’s research and education programs. MTI’s extensive collection of transportation- related publications is
integrated into San José State University’s world- class Martin Luther King, Jr. Library.
DISCLAIMER
The contents of this report reflect the views of the authors, who are responsible for the facts and accuracy of the information presented herein. This document is disseminated under
the sponsorship of the U. S. Department of Transportation, University Transportation Centers Program and the California Department of Transportation, in the interest of
information exchange. This report does not necessarily reflect the official views or policies of the U. S. government, State of California, or the Mineta Transportation Institute, who
assume no liability for the contents or use thereof. This report does not constitute a standard specification, design standard, or regulation.
a publication of the
Mineta Transportation Institute
College of Business
San José State University
San José, CA 95192- 0219
Created by Congress in 1991
MTI REPORT 06- 06
HOW FAR, BY WHICH ROUTE, AND WHY? A
SPATIAL ANALYSIS OF PEDESTRIAN PREFERENCE
June 2007
Marc Schlossberg, Ph. D.
Asha Weinstein Agrawal, Ph. D.
Katja Irvin
Vanessa Louise Bekkouche
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San José State University
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June 2007
Marc Schlossberg, Asha Weinstein Agrawal, Katja Irvin, and Vanessa
Louise Bekkouche
MTI 06- 06
There is an increasing interest in community walkability, as reflected in the growing number of state and federal initiatives on
Safe Routes to School, the new concern over a national obesity epidemic, and the rising interest in smart growth and related
policy approaches to urban development. In each of these cases, walking is recognized as a key mode of travel, and increasing
walking is viewed as a key goal.
Despite the seeming simplicity of the goal of building communities that are good places to walk, we actually know very little
about how our local infrastructure affects people’s willingness or capacity to walk to access their desired destinations. The
central research questions for this study are thus:
• How far do pedestrians walk to rail transit stations?
• What environmental factors influence their route choice?
This research project surveyed people who walked to five rail transit stops to find out what route they walked and their
preferences in choosing a walking route. In addition, we conducted an environmental audit of the streets and intersections
around those stations.
Combing the results from both activities, our analysis generated five key findings about pedestrian behaviors and preferences,
including the finding that the average survey respondent walked a half mile, far farther than the quarter to a third of a mile
assumed by many to be the maximum distance that Americans will walk. In addition, pedestrians in the study believed that
their primary consideration in choosing a route is minimizing time and distance. Secondary factors influencing their route
choice were safety from traffic and, to a lesser extent, attractiveness of the route, sidewalk quality, and the absence of long waits
at traffic lights.
Through the data collection and analysis process, we developed several recommendations related to the methodology for doing
such detailed, block- by- block analysis. Three of these focus on how to reduce the time burden of collecting the data, allowing a
research team to hone in on collecting only the most useful data. The final two findings address the practicalities of collecting
the data— whether to use Pocket PCs or pen and paper, and the importance of ground testing maps if one uses a GIS- based
system running on Pocket PCs.
Landscaping; Land use
planning; Pedestrian
walkways; Transit- oriented
development; Walking
86
FHWA/ CA/ OR- 2006/ 24
How Far, By Which Route, and Why? A Spatial Analysis of
Pedestrian Preference
Final Report
by Mineta Transportation Institute
All rights reserved
To order this publication, please contact the following:
Mineta Transportation Institute
College of Business
San José State University
San José, CA 95192- 0219
Tel ( 408) 924- 7560
Fax ( 408) 924- 7565
E- mail: mti@ mti. sjsu. edu
http:// transweb. sjsu. edu
Copyright © 2007
Library of Congress Catalog Card Number: 2007924737
ACKNOWLEDGMENTS
We would like to thank the leadership and staff at the Mineta Transportation Institute for
their support of all phases of this work, including Trixie Johnson, Sonya Cardenas- Carter, and
Brendan McCarthy. We would also like to thank Laurie Miskimins and Janel Sterbentz,
Portland State University graduate students who helped with survey distribution and other
aspects of the Portland research sites.
Thanks are also offered to MTI staff, including Communications Director Leslee Hamilton
and Graphic Artist Shun Nelson. Editing and publication services were provided by Catherine
Frazier and Tricia Lawrence.
Mineta Transportation Institute
i
TABLE OF CONTENTS
EXECUTIVE SUMMARY 1
INTRODUCTION 7
LITERATURE REVIEW: PEDESTRIAN ROUTE CHOICE AND DISTANCES
WALKED 9
DATA COLLECTION METHODS 13
ANALYSIS OF SURVEY FINDINGS 23
ANALYSIS OF WALKABILITY AUDIT DATA 33
CONCLUSIONS 45
APPENDIX A: SURVEY QUESTIONNAIRE 55
APPENDIX B: AUDIT INSTRUMENT 65
ENDNOTES 73
ABBREVIATIONS AND ACRONYMS 77
BIBLIOGRAPHY 79
ABOUT THE AUTHORS 83
PEER REVIEW 85
ii Table of Contents
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iii
LIST OF FIGURES
1. Looking East to Japantown Station 14
2. Looking East to El Cerrito Plaza Station 15
3. The Hollywood Station, Located Between Heavy Rail and the Freeway 15
4. Looking at Both Sides of the Gresham Station 16
5. The Westbound Train at the Rockwood Station 17
6. Audit Tool 20
7. Examples of the Walkability Audit Data Entry Forms 21
8. Survey Respondent Origins, El Cerrito BART Station 24
9. Actual Walking Routes— Japantown Station 34
10. Pedestrian Volume With Safe From Crime Audit Data 35
11. General Appearance Index, Japantown 37
12. Overall Greenery Index, Japantown 38
13. Overall Appearance Index, Japantown 40
14. Japantown Attractiveness and Crime Subjective Assessments 41
15. Julian Street Drill- Down Using Objective Criteria Indexes 42
16. Julian Street 43
17. An Example of an Audit Tool Customized by Street Type 51
iv List of Figures
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v
LIST OF TABLES
1. Survey Response Rates by Station 18
2. Variables Included in Walkability Audit 20
3. Demographics of Survey Respondents 25
4. Trip Purposes by Station 25
5. How Many People Stopped, For What Reason, and How Long 26
6. Self- Reported Distance Walked in Miles, Blocks, and Minutes 26
7. Actual Distances Walked 27
8. Accuracy of Self- Reported Trip Distances 27
9. Consistency of Route Choice 28
10. Percent of People Volunteering a Factor as Influencing Their Route Choice 29
11. Respondent Ratings: Importance of Factors That Might Influence Their Route
Choice 30
12. Attitudes Toward Walking 31
13. General Appearance Index 36
14. Greenery Index 38
15. Overall Appearance Index 39
vi List of Tables
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1
EXECUTIVE SUMMARY
STUDY OBJECTIVES AND METHODOLOGY
There is an increasing interest in community walkability, as reflected in the growing number
of state and federal initiatives on Safe Routes to School, the new concern over a national
obesity epidemic, and the rising interest in smart growth and related policy approaches to
urban development. In each of these cases, walking is recognized as a key mode of travel, and
increased walking is viewed as a key goal.
Despite the seeming simplicity of the goal of building communities that are good places to
walk, planners and policymakers actually know very little about how the local built
environment affects people’s willingness or capacity to walk to access their desired
destinations. The central research questions for this study are thus:
• How far do pedestrians walk to rail transit stations?
• What environmental factors influence their route choice?
This research project collected two types of data to assess how far people walk to rail stations
and the environmental factors that influence their route choices:
1. Pedestrian survey: People who walked to five rail stations in Portland and the San
Francisco Bay Area were given surveys and asked questions on walking behavior,
preferences, and route choice. In addition, respondents were given a map and asked to trace
their walking route, as well as to mark intersections and streets they avoided on their walk.
A total of 328 surveys were returned, for a 45 percent response rate.
2. Walkability audit: A geographic information systems ( GIS) and Pocket PC tool was
developed to evaluate specific elements of the walking environment at a streetscape scale
that previous researchers have identified as likely to affect a neighborhood’s walkability.
The audit tool assessed block segments and intersections separately, since pedestrians
experience the two in different ways. For each block segment, the auditor gathered
holistic, subjective assessments about the block as well as collected detailed data on the
block’s maintenance and cleanliness, amenities, sidewalk characteristics, buffer zone
characteristics, front zone characteristics, and roadway characteristics. For each
intersection, the audit collected data on factors affecting the ease of crossing the street,
such as the presence of traffic control devices, crosswalks, and curb cuts. Audit data was
collected for all streets in a half- mile radius around two stations.
STUDY FINDINGS
After this data was collected, the survey results were analyzed to assess respondents’ own
perceptions of how far they walked and why they chose their route. In addition, the
walkability audit data was used to analyze the built environment characteristics of the routes
people chose to walk and the places they avoided.
2 Executive Summary
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Findings on Walkability: Implications for Planning Practice
Three findings about walkability from the survey stood out as particularly relevant for future
planning efforts. First, the survey showed that pedestrians walk considerably farther than
commonly is acknowledged. In addition, the survey responses indicated that the respondents’
primary goal in choosing a route was to minimize distance and time, but that safety and
aesthetic considerations were also important to them.
Finding 1: Pedestrians walk considerably farther to access rail stations than commonly
assumed.
Conventional wisdom among planners has been that pedestrians in the United States will only
walk a quarter to a third of a mile for any reason, including to access transit. The results of our
study suggest quite differently, at least for walk trips to access rail transit. The median trip
distance was 0.47 miles, showing that fully half the people surveyed walked at least a
half- mile to access the train station. The study results therefore show that the conventional
wisdom underestimates actual pedestrian behavior, at least for the conditions we studied.
The study finding that pedestrians walk unexpectedly long distances suggests that
transportation and land- use planners designing transit- oriented developments should plan to
provide pedestrian infrastructure and pedestrian- scaled design within a larger radius than
previously assumed.
Finding 2: Pedestrians believe that their primary consideration in choosing a route is
minimizing time and distance.
The survey explored the reasons that pedestrians choose particular routes in two ways, first
asking about route choice factors as an open- ended question and then asking respondents to
rate the importance of a list of factors that might have influenced them. In both cases,
respondents overwhelming indicated that their first priority was to choose the most direct
and/ or quickest route. Because almost all of our respondents were making a morning commute
trip, it is not surprising that time would be a strong consideration for them.
These results suggest that land- use planners who want to increase walk trips should ensure
that pedestrians have available direct routes to their destinations. Grid street patterns are a
good choice because they provide direct routes ( as well as route choice). If the grid has very
long blocks, planners might want to consider adding mid- block footpaths through the center
of the block. In neighborhoods that have been designed on a cul- de- sac pattern, planners could
create pedestrian cut- through passages that allow walkers direct access to many different
destinations.
Finding 3: Secondary factors influencing route choice are safety and, to a lesser extent,
attractiveness of the route, sidewalk quality, and the absence of long waits at traffic lights.
In both the open- ended and closed- ended questions about route choice, the most highly rated
factors after distance had to do with safety. In the open- ended question, safety factors were the
only other issue listed by over a quarter of respondents. In the closed- ended questions, about
Mineta Transportation Institute
Executive Summary 3
half of respondents rated it as “ very important” to have traffic devices present and traffic
driving at safe speeds. The next most- cited “ very important” factor was having sidewalks in
good condition ( 43 percent). Aesthetic factors, in the sense of attractive landscaping or
buildings, were rated as very important by 35 percent of respondents, but raised by only 8
percent of the respondents in the open- ended question. The only other issues rated as “ very
important” by at least a quarter of respondents were having other people present ( which may
be a safety- related concern), and the absence of traffic lights with a long wait.
These results suggest that transportation planners and traffic engineers wanting to encourage
walking should pay particular attention to ensuring that pedestrians feel safe crossing streets
by keeping traffic to safe speeds and having traffic control devices present to help pedestrians
cross intersections. Other transportation infrastructure issues to address are sidewalk
availability and the length of time pedestrians must wait at traffic lights.
