The Role of the Access Environment in Metro Commute Travel Satisfaction

Abstract

The contributions of the access and egress portions of public transport trips to overall travel satisfaction merit more attention. This study collected responses from intercepted regular urban rail travelers at three metro stations with distinct built-form and land-use characteristics. Local conditions of access on foot, by bicycle, or on a bus were evaluated retrospectively on a five-point satisfaction scale and compared with an independent survey of the same access routes. Three-factor theory and dummy variable regression methods were used to identify the factor structure of environmental attributes under different access means. In the results, access and egress satisfaction were more important than metro trip satisfaction in overall trip satisfaction for walking and cycling modes. Access distance was not significant for walking and marginally negative for cycling satisfaction. For pedestrians, street connectivity, pathway directness, shade, greenery, and crossing safety were all significant (p < 0.05), explaining 51% of the variance in expressed satisfaction. For bicyclists, directness, distance, service, and parking facilities were significant in satisfaction, accounting for 62% of variance. In the bus access model, we found that bus stop location is very important, with passengers also very concerned about the walk experience to the bus stop. Satisfaction with access and egress environments is important in overall satisfaction with travel by public transport.

Full text

Citation: Zacharias, J.; Liu, X. The
Role of the Access Environment in
Metro Commute Travel Satisfaction.
Sustainability 2022,14, 15322.
https://doi.org/10.3390/
su142215322
Academic Editor: Mónica
Gómez-Suárez
Received: 13 October 2022
Accepted: 16 November 2022
Published: 18 November 2022
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4.0/).
sustainability
Article
The Role of the Access Environment in Metro Commute
Travel Satisfaction
John Zacharias * and Xinyi Liu
Laboratory for Urban Process Modelling and Applications, Peking University, Beijing 110871, China
*Correspondence: johnzacharias@pku.edu.cn
Abstract:
The contributions of the access and egress portions of public transport trips to overall travel
satisfaction merit more attention. This study collected responses from intercepted regular urban rail
travelers at three metro stations with distinct built-form and land-use characteristics. Local conditions
of access on foot, by bicycle, or on a bus were evaluated retrospectively on a five-point satisfaction
scale and compared with an independent survey of the same access routes. Three-factor theory and
dummy variable regression methods were used to identify the factor structure of environmental
attributes under different access means. In the results, access and egress satisfaction were more
important than metro trip satisfaction in overall trip satisfaction for walking and cycling modes.
Access distance was not significant for walking and marginally negative for cycling satisfaction.
For pedestrians, street connectivity, pathway directness, shade, greenery, and crossing safety were
all significant (p< 0.05), explaining 51% of the variance in expressed satisfaction. For bicyclists,
directness, distance, service, and parking facilities were significant in satisfaction, accounting for
62% of variance. In the bus access model, we found that bus stop location is very important, with
passengers also very concerned about the walk experience to the bus stop. Satisfaction with access
and egress environments is important in overall satisfaction with travel by public transport.
Keywords: travel satisfaction; pedestrians; bicycles; access; travel environment
1. Introduction
Travel satisfaction is a fundamental good in urban life and is also linked with modal
choice. As travel mode choices increase, travel satisfaction plays an increasing role in mode
choice. Enhancing travel satisfaction may aid in the effort for a better environment by
adding value to low-energy systems while making daily life more agreeable. The potential
for improvement in public transport (PT) and non-motorized transport is considerable and
touches on several areas for improvement, in contrast with private car travel which is more
constrained by the limits of infrastructure.
There has been considerable attention to the intrinsic qualities and services of public
transport service for its effects on mode choice [
1
,
2
]. Operations management, pricing,
and service quality figure prominently in the efforts of transit operators to address the
modal shift to PT [
3
]. Crowding, long waiting times, and service unreliability all lowered
satisfaction levels in the main commute transport mode in Dublin [
4
]. The remembered
experiences of travel, including service courtesies, have direct effect on the decision to
travel by PT. Cumulative satisfaction with the transport service is highly related to the
accumulation of negative, critical incidents remembered from the past. Factors figuring into
those critical negative incidents include treatment by an employee of the service, simplicity
of information, and design [
5
]. The intention to maintain use of urban rail for routine
journeys is heavily dependent on impressions of service quality in cases from Spain [
6
] and
Portugal [
7
]. The attention to the internal operations of PT that dominates the literature is
understandable given the purview of operators and the much greater difficulties in dealing
with the urban context in which PT operates. This literature demonstrates that passengers
Sustainability 2022,14, 15322. https://doi.org/10.3390/su142215322 https://www.mdpi.com/journal/sustainability

Sustainability 2022,14, 15322 2 of 18
are sensitive to a wide variety of service attributes for PT that have direct impact on the
decision to use the service.
There is much less attention in the literature to the effects of linked parts of the journey
and, in particular, access and egress from the central part of the journey, in this case on urban
rail. The concern with distance accessibility is often related to network characteristics [
8
]
and obstacles to movement. The built environment of the station catchment area [
9
] and
land uses [
10
] have been studied with regard to mode choice, but very rarely with regard
to travel satisfaction with the access trip. A limited number of studies have examined
traveler evaluations of certain qualities of the access trip [
11
–
14
]. We have yet to link these
evaluations with specific elements of the access and egress urban rail trips.
Trip satisfaction generally declines with increasing distance and time of the main
commute. This decline in satisfaction is linked with activities in other life domains that may
be curtailed or rescheduled after introducing the fixed parameters of travel [
15
]. It is not
yet clear that satisfaction is uniformly a negative linear response to travel time. The comfort
of the experience before the main transport mode results in lower travel time estimates,
for example [
16
]. The sensed time and dimensions of experienced space vary according to
cognitive and spatial abilities of individuals, but also as a function of direct experience of
the space. The great differences in satisfaction between travel by active and by motorized
modes, as well as substantial variation in the order and amplitude of responses among
studies, suggest that the effect of distance might be further modified. As a result, distance
and time should also be retained in satisfaction studies as control variables.
In this research, we hypothesize that the evaluation of the access trip has impact on
the evaluation of the whole origin–destination journey, as well as on the PT portion of the
journey. Why would the experience of the access trip have impact on satisfaction with the
main PT part of the trip? Why in particular might walking in the access trip have a positive
impact on subsequent travel satisfaction? A brief review of the physiological and psycholog-
ical outcomes of walking provides explanations why evaluations of the first walking stage
of travel may have direct impact on evaluations of the public carrier. Improved mood while
walking may be partly attributed to physiological and biochemical mechanisms, including
the production of endorphins [
17
]. Normal walking is also associated with lowered blood
pressure but also with the increased production of beta-endorphin [
18
], which is known
to activate opioid receptors that bring about feelings of general wellbeing. Many of these
physiological responses are maintained for some time after the walk. Psychological mecha-
nisms may include responses to moderate levels of stimulus, as an abundance of literature
has shown, but also heightened feelings of self-efficacy. Self-efficacy is linked to improved
performance [
19
]. The physiological outcomes may be transitory but are sustained into the
main part of the trip when the legs of the journey are chained. Psychological effects tend to
have longer duration and are variable [20].
The general finding that travel by public transport is evaluated as less pleasant than
being at home, while walking is often evaluated as more pleasant, prompts investigation
of the potential of the earlier travel stage influencing the second. The majority of access
trips to the metro station are by walking. Given the conditioning of the walk sequence and
heightened state of awareness and wellbeing, we might expect a more positive assessment
of PT. Similarly, we might examine the relative effect of other modes, such as the bus, on
the subsequent travel experience. Consistent with the first hypothesis concerning transfers
from walking to PT, we could expect a decline in evaluations of PT when the access trip
is by bus, in light of generally lower levels of appreciation for this mode. Such a focus is
consistent with the general lines of work on travel as an integrated spatial and temporal
entity, defined by purpose, others, and physical context.
In the next section, we examine these hypotheses in greater detail and with reference to
the literature. This reading informs our research instrument, which is presented in Section 3.
Firstly, we present the three-factor model (Section 3.1) and the Beijing case study areas
(Section 3.2). The commuter survey instrument is then introduced (Section 3.3) followed by
the mystery consumer survey (Section 3.4). The analytical approach is outlined (Section 3.5).

