Global interest in walking accessibility: a scoping review

Abstract

We conduct a systematic scoping review of the academic literature concerning pedestrian accessibility. We distinguish “walking accessibility” from the broader topic of “walkability” by two criteria: papers must consider one or more destination type(s), and papers must address the issue of distance or impedance. After searching Web of Science, TRID, and Google Scholar databases and conducting screening, we identify 85 papers meeting these criteria. We organise the literature review according to the four components of accessibility identified by Geurs and van Wee [(2004). Accessibility evaluation of land-use and transport strategies: Review and research directions. Journal of Transport Geography, 12(2), 127–140]: (1) Land use; (2) transport; (3) temporal; and (4) individual and also add a section on the topic of impedance. Walking accessibility studies address a much greater range of land uses or destination types than is typically found for other modes. The transportation component is relatively undeveloped, as pedestrian infrastructure includes many influential elements not currently tracked in GIS systems. Few studies address the temporal component of walk accessibility, which varies according to climatic and nighttime conditions. Most papers do not account for the significant variation across individual capabilities and preferences regarding walking. We note that developing detailed pedestrian networks is a key first step, as most published analysis is conducted on roadway networks. A second major recommendation is to consider individual variations in walk accessibility across demographic classifications, accounting for varying levels of physical mobility.

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Global Interest in Walking Accessibility:
A Scoping Review
Author Information
Louis A. Merlin, AICP, Ph.D.
Associate Professor
Department of Urban and Regional Planning
Florida Atlantic University
Boca Raton, Florida, USA
Email: lmerlin@fau.edu
ORC ID: 0000-0002-9267-5712
Ulrike Jehle
Ph.D. Candidate
Department of Urban Structure and Transport Planning
Technical University of Munich
Munich, Germany
Email: ulrike.jehle@tum.de
ORC ID: 0000-0002-5962-6679
Acknowledgments
We would like to thank our colleague Elias Parajes for reviewing a draft of this paper and providing
helpful commentary.
No research funding was used in support of this project.

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Abstract
We conduct a systematic scoping review of the academic literature concerning pedestrian accessibility.
We distinguish “walk accessibility” from the broader topic of “walkability” by two criteria: papers must
consider one or more destination type(s), and papers must address the issue of distance or impedance.
After searching Web of Science, TRID, and Google Scholar databases and conducting screening, we
identify 85 papers meeting these criteria.
We organize the literature review according to the four components of accessibility identified by Geurs
and van Wee (2004): 1) Land use; 2) transport; 3) temporal; and 4) individual and also add a section on
the topic of impedance. Walk accessibility studies address a much greater range of land uses or
destination types than is typically found for other modes. The transportation component is relatively
undeveloped, as pedestrian infrastructure includes many influential elements not currently tracked in
GIS systems. Few studies address the temporal component of walk accessibility, which varies according
to climatic and nighttime conditions. Most papers do not account for the significant variation across
individual capabilities and preferences regarding walking. We note that developing detailed pedestrian
networks is a key first step, as most published analysis is conducted on roadway networks. A second
major recommendation is to consider individual variations in walk accessibility across demographic
classifications, accounting for varying levels of physical mobility.
Keywords
Pedestrians; Accessibility; Impedance; Transportation Infrastructure; GIS

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Main Text
1. Introduction
1.1. Background
Understanding pedestrian accessibility is a topic of widespread concern for transport planners and
society generally. Walking is the most universal of modes – available for most persons for most of their
life span and available across the globe, regardless of a country's level of economic development. In this
paper, we include personal transport by wheelchair within the scope of walking, as people traveling by
wheelchair usually rely on the same infrastructure as pedestrians in cities and travel at compatible
speeds. Walking is an essential component of travel in cities, as other modes – such as driving, transit, or
even airplane travel – have a walk-access and a walk-egress component. Scholars and planners who
advocate for better transit systems are also concerned with the ability of pedestrians to access transit
stops (see for example Bivina et al., 2020; Jehle et al., 2022; Sarker et al., 2019).
Travel by foot is perhaps the most sustainable mode (Jou, 2011; Replogle & Fulton, 2014). Walking
generates no greenhouse gases or other air pollution, and the infrastructure it requires is relatively
inexpensive and space-efficient compared to motorized modes. Because people take little space while
walking and travel at slow speeds, pedestrians are rarely subject to congestion, only in the most
crowded cities. The safety costs of a "crash" between pedestrians are minimal. Walking has significant
physical and mental health benefits (Chin et al., 2008; Moniruzzaman et al., 2014; US Dept. of Health
and Human Services, 2015). From an equity perspective, walking offers mixed benefits: travel by foot is
one of the most affordable means of travel, yet the physical capability for walking can vary widely.
Hence, destinations that are only accessible by foot may exclude some population segments.
In this paper, we focus on walking accessibility and distinguish it from the more general concern of
walkability. Walkability is a broad and, at times, vague concept that can include the enjoyment of
pedestrians of a particular facility or place (Jacobs, 1993), the compatibility of an environment with
urban design principles (Ewing, 1999), or a prediction the amount of walking likely present in a specified
area (Frank et al., 2010). Walking accessibility, on the other hand, is tethered to the accessibility
concept, which concerns the ability of persons of varying abilities to reach specified destinations (Levine
et al., 2019). Therefore, our concern here is for utilitarian walking for travel rather than undirected
recreational walking or walking for health reasons alone.
Measuring walking accessibility presents various challenges that are not as pronounced for motorized
modes. First, pedestrian infrastructure is very fine-grained and rarely recorded at the necessary level of
detail to accurately calculate walking accessibility (Iacono et al., 2010). Pedestrian infrastructure also
plays a more prominent role because pedestrians are more sensitive and vulnerable to environmental
conditions, threats, and the hazards of poor infrastructure. Second, the ability of individuals to walk, and
their attitudes towards walking, vary greatly. Therefore, much more attention must be paid to the
individual component compared to other modes. Generally, the ability of drivers to overcome distance
on a particular roadway segment at a particular time of day is assumed to be relatively constant and
does not vary by whom is driving. In contrast, walking abilities, needs, and preferences differ highly

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based on personal characteristics. The same walking infrastructure may be perceived quite differently by
different persons. Recent studies (e.g. Gebel et al. 2011; Jehle et al. 2022; Ryan & Pereira 2021) have
therefore discovered a mismatch between objective and perceived accessibility and highlighted further
research needs to explore this divergence (De Vos et al. 2022).
Walking accessibility has not received as much attention as accessibility by private vehicle or public
transit. Nevertheless, our search indicates an accelerating interest in the topic (see Figure 1). Our
earliest paper is from 1997, but we find more than five (5) papers each year from 2017 to 2021. We
attribute this increasing interest to improved utilization of geographic information systems (GIS), which
are an essential instrument for nearly all accessibility studies, and the availability of increasingly detailed
data concerning the built environment.
Figure 1: Publication per Year on the Topic of Pedestrian Accessibility
1.2. Theoretical Framework
We employ two key criteria for inclusion in our literature review: the specification of at least one specific
destination type and the application of a measure of impedance. These criteria correspond with the two
essential elements of accessibility identified by Wu and Levinson (2020) in their paper Unifying Access.
In this paper, Wu and Levinson (2020) survey the wide number of mathematical formulae for computing
accessibility, all of which can be connected to the generalized formula (also adapted from Páez et al.
(2010):
(1) 
 
󰇡
󰇢


Where Ai is an accessibility measure from an origin i, at time t, to destination type k, for person type p.,
g(Otjk) is a measure of the attractiveness of opportunities of type k at destination j available at time t,
and f(Cpij) is a measure of the impedance of traveling from origin i to destination j for person type p.

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Note that the functional form f() concerns an impedance decay function, while Cpij is the generalized
travel cost, which may take into account factors such as time, distance, and effort.
Following Geurs and van Wee (2004) we examine each paper along the four components of accessibility
measurement: land use, transport, temporal, and individual. Per Geurs and van Wee:
● The land use component includes the spatial location of origins and destinations, the amount
and quality of destinations, and the potential competition for destinations when demand outstrips
supply;
● The transport component includes transport infrastructure and services and how those
translate into a disutility for travel from a particular origin to a particular destination, including factors
such as travel time, cost, and effort;
● The temporal component incorporates scheduling constraints due to time commitments and
scheduling availability;
● And the individual component addresses the varying needs, goals, and capabilities across
persons, noting that all modes are not equally available to all.
Our definitions of these components largely conform with the suggestions of Geurs and van Wee (2004)
as above. We expand the temporal component to include not only time constraints and the time
availability of destinations, but also how nighttime conditions and weather can impinge upon the
impedance experienced during pedestrian travel.
Figure 2 illustrates our conceptual framework regarding the four components and how they relate to the
opportunities and the impedance articulated in Equation 1. The availability and attractiveness of
destinations, g(Oj), is primarily encapsulated by the land use component – though it could also be
influenced by differences between individuals in how they value destination types, and by temporal
components concerning when facilities are available. However, we find that the impedance element of
pedestrian accessibility – f(Cij) – is influenced by each of the four components, as well as the interactions
between these components. Geurs and van Wee (2004), suggest that the individual component
interacts with each of the other components, i.e. “a person’s needs and abilities that influence the
(valuation of) time, cost and effort of movement, types of relevant activities and the times in which one
engages in specific activities.” However, we find evidence of interactions across all four components
relative to the impedance element. For example, transport and temporal considerations overlap when
taking into account how lighting influences the ability to walk at night; land use and temporal
considerations interact when accounting for how the value of shade depends upon the prevailing
weather; and the land uses present along a segment influence the disutility experienced, demonstrating
an interaction between land use and transportation infrastructure. Thus, this diagram illustrates how
each of the four components feeds in turn into the two mathematical elements of destination
attractiveness and travel impedance. Our review focuses on this latter aspect – how each of the four
components influences travel impedance as experienced by different pedestrians and through their
varying perceptions.