The fact that respondents significantly prioritized time saving over aesthetic qualities of the
built environment raises the question of whether pedestrian planners need not initially worry
too much about the urban design details that pedestrians experience. Our results suggest that
if people have a quality destination that they can walk to, they will walk unless there is some
significant barrier that prevents them from doing so. Perhaps the key to increasing the
number of walk trips is not to design pedestrian environments full of amenities such as
benches, tree cover, awnings, and wide sidewalks— although there is no doubt those assets can
greatly enhance the pedestrian experience— but rather to prioritize giving people places to
walk in an environment without any major barriers to walking.
Findings on the Survey Methodology: Implications for Research
The survey generated two key lessons for designing and interpreting research that collects
information on how far people walk and the routes they take. First, the study demonstrated
that asking participants to draw their route on a map works well. In addition, the study
demonstrated that data derived from questions asking pedestrians to estimate the distance
they walked must be interpreted cautiously.
Finding 4: Asking survey respondents to trace their walking route on a local map is an
effective research technique.
Asking respondents to draw their route on a map has been a relatively undocumented survey
technique, but the study results show that the technique is highly effective. Of the 328
surveys received, the map was filled out correctly 93 percent of the time, generating 261
routes that could be analyzed for actual distance and other route characteristics. The route
tracings were legible and precise enough that the research team had no trouble transferring the
exact routes into a GIS database where the distance could be automatically calculated and
walking routes recorded. In addition, the relatively high response rate for the survey overall
( 45 percent) shows that the presence of the map did not discourage people from completing
the survey.
4 Executive Summary
Mineta Transportation Institute
The results of the map question on the survey suggest that asking respondents to draw a route
on a map is an effective research technique that can gather high response rates. In addition to
generating data on walking routes, it is a useful way to assess walk trip distances.
Finding 5: Pedestrians vary considerably in how accurately they estimate the distance of a
regular walk trip.
Many travel surveys ask respondents to self- report the distances they travel. To date, there has
been little published research into how accurate those self- reported estimates might be. This
study found that the average difference between actual and perceived distance is modest,
although a significant minority of respondents were also fairly far off. At least half of all
respondents guessed within 0.13 miles of their actual route length. However, 25 percent of
respondents’ guesses were off by more than 50 percent or a quarter of a mile, suggesting that a
substantial minority do not have a precise idea of how far they walked. A few of the individual
guesses were also substantially off in terms of distance, as well as percent: guesses ranged from
up to 1.07 miles over to 0.88 miles under the correct distance.
The findings on reported walking distances suggest that researchers cannot assume that
pedestrians will provide a highly accurate estimate of the distances they walk, even for short
and routine trips. This finding is useful for assessing the value of survey data that ask for
self- reported walking distances. However, these study findings should be interpreted carefully
when applying them to other surveys. Our survey asked people to estimate the distance of a
route they walk routinely, so they may well have a more accurate sense of distance than they
would on a less familiar trip. Other surveys asking people to report the distances of routine
trips might have similar ( in) accuracies, but the study results should not be assumed to hold
true for other types of trips that surveyors ask about.
Findings on the Walkability Audit Methodology: Implications for Research
Through the data collection and analysis process, we developed several recommendations for
how best to conduct detailed, block- by- block walkability analyses. Findings six through eight
focus on ways to reduce the time burden of collecting walkability audit data, allowing a
research team to hone in on collecting only the most useful data. The final two findings
address the practicalities of collecting the data— whether to use Pocket PCs or pen and paper,
and the importance of ground testing maps if one uses a GIS- based system running on Pocket
PCs.
Finding 6: Spatially target the areas in which to collect walkability audit data.
Collecting data about the quality of street segments and intersections that pedestrians travel
through generated very interesting findings that correlated with respondents’ route choices,
but we quickly realized that applying such a tool ( or any walkability evaluation instrument) to
every location was an inefficient use of time. For many neighborhoods, one useful way to limit
the data collection burden is to focus on arterials and collector streets. It was also apparent
from our study sites that, in some study neighborhoods, it was almost unnecessary to audit
Mineta Transportation Institute
Executive Summary 5
residential streets because they were similar to each other and provided an adequate walking
environment.
Focusing the audit on arterials, collectors, and their associated intersections may be a better
use of data collection time for future projects. In essence, the more focused question could be
“ what makes a major automobile road more or less pedestrian friendly?” An alternative
research approach to streamline the walkability audit data collection process may be to audit
only those locations in a study area that have been identified as problematic.
Finding 7: Customize data collection by street type.
Based on the study experience, we concluded that walkability audit instruments should
differentiate among street types, so that surveyors only have to collect the data most relevant
to each type of street or path. It became clear during the walkability audit that arterial and
collector streets presented a different set of attributes that needed documentation compared to
neighborhood streets. Customizing data entry variables for different types of streets would
streamline the data collection process and allow a greater range of streets to be surveyed in a
shorter period of time. This strategy would also produce a more streamlined and relevant set of
data for analysis, reducing the time needed for the data analysis.
Finding 8: Consider using holistic, subjective measures of walkability instead of more
detailed quantitative measures.
We found that, in many cases, the subjective assessment of how safe or attractive a block was
seemed to better capture the pedestrian environment than did the many quantitative measures
included in the walkability audit. These subjective measures are also obviously much quicker
to collect, so future researchers may wish to concentrate on a few subjective measures only, to
save data collection time.
One limitation of relying solely on broad subjective evaluations of walkability is that these do
not provide decision makers with any guidance on how to design or retrofit areas targeted for
pedestrian improvements. However, for studies of pedestrian route preference, such subjective
measures may be enough to determine whether urban design features impact route choices or
not, or whether shortest routes are the predominant factor in influencing trip making. More
detailed audits of the design features in a neighborhood could be reserved for planning studies
where planners and decision makers wish to identify specific environmental features that need
to be upgraded.
Finding 9: Weigh carefully the benefits of collecting audit data on paper vs. on a Pocket
PC.
Lastly we reflect on the utility of an electronic and GIS- enabled approach to audit data
gathering versus a more traditional approach of paper, pen, and clipboard. The obvious benefit
of the handheld GIS computer approach is that by collecting data both in an electronic and a
GIS format, there is no need for subsequent data entry once the audit is complete. The GIS
data collection approach also eliminates the danger that data collected on paper will be
6 Executive Summary
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incorrectly entered into the computer database when later converting the data to a GIS
environment. Also, the GIS technology greatly reduces the total time involved, because the
data does not have to later be converted to GIS from a paper form or electronic database.
Disadvantages of the GIS technology were that recording field notes can be more difficult or
even impossible; audit questions must be answered in the order they are written, not as they
are observed; the battery life of Pocket PCs can be too short for all- day auditing unless
extended batteries are purchased; some people just find the Pocket PC too cumbersome to use;
carrying expensive computers while analyzing neighborhood streets and sidewalks can be
unsafe in certain neighborhoods ( or make auditors feel unsafe), and cost and technological
accessibility could be a problem for projects with limited budgets.
Finding 10: Ground truth base maps.
Although we found that collecting GIS- enabled data at a streetscape level was generally
straightforward, we did learn ( the hard way) that it is critical to ground truth the street base
map that will form the core of the data set before using the tool in the field. It is possible to
add or delete street segments or adjust street ranges in the field by using the ArcPad program
running on the Pocket PC, but it is critically important that some basic ground truthing of
the base GIS data be conducted prior to auditing the environment. It is also important to
check the address ranges of the streets within the TIGER data after uploading data to ArcPad
to ensure they are consistent with actual address ranges of the streets. We found address ranges
that were one block off, meaning we had to correct these errors in the map by hand before it
was possible to accurately geocode our survey data.
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7
INTRODUCTION
Understanding how the built environment impacts walking decisions is critically important as
our society begins to recognize that the unwalkable development patterns of the last sixty
years are unsustainable in terms of energy use, public health, and social cohesion. The
increasing interest in community walkability is reflected in the growing number of state and
federal initiatives on Safe Routes to School, the new concern over a national obesity epidemic
( especially in children), and the rising interest in smart growth and related policy approaches
to urban development. In each of these cases, walking is recognized as a key mode of travel,
and increased walking is viewed as a key goal.
Despite the seeming simplicity of the goal of building communities that are good places to
walk, we actually know very little about how our local infrastructure affects people’s
willingness or capacity to walk to access their desired destinations. A formidable challenge,
then, is to characterize the local environment from a pedestrian point of view, understanding
both the distance people are willing to walk to access a location and the characteristics of their
preferred routes.
The central research questions for this study are thus:
1. How far do pedestrians walk to rail transit stations?
2. What environmental factors influence their route choice?
The first question, about the distances people walk, provides data the transportation planning
community needs in order to plan communities that facilitate walking for residents. For
decades, community planners have tended to assume that pedestrians will only walk a quarter
or a third of a mile and planned neighborhoods accordingly. However, there is little evidence
to back up this rule of thumb. Indeed, there is very little evidence about how far people walk
for any type of trip. This paper helps to fill these gaps in knowledge about walking distances
by providing data on the distances people walk for one type of trip: commute trips to access
rail transit.
The second question recognizes that people base their decisions about walking on more than
simply whether or not it is possible to get to a destination within a reasonable distance; the
characteristics of each section of path that a pedestrian potentially uses could encourage or
dissuade a person from walking. Too often advocates for increased walking assume that low
rates of walking are a result of personality flaws such as laziness and thereby minimize the
larger impact of urban form on people’s capacity or desire to walk. This research report focuses
on how pedestrians experience the most local, micro- scale aspects of the physical environment
through which they walk, such as traffic control features or the presence or absence of
greenery.
Although there is growing interest among researchers in how pedestrians react to the
micro- level environment, few conclusive results have emerged from the body of work, as
8 Introduction
Mineta Transportation Institute
discussed in the next section of the paper. This report adds to that developing body of
literature. Conclusions from this research can then assist transportation and city planners to
plan, develop, and retrofit urban spaces that will support walking.
This research project collected two types of data to assess how far people walk to rail stations
and the environmental factors that influence their route choices:
1. Pedestrian survey: People who walked to train stations in Portland and the San Francisco
Bay Area were given surveys and asked about their pedestrian preferences and their
walking trip, including tracing their route on a map.
2. Walkability audit: A GIS and Pocket PC tool was developed to evaluate specific elements
of the walking environment at a streetscape scale within the project study areas.
After this data was collected, the survey results were analyzed to assess respondents’ own
perceptions of how far they walked and why they chose their route. In addition, the
walkability audit data was used to analyze the built environment characteristics of the routes
people chose to walk and the places they chose to avoid.
The remaining sections of the report discuss the body of literature to which our study
contributes, the study methodology, the results of the survey, and the analysis of the
walkability audit data. The study concludes with a series of ten findings and associated
recommendations for planning practice and future research methods.
Three findings from the survey about walkability stood out as particularly relevant for future
planning efforts. First, the survey showed that pedestrians walk considerably farther than
commonly is acknowledged. In addition, the survey responses indicated that the respondents’
primary goal in choosing a route was to minimize distance and time, but that safety and
aesthetic considerations were also important to them.
The survey generated two key lessons for designing and interpreting research that collects
information on how far people walk and the routes they take. First, the study demonstrated
that asking participants to draw their route on a map works well. In addition, the study
demonstrated that data derived from questions asking pedestrians to estimate the distance
they walked must be interpreted cautiously.
Through the data collection and analysis process, we developed several recommendations
related to the methodology for doing such detailed, block- by- block analysis. Three of these
focus on how to reduce the time burden of collecting the data, allowing a research team to
hone in on collecting only the most useful data. The final two recommendations address the
practicalities of collecting the data— whether to use Pocket PCs or pen and paper, and the
importance of ground- testing maps if one uses GIS running on Pocket PCs.
