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Meso- or micro-scale? Environmental factors influencing
pedestrian satisfaction
Saehoon Kim
a,b,1
, Sungjin Park
c,2
, Jae Seung Lee
d,
⇑
a
Department of Landscape Architecture with Urban Design concentration, Graduate School of Environmental Studies, Seoul National University,
1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea
b
Interdisciplinary Program in Landscape Architecture, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea
c
Department of Urban Design and Planning, Hongik University, K521, 94 Wausan-ro, Mapo-gu, Seoul 121-791, Republic of Korea
d
Department of Urban Design and Planning, Hongik University, K520, 94 Wausan-ro, Mapo-gu, Seoul 121-791, Republic of Korea
article info
Keywords:
Pedestrian satisfaction
Meso-scale environmental attributes
Micro-scale environmental attributes
Multilevel modeling
abstract
Despite interest in walking and its environmental, health, and social benefits, little research
has investigated pedestrian satisfaction, and its potential influence on walking decisions.
The present study examines the relationships between pedestrian satisfaction, and a
variety of built environment factors, in order to gain insight into urban design strategies
that can improve pedestrian satisfaction. We analyzed a pedestrian survey, carried out in
Seoul, Korea, which includes questions about personal characteristics, micro-scale environ-
mental attributes, and the level of pedestrian satisfaction, together with Seoul GIS data,
which provided meso-scale environmental variables. The multilevel models estimated
the effects of environmental factors on the level of satisfaction of utilitarian and recrea-
tional walkers. The analysis identified significant effects of both meso-scale (e.g. density,
intersection density, hilliness, and the presence of bus stops), and micro-scale (e.g.
sidewalk width, and the presence of bus dedicated lanes, crossings, lamps, and trees)
variables on pedestrian satisfaction. The results calls on researchers to investigate a
comprehensive set of psychological and environmental factors, in order to understand
the various aspects of pedestrian satisfaction, and the diverse motivations behind it, as well
as on planners, to adopt diverse design approaches that will produce more satisfactory
pedestrian environments.
Ó2014 Elsevier Ltd. All rights reserved.
Introduction
Interest in walking, and its environmental, health, and social benefits, have grown in urban planning, transportation, and
public health studies. Researchers have investigated walking, with regard to environmental and health issues, such as air
pollution, traffic congestion, and obesity risk (Marshall et al., 2009; Hoehner et al., 2011). Studies in public health identified
the health benefits of physical activities, including walking: moderate levels of physical activities tend to reduce health risks,
such as high blood pressure, heart disease, colon cancer, and diabetes (Nelson et al., 2007). As the promotion of walking
emerges as a policy agenda, a growing number of urban planning experts have become interested in enhancing walkability
http://dx.doi.org/10.1016/j.trd.2014.05.005
1361-9209/Ó2014 Elsevier Ltd. All rights reserved.
⇑
Corresponding author. Tel.: +82 2 320 1667; fax: +82 2 336 7416.
E-mail addresses: skim5@snu.ac.kr (S. Kim), sungjin.park1@gmail.com (S. Park), jaeseung74@gmail.com (J.S. Lee).
1
Tel.: +82 2 880 5662; fax: +82 2 874 7181.
2
Tel.: +82 2 320 1664.
Transportation Research Part D 30 (2014) 10–20
Contents lists available at ScienceDirect
Transportation Research Part D
journal homepage: www.elsevier.com/locate/trd
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as a policy intervention tool. For example, Transit-oriented Developments (TOD), which pursues high-density, mixed-use
compact urban form, aims to reduce private automobile-based travel, and to promote public transportation use and greater
walking activities. In particular, since walking is essential to access public transportation, it is expected that creating pedes-
trian-friendly environments is important for the success of TOD (Park et al., 2014).
The promise of the planning and policy actions to improve walkability is that walking can be encouraged, by enhancing
the quality of the built environment. Hence, most pedestrian-related studies have focused on the behavioral aspects associ-
ated with the built environment, such as walking distance, walking time, or walking mode choice. However, only a few stud-
ies investigated psychological aspects – such as personal motivation, residential preferences, travel-related attitudes, and
pedestrian satisfaction – that may influence walking decisions at a specific site. Omission of these psychological factors in
behavioral models can result in overestimation of the influence of the built environment on travel behavior (Cao et al.,
2009). In particular, the level of satisfaction experienced by pedestrians is an under-explored topic in travel behavior
research. Although it is not easy to operationalize the concept of pedestrian satisfaction, individual pedestrian satisfaction
level of the built environment can help reveal potential environmental factors for improving pedestrian environments.
How can we create a satisfactory walking environment to promote public transportation uses, and greater physical activities,
like walking in cities? Satisfaction with the walking environment is important, not only because satisfaction is relevant to the
policies for well-being, but also because satisfactory walking experienced by travelers is more likely to be chosen again next
time, and sustained as a habitual behavior (Ettema et al., 2011). Therefore, the present study investigates the relationships
between pedestrian satisfaction and a variety of built environment factors, in order to gain insight into urban design strat-
egies that can improve both pedestrian satisfaction and activities.