Sustainability 2022,14, 15322 3 of 18
The results are in five parts: the descriptive results (Section 4.1); the comparison between
the mystery consumer survey results and the commuter survey results (Section 4.1); the
multivariate analysis for all environmental attributes; and the commuter assessments of
those environmental attributes by mode of access (Section 4.2). Finally, we interpret these
results in terms of the three-factor model.
2. Literature Supporting the Hypotheses
There are three topics that relate directly to the hypotheses, beginning with the superior
psychological wellbeing in active modes. The presumed relation between access and egress
with respect to the main mode is discussed with the relevant findings. The relation between
satisfaction and travel distance or time is reviewed, which can then be related to the
trade-off between access time and public transport level of service. Finally, we review
the evidence for positive affect from specific features of the environment of experience
while walking.
Studies that have directly compared active and motorized travel modes generally find
higher levels of travel satisfaction with active travel modes [
21
–
23
]. While it is possible that
dedicated pedestrians and cyclists are acting out deep-seated life constructs, even to the
choice of habitats that allow them to play out these preferences, improved psychological
wellbeing is also observed for those who switched from car driving to active commutes [
24
].
Some of the higher ratings for walking and cycling, when compared with public transport,
may relate to the greater logistical simplicity of the active modes. Such trips are monomodal,
which are generally preferred over multimodal trips [
25
]. Trips on urban rail are by
definition multimodal, such that we could expect lower satisfaction than for similar distance
trips executed on foot or bicycle. In a Beijing study, 11% of pedestrians walked to their
destination when it was more than 2 km away on a corridor where metro and bus services
were available [
26
]. Travelers value aspects of their experience that weigh in favor of the
non-motorized choice, where it is possible to do so.
Studies tend to support the view that longer commutes are associated with lower over-
all life satisfaction [
23
,
27
–
30
]. With longer duration travel, individuals are less enthusiastic
about their travel, less relaxed, and tend to evaluate the quality and efficiency of the trip as
lower. Travel time is not always regarded as wasted time, however, and may be perceived
as positive experience [
31
,
32
]. The very long commutes experienced by suburban Beijingers
or Tokyoites are assessed negatively. In the Beijing case, long commutes by rail accompany
intentions by the great majority of such commuters to switch to private car [33].
To the extent that travel is a disruption of routines in a variety of purpose settings
and travel results in lowered levels of satisfaction [
20
]. Active travel might be perceived
even more positively than some elective sedentary activity for the physiological and
psychological reasons mentioned above. Just as service reliability of PT figures prominently
in many studies of its desired qualities, the accommodation and realizability of a day
schedule may also be key factors in raising appreciation of the walk cycle. The wide range
of responses to the environments of such movement suggest that such satisfaction is highly
related to the conditions of the environment [
14
], which is addressed in the last part of
this section.
The body’s response to walking or cycling by producing more endorphins [
17
] is expe-
rienced during the walk cycle but also has residual effect. The walk also results in lowered
blood pressure among other signs of relaxation [
34
]. Essentially, we feel better physically and
psychologically by virtue of exercise in the first stage of the journey, which may have spill-over
effects when evaluating subsequent experience. Few studies examine the relationship between
access and travel by the main mode with regard to satisfaction [
13
,
14
,
29
,
35
]. In a study of
bicycle and public transport access to the train in the Netherlands, access conditions and
management of the station space had significant effects on the evaluation of the rail portion
of the trip [
11
]. In a Swedish study, overall satisfaction with public transport was related
to satisfaction with access (0.684, p< 0.01) and egress (0.522, p< 0.01) [
13
]. Based on these
insights from multiple sources, we could expect a positive spin-off from the first leg of the

Sustainability 2022,14, 15322 4 of 18
journey onto the second but our knowledge of the details of the walk or bicycle environment
that relate positively to overall satisfaction is partial and fragmentary.
An additional element entering into the evaluation of time is the weight of external
benefits that derive from comfort and frequency of service for a reduced, higher-quality
network. It is well recognized that pedestrians are willing to walk farther to urban rail [
14
]
because of greater comfort, reliability, and frequency of service when compared with bus
service. The operational advantages of urban rail might be tempered by higher volumes
of passenger traffic that lead to crowding or even delays while waiting to enter an over-
crowded station. For these reasons, as well as possible others, walked distance is not clearly
related to satisfaction with the walk portion of the trip.
Finally, our experience of walking is complex because it is multisensory and within an
environment that is constantly changing as we move. The literature demonstrates the ability
of individuals to globally assess the comfort and convenience of their walk to PT although
it is more difficult to relate these assessments to particular features of the environment.
Positive affects in walking may relate to higher concentrations of human activity but are also
observed in naturalistic environments with trees and water bodies [
28
]. Safety and security
figure prominently in many studies on access to urban rail [
14
], although we need to know
more about the specific elements in the public environment triggering these assessments.
Some of the concern with safety relates to features of the physical environment, including
interactions with motor traffic, quality of walking surfaces, clarity of movement priority,
lighting, and the presence of others. Pedestrians walking to urban rail are highly sensitive
to details of sidewalks, stairways, and other pedestrian facilities. When pedestrians have
available path choices, they will tend to choose routes with certain physical attributes that
might include services, whether used or not [
36
]. The availability of path choice may also
have effects on satisfaction levels, given that choice confers a measure of control.
The literature on access to urban rail merits development, particularly with regard
to the interaction between stages of travel. Although much has been done to investigate
the co-variates of walking for leisure, commuting, and other purposes, we need specific
investigation of the access trip to urban rail.
3. Materials and Methods
3.1. The Three-Factor Model
The model relates performance factors to satisfaction. The three groups of factors are
described in Table 1following the literature [37,38].
Table 1. Definitions of three factors.
Attributes
Basic factors
When such attributes are delivered well, they will not have a positive effect on overall satisfaction, and
when they are delivered poorly, they can cause dissatisfaction. They are the basic and expected attributes
that all services should adequately provide. Their relationship with overall satisfaction is asymmetric and
non-linear.
Performance factors
Such attributes can lead to satisfaction and dissatisfaction, respectively, depending on whether their
performance is high or low. They have a linear and symmetrical relationship with overall travel
satisfaction.
Exciting factors
This category is in contrast to the basic factors. The attributes belonging to this category are unexpected
attributes that can only bring happiness and satisfaction from the service. Their relationship with overall
satisfaction is asymmetric and non-linear.
The basic factors have a negative impact on overall satisfaction if their performance
is low, but have no material impact on overall satisfaction if they perform well (Figure 1).
In contrast, exciting factors increase overall satisfaction during high performance, but do
not affect overall satisfaction during low performance. These two factors represent the
nonlinear and asymmetric effects of service attributes on overall satisfaction. On the other
hand, performance factors have a linear effect on overall satisfaction. When the basic factors

