Assessing bikeability with street view imagery and computer vision
Koichi Itoa,Filip Biljeckia,b,∗
aDepartment of Architecture, National University of Singapore, Singapore
bDepartment of Real Estate, National University of Singapore, Singapore
Google Street View
Studies evaluating bikeability usually compute spatial indicators shaping cycling conditions and
conﬂate them in a quantitative index. Much research involves site visits or conventional geospa-
tial approaches, and few studies have leveraged street view imagery (SVI) for conducting virtual
audits. These have assessed a limited range of aspects, and not all have been automated using
computer vision (CV). Furthermore, studies have not yet zeroed in on gauging the usability of
these technologies thoroughly. We investigate, with experiments at a ﬁne spatial scale and across
multiple geographies (Singapore and Tokyo), whether we can use SVI and CV to assess bike-
ability comprehensively. Extending related work, we develop an exhaustive index of bikeability
composed of 34 indicators. The results suggest that SVI and CV are adequate to evaluate bike-
ability in cities comprehensively. As they outperformed non-SVI counterparts by a wide margin,
SVI indicators are also found to be superior in assessing urban bikeability and potentially can
be used independently, replacing traditional techniques. However, the paper exposes some lim-
itations, suggesting that the best way forward is combining both SVI and non-SVI approaches.
The new bikeability index presents a contribution in transportation and urban analytics, and it is
scalable to assess cycling appeal widely.
This is the Accepted Manuscript version of an article published by Elsevier in the journal Transportation Research Part C: Emerging Technologies
in 2021, which is available at: https://doi.org/10.1016/j.trc.2021.103371. Cite as: Ito K, Biljecki F (2021): Assessing bikeability
with street view imagery and computer vision. Transportation Research Part C, 132: 103371.
©2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (http://creativecommons.org/licenses/by-nc- nd/4.0/)
Bicycles play an important role in making cities environmentally sustainable, healthy, and economically vibrant
(Neves and Brand,2019;Cao and Shen,2019;Rojas-Rueda et al.,2011;Volker and Handy,2021;Wang et al.,2016;
Horacek et al.,2018;Wang et al.,2019a;Yeh et al.,2019;Chen et al.,2020b;Alaoui and Tekouabou,2021). To
evaluate the extent to which cycling is facilitated, the notion of bikeability was created by complementing the concept
of walkability. Subsequently, index systems, as instruments to quantify it, have been developed by many studies (Porter
et al.,2020;Cain et al.,2018;Manton et al.,2016;Gullón et al.,2015;Koh and Wong,2013;Horacek et al.,2012;
Wahlgren et al.,2010;Hoedl et al.,2010;Clifton et al.,2007;Titze et al.,2012;Arellana et al.,2020;Winters et al.,
2013;Guler and Yomralioglu,2021;Lin and Wei,2018;Gholamialam and Matisziw,2019;Resch et al.,2020;Kamel
et al.,2020;Grigore et al.,2019;Osama et al.,2020;Chevalier and Xu,2020;Kang et al.,2019;Schmid-Querg et al.,
2021;Galanis et al.,2018;Lowry et al.,2016;Faghih Imani et al.,2019;Boongaling et al.,2021). This topic became
even more relevant with the advent of bicycle-sharing systems (Du et al.,2019;Luo et al.,2020). Data collection
methodologies to calculate bikeability indexes are for a large part derived from ﬁeld observations, entailing time-
consuming manual work and limiting the spatial extent that can be surveyed. As technologies have advanced, these
assessments have been supplemented by new data sources, such as crowdsourcing and virtual observations (Kalvelage
et al.,2018;Gullón et al.,2015;Abadi and Hurwitz,2018). Nevertheless, previous studies face various issues, such as
slow data collection process, the balance between subjectivity and objectivity of data, lack of street-level information,
and standardization of spatial granularity. The recent availability of street view imagery (SVI) has yielded opportunities
for new approaches to urban studies (Biljecki and Ito,2021), enabling a wealth of images from the pedestrian and cyclist
perspective that may be used to assess walkability and bikeability and that is available remotely (Figure 1). In parallel,
developments in computer vision (CV) have catalyzed the means to process the profusion of photos automatically and
eﬃciently. They have already been utilized for assessing walkability (Nagata et al.,2020). SVI and CV are not entirely
new to bikeability either. For example, studies by Tran et al. (2020) and Gu et al. (2018) have used CV and SVI to assess
particular aspects of bikeability. However, no comprehensive bikeability assessment study has been conducted yet, and
there has been no critical evaluation of the usability of such technologies in comparison to conventional methods that
have been dominating the ﬁeld so far.
email@example.com (F. Biljecki)
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Figure 1: Illustration of an urban setting together with one of the corresponding street-level views, highlighting several
aspects that may indicate bikeability. The method presented in this paper takes advantage of a substantial number of
visual features that may be extracted automatically from street view images and engage them to generate a composite
index that suggests cycling appeal at a ﬁne spatial scale and across multiple cities.
Considering the developments in computer vision and proliferation of street view imagery (e.g. increased coverage
of commercial services such as Google Street View and introduction of crowdsourced alternatives), we believe that
such research is needed and timely. This study tests a hypothesis that CV and SVI coupled together have a strong
potential to overcome the issues faced by conventional methods in gauging how friendly urban streets are to cyclists.
Further, as the availability of SVI is now considered to be virtually omnipresent, a notable research gap is comparative
studies that involve more than one city. Thus, in this study we aim to answer the following research questions: can we
use CV techniques and SVI data to comprehensively assess bikeability within and among cities? If yes, can SVI and CV
alone be used to assess bikeability, replacing traditional techniques entirely? To answer these questions, we develop a
bikeability index with 34 indicators under ﬁve categories and implement it in Singapore and Tokyo. The contributions
of our study are the novel investigation of CV techniques’ and SVI indicators’ usefulness in comprehensive bikeability
assessment, construction of a new comprehensive bikeability index regarding an unprecedented amount of aspects,
and provision of a new data collection method that can extract subjective and objective indicators from street-level
information at a larger spatial scale and across multiple geographies, thereby overcoming the issues found by the
previous studies. We believe that the method and the index may be scaled around the world, including additional cities
in future work. It is important to note that our method includes also a survey with a large number of human participants
to investigate whether CV techniques may estimate human perception of cycling appeal automatically.