Most walkability studies have paid attention to the meso-scale (or neighborhood-scale) built environment, investigating
its impact on walking behavior. The meso-level built environment is measured by environmental factors, such as housing
density, land use diversity, and street patterns within certain areas, since Cervero and Kockelman (1997) proposed the sem-
inal concept of ‘‘3D’’ (density, diversity, and design). The 3Ds, originally developed for estimating the influence of the built
environment on motorized travel behavior, are broadly used today for research on walking behavior, under the generic ter-
minology termed ‘‘neighborhood walkability’’. Despite their popularity in walkability research, meso-scale measures in gen-
eral have drawbacks as variables for capturing micro-scale (or street-level) built environment characteristics, such as the
presence of trees, the width of sidewalks, and the quality of streets. However, developing objective and reliable measures
of the micro-scale built environment is elusive and costly, leading to the heavy reliance of a large number of walking-behav-
ior researches on meso-level variables. Consequently, the influence of micro-scale factors on walking behavior has rarely
been thoroughly tested. Here, using a unique dataset collected in Seoul, Korea that includes both meso- and micro-scale
environmental attributes, we attempt to investigate the effect and relative importance of the environmental factors at
different scales, with regard to affecting pedestrian satisfaction.
The present research is carried out in Seoul, which is the capital city of the Republic of Korea. Although the city’s public
transportation system is well distributed across the city, the pedestrian environments in Seoul are not sufficiently conve-
nient and safe. The present study estimates pedestrian satisfaction with explanatory variables, including built environment
attributes, and pedestrian characteristics. The study uses the pedestrian survey that was carried out by the Seoul Metropol-
itan Government in 2009. The selection of survey locations was designed to include major activity centers, transportation
nodes, and major land uses of Seoul. The survey instrument consisted of questions about personal characteristics, travel pur-
pose and frequency, and the level of pedestrian satisfaction with their walking environment. At each survey location, survey-
ors not only collected responses from pedestrians, but also measured a variety of micro-scale built environment
characteristics. The micro-scale attributes, measured within 50 m from the survey locations, are geographically distin-
guished from the meso-scale attributes, extracted from Seoul’s geographic information systems (GIS) dataset within
400 m network distances. The survey data, combined with Seoul GIS data, provides an extraordinary opportunity to examine
the influence of micro-scale environments on walkability, with a particular emphasis on pedestrian satisfaction, which can
offer valuable policy and design insight into building desirable walking environments.
The rest of this paper is structured as follows. The next section reviews theoretical and empirical studies of travel behav-
ior, the built environment, and residential and travel behavior. The settings and methods are then presented. The results of
pedestrian satisfaction models follow. The final section discusses the implications, limitations, and future directions of the
research.
Theoretical and empirical background
Satisfaction and the built environment
The level of satisfaction with environmental quality has been studied as an important factor that affects the quality of life
of residents, and is known to trigger certain behavioral outcomes. Residential satisfaction is a ‘‘positive affective’’ psycholog-
ical state that individuals experience toward the residential environment (Amerigo and Aragones, 1997). In the previous
study, the level of satisfaction is determined by the interaction between objective and subjective environmental attributes,
and residents’ characteristics. Residents subjectively evaluate the objective attributes of the built environment, leading to a
certain level of satisfaction. Hence, the individual’s socio-demographic and personal characteristics affect the subjective
S. Kim et al. /Transportation Research Part D 30 (2014) 10–20 11
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environmental attributes (Amerigo and Aragones, 1997). Thus, most empirical studies focused on residential satisfaction,
examining residents’ characteristics and environmental characteristics (both physical and social) of living environments.
The studies pertaining to residential satisfaction can be categorized into two groups. The first group of studies deals with
residential satisfaction as a criterion of environmental quality evaluation. Thus, this group treats residential satisfaction
as a dependent variable (e.g. Gifford, 1987; De Jong et al., 2012; Van Dyck et al., 2011). The second group of research regards
residential satisfaction as a predictor of behavioral and psychological outcomes, and therefore, as an independent variable
(e.g. Prieto-Flores et al., 2011; Speare, 1974).
In the field of transportation, utility theory that explains how travelers choose activity, destination, and travel mode deals
with travelers’ satisfaction derived from observed travelers’ choice, assuming that travelers make rational decisions to max-
imize their utility, mostly by minimizing travel time and costs (disutility), under given time and budget constraints
(McFadden, 2001). However, the weakness of the derived utility-based assessment of satisfaction is that anticipated and
actual experiences may be significantly different, due to the underestimation of the intensity of experience (Ettema et al.,
2010; Wilson and Gilbert, 2003). Hence, other studies have investigated ‘‘experienced’’ utility by travelers. Indeed, travelers’
satisfaction is likely to be influenced by experienced events during travel. Studies show that single critical events and their
frequencies influence public transportation users’ satisfaction (Friman et al., 1998, 2001). Stradling et al. (2007) explored fac-
tors influencing satisfaction with bus service and walking trips, finding that non-instrumental factors, such as cleanliness,
privacy, safety, convenience, and scenery, affect satisfaction with bus service. They also revealed that satisfaction with walk-
ing trips is influenced by micro-scale factors, including crowdedness, air quality, presence of trees and flowers, presence of
beggars, and pavement condition. Investigating satisfaction with travel and subjective well-being (SWB), Ettema et al. (2011)
showed that SWB is influenced by travel mode, travel time, and access to bus stops. The study of Manaugh and El-Geneidy
(2013) investigated the relationship between walking distance and satisfaction with walking trips, and found that people
who are most concerned with environmental issues are willing to walk longer distance, and tend to be more satisfied. Over-
all, travelers’ satisfaction, including pedestrian satisfaction, is influenced not only by travel time and travel cost, but also by
physical and nonphysical environments that affect the type of events experienced by travelers.