Sustainability 2022,14, 15322 5 of 18
are not performing well, they need to be taken seriously, because passengers want these
factors to be satisfied. Exciting factors have the lowest priority, because passengers do not
expect them, even if they may make passengers happy [
38
]. Zhang et al. [
39
] found that
customer service, ride comfort, safety while waiting, and cost constituted basic factors,
while performance factors included safety while riding, reliability, convenience, travel
time, and ease of use. Busacca and Padula [
37
] detail how the three factors interact and
recommend regression analysis with the dummy variables of high and low performance,
which is the approach taken in this study.
Sustainability 2022, 14, x FOR PEER REVIEW 5 of 19
Exciting factors
This category is in contrast to the basic factors. The attributes belonging to this category are un-
expected attributes that can only bring happiness and satisfaction from the service. Their rela-
tionship with overall satisfaction is asymmetric and non-linear.
The basic factors have a negative impact on overall satisfaction if their performance
is low, but have no material impact on overall satisfaction if they perform well (Figure 1).
In contrast, exciting factors increase overall satisfaction during high performance, but do
not affect overall satisfaction during low performance. These two factors represent the
nonlinear and asymmetric effects of service attributes on overall satisfaction. On the other
hand, performance factors have a linear effect on overall satisfaction. When the basic fac-
tors are not performing well, they need to be taken seriously, because passengers want
these factors to be satisfied. Exciting factors have the lowest priority, because passengers
do not expect them, even if they may make passengers happy [38]. Zhang et al. [39] found
that customer service, ride comfort, safety while waiting, and cost constituted basic fac-
tors, while performance factors included safety while riding, reliability, convenience,
travel time, and ease of use. Busacca and Padula [37] detail how the three factors interact
and recommend regression analysis with the dummy variables of high and low perfor-
mance, which is the approach taken in this study.
Figure 1. The three-factor structure.
3.2. Study Areas
Beijing has invested heavily in its metro system in the last twenty years to respond
to mobility demands. In 2019, before the onset of the pandemic, 21.5 m inhabitants gener-
ated a total of 3.95 billion metro-based trips [40]. The planned extensions to the existing
system of 700 km of lines and 405 stations are intended to capture an increasing propor-
tion of new internal trips. To the extent that access and egress satisfaction are relevant to
the decision to use metro, they are relevant to this transport plan. This study is in part
focused on local conditions for access to stations, so three distinctly different environ-
ments were chosen for the field study (Figure 2). The three stations are Xisi Station (XS),
Mudanyuan Station (MDY), and Huilongguan Station (HLG) (Figure 3). The circles rep-
resent the 800 m study areas. XS is a residential area with traditional Beijing urban tex-
ture—small blocks, high road density—offering pedestrians more path choice. HLG is in
a new suburban residential area, with large blocks, dense residential use, and low land
use diversity. The built environment around MDY is typical of residential areas built be-
fore 2000 in Beijing, with a relatively moderate street scale and land use diversity. Figure
Fully
delivere
d
Not
delivered
S
at
i
s
f
act
i
on
Dissatisfaction
Exciting
factors
Performance
factors
Basic
factors
Figure 1. The three-factor structure.
3.2. Study Areas
Beijing has invested heavily in its metro system in the last twenty years to respond to
mobility demands. In 2019, before the onset of the pandemic, 21.5 m inhabitants generated
a total of 3.95 billion metro-based trips [
40
]. The planned extensions to the existing system
of 700 km of lines and 405 stations are intended to capture an increasing proportion of
new internal trips. To the extent that access and egress satisfaction are relevant to the
decision to use metro, they are relevant to this transport plan. This study is in part focused
on local conditions for access to stations, so three distinctly different environments were
chosen for the field study (Figure 2). The three stations are Xisi Station (XS), Mudanyuan
Station (MDY), and Huilongguan Station (HLG) (Figure 3). The circles represent the 800 m
study areas. XS is a residential area with traditional Beijing urban texture—small blocks,
high road density—offering pedestrians more path choice. HLG is in a new suburban
residential area, with large blocks, dense residential use, and low land use diversity. The
built environment around MDY is typical of residential areas built before 2000 in Beijing,
with a relatively moderate street scale and land use diversity. Figure 2shows the built
environment around the three stations. In the first row are street intersections. The second
row illustrates privatized urban blocks with the public thoroughfares between them. The
last row illustrates land use, with yellow as exclusively residential use, red as exclusively
commercial use, and pink with mixed commercial and residential uses. The green areas
are parks.

Sustainability 2022,14, 15322 6 of 18
Sustainability 2022, 14, x FOR PEER REVIEW 6 of 19
2 shows the built environment around the three stations. In the first row are street inter-
sections. The second row illustrates privatized urban blocks with the public thoroughfares
between them. The last row illustrates land use, with yellow as exclusively residential use,
red as exclusively commercial use, and pink with mixed commercial and residential uses.
The green areas are parks.
Figure 2. The Beijing metro system in 2019 with the three station areas of the present study.
Figure 3. The built environment of the three station areas, showing respectively from top to bottom,
intersections, blocks, and land uses (red is commercial, yellow is residential, green is park and pink
is mixed use).
Figure 2. The Beijing metro system in 2019 with the three station areas of the present study.
Sustainability 2022, 14, x FOR PEER REVIEW 6 of 19
2 shows the built environment around the three stations. In the first row are street inter-
sections. The second row illustrates privatized urban blocks with the public thoroughfares
between them. The last row illustrates land use, with yellow as exclusively residential use,
red as exclusively commercial use, and pink with mixed commercial and residential uses.
The green areas are parks.
Figure 2. The Beijing metro system in 2019 with the three station areas of the present study.
Figure 3. The built environment of the three station areas, showing respectively from top to bottom,
intersections, blocks, and land uses (red is commercial, yellow is residential, green is park and pink
is mixed use).
Figure 3.
The built environment of the three station areas, showing respectively from top to bottom,
intersections, blocks, and land uses (red is commercial, yellow is residential, green is park and pink is
mixed use).
3.3. Questionnaire Design and Data Collection
Capturing impressions of a perceptually rich environment is a methodological chal-
lenge because of uncontrolled phenomena in a dynamic environment. The brain retains
selected memories for future action purposes. The selected experiences that are needed for

Sustainability 2022,14, 15322 7 of 18
future trips can be stored in long-term memory, while multi-sensory experiences and trip
details belong to short-term memory. Applying the survey immediately following the ac-
cess trip was intended to capture impressions from both short- and long-term memory. The
survey was conducted on the metro station platform by approaching waiting passengers
using a randomizing protocol and then asking an eligibility question. Participants needed
to be executing a routine trip from home with one rail-based segment of the journey and
monomodal access and egress segments. The survey was conducted simultaneously at
three stations between 8 a.m. and 10 a.m. in July 2019. A pilot survey with 60 participants,
recruited on the same metro platforms as for the main survey, allowed us to verify that
the questionnaire was understandable and applicable and that responses were distributed.
The final survey was of 332 participants with completed questionnaires, or a 40% response
rate. This number is sufficient given the psycho-metric nature of the survey, as evidenced
generally in such studies on environmental perceptions.
The questionnaire consisted of four parts: 1—trip characteristics; 2—evaluation of
different trip stages and environmental variables; 3—socio-demographic characteristics;
and 4—estimates of access and egress travel time. Trip characteristics included purpose,
origin, and destination location as well as chosen mode for both access and egress, given
that these are often different. The access, egress, and main parts of the trip were evaluated
for satisfaction on a 5-point Likert scale with 5 as most satisfied. Actual distance was
measured in GIS using the reported origin and destination locations. The set of built
environment attributes considered and their definitions are as follows (Table 2):
Table 2. Built environment attributes and definitions.
Attribute Definition
Access distance Distance measured in GIS using reported origin and destination locations
Safety of street crossing Degree of perceived safety when crossing access or egress street crossings
Connectivity of the road The degree of satisfaction with the ratio of waiting time at the traffic light to the total
connection time
Service facility The number of food services on the way
Sidewalk width Satisfaction with the level of the provision
Pedestrian route directness (PRD) Perceived ratio of actual walking distance to straight-line distance
Greenery Perception of greenery and scenic beauty
Shade area Perception of the degree of shade
Bicycle parking facilities Evaluation of the adequacy of bicycle parking provisions
Separated bikeway Existence of separated bicycle lanes
Location of bus stop Convenience or suitability of bus stop location
Bus station security Evaluation of the level of security
Walk to the bicycle Walking comfort level
Walk to bus stop Walking comfort level
3.4. Mystery Consumer Survey
Considering possible reliability issues due to recall for the evaluation of service qual-
ity, a mystery consumer survey is also implemented. The method involves independent
observers retracing the reported access and egress paths and reporting evaluations accord-
ing to the survey grid administered to the commuter participants. Four investigators are
trained with regard to the above factors and the evaluation grid. All of them conduct
evaluations at all three stations and on access and egress pathways where there were more
than three participants, for a total of 72 distinct paths.