In Section 2, we conduct a comprehensive literature review to aﬃrm the research gap. The comprehensive and
structured overview of the state of the art is another contribution of this paper. Section 3explains the data sources and
methodologies used in this study. Section 4describes the results of this study together with a discussion. Finally, we
conclude our study as well as discuss further directions for future research in Section 6.
2. Related work
2.1. Bikeability studies
Many studies have explored various aspects of the built environment that can inﬂuence people’s cycling behavior
(Bauman et al.,2012;Nielsen and Skov-Petersen,2018;Pritchard et al.,2019;Daraei et al.,2021;Kraus and Koch,
2021;Nazemi et al.,2021;McNeil,2011;Ma and Dill,2017;Cicchino et al.,2020;Nogal and Jiménez,2020;Sottile
et al.,2019;Berger and Dörrzapf,2018;Porter et al.,2018;Aldred et al.,2020;Long and Zhao,2020;Doubleday et al.,
2021;Brüchert et al.,2020;Martin et al.,2021;Attard et al.,2021). Studies on the association of the built environment
and cycling conditions became well-established, and many researchers developed indexes to assess speciﬁc aspects
of the built environment that can aﬀect cycling behavior and comprehensively quantify bikeability, i.e. the extent to
which an environment is friendly for bicycling.
In the early days of related work between 2010 and 2015, methodologies to collect data are mostly conducted
through ﬁeld surveys, thus tending to be time- and resource-intensive. Studies by Hoedl et al. (2010), Horacek et al.
(2012), and Koh and Wong (2013) involve ﬁeld observation by experts, and such a methodological limitations precludes
bikeability assessment at large scales. Moreover, only a few studies (Koh and Wong,2013) include both objective and
First Author et al.: Preprint submitted to Elsevier Page 2 of 27
subjective indicators, and most of the studies (Wahlgren et al.,2010;Hoedl et al.,2010;Horacek et al.,2012) only
focused on either one of the indicators. The spatial granularity of sample points is loosely deﬁned and not standardized
in some studies as well (Wahlgren et al.,2010;Horacek et al.,2012).
Recent bikeability studies have become more standardized and scalable. Although some studies still use ﬁeld ob-
servation (Manton et al.,2016;Cain et al.,2018), more studies apply emerging technologies and data sources such
as remote sensing images, manual virtual auditing using SVI, and crowdsourcing, to collect data (Krenn et al.,2015;
Gullón et al.,2015;Winters et al.,2016;Kalvelage et al.,2018), enabling large-scale and comparative assessment of
bikeability. However, as data collection methods become more scalable, subjective indicators are excluded by many
studies (Krenn et al.,2015;Manton et al.,2016;Winters et al.,2016;Cain et al.,2018;Kalvelage et al.,2018). More-
over, remotely sensed imagery cannot capture street-level information, and manual virtual auditing and crowdsourcing
data collection require a large amount of time and resources. For example, a recent study by Arellana et al. (2020)
utilizes virtual auditing in SVI, but this data collection process was reported to be six months long for a city-scale study
area, suﬀering from the same issue of a time-intensive method as the aforementioned studies. To overcome the issue of
the balance between street-level information and scalability, recent studies couple SVI with CV techniques to automate
the indicator extraction (Gu et al.,2018;Tran et al.,2020), but the number of indicators extracted by such methods
remains still limited. Moreover, subjective indicators for bikeability assessment have not been extracted from SVI by
using CV yet. Therefore, there is a need for further studies to examine the possibility of extracting more indicators —
both objective and subjective — from SVI by using CV.
Structuring the rundown on related work, Table 1 summarizes indicators used in the reviewed studies. Most of
the indicators could be categorized into connectivity, environment, infrastructure, vehicle–cyclist interaction (V–C
interaction), and perception. In developing bikeability indexes, the previous studies have faced the issues of the time-
intensive data collection process, the balance between subjectivity and objectivity, extraction of street-level informa-
tion, and standardization of spatial granularity. Table 2 summarizes the issues mentioned above. Literature reviews
on bikeability by Kellstedt et al. (2021) and Castañon and Ribeiro (2021) indicate that the past development of bike-
ability assessment has been driven by innovative uses of advanced new technologies, thereby suggesting that newer
technologies may overcome these issues mentioned above. While there have been studies that have utilized SVI and
CV techniques to assess bikeability, and thus indicating the reliability of SVI as a data source that can be used in
a scalable manner, they suﬀer from shortcomings that we seek to mitigate in our work. Primarily, previous studies
used these technologies to assess very limited aspects of bikeability (i.e. only up to a few indicators), did not collect
and assess subjective indicators, and have been evaluated on limited areas. Such a gap necessitates further studies to
examine how much SVI and CV techniques are usable to assess bikeability comprehensively.
2.2. Street view imagery in urban studies
The growth of the spatial coverage of street view imagery and the development of computer vision techniques have
catalyzed the recent proliferation of studies that utilize them, both in transportation and urban planning and beyond
(Wang et al.,2019b;Song et al.,2020;Wu and Biljecki,2021;Ye et al.,2020;Fan et al.,2021;Chen et al.,2021a).
This section focuses on studies that use SVI to extract information of data used in previous bikeability studies under
four categories that have been delineated by this study (i.e. environment, infrastructure, vehicle-cyclist interaction, and
perception), and to examine subjects pertaining to cycling.
One of the more explored aspects in related research is urban greenery. Quantiﬁcation of greenery by using image
segmentation, CV techniques that can classify categories of objects at a pixel-level, has enabled many various urban
studies ranging from simple assessment of the distribution of vegetation and interdisciplinary examinations of the
relationships between greenery and various aspects of cities, such as physical activities of residents and real estate (Ye
et al.,2019a;Lu,2019;Ye et al.,2019b). Other studies quantify greenery, sky view factor (i.e. openness), and buildings
(i.e. enclosure) with semantic segmentation to measure characteristics of cities in a scalable manner (Li et al.,2017;
Gong et al.,2018,2019;Li and Ratti,2019;Toikka et al.,2020;Wang and Vermeulen,2020;Ma et al.,2021;Zhou
et al.,2021). These features that are extracted from SVI, which have been leveraged for a variety of applications, can
be also used to evaluate bikeability.