Meso-scale environmental factors and walking
Meso-scale environmental factors are area-based measures within certain boundaries. Cervero and Kockelman (1997)
proposed the concept of the ‘‘3Ds’’ (density, diversity, and design), which became the most commonly used environmental
variables in travel behavior research. The 3D attributes’ popularity has been growing with the increasing availability of GIS
and spatial data. Later, Cervero et al. (2009) proposed ‘‘5D’’ (3D plus ‘‘destination accessibility’’ and ‘‘distance to transit’’),
integrating meso-scale urban form and accessibility. The 5D attributes are also widely used to capture ‘‘neighborhood walk-
ability’’. Walking behavior research consistently identified positive associations between utilitarian walking and meso-scale
factors, such as higher residential and job density, mixed land use, better street connectivity, and proximity to destinations
and public transit (e.g. Baran et al., 2008; Giles-Corti and Donovan, 2002; Huston et al., 2003; Lee and Moudon, 2006a; Lee
et al., 2013; Moudon et al., 2005; Saelens et al., 2003a). While research focused on recreational walking yielded inconsistent
results (Owen et al., 2004), some studies revealed the effect of accessible destinations (Giles-Corti et al., 2005), and hilliness
(Lee and Moudon, 2006b), on recreational walking.
Despite the popularity of the 5Ds, there has been controversy over the appropriate boundary of a neighborhood (Lin and
Long, 2008). More broadly, meso-scale environmental factors have problems associated with the size and scale of areal mea-
surement units. For example, the modifiable areal unit problem (MAUP) may occur, if a multivariate analysis is made based
on spatially aggregated datasets, resulting in erroneous results, depending on the size and scale of the areal measurement
unit (Fotheringham and Wong, 1991; Guo and Bhat, 2007; Kwan and Weber, 2008). In order to minimize the modifiable area
unit problem, some walkability studies match behavioral outcomes of interest (walking) to an areal unit, utilizing network
buffers to account for the actual distance a walker travels from origin (typically home), to nearby destinations, on the exist-
ing street network. Yet, the network distance utilized greatly differs, for example, 1 km (Frank et al., 2008), 0.5 mile (Coogan
et al., 2009), and 0.25 mile (Wells and Yang, 2008; Zegras et al., 2012).
Micro-scale environmental factors and walking
The quality of the micro-scale walking environment has long been of interest to urban designers and planners. Urban
designers presented a variety of theories related to the street-level walkability, for example, ‘‘eyes upon the street’’
(Jacobs, 1961), ‘‘path quality’’ (Lynch and Southworth, 1974), ‘‘street enclosure’’ (Alexander et al., 1977), ‘‘livable streets’’
(Appleyard, 1981), and ‘‘soft edges’’ (Gehl, 1987). While these concepts contributed to the definition of good walkability,
few empirical studies developed objective and systematic measuring instruments.
As the health benefits of walking emerged as a crucial policy and design issue, studies focusing on walkability yielded
walkability measurement tools, such as the Neighborhood Environmental Walkability Survey (Saelens et al., 2003b), System-
atic Pedestrian and Cycling Environmental Scan (Pikora et al., 2003), and Measurement Instrument for Urban Design Qual-
ities Related to Walkability (Ewing et al., 2006). These tools considerably contributed to defining and measuring micro-scale
walkability in a comprehensive way. In particular, Ewing and Handy (2009) tried to objectively measure, and to quantify
12 S. Kim et al. /Transportation Research Part D 30 (2014) 10–20
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qualitative design attributes, measuring approximately one hundred physical and non-physical features of sampled streets,
in order to test possible correlations with expert ratings on walkability.
Despite the progress in developing micro-scale measurement tools, few walkability studies included micro-scale environ-
mental factors; instead, they primarily focused on meso-scale factors. This is because micro-scale factors require consider-
able effort and time to collect sufficient data. Also, some qualitative and subjective measures, such as pavement cleanliness,
building design, and perception of safety, suffered from reliability issues. However, the urban design theories and empirical
studies suggest that micro-scale environmental factors play an important role in walkability. Therefore, the omission of
micro-scale factors in empirical studies can lead to inaccurate results. Overall, little research comprehensively investigated
the effect of both meso- and micro-scale environmental factors, at the same time as placing a particular emphasis on pedes-
trian satisfaction. The present study attempts to fill this gap, testing and comparing the effects of meso- and micro-scale
environmental factors on pedestrian satisfaction.