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Figure 2: Concept Framework
We acknowledge that more complex conceptual frameworks are viable and may shed light on different
issues. For example, the concept framework recently proposed by De Vos et al. (2022) highlights the
difference between perceived and objective accessibility, and their potential differential effects on
outcomes some as walking behavior and walking satisfaction. However, in this effort, we focus on the
four components outlined by Geurs and van Wee (2004) and how they are differentially operationalized
into mathematical formulae for the computation of pedestrian accessibility.
1.3. Structure of the Paper
The paper proceeds as follows. Section 2 describes our methods for searching for relevant literature,
screening, and data collection from each piece of work. In Section 3, we explore how the body of
literature approaches each of the major components of walking accessibility: land use, transportation,
individual, and temporal. We note the most common approaches to each component as well as discuss
novel and promising approaches. Then we discuss how each of these components influences the
calculation of impedance, or the second element in Equation 1. In Section 4, we offer a discussion and
recommendations regarding how research and practice concerning walking accessibility might be
improved. Following that, we offer a summary of major points in the conclusion (Section 5).
2. Methodology
We conduct a scoping literature review on the topic of walking accessibility. A scoping literature review
aims to provide a comprehensive picture of approaches within a given field, with the objective of
defining a field’s conceptual boundaries (Xiao and Watson, 2019). This approach helps to identify the
strengths and weaknesses of existing research approaches and identify research gaps.
In the first stage, we searched the literature with three research databases: Web of Science, Google
Scholar, and the TRID database provided by the Transportation Research Board (final search date: 24th
of May, 2022). Within each of these databases, we look for at least one title term related to walking,
including “pedestrian”, “walk”, “nonmotorized”, and “non-motorized” as well as one term related to

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accessibility, for which we solely relied upon the term “accessibility.” We also conducted searches with
the term “access” but found too many results to be meaningfully reviewed.
Within Web of Science, we found 112 unique articles; in TRID, 63; and in Google Scholar, we limited the
search to the topic “pedestrian accessibility” and the results to the first 100 as listed by relevance. After
combining the results of the three searches, we netted 181 articles from the raw literature search (see
Table 1). In the few cases where articles were unavailable from online databases, we emailed authors
requesting a copy.
Table 1: Summary of Literature Search Strategy
Database
Title Search
Number of Results
Web of Science Core Collection
“nonmotorized” and “accessibility”
2
Σ 112
“non-motorized” and “accessibility”
8
“walk” and “accessibility”
53
“pedestrian” and “accessibility”
52
TRID
“nonmotorized” and “accessibility”
3
Σ 62
“non-motorized” and “accessibility”
7
“walk” and “accessibility”
12
“pedestrian” and “accessibility”
51
Google Scholar
Topic Search: “pedestrian accessibility”
limited to top 100 results by relevancy
56 Σ 56
Total
Σ 230
Duplicates Removed
49
181
In the next stage, we screened the articles by reviewing their titles and abstracts. Our inclusion criteria
were: 1) The article must be in English; 2) the primary mode of travel analyzed must be walking; 3) the
paper must discuss walking to destinations; and 4) the article must discuss some measure of distance,
cost, or impedance. We opted for inclusion in those cases where we were uncertain about whether an
article met these criteria. After screening, 82 articles remained. During our review process, we came
across three (3) additional articles meeting our criteria, resulting in a total of 85 articles reviewed.
For the purposes of data extraction, we identified the following fields of interest: Study title, journal of
publication, study area (city or metro), study continent, measurement of impedance or distance, land
use component (origins, and destinations), transport component (pedestrian network), inclusion of an

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individual component, inclusion of a temporal component, if the study was on perceived or objective
accessibility, analysis method, results, innovative features, and limitations. Only one member from the
research team reviewed each paper. We did an initial pass reviewing 8 papers together and discussing
our findings to synchronize our understanding of key concepts and data extraction methods. A
condensed summary of the data extraction is available in the appendix.
The vetted papers represent a genuinely global geography, as indicated in Figure 3. Though European
and North American publications are most numerous, every continent besides Antarctica is represented.
The fields of study housing publications concerning walking accessibility are also diverse. While 35
publications are from transport-related journals, there are also 11 from general planning, 11 from
geography and GIS, five from health-related journals, and four from computer science journals or
conferences (note that these counts are not mutually exclusive).
Figure 3: Map of Study Area Locations
3. Literature Review Findings
In this section, we are discussing the findings from the literature review. It is divided into six subsections
(transport component, land use component, individual component, temporal component, impedance
calculation, and objective vs. perceived accessibility). In each of them, first a general overview of the
most common practice is given, followed by a deeper dive into some exceptional examples.

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3.1. Transport Component
The first step in computing pedestrian accessibility is to build a pedestrian network. Analysts often
accomplish this by starting with the road network and subtracting out roads that do not accommodate
pedestrians (Carpio-Pinedo et al., 2021; Roblot et al., 2021; Vale and Pereira, 2017). OpenStreetMap
(OSM), the open, crowdsourced geographic database, is freely available and offers global coverage and
so can be an appropriate starting point for streets (Liu et al., 2021; Pearce, Matsunaka, & Oba, 2021;
Tiran et al., 2019). However, many attributes that are important for pedestrians (such as sidewalk
availability, width, lit, and surface) are often not complete within OSM. In a few cases, researchers have
gone to significant trouble to build accurate pedestrian networks far beyond the roadway network
(Amaya et al., 2022; D’Orso & Migliore, 2018; Erath et al., 2017; Jonietz and Timpf, 2012; Pearce,
Matsunaka, and Oba, 2021; Sinagra, 2019; Sun et al., 2015; Tang et al., 2021). Figure 4 provides an
overview of features and their corresponding measurements that could be considered in calculating
accessibility. The large number of potentially relevant items illustrates the difficulty of collecting
comprehensive pedestrian network data.
Accurate pedestrian networks are a substantial challenge to build, both because local authorities often
do not collect thorough pedestrian data and because many environmental features can affect the
pedestrian experience (Parmenter et al., 2008). Pedestrian networks can include not only sidewalks but
also plazas, parks, shared-use paths, pedestrian bridges and underpasses, off-street paths, stairways,
escalators, street crossings, interior corridors, and building entrance locations. Relevant data on the
individual segments may include surface, evenness, slope, obstacles, width, greenery, presence of
certain land use activities, and more (Alfonzo, 2005; Lo, 2009; Wimbardana et al., 2018). Laakso et al.
(2013) propose a comprehensive information model for capturing data regarding pedestrian accessibility
and pedestrian routing. They capture a variety of pedestrian pathway types (including crossings)
through linear features classified as “PedestrianPassage” and a range of obstacle types through primarily
point features classified as “PedestrianObstacle.” Li et al. (2018) propose a streamlined method for
developing a sidewalk and crosswalk network where none exists, building upon existing parcel and
roadway network GIS files. The method is semi-automated, but some hands-on data cleaning is
required.
Van Eggermond and Erath (2016) developed two contrasting pedestrian networks for Singapore and
examine accessibility differences across these two networks. The first network is simply a network of
applicable roadway center lines. For the second network, they developed sidewalks for both sides of the
street by using an offset from roadway centerlines. They also identified three types of roadway
crossings: overhead bridges, pedestrian underpasses, and painted crosswalks. They find that detailed
pedestrian networks more accurately capture pedestrian accessibility, but a substantial amount of
ground truthing is required to develop correct networks. Several other researchers use site visits, Google
Street view, or aerial maps to construct detailed pedestrian networks (Achuthan et al., 2007; Amaya et
al., 2022; Blečić et al., 2013; D’Orso & Migliore, 2018; Jonietz & Timpf, 2012; Laakso et al., 2013; Pearce
et al., 2021; Sinagra, 2019; Stewart, 2016; Tang et al., 2021; Wilhelm, 2007; Xu, 2014).
Adding off-street pedestrian network features, such as shared-use paths, is also relevant; these features
are usually not captured in roadway networks. Off-street pedestrian networks, when they provide
additional options beyond the street-based network, can result in shorter paths and improved

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accessibility (Tal & Handy, 2012). Shared use paths may also be preferred routes for reasons of safety
and aesthetics (Erath et al., 2015).
The infrastructure element of accessibility is interwoven with the individual element; depending upon
which population segments researchers are considering, their data collection needs may differ. In
particular, when considering the accessibility needs of persons in wheelchairs or with constrained
mobility, obstacles and surface unevenness become more severe constraints (Laakso et al., 2011, 2013;
Orellana et al., 2020; Sinagra, 2019; Wilhelm, 2007). To capture the needs of these populations,
investigators must detail features such as physical obstacles present in the pedestrian pathway, curb
ramps, driveway ramps, textural changes (for the visually impaired), roughness, cross slope, tripping
hazards, and dips. Likewise, Amaya et al., (2022) consider the presence of benches when evaluating
walking accessibility for older adults.
Although the overwhelming majority of papers investigating walking accessibility employ a graph-based
model of pedestrian infrastructure (where the pedestrian network consists of nodes and edges
connecting them), there are exceptions. For example, Blanford et al. (2012) consider walking
accessibility to health care facilities in Niger in rural areas where road networks may be lacking.
Therefore, they consider pedestrian raster-based routing that considers elevation and land cover as
potential pedestrian obstacles that increase impedance. Paez et al. (2020) perform a similar analysis in
Kenya while examining residents’ access to water, primarily considering the distance and slope of
alternative raster-based routes. Rossetti et al. (2020) also employ a raster-based approach, this time in
urban Italy. Their reasoning for employing a rasterized method is that pedestrian routing through parks
and plazas involves a high degree of discretion and cannot readily be simplified into a discrete set of
possible linear pathways. Raster-based routing methods offer an appealing alternative when formal
pedestrian infrastructure is absent but involves much more computational load than a network
approach, so its applicability is likely of limited use.
Figure 4: Potential Measurable Characteristics of Pedestrian Links
3.2. Land-Use Component
Land use features in the consideration of pedestrian accessibility in three ways: as origins, as
destinations, and as a feature of segments alongside pedestrian routes.