Mineta Transportation Institute
9
LITERATURE REVIEW: PEDESTRIAN ROUTE CHOICE
AND DISTANCES WALKED
As explained in the introduction, the study addressed two primary questions:
1. How far do pedestrians walk to rail stations?
2. What environmental factors influence their route choice?
For neither question is there a well- established literature providing firm answers. Rules of
thumb and educated guesses about walking behavior abound; however, relatively little
research exists regarding these topics in particular. Until the mid- 1990s, pedestrian behavior
was largely ignored in the transportation and planning literatures. In the last decade— and
especially the last five years— the topic has suddenly become popular and many studies about
pedestrians have been published or are underway. Much of the new literature has come from
the public health community, complementing work done by planning and transportation
researchers. Despite this outburst of activity, however, little of it has documented walk trip
distances and there is also little consensus about which environmental factors influence
pedestrians most.
WALK TRIP DISTANCES
Very little published literature looks specifically at how far pedestrians walk to any
destination, including rail stations. The main sources of information on walk trip distances are
the U. S. Census, National Household Travel Survey ( NHTS), and regional household travel
surveys. These surveys often report the number of walk trips made, but do not necessarily
include trip distances, and even when they do, the data is often suspect. In the 2001 NHTS,
for example, surveyors recoded many walk trip distances to the nearest mile. 1 Given that most
walk trips are quite short, this recording method makes the data almost useless for
understanding walk trip distances with any precision.
In terms of how far pedestrians walk to access rail transit specifically, most of the existing data
is collected when transit agencies conduct internal surveys of their passengers. Researchers
usually do not have easy access to this data, since transit agencies rarely publish their findings.
In addition, such surveys usually ask respondents to estimate the distance they walked, so the
data accuracy has been questionable because there is little research testing the reliability of
these estimates. One published study from the mid- 1990s, however, gathered a few such
surveys from the United States and Canada and conducted an additional survey of light rail
riders in Calgary, Canada. The authors found that the median walking distance to a rail station
in Calgary was about a fifth of a mile, though at suburban stations it was twice that distance. 2
10 Literature Review: Pedestrian Route Choice and Distances Walked
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THE INTERACTION BETWEEN WALKING AND THE BUILT ENVIRONMENT
Researchers have studied the second question, the environmental factors that influence route
choice, in somewhat more detail than walk trip distances, but the field is still very much in
development. The earliest and largest body of research on pedestrian behavior, which comes
from the transportation planning community, assesses the environmental factors that
influence people to choose one mode of travel instead of another. In general, the authors of
these studies want to understand how to shift Americans away from solo driving trips and
toward transit, biking, or walking. Because the research was usually designed solely to
discover why people choose to walk instead of drive, most studies did not examine the
distances or routes walkers traveled. The majority of these studies claim to look at what is
often called the “ three Ds,” density, diversity, and design, but in reality the studies tend to
focus on the first of the two Ds, density and diversity of land uses. Many researchers have
concluded that residents are more likely to walk in dense neighborhoods that include a diverse
mix of nonresidential uses within a short distance, 3 although a subset of the research
community remains unconvinced that the association is very strong, except for comparisons
between extremely high and extremely low densities. 4
Despite the rhetoric about the three Ds, these planning and transportation studies assessing
mode choice usually ignored micro- scale urban design and environmental factors, likely
because no pre- existing datasets captured design factors such as the presence of greenery,
attractive buildings, sidewalk quality, traffic control devices that aid pedestrians crossing the
street, or the presence of heavy traffic.
Nevertheless, in North America and Europe scattered studies starting in the 1970s
investigated such design factors, many focusing on how heavy traffic volumes discourage
walkers. 5 Since 2000, a burst of new research is taking on the design question more rigorously,
with a number of studies on the topic appearing in the last decade. 6 However, researchers have
quickly discovered that pedestrian behavior is highly complex and difficult to study, and the
existing body of research points to few consistent findings. One exhaustive review of the
evidence linking physical activity with the built environment concluded that there is limited
evidence showing a connection between neighborhood design and walking, but that further
research is needed to determine if there is truly no link or if existing research has not been
designed properly to reveal real relationships. 7
A new body of research recently trying to better understand how design impacts pedestrians
focuses on developing audit tools to collect data on and measure the variety of streetscape
elements that might promote or hinder walking behavior. These audit tools try to define the
context of the relationship between walking and urban form at a much finer geographic scale
and much more comprehensively than has been done before.
Moudon and Lee developed an audit tool and conceptual framework for measuring
walkability, both to set current work into a theoretical context and to help direct future
research efforts. To develop their framework, they performed an exhaustive review of over
thirty published methodologies and inventorying tools that have been developed to assess
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Literature Review: Pedestrian Route Choice and Distances Walked 11
walkability. They outlined a theoretical framework called the Behavioral Model of
Environments that seeks to account for personal, physical, and internal response factors that
may explain the connection between individual pedestrians and their walking environment. In
essence, Moudon and Lee attempt to lay the theoretical groundwork describing the
characteristics of place and urban form that influence pedestrian behavior. Because Moudon
and Lee do not test their variables in research with real pedestrians, their work provides no
evidence linking the urban design elements to actual walking behavior at the streetscape
scale. 8
Ewing et al. approached urban design professionals to gather professional opinions about
design and walkability to: ( 1) develop operational definitions of the built environment
relevant to pedestrians; and ( 2) translate those definitions into a field survey instrument. 9 The
basic goal of this research was to identify those more subtle urban design qualities that may
intervene between the nature of the built form and walking behavior. The study identified five
areas— imageability, enclosure, human scale, transparency, and complexity— that could be
reasonably measured to test the link between design and behavior.
One of the outputs of the research by Ewing et al. is a scoring sheet to measure specific design
elements within the five urban design categories of imageability, enclosure, human scale,
transparency, and complexity. Examples of the measurements include the number of
courtyards, plazas, and parks ( imageability), number of long sight lines ( enclosure and human
scale), proportion of windows at the street level ( transparency), and number of basic building
colors ( complexity). This work provides an important contribution in linking the pedestrian
experience in a specific space to the larger design elements of both the block and the city. It is
not clear, however, if these more subtle urban design elements impact pedestrian behavior or
preference for one route over another. It also seems that this work is geared more to casual
urban strolling rather than walking as an efficient mode of travel to access particular
destinations such as a transit stop. Finally, although this study presents characteristics of
urban design that may influence pedestrian perceptions, the study offers no evidence that the
measures do in fact influence pedestrian or route choice behavior because actual pedestrian
behavior was not incorporated into the study. 10
One pedestrian and urban design assessment tool that is looked upon as a standard in this
emerging field is an environmental audit instrument called SPACES. It is a comprehensive
tool that inventories the characteristics of the built environment along a roadway segment. 11
The authors categorize different factors of a walking environment into five classifications:
1. functional ( physical attributes of the street)
2. safety ( characteristics of a safe environment)
3. aesthetic ( elements such as trees or gardens)
4. destination ( relationship of neighborhood services to residences)
5. subjective ( attractiveness and difficulty)
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Examples of the measures include intersection design, path continuity, path design, path
maintenance, path surface, traffic speed, cleanliness, trees, and lighting.
Building on SPACES, Clifton and Livi developed the Pedestrian Environment Data Scan
( PEDS) audit tool, which includes 78 measures of streetscape characteristics that other
research has shown to influence walkability. Clifton and Livi studied the inter- rater reliability
of the instrument in order to understand the potential of such tools to be used in broad
geographic areas with a diversity of audit administrators. They found relatively high
reliability scores for many of the questions contained within the audit instrument, despite a
wide range of street segment uses, conditions, and aesthetics. 12
Finally, despite the development of these new conceptual and operational frameworks for
assessing local walkability, researchers have been limited by the amount of time required to
conduct block- by- block assessments of every street segment and intersection within a study
area. As researchers identify more aspects of the built environment that may be important in
creating walkable environments, the burden of applying those measures to each street segment
grows. Thus, actual application of walkability audit tools has lagged despite a growing
number of them being available to planners.
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13
DATA COLLECTION METHODS
This study used two primary data collection methods: 1) a pedestrian survey; and 2) a
block- by- block audit of the walking environment in two neighborhoods. Surveys were
distributed at five transit stations. Two were in California’s San Francisco Bay Area: one in San
José ( Japantown) and one in El Cerrito ( El Cerrito Plaza). The other three were in Portland,
Oregon ( Hollywood, Gresham, and Rockwood). The Walkability Audit focused on the El
Cerrito Plaza and Japantown station areas. Details of the methods and the study sites are
presented below.
STATION AREA SELECTION
The primary criteria for selecting the station areas was to find neighborhoods where
pedestrians would have a reasonably high number of different route options. Because we
assumed that people would not be willing to walk more than a little bit out of their way to
find a nicer route, we selected only neighborhoods with streets laid out in a grid pattern. With
a grid street pattern, respondents had multiple routes to choose from that were all
approximately the same distance. We also chose neighborhoods where walkers would have a
mix of local and collector or arterial streets, as well as both residential and mixed- use or
commercial streets. The stations finally selected were chosen after a combination of site visits,
visual overview from aerial photographs, and review of basic census and transit agency
ridership information in order to choose stations that had a potentially sufficient number of
people who accessed transit by foot.
Japantown
The Japantown station, in San José, California, is part of the Santa Clara Valley Transportation
Authority’s light rail system ( see Figure 1). The light rail system has 62 stations and 77 miles
of tracks, and it serves northern Santa Clara County. Overall ridership is relatively small, with
about 21,000 weekday boardings in 2005.13
The station is located in historic Japantown, an area of traditional neighborhoods just outside
of downtown San José. Built environment conditions in the area are slowly improving, but
maintenance and other conditions still vary substantially from block to block. Several
medium- and high- density residential projects have been completed since 2000, to the east
near Japantown and to the south near First and Julian Streets. In contrast to this walkable
environment, the area west of Highway 87 is largely designated for open space to protect the
airport flight path, which further strengthens the pedestrian boundary created by the freeway.
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Figure 1 Looking East to Japantown Station
El Cerrito
The El Cerrito Station ( see Figure 2) is part of the Bay Area Rapid Transit system ( BART),
which serves four counties in the Bay Area region. The system has 101 miles of tracks and 66
stations. 14 In 2005, BART reported almost 93 million passenger trips. 15
The neighborhood around the El Cerrito Plaza BART station is laid out in a grid street
network. The area is primarily residential, with several commercial streets, plus a large
shopping center to the south of the BART station. Underneath the BART tracks runs a
popular bicycle and pedestrian path, the Ohlone Greenway.
The catchment area for potential walkers to the BART station is quite large. There are no
competing BART stations within walking distance, although there is frequent bus service
along San Pablo Avenue, as well as lines that run along Fairmount, Central, and Pierce, all of
which stop at the BART station. There are no major barriers created by freeways or other
features of the built environment. To the east of the station, the neighborhoods rise up a
moderately steep hill. To the west, the land is relatively flat except for a large hill about 1/ 3
mile to the southwest.
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Data Collection Methods 15
Figure 2 Looking East to El Cerrito Plaza Station
Hollywood ( Portland)
The three Portland area stations are all on the TriMet Max Light Rail system, east of
downtown Portland. There are 44 miles of track and 66 stations on the system’s three lines.
Average weekday boarding across the light rail system is about 100,000 per day. 16
The Hollywood station ( see Figure 3) lies between a freeway and a heavy rail line, and is
accessed from either side by a pedestrian foot bridge. One side of the station consists mainly of
residential housing, with mostly residential streets closest to the station. The other side of the
station is bordered by a bus drop- off zone, commercial and office space, and a combination of
multi- family and single- family residential sections. This side also has two fairly heavily used
arterials bisecting the space.