Setting and methods
Context
The present research is carried out in Seoul, which is the capital city of the Republic of Korea. Seoul is one of the largest
and densest cities in the world. Seoul is inhabited by 10.4 million citizens, as of 2013, within its 605.25 km
2
boundary, yield-
ing a population density of approximately 17,200 inhabitants per km
2
. The districts of Seoul are categorized into commercial,
green, semi-industrial, and residential areas (Fig. 1(a)). The main commercial area is located in the historic city center, and
other commercial areas have emerged in other areas in Seoul. Green areas include parks and natural features, such as moun-
tains and streams. Semi-industrial areas can be characterized as light industries mixed with retail and housing. Residential
areas consist of diverse housing types, and neighborhood retail outlets.
The public transportation system of Seoul is relatively well developed (Fig. 1(b)). Currently, nine subway lines with 306
stations cover 64.4% of Seoul’s total area, within one kilometer distance from each subway station (Jang, 2008). Seoul’s bus
system complements the public transportation system, connecting destinations not easily accessible by subway. In partic-
ular, dedicated bus lines in the center of the road, implemented in the major areas around Seoul, enhance the efficiency
of the bus system.
As one of the oldest cities in Korea, Seoul exhibits diverse urban forms, with a variety of building types, street patterns,
and land use patterns. However, from a global perspective, Seoul may not be one of the highly walkable cities. Major roads
tend to be very wide, making it difficult for pedestrians to cross roads; and many neighborhood streets do not have appro-
priate sidewalks. Also, Korea shows the highest pedestrian death rate (11.3 in every 100,000 people in 2010), among all of
the OECD countries.
3
The national and city governments recently strived to improve the pedestrian environment, implementing
a sizable number of pedestrian-only streets and new sidewalks, as well as designating several ‘‘pedestrian-friendly’’ districts
across the city. However, the quality of the pedestrian environment in Seoul still varies. Hence, the urban setting of Seoul offers
a heterogeneous context in which to examine how the built environment influences pedestrian satisfaction.
Survey design and data
The Seoul Metropolitan Government carried out a comprehensive pedestrian study in 2009. The government hired and
educated 2,000 auditors, in order to measure pedestrian volume, and audit the built environment characteristics at
10,000 locations in Seoul. The pedestrian survey was conducted at approximately 10% of the 10,000 locations (1170 loca-
tions, see Fig. 1(c)). The selection of the locations was designed to include major activity centers, transportation nodes,
and major land uses of Seoul. The survey instrument consisted of questions about personal characteristics, travel purpose
and frequency, and the level of pedestrian environment satisfaction. At each survey location, surveyors collected responses
from 24 pedestrians on Tuesdays, Wednesdays, and Fridays, yielding 72 responses per location. Excluding missing values, the
final dataset contains 83,291 responses. The auditors also collected micro-scale built environment characteristics, within
50 m from each location.
In order to gather meso-scale built environment characteristics, the survey data were combined with the city’s spatial
data, including building footprints and heights, roads, parcels, land use, and transportation systems that came from Seoul’s
GIS dataset. We generated 400 m network buffers for each location, to measure the physical characteristics around the loca-
tions. The 400 m network buffers were drawn according to walking paths along streets, rather than a buffer based on a
straight line radius from the locations, based on the assumption that only physical characteristics within a certain walking
distance of the location affect walking behavior. The street network used is based on the roads data, excluding highways,
since our focus is on pedestrians. Within each buffer, the following meso-scale variables are measured:
Gross density: A floor area ratio (FAR) is measured, to represent the amount of built activity space:
FAR ¼total floor area=total area of a buffer
3
IRTAD, Road Safety Annual Report 2011
S. Kim et al. /Transportation Research Part D 30 (2014) 10–20 13
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Diversity: The diversity index (DI) measures the mix of land uses, ranging from 0 for a single use, to 1 for perfect mixing of
uses. Four land uses are used in the calculation: commercial, green, semi-industrial, and residential. The DI is expressed as
(Rajamani et al., 2003):
DI ¼1
r
T
1
6
þ
c
T
1
6
þ
i
T
1
6
þ
g
T
1
6
3
2
()
;
Fig. 1. Land use, public transportation, and survey locations of Seoul, Korea.
14 S. Kim et al. /Transportation Research Part D 30 (2014) 10–20
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where, r= area in residential use (single and multifamily housing); c= area in commercial use; i= area in semi-industrial
use; g= green area; and the total area, T=r+c+i+g.
Design: A variety of design elements, such as street connectivity, measured by intersection density (Dill, 2004) and hill-
iness, may influence pedestrian satisfaction:
Intersection density = the number of true intersections (three-way and more)/road length (km)
Hilliness ¼the average slopeð%riseÞ
Transportation: The presence of transportation services is also expected to influence the level of pedestrian satisfaction:
Subway ¼presence of subway stations
Bus ¼presence of bus stops
Measures and descriptive statistics
Table 1 presents definitions and descriptive statistics of key variables. The majority of observations (91%) are utilitarian
walking, and recreational walking accounts for only 9% of the total observations. The recreational pedestrians’ average sat-
isfaction level is higher than that of utilitarian pedestrians. The data structure is multi-level: individual pedestrian’s
responses (level-1) are clustered at 1,170 locations (level-2). Level-2 variables are meso- and micro-scale built environment
characteristics, measured from each location. Meso-scale variables measure the 3Ds and transportation characteristics.