The high scalability of CV techniques and SVI has also multiplied opportunities for infrastructure assessment at a
city scale. Hall et al. (2018) propose a methodology to detect and classify traﬃc signals, and Chacra and Zelek (2018)
develop a CV-based model to detect infrastructure anomalies from SVI. Assessment of urban accessibility is conducted
by Najaﬁzadeh and Froehlich (2018), developing models to detect accessibility problems from SVI, such as missing
curb ramps and street surface issues. Further, Ding et al. (2021) use object detection and classiﬁcation CV models to
First Author et al.: Preprint submitted to Elsevier Page 3 of 27
map bike lane networks from SVI. Mapillary, a crowdsourced SVI service, developed a dataset called Vistas, which
contains 25,000 images collected from around the world, and annotated according to 66 categories (Neuhold et al.,
2017). Cityscapes is another frequently used street-level dataset for segmentation (Cordts et al.,2016;Gong et al.,
2019;Nagata et al.,2020). Although Mapillary Vistas has many infrastructure-related categories (e.g. bike lane and
bike parking) that other segmentation training datasets do not regard, it has not been widely used in urban studies.
Thus, using this dataset might expand the prospects of SVI for bikeability assessment.
These techniques have been utilized in transport studies as well. Goel et al. (2018) ﬁnd that the number of cyclists
manually counted in GSV images is strongly correlated with the cycling mode share reported by cities in the UK
(r = 0.92). Zhang et al. (2019a) and Chen et al. (2020a) also examine the relationship between visual features and
urban traﬃc volume and reveal that SVI can be used to explain more than 65 percent of the spatiotemporal mobility
pattern. These ﬁndings hint that SVI may be used to estimate traﬃc volume, which is an important aspect of bikeability
(Labetski and Chum,2020).
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An overview of indicators used by previous studies grouped into ﬁve categories.
Publication Connectivity Environment Infrastructure V-C interaction Perception
Clifton et al. (2007) Cul-de-sac Land use Sidewalk Attractiveness
Continuity Slope Pavement Traﬃc speed Safety
Greenery Path obstruction Street parking Cleanliness
Enclosure Sidewalk buﬀer Traﬃc control
Building design Road condition
Setback Curb cuts
Hoedl et al. (2010) N/A Greenery Bikelane N/A
Land use Sidewalk Traﬃc speed
Billboards Traﬃc volume
Wahlgren et al. (2010) Directness Air quality N/A Traﬃc speed Attractiveness
Intersection Noise Traﬃc volume Crowdedness
Greenery Cylist speed Safety
Slope Traﬃc separation Beauty
Horacek et al. (2012) N/A Slope Pavement Traﬃc speed Beauty
Street light Traﬃc volume
Potholes Traﬃc control
Koh and Wong (2013) Detour Slope Directional sign N/A Safety
Intersection POIs Pavement Crowdedness
Gullón et al. (2015) Diﬀerent routes Greenery Pavement Cleanliness
Land use mix Street amenity Traﬃc control Beauty
Street light Attractiveness
Krenn et al. (2015) N/A Greenery Bike lane Traﬃc separation N/A
Manton et al. (2016) Intersection N/A Road width Traﬃc volume N/A
Winters et al. (2016) Intersection Slope Bike lanes Mode share N/A
Distance to POIs
Hartanto et al. (2017) Intersection Water Road type Traﬃc speed N/A
Directness Greenery Pavement Traﬃc volume
Buildings Street light
Slope Bike parking
Cain et al. (2018) Intersection Land use Transit facilities Traﬃc control N/A
Informal path No. of pedestrian Roll-over curb
Cul-de-sac Water Street amenity
Landscape Bike parking
Hardscape Curb cuts
Graﬃti Bike lane
Shade Road width
Building design Sidewalk buﬀer
Gu et al. (2018) Street density Shade Bike lane Traﬃc separation N/A
Arellana et al. (2020) N/A Slope Bike lane Traﬃc control N/A
Greenery Sidewalk Mode share
Building design Street obstruction Traﬃc speed
Crime presence Bike lane width
Tran et al. (2020) Directness POIs Bike lane N/A N/A
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Characteristics of methods used by previous studies.
Publication Data Weighting system No. of indicators
Clifton et al. (2007) Objective & Individual assessment 0 Yes
Hoedl et al. (2010) Objective Individual assessment 0 Yes
Wahlgren et al. (2010) Subjective Individual assessment 0 Yes
Horacek et al. (2012) Objective Unequal arbitrary weight 0 Yes
Koh and Wong (2013) Objective & Survey-based weight 0 Yes
Gullón et al. (2015) Objective & Unequal arbitrary weight 0 Yes
Krenn et al. (2015) Objective Equal weight 0 No
Manton et al. (2016) Objective Regression modeling 0 Yes
Winters et al. (2016) Objective Equal weight 0 No
Hartanto et al. (2017) Objective Equal weight 0 Yes
Cain et al. (2018) Objective Unequal arbitrary weight 0 Yes
Gu et al. (2018) Objective Entropy weight 3 No
Arellana et al. (2020) Objective Survey-based weight 0 Yes
Tran et al. (2020) Objective Equal weight 3 Yes
Extraction of information from images has also enabled the prediction of urban perception based on SVI. Naik
et al. (2014) conduct surveys on safety, in which 7,872 unique participants from 91 countries ranked 4,109 images
using 208,738 pairwise comparisons. Dubey et al. (2016) develop a dataset called Place Pulse 2.0, which consist of
110,988 images from 56 cities and 1,170,000 pairwise comparisons answered by 81,630 online survey participants on
six perception scores, and predicted these scores with convolutional neural network models, such as VGGNet, and this
study’s dataset and methodology have been utilized and replicated by other studies (Kang et al.,2021;Qiu et al.,2021).
Yao et al. (2021) conduct a similar study to predict perception scores by surveying 20 volunteers on 1,000-2,000 images
and predicting perception scores using a random forest model and features extracted by FCN-8s as inputs. Verma et al.
(2020) also recruit only 79 participants to rate 200 images, extracting high- and low-level features from SVI by using
CV techniques (e.g. image segmentation, object detection, classiﬁcation, and edge detection), and designing models
to predict fourteen perception scores, such as “pleasant”, “boring”, and “safe”. Moreover, the validity of conducting
perception surveys has been examined and found to be as reliable as surveys based on the real environment by Feng
et al. (2021).
Lu et al. (2019) and Wang et al. (2020) examine the association between urban greenery (extracted from SVI) and
cycling behaviors, and Hollander et al. (2020) study the correlation between transportation planning and the perceived
safety of the built environment. A study by Tran et al. (2020) is the only study that relies on SVI to create a bikeability
index, but this study uses SVI only to assess very limited aspects of bikeability, such as greenery and enclosure.