Sustainability 2022,14, 15322 8 of 18
3.5. Analytical Approach
Regression with dummy variables is chosen to discriminate the way service attribute
performance impacts overall travel satisfaction. Since the dependent variable—overall
travel satisfaction—is in order, from 1 or totally disagree, to 5 or totally agree, in this case
an Ordered Logit Model (OLM) is most appropriate. In general, OLM can be expressed as:
ykˆ
Sustainability 2022, 14, x FOR PEER REVIEW 8 of 19
are trained with regard to the above factors and the evaluation grid. All of them conduct
evaluations at all three stations and on access and egress pathways where there were more
than three participants, for a total of 72 distinct paths.
3.5. Analytical Approach
Regression with dummy variables is chosen to discriminate the way service attribute
performance impacts overall travel satisfaction. Since the dependent variable—overall
travel satisfaction—is in order, from 1 or totally disagree, to 5 or totally agree, in this case
an Ordered Logit Model (OLM) is most appropriate. In general, OLM can be expressed
as:
yk^ ⋇ = Xk β + εk
where k^⋇ is the latent dependent variable of individual k, and Xk is the explanatory var-
iable set of individual k, which contains all the independent variables of individual k. β is
the vector corresponding to the parameter to be estimated. εk is the error term, which is
assumed to be a logistic error term of the same distribution.
In this research, level 5, or very satisfied, is set as the reference variable for each group
of variables, for determining to which factor in the three-factor model the attribute be-
longs. When there is a significant negative impact relative to the reference variable, at 1 to
2 on the satisfaction scale and no significant impact at 4, the attribute is taken as a basic
factor. When a score of 4 has significant negative effect compared with a score of 5, but a
score of 1 to 2 has no significant effect, the attribute is an exciting factor. When attributes
have significant negative effects from 1 to 4, and the impact factor is basically linear, the
attribute is treated as a covariate, i.e., belonging to performance factors.
4. Results
4.1. Descriptive Results
Table 3 summarizes the descriptive data from the three stations. The recruited par-
ticipants were evenly distributed between males and females, with about 80% between
the ages of 20 and 39. Nearly half of survey participants (48.0%) owned a private car or
bicycle. While HLG had a significantly lower rate of car ownership among the three areas,
a larger proportion (11.1%) chose car as the access mode than in MDY or XS. There was
no significant difference in car ownership among the three urban areas (p > 0.05) but sig-
nificant difference in access mode choice (c2 = 24.3, p = 0.000). Access distance in HLG was
on average greater than in XS. XS had the shortest access distance, but also a smaller pro-
portion of trips by bus and more by shared bicycle. The walking share was greater for
egress than for access overall, with a lower proportion of those taking a bus after the metro
ride compared with the access stage of the trip. The longest metro journeys were from
HLG, given its location in peri-urban Beijing.
Access distance was important in the choice of mode at certain thresholds. The access
distance has a significant effect on the selection of access mode (c2 = 190.5, p = 0.00). Walk-
ing predominates at under 1.8 km, with a switch to bus and bicycling thereafter. Car use
increases after 3 km of access distance.
Table 3. Sample summary statistics.
Percent (%)
Attributes Distribution HLG (n = 126) MDY(n = 122) XS (n = 84) Overall (n = 332)
Personal characteristics
Age <20 4.8 12.3 13.1 9.6
20–29 63.5 63.1 41.7 57.8
29–39 27.8 18.9 36.9 26.8
40–494.0 4.9 8.3 5.4
= Xk β+εk
where kˆ
Sustainability 2022, 14, x FOR PEER REVIEW 8 of 19
are trained with regard to the above factors and the evaluation grid. All of them conduct
evaluations at all three stations and on access and egress pathways where there were more
than three participants, for a total of 72 distinct paths.
3.5. Analytical Approach
Regression with dummy variables is chosen to discriminate the way service attribute
performance impacts overall travel satisfaction. Since the dependent variable—overall
travel satisfaction—is in order, from 1 or totally disagree, to 5 or totally agree, in this case
an Ordered Logit Model (OLM) is most appropriate. In general, OLM can be expressed
as:
yk^ ⋇ = Xk β + εk
where k^⋇ is the latent dependent variable of individual k, and Xk is the explanatory var-
iable set of individual k, which contains all the independent variables of individual k. β is
the vector corresponding to the parameter to be estimated. εk is the error term, which is
assumed to be a logistic error term of the same distribution.
In this research, level 5, or very satisfied, is set as the reference variable for each group
of variables, for determining to which factor in the three-factor model the attribute be-
longs. When there is a significant negative impact relative to the reference variable, at 1 to
2 on the satisfaction scale and no significant impact at 4, the attribute is taken as a basic
factor. When a score of 4 has significant negative effect compared with a score of 5, but a
score of 1 to 2 has no significant effect, the attribute is an exciting factor. When attributes
have significant negative effects from 1 to 4, and the impact factor is basically linear, the
attribute is treated as a covariate, i.e., belonging to performance factors.
4. Results
4.1. Descriptive Results
Table 3 summarizes the descriptive data from the three stations. The recruited par-
ticipants were evenly distributed between males and females, with about 80% between
the ages of 20 and 39. Nearly half of survey participants (48.0%) owned a private car or
bicycle. While HLG had a significantly lower rate of car ownership among the three areas,
a larger proportion (11.1%) chose car as the access mode than in MDY or XS. There was
no significant difference in car ownership among the three urban areas (p > 0.05) but sig-
nificant difference in access mode choice (c2 = 24.3, p = 0.000). Access distance in HLG was
on average greater than in XS. XS had the shortest access distance, but also a smaller pro-
portion of trips by bus and more by shared bicycle. The walking share was greater for
egress than for access overall, with a lower proportion of those taking a bus after the metro
ride compared with the access stage of the trip. The longest metro journeys were from
HLG, given its location in peri-urban Beijing.
Access distance was important in the choice of mode at certain thresholds. The access
distance has a significant effect on the selection of access mode (c2 = 190.5, p = 0.00). Walk-
ing predominates at under 1.8 km, with a switch to bus and bicycling thereafter. Car use
increases after 3 km of access distance.
Table 3. Sample summary statistics.
Percent (%)
Attributes Distribution HLG (n = 126) MDY(n = 122) XS (n = 84) Overall (n = 332)
Personal characteristics
Age <20 4.8 12.3 13.1 9.6
20–29 63.5 63.1 41.7 57.8
29–39 27.8 18.9 36.9 26.8
40–494.0 4.9 8.3 5.4
is the latent dependent variable of individual k, and Xk is the explanatory
variable set of individual k, which contains all the independent variables of individual k.
β
is the vector corresponding to the parameter to be estimated.
ε
k is the error term, which is
assumed to be a logistic error term of the same distribution.
In this research, level 5, or very satisfied, is set as the reference variable for each group
of variables, for determining to which factor in the three-factor model the attribute belongs.
When there is a significant negative impact relative to the reference variable, at 1 to 2 on
the satisfaction scale and no significant impact at 4, the attribute is taken as a basic factor.
When a score of 4 has significant negative effect compared with a score of 5, but a score
of 1 to 2 has no significant effect, the attribute is an exciting factor. When attributes have
significant negative effects from 1 to 4, and the impact factor is basically linear, the attribute
is treated as a covariate, i.e., belonging to performance factors.
4. Results
4.1. Descriptive Results
Table 3summarizes the descriptive data from the three stations. The recruited par-
ticipants were evenly distributed between males and females, with about 80% between
the ages of 20 and 39. Nearly half of survey participants (48.0%) owned a private car
or bicycle. While HLG had a significantly lower rate of car ownership among the three
areas, a larger proportion (11.1%) chose car as the access mode than in MDY or XS. There
was no significant difference in car ownership among the three urban areas (p> 0.05) but
significant difference in access mode choice (c
2
= 24.3, p= 0.000). Access distance in HLG
was on average greater than in XS. XS had the shortest access distance, but also a smaller
proportion of trips by bus and more by shared bicycle. The walking share was greater for
egress than for access overall, with a lower proportion of those taking a bus after the metro
ride compared with the access stage of the trip. The longest metro journeys were from
HLG, given its location in peri-urban Beijing.
Access distance was important in the choice of mode at certain thresholds. The access
distance has a significant effect on the selection of access mode (c
2
= 190.5, p= 0.00).
Walking predominates at under 1.8 km, with a switch to bus and bicycling thereafter. Car
use increases after 3 km of access distance.
Firstly, we examine overall satisfaction by mode and trip segment (Tables 4and 5).
Access and egress satisfaction are highly related to overall satisfaction, contributing as
much or more than the main segment of travel by metro. From the overall data, compared
with the average satisfaction of the access part and the egress part, the satisfaction of
the main travel stage of the metro was highest (3.69
±
0.09), and the access part was
lowest (3.61
±
0.09). Metro passengers arriving by car have the highest mean score of
trip satisfaction (3.71
±
0.35), while those who chose bus as access mode have the lowest
(3.43
±
0.23). The scores of access mode were significantly different among the three sites
(F = 5.126, p< 0.01), among which XS had the highest average satisfaction of all stages, while
HLG had the lowest. When arriving at the station by foot and bicycle, visitors’ satisfaction
was significantly higher at the XS Station than at the HLG Station (p< 0.05), although the
latter tended to have longer trips.