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Reviewed papers most commonly consider residences as the primary origin of interest, i.e., walk trips
starting from home, for 32 out of the 85 papers. Several papers consider any possible location in the city
as an origin (Ariza-Alvarez et al., 2021; Liu et al., 2021; Roblot et al., 2021) or consider any building as an
origin (Erath et al., 2017; Higgins et al. 2019; Sun et al., 2015). This finer building-scale analysis is
relevant when considering walking accessibility, where even building location entrances and under- or
overpasses can be accounted for (Desjardins et al., 2022; Tang et al., 2021). Several papers consider
public transport stops (Chandra et al., 2017) or parking lots (Joneitz & Timpf, 2012) as origins.
Destinations are notably more diverse than in the study of transit and auto accessibility, where most
studies focus on accessibility to jobs. Destination types considered include jobs, parks and green space,
health service facilities, schools, childcare facilities, shopping centers, and transit stops. The
categorization and nomenclature of destinations are not standardized, and different papers employ
differing terminologies for what appear to be the same concepts, i.e., “commercial areas” versus
“shopping” versus “services.” The most common destinations analyzed are a variety of points of interest
(23/85), parks and recreational amenities (13/85), transit stops (11/85), and shopping (10/85).
Land uses occurring along pedestrian segments can influence perceived impedance, though few
reviewed studies examine this. Erath and van Eggermond (2015, 2017) employ a stated preference
survey to determine that pedestrians in Singapore prefer to walk along segments with greenery and
with shops. Likewise, Broach and Dill (2015) found that perceived impedance is lower along main streets
(commercial streets) in Portland, Oregon, via a revealed preference survey using GPS. Several other
papers consider the effect of greenery or commercial activity on pedestrian impedance (Blečić et al.,
2015; D’Orso & Migliore, 2018; Gaglione et al., 2021).
3.3. Individual Component
While most papers that we reviewed do not distinguish between different populations, there is a rich
and growing literature on the individualized aspects of walking accessibility. Researchers considered the
demographic components of age, gender, income, health, mobility constraints, vision constraints,
vehicle ownership, and more in the papers we reviewed. There are two distinct approaches to
examining individual differences. One approach is to identify population segments with distinctive
characteristics and consider their particular needs (Orellana et al., 2020; Papa et al., 2018; Wilhelm,
2007). The second approach is to integrate multiple population characteristics into a statistical formula
for fully individualized accessibility profiles (García-Palomares et al., 2013; Marquet & Miralles-Guasch,
2014; Reyes et al., 2014).
Several of the reviewed papers examine walking accessibility for older adults (Amaya et al., 2022; Ariza-
Alvarez et al., 2021; Boakye-Dankwa et al., 2019; Borowska-Stefańska & Wiśniewski, 2017; Cheng et al.,
2019; Colclough, 2009; Erath et al., 2015; Gaglione et al., 2019, 2021; García-Palomares et al., 2013;
Marquet et al., 2017; Pajares et al., 2021; Papa et al., 2018; van der Vlugt et al., 2022). Gaglione et al.
(2021) examine pedestrian access of older adults to urban services in Naples, Italy, and Aberdeen,
Scotland, by utilizing a value of perceived travel time that considers the traveler's age. They aim to
identify areas of the pedestrian network where interventions could increase senior access. Jehle (2020)
examines how elderly accessibility varies from a “standard” pedestrian profile in Munich using the open-
source software tool GOAT. Using differential speeds across traveler types, they find a much lower level

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of walking accessibility among the elderly. Cheng et al. (2019) calculate cumulative opportunity
accessibility measures with varying distance thresholds based upon multiple individual and household
level characteristics, including age. Therefore, the level of accessibility depends not just upon location
and infrastructure but also on individual social and demographic characteristics. They find that older
adults have lower access to recreational facilities than their younger counterparts.
Surprisingly, walking accessibility for children was much less frequently analyzed in our set of papers.
Reyes et al. (2014) examine children’s walk accessibility to urban parks in Montreal employing a spatial
expansion model. Garcia-Palomares et al. (2013) consider the accessibility of different population
groups to metro stations and incorporate children within their analysis. But few reviewed papers
consider children’s walking access, despite the unique needs of this group, and children’s greater
dependence on walking as a mode of transport.
Wheelchair users have distinctive needs regarding pedestrian infrastructure, so several researchers have
delved into methods for accurately assessing their accessibility (Church & Marston, 2003; Jehle, 2020;
Laakso et al., 2011, 2013; Orellana et al., 2020; Sinagra, 2019; Wilhelm, 2007). Detailed data sets are
necessary that include the collection of curb ramps, driveway ramps, sidewalk texture, obstacles, and
slope (Jehle, 2020; Laakso et al., 2011, 2013; Orellana et al., 2020; Sinagra, 2019; Wilhelm, 2007). Some
of these features can present absolute obstacles to wheelchair travel, while others represent an
impediment that makes wheelchair travel more burdensome, creating a higher impedance (Sinagra,
2019; Wilhelm, 2007).
Socioeconomic variables, such as income, vehicle ownership, and housing type, are also sometimes used
to evaluate differences in pedestrian accessibility (Anciaes et al., 2015; Ariza-Alvarez et al., 2021;
Boakye-Dankwa et al., 2019; Chandra et al., 2017; Cheng et al., 2019; Damurski et al., 2020; García-
Palomares et al., 2013; Goodwin, 2005; Higgins, et al., 2019; Marquet et al., 2017; Morar et al., 2014;
Reyes et al., 2014; van der Vlugt et al., 2022; Xu, 2014). Ariza-Alvarez et al. (2021) calibrate different
gravity decay functions based on various socioeconomic characteristics, including income and vehicle
availability. Marquet et al. (2017) consider which population segments are most likely to take shorter
“proximity trips” to understand what groups would most value local accessibility. They find that age,
gender, and economic status are correlated with the likelihood of taking proximity trips. Socioeconomic
variables may help analysts identify accessibility differences across populations as well as observed
differences in the likelihood of overcoming a particular distance.
3.4. Temporal Component
Relative to the land-use and transport components of walking accessibility, the temporal component is
relatively less studied. Only nine of the reviewed papers consider the effect of nighttime (Chandra et al.,
2017; Jehle, 2020) or variations in weather (Erath et al., 2015) on pedestrian accessibility. Erath et al.
(2015) note that shaded and covered routes are preferred in hot, sunny Singapore and that the value of
covered routes increases during rainfall events. Jehle (2020) examine how accessibility to POIs varies
based on the opening hours each POI is available.

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3.5. Impedance Calculation
Each of the accessibility components – transport, land use, individual, and temporal – can have an
independent or combined effect on the calculation of impedance along a given route. Transportation
infrastructure defines which routes are shorter as well as the relative difficulty of traversing each route.
Land use determines the location of origins and destinations, their quality, and whether or not there are
interesting diversions along the route for pedestrians. The individual component defines each person’s
physical ability to overcome spatial separation and their variable degree of sensitivity to infrastructure
conditions. The temporal component – through reduced illumination or weather – can also serve to
increase impedance or effectively remove specific unilluminated travel paths from the network.
Despite these manifold possibilities, the most common approach in the literature to calculating
impedance is simply distance, which was employed in 25 out of the 85 papers reviewed, with modified
versions of distance employed in an additional 11. In almost all cases, analysts used pedestrian network
distance, not straight-line distance. Researchers often applied cumulative opportunity measures and
distance-decay measures of route distance to destinations. In some cases, distances were modified by
considering aspects of transportation infrastructure that increased “effective” distance, such as slope
(Blečić et al., 2013, 2015, 2018; D’Orso & Migliore, 2018; Kuzmyak et al., 2006). Others tried to
incorporate perceived distance by adding impedances based on the quality of the path, assigning
greater lengths to unpleasant routes (Blečić et al., 2015; Blečić et al., 2018; D’Orso & Migliore, 2018;
Jang et al., 2020; Joneitz & Timpf, 2012).
The second most common method for measuring impedance was travel time, which was employed in 31
out of 85 papers. Travel time can be translated to distance if an expected walking speed is assumed;
however, different individuals walk at different speeds (and with differing levels of physical effort).
Several analyses account for differing travel speeds across population segments (Amaya et al., 2022;
Gaglione et al., 2021; Jehle, 2020). Interestingly, some research indicates that people better estimate
walk times than walk distances (Vale & Pereira, 2017). Travel time can also be adapted to account for
infrastructure characteristics, therefore increasing the walk time along any particular segment due to
slope or impediments (Amaya et al., 2022; Gaglione et al., 2021; Li et al., 2018; Tang et al., 2021;
Wilhelm, 2007). Lastly, several investigations examine perceived travel time as an impedance measure
by asking travelers about their perceptions or willingness to travel along specific routes based on each
route’s characteristics or attractiveness (Boakye-Dankwa et al., 2019; Erath et al., 2017; Gaglione et al.,
2021; Sun et al., 2015).
There are several ways to account for perceived travel time. Four methods we identified are: 1) Based
upon revealed route-choice behavior (Broach and Dill, 2015), 2) Based upon stated preference surveys
(Erath et al., 2015), 3) Based upon survey questions to study participants about perceived travel time
(Sun et al., 2015), or 4) Based upon assumptions about perceived travel time (Gaglione et al., 2021). For
example, Boakye et al. (2019) analyzed the connection between perceived accessibility to destinations
at different distances from home and self-reported amounts of walking for different purposes among
older adults in Brisbane and Hong Kong. Their survey found that higher perceived destination
accessibility was positively associated with the likelihood of walking only in Brisbane. Differing results
across the two study cities suggest that local contexts may affect how accessibility is experienced. Sun et
al. (2015) investigate the differences between actual and perceived travel time for various routes within