Therefore, there has not been any study that developed a comprehensive bikeability index mirroring the wide array of
aspects jointly covered by related work and one that has utilized SVI as a major data source to calculate it, a gap that
is bridged by our study.
The literature review elucidates the proliferation of urban studies that utilize SVI and CV techniques and, at the
same time, it exposes the absence of studies that take advantage of them for assessing cycling appeal and developing
a bikeability index. Such omission is possibly caused by obstacles of using CV techniques and the complexities
of developing a comprehensive bikeability index system. However, previous studies demonstrate the possibility of
replacing traditional data collection for bikeability indicators, as they collect information on similar aspects that may
indicate cycling appeal.
This paper aims to bridge this gap by designing a thorough bikeability index that is largely derived using SVI and
CV, and it also uses the opportunity to critically investigate their value and independence when doing so. Moreover,
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as SVI data has virtually global coverage, the study puts scalability under the spotlight; thus, the combination of
scalability of SVI and eﬃcient extraction of indicators through CV can reduce the time and resource cost of bikeability
assessment compared to conventional data sources and methods. Finally, potential barriers of this approach for urban
planners are also examined to understand its application and prospects for adoption.
This study uses six data sources (i.e. SVI, surveys, OpenStreetMap (OSM), Land Use (LU), Digital Elevation
Model (DEM), and Air Quality Index (AQI)) to evaluate 34 indicators under ﬁve categories (i.e. connectivity, envi-
ronment, infrastructure, perception, and vehicle-cyclist interaction). The comprehensive selection of these indicators
is a contribution on its own, as its scope is unprecedented, and may serve as a resource to develop indexes in other
domains. Testing the scalability of the method, Singapore and Tokyo, as two geographies with disparate characteris-
tics, are selected as study areas. Thus, data retrieval and indicator extractions are conducted to develop the composite
index by weighting each category and indicator equally. Figure 1 highlights some examples of phenomena in SVI that
are characterized in this method.
3.1. Selection of indicators
Conducting a review of studies that developed bikeability indexes, this study devised an exhaustive list of indicators
used by them (Hartanto et al.,2017;Winters et al.,2016;Gullón et al.,2015;Wahlgren et al.,2010;Horacek et al.,
2012;Hoedl et al.,2010;Clifton et al.,2007;Manton et al.,2016;Cain et al.,2018;Koh and Wong,2013). This
inventory was used to create an own index to expand related work and minimize the bias when selecting indicators.
Duplicates and those indicators that cannot be obtained and/or are unsuitable for this study have been excluded, such as
noise and the presence of informal paths and crimes. After ﬁltering, out of 65 unique indicators found in the previous
studies in total, 34 indicators (i.e. about 52%) were kept, forming the most extensive instance to date. Future studies
can use our method while partially modifying this list to incorporate more or fewer indicators as long as they can be
extracted from the data sources we used.
These 34 unique indicators are categorized into ﬁve categories, namely, connectivity, environment, infrastructure,
perception, and vehicle-cyclist interaction (see Table 3). Regarding data sources, 21 indicators are to be extracted from
SVI, 10 from OSM, one from each of LU, DEM, and AQI.
Although this study does not examine if the selected indicators can predict bike usage, most of the reviewed previous
studies also selected them based on literature reviews, except for a few (Winters et al.,2016;Arellana et al.,2020) that
use regression analysis. Moreover, higher bikeability does not necessarily lead to higher counts of bike usage. For
example, Arellana et al. (2020) examine and ﬁnd that other factors, such as population density and socio-economic
characteristics, also play a role (Munira et al.,2021). Therefore, investigating whether the bikeability index can predict
bike usage is out of the scope of this research.
Table 3 lists indicators with their data sources, extraction methods, and scaling methods. For scaling methods,
min-max scaling and negative min-max scaling were used (see Equation 1 and Equation 2). The sampling method is
further explained in the following sections.
𝑥Min-Max Scaled =𝑥− min(𝑥)
max(𝑥) − min(𝑥)(1)
𝑥Negative Min-Max Scaled = 1 − 𝑥− min(𝑥)
max(𝑥) − min(𝑥)(2)
3.2. OSM and SVI data retrieval
This study relies on OSM to retrieve information on indicators and also for the retrieval of locations on streets that
are used to fetch street view images. The completeness of OSM in the study area is deemed adequate (Barrington-Leigh
and Millard-Ball,2017;Biljecki,2020). To retrieve the OSM data, OSMnx (Boeing,2017) was used, obtaining about
250,000 points and 440,000 points for Singapore and Tokyo, respectively. After that, 7,142 points were randomly
selected. The number of points to be collected was set at 7,142 because this is the maximum number of SVI images
that can be retrieved within the free credit provided by GSV API every month. Future studies can also utilize the
initial trial credit as well, but if there are ﬁnancial constraints, the rise of volunteered SVI, such as KartaView and
Mapillary, may ameliorate this issue in the future. For this study, the API of GSV was used to collect images because
of its extensive coverage and high quality in both metropolises.
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Indicators forming the comprehensive bikeability index introduced by this study.