Figure 3 The Hollywood Station, Located Between Heavy Rail and the Freeway
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Gresham ( Portland)
The Gresham station is adjacent to a centralized bus hub, and the two transit facilities
combined are considered one of TriMet’s transit centers. The Gresham area was developed
prior to World War II and to the south and east of the station there is a street grid pattern
typical of that era. There are no arterials or other major roads between this residential area and
the station. Outside this gridded area, there are a number of major roads, some within the
quarter- mile area. There are also large commercial areas and offices nearby, and a mixture of
both single- family and multi- family residential areas ( see Figure 4).
Figure 4 Looking at Both Sides of the Gresham Station
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Data Collection Methods 17
Rockwood ( Portland)
The Rockwood Transit Station ( see Figure 5) is located on 188th and East Burnside in
Gresham. The east and westbound platforms are separated by the signal at 188th. The station
sits on a busy commercial corridor with multi- family and single- family residences adjacent to
it in all directions.
The station is accessible via one bus line and there are sidewalks throughout the neighborhood
area around the transit stop. There are signalized crossings at Burnside and 188th, but the
distance to cross is quite long because the streets are major arterials. Directly across from the
westbound platform sits a large commercial lot that is currently unoccupied, although it has
become an informal park- and- ride lot.
Figure 5 The Westbound Train at the Rockwood Station
PEDESTRIAN SURVEY
In the survey conducted for this research, respondents were asked a series of questions about
how far and how long they walked to the station, what factors influenced their choice of route,
their attitudes toward walking, and some basic demographic questions. The survey
questionnaire is included in Appendix A.
Surveys were distributed at transit stations to people who walked to the transit stop. Between
one and three surveyors distributed surveys, depending on the day and station, and they
worked between 6 A. M. and 10 A. M. on mostly weekday mornings from February to May
2006. The surveyors followed a script for consistency. At four of the stations, surveyors
approached all people waiting at the station and ask how they arrived at the station. At the El
Cerrito BART station, which has higher ridership, the surveyors selected a random sample of
the riders waiting on the platform. 17
Those people who responded that they walked to the station were asked follow- up questions to
determine their eligibility for the study: ( 1) if they were over 18 years of age, and ( 2) if they
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would be willing to participate in the study anonymously. Willing survey respondents
received a six- page written survey, a pen, and a pre- addressed and stamped return envelope.
They were asked to either return the completed survey to the surveyor at the station or mail it
back in the pre- stamped envelope. Very few respondents returned surveys at the station
because the trains arrived quite frequently.
The survey included three sections:
1. Questions on walking behavior, preferences, and route choice.
2. A map inserted in the survey on which respondents were asked to trace their walking
route. Respondents were also asked to mark intersections and streets they avoided on their
walk.
3. Basic demographic questions.
A total of 328 surveys were returned. Table 1 shows the number returned per station, as well
as the response rate per station. Almost two- thirds of the surveys ( 64 percent) came from the
two Bay Area stations; over a third of the surveys came from El Cerrito Plaza station and just
over another quarter came from the Japantown. Of the remaining surveys, almost a quarter
came from Portland’s Hollywood station ( 24 percent), and the Gresham and Rockwood
stations in Portland generated the remaining few.
The response rate for the survey was quite high. For the total population, the response rate was
45 percent. El Cerrito Plaza had the highest response rate at 71 percent, whereas response rates
from the other stations ranged from 15 percent to 49 percent. We calculated the response rate
as the number of surveys returned as a proportion of the number of surveys distributed. Some
transit riders approached by our surveyors were not given a survey to complete because they
did not wish to participate, had not walked to the station, or were under age 18, or because the
train approached too quickly after they arrived on the station platform.
Table 1 Survey Response Rates by Station
Station Number of Completed Surveys Response Rate% a
a. Response rate is defines as the number of surveys returned as a proportion of the number of
surveys distributed. Some riders contacted were not given a survey because they had not
walked or refused to participate.
El Cerrito Plaza 120 71
Japantown 90 49
Hollywood 78 45
Gresham 15 15
Rockwood 25 23
Total 328 45
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Data Collection Methods 19
Although some surveys had missing responses for a few individual questions, all of the surveys
were complete enough to be included in the final data set. The number of completed responses
varied slightly for each question, however. Of the 328 surveys received, the map was filled out
correctly 93 percent of the time, generating 261 routes that could be analyzed for actual
distance and other route characteristics.
WALKABILITY AUDIT
A comprehensive audit of the physical environment within 1/ 2 to 3/ 4 mile of the Japantown
light rail station and the El Cerrito BART station was conducted to assess various aspects of
the built environment that previous researchers have identified as likely to affect a
neighborhood’s walkability. The audit instrument developed for this study is included in
Appendix B.
The audit tool assessed block segments and intersections separately, because pedestrians
experience the two in different ways. For each block segment, the auditor assessed the
characteristics listed in Table 2. The first three questions asked the auditor to enter holistic,
subjective assessments: how attractive the block segment was, how safe from traffic the auditor
felt walking there, and how safe from crime the auditor felt. These holistic and rather
subjective assessments were followed by questions about a detailed set of specific factors
addressing maintenance and cleanliness, amenities, sidewalk characteristics, buffer zone
characteristics, front zone characteristics, and roadway characteristics. These questions were
designed to collect more quantitative data. For each intersection, the audit collected data on
factors affecting the ease of crossing the street, such as the presence of traffic control devices,
crosswalks, and curb cuts ( for more details, see Table 2 and Appendix ). The intersection audit
collected data on just six variables, including traffic control devices and crossing infrastructure
( see Figure 6 for a photo of the audit tool; Figure 7 for sample screenshots of the tool in use).
20 Data Collection Methods
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Figure 6 Audit Tool
Table 2 Variables Included in Walkability Audit
Street Characteristics Intersection Characteristics
Attractive for walking Traffic signals
Safe from crime Safe crossing
Safe from traffic Pedestrian crossing signs
Landscape maintenance Number of curb cuts
Building maintenance Crosswalks
Broken, boarded, or bars on
windows
Litter
Graffiti
Benches
Buffer width
Grass/ hedges/ cement in buffer
Number of street trees
Slope
Sidewalk width
Sidewalk condition
Walk through parking lots to
buildings
Number of off- street parking
spaces
Percent of block used for
off- street parking
Number of medium/ high
volume driveways
One- way or two- way street
Number of traffic lanes
On- street parking ( 0, 1, 2 sides)
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Data Collection Methods 21
Figure 7 Examples of the Walkability Audit Data Entry Forms
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Mineta Transportation Institute
23
ANALYSIS OF SURVEY FINDINGS
This section first describes the basic sociodemographic characteristics of the survey
respondents, and then discusses the results of the survey. The results discussed include
respondents’ trip purposes, how many people stopped along their walk and what for, how far
respondents said they walked, our own calculations of the distances they traveled, the factors
that influenced their route choices, and their attitudes toward walking.
WHO WERE THE SURVEY RESPONDENTS?
Table 3 summarizes some sociodemographic statistics about the survey respondents. They
were roughly half male and half female, about three- quarters self identified as white, and
three- quarters were adults between the ages of 30 and 59. The median household income was
$ 60,000, and slightly over half the respondents were renters rather than homeowners. Almost
one- third of the group rarely or never had access to a car, indicating that a fairly high
proportion of the respondents were transit dependent.
The groups of respondents from each station were roughly similar to the total population of
respondents, with just a few notable differences. The Bay Area respondents were a racially
diverse group, whereas the Portland respondents were nearly all white. Also, the small sample
of respondents from the Portland stations of Gresham and Rockwood had considerably lower
household incomes and, correspondingly, were more likely to rent than own their homes. The
Rockwood population was also highly transit dependent, with 67 percent saying that they
never or only occasionally had access to a car.
TRIP PURPOSES AND ORIGINS
Most respondents made home- based trips to work ( see Table 4). Among the full population,
81 percent made commute trips, another 5 percent made trips to school, and 8 percent made
personal shopping trips. This pattern held roughly consistent across all the stations, except
that Japantown had fewer commute trips and considerably more shopping trips ( 21 percent),
whereas Gresham riders made fewer commute trips and more trips to school ( 33 percent).
Respondents walked to the stations from a wide variety of origins. Figure 8, for example,
shows a map of El Cerrito respondents’ origin points.
24 Analysis of Survey Findings
Mineta Transportation Institute
Figure 8 Survey Respondent Origins, El Cerrito BART Station
STOPS DURING THE TRIPS
After reporting how far and for how long they walked, respondents were asked if they had
stopped along the way. If they had, follow- up questions probed the reason for the stop and
how long they stopped for. The vast majority, 87 percent, did not stop ( see Table 5). Of the 13
percent of respondents who did stop, about half stopped to buy food or a drink; the others
stopped either to buy a newspaper, to talk to somebody, or for “ other” reasons. The median
time for these stops was just three minutes, consistent with stops made by people popping
into a small business to make a quick purchase. The average stop times were longer, however,
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Analysis of Survey Findings 25
reflecting the fact that some people did stop for much longer time periods ( up to 45 minutes
for the longest stop).
Table 3 Demographics of Survey Respondents
Bay Area Portland
All
Stations El Cerrito Japantown Gresham Hollywood Rockwood
Gender
Male 53% 49% 66% 40% 47% 52%
Female 47% 51% 34% 60% 53% 48%
Race
White 74% 68% 59% 93% 96% 86%
Black 2% 0% 2% 0% 1% 9%
Asian/ Pacific
Islander 15% 23% 20% 0% 1% 5%
Other 5% 4% 11% 0% 0% 0%
Mixed race 5% 5% 9% 7% 1% 0%
Age
18– 29 19% 15% 25% 23% 15% 29%
30– 39 30% 34% 26% 15% 32% 29%
40– 49 23% 20% 26% 39% 25% 13%
50– 59 20% 25% 12% 8% 23% 25%
60+ 8% 7% 10% 15% 6% 4%
Household income
Median $ 60,000 $ 80,000 $ 60,000 $ 35,000 $ 70,000 $ 20,000
Own/ rent home
Own 44% 45% 38% 29% 60% 21%
Rent 56% 55% 62% 71% 40% 79%
Driver’s license?
Yes 84% 91% 76% 80% 86% 75%
No 16% 9% 24% 20% 14% 25%
Access to a car?
Never/ occasionally 30% 16% 36% 33% 32% 67%
Most of the
time/ always 70% 84% 64% 67% 60% 33%
Table 4 Trip Purposes by Station
Bay Area Portland
Trip Purpose All Stations El Cerrito Japantown Gresham Hollywood Rockwood
Work 81% 87% 68% 86% 84% 67%
School 5% 4% 2% 4% 4% 33%
Personal
shopping 8% 3% 21% 6% 4% 0%
Other origin 6% 6% 8% 4% 8% 0%
Home 96% 99% 92% 95% 100% 100%
Work 1% 1% 1% 1% 0% 0%
Other 3% 0% 7% 4% 0% 0%
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TRIP DISTANCES
Self- reported distances
Respondents were asked how far they had walked, in both miles and blocks. Almost all
respondents entered the number of blocks ( 91 percent), but only 64 percent entered the
distance in miles. 18 For the full group of respondents, the mean reported distance was 0.58
miles ( see Table 6). Looking at how the data broke out in quartiles shows that a quarter of
people reported walking just a quarter of a mile or less, the second quartile of people reported
walking between a quarter- mile and a half- mile, the third quartile reported walking between
half a mile and almost a full mile ( 0.95 miles), and the final quarter said they walked more
than 0.95 miles. The responses clustered around a quarter mile, half mile, and one mile,
indicating the tendency of people to round off distances.