Approximately 46% of survey locations have subway stations, and 96% have bus stops, within 400 m.
Micro-scale variables are street and sidewalk characteristics, and street furniture. Approximately, 11% of roads have ded-
icated bus lanes. About half of the locations are within 50 m from a pedestrian crossing. The average sidewalk width in the
data set is 4.16 m. One-fifth of streets have fences, to separate pedestrians from vehicular circulations. Other noticeable
street elements are street lamps, ramps, and trees. Only a few streets have pedestrian signal control devices and trashcans.
Level-1 variables are personal characteristics of pedestrians clustered at each location. Approximately one-fourth of
pedestrians are accompanied, and most pedestrians are familiar with their locations, walking through at least 1 or 2 times
a week. Among respondents, 45% are male pedestrians. Most pedestrians (79%) are adults aged between 20 and 59.
Table 1
Definitions and descriptive statistics of key variables.
Variables Definition Total Utilitarian walking Recreational walking
Mean (S.D.) Mean (S.D.) Mean (S.D.)
Dependent variable
Satisfaction How satisfied are you with walking here? 3.27 (0.95) 3.25 (0.94) 3.41 (0.99)
(1. Very dissatisfied; 2. Dissatisfied; 3. Neutral;
4. Satisfied; 5. Very satisfied)
n82,211 74,644 7567
Level-2 variables
Meso-scale
FAR Gross floor area ratio 1.32 (0.76) – –
Diversity index Land use diversity index 0.15 (0.16) – –
Intersection density Number of true intersections/road length (km) 12.11 (4.56) – –
Slope % Rise 2.52 (2.31) – –
Subway Presence of subway stations (0. no; 1. yes) 0.46 – –
Bus Presence of bus stops (0. no; 1. yes) 0.96 – –
Micro-scale
Bus lane Presence of bus-dedicated lanes (0. no; 1. yes) 0.11 – –
Crossing Presence of crossings (0. no; 1. yes) 0.57 – –
Sidewalk width Sidewalk width (m) 4.16 (2.30) – –
Fence Presence of sidewalk fences (0. no; 1. yes) 0.19 – –
Signal Presence of signal control device (0. no; 1. yes) 0.02 – –
Lamp Presence of street lamps (0. no; 1. yes) 0.15 – –
Ramp Presence of ramps (0. no; 1. yes) 0.25 – –
Tree Presence of trees (0. no; 1. yes) 0.28 – –
Trashcan Presence of trashcans (0. no; 1. yes) 0.01 – –
N1170 – –
Level-1 variables
Together Walking with companions (0. no; 1. yes) 0.27 0.27 0.28
Familiar Walk here at least 1 or 2 times/week (0. no; 1. yes) 0.86 0.85 0.93
Male Male (0. no; 1. yes) 0.45 0.44 0.46
Teenager Age 15 and 19 (0. no; 1. yes) 0.07 0.08 0.01
Adult Age 20 and 59 (0. no; 1. yes), base category 0.79 0.81 0.59
Senior Age 60 and over (0. no; 1. yes) 0.14 0.11 0.40
n82,211 74,644 7567
S. Kim et al. /Transportation Research Part D 30 (2014) 10–20 15
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A potential complication is that environmental attributes are often highly correlated, leading to the multicollinearity that
can yield invalid results in statistical models (Lee and Moudon, 2006a). Table 2 shows the Pearson correlation matrix of
meso- and micro-scale environmental measures. None of the correlation coefficients are greater than 0.3, implying that
the multicollinearity problem may not arise in our analysis.
Modeling
The multilevel structure of the dataset raises an analytical challenge, in estimating the level of pedestrian satisfaction
with the built environment factors and personal characteristics. Level-1 units (pedestrians) are clustered within level-2 units
(survey locations). Pedestrians at the same location tend to be more similar to each other, than to pedestrians at other loca-
tions, due to common environments, and experiences at the same location. The potential similarity or mutual influences
among pedestrians at the same location can lead to invalid statistical analysis results, by violating the ordinary regression
models’ assumption that individual observations (pedestrian satisfaction levels in this case) should not influence or influ-
enced by other observations. Multilevel modeling deals with this spatial autocorrelation issue, by accounting for both the
variability among survey locations (level-2 units) and the variability among pedestrians (level-1 units).