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a Hong Kong-based campus that includes significant elevation changes. They hypothesize that perceived
walking time is primarily a function of distance and elevation change and calibrate a regression of
perceived walking time to these variables. Pot et al. (2021) consider perceived accessibility to be its own
category of accessibility measurement and relate it to the other traditional accessibility components.
Paez et al. (2020) employ a distinctive method for calculating pedestrian impedance, estimating the
metabolic energy expended in pedestrian travel across varying routes. Biologically, it is intuitive that
humans would endeavor to economize on metabolic energy expenditure while walking. Paez et al.’s
(2020) method is also appropriate because of the study context – a rural part of Kenya with few
roadways present. Therefore, most pedestrian paths traverse an unpaved landscape, and the metabolic
energy expended can vary based upon the landcover and slope. They find that the shortest paths as
determined by metabolic energy expenditure are rather different from those that minimize either time
or distance.
3.6. Objective vs. Perceived Accessibility
Accessibility studies can be divided in two approaches: objective and perceived analysis. Objective
analyses calculate accessibility using spatial data, while perceived analysis are based on survey or
reported data. The majority of the accessibility studies we reviewed used an objective approach. Six
papers were identified which used a perceived approach (Abrahams, 2010; Arranz-López et al., 2021;
Boakye-Dankwa et al., 2019; Erath et al., 2015; Iacono, et al., 2008; van der Vlugt et al., 2022). Boakye-
Dankwa et al. (2019), Erath et al. (2015), Iacono et al. (2008) and van der Vlugt et al. (2022) leveraged
survey data to analyze perceived accessibility, while Abrahams (2010) conducted face-to-face interviews
with selected experts and incorporated passive field observations. The study by Arranz-López et al.
(2021) compared differing visualizations of accessibility in terms of their comprehensibility.
Eleven studies combined objective and perceived methods (Adams, 2002; Arranz-López et al., 2017;
Arranz-López et al., 2019; Damurski, Pluta, and Zipser, 2020; Erath et al., 2017; García-Palomares et al.,
2013; Iacono et al., 2010; Kang, 2015; Reyes et al., 2014; Sun et al., 2015). Arranz-López et al. (2017,
2019) survey residents to define different distance-decay functions across user groups; Iacano et al.
(2010) do the same for different trip purposes. Damurski et al. (2020) compare objective and perceived
accessibility and find disparities across the two measures. Kang (2015) juxtaposed pedestrian volumes
with accessibility values and found that the land use present along pathways impacts the level of
walking activity. To account for environmental perceptions, path characteristics and destination
attractiveness, Erath et al. (2017) employ behavioral data from surveys to calibrate accessibility
indicators. Sun et al. (2015) used the walking diary of 169 students to derive perceptions of walking
uphill and incorporate them into a 3D walking accessibility model. Some authors (e.g. Blecic et al.,2013;
Blečić et al., 2018; D’Orso & Migliore, 2018); Joneitz & Timpf (2012) and Amaya et al. (2022)) include
walkability attributes in the impedance function, hoping to impute their impact on perceived
impedance.
Throughout the papers, different terms are used for perceived accessibility. Most of the authors (e.g.
van der Vlugt et al., 2022; Boakye-Dankwa et al., 2019) use the term perceived accessibility and the term
objective accessibility as its counterpart. Damurski et al. (2020) write subjective accessibility and Arranz-

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López et al. (2021) speak of relative accessibility, which is explained to be “subjective and shaped by
individual circumstances (e.g., individual preferences, habits, and cultural norms)”.
4. Discussion and Recommendations
Our recommendations for research and practice are summarized in Table 2, which we discuss in this
section. The column indicating current practice describes the most common or modal practice from the
body of literature we reviewed; the second column or recommended practice briefly describes our
recommendations for future walking accessibility studies.
The first step to improved analysis of pedestrian accessibility is to shift from the use of roadway
networks to pedestrian networks. As noted above, the quality of the pedestrian network can vary
widely, and detailed and precise data on the pedestrian network is a necessary precondition for
accurately analyzing pedestrian accessibility. Fortunately, several researchers have developed thorough
data models for capturing the necessary information (Laakso et al., 2013; Sinagra, 2019; Xu, 2014).
Although few jurisdictions have accurate and detailed pedestrian network data, we argue that the time
for both researchers and practitioners to start building such networks is now.
Table 2: Current vs. Recommended Practice for Analyzing Pedestrian Accessibility
Component
Current Practice
Recommended Practice
Transport
Roadway Network
Pedestrian Network, including
Street Crossings
Land Use
Administrative Zones as
Origins
Specific Destination Types
Buildings as Origins or
Grid-Type Zones as Origins
Specific Destination Types
Individual
All Persons the Same
Distinct Population Segments
Temporal
Not Considered
Consider the Effect of Weather
and Nighttime
Impedance
Distance
Time
Objective vs.
Perceived
Objective
Imputed Perception
One of the most critical features of the pedestrian network that is currently often missing is street
crossings. Street crossings may pose either a minor or major obstacle to pedestrian travel (Anciaes &
Jones, 2020; Broach & Dill, 2015; Erath et al., 2015; Montgomery County Planning Department, 2020). In
most analyses based on the roadway networks, pedestrian crossings are not considered. Factors that
may influence pedestrians’ ability to cross include the number of traffic lanes, the presence of a median
island, prevailing vehicular speeds, the presence of a crosswalk, the presence of a signalized traffic light,
and the presence of an overpass or underpass (Anciaes & Jones, 2020; Montgomery County Planning
Department, 2020).

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The current dominant practice is to employ existing administrative zones as origins. However, as more
detailed GIS data becomes available, it may be possible to use individual buildings as origins, especially
major residential complexes with many housing units. Pedestrian travel can vary significantly on the
microscale in places where blocks are long or street crossings are infrequent, so the use of individual
buildings as origins and destinations is desirable. On the other hand, it may still be preferable to conduct
a zonal analysis if the study area is extensive or for the analysis is of an area with many single-family
homes. Another reason to prefer zonal analysis is for rapid, sketch-planning type tools (Pajares et al.,
2021). A middle route is to create a regular grid of small zones and to interpolate the population to that
grid (Desjardins et al., 2022; Liu et al. 2021).
In the research world, the current modal practice for understanding walking accessibility already
considers specific destination types. For example, parks, grocery stores, and schools have all been
analyzed as potential walk destinations. We recommend that the disaggregation of destination types be
the standard and that this approach also be adopted by those analyzing pedestrian accessibility in
practice. Each destination type may have distinct demographic segments that it attracts; distinct
patterns in hours of activity; and even differential ability to attract pedestrian trips; therefore, we argue
against the aggregation of various destinations into an overall walkability index. As increasingly accurate
and detailed information becomes available for POIs, such disaggregated analysis by destination type
should become more achievable. In addition, such analyses could take into account the quality of
destinations where adequate data is available.
As we have documented here, there is growing attention to the individual aspect of walking
accessibility; however, the norm in the literature is still to consider the entire population as experiencing
the same opportunity for walking to destinations. We recommend the consideration of different
population segments, especially concerning their differential ability to overcome distance, cross streets,
and access POIs. The population segments that appear to need attention the most would be older
adults, children, those using human-powered wheels (including persons in wheelchairs and persons
pushing strollers), and persons with other mobility impairments, i.e., cane users. Each group has specific
needs and requirements for walking infrastructure in terms of safety, freedom from barriers, etc.
Considering that metabolic energy may be an appropriate way to measure pedestrian impedance, there
may be cases where the pedestrian accessibility needs of the overweight and the obese could also be
considered in a differential fashion. Still another option that accounts for individual differentiation
would be comfort-based accessibility measures that include perceived route attractiveness factors.
However, we do not recommend a fully individualized approach to accessibility, where multiple
characteristics of each individual are integrated into a customized accessibility profile. We argue against
this for primarily practical reasons. The state of knowledge is not sufficiently developed to make such
individualized measures accurate. Moreover, decision-makers plan the built environment for general
populations, not specific individuals. Individualized accessibility measures complicate identifying
significant variations across major population segments. However, analyses disaggregated by population
groups (e.g., children's accessibility to kindergartens and primary schools with particular emphasis on
the safety of street crossings) may be beneficial to planners to highlight shortcomings in the walking
network to essential amenities.