Indicators Data Extraction Scale
No. of intersection with lights OSM Aggregation (500m) 0-1 (Negative Min-Max)
No. of intersection without lights OSM Aggregation (500m) 0-1 (Negative Min-Max)
No. of cul-de-sac OSM Aggregation (500m) 0-1 (Negative Min-Max)
Slope DEM Calculation 0-1 (Negative Min-Max)
No. of POI OSM Aggregation (500m) 0-1 (Min-Max)
Shannon land use mix index Land use Aggregation (500m) 0-1 (Min-Max)
Air quality index AQI Spatial interpolation 0-1 (Negative Min-Max)
Scenery: greenery SVI Segmentation 0-1 (Min-Max)
Scenery: buildings SVI Segmentation 0-1 (Min-Max)
Scenery: water SVI Segmentation 0-1 (Min-Max)
Type of road OSM Aggregation (100m) service, track = 0.1
primary, primary_link = 0.2
secondary, secondary_link = 0.4
tertiary, tertiary_link = 0.5
unclassiﬁed = 0.6
pedestrian path = 0.8
cycleway = 1
Others = 0
Presence of potholes SVI Segmentation 1 if not present
0 if present
Presence of street light SVI Segmentation 1 if present
0 if not present
Presence of bike lanes SVI Segmentation 1 if present
0 if not present
No. of transit facilities OSM Aggregation (500m) 0-1 (Min-Max Scale)
Type of pavement OSM Aggregation (100m) unhewn_cobblestone, cobblestone = 0.2
sett, metal, wood = 0.4
paved = 0.5
concrete:lanes, plates, paving_stones = 0.6
asphalt, concrete = 1
Others = 0
Presence of street amenities SVI Segmentation 1 if present
0 if not present
Presence of utility pole SVI Segmentation 1 if not present
0 if present
Presence of bike parking SVI Segmentation 1 if present
0 if not present
Road width OSM Aggregation (100m) 0-1 (Divide by 10)
1 if width is larger than 10m
Presence of sidewalk SVI Segmentation 1 if present
0 if not present
Presense of crosswalk SVI Segmentation 1 if present
0 if not present
Presence of curb cuts SVI Segmentation 1 if present
0 if not present
Attractiveness for cycling SVI Surveys, Modeling 0-1 (Min-Max)
Spaciousness SVI Surveys, Modeling 0-1 (Min-Max)
Cleanliness SVI Surveys, Modeling 0-1 (Min-Max)
Building design attractiveness SVI Surveys, Modeling 0-1 (Min-Max)
Safety as a cyclist SVI Surveys, Modeling 0-1 (Min-Max)
Beauty SVI Surveys, Modeling 0-1 (Min-Max)
Attractiveness for living SVI Surveys, Modeling 0-1 (Min-Max)
No. of vehicles SVI Detection, Aggregation (500m) 0-1 (Negative Min-Max)
Presence of on-street parking OSM Aggregation (100m) 1 if not present
0 if present
Presence of traﬃc lights / stop signs SVI Segmentation 1 if present
0 if not present
No. of speed control devices OSM Aggregation (100m) 1 if present
0 if not present
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3.3. Extraction of indicators
The feature extraction process for indicators diﬀers among diﬀerent categories and data sources. Feature extractions
for land use, topography, and AQI are relatively straightforward. For land use, we use an entropy formula derived from
the Shannon index developed by Frank et al. (2005) within 500m buﬀers from sample points (see Equation 3).
𝐿𝑈 𝑀 = −1 𝑛
𝑝𝑖∗ ln(𝑝𝑖)∕ ln(𝑛)(3)
In this formula, 𝐿𝑈 𝑀 is the land-use mix score, 𝑝𝑖 is the proportion of the neighborhood covered by the land use
𝑖against the total area for all the land-use categories, and 𝑛is the number of land-use categories. A land-use mix score
of 1 indicates the highest mix possible while a score of 0 indicates the area contains a single land use. This results in
values between 0, the lowest mixed-use level, and 1, the highest mixed-use level.
For topography, the slope is calculated from DEM data created by Yamazaki et al. (2017) and sampled at each
sample point, and the value was scaled with min-max scaling.
AQI data is collected from the Air Quality Index1by automating the retrieval of stations and AQI data in both cities
with a Python library called Selenium. The annual average AQI of stations in the cities is calculated by taking the daily
maximum value of various pollutants (e.g. PM2.5) in the unit of µg/m3, and then spatial interpolation was conducted
to estimate the average AQI at the sample points in this study by using inverse distance weighted interpolation (see
In this formula, 𝑧𝑝denotes the interpolated values of the target points, 𝑛represents the number of points used to
interpolate from, 𝑧𝑖shows the value being interpolated from, and 𝑑𝑝
𝑖is the distance between the target point and the
point being used to interpolate from. The calculated AQI is then scaled into values between 0 and 1 by using min-max
Feature extractions from OSM are conducted by taking buﬀers from sample points. For the density of intersection
with/without lights, cul-de-sac, shops along the route, and transit facilities, we created 500m buﬀers from sample
points and aggregated the number of these indicators because we need to consider the surrounding contexts as well.
Other indicators collected from OSM, such as the type of roads and the type of pavement, are collected by creating
100m buﬀers from sample points and converting categorical values to numerical if necessary.
Finally, this study conducts feature extractions from SVI in two ways. For objective indicators, two CV techniques
— segmentation and object detection — were used to extract features. For segmentation, the In-Place Activated
BatchNorm model trained on Mapillary Vistas with WideResNet38 and DeepLab3 developed by Bulò et al. (2018) is
selected because of its high accuracy of the mean intersection over union of 53.42%. Indicators such as the scenery
along bike lanes (i.e. built-up area, greenery, sky view factor, water) are quantiﬁed by calculating the ratios of the
pixels categorized as them over the total number of pixels in the image, which are then scaled into between 0 and 1
with min-max scaling. Because it is not meaningful to calculate pixels of other objects such as street lights, bike lanes,
street amenities, and bike parking, they are quantiﬁed in a binary manner (i.e. score 1 if they are in the image and 0
if they are not in the image). Object detection is used for one indicator, that is, the number of vehicles, as a previous
study suggests that SVI can be used to estimate the traﬃc in the area (Zhang et al.,2019a). For this task, we opt for the
GluonCV’s model zoo developed by Guo et al. (2020) and select a YOLOv3 model pre-trained on Pascal VOC dataset
with Darknet53 as the base model, which can detect the number of bicycles, pedestrians, and vehicles with an average
precision of 58.2% at the intersection over a union of 0.5. We chose this model because of its reported relatively
high speed and accuracy compared to other models, such as Faster Region-based Convolutional Neural Networks and
Single Shot Detection (Srivastava et al.,2021;Li et al.,2020). Moreover, the core of our paper is separate from the
models, thus if necessary, new models can be used in any part of this methodology, as our approach is model-agnostic.
First Author et al.: Preprint submitted to Elsevier Page 9 of 27
Because a street view image captures only the traﬃc volume of the road segment at that moment, it is not entirely
reliable to estimate the traﬃc based on just one photo at a point. Therefore, a buﬀer of 500m was generated for each
point, aggregating the number of vehicles found in the plentiful imagery in the buﬀer. After this processing, the mode
share was also scaled into values between 0 and 1 by min-max scaling. A previous study (Chen et al.,2020a) uses a
similar method to estimate the number of pedestrians and obtains Cronbach’s alpha above 0.8, indicating SVI’s high
reliability as a data source. However, it should be noted that the number of images within each buﬀer varies because
the sample points were randomly selected, which might produce some bias. Also, duplication of vehicles detected
in SVI can cause bias because some vehicles could have driven together with the vehicles collecting the street view
images. The aforementioned study (Chen et al.,2020a) did not consider this issue and still obtained high reliability;
thus, we leave improvement in methodology to detect the same vehicles with models such as Siamese-convolutional
neural network for future studies.