Table 5 How Many People Stopped, For What Reason, and How Long
Bay Area Portland
Combined El Cerrito Japantown Hollywood Rockwood Gresham
% stopping for any
reason 13% 10% 12% 14% 32% 0%
% stopping for:
Food 7% 10% 4% 9% 28% 0%
Newspaper 2% 4% 2% 0% 4% 0%
To talk 2% 8% 6% 0% 0% 0%
Other 4% 2% 3% 8% 0% 0%
Time stopped
Mean 6 min. 6 min. 5 min. 7 min. 7 min. n/ a
Median 3 min. 3 min. 2 min. 3 min. 6 min. n/ a
Table 6 Self- Reported Distance Walked in Miles, Blocks, and Minutes
Distance in miles
( percentiles)
Distance in blocks
( percentiles)
Time in minutes
( percentiles)
mean 25th 50th 75th mean 25th 50th 75th mean 25th 50th 75th
All stations 0.58 0.25 0.5 0.95 6 3 5 8 10 5 10 12
Bay Area
El Cerrito 0.65 0.25 0.5 1 6 3 5 8 11 6 10 15
Japantown 0.45 0.13 0.28 0.69 4 2 4 6 8 5 6 10
Portland
Gresham 0.43 0.11 0.3 0.8 4 2 2 4 7 3 6 10
Hollywood 0.62 0.39 0.5 1 8 4 6 10 11 5 10 13
Rockwood 0.49 0.25 0.5 0.75 5 2 3 6 10 5 10 13
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Analysis of Survey Findings 27
Actual distances
We asked respondents to trace on a map the route they walked. For the El Cerrito, Japantown,
and Hollywood stations, these routes were entered into a GIS database and the information
used to calculate the exact length of each trip. The mean trip distance was just over a half mile
( see Table 7), with the shortest trip 0.02 miles and the longest 1.88 miles. Looking at the
distance data broken into quartiles shows that a quarter of respondents walked a quarter mile
or less, the next quartile walked between a quarter and half mile, the third quartile walked
between a half and two- thirds of a mile, and the final quarter walked over two- thirds of a mile.
The accuracy of self- reported distances
We were interested to learn how accurately respondents estimated the distances they had
walked. Many travel surveys ask respondents to estimate the distances they walk, but little is
known about how accurate these estimates are. Close to half of the responses analyzed ( 43
percent) were quite accurate guesses, within a tenth of a mile ( see Table 8). 19 However, other
guesses were highly inaccurate, ranging from up to 1.07 miles over to 0.88 miles under the
correct distance. The average guess was off by about 0.2 miles. Percentage- wise, guesses were
off by 45 percent of the actual distance on average, with 25 percent of respondents guessing
within 11 percent and half guessing within 30 percent of the correct distance in miles. On the
other hand, 25 percent of respondents’ guesses were off by more than 50 percent, a
surprisingly large error, and 10 percent were off by more than 90 percent. It should be noted
that, because the distances walked were short, the actual error in miles was trivial for most
respondents, although 26 percent of respondents made guesses that were off by a quarter of a
mile or more.
Table 7 Actual Distances Walked
Distance
( miles)
Mean 0.52
Medium 0.02
Maximum 1.88
25th percentile 0.27
50th percentile 0.47
75th percentile 0.68
Table 8 Accuracy of Self- Reported Trip Distances
Accuracy of Distance Estimate Percent
Cumulative
Percent
Within .1 mile 43 43
Off by .1 to. 25 mile 31 74
28 Analysis of Survey Findings
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CONSISTENCY OF ROUTE CHOICES
The survey asked respondents two questions designed to identify how much they varied their
route from day to day. After respondents drew on the map the route they had walked that day,
the survey asked, “ The last time you walked here from the same place, did you take the exact
same route?” ( See Appendix A, Question 5). Virtually all ( 92 percent) said that they had. A
follow- up question asked respondents how many different routes they took during the last five
times they walked to the station when leaving from the same destination ( Appendix A,
Question 6). This question revealed only slightly more variation. Seventy- four percent said
that they took the same route for all five trips, and another 19 percent reported taking only
two different routes over the five trips ( see Table 9). To look at the data another way, only six
percent varied their route frequently, taking three or more routes over the course of five trips.
FACTORS INFLUENCING ROUTE CHOICES
After respondents traced their walking route on the map, the survey asked them to identify
the factors that led them to choose a particular route. The survey addressed this issue in three
steps. First, respondents were asked the open- ended question, “ What are the main reasons why
you chose your route today?” and given space to write three answers in their own words. On
the next page, respondents were asked to rank the importance of 11 potential factors that
might have influenced their route choice. The instructions read, “ Below is a list of factors that
other researchers have found to influence the routes people walk along. For each one, please
Off by .25 to .5 mile 20 94
Off by > .5 mile 6 100
Table 9 Consistency of Route Choice
# of Different
Routes Last 5
Times
Walking
Percent
1 74a
a. Includes people who
responded “ zero,” which
we assume was an error
and intended to be “ 1.”
2 19
3 5
4 1
5 1
Table 8 Accuracy of Self- Reported Trip Distances
Accuracy of Distance Estimate Percent
Cumulative
Percent
Mineta Transportation Institute
Analysis of Survey Findings 29
mark how important it is to you.” Finally, a last open- ended question asked, “ Are there any
other factors, positive or negative, that influenced your choice of route today?” Relatively few
people answered this final question, so only the results of the first two questions are discussed
below.
The first, open- ended question showed that by far the most important factor was choosing the
shortest or fastest route. As shown in Table 10, 52 percent of respondents wrote this as the
first item in their list, and almost two- thirds mentioned this factor somewhere among their
three responses. An additional 9 percent of respondents mentioned “ convenience” as an
important factor, and it may well be that convenience was their way of expressing the same
concept— choosing the quickest route.
The second most common set of responses had to do with safety, mentioned by 28 percent of
respondents. Most of these responses related in some way to safety from traffic, such as low
traffic volumes or an intersection where it was easy to cross a large street. Only a small number
of people described safety issues in terms of crime. Although safety was a fairly common
response somewhere in the list of three answers, only 8 percent of people put it as their first
item on the list. Safety was somewhat more common as the second item, appearing here 14
percent of the time.
Finally, very small numbers of respondents mentioned choosing their routes based either on
the attractiveness of the route ( e. g., nice landscaping or attractive buildings) or because they
wanted to stop at a particular business.
These priorities were partially validated in the next question, which asked respondents to rate
the importance of 11 different factors. As shown in Table 11, 99 percent of respondents rated
choosing the shortest route as either very important or somewhat important, with the bulk of
those saying it was very important ( 82 percent of respondents). This finding confirms the
results of the open- ended question, where responses related to distance predominated. On the
other hand, safety considerations showed up as considerably more important in the second
Table 10 Percent of People Volunteering a Factor as Influencing Their Route Choice
Factor Type
Anywhere
in List
First Second Third
Shortest/ fastest 64 52 10 3
Safety 28 8 14 6
Convenience 9 6 2 1
Attractive 8 2 3 2
Habit 6 3 1 2
Stopped at a business 3 2 2 0
Other 27 13 9 5
Meaning of response unclear 16 9 5 3
Left blank n/ a 3 50 77
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question than they did in the previous, open- ended question. About half of respondents rated
as “ very important” having traffic devices present and having traffic drive at safe speeds, and
those numbers jumped considerably, to 85 percent and 87 percent, when one combines the
responses of people who responded that these factors were either very or somewhat important.
Other factors rated as “ very” or “ somewhat” important by at least 50 percent of respondents
were: having sidewalks in good condition; the presence of attractive buildings, trees, and
landscaping; having no traffic lights where it took a long time to cross; the presence of other
people out walking; and having shops or businesses to stop in. Only the first two of these
( sidewalks and attractive buildings) were rated as “ very important” by at least a third of
respondents, however. Finally, three factors rated as important by relatively few people were
having shops or businesses with windows to look at, having benches or other places to sit, and
having a friend or neighbor along the route.
Note: Factors were ordered differently in the survey itself.
ATTITUDES TOWARD WALKING
Toward the end of the survey, respondents were asked how strongly they agreed with a series
of statements describing different reasons that they might choose to walk. Overall,
respondents had very positive attitudes toward walking, which may explain their high level of
willingness to complete and return the survey. The first two questions asked if people liked
Table 11 Respondent Ratings: Importance of Factors That Might Influence Their Route
Choice
Question: Below is a list of factors that other researchers have found to influence the
routes people walk along. For each one, please mark how important it is to you.
Statement
Strongly
Agree
(%)
Agree
(%)
Disagree
or
Strongly
Disagree
(%)
Shortest route 82 17 1
Traffic devices are present 55 30 15
Traffic drives at safe speeds 46 41 13
Sidewalks in good condition 43 44 13
Presence of attractive buildings, trees, and landscaping 35 44 21
No traffic lights where it takes a long time to cross 29 39 32
Other people out walking 23 37 40
Shops/ businesses to stop in 14 32 54
Shops/ businesses with window to look in 11 25 65
Benches/ places to sit 11 15 75
Friend/ neighbor along the route 7 18 75
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Analysis of Survey Findings 31
walking and if they found walking relaxing, and in both cases 97 percent either strongly
agreed or agreed with the statement ( see Table 12). Another question asked if respondents
walked in order to get exercise or health benefits, and again virtually all agreed or strongly
agreed ( 94 percent). Slightly lower percentages of people agreed that they sometimes walk
because it is the most convenient mode of travel ( 89 percent) or because it is the cheapest way
to travel ( 80 percent).
In sum, the survey results show that pedestrians walking to a rail station for their morning
commute are willing to walk considerably longer than previously thought, desire to minimize
their walk distance and time, pay attention to safety and their walking environment, and do
not often vary their route.
The following section takes a closer look at how safety and the walking environment were
evaluated using a walkability audit tool designed to rate specific characteristics of the walking
environments in the station areas.
Table 12 Attitudes Toward Walking
Question: For each statement below, please mark how strongly you agree or disagree with it.
Statement
Strongly
Agree
(%)
Agree
(%)
Disagree or
Strongly
Disagree
(%)
I like walking 78 19 3
Walking is relaxing 70 27 3
I walk to get exercise or other health benefits 71 23 6
I sometimes walk because it is the fastest and/ or most convenient
way to get somewhere 55 34 12
I sometimes walk because it is the cheapest way to get around 46 34 19
32 Analysis of Survey Findings
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33
ANALYSIS OF WALKABILITY AUDIT DATA
The next step of the research was to evaluate and measure the features of the built environment
around the El Cerrito BART and Japantown light rail stations. Once the built environment
features were identified, measured, and mapped, we could evaluate the built environment
characteristics of the actual routes people chose to walk in order to identify any patterns.
The study area locations are served by fairly gridded street patterns which offer alternative
routes with similar overall distances. The question, then, is: when a pedestrian chooses one
shortest path over another, what factors in the built environment ( if any) influence that
choice? One could imagine that a pedestrian either chooses a certain path because of its
“ pedestrian friendliness” or alternatively chooses a path that avoids areas that are “ unfriendly”;
that is, a good path may be one that has an absence of repulsion. This section presents findings
from the comparison between the walkability audit and the actual routes that people walked
using data collected at the Japantown light rail station.
The walkability audit tool was described in more detail earlier in this report. As an overview,
however, the audit tool contained about 60 different built environment variables that ranged
from subjective questions such as “ How attractive is this street segment?” to specifying
objective characteristics such as whether a buffer exists between the sidewalk and street and
whether this buffer is made of grass, trees, concrete, other landscaping, or some combination of
these attributes.
Each built environment factor was a numeric score depending on how it was rated ( e. g., a
sidewalk in good condition may receive a score of 3 and a sidewalk in fair condition may
receive a score of 1). In some cases, factors were looked at individually and in others they were
combined to create indices of built environment characteristics. Maps of the audited
characteristics were then produced and used to highlight street segments that were either very
good or very poor quality walking environments in terms of the urban design and
environmental characteristics measured by the audit.
INTEGRATING THE SURVEY AND AUDIT DATA
The survey asked respondents to trace their actual walking route on a map of the station area.