In other words, ordinary regression estimation with multilevel data results in incorrect standard errors. Multilevel mod-
eling overcomes this challenge, by including explanatory variables at different levels, and by attributing unexplained vari-
ability (residuals) to the different levels (Rabe-Hesketh and Skrondal, 2012). We specify models for ordinal responses of
the dependent variable, pedestrian satisfaction, by using a latent-response formulation. Multilevel modeling distinguishes
within-cluster (location) effects from between-cluster effects, by including a location-specific random intercept f
j
in the
model
Y
ij
¼b
2
X
ij
þb
3
X
j
þf
j
þ
ij
Y
ij
¼
1if <y
ij
k
1
2ifk
1
<y
ij
k
2
3ifk
2
<y
ij
k
3
4ifk
3
<y
ij
k
4
5if k
4
<y
ij
8
>
>
>
>
>
>
>
<
>
>
>
>
>
>
>
:
where, Y
ij
is the latent response of pedestrian iin cluster (here location) j. K
s
are category specific parameters or thresholds.
X
ij
are level-1 variables, and X
j
are level-2 variables.
e
ij
are residuals that are uncorrelated with both locations and pedestri-
ans. The location-specific random intercept f
j
N(0,
w
) is assumed to be independently distributed over locations, and to be
independent from explanatory variables X
ij
.
The within-location dependency is modeled by calculating the intraclass (intra-location) correlation (
q
), as defined
below. The models are estimated by using the command gllamm in the statistical software, Stata 11. Ordinal logit is fitted
in gllamm with the link(ologit) option.
^
q
¼
^
w
^
wþ
p
2
=3
Table 2
Correlations of meso- and micro-scale variables.
Satisfaction FAR Diversity
index
Intersection
density
Slope Subway Bus Bus
lane
Crossing Sidewalk
width
Fence Signal Lamp Ramp Tree Trashcan
Satisfaction 1.00
FAR 0.08 1.00
Diversity
index
0.06 0.12 1.00
Intersection
density
0.14 0.29 0.24 1.00
Slope 0.09 0.18 0.12 0.20 1.00
Subway 0.01 0.15 0.09 0.07 0.11 1.00
Bus 0.04 0.09 0.05 0.04 0.07 0.12 1.00
Bus lane 0.06 0.02 0.07 0.04 0.08 0.07 0.06 1.00
Crossing 0.07 0.02 0.03 0.11 0.10 0.02 0.09 0.13 1.00
Sidewalk
width
0.04 0.11 0.12 0.08 0.07 0.13 0.01 0.06 0.10 1.00
Fence 0.06 0.06 0.10 0.17 0.05 0.01 0.05 0.16 0.22 0.01 1.00
Signal 0.00 0.04 0.00 0.02 0.01 0.02 0.03 0.01 0.04 0.03 0.02 1.00
Lamp 0.00 0.02 0.01 0.05 0.02 0.02 0.07 0.02 0.01 0.03 0.06 0.06 1.00
Ramp 0.03 0.00 0.01 0.08 0.23 0.04 0.05 0.05 0.01 0.00 0.07 0.05 0.03 1.00
Tree 0.09 0.03 0.03 0.09 0.01 0.06 0.00 0.06 0.23 0.08 0.20 0.09 0.26 0.03 1.00
Trashcan 0.02 0.07 0.06 0.04 0.01 0.02 0.02 0.03 0.06 0.01 0.04 0.01 0.04 0.01 0.06 1.00
16 S. Kim et al. / Transportation Research Part D 30 (2014) 10–20
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Lastly, we use a likelihood ratio test for nested models, to evaluate the relative importance of grouped explanatory variables,
according to the second hypotheses (Lichstein et al., 2002; Tognelli and Kelt, 2004). The three reduced models – without
meso-scale, micro-scale, and personal variables, respectively – are contained within the full model (model containing all
variables):
LR ¼2ðLL
reduced
LL
full
Þ
where, LR is the likelihood ratio test statistic. LL
reduced
and LL
full
are the log-likelihood of the reduced and full models, respec-
tively. For example, a reduced model is a model lacking meso-scale variables; whereas, the full model is the model contain-
ing all variables. In this case, larger LR values indicate a greater contribution of meso-scale variables as a group to the model.
Results
Utilitarian walking model result
Table 3 presents the utilitarian walking models that estimate the level of pedestrian satisfaction. The rho value indicates
that 27.4% of the variation in reported pedestrian satisfaction is attributable to locational differences. Therefore, ordinary
ordered logit produces incorrect standard errors. The pseudo R-squared value indicates that between-location residual var-
iance declines, as independent variables are added from the null model without independent variables. The results partially
confirm our first hypothesis, that the built environment characteristics are correlated with pedestrian satisfaction. Among
meso-scale environmental variables, the effects of intensity of activities (represented as FAR) and availability of bus stops
are positive, and significant. The effect of intersection density is significant and negative, indicating that when they need
to cross more intersections, pedestrians tend to be less satisfied. Pedestrians tend to be less satisfied when they walk through
hillier routes. Also, several micro-scale environmental variables are significantly associated with pedestrian satisfaction. The
presence of dedicated bus lanes and available pedestrian crossings are positively correlated with the pedestrian satisfaction
level. When pedestrians walk on wider sidewalks, the level of satisfaction tends to be higher. Among micro-elements, the
presence of trees is also positively associated with the satisfaction level. Level-1 personal characteristics are also statistically
significant. Pedestrians tend to be less satisfied being accompanied, than walking alone. Walking familiar streets tends to be
more satisfactory, than walking unfamiliar streets. Senior pedestrians’ satisfaction level is significantly higher, than that of
adult pedestrians.