Subjective indicators are under the perception category, including attractiveness for cycling, spaciousness, cleanli-
ness, building design attractiveness, and perception of safety as a cyclist, and these indicators were predicted by using
features extracted from SVI. We follow the methodology used by Verma et al. (2020), where high- and low-level fea-
tures of images are used to predict perceptions of images (see Table 4). Low-level features can be extracted through
edge detection, blob detection, and Hue-Saturation-Lightness (HSL) extraction. Edges are quantiﬁed by detecting
edges and calculating the ratio of pixels that are categorized as edges over the total number of pixels. Blob detection
was used to calculate the number of blobs in SVI, and HSL extraction is conducted to calculate the average and stan-
dard deviation of each hue, saturation, and lightness. It should be noted that lightness might have some bias because of
variances in the time when the images were collected, although GSV has a standardized data collection procedure and
post-processing of images (Google,2018). As for high-level features, image classiﬁcation (IC), object detection (OD),
and semantic segmentation (SS) are used to extract them. For OD and SS, this study uses the same models mentioned
above, and a ResNet50 model trained on Places365 data with an accuracy of 85.07% is used for IC (Zhou et al.,2018).
These extracted features are dependent variables to predict the perception indicators.
To collect the training data set and build models to predict indicators suggesting perception, a survey is conducted
on Amazon Mechanical Turk, for which this study has received an exemption from the Institutional Review Board
of the National University of Singapore, and in which the participants were compensated ﬁnancially. For each city,
400 SVIs, i.e. 800 in total, were randomly selected for the survey, which was designed to have at least eight diﬀerent
participants rate the images on the ﬁve indicators on a scale of 0 to 10. The large number of participants to rate each
image ensures reliability. The occasional and inevitable outliers among the responses were detected with the median
absolute deviation method and removed when the output is above three (see Equation 5).
MAD = median 𝑋𝑖−̃
𝑋𝑖denotes each observation, and ˜
Xrepresents the median of all the observations.
The collected dataset is split into training and validation data sets in an 80:20 ratio to build predictive models with
LightGBM (Ke et al.,2017), which is gaining momentum in urban studies (Chen et al.,2021b) for its high accuracy
and low computational cost in training (Zhang et al.,2019b;Deng et al.,2018). In this study, we tuned the following
hyperparameters with 10-fold cross-validation: num_leaves, max_depth, min_child_samples, min_child_weight, sub-
sample, colsample_bytree. After conducting the prediction for the rest of the points, the predicted perception indicators
are scaled into values between 0 and 1 with min-max scaling.
Moreover, this study explores the relationships between all the features and perception scores by grouping obser-
vations into below- and above-average values of features and conducting Welch’s t-test (Delacre et al.,2017). We aim
to reveal the underlying visual eﬀects of each feature on human perceptions.
3.4. Development of a new composite index to assess bikeability
The composite index of this study, one of its advancements and key contributions, is developed based on conﬂating
bikeability indexes developed by previous studies. There are three types of weighting systems developed by them,
which warrant a brief overview. One of them is the independent assessment of indicators. This method only evaluates
each indicator but does not give weights to them, and it is used by Wahlgren et al. (2010) and Hoedl et al. (2010).
Another type is the arbitrary weight. This method gives arbitrary weights to categories and indicators, which is adopted
by Cain et al. (2018); Horacek et al. (2012). The last type is equal weight. This method gives equal consideration to
all the categories and indicators, which actually — strictly speaking — belongs to the arbitrary weighting system, but
First Author et al.: Preprint submitted to Elsevier Page 10 of 27
Features for predicting perception.
Visual features Deﬁnitions
tree_ss % of pixels classiﬁed as trees.
sky_ss % of pixels classiﬁed as the sky.
street_ss % of pixels classiﬁed as street and sidewalks.
built_ss % of pixels classiﬁed as buildings.
others_ss % of pixels classiﬁed as other remaining outdoor classes.
nature % of pixels classiﬁed as natural elements such as sky, tree, and water.
shannon Shannon entropy values calculated on SS task.
slum_ic Probability of being classiﬁed as Slum/Alley/Junkyard in IC task.
market_ic Probability of being classiﬁed as Bazaar/Flea market/Market in IC task.
built_other_ic Probability of being classiﬁed as Downtown/Embassy/Plaza in IC task.
green_other_ic Probability of being classiﬁed as Forest path/Forest road in IC task.
bicycle_od No. of bicycles detected in OD task.
bus_od No. of buses detected in OD task.
car_od No. of cars detected in OD task.
motorcycle_od No. of motorcycles/scooters detected in OD task.
person_od No. of persons detected in OD task.
traﬃc_light_od No. of traﬃc lights detected in OD task.
truck_od No. of trucks/auto rickshaws detected in OD task.
canny_edge_llf % of pixels detected as edges.
no_of_blobs_llf No. of blobs.
hue_mean_llf The mean value of the hue dimension in HSL color space.
hue_std_llf The standard deviation of the hue dimension in HSL color space.
lightness_mean_llf The mean value of the lightness dimension in HSL color space.
lightness_std_llf The standard deviation of the lightness dimension in HSL color space.
saturation_mean_llf The mean value of the saturation dimension in HSL color space.
saturation_std_llf The standard deviation of the saturation dimension in HSL color space.
this study diﬀerentiated them for clarity. This method is used by Hartanto et al. (2017), Winters et al. (2016), and
Tran et al. (2020), and it is a common system among the reviewed studies. This study adopts the equal weight system
for its simplicity and scaled all the indicators into values between 0 and 1 to prevent any indicators from excessively
inﬂuencing the composite index at the end (see Equation 6).
𝑖=1 𝑥𝑖∗ (100∕(𝑁𝑐∗𝑁𝑐𝑖 ))(6)
In this equation, Index represents the bikeability index, 𝑥𝑖denotes the value of each indicator 𝑖,𝑁𝑐show the number
of categories, and 𝑁𝑐𝑖 stand for the number of indicators in the respective category.
Facilitating a critical analysis of the value of SVI over non-SVI counterparts, as one of the principal aims of this
study, the following indexes with diﬀerent types of indicators are designed:
1. Index with SVI indicators and non-SVI indicators
2. Index with only SVI indicators
3. Index with only non-SVI indicators
These indexes and their sub-categories were compared with each other to examine how much SVI and non-SVI indi-
cators explain the overall variance. Finally, Figure 2 sums up the methodology.
First Author et al.: Preprint submitted to Elsevier Page 11 of 27
Figure 2: Illustration of the methodology of this study.