Each of these traced routes was then converted into a digital form for analysis within the GIS
mapping environment ( see Figure 9). The discussion below summarizes the results from the
micro- scaled analysis of streetscape and individual route choice of our study sample.
34 Analysis of Walkability Audit Data
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Figure 9 Actual Walking Routes— Japantown Station
AUDIT DATA ANALYSIS— A SPATIAL OVERVIEW
Combining the survey and audit data into the same maps allowed us to compare our
assessments of the physical walking environment with the routes that the survey respondents
took while walking to their transit stop. The types of data used in the analysis were the audit
data, the pedestrian origin points, the actual walking routes, and the streets and intersections
that survey respondents said they avoided. Basic analysis of the audit data involved mapping
each audit characteristic and examining the results for nodes or corridors where the streets
varied substantially from those in the area as a whole.
For some of the audit measures, there was little variability in the study areas, so analysis to see
if people avoided or sought out routes exhibiting those traits was impossible. In particular,
sidewalk conditions, which were found to be important in previous studies, were quite good
throughout both areas and therefore did not appear to influence route choices. In addition, the
study areas were safe from traffic overall, so analysis of this factor was also impossible. Few
traffic calming devices were found in the station areas, so it was not possible to analyze the
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Analysis of Walkability Audit Data 35
influence of features such as traffic circles and curb extensions. However, the subjective
measures, and measures related to green buffers, street trees, home and landscape maintenance,
and litter displayed more variability.
Integrating Survey Data
We were interested to see if our survey respondents chose or avoided segments shown by the
walkability audit to have especially agreeable or disagreeable characteristics. If such
correlations were found, perhaps a pattern of characteristics that most influence pedestrian
choices would also be revealed.
Pedestrian volumes along each street segment were calculated so that we could add this to our
analysis of the layer. The map in Figure 10 shows an example of pedestrian volumes overlaid
on the “ safe from crime” audit variable for the Japantown area. There is some indication that
respondents avoided streets rated as very or somewhat unsafe.
Figure 10 Pedestrian Volume With Safe From Crime Audit Data
36 Analysis of Walkability Audit Data
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AUDIT- BASED INDICES
As our analysis evolved, we realized that although it was easy to understand the relevance of
the more general, subjective measures ( attractiveness for walking, safety from crime, safety
from traffic, and safe crossing at intersections), it was difficult to make use of the more specific
audit characteristics such as the presence of litter and the number of street trees per 1000 feet.
It seemed unlikely that any single one of these characteristics would, on its own, influence
walking routes. Therefore, many of these specific audit measures were combined to form three
composite measures: a General Appearance Index, a Greenery Index, and an Overall Index.
These indices are discussed below using data from the Japantown station area. 20
General Appearance Index
A high score for the general appearance index represents an attractive block, which we defined
as a litter- free street segment with well- kept buildings and gardens. Table 13 lists the
individual variables that make up the General Appearance Index and the associated weighting
of each potential variable response.
Figure 11 shows this index applied to a map of the Japantown area. Most of the blocks around
each study area were of average to good appearance. Very few street segments had a poor
overall appearance.
Table 13 General Appearance Index
Measure Response Values Index Valuesa
a. Index range is 0 to 5; Low = 0 to 2, Medium = 2.5 to 3.5, High = 4 to 5
Weight
< 50% = 1 0
Landscape Maintenance 50% to 75% = 2 0.5 Unweighted
> 75% = 3 1
< 50% = 1 0
Building Maintenance 50% to 75% = 2 0.5 Unweighted
> 75% = 3 1
None or almost none = 0 1
Litter Some = 1 0.5 Unweighted
Lots = 2 0
None or almost none = 0 1
Graffiti Some = 1 0.5 Unweighted
Lots = 2 0
Bars/ Boarded/ Broken
Windows?
Yes = T 0 Unweighted
No = F 1
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Analysis of Walkability Audit Data 37
Figure 11 General Appearance Index, Japantown
Greenery Index
A high score for the greenery index represents a street segment with an extensive green canopy
or environment. Table 14 lists the individual variables that make up the Greenery Index and
the associated weighting of each potential variable response. Figure 12 shows this index
spatially presented for the Japantown area. Most of the area surrounding the transit stops had
average or good scores on the greenery index, accurately reflecting the common presence of
trees and grass sidewalk buffers.
38 Analysis of Walkability Audit Data
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Figure 12 Overall Greenery Index, Japantown
Table 14 Greenery Index
Measure Response Values Index Valuesa
a. Index range is 0 to 5, with Low = 0 to 2; Medium = 2.5 to 3.5; High = 4 to 5
Weight
Only cement 0
Buffer Greenery Cement/ grass/ hedges 0.5 Unweighted
Only grass/ hedges 1
0 to 15 0
Trees per 1,000 Feet 15.01 to 25 0.5 2
> 25 1
Buffer Width
No buffer = 0 0
Unweighted
< 1 foot = 1 0
1 foot to 4 feet = 2 0.5
> 4 feet = 3 1
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Analysis of Walkability Audit Data 39
Overall Appearance Index
The overall appearance combines the general appearance index with two measures from the
greenery index and two parking measures ( see Table 15). A high score for the overall
appearance index represents a clean street segment with well- kept buildings and gardens, a
relatively high number of street trees, greenery in the buffer, and few or no large parking lots
visible from the sidewalk. Table 15 lists the individual variables that make up the Greenery
Index and the associated weighting of each potential variable response. Figure 13 shows this
index spatially presented for the Japantown area. As with the other two indices, the map shows
that the general appearance was decent or good in most of the surrounding street segments.
Table 15 Overall Appearance Index
Measure Response Values Index Valuesa
a. Index range is 0 to 9; Low = 0 to 4, Medium = 4.5 to 6.5, High = 7 to 9
Weight
< 50% = 1 0
Landscape Maintenance 50% to 75% = 2 0.5 Unweighted
> 75% = 3 1
< 50% = 1 0
Building Maintenance 50% to 75% = 2 0.5 Unweighted
> 75% = 3 1
None or almost none = 0 1
Litter Some = 1 0.5 Unweighted
Lots = 2 0
None or almost none = 0 1
Graffiti Some = 1 0.5 Unweighted
Lots = 2 0
Bars/ Boarded/ Broken
Windows?
Yes = T 0
Unweighted
No = F 1
Only cement 0
Buffer Greenery Cement/ grass/ hedges 0.5 Unweighted
Only grass/ hedges 1
0 to 15 0
Trees per 1,000 Feet 15.1 to 25 0.5
> 25 1 2
Walk Through Parking Lots?
No = F 1
0.5
Yes = T 0
Percent of Block Used by
Parking Lots
None = 0 1
0.5
< 30% = 1 1
31% to 60% = 2 0
> 60% = 3 0
40 Analysis of Walkability Audit Data
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Figure 13 Overall Appearance Index, Japantown
Reflection and Use of Audit Data
It is fairly clear after viewing the maps above that there is little variability across most streets
in the study areas. In addition, the actual routes that our respondents took, as well as their
reasonable alternative route choices, for the most part were all reasonably accommodating
pedestrian environments. Thus, understanding why one path was chosen over another is hard
to determine. Further, because respondents so clearly put a priority on finding the shortest and
quickest route on their morning commute walk ( the time when the survey was administered),
slight variations in pedestrian environments likely would have little significant influence.
That said, the areas that showed the most variability and were rated more poorly as walking
environments almost all occurred on arterials or collectors, rather than residential streets.
Focusing on these potential pedestrian impediments may provide some insight into route
choice and pedestrian decision making. The audit data allows for a more focused investigation
of those poorly rated areas at the streetscape scale, allowing researchers or planners to
understand what makes the walking environment more or less hospitable to pedestrians. An
example of how this audit data may be used to understand very specific environments is
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Analysis of Walkability Audit Data 41
presented below, using Julian Street in the Japantown transit station area. Julian Street was
one that some survey respondents identified as a street they avoid or carefully consider where
to cross when accessing the transit stop. With such feedback from pedestrians, these maps give
the researcher or planner an idea of the different elements of that particular area that may be
causing pedestrians to try to avoid it.
Figure 14 shows the individual subjective audit variables of attractiveness and safety of each
individual street segment. Looking at the entire study area in this manner allows problem
areas to be pinpointed. The map shows a mixture of ratings within the Japantown area and on
Julian Street in particular.
Figure 14 Japantown Attractiveness and Crime Subjective Assessments
42 Analysis of Walkability Audit Data
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Figure 15 gives a little more detail about the streetscape along Julian Street. Looking at the
different indices presented, it appears that one of the negative attributes that may be
influencing pedestrian decision making may have to do with the low presence of greenery. The
General Appearance Index and the Overall Index do not show much more variability in
condition compared to the transit area as a whole, but the Greenery Index is quite different for
Julian Street than other locations. It may be that the lack of greenery negatively impacts
pedestrians’ perception of Julian Street and causes them to avoid that location if possible. It
may also be that potential pedestrians may be dissuaded from walking to the transit stop
because of the barrier presented by Julian Street.
Figure 15 Julian Street Drill- Down Using Objective Criteria Indexes
Mineta Transportation Institute
Analysis of Walkability Audit Data 43
Figure 16 Julian Street
This analysis of Julian Street demonstrates the types of analyses and micro- scaled
investigations that could be possible when investigating areas with more variability and lower
ratings than the larger study area. If walking decisions are influenced in part by the condition
of the surrounding environment, then utilizing tools that adequately capture those local
conditions can be very important for both research and applied applications. There is, of
course, a trade off between collecting extensive data at the micro scale and the time investment
needed to collect such data over a significant geographic area. Trade- offs and ideas for more
focused application of walkability audit tools are presented in the following section.
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45
CONCLUSIONS
This study surveyed pedestrians walking to five different rail stations to determine how far
they walked and the environmental factors that they believed influenced their choice of route.
An additional audit of walkability conditions conducted by the authors was used to compare
with the respondents’ own evaluations of the environmental factors that influenced them. This
section summarizes the primary conclusions from the study and assesses their implications for
planning practice and future research. The first part discusses three key findings about
pedestrian behavior, followed by findings about the survey methodology and then the
walkability audit methodology.
FINDINGS ON WALKABILITY: IMPLICATIONS FOR PLANNING PRACTICE
Three findings about walkability from the survey stood out as particularly relevant for future
planning efforts. First, the survey showed that pedestrians walk considerably farther than
commonly is acknowledged. In addition, the survey responses indicated that the respondents’
primary goal in choosing a route was to minimize distance and time, but that safety and
aesthetic considerations were also important to them.
Finding 1: Pedestrians walk considerably farther to access rail stations than commonly
assumed.
Conventional wisdom among planners has often been that pedestrians in the United States
will only walk a quarter to a third of a mile for any reason, including to access transit. A paper
from the mid- 1990s looking at how far transit agencies and transportation modelers assume
that pedestrians will walk to a light rail station found very short distances, most well under a
half mile. 21 The results of our study suggest quite differently, at least for walk trips to access
rail transit. The median trip distance was 0.47 miles, showing that fully half the people
surveyed walked at least a half mile to access the train station. The study results therefore
contradict the common wisdom, supported in part by past research, that says people are only
willing to walk a quarter to a third of a mile to a destination, transit or otherwise. Those rules
of thumb are shown to underestimate actual pedestrian behavior, at least for the conditions we
studied.
The study finding about the relatively long distances that pedestrians walk suggests that
transportation and land- use planners designing transit- oriented developments should assume
many train riders will walk considerably farther than they may have previously thought, at
least for commute trips to a rail station. For planning practice, this suggests that the
pedestrian zones around key destinations ( transit, schools, markets, parks) are larger than
previously acknowledged. Planners should plan for good pedestrian infrastructure and
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pedestrian- scaled design within a large radius around major destinations such as schools,
transit centers, or shopping areas.