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As noted previously, the temporal component is neglected in research and in practice. Despite this,
there is evidence that heat, cold, and rain may significantly affect the willingness of persons to walk and
may influence the distance they are willing to walk (Merlin et al., 2021). In addition, intuition tells us
that walking at night differs from walking during the day and presents unique challenges (Chandra et al.,
2017; Jehle, 2020). Since little data has been gathered on this topic, the temporal component of walking
accessibility may be more suitable for researchers than practitioners at this time. However, in
challenging environments, practitioners should undoubtedly consider designs that mitigate extreme
weather (Erath et al., 2017).
The most common unit of impedance measurement for pedestrian accessibility is distance. Distance has
several advantages – it is easy to conceptualize and map for planning purposes. Moreover, distance can
be adapted to consider each of the accessibility components – for example, expanding the distance
when someone is walking upslope or contracting it when someone is walking by a park. However, travel
time may be a more advantageous unit of analysis. Firstly, there is evidence that people report time
more accurately than distance (Vale & Pereira, 2017). Secondly, different accessibility analyses can easily
be adapted to different populations by incorporating their variability in walking speeds. Lastly, street
crossing time can be added to route times as a rough way to account for the additional impedance
created by street crossings.
Most of the reviewed studies focus on objective accessibility. The advantage of objective analyses is that
these measures can readily be transferred worldwide if the corresponding spatial data is available.
However, in doing so, they neglect variations in perception, which may vary systematically not only
across individuals but also across cultures (Golan et al. 2019). Perceived measures, on the other hand,
are based on questionnaires conducted for the respective study area and thus the results are not per se
transferable. Similar to Damurski et al. (2020), several studies have discovered a mismatch between
objective and perceived accessibility (Curl et al., 2015; Gebel et al., 2011; Lättman et al., 2018;
McCormack et al., 2008; Pot et al., 2021; Ryan and Pereira, 2021; Ryan et al., 2016; van der Vlugt et al.,
2022). In order to obtain realistic and holistic results, we recommend imputing perceived accessibility
based on empirical evidence from appropriate studies - thus using the advantages of both approaches.
Finally, we also want to argue not to make the perfect the enemy of the good. While the characteristics
outlined in Table 2 are desirable, they may not be necessary or appropriate in every context. We would
rather see more frequent pedestrian accessibility analysis even if it does not meet all of our
recommended criteria in Table 2. Rather, it is intended to be a guide for the general direction of
improvement in pedestrian accessibility analyses.
Our study has several limitations. We only examined three databases and only included papers
published in English; in fact, we found several relevant papers published in other languages. Also, we did
not receive papers from all authors upon request. It is possible that some highly relevant papers were
omitted from our review. Each paper was only reviewed by a single member of the research team;
double reviews could improve data extraction. However, given the inclusiveness of our search, we
believe we have drawn an accurate picture of the current state of pedestrian accessibility analysis.

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5. Conclusion
In this paper, we review the global, multidisciplinary literature on pedestrian accessibility, surveying 85
publications. We differentiate the literature concerning pedestrian accessibility from the broader
literature on walkability by screening out papers that do not concern reaching destinations or do not
incorporate some measure of impedance or distance. This distinguishes our review from a previous
effort that did not limit their scope to walking to destinations (Vale et al., 2016). For the purposes of
data extraction, we gathered data on the following topics of interest: Study title, publication journal,
study area, study continent, measurement of impedance or distance, origins, destinations, method of
developing the pedestrian network, variation by population group, temporal considerations, analysis or
calculation method, results, innovative features, and limitations.
In order to structure our critique of this body of literature, we employ the framework proposed by Geurs
and van Wee (2004), which identifies four components of the accessibility concept: transportation, land
use, individual and temporal. We conceive of these four components, in turn, influencing the two
elements of the generalized equation for accessibility from Levinson and Wu (2020) (Equation 1), an
element calculating attractiveness and an element calculating impedance or cost (see Figure 2). Our
analysis indicates that all four components, as well as interactions between the four components, can
influence the calculation of impedance.
Based upon the content of our review, we issue several recommendations for research and practice
concerning the study of walking accessibility, summarized in Table 2. We recommend that, when
possible, analysts use pedestrian networks that account for street crossings and other distinctive
features of the pedestrian environment rather than relying solely upon roadway networks. Because of
the importance of small-scale features of the pedestrian environment to impedance, we recommend
that building-level accessibility measures be calculated when feasible. Concerning the individual
component of accessibility, pedestrians are subject to much more significant variation than motorized
modes, and therefore we argue that differentiation across population groups should be considered. Two
populations that have often been considered are older adults and those in wheelchairs, but other
population segments with distinct pedestrian needs exist. We also note that the temporal component of
walking accessibility is generally neglected. Accounting for the effects of weather, nighttime, and
opening hours would be a fruitful way to consider temporal aspects. Lastly, we point to several
advantages of using travel time as the unit of analysis, rather than distance, for measuring pedestrian
impedance, while noting that other measures such as perceived impedance may also be relevant.
In any given specific implementation of pedestrian accessibility analysis, a trade-off must be made
between detailed data gathering and computational efficiency. Because of the large number of factors
that influence pedestrian accessibility, gathering comprehensive data on all aspects of pedestrian
accessibility may be cost-prohibitive in any given circumstance. Each analysis must consider the costs
and benefits of each additional component of data to be gathered, and the overall effort should be
guided by which kinds of data are most relevant to the populations and built environment contexts
under study. Therefore, the most appropriate level of data gathering for each pedestrian accessibility
analysis effort is a strategic consideration that should be carefully considered at the start of any
pedestrian accessibility analysis project.

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References
Abrahams, Petrine M. (2010). ‘Stakeholders’ Perceptions of Pedestrian Accessibility to Green
Infrastructure: Fort Worth’s Urban Villages’,
Achuthan, K., Titheridge, H., & Mackett, R. (2007). Measuring pedestrian accessibility. Proceedings of the
Geographical Information Science Research UK (GISRUK) Conference, National Centre for
Geocomputation, National University of Ireland, 264–269.
Adams, Sherry. (2002). ‘Pedestrian Access’. Pedestrian Accessibility. 2002.
https://sherrynadams.tripod.com/trans/pedaccess.html.
Alfonzo, M. A. (2005). To Walk or Not to Walk? The Hierarchy of Walking Needs. Environment and
Behavior, 37(6), 808–836. https://doi.org/10.1177/0013916504274016
Amaya, V., Moulaert, T., Gwiazdzinski, L., & Vuillerme, N. (2022). Assessing and qualifying neighborhood
walkability for older adults: construction and initial testing of a multivariate spatial accessibility model.
International journal of environmental research and public health, 19(3), 1808.
Anciaes, P., and Jones, P. (2020). A comprehensive approach for the appraisal of the barrier effect of
roads on pedestrians. Transportation Research Part A: Policy and Practice, 134(June 2019), 227–250.
https://doi.org/10.1016/j.tra.2020.02.003
Anciaes, P., Nascimento, J., and Silva, S. (2015). Mapping pedestrian accessibility and the quality of
walking in an African city: Praia, Cape Verde. CODATU.
Ariza-Alvarez, A., Soria-Lara, J. A., & Arranz-López, A. (2021). Comparing walking accessibility variations
between groceries and other retail activities for seniors. Research in Transportation Economics,
87(September), 100745. https://doi.org/10.1016/j.retrec.2019.100745
Ariza-Alvarez, A., Soria-Lara, J. A., and Arranz-López, A. (2021). Comparing walking accessibility
variations between groceries and other retail activities for seniors. Research in Transportation
Economics, 87(September), 100745. https://doi.org/10.1016/j.retrec.2019.100745
Arranz-López, A., Soria-Lara, J. A., López-Escolano, C. and Campos, A. P. (2017). ‘Retail Mobility
Environments: A Methodological Framework for Integrating Retail Activity and Non-Motorised
Accessibility in Zaragoza, Spain’. Journal of Transport Geography 58 (January): 92–103.
https://doi.org/10.1016/j.jtrangeo.2016.11.010.
Arranz-López, A., Soria-Lara, J. A., & Campos, A. P. (2019). ‘Social and Spatial Equity Effects of Non-
Motorised Accessibility to Retail’. Cities 86 (March): 71–82. https://doi.org/10.1016/j.cities.2018.12.012.
Arranz-López, A., Soria-Lara, J. A., Witlox, F. & Páez, A.. (2019). ‘Measuring Relative Non-Motorized
Accessibility to Retail Activities’. International Journal of Sustainable Transportation 13 (9): 639–51.
https://doi.org/10.1080/15568318.2018.1498563.
Aultman-Hall, L., Roorda, M. ;, & W. Baetr, B. (1997). Using GIS for Evaluation of Neighborhood
Pedestrian Accessibility. Journal of Urban Planning and Development, 123(1), 10–17.