Sustainability 2022,14, 15322 9 of 18
Table 3. Sample summary statistics.
Percent (%)
Attributes Distribution HLG (n= 126) MDY (n= 122) XS (n= 84) Overall (n= 332)
Personal characteristics
Age <20 4.8 12.3 13.1 9.6
20–29 63.5 63.1 41.7 57.8
29–39 27.8 18.9 36.9 26.8
40–49 4.0 4.9 8.3 5.4
>50 0.0 0.8 0.0 0.3
Sex male 49.2 36.1 59.5 47
female 50.8 63.9 40.5 53
Vehicle ownership none 61.9 46.7 45.2 52.1
bicycle 18.3 22.1 16.7 19.3
car 12.7 17.2 15.5 15.1
both 7.1 13.9 22.6 13.6
Trip characteristics
Trip purpose working 91.3 73.8 66.7 78.6
study 2.4 4.1 7.1 4.2
leisure 2.4 12.3 8.3 7.5
other 4.0 9.8 17.9 9.6
Access mode walking 61.9 62.3 73.8 65.1
bike (shared) 7.1 12.3 11.9 10.2
bike (private) 2.4 5.7 4.8 4.2
bus 17.5 18.9 7.1 15.4
car 11.1 0.8 2.4 5.1
Egress mode walking 70.6 77.0 72.6 73.5
bike (shared) 11.1 9.0 15.5 11.4
bike (private) 0.0 0.8 1.2 0.6
bus 9.5 9.8 9.5 9.6
car 4.8 1.6 0.0 2.4
other 1.6 1.2 1.8 2.4
Access time (min) ≤5 11.1 11.4 16.7 12.6
5–10 50.8 54.1 50.0 51.8
10–20 28.6 18.9 23.8 23.8
20–30 3.2 6.6 3.6 4.5
>30 6.3 9.0 6.0 7.2
Access distance (m) ≤500 17.5 29.5 14.3 21.1
500–1000 37.3 36.9 65.5 44.3
1000–1500 17.5 8.2 10.7 12.3
1500–2000 14.3 9.0 6.0 10.2
2000–2500 2.4 9.0 1.2 4.5
2500–3000 0.8 1.6 0.0 0.9
>3000 10.3 5.7 2.4 6.6
Mean access distance (m) 1374.5 1137.1 921.5 1172.7
Mean trip distance (km) 20.8 13.2 14.0 16.3
Built environment around
stations
Land use diversity (/km
2
)
0.30 0.66 0.70
Intersection density
(/km2)10.2 8.0 36.0
Block length (m) 367 245 134

Sustainability 2022,14, 15322 10 of 18
Table 4.
Means and 95% confidence intervals of satisfaction at each stage (by different access mode).
Variable
Walking
(n= 216)
Bike
(n= 48)
Bus
(n= 51)
Car
(n= 17)
Overall
(n= 332)
Mean Mean Mean Mean Mean
Overall
Satisfaction 3.73 ±0.11 3.46 ±0.24 3.43 ±0.23 3.71 ±0.35 3.64 ±0.08
Access Satisfaction 3.70 ±0.12 3.42 ±0.22 3.35 ±0.26 3.76 ±0.46 3.61 ±0.09
Metro Satisfaction 3.73 ±0.12 3.60 ±0.39 3.55 ±0.25 3.76 ±0.34 3.69 ±0.09
Egress Satisfaction 3.70 ±0.12 3.60 ±0.26 3.43 ±0.29 3.65 ±0.55 3.64 ±0.10
Table 5. Means and 95% confidence intervals of satisfaction at each stage (different rail stations).
Variable HLG (n= 126) MDY (n= 122) XS (n= 84) Overall (n= 332)
Mean Mean Mean Mean
Overall Satisfaction 3.41 ±0.13 3.72 ±0.15 3.88 ±0.18 3.64 ±0.08
Access Satisfaction 3.45 ±0.14 3.62 ±0.16 3.83 ±0.18 3.61 ±0.09
Metro Satisfaction 3.44 ±0.13 3.81 ±0.17 3.88 ±0.18 3.69 ±0.09
Egress Satisfaction 3.49 ±0.14 3.68 ±0.17 3.81 ±0.19 3.64 ±0.10
Pedestrians are most satisfied with the continuity and lack of detour in the street
network, while they are most dissatisfied with shade and greenery (Table 6). They rate
walking to the bus stop (mean = 3.49) or cycling (mean = 3.38) as less satisfying than
walking to the metro (mean = 3.73). Pedestrians are more satisfied with services than are
bicyclists. Those biking to the station express generally lower levels of satisfaction with the
metro portion of the trip, similar to the effect of riding to the station by bus. Bicyclists are
less satisfied with separated bikeway (mean = 3.29) and bike parking facilities (mean = 3.33),
but show highest satisfaction with road connectivity (mean = 3.69). People have a high
degree of satisfaction with the built environment when traveling by bus, especially the
safety of the station (mean = 3.80). The higher satisfaction with the metro for bus riders
may be illustrative of the contrast they feel between their access mode and the main mode.
Table 6. Mean satisfaction and 95% confidence intervals for access environment attributes.
Variables Walking (n= 216) Bike (n= 48) Bus (n= 51)
Attribute Satisfaction Mean Mean Mean
Width of sidewalk 3.64 ±0.13
Safety of crossing 3.75 ±0.11
Connectivity of the road 3.89 ±0.12 3.69 ±0.25
Directness of the road 3.90 ±0.12 3.50 ±0.30
Service facility 3.54 ±0.14 3.40 ±0.28
Greenery 3.55 ±0.12 3.46 ±0.28
Shade 3.39 ±0.14
Bicycle parking facilities 3.33 ±0.32
Separated bikeway 3.29 ±0.32
Environment of walking to bike 3.38 ±0.31
Environment of walking to bus stop 3.49 ±0.25
Location of bus stop 3.71 ±0.22
Safety of bus station 3.80 ±0.22
XS has the highest average score of road connectivity, detour and crossing safety, and
service facilities among the three stations, but the width of the sidewalk has the worst
performance among the three sets of data. The sidewalk in XS is not only narrow but
also impossible to walk due to illegal car parking and bicycle parking. XS commercial
and residential uses are combined, with both sides of the road providing a wealth of