Of course, the study may be capturing the high end of the pedestrian spectrum, because we
surveyed current walkers to transit, and it would be reasonable to expect that other walkers
may be more inclined to walk shorter distances. However, just as maximum periods of usage
are considered when building parking lots and road systems, planners should consider these
maximum likely walking distances when making land use and transportation decisions.
Finding 2: Pedestrians believe that their primary consideration in choosing a route is
minimizing time and distance.
The survey explored the reasons that pedestrians choose particular routes in two ways, first
asking about route choice factors as an open- ended question and then asking respondents to
rate the importance of a list of factors that might have influenced them. In both cases,
respondents overwhelming indicated that their first priority was to choose the most direct
and/ or quickest route. Because almost all of our respondents were making a morning commute
trip, it is not surprising that time would be a strong consideration for them.
These results suggest that land use planners who want to increase walk trips should ensure
that pedestrians have available fairly direct routes to their destinations. Grid street patterns
generally provide direct routes ( as well as route choice), so planners are advised to adopt grid
street patterns for pedestrian infrastructure when laying out new communities. If the grid has
very long blocks, planners might want to consider adding mid- block footpaths through the
center of the block. Neighborhoods that do not follow a grid pattern tend to require that
travelers cover much longer distances to reach their destinations. In such cases, planners
should try to create pedestrian cut- through passages that allow walkers direct access to many
different destinations.
Finding 3: Secondary factors influencing route choice are safety and, to a lesser extent,
attractiveness of the route, sidewalk quality, and the absence of long waits at traffic lights.
In both the open- ended and closed- ended questions about route choice, the most highly rated
factors after distance had to do with safety. In the open- ended question, safety factors were the
only other issue listed by over a quarter of respondents. In the closed- ended questions, about
half of respondents rated it as “ very important” to have traffic devices present and traffic
driving at safe speeds. The next most- cited “ very important” factor was having sidewalks in
good condition ( 43 percent). Aesthetic factors, in the sense of attractive landscaping or
buildings, were rated as very important by 35 percent of respondents, but raised by only 8
percent of the respondents in the open- ended question. The only other issues rated as “ very
important” by at least a quarter of respondents were having other people present ( which may
be a safety- related concern), and the absence of traffic lights with a long wait.
When interpreting these results, it is important to keep in mind the context in which the
respondents answered. First, all were thinking about a commute trip in the morning; for other
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Conclusions 47
trip purposes, their responses might vary. In addition, the audits conducted around two of the
stations in this study showed that the pedestrian environment was relatively safe from crime
and traffic, and most of the residential streets were of at least average attractiveness in terms of
the built environment. Had the survey been conducted in extremely run- down neighborhoods,
respondents might have placed higher priority on the visual quality and maintenance of the
built environment.
These results suggest that transportation planners and traffic engineers wanting to encourage
walking should pay particular attention to ensuring that pedestrians feel safe crossing streets,
including keeping traffic to safe speeds and having traffic control devices present to help
pedestrians cross intersections. Other transportation infrastructure issues to address are
sidewalk quality and the length of time pedestrians must wait at traffic lights. Finally,
planners who work with communities to improve the aesthetics of the built environment
might see somewhat increased walking as a result, in addition to the other numerous benefits
associated with attractive neighborhoods.
FINDINGS ON THE SURVEY METHODOLOGY: IMPLICATIONS FOR
RESEARCH
The survey generated two key lessons for designing and interpreting research that collects
information on how far people walk and the routes they take. First, the study demonstrated
that asking participants to draw their route on a map works well. In addition, the study
demonstrated that data derived from questions asking pedestrians to estimate the distance
they walked must be interpreted cautiously.
Finding 4: Asking survey respondents to trace their walking route on a local map is an
effective research technique.
Asking respondents to draw their route on a map is a relatively undocumented survey
technique. We were unsure whether respondents would be willing to provide this
information, or if they would fill out the map correctly so that the data would be useful. The
study results show that the survey technique is highly effective. Of the 328 surveys received,
the map was filled out correctly 93 percent of the time, generating 261 routes that could be
analyzed for actual distance and other route characteristics. The route tracings were legible and
precise enough that the research team had no trouble transferring the exact routes into a GIS
database where the distance could be automatically calculated and walking routes recorded. In
addition, the relatively high response rate for the survey overall ( 45 percent) shows that the
presence of the map did not discourage people from completing the survey.
The results of the map question on the survey suggest that asking respondents to draw a route
on a map is an effective research technique that can gather high response rates. In addition to
generating data on walking routes, it is a useful way to assess walk trip distances. If researchers
wish to collect accurate data about how far people walk, this method proved reliable and is
48 Conclusions
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cheaper and less burdensome to respondents than the currently popular alternatives of asking
respondents to wear a GPS device to track their movements or to wear a pedometer that counts
overall steps.
Finding 5: Pedestrians vary considerably in how accurately they estimate the distance of a
regular walk trip.
Many travel surveys ask respondents to self report the distances they travel. To date, there has
been little published research into how accurate those self- reported estimates might be. This
study found that the average difference between actual and perceived distance is modest,
though a significant minority of respondents were also fairly far off. At least half of all
respondents guessed within 0.13 miles of their actual route length. However, 25 percent of
respondents’ guesses were off by more than 50 percent or a quarter of a mile, suggesting that a
substantial minority do not have a precise idea of how far they walked. A few of the individual
guesses were also substantially off in terms of distance, as well as percent: guesses ranged from
up to 1.07 miles over to 0.88 miles under the correct distance.
The findings on these reported walking distances suggest that researchers cannot assume that
pedestrians will provide a highly accurate estimate of the distances they walk, even for short
and routine trips. This finding is useful for assessing the value of other surveys that ask for
self- reported walk distances, though it should be interpreted carefully when applying it to
other surveys. Our survey asked people to estimate the distance of a route they walk routinely,
so they may well have a more accurate sense of distance than they would on a less familiar trip.
It seems likely that other surveys asking people to report the distances about routine trips
might have similar ( in) accuracies, but the study results should not be assumed to hold true for
other types of trips that surveyors ask about. In addition, it may be that people making
significantly longer trips would estimate distances less accurately than did our respondents,
who were walking relatively short distances.
To counter this problem of inaccurate distance estimates, we recommend that future travel
surveys ask residents to provide the address ( or nearest intersection) of the trip origin and
destination. This will allow surveyors to use automated GIS processes to estimate the distance
along the shortest route on the street network.
FINDINGS ON THE WALKABILITY AUDIT METHODOLOGY: IMPLICATIONS
FOR RESEARCH
Through the data collection and analysis process, we developed several recommendations for
how best to conduct detailed, block- by- block walkability analyses. Findings six through eight
focus on ways to reduce the time burden of collecting walkability audit data, allowing a
research team to hone in on collecting only the most useful data. The final two findings
address the practicalities of collecting the data— whether to use Pocket PCs or pen and paper,
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Conclusions 49
and the importance of ground testing maps if one uses a GIS- based system running on Pocket
PCs.
Finding 6: Spatially target the areas in which to collect walkability audit data.
Collecting data about the quality of street segments and intersections that pedestrians travel
through generated very interesting findings that correlated with respondents’ route choices,
but we quickly realized that applying such a tool ( or any walkability evaluation instrument) to
every location was an inefficient use of time. Auditing all the streets is a lot of work for results
that may not vary greatly over space ( e. g., if residential streets throughout a study area do not
vary much).
For many neighborhoods, one useful way to limit the data collection burden is to focus on
arterials and collector streets. It was also apparent from our study sites that, in some study
neighborhoods, it was almost unnecessary to audit residential streets and that focusing the
audit on arterials, collectors, and their associated intersections may have been a better use of
data collection time. In some neighborhoods, all residential streets had sidewalks and were
pleasant and safe enough to walk along. In such cases, the key to evaluating the potential
pedestrian friendliness of one’s journey from home to transit ( or other destination) was to
examine the attributes of the major roads and the intersections between neighborhood roads
and major roads.
In essence, the more focused question could be: “ What makes a major automobile road more or
less pedestrian friendly?” In this approach, all neighborhood streets could be assumed to be
generally walkable and the focus would concentrate on locations where pedestrians had to
travel on or across streets with high volumes of automobiles and/ or high- speed automobiles. It
is in these locations that interventions on behalf of walking might be best targeted.
Comparing route choices and route avoidance by pedestrians along these major streets would
allow planners and policymakers to focus resources and interventions where they are most
needed, and the audit data could point these decision makers into appropriate directions for
their interventions. Of course, in study areas where sidewalks are not universally present, or
where street widths in particular vary quite a bit and could be deemed important barriers for
walking, then including neighborhood roads in the audit may be important.
An alternative research approach may be to audit only those locations in a study area that have
been identified as problematic. Researchers could first survey pedestrians to ask what blocks or
intersections they avoid. Once these barriers have been identified, then planners could audit
those areas to assess and document conditions precisely. In this approach, the assumption is
that pedestrians choose to avoid hostile areas more than they seek friendly ones. By surveying
pedestrians ( or potential pedestrians) about their walking barriers, use of the audit tool can be
better targeted to areas where the greatest concern exists. Research time can therefore be
focused on areas that citizens have specifically identified as barriers instead of gathering
extensive lists of built environment characteristics that may not be necessary or useful.
50 Conclusions
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Finding 7: Customize data collection by street type.
Based on the study experience, we concluded that walkability audit instruments should
differentiate among street types, so that surveyors only have to collect data relevant to each
type of street or path. It became clear during the walkability audit that arterial and collector
streets presented a different set of attributes that needed documentation compared to
neighborhood streets. For example, street width, sidewalk buffers, on- street parking, and the
number of high- volume driveways to cross were all much more important on arterials and
collectors, where the volume and speed of vehicles presents much more of a safety threat and
level of discomfort, compared to neighborhood streets. On neighborhood streets, at least in
our study areas, the features in the built environment seemed unlikely to influence walking
behavior. For these streets, perhaps the one exception to that rule would be to document
whether or not the streets have sidewalks.
Customizing data entry variables for different types of streets would streamline the data
collection process and allow a greater range of streets to be surveyed in a shorter period of
time. This strategy would also produce a more streamlined and relevant set of data for analysis,
reducing the time needed for the data analysis.
Figure 17 shows an example of a potential data filtering system by street type. These are two
screenshots from a new tool, the School Environment Assessment Tool ( SEAT), being
developed to audit walkability for Safe Routes to School. The image on the left is the initial
data entry page that appears and it provides an initial filter as to the street type being audited.
Subsequent pages are customized based on which street type is selected. The image on the
right is the data entry screen that appears for a street segment that ends with a cul- de- sac.
Most streets that end in a cul- de- sac are neighborhood roads with low volumes of cars and are
most likely not severely impacted by different measures of walkability. Documenting whether
a pedestrian can cut through the end of the cul- de- sac, however, is important, but because it
only pertains to segments ending in cul- de- sacs, this question only appears for streets selected
as cul- de- sacs on the first data entry page.
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Conclusions 51
Figure 17 An Example of an Audit Tool Customized by Street Type
Finding 8: Consider using holistic, subjective measures of walkability instead of more
detailed quantitative measures.
We found that in many cases the subjective assessment of how safe or attractive a block was
seemed to better capture the pedestrian environment than did the many quantitative measures
included in the walkability audit. These subjective measures are also obviously much quicker
to collect, so future researchers may wish to concentrate on a few subjective measures only, to
save data collection time.