First Author et al.: Preprint submitted to Elsevier Page 12 of 27
Figure 3: Distribution of indicators describing connectivity across numerous locations in Singapore and Tokyo.
4.1. Data collection
For OSM data, street network data and point data were retrieved. For street network data, 252,369 street segments
and 450,379 street segments were retrieved for Singapore and Tokyo, respectively. As for point data, 12,640 POIs
and 157 mass rail transit stations were collected from Singapore, 75,203 POIs, and 606 stations were collected from
Tokyo. For SVI, after removing indoor images and grey images from 7,142 images, 5,833 and 6,181 panorama images
remained for the two cities.
For LU, each city’s open data by the local government was used (Tokyo Metropolitan Government,2018;Singapore
Government,2020b). Because the categorizations of LU in each city were diﬀerent, LU categories were harmonized
to residential, commercial, and industrial in this study, and other categories were excluded. AQI data in 2020 are
obtained for 5 and 126 stations in the city-state and the Japanese capital, respectively.
4.2. Extracted indicators and composite index
Connectivity is assessed based on the number of intersections with lights, intersections without lights, and cul-de-
sacs. Comparing Singapore and Tokyo, the latter achieves higher scores for connectivity (see Table 6 and Figure 8).
Due to a large number of indicators not all of them can be detailed in this paper, but as an example, Figure 3 illustrates
the distribution of values in this particular indicator. The score for the number of intersections without traﬃc lights is
much lower for Singapore while the other two indicators have a similar distribution.
The environment is evaluated based on slope, the number of POIs, land use mix, AQI, and pixel ratios of greenery,
buildings, and water, aiming to evaluate the natural and built environment. Compared to Singapore, Tokyo has a
higher mean score for this category (see Table 6 and Figure 8). The distributions of each indicator suggest that Tokyo
obtains noticeably higher scores for slope, land use, the pixel ratio of buildings. Such results reﬂect Tokyo’s relatively
First Author et al.: Preprint submitted to Elsevier Page 13 of 27
ﬂat topography, organic distribution of land uses, and densely built buildings that create more enclosures for cyclists.
Singapore achieved higher scores in AQI and greenery, which also reﬂects its strategic environmental management as
a garden city (Han,2017;Palliwal et al.,2021).
Infrastructure is evaluated based on the type of road and pavement, the width of the road, number of transit facil-
ities, and presence of potholes, street lights, bike lanes, street amenities, utility poles, bike parking spaces, sidewalks,
crosswalks, and curb cuts. This category aims to comprehensively evaluate various elements in the realm of infrastruc-
ture. Compared to Tokyo, Singapore achieves a much higher mean score for this category (see Table 6 and Figure 8).
Singapore obtains higher scores in the type of pavement (i.e. surface), the presence of street amenity, and, especially,
utility poles, which show the nearly opposite result from Tokyo. This result reﬂects Tokyo’s issue with many utility
poles, which not only deteriorates the beauty of the city but also can cause obstruction for pedestrians and cyclists
(Inajima and Urabe,2017).
4.2.4. Vehicle-cyclist interaction
Vehicle-cyclist interaction was evaluated based on the number of vehicles and speed control devices and the pres-
ence of on-street parking and traﬃc lights/stop signs. This category aims to assess how safely cyclists interact with
vehicles; thus, fewer and slower traﬃc leads to higher scores. The mean scores for both cities are also similar, while
Singapore has a slightly higher mean and standard deviation (see Table 6 and Figure 8). The analysis suggests very
small variances in the indicators and similar distributions for both cities. Such a result can potentially be explained:
Singapore and Tokyo have similar restrictions on vehicles; for example, on-street parking is strongly discouraged in
both cities (Barter,2010;Russo et al.,2019), and the numbers of vehicles per capita are similar: 0.22 and 0.17 (Barter,
The perception is evaluated based on attractiveness for cycling, spaciousness, cleanliness, building design attrac-
tiveness, safety as cyclists, beauty, attractiveness for living. In this category, we will discuss the survey result, the
relationships between perception scores and high- and low-level features, the result of predictive modeling, and the
result of inferences from the models.
Surveys on 800 images, 400 images from each city, were conducted on Amazon Mechanical Turk to recruit eight
unique participants to rate each image’s perception scores mentioned above on a scale of 0 to 10. Before proceed-
ing further, it is important to assert that while our work strives for a high degree of automation, as in most studies
involving machine learning, a portion of the work is manual, i.e. the labeling of training data was done through crowd-
sourcing (Section 3.3). However, after the predictive modeling using the data obtained from the survey, the process of
determining the scores was automated.
The results of the survey indicate strong positive correlations among the perception scores, ranging from 0.58 to
0.79 in squared R, and exhibit no visible skewness in the data distribution (see Figure 4 and Figure 5). Figure 6 indicates
the results of the survey after excluding outliers, which reveal similar results for all the scores and illustrates that the
majority of responses are between three and eight. The ﬁgure also suggests that images from Singapore generally had
higher scores across all the measures.
Visual features from SVI are extracted through high-level feature extraction (i.e. semantic segmentation, classiﬁ-
cation, and object detection) and low-level feature extraction (i.e. edge detection, blob detection, and HLS statistics).
After labeling each observation below or above the mean value of each feature, Welch’s t-test is conducted to ﬁnd
features with statistically signiﬁcant eﬀects on perception scores based on the labeling. The total number of extracted
features is 519, and the number of scores is seven; therefore, there are 3,633 unique feature-score pairs.
For beauty, we found that features that contribute to better infrastructures such as curb and terrain contribute to
higher scores, while features that damage beauty such as utility poles and junkyards contribute to lower scores. Also,
for low-level features, larger standard deviations of HLS are associated with lower beauty scores.
As for building attractiveness, we realize that a higher pixel ratio of buildings and classiﬁcation as slums leads to
lower scores, implying that participants did not prefer dense buildings. On the other hand, classiﬁcation as residen-
tial neighborhood and campus leads to higher scores, suggesting residential and education as potential land uses and
typologies that make people feel attracted to buildings.
The result of cleanliness suggests that classiﬁcation as campus and higher pixel ratio of curb leads to higher clean-
First Author et al.: Preprint submitted to Elsevier Page 14 of 27
Figure 4: A scatter plot matrix of the perception scores
obtained from the survey.