Table 3
Utilitarian and recreational walking model results estimating pedestrian satisfaction.
Utilitarian walking multilevel model Recreational walking multilevel model
Coef. (S.E.) p-Value Coef. (S.E.) p-Value
Level-2 variables
Meso-scale FAR 0.151
c
(0.036) 0.000 0.118 (0.079) 0.135
Diversity index 0.208 (0.202) 0.305 0.109 (0.323) 0.735
Intersection density 0.046
c
(0.007) 0.000 0.057
c
(0.012) 0.000
Slope 0.053
c
(0.015) 0.000 0.056
b
(0.022) 0.011
Subway 0.019 (0.065) 0.765 0.140 (0.105) 0.180
Bus 0.435
c
(0.128) 0.001 0.273 (0.227) 0.229
Micro-Scale Bus lane 0.215
b
(0.100) 0.032 0.376
c
(0.174) 0.030
Crossing 0.152
b
(0.068) 0.027 0.080 (0.107) 0.453
Sidewalk width 0.038
c
(0.013) 0.005 0.020 (0.023) 0.373
Fence 0.037 (0.083) 0.653 0.139 (0.135) 0.305
Signal 0.167 (0.248) 0.501 0.663 (0.370) 0.073
Lamp 0.175
a
(0.090) 0.053 0.345
b
(0.147) 0.019
Ramp 0.015 (0.077) 0.846 0.018 (0.118) 0.881
Tree 0.401
c
(0.077) 0.000 0.502
c
(0.120) 0.000
Trashcan 0.146 (0.200) 0.465 0.506 (0.694) 0.466
N1170 1057
Level-1 variables
Personal Together 0.090
c
(0.017) 0.000 0.027 (0.057) 0.639
Familiar 0.158
c
(0.021) 0.000 0.189
a
(0.102) 0.064
Male 0.018 (0.014) 0.224 0.002 (0.049) 0.967
Children 0.001 (0.027) 0.971 0.129 (0.229) 0.573
Senior 0.142
c
(0.023) 0.000 0.046 (0.052) 0.372
n74,644 7567
Variance
u
1.244 (0.054) 1.742 (0.121)
Rho 0.274 0.346
Pseudo R
2
u
0.127 0.133
a
p< .10,
b
p< .05,
c
p< .01.
S. Kim et al. / Transportation Research Part D 30 (2014) 10–20 17
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Recreational walking model result
Recreational walking model results are presented in Table 3. The intraclass correlation of recreational pedestrian satisfac-
tion is 0.346, justifying the multilevel model over an ordinary ordered logit model. The random-intercept ordered logit
model includes independent variables. The pseudo R-squared value indicates that the multilevel model with independent
variables is improved from the null model. Two meso-scale variables, the effects of intersection density and hilliness, are
negatively associated with the level of pedestrian satisfaction. Among micro-level variables, the presence of bus lanes is pos-
itively associated with satisfaction levels. The presence of pedestrian amenities, such as lamps and trees, positively affects
recreational walkers’ satisfaction levels. Personal variables are not statistically significant.
Likelihood ratio test result
Table 4 presents the relative importance of meso-scale, micro-scale, and personal variables, as groups. In the utilitarian
walking model, the explanatory variables that have the highest relative importance are personal variables. Meso-scale envi-
ronmental variables rank the second in importance. The micro-scale environmental variables’ contribution to the model is
the smallest. In the recreational walking model, variables that rank among the most important are meso-scale variables. The
contribution of micro-scale variables is a little smaller, ranking second. The importance of personal variables is the lowest in
the recreational model.
Implications and conclusions
Implications
The present study investigates the effect of meso- and micro-scale environmental factors on pedestrian satisfaction, ana-
lyzing a unique large-scale pedestrian survey in Seoul. The relative importance of the two scale environmental variables are
also compared as groups. The utilitarian pedestrian model shows that both meso- and micro-scale factors play a significant
role in influencing pedestrian satisfaction. Utilitarian pedestrians are likely to be more satisfied with higher density that pro-
vides greater chances of multiple activities and events. The significant effect of the availability of the bus system indicates
that better public transportation services may increase the satisfaction level of utilitarian pedestrians, implying that utilitar-
ian pedestrians are potential bus users. However, the negative effect of intersection density is somewhat unexpected,
because previous studies identified a positive correlation between better street connectivity and walking levels (e.g.
Baran et al., 2008). A possible explanation may be that pedestrians tend to dislike frequently having to stop to cross roads,
which may decrease their satisfaction levels. Lastly, hilly streets may make walking physical difficult, decreasing pedestrian
satisfaction levels.