Sustainability 2022,14, 15322 11 of 18
service facilities. Although the smaller block size of XS provides more convenience, other
attributes are lacking, such as landscape greening. The greenery and shading of the MDY
are obviously the best of the three stations. Compared with the XS and HLG, the sidewalk
width at MDY is the most satisfactory for commuters. MDY also has more green space than
the other station areas. The average value of all attributes of HLG is lower than that of the
other two stations, especially the service facilities, which are far lower than that of XS. Due
to the barrier of railway traffic in HLG, the satisfaction of PRD is low. At the same time, the
wide road also reduces the road connectivity and crossing safety.
The mystery consumer survey results are compared with those of the commuter
participants in Figure 4, with an overall correlation of 0.97 (p= 0.000). Except for road
connectivity, the average score of each attribute in the mystery consumer experiment results
was lower than that in the commuter satisfaction survey, with an absolute difference of less
than 0.20. We conclude that commuter participants had a clear recall of access and egress
conditions, ranking the environments in much the same way as the mystery consumer
investigators, who had the advantages of comparison and purpose.
Sustainability 2022, 14, x FOR PEER REVIEW 11 of 19
XS has the highest average score of road connectivity, detour and crossing safety, and
service facilities among the three stations, but the width of the sidewalk has the worst
performance among the three sets of data. The sidewalk in XS is not only narrow but also
impossible to walk due to illegal car parking and bicycle parking. XS commercial and res-
idential uses are combined, with both sides of the road providing a wealth of service fa-
cilities. Although the smaller block size of XS provides more convenience, other attributes
are lacking, such as landscape greening. The greenery and shading of the MDY are obvi-
ously the best of the three stations. Compared with the XS and HLG, the sidewalk width
at MDY is the most satisfactory for commuters. MDY also has more green space than the
other station areas. The average value of all attributes of HLG is lower than that of the
other two stations, especially the service facilities, which are far lower than that of XS. Due
to the barrier of railway traffic in HLG, the satisfaction of PRD is low. At the same time,
the wide road also reduces the road connectivity and crossing safety.
The mystery consumer survey results are compared with those of the commuter par-
ticipants in Figure 4, with an overall correlation of 0.97 (p = 0.000). Except for road connec-
tivity, the average score of each attribute in the mystery consumer experiment results was
lower than that in the commuter satisfaction survey, with an absolute difference of less
than 0.20. We conclude that commuter participants had a clear recall of access and egress
conditions, ranking the environments in much the same way as the mystery consumer
investigators, who had the advantages of comparison and purpose.
Figure 4. Mystery consumer and passenger satisfaction questionnaire attribute mean ratio.
4.2. Multivariate Analysis
In the walking access mode, all factors except PRD have a significant impact on over-
all trip satisfaction, while all environmental attributes are significant for bicycling access.
Bus access distance is unrelated to satisfaction, consistent with Abenoza et al. (2019a).
All travel stage satisfaction levels are closely related to overall travel satisfaction us-
ing Spearman’s coefficient (r = 0.6, p < 0.01). Based on the correlation test, the ordered
regression models (Tables 7–10) are presented for satisfaction at different stages and for
different access modes. Satisfaction with each stage of the journey can explain up to 74.6%
of overall travel satisfaction (Table 7). In general, the influence of the access stage is higher
than the influence of the other stages. Recall that our survey was conducted upon the
participant’s recent completion of the access stage. Satisfaction of the connecting part of
travel has a greater impact on overall satisfaction than satisfaction with the main ride
stage. The access part in the bus access mode has greater impact.
Figure 4. Mystery consumer and passenger satisfaction questionnaire attribute mean ratio.
4.2. Multivariate Analysis
In the walking access mode, all factors except PRD have a significant impact on overall
trip satisfaction, while all environmental attributes are significant for bicycling access. Bus
access distance is unrelated to satisfaction, consistent with Abenoza et al. (2019a).
All travel stage satisfaction levels are closely related to overall travel satisfaction
using Spearman’s coefficient (r = 0.6, p< 0.01). Based on the correlation test, the ordered
regression models (Tables 7–10) are presented for satisfaction at different stages and for
different access modes. Satisfaction with each stage of the journey can explain up to 74.6%
of overall travel satisfaction (Table 7). In general, the influence of the access stage is higher
than the influence of the other stages. Recall that our survey was conducted upon the
participant’s recent completion of the access stage. Satisfaction of the connecting part of
travel has a greater impact on overall satisfaction than satisfaction with the main ride stage.
The access part in the bus access mode has greater impact.

Sustainability 2022,14, 15322 12 of 18
Table 7.
Satisfaction averages and 95% confidence intervals for environmental attributes among the
three stations.
HLG (N = 28) MDY (N = 20) XS (N = 24)
Attribute Satisfaction Mean Mean Mean
Width of sidewalk 3.50 ±0.25 3.75 ±0.60 3.33 ±0.27
Safety of crossing 3.14 ±0.36 3.75 ±0.52 4.17 ±0.16
PRD 3.57 ±0.41 3.85 ±0.46 4.00 ±0.39
Connectivity of the road 3.43 ±0.37 4.10 ±0.40 4.50 ±0.22
Greenery 3.14 ±0.21 3.65 ±0.27 3.38 ±0.35
Shade 3.11 ±0.19 3.60 ±0.38 3.13 ±0.25
Service facility 2.82 ±0.28 3.10 ±0.26 4.17 ±0.36
Table 8. M1, overall travel—each travel stage satisfaction model.
Stage Satisfaction General Access = Walk Access = Bike Access = Bus
Estim. Sig. Estim. Sig. Estim. Sig. Estim. Sig.
Access 1.678 ** 0.000 1.555 ** 0.000 1.464 * 0.019 1.911 * 0.003
Metro 1.462 ** 0.000 1.400 ** 0.000 1.330 ** 0.003 1.445 * 0.009
Egress 1.250 ** 0.000 1.704 ** 0.000 1.833 ** 0.002 0.264 0.620
−2 Log likelihood 197.083 118.077 44.966 56.539
Nagelkerke R20.746 0.763 0.769 0.675
N 332 216 48 51
*p< 0.05, ** p< 0.01.
Table 9. M2, the model of built environmental factors and overall satisfaction (access = walking).
Estim. Sig.
Access distance 0.000 0.706
Road connectivity satisfaction 0.914 0.000
Shade satisfaction (very dissatisfied) −1.246 0.237
(dissatisfied) −1.844 0.037
(general) −2.242 0.007
(satisfied) −1.449 0.063
(very satisfied) Ref. value
Greenery satisfaction (very dissatisfied) −2.714 0.040
(dissatisfied) −2.168 0.020
(general) −0.663 0.381
(satisfied) −0.874 0.222
(very satisfied) Ref. value
Safety of crossing satisfaction (very dissatisfied) 3.912 0.173
(dissatisfied) −2.034 0.061
(general) −1.004 0.186
(satisfied) −0.856 0.239
(very satisfied) Ref. value
Sidewalk width satisfaction (very dissatisfied) −1.037 0.512
(dissatisfied) 0.925 0.288
(general) 1.112 0.155
(satisfied) 0.939 0.197
(very satisfied) Ref. value
Service facility satisfaction (very dissatisfied) −0.792 0.502
(dissatisfied) −0.712 0.397
(general) −0.288 0.698
(satisfied) −0.268 0.702
(very satisfied) Ref. value
−2 Log likelihood 346.460
Nagelkerke R20.513
N 216