19
Preprint. Final article is available at Transport Reviews:
https://doi.org/10.1080/01441647.2023.2189323
Badami, M. G. (2009). Urban transport policy as if people and the environment mattered: Pedestrian
accessibility the first step. Economic and Political Weekly, 44(33), 43–51.
Bivina, G., Gupta, A., & Manoranjan, P. (2020). Walk accessibility to metro stations: An analysis based on
meso- or micro-scale built environment factors. Sustainable Cities and Society, 55.
10.1016/j.scs.2020.102047.
Blanford, J. I., Kumar, S., Luo, W., & MacEachren, A. M. (2012). It’s a long, long walk: accessibility to
hospitals, maternity and integrated health centers in Niger. INTERNATIONAL JOURNAL OF HEALTH
GEOGRAPHICS, 11. https://doi.org/10.1186/1476-072X-11-24
Blečić, I., Cecchini, A., Congiu, T., & Fancello, G. (2015). International Journal of Geographical Evaluating
walkability : a capability- wise planning and design support system. International Journal of Geographical
Information Science, May 2015, 37–41. https://doi.org/10.1080/13658816.2015.1026824
Blečić, I., Cecchini, A., Congiu, T., Pazzola, M., & Trunfio, G. A. (2013). A design and planning support
system for walkability and pedestrian accessibility. International Conference on Computational Science
and Its Applications, 284–293. https://doi.org/10.1007/978-3-642-39649-6_20
Blečić, I., Cecchini, A., & Trunfio, G. A. (2018). Towards automatic assessment of perceived walkability.
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics), 10962 LNCS, 351–365. https://doi.org/10.1007/978-3-319-95168-3_24
Boakye-Dankwa, E., Barnett, A., Pachana, N. A., Turrell, G., & Cerin, E. (2019). Associations Between
Latent Classes of Perceived Neighborhood Destination Accessibility and Walking Behaviors in Older
Adults of a Low-Density and a High-Density City. Journal Of Aging and Physical Activity, 27(4), 553–564.
https://doi.org/10.1123/japa.2018-0297
Borowska-Stefańska, M., & Wiśniewski, S. (2017). Pedestrian accessibility to parks in Łódź. Studia
Miejskie, 27, 39–50.
Broach, J., & Dill, J. L. (2015). Pedestrian route choice model estimated from revealed preference GPS
data. Transportation Research Board 94th Annual Meeting, 15, 1–22.
Carpio-Pinedo, J., Benito-Moreno, M., & Lamíquiz-Daudén, P. J. (2021). Beyond land use mix, walkable
trips. An approach based on parcel-level land use data and network analysis. Journal of Maps, 17(1), 23–
30. https://doi.org/10.1080/17445647.2021.1875063
Chandra, S., Jimenez, J., & Radhakrishnan, R. (2017). Accessibility evaluations for nighttime walking and
bicycling for low-income shift workers. Journal of Transport Geography, 64, 97–108.
https://doi.org/10.1016/j.jtrangeo.2017.08.010
Church, R. L., & Marston, J. R. (2003). Measuring accessibility for people with a disability. Geographical
Analysis, 35(1), 83–96.
Cheng, L., Caset, F., De Vos, J., Derudder, B., & Witlox, F. (2019). Investigating walking accessibility to
recreational amenities for elderly people in Nanjing, China. Transportation Research Part D-Transport
And Environment, 76, 85–99. https://doi.org/10.1016/j.trd.2019.09.019

20
Preprint. Final article is available at Transport Reviews:
https://doi.org/10.1080/01441647.2023.2189323
Chin, G. K. W., van Niel, K. P., Giles-Corti, B., & Knuiman, M. (2008). Accessibility and connectivity in
physical activity studies: The impact of missing pedestrian data. Preventive Medicine, 46(1), 41–45.
https://doi.org/10.1016/j.ypmed.2007.08.004
Colclough, J. G., & (AET), A. for E. T. (2009). Modelling Pedestrian Accessibility using GIS Techniques to
Assess Development Sustainability (p. 9p).
Curl, Angela, John D. Nelson, and Jillian Anable. (2015). ‘Same Question, Different Answer: A Comparison
of GIS-Based Journey Time Accessibility with Self-Reported Measures from the National Travel Survey in
England’. Computers, Environment and Urban Systems 49 (January): 86–97.
https://doi.org/10.1016/j.compenvurbsys.2013.10.006.
Damurski, L., Pluta, J., & Zipser, W. (2020). Pedestrian accessibility of services as a measure of territorial
cohesion at the neighbourhood level. BULLETIN OF GEOGRAPHY-SOCIO-ECONOMIC SERIES, 49(49), 31–
48. https://doi.org/10.2478/bog-2020-0022
Desjardins, E., Higgins, C. D., & Paez, A. (2022). Examining equity in accessibility to bike share: A
balanced floating catchment area approach. Transportation Research Part D: Transport and
Environment, 102, 103091.
De Vos, J., Lättman, K., van der Vlugt, A. L., Welsch, J., & Otsuka, N. (2022). Determinants and effects of
perceived walkability: A literature review, conceptual model and research agenda. Transport Reviews.
https://doi.org/10.1080/01441647.2022.2101072
D’Orso, G., & Migliore, M. (2018). A GIS-based method to assess the pedestrian accessibility to the
railway stations. International Conference on Computational Science and Its Applications, 19–30.
Eizaguirre-Iribar, A., Iginiz, L. E., & Hernandez-Minguillon, R. J. (2016). A multilevel approach of non-
motorised accessibility in disused railway systems: The case-study of the Vasco-Navarro railway.
JOURNAL OF TRANSPORT GEOGRAPHY, 57, 35–43. https://doi.org/10.1016/j.jtrangeo.2016.09.009
Erath, A. L., Eggermond, M. Van, Ordonez, S., & Axhausen, K. W. (2015). Modelling for Walkability:
understanding pedestrians’ preferences in Singapore. IATBR 2015, 1–16.
Erath, A. L., van Eggermond, M. A. B., Axhausen, K. W., & Ordóñez, S. A. (2017). Introducing the
pedestrian accessibility tool (PAT): Open source GIS-Based walkability analysis. Transportation Research
Record, 2661, 51–61. https://doi.org/10.3141/2661-06
Ewing, R. H. (1999). Pedestrian-and transit-friendly design: A primer for smart growth. Smart Growth
Network Washington, DC.
Foda, M. A., & Osman, A. O. (2010). Using GIS for Measuring Transit Stop Accessibility Considering Actual
Pedestrian Road Network. Journal of Public Transportation, 13(4), pp 23-40.
https://doi.org/http://doi.org/10.5038/2375-0901.13.4.2
Foti, F., Waddell, P., & Luxen, D. (2012). A generalized computational framework for accessibility: from
the pedestrian to the metropolitan scale. Proceedings of the 4th TRB Conference on Innovations in Travel
Modeling. Transportation Research Board.

21
Preprint. Final article is available at Transport Reviews:
https://doi.org/10.1080/01441647.2023.2189323
Frank, L. D., Sallis, J. F., Saelens, B. E., Leary, L., Cain, K., Conway, T. L., & Hess, P. M. (2010). The
development of a walkability index: application to the Neighborhood Quality of Life Study. British
Journal of Sports Medicine, 44, 924–933.
Gaglione, F., Cottrill, C., & Gargiulo, C. (2021). Urban services, pedestrian networks and behaviors to
measure elderly accessibility. Transportation Research Part D: Transport and Environment, 90.
https://doi.org/10.1016/j.trd.2020.102687
Gaglione, F., Gargiulo, C., & Zucaro, G. (2019). Elders quality of life and urban accessibility: A method
proposal for spatial planning. TEMA: Journal Of Land Use Mobility And Environment, 12(3), 295–312.
https://doi.org/http://dx.doi.org/10.6092/1970-9870/6272
García-Palomares, J. C., Gutiérrez, J., & Cardozo, O. D. (2013). Walking accessibility to public transport:
An analysis based on microdata and GIS. Environment and Planning B: Planning and Design, 40(6), 1087–
1102. https://doi.org/10.1068/b39008
Gebel, Klaus, Adrian E. Bauman, Takemi Sugiyama, and Neville Owen. 2011. ‘Mismatch between
Perceived and Objectively Assessed Neighborhood Walkability Attributes: Prospective Relationships with
Walking and Weight Gain’. Health & Place 17 (2): 519–24.
https://doi.org/10.1016/j.healthplace.2010.12.008.
Geurs, K. T., & van Wee, B. (2004). Accessibility evaluation of land-use and transport strategies: Review
and research directions. Journal of Transport Geography, 12(2), 127–140.
https://doi.org/10.1016/j.jtrangeo.2003.10.005
Goldsberry, K., Duvall, C. S., Howard, P. H., & Stevens, J. E. (2010). Visualizing nutritional terrain: a
geospatial analysis of pedestrian produce accessibility in Lansing, Michigan, USA. GEOCARTO
INTERNATIONAL, 25(6), 485–499. https://doi.org/10.1080/10106049.2010.510583
Goodwin, J. M. (2005). Where the Sidewalk Begins: Pedestrian Accessibility Analysis in Suburban
Cincinnati. Ohio University.
Golan, Yael, Nancy Wilkinson, Jason M Henderson, and Aiko Weverka. (2019). ‘Gendered Walkability:
Building a Daytime Walkability Index for Women’. Journal of Transport and Land Use 12 (1).
https://doi.org/10.5198/jtlu.2019.1472.
Grecu, V., & Morar, T. (2013). A decision support system for improving pedestrian accessibility in
neighborhoods. Procedia-Social and Behavioral Sciences, 92, 588–593.
Higgins, C. D., Darren, N., & Gerhard, B. (2019). Slope, Layers, and Walkability: Estimating the Link
Between Pedestrian Accessibility and Land Values in the Morphology of High Density Cities. Urban
Morphological Methods and Techniques, 719–728.
Holzer, R. (2018). Analyzing Pedestrian Accessibility Using QGIS and OpenStreetMaps Data Background.
https://www.rickyholzer.com/2018/analyzing- pedestrian-accessibility-using-qgis-and-openstreetmaps-
data