There were street segments in our audit evaluation that felt like poor environments to walk
along due to aesthetics, proximity to heavy traffic, and just a general feeling of being
uncomfortable places. It would be easy to imagine that pedestrians would simply choose
parallel paths to walk along. However, analyzing the objective variables contained in the audit
tool did not always convey the general impression the surveyors received about the street
segment. For example, one of these uncomfortable walking streets had a buffer between
sidewalk and street, trees in this buffer, on- street parking, only two travel lanes in each
direction, and properties that were decently maintained. In short, the segment had all the
attributes that one would expect to make for a safe and attractive walking environment— even
though the overall impression was otherwise. A similar conclusion about the value of
subjective audit questions was reached in a study where the authors found that “ walking
behavior is better explained by perceptions than sociodemographics or objective assessment of
the environment.” 22
52 Conclusions
Mineta Transportation Institute
One limitation of relying solely on broad subjective evaluations of walkability is that these do
not provide decision makers with any guidance on how to design or retrofit areas targeted for
pedestrian improvements. However, for studies of pedestrian route preference, such subjective
measures may be enough to determine whether urban design has impact on route choices or
not, or whether shortest routes are the predominant factor in influencing trip making. More
detailed audits of the design features in a neighborhood could be reserved for planning studies
where planners and decision makers wish to identify specific environmental features that need
to be upgraded.
Finding 9: Weigh carefully the benefits of collecting audit data on paper vs. on a Pocket
PC.
Lastly we reflect on the utility of an electronic and GIS- enabled approach to audit data
gathering versus a more traditional approach of paper, pen, and clipboard. The obvious benefit
of the handheld GIS computer approach is that by collecting data both in an electronic and a
GIS format, there is no need for subsequent data entry once the audit is complete. The GIS
data collection approach also eliminates the danger that data collected on paper will be
incorrectly entered into the computer database when later converting the data to a GIS
environment. With handheld GIS technology that risk is minimized, because data can be
collected in closed- ended questions directly within a GIS environment. Also, the GIS
technology greatly reduces the total time involved, because the data does not have to later be
converted to GIS from a paper form or electronic database.
The handheld computer approach has the additional benefit of instant map making, which
may be important for community- based approaches to walkability assessments. For example,
planners or researchers may wish to have a group of community or elementary school
volunteers use the audit tool to assess streets and intersections within a mile of a target school
and then immediately show the results to the volunteers. With the handheld GIS approach to
conducting walkability audits, it would be possible for this group of volunteers to easily
collect data in a few hour period, gather together at the end of data collection, and synthesize
the data from each handheld device used into a single data file that can be mapped on the spot.
Incorporating portable printer technology would allow each volunteer to leave the day’s
auditing with initial walkability maps based on data collected that day. For community- based
approaches to walking issues, the ability to transform volunteer energy into a tangible map
can be vital in sustaining community interest and catalyzing decision makers into taking
appropriate action in regards to the needs of pedestrians.
Of course, the use of this advanced technology in assessing the walking environment can also
be limiting or carry risks. Perhaps the biggest limitation of handheld computer technology is
that recording field notes can be more difficult or even impossible. When conducting a
walkability audit, auditors sometimes wish to make specific notes about an audit variable, and
unless the Pocket PCs are specifically programmed to allow this, handheld computers may
offer limited note- taking capabilities. There are potential technological fixes to this problem,
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Conclusions 53
such as using the built- in word processing, voice- recording, or picture- taking capabilities of
Pocket PCs, but writing observations or comments directly onto a survey form is probably still
easier to do with a pen- and- paper audit.
Another limitation of the digital approach is that audit questions are permanently pre- ordered
and auditors are forced to answer audit questions as they are written, not as they are observed.
Paper versions of audits allow the auditor to answer questions in the most logical order for
what is being observed, but electronic approaches make this approach too cumbersome to be
useful. Some auditors in projects similar to this study have complained to the study authors
that they can record the data much faster on paper than using a Pocket PC.
Other technology issues are that battery life of Pocket PCs can be short for all- day auditing
unless extended batteries are purchased. Also, some people just find the Pocket PC too
cumbersome to use. Good training and preparation can overcome this hurdle, however.
Finally, carrying expensive computers while analyzing neighborhood streets and sidewalks can
be unsafe in certain neighborhoods ( or make auditors feel unsafe), especially if auditing teams
are perceived as outsiders to that neighborhood. Making good community connections, as
should be done with any project where a potential problem of outsider vs. insider may exist,
should be a prerequisite to doing the auditing work.
Finally, cost and technological accessibility could be a problem with the electronic approach.
The cost of a PDA plus an extended battery, available from a variety of vendors such as Dell or
HP, is about $ 500 per unit. High- end units with integrated GPS can cost as much as $ 2,500.
The software needed to program the PDA with a GIS- based audit tool is called ArcPad and
ArcPad Application Builder. It is available from ESRI, the maker of the popular ArcGIS suite
of tools, for around $ 1,500.
Finding 10: Ground truth base maps.
Although we found that collecting GIS- enabled data at a streetscape level was generally
straightforward, we did learn ( the hard way) that it is critical to ground truth the street base
map that will form the core of the data set before using the tool in the field. We used the
Topologically Integrated Geographic Encoding and Referencing ( TIGER) street file as our
base map because we wanted to use a freely accessible source of data that would be available to
any community in the United States. As is often the case with TIGER data, the map did not
always accurately reflect existing streets. In some cases, the TIGER data included streets that
did not exist, and in others, streets existed that were not included in the TIGER data. It is
possible to add or delete street segments or adjust street ranges in the field by using the
ArcPad program running on the Pocket PC, but it is critically important that some basic
ground truthing of the base GIS data be conducted prior to auditing the environment.
It is also important to check the address ranges of the streets within the TIGER data after
uploading data to ArcPad to ensure they are consistent with actual address ranges of the
streets. We found address ranges that were one block off, meaning we had to correct these
errors in the map by hand before it was possible to accurately geocode our survey data.
54 Conclusions
Mineta Transportation Institute
RECOMMENDATIONS FOR FUTURE RESEARCH
This study has shown the feasibility of the map- based survey method combined with
walkability audits as a method to explore pedestrian route choices and distances walked. The
results should be extended by applying the methods developed to study different kinds of walk
trips, walkers, and neighborhoods.
One useful variation on this study would be to survey people taking trips for purposes other
than a morning commute. For the commuters surveyed in this study, the key factor in their
route choice was minimizing distance and time. Although it is unsurprising that people on
their way to work in the morning want to minimize their travel time, walkers on other types
of trips may be less sensitive to time and more sensitive to their surroundings. Future studies
could target pedestrians walking to destinations, such as shopping, local services, or schools, to
see how far they actually travel and what route choices they make.
Second, the methods could be applied to different populations to see if the study results are
unique in any way to commuters. The elderly, children, and adults who do not work during
the daytime are examples of groups who might have very different walking habits and
preferences for both route choice and distance.
A third useful application of the study methodologies would be to research a neighborhood
with more overtly unpleasant walking conditions. The study areas investigated were relatively
safe, and although not all corridors were exactly beautiful, there were not many obvious
deterrents to walking, such as huge vacant lots, abandoned buildings, or highly dangerous
intersections.
Mineta Transportation Institute
55
APPENDIX A
SURVEY QUESTIONNAIRE
Instructions for Surveyor
Ask verbally. DO NOT read the list of options, but check off the right option based on the
response.)
Hello, I’m with the University of Oregon and I’m surveying people about how they got to the
BART station today. Would you mind answering a few questions while you’re waiting for the
train?
Could you tell me how you got to this station today?
Keep a tally ( hatch marks) of modes of travel to the station for the following categories as you pass out the
surveys.
If subject DID NOT walk, make sure to record their mode of transportation below and say:
For this study, we’re focusing on the various routes people used to walk here, instead of
< biked/ drove/ took the bus>. But thank you for taking the time to speak with me. Have a
good day!
If subject DID walk, say:
We’re interested in finding out more information from people who walk to the station. Do
you have five minutes to complete a survey for me? Your participation is voluntary, and all
information you provide will be kept completely anonymous. ( if they don’t want to
participate, record in the table.)
Before we start, I have to confirm that you’re at least 18 years old. Are you? ( If they are
obviously over 18, do not bother to ask.)
If under 18, record above and thank them. Okay. Unfortunately, because of research restrictions,
we can only survey people over 18 years of age. Thank you for taking the time to speak with
me. Have a good day!
Here’s the survey. As part of the survey we ask you to draw the route you walked on this map
[ show map]. If you finish before the train comes, you can give it back to me. Otherwise, you
can mail it to us in this postage- paid envelope [ show envelope]. Also, here’s a pen that is a
small thank you gift from us, in appreciation of your time.
56 Appendix A Survey Questionnaire
Mineta Transportation Institute
Mineta Transportation Institute Survey: Walking to the Transit Station
For this survey, we are interested in the walking route you used to get to the station today, and
why you chose it.
After you complete the survey, please hand it back to one of the surveyors. If you do not finish
the survey before your train arrives, please complete the survey on the train and mail it back in
the stamped envelope provided.
If you have any questions about the survey, the last page provides you with information about
how to contact the researchers, who are based at the Mineta Transportation Institute at San
José State University and the University of Oregon.
________________________________________________________________________
1. How far do you estimate that you walked to get here? Please respond in both miles and
blocks, and be as precise as possible.
____ Miles ____ Blocks
2. How long did it take you to walk to the station?
____ Minutes
3. Did you stop along the way to buy something, talk to somebody, or for any other purpose?
___ Yes ___ No
If yes, continue to questions 3a – 3c
If no, continue to question 4
Mineta Transportation Institute
Appendix A Survey Questionnaire 57
3a. What did you stop to do?
___ Buy food/ drink
___ Buy newspaper or other retail good
___ Talk to somebody
___ Other ( please indicate): ___________________________
3b. How long did you stop for?
____ Minutes
3c. If you had not stopped, what would your actual walking time have been? Please
estimate to the nearest minute.
____ Minutes
4. For the attached map, please do the following:
- Trace the route that you took today on the attached map, being as specific as possible
about your starting point.
- Mark an X on any roads that you purposefully avoid.
- Circle any intersections that you purposefully avoid, or write them in the space
provided below:
Intersection of ____________________ and _______________________
Intersection of ____________________ and _______________________
Intersection of ____________________ and _______________________
58 Appendix A Survey Questionnaire
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Mineta Transportation Institute
Appendix A Survey Questionnaire 59
5. The last time you walked here from the same place, did you take the exact same route?
􀂆 Yes
􀂆 No
6. The last five times you walked to this station, leaving from the same place, how many
different routes did you take?
􀂆 1
􀂆 2
􀂆 3
􀂆 4
􀂆 5
7. What are the main reasons why you chose your route today?
i.
ii.
iii.
8. Below is a list of factors that other researchers have found to influence the routes people
walk along. For each one, please mark how important it is to you.
Factors in Route Choice
Very
Important
Somewhat
Important
Not
Important
Traffic drives at safe speeds 􀂆 􀂆 􀂆
There are traffic control devices like traffic lights,
stop signs, and crosswalks 􀂆 􀂆 􀂆
There are no traffic lights where I have to wait a long
time to cross 􀂆 􀂆 􀂆
There are attractive trees, landscaping, or buildings
along the street 􀂆 􀂆 􀂆
The sidewalks are in good condition, without litter,
cracks, or obstacles 􀂆 􀂆 􀂆
60 Appendix A Survey Questionnaire
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9. Are there any other factors, positive or negative, that influenced your choice of route
today?
10. For each statement below, please mark how strongly you agree or disagree with it.
There are shops or businesses that I like to stop in 􀂆 􀂆 􀂆
There are shops or businesses with windows I like to
look at 􀂆 􀂆 􀂆
A friend or neighbor is along the route 􀂆 􀂆 􀂆
There are benches and/ or places to sit 􀂆 􀂆 􀂆
There are other people out walking 􀂆 􀂆 􀂆
It is the shortest route 􀂆 􀂆 􀂆
Statement
Strongly
Agree
Somewhat
Agree
Somewhat
Disagree
Strongly
Disagree
a. I like walking 􀂆 􀂆 􀂆 􀂆
b. Walking is relaxing 􀂆 􀂆 􀂆 􀂆