Sustainability 2022,14, 15322 13 of 18
Table 10. M3, the model of built environmental factors and overall satisfaction (access = biking).
Estimate Sig.
Pedestrian Route Directness (PRD) −2.539 ** 0.024
Access distance −0.002 *** 0.006
Parking facility satisfaction (very dissatisfied) −3.883 ** 0.018
(dissatisfied) −1.797 0.265
(general) −1.199 0.334
(satisfied) −1.585 0.191
(very satisfied) Ref. value
Service facility satisfaction (dissatisfied) −4.695 ** 0.003
(general) −4.951 *** 0.001
(satisfied) −2.564 ** 0.043
(very satisfied) Ref. value
−2 Log likelihood 74.784
Nagelkerke R20.676
N 48
** p< 0.05, *** p< 0.01.
Table 8presents the estimated coefficients for overall satisfaction under the walking
access mode. The model can explain 51% of overall travel satisfaction. On the whole, road
connectivity features prominently in overall satisfaction. Since there is a linear relation-
ship between this factor and overall satisfaction, we can conclude that more connectivity
brings higher overall satisfaction. When the shading condition is poor, there is little change
in satisfaction, but when the shading condition is better, it will bring a significant in-
crease in satisfaction. This fulfills the condition of the exciting factors in the three-factor
classification—unexpected but welcome. Greening and crossing safety attributes have the
opposite effect to shading. When the satisfaction of greening and crossing safety attributes
is lower than the basic value, it has a significant negative impact on the overall travel
satisfaction, but when the greening attribute satisfaction exceeds the basic satisfaction, the
impact on overall satisfaction no longer increases linearly with the increase in greening
satisfaction. This shows that greening and street crossing safety attributes are basic factors.
Table 9presents the estimated coefficients under the cycling access mode. Due to data
limitations, non-significant factors are eliminated. The model can explain 67% of the overall
travel satisfaction. The results show that service facilities, parking facilities, detour and
access distance all have a significant impact on the travel satisfaction of cyclists. Access
distance and PRD have a linear effect on overall satisfaction. The greater the access distance,
the greater the detour, and the lower the overall satisfaction. When passengers are very
dissatisfied with the parking facilities, there is a significant negative impact on the overall
trip satisfaction; however, if satisfaction continues to increase, the impact on the overall
satisfaction does not increase linearly, which indicates that parking facilities are basic
attributes. The higher the passengers’ satisfaction evaluation of service facilities, the higher
the overall satisfaction is; however, when service facility satisfaction falls below 3, overall
satisfaction is no longer significantly decreased. Therefore, service facilities are an exciting
factor in this model—unexpected but when present, lead to a better travel experience.
Table 10 presents estimated coefficients under the bus access mode. The model can
explain 62% of the overall travel satisfaction. The results show that the higher the walking
experience satisfaction, the higher the overall travel satisfaction. The experience of walking
to the bus is not a specific attribute in itself (Table 11). When passengers are dissatisfied
with the station’s location, however, there is a significant negative impact on overall travel
satisfaction. When the satisfaction increases to a normal value, overall satisfaction will no
longer increase as satisfaction with the station’s location increases. Therefore, the position
of the station is a basic factor in this model.

Sustainability 2022,14, 15322 14 of 18
Table 11. M4, the model of built environmental factors and overall satisfaction (access = bus).
Estim. Sig.
Bus station safety −0.036 0.952
Comfort of the walk to bus stop 1.588 *** 0.005
Location of bus stop
(dissatisfied) −5.354 ** 0.013
(general) −3.238 ** 0.031
(satisfied) −1.771 0.135
(very satisfied) Ref. value
−2 Log likelihood 45.605
Nagelkerke R20.623
N 51
** p< 0.05, *** p< 0.01.
Figure 5summarizes the multi-level organization of the built environment attributes
of metro passengers, following the previous interpretation of the regression model. Basic re-
quirements are made up of landscape greening, bicycle parking facilities, bus stop location,
and crossing safety. The second level of performance factors are mainly connection distance
and detour and road connectivity, which can produce similar positive and negative effects
according to their performance, and have a linear relationship with overall trip satisfaction.
The shorter the access distance, the higher the overall satisfaction; the lower the amount of
detour, the higher the overall satisfaction, etc. The third level of exciting factors is composed
of shade and service facilities. If such conditions are available, they can increase travel
satisfaction but are not necessary conditions for travel satisfaction.
Sustainability 2022, 14, x FOR PEER REVIEW 14 of 19
Table 10 presents estimated coefficients under the bus access mode. The model can
explain 62% of the overall travel satisfaction. The results show that the higher the walking
experience satisfaction, the higher the overall travel satisfaction. The experience of walk-
ing to the bus is not a specific attribute in itself (Table 11). When passengers are dissatis-
fied with the station’s location, however, there is a significant negative impact on overall
travel satisfaction. When the satisfaction increases to a normal value, overall satisfaction
will no longer increase as satisfaction with the station’s location increases. Therefore, the
position of the station is a basic factor in this model.
Table 11. M4, the model of built environmental factors and overall satisfaction (access = bus).
Estim. Sig.
Bus station safety −0.036 0.952
Comfort of the walk to bus stop 1.588 *** 0.005
Location of bus stop
(dissatisfied) −5.354 ** 0.013
(general) −3.238 ** 0.031
(satisfied) −1.771 0.135
(very satisfied) Ref. value
−2 Log likelihood 45.605
Nagelkerke R2 0.623
N 51
** p < 0.05, *** p < 0.01.
Figure 5 summarizes the multi-level organization of the built environment attributes
of metro passengers, following the previous interpretation of the regression model. Basic
requirements are made up of landscape greening, bicycle parking facilities, bus stop loca-
tion, and crossing safety. The second level of performance factors are mainly connection
distance and detour and road connectivity, which can produce similar positive and nega-
tive effects according to their performance, and have a linear relationship with overall trip
satisfaction. The shorter the access distance, the higher the overall satisfaction; the lower
the amount of detour, the higher the overall satisfaction, etc. The third level of exciting
factors is composed of shade and service facilities. If such conditions are available, they
can increase travel satisfaction but are not necessary conditions for travel satisfaction.
Figure 5. Metro access attribute satisfaction in the three-factor model.
5. Discussion
The literature has generally found that the main, central, or public transit mode was
dominant in travel satisfaction with the whole journey [29,35]. Our result is that the access
and to some extent, egress, have greater weight than the main travel component in overall
trip satisfaction for commuting travel on the metro. The preponderance of walking and
Exciting
factors
Shade
Service facilities
Performance
factors
Access distance
Road connectivity
Pedestrian route directness
Greenery
Crossing safety
Parking facility
Bus stop location
Basic
factors
Figure 5. Metro access attribute satisfaction in the three-factor model.
5. Discussion
The literature has generally found that the main, central, or public transit mode was
dominant in travel satisfaction with the whole journey [
29
,
35
]. Our result is that the access
and to some extent, egress, have greater weight than the main travel component in overall trip
satisfaction for commuting travel on the metro. The preponderance of walking and bicycling
trips in the result are important. Moreover, our survey was conducted with the access trip
just completed, presumably vivid in memory and perhaps having significant effect on mood
generally, and on the specifics that were part of our survey. Rietveld’s remark (2000) [
41
] that
“the market potential of railway services depends to a considerable extent on the quality of
the total chain from residence to place of activity and vice versa” is to the point.
Previous work on the access and egress parts of the journey involving urban rail has
centered on distance and time issues, safety and security issues [
42
], and conditions at the
transfer point [
43
]. The present analysis contributes evaluations of some qualitative aspects
of the experience and shows that such evaluations have relation with broader assessments