22
Preprint. Final article is available at Transport Reviews:
https://doi.org/10.1080/01441647.2023.2189323
Iacono, M., Krizek, K. & El-Geneidy, A.. (2008). ‘Access to Destinations: How Close Is Close Enough?
Estimating Accurate Distance Decay Functions for Multiple Modes and Different Purposes’.
Iacono, M., Krizek, K. J. & El-Geneidy, A. (2010). Measuring non-motorized accessibility: issues,
alternatives, and execution, Journal of Transport Geography, 18(1), 133-140, ISSN 0966-6923,
https://doi.org/10.1016/j.jtrangeo.2009.02.002.
Allan, J. (1995). Great streets.
Jang, K. M., Kim, J., Lee, H. Y., Cho, H., & Kim, Y. (2020). Urban Green Accessibility Index: A Measure of
Pedestrian-Centered Accessibility to Every Green Point in an Urban Area. ISPRS International Journal Of
Geo-Information, 9(10). https://doi.org/10.3390/ijgi9100586
Jehle, U. (2020). Incorporating the four accessibility components into an interactive accessibility
instrument (Issue January). Technical University of Munich.
Jehle, U., Coetzee, C., Büttner, B., Pajares, E. and Wulfhorst, G. (2022). Connecting people and places:
Analysis of perceived pedestrian accessibility to railway stations by Bavarian case studies. Journal of
Urban Mobility, Volume 2, 100025, ISSN 2667-0917, https://doi.org/10.1016/j.urbmob.2022.100025.
Jonietz, D., & Timpf, S. (2012). Incorporating the Influence of Walkability into a Model of Pedestrian
Accessibility. In G. Wulfhorst and B. Buttner (Eds.), TUM 2012 International Scientific Conference on
Mobility and Transport (p. 280). Techinical University of Munich.
Jou, K. K. (2011). Pedestrian Areas and Sustainable Development. International Journal of Architectural
and Environmental Engineering, 5(5), 228–235.
Kang, Chang-Deok. (2015). ‘The Effects of Spatial Accessibility and Centrality to Land Use on Walking in
Seoul, Korea’. Cities 46 (August): 94–103. https://doi.org/10.1016/j.cities.2015.05.006.
Kuzmyak, J. R., Baber, C., & Savory, D. (2006). Use of Walk Opportunities Index to Quantify Local
Accessibility. Transportation Research Record: Journal of the Transportation Research Board, 1977, pp
145-153. https://doi.org/https://doi.org/10.3141/1977-19
Laakso, M., Sarjakoski, T., Lehto, L., & Sarjakoski, L. T. (2013). An information model for pedestrian
routing and navigation databases supporting universal accessibility. Cartographica: The International
Journal for Geographic Information and Geovisualization, 48(2), 89–99.
Laakso, M., Sarjakoski, T., & Sarjakoski, L. T. (2011). Improving accessibility information in pedestrian
maps and databases. Cartographica: The International Journal for Geographic Information and
Geovisualization, 46(2), 101–108.
Lättman, Katrin, Lars E. Olsson, and Margareta Friman. (2018). ‘A New Approach to Accessibility –
Examining Perceived Accessibility in Contrast to Objectively Measured Accessibility in Daily Travel’.
Research in Transportation Economics, Competition and Ownership in Land Passenger Transport
(selected papers from the Thredbo 15 conference), 69 (September): 501–11.
https://doi.org/10.1016/j.retrec.2018.06.002.

23
Preprint. Final article is available at Transport Reviews:
https://doi.org/10.1080/01441647.2023.2189323
Levine, J., Grengs, J., & Merlin, L. A. (2019). From Mobility to Accessibility: Transforming Urban
Transportation and Land-Use Planning. Cornell University Press.
Li, H. Y., Cebe, J., Khoeini, S., Xu, Y., Dyess, C., & Guensler, R. (2018). A Semi-Automated Method to
Generate GIS-Based Sidewalk Networks for Asset Management and Pedestrian Accessibility Assessment.
TRANSPORTATION RESEARCH RECORD, 2672(44), 1–9. https://doi.org/10.1177/0361198118757981
Liang, H. L., Chen, D., & Zhang, Q. P. (2017). Walking accessibility of urban parks in a compact megacity.
Proceedings of the Institution of Civil Engineers-Urban Design And Planning, 170(2), 59–71.
https://doi.org/10.1680/jurdp.16.00030
Liu, S. Q., Higgs, C., Arundel, J., Boeing, G., Cerdera, N., Moctezuma, D., Cerin, E., Adlakha, D., Lowe, M.,
& Giles-Corti, B. (2021). A Generalized Framework for Measuring Pedestrian Accessibility around the
World Using Open Data. Geographic Analysis. https://doi.org/10.1111/gean.12290
Lo, R. H. (2009). Walkability: what is it? Journal of Urbanism: International Research on Placemaking and
Urban Sustainability, 2(2), 145–166. https://doi.org/10.1080/17549170903092867
Marquet, O., Bedoya, V. R., & Miralles-Guasch, C. (2017). Local accessibility inequalities and willingness
to walk in Latin-American cities: Findings from Medellin, Colombia. INTERNATIONAL JOURNAL OF
SUSTAINABLE TRANSPORTATION, 11(3), 186–196. https://doi.org/10.1080/15568318.2016.1230804
Marquet, O., and Miralles-Guasch, C. (2014). Walking short distances. The socioeconomic drivers for the
use of proximity in everyday mobility in Barcelona. TRANSPORTATION RESEARCH PART A-POLICY AND
PRACTICE, 70, 210–222. https://doi.org/10.1016/j.tra.2014.10.007
McCormack, Gavin R., Ester Cerin, Eva Leslie, Lorinne Du Toit, and Neville Owen. (2008). ‘Objective
Versus Perceived Walking Distances to Destinations: Correspondence and Predictive Validity’.
Environment and Behavior 40 (3): 401–25. https://doi.org/10.1177/0013916507300560.
Merlin, L. A., Teoman, D., Viola, M., Vaughn, H., & Buehler, R. (2021). Redrawing the Planners’ Circle:
Analyzing Trip-Level Walk Distances Across Two National Surveys. Journal of the American Planning
Association, 0–2. https://doi.org/10.1080/01944363.2021.1877181
Montgomery County Planning Department. (2020). Pedestrian Level of Comfort Methodology.
Moniruzzaman, M., Páez, A., & Morency, C. (2014). Compliance potential mapping: A tool to assess
potential contributions of walking towards physical activity guidelines. BMC Public Health, 14(1).
https://doi.org/10.1186/1471-2458-14-511
Morar, T., Radoslav, R., Spirdon, L. C., & Pacurar, L. (2014). Assessing Pedestrian Accessibility to Green
Space Using GIS. Transylvanian Review of Administrative Sciences, 42E, 116–139.
Orellana, D., Bustos, M. E., Marin-Palacios, M., Cabrera-Jara, N., & Hermida, M. A. (2020). Walk’n’roll:
Mapping street-level accessibility for different mobility conditions in Cuenca, Ecuador. Journal of
Transport and Health, 16. https://doi.org/10.1016/j.jth.2020.100821

24
Preprint. Final article is available at Transport Reviews:
https://doi.org/10.1080/01441647.2023.2189323
Páez, A. Mercado, R. G., Farber, S. Morency, C. and Roorda, M. (2010). Relative accessibility deprivation
indicators for urban settings: Definition and applications to food deserts in Montreal. Urban Studies.
47(7), 1415-1438.
Páez, A., Anjum, Z., Dickson-Anderson, S. E., Schuster-Wallace, C. J., Ramos, B. M., & Higgins, C. D.
(2020). Comparing distance, time, and metabolic energy cost functions for walking accessibility in
infrastructure-poor regions. Journal of Transport Geography, 82.
https://doi.org/10.1016/j.jtrangeo.2019.102564
Pajares, E., Büttner, B., Jehle, U., Nichols, A., & Wulfhorst, G. (2021). Accessibility by proximity:
Addressing the lack of interactive accessibility instruments for active mobility. Journal of Transport
Geography, 93, 103080. https://doi.org/10.1016/j.jtrangeo.2021.103080
Papa, E. E., Carpentieri, G., & Guida, C. (2018). Measuring walking accessibility to public transport of the
elderly: The case of Naples. Tema. Journal of Land Use, Mobility and Environment, 2, 105–116.
https://doi.org/10.6092/1970-9870/5766
Parmenter, B., McMillan, T., Cubbin, C., & Lee, R. E. (2008). Developing geospatial data management,
recruitment, and analysis techniques for physical activity research. Journal of the Urban and Regional
Information Systems Association, 20(2), 13–19.
Pearce, D. M., Matsunaka, R., & Oba, T. (2021). Comparing accessibility and connectivity metrics derived
from dedicated pedestrian networks and street networks in the context of Asian cities. Asian Transport
Studies, 7(June 2020), 100036. https://doi.org/10.1016/j.eastsj.2021.100036
Pearce, D. M., Matsunaka, R., Oba, T., & Yamamoto, S. (2021). Utilising dedicated pedestrian networks
to understand the relationship between accessibility and pedestrian density in Asian cities. International
Journal of Transport Development and Integration, 5(2), 150–161.
Pot, F. J., van Wee, B., & Tillema, T. (2021). Perceived accessibility: What it is and why it differs from
calculated accessibility measures based on spatial data. Journal of Transport Geography, 94, 103090.
https://doi.org/https://doi.org/10.1016/j.jtrangeo.2021.103090
Replogle, M. A., & Fulton, L. M. (2014). A global high shift scenario: impacts and potential for more
public transport, walking, and cycling with lower car use.
Reyes, M., Páez, A., & Morency, C. (2014). Walking accessibility to urban parks by children: A case study
of Montreal. Landscape and Urban Planning, 125, 38–47.
https://doi.org/10.1016/j.landurbplan.2014.02.002
Roblot, M., Boisjoly, G., Francesco, C., & Martin, T. (2021). Participation in Shared Mobility: An Analysis
of the Influence of Walking and Public Transport Accessibility to Vehicles on Carsharing Membership in
Montreal, Canada. Transportation Research Record, 2675(12), 1160–1171.
https://doi.org/10.1177/03611981211032221
Rossetti, S., Tiboni, M., Vetturi, D., & Calderòn, E. J. (2015). Pedestrian mobility and accessibility
planning: some remarks towards the implementation of travel time maps. CSE Journal, 1(1), 67–78.