Several micro-scale variables also seem to significantly influence pedestrian satisfaction. The significant effect of dedi-
cated bus lanes implies that, again, a convenient bus system can increase the satisfaction levels of pedestrians, who are
mostly potential bus users. The availability of crossings contributes to a higher level of pedestrian satisfaction, by making
it easier for walkers to cross roads. Pedestrians feel more satisfied with wider streets that provide more space for easier walk-
ing. The positive effect of green elements, such as trees, on satisfaction is consistent with previous findings (e.g. De Jong et al.,
2012; Stradling et al., 2007). Comparing the importance of meso- and micro-scale variables, meso-scale variables are more
important than micro-scale variables as groups. However, the importance of micro-scale variables is not negligible.
The recreational model identifies the negative influence of higher intersection density and greater hilliness. The availabil-
ity of bus-dedicated lanes also influences recreational walkers’ satisfaction. The model also shows that street amenities, such
as lamps and trees, can influence recreational pedestrians. The relative importance of meso- and micro-scale variables are
similar, while the meso-scale variables’ importance is a little greater. In addition, personal characteristics tend to influence
utilitarian walkers, rather than recreational walkers. A possible explanation may be that utilitarian walking can be non-vol-
untary, while recreational walking may be generally voluntary. Hence, the satisfaction levels of utilitarian walkers who need
to walk may vary, depending on the individuals’ condition; whereas, the satisfaction levels of recreational walkers who are
willing to walk may be relatively stable over individuals.
The present findings have several design and policy implications. In metropolitan cities like Seoul, with high parking fees,
creating a satisfactory pedestrian environment is essential for successful Transit-oriented Developments, because walking is
generally necessary to use public transportation. Urban design and planning approaches (e.g. Smart Growth and New Urban-
ism) suggested meso-scale approaches, such as achieving higher density, mixed land-use, and better street connectivity.
Table 4
Relative importances (likelihood ratio) of meso-scale, micro-scale, and personal variables.
Meso-scale variables Micro-scale variables Personal variables
LR utilitarian 85.17 58.02 148.03
LR recreational 46.51 42.10 11.28
18 S. Kim et al. / Transportation Research Part D 30 (2014) 10–20
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However, these types of approaches are not easy to achieve in practice, generally requiring massive change of urban struc-
ture. In contrast, micro-scale approaches, which improve street-level environments, are relatively easy and feasible to real-
ize. While implying that both scales of environmental factors are important for pedestrian satisfaction, micro-scale
intervention can be an effective approach to improving environmental quality.
Academically, most previous walkability studies have focused on either meso- or micro-scale environmental variables,
either of which is highly likely to produce incorrect results of the environmental effect, because as the present study shows,
both scales of variables are considerably important in influencing pedestrian satisfaction, and possibly further walking
behavior. Also, the significant correlation between pedestrian satisfaction and environmental factors implies the importance
of considering psychological factors for walkability studies. In travel behavior research, a considerable amount of studies has
focused on residential preference and traveler’s attitude, in pursuit of controlling for self-selection (e.g. Ewing and Handy,
2009; Mokhtarian and Cao, 2008). Other studies examined perceptions of distance, safety, convenience, monetary cost,
and travel time related to walking (Vojnovoc, 2006). However, only little research focused on the role of satisfaction in inter-
actions between behavior and the built environment. While pedestrian satisfaction is expected to encourage walking
(Ettema et al., 2011), few empirical studies examined the effect of satisfactory walking environments on the level of walking
activities. Also, little is known about the influence of various aspects of pedestrian satisfaction – for example, satisfaction
with safety, security, convenience, comfort, easiness for crossing, and visual experience – on walking. Therefore, the present
analysis suggests that further investigation of pedestrian satisfaction can contribute to a better understanding of walkability.
Overall, this study calls on researchers to investigate a comprehensive set of psychological and environmental factors, in
order to understand the various aspects of pedestrian satisfaction, and the diverse motivations behind them, as well as on
planners, to adopt diverse design approaches that will produce more satisfactory pedestrian environments.
Limitations and future research
Despite the findings, an important limitation of the present research is the pedestrian survey that includes only one ques-
tion addressing overall pedestrian satisfaction. In order to sufficiently examine pedestrian satisfaction, various aspects of sat-
isfaction, for example comfort, safety, ease, and aesthetics of walking should be determined. However, few large-scale travel
behavior surveys include these types of questions. Surveys that investigated these aspects of satisfaction would contribute to
better understanding of walking behavior, and related policy outcomes. Hence, future research will examine pedestrian sat-
isfaction in sufficient detail. Also, focusing on pedestrians in Seoul, the findings of the present study may be applied to large
cities in Asia that are similar to Seoul. Parallel studies in other cities should enhance the generalizability of the study. Lastly,
while our study investigated the effect of the built environment on pedestrian satisfaction, the influence of pedestrian
satisfaction level on the actual level of walking activity is not sufficiently investigated in walkability research. Future studies
that reveal the role of pedestrian satisfaction in promoting walking participation, may contribute to better design and health
policies for healthy environments.
Acknowledgments
This work is supported by a National Research Foundation of Korea Grant, funded by the Korea government (MSIP)
(NRF-2010-0029452). This work is also supported by the 2013 Hongik University Research Fund.
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