Sustainability 2022,14, 15322 15 of 18
of travel by urban rail. Distance remains important for the PT portion of the trip but is not
significant for the distances covered by the walking and bicycling modes. The walking
distances at HLG and MDY are nevertheless considerable, while bicycling distances are
about double the walking distance in the three study areas. The most notable difference
in mode choice among the three study areas concerns HLG, with its contemporary layout
of very large blocks and wide streets. Car use is higher in HLG, even if car ownership
is lower, which seems likely to result from urban layout adapted to the car. The sharp
differences in modal split and related access distances in these three sample datasets attest
to the influence of the environment on the travel outcomes.
Satisfaction with walking is higher than with public transport, a finding similar with
that of St-Louis et al. [
23
]. When access distance exceeds 1 km, there is a notable shift
from the walking mode to bus and car. At greater distance, there is time saving with the
bus on the access journey, which helps explain why distance is less important while time
becomes more important. This also helps explain why the waiting time for the bus figures
prominently in the bus riders’ responses. This may be because the access part occupies
more waiting and transfer time during the bus access process. Travel phases that include
more waiting and transfer time have a higher impact on overall travel satisfaction.
Relatively high satisfaction with bus access to the station might appear counter-
intuitive, particularly in light of the less than satisfying ratings for crowdedness and seat
capacity. During the time of the survey, it could be expected that most buses arriving at the
station would be full, with standing room only. Crowding has serious negative effects on
the perceived utility of travel by public transport [
42
]. Bus riders are likely to evaluate their
condition within a different frame of reference than that of pedestrians and cyclists. Factors
that might impinge on their satisfaction are also largely out of their control, including
waiting time, crowdedness, location of the bus stop, and traffic speed. Within that frame,
the efficiency of the trip will tend to be more important, with time savings in particular.
The car does not have a prominent role in the access or egress trips with metro as the
central mode. Firstly, driving and non-driving days are determined by license plate number
in Beijing. Secondly, there is much more uncertainty about travel time and conditions by
car while the non-motorized access and egress modes together with the metro main mode
are highly reliable. As a result, many car owners do not use their cars and also have higher
levels of satisfaction with the travel, as they are PT travelers by choice. This is consistent
with the literature. A large proportion of the PT travelers by choice likely did not experience
restrictions on the use of their private car but chose other and distinct mode combinations
according to local conditions.
Access environment has a significant role in access trip satisfaction, which is consistent
with the literature [
43
,
44
] and may explain specifically why XS has the highest access
satisfaction. Although the access distance for XS was the shortest among the three, short
distance did not have a significant effect on expressed satisfaction. In contrast, it is access
to the physical environment that plays an important role. These findings might suggest
that traditional neighborhood designs are more suitable for walking and bicycling than
contemporary high-rise neighborhoods in Mainland China. Furthermore, we find that
when people choose to use a vehicle to get to a metro station, the comfort of getting to that
vehicle also plays an important role. Therefore, we can add to previous findings in the
literature that the built environment impacts not only walking but also the other access
modes including bicycling and bus.
With regard to the classification of attribute factors, there are differences and similari-
ties with previous work. For urban rail transit, network structure defined as the distance,
detour, and connectivity, is classified as a performance factor and has a linear influence on
satisfaction. Attributes such as greening and shade show significant nonlinear effects in
this model.
Although the nature of the three-factor approach by itself does not permit the estab-
lishment of preferences and priorities among service attributes, it can serve as a reference
point for strategic investments. Stakeholders need to consider not only the level of necessity

Sustainability 2022,14, 15322 16 of 18
for a given attribute but also a number of other important aspects, including the actual
level of performance of the attribute and the cost of improving performance, as well as the
degree of positive and negative impacts on performance.
Overall, we have the sense that the access and egress portions of the travel involving
the metro are important, not only for choice but also for the overall satisfaction with the
chosen method of urban travel. In many cases, certainly the case in Beijing, urban rail is
retrofitted into the existing urban environment with little or no alteration to the conditions
in the local environment for access. Those local conditions deserve more attention, based
on the evaluations of metro riders’ experience.
The study has limitations. The relatively small sample size does not allow us to
examine in detail the car access mode. A further investigation on the egress segment of
the trip would be useful, particularly given the somewhat different mix of modes used
for egress compared with access. Moreover, this research did not control the influence
of personal social attributes and preferences [
45
]. Finally, when considering the response
differences between local areas, one needs to admit the possibility of self-selection with
regard to mode choice and response to the attributes of those particular local environments.
6. Conclusions
This research measured the impact of access and egress stages of travel in metro-based
commuter trips on overall travel satisfaction. Specific features and qualities of access and
egress trips were also evaluated by commuters who routinely made the journey in progress
and had just completed the access trip. The reported access and egress itineraries were
independently evaluated by a team of investigators to validate the responses of survey
participants. A relatively wide range of features were examined by applying the survey at
three metro stations in a centrally located traditional area, an area built up after market reforms
in 1978 and a peri-urban community built up after the year 2000. The investigations by the
mystery consumer team across all environments and paths turned up satisfaction responses
that strongly resembled those of the commuter participants, suggesting that the participants
were well able to discriminate their local access conditions and to agree on those conditions.
The access and egress portions of the journey by rail have a major impact on overall
travel satisfaction with the metro in the case of Beijing, with their contributions to overall
satisfaction somewhat greater than that attributable to the rail portion of the journey. Given
Beijing’s intention to raise the metro rail share of intra-urban travel with continued buildup of
the system, it is very important to improve the local environments of access. Access to the bus
stop is also important in satisfaction with this mode when the bus is used to access the metro.
Attributes of the access and egress stages of the trip were evaluated on a five-point
satisfaction scale for each of the modes of walking, bicycling, bus, and car. A three-
factor analysis was applied to the result to determine whether the attributes belong to
basic, performance, and excitatory factor categories. An ordered logistic regression model
reveals the relative importance of each in a combined explanatory model. In the case
of pedestrian access, road connectivity, shading, greenery, and perceived street crossing
safety collectively account for 51% of satisfaction with the whole journey. Examination
of the regression effects reveals that greening and travel security can be considered basic
factors, connectivity is a performance factor, and shading is an excitatory factor. In the
bicycling access mode, it is found that pedestrian route directness (PRD), access distance,
and service and parking facilities together can explain 62% of the variance in expressed
satisfaction with the whole journey. Analysis of the regression results for the three-factor
model reveals that parking facilities are a basic factor, while PRD, connection distance,
and service facilities are performance factors. In the case of bus access, bus stop location
is a basic factor for commuters along with the walking environment and distance to the
bus stop. On the whole, the built environment has significant impact on overall travel
satisfaction for commuters accessing rail on foot and by bicycle. The built environment is
of less concern for commuters accessing rail by bus, while waiting time for the bus and
service attributes of the bus are main concerns.

Sustainability 2022,14, 15322 17 of 18
The three-factor model enables us to overcome to some extent the limitations of previ-
ous studies that assume linearity and symmetry in the response to various environmental
attributes. The model enables the categorization of attributes by mode according to basic,
performance, and exciting factors. The three-factor model suggests that priority areas
for improvement are the connection time, greening, bicycle parking facilities, bus stop
location, and perceived safety of crossing the street. Shorter connection time is achievable
by reducing the time between cycles at controlled intersections. The next order of priority
is the reduction in connection distance through reducing detours. Shading, greening, and
service facility provision are at the lowest priority in terms of their impact on overall travel
satisfaction although they are highly appreciated in the access and egress stages of travel.
Author Contributions:
J.Z. conceptualized the study. X.L. refined the research instrument and carried
out the study. X.L. conducted most of the analysis and wrote a first draft. J.Z. completed the analysis
and wrote the final text. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Helsinki for studies involving humans. The study was conducted as supervised Master thesis
work, to be supervised for ethical protocols by the supervisor and according to rules set down by
Peking University. All intercepted, anonymous participants were read a consent form and agreed to
it before answering questions verbally.
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Data may be obtained by contacting the authors directly.
Conflicts of Interest: The authors declare no conflict of interest.
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