25
Preprint. Final article is available at Transport Reviews:
https://doi.org/10.1080/01441647.2023.2189323
Rossetti, S., Tiboni, M., Vetturi, D., Zazzi, M., & Caselli, B. (2020). Measuring Pedestrian Accessibility to
Public Transport in Urban Areas: a GIS-based Discretisation Approach. EUROPEAN TRANSPORT-
TRASPORTI EUROPEI, 76.
Ryan, Jean, and Rafael H.M. Pereira. (2021). ‘What Are We Missing When We Measure Accessibility?
Comparing Calculated and Self-Reported Accounts among Older People’. Journal of Transport
Geography 93 (May): 103086. https://doi.org/10.1016/j.jtrangeo.2021.103086.
Ryan, M., T. Lin, Jianhong (Cecilia) Xia, and Todd Robinson. (2016). ‘Comparison of Perceived and
Measured Accessibility between Different Age Groups and Travel Modes at Greenwood Station, Perth,
Australia’. European Journal of Transport and Infrastructure Research 16 (2): 406–23.
Saghapour, T., Moridpour, S., & Thompson, R. G. (2017). Measuring Walking Accessibility in
Metropolitan Areas. Transportation Research Record, 2661, 111–119. https://doi.org/10.3141/2661-13
Saghapour, T., Moridpour, S., & Thompson, R. G. (2018). Enhancing active transport demand Modelling
by incorporating accessibility measures. Cities, 78(September 2017), 206–215.
https://doi.org/10.1016/j.cities.2018.02.015
Saghapour, T., Moridpour, S., & Thompson, R. (2019). Sustainable transport in neighbourhoods: effect of
accessibility on walking and bicycling. Transportmetrica A-Transport Science, 15(2), 849–871.
https://doi.org/10.1080/23249935.2018.1540502
Sarker, R. I., Mailer, M., & Sikder, S. (2019). Walking to a public transport station: Empirical evidence on
willingness and acceptance in Munich, Germany. Smart and Sustainable Built Environment, 9(1), 38–53.
10.1108/SASBE-07-2017-0031. Publisher: Emerald Publishing Limited
Seisenberger, S. (2021). Evaluating walking accessibility and equity to essential services with and without
competition using the interactive accessibility instrument GOAT [Master’s Thesis, Technical University of
Munich].
Sevtsuk, A., Kalvo, R., & Ekmekci, O. (2016). Pedestrian accessibility in grid layouts: the role of block, plot
and street components. Urban Morphology, 20(2), 89–106.
Sinagra, E. (2019). Development of pathNav: A Pedestrian Navigation Tool that Utilizes Smart Data for
Improved Accessibility and Walkability. In TRANSIT-IDEA Program Project Final Report (Final Repo, Issue
87, p. 45p). Transportation Research Board.
Stewart, N. (2016). Using Pedestrian Accessibility Indicators to Locate Schools: A Site Suitability Analysis
in Greenville County, South Carolina. University of Southern California.
Sun, G. B., Haining, R., Lin, H., Oreskovic, N. M., & He, J. (2015). Comparing the perception with the
reality of walking in a hilly environment: an accessibility method applied to a University campus in Hong
Kong. GEOSPATIAL HEALTH, 10(1), 32–39. https://doi.org/10.4081/gh.2015.340
Tal, G., & Handy, S. (2012). Measuring Nonmotorized Accessibility and Connectivity in a Robust
Pedestrian Network. Transportation Research Record, 2299, 48–56. https://doi.org/10.3141/2299-06

26
Preprint. Final article is available at Transport Reviews:
https://doi.org/10.1080/01441647.2023.2189323
Tang, B. S., Wong, K. K. H., Tang, K. S. S., & Wai Wong, S. (2021). Walking accessibility to neighbourhood
open space in a multi-level urban environment of Hong Kong. Environment and Planning B: Urban
Analytics and City Science, 48(5), 1340–1356. https://doi.org/10.1177/2399808320932575
Tasic, I. M., Musunuru, A., & Porter, R. J. (2014, January). Quantifying Accessibility of Non-Motorized
Transportation Modes in Recreational Areas: Case Study of Mill Creek Canyon, Utah. Transportation
Research Board 93rd Annual Meeting.
Tiran, J., Lakner, M., & Drobne, S. (2019). Modelling walking accessibility: A case study of Ljubljana,
Slovenia. Moravian Geographical Reports, 27(4), 194–206. https://doi.org/10.2478/mgr-2019-0015
US Dept. of Health and Human Services. (2015). Step It Up! The Surgeon General’s Call To Action To
Promote Walking And Walkable Communities U.S. Department Of Health And Human Services.
www.surgeongeneral.gov.
Vale, D. S., Saraiva, M., & Pereira, M. (2015). Active accessibility: A review of operational measures of
walking and cycling accessibility. Journal of Transport and Land Use, 1, 1–27.
https://doi.org/10.5198/jlu.2015.593
Vale, D. S., & Pereira, M. (2017). The influence of the impedance function on gravity-based pedestrian
accessibility measures: A comparative analysis. Environment and Planning B-Urban Analytics and City
Science, 44(4), 740–763. https://doi.org/10.1177/0265813516641685
van der Vlugt, A. L., Curl, A., & Scheiner, J. (2022). The influence of travel attitudes on perceived walking
accessibility and walking behaviour. Travel Behaviour and Society, 27(November 2021), 47–56.
https://doi.org/10.1016/j.tbs.2021.11.002
Van Eggermond, M. A., & Erath, A. L. (2016). Pedestrian and transit accessibility on a micro level: Results
and challenges. Journal of Transport and Land Use, 9(3), 127–143. https://doi.org/10.5198/jtlu.2015.677
Verma, A., Verma, M., Rahul, T. M., Khurana, S., & Rai, A. (2019). Measuring accessibility of various
facilities by walking in world’s largest mass religious gathering - Kumbh Mela. SUSTAINABLE CITIES AND
SOCIETY, 45, 79–86. https://doi.org/10.1016/j.scs.2018.11.038
Wilhelm, M. E. (2007). Analysis of pedestrian accessibility as applied to Spokane city parks. Citeseer.
Wimbardana, R., Tarigan, A. K. M., & Sagala, S. (2018). Does a pedestrian environment promote
walkability? Auditing a pedestrian environment using the pedestrian environmental data scan
instrument. Journal of Regional and City Planning, 29(1), 57–66.
https://doi.org/10.5614/jrcp.2018.29.1.5
Wu, H., & Levinson, D. (2020). Unifying access. Transportation Research Part D: Transport and
Environment, 83(April), 102355. https://doi.org/10.1016/j.trd.2020.102355
Xiao, Y., & Watson, M. (2019). Guidance on Conducting a Systematic Literature Review. Journal of
Planning Education and Research, 39(1), 93–112. https://doi.org/10.1177/0739456X17723971

27
Preprint. Final article is available at Transport Reviews:
https://doi.org/10.1080/01441647.2023.2189323
Xu, M. (2014). A GIS-based pedestrian network model for assessment of spatial accessibility equity and
improvement prioritization and its application to the Spokane public transit benefit area. Washington
State University
Yang, S. Q., Tan, W. K., & Yan, L. X. (2021). Evaluating Accessibility Benefits of Opening Gated
Communities for Pedestrians and Cyclists in China: A Case Study of Shanghai. Sustainability, 13(2).
https://doi.org/10.3390/su13020598
Yang, M., & Wang, Y. J. (2013). Modelling The Accessibility Classification Of Railway Lines: A Case Study
Of Northeast China Railway Network. Promet-Traffic & Transportation, 25(5), 467–474.
Zahra, A. A., & Ahyudanari, E. (2021). Measuring pedestrian accessibility of Transit Oriented
Development area in Surabaya (a study case: Joyoboyo Terminal). IOP Conference Series: Earth and
Environmental Science, 778(1), 12003.
Zecca, C., Gaglione, F., Laing, R., & Gargiulo, C. (2020). Pedestrian routes and accessibility to urban
services: An urban rhythmic analysis on people’s behaviour during the Covid-19. TEMA-Journal Of Land
Use Mobility And Environment, 13(2), 241–256. https://doi.org/10.6092/1970-9870/7051
Zuo, T., Wei, H., & Rohne, A. (2018). Determining transit service coverage by non-motorized accessibility
to transit: Case study of applying GPS data in Cincinnati metropolitan area. Journal of Transport
Geography, 67, 1–11. https://doi.org/10.1016/j.jtrangeo.2018.01.002