Figure 5: A correlation matrix of the perception scores
obtained from the survey.
liness scores, which might imply that these places and infrastructures are relatively better maintained. On the other
hand, classiﬁcation as junkyard and landﬁll led to lower cleanliness scores, which is intuitively understandable because
of their strong associations with garbage. As for cycling attractiveness, natural elements such as terrain and vegetation
as well as classiﬁcation as residential neighborhoods leads to higher cycling attractiveness scores, while utility poles
and construction sites are associated with lower scores. Attractiveness for living obtains a similar pattern to other
scores. A higher pixel ratio of terrain and curb entails higher scores, and classiﬁcation as slum and landﬁll causes
lower scores. The result of safety shows that terrain and campus achieve higher scores and that other features such as
gas stations, junkyards, and slums were associated with lower safety scores. Lastly, spaciousness gains higher scores
when terrain, curb, and campus are present, while other features, such as utility poles, junkyards, and larger standard
deviations of HLS statistics are detrimental.
In this exploratory analysis, some parts of the study by Verma et al. (2020) are replicated such as low associations
among perception scores and edge detection and blob detection. However, other parts of the study such as the strong
inﬂuence of cars in object detection could not be reproduced, suggesting that the geography of the study and/or training
data may play a role. Moreover, more features from image classiﬁcation are found to have an inﬂuence on perception
scores than other features.
Based on the study by Verma et al. (2020), predictive modeling is conducted for each perception score between 0
and 10 with high- and low-level features (see Table 4). Table 5 underscores the results of the modeling with diﬀerent
performance metrics, where one can see low mean absolute error (MAE) around 0.65, mean absolute percentage error
(MAPE) around 0.1, and root mean squared error (RMSE) around 0.8; however, all the 𝑅2values were below 0. This
means that the models were worse than predicting the constant values regardless of input data.
Although Verma et al. (2020) achieves 𝑅2as high as 0.66 by using the same visual features, our study could not
achieve comparable results. This discrepancy might be due to the disparate level of data quality in the SVI training set.
After the modeling, we randomly selected SVI images with higher and lower scores and found that some partially grey
images and indoor images were still in the set despite the eﬀorts to clean the data prior to the modeling. Such noise in
SVI data is not often discussed in previous studies using SVI, and this issue remains to be solved in the future (Biljecki
and Ito,2021). To further improve the prediction, other visual feature extractions and selection methods need to be
explored as well.
After incorporating scores across all categories, bikeability scores were calculated at a ﬁne spatial scale (Figure 7).
In Singapore, bikeability scores are generally distributed homogeneously barring a few outliers. The results for Tokyo
First Author et al.: Preprint submitted to Elsevier Page 15 of 27
Figure 6: Distribution of seven perception scores obtained from the survey by each city.
Results of predictive modeling of perception indicators.
Target_variable MAE MAPE RMSE R2
beauty 0.63 0.10 0.81 -0.20
building_attractiveness 0.63 0.10 0.78 -0.14
cleanliness 0.63 0.10 0.79 -0.12
cycling_attractiveness 0.65 0.11 0.83 -0.18
living_attractiveness 0.69 0.12 0.87 -0.10
safety 0.72 0.12 0.89 -0.29
spaciousness 0.67 0.11 0.83 -0.19
seem to be more heterogeneous, with a lower score in the central area and peripheral areas and higher scores in between.
The distribution of data indicates no skewness in the data, and a comparison between Singapore and Tokyo in Table 6
hints that Singapore achieved a slightly higher mean value and lower standard deviation than Tokyo.
This bikeability index, however, has some issues that need to be exposed. Firstly, the perception prediction’s result
is not entirely reliable. Although MAE, MAPE, and RMSE of models turned out to be moderately acceptable, 𝑅2
values resulted in negative values, indicating the models are more inaccurate than simply predicting the mean values
First Author et al.: Preprint submitted to Elsevier Page 16 of 27
Figure 7: One of the key outputs of this study: maps of bikeability across Singapore and Tokyo, generated from the scores
at numerous locations in the two cities.
of the scores. Low variances among observations in some categories also need to be considered. Summary statistics
shown in Table 6 and data distribution shown in Figure 8 suggest some categories such as vehicle-cyclist interaction
had low standard deviation and skewed data distribution with a few outliers. This poses a question regarding the
appropriate indicators to be used that can diﬀerentiate bikeability scores within and between cities because most of the
sample points obtain highly similar scores for such categories. This question needs to be examined by expanding study
areas in diﬀerent contexts to observe whether this phenomenon is simply caused by similar characteristics of streets in
the Asian context.
4.3. Comparison between SVI and Non-SVI indicators
This study compares three types of indexes to answer one of the research questions gauging the value of SVI in
bikeability studies. Besides the comprehensive index developed with both SVI and non-SVI indicators, indexes with
only SVI and non-SVI indicators are developed and compared with each other. Each category’s score was recalculated
based on the indicator types, and, for bikeability, we compare the indexes by plotting scores developed with diﬀerent
indicators on the same sample points. Connectivity and perception were excluded from this analysis because these
categories only had one type of indicator, only non-SVI indicators, and only SVI indicators, respectively.
Indexes of the environment developed with SVI and non-SVI indicators are not correlated with each other and
that non-SVI indicators are more strongly correlated with the index developed with both of the indicators. On the
other hand, SVI indicators had more inﬂuence on infrastructure and vehicle-cyclist interaction. After combining all
the categories, bikeability scores were compared among diﬀerent indicator types, and SVI indicators turned out to
have a stronger correlation with the index with both indicator types and lower kurtosis than non-SVI indicators (see
Figure 10 and Figure 9). Although this result is not a surprise given the larger number of SVI indicators than non-SVI
indicators, this result indicates the potential of SVI indicators to be used alone to evaluate bikeability because of its
high correlation, 𝑅2of 0.85, with the index with both indicators and capability to explain most of the variance.
Although it was found possible to estimate bikeability only with SVI indicators, some relevant non-SVI indicators
First Author et al.: Preprint submitted to Elsevier Page 17 of 27
Figure 8: Distribution of scores across categories in Singapore and Tokyo.
are either diﬃcult or impossible to replace with SVI. For example, transit facilities, POIs, and land uses are not impossi-
ble but complicated to obtain from SVI and are much more straightforward to collect from OSM and other data sources,
which are anyway frequently available. Also, the slope and air quality are unnecessarily challenging to estimate from
SVI, and other data sources are much less involved to collect and much more accurate than it would be if estimating
them from SVI. Therefore, with these outcomes in mind, it is more beneﬁcial for urban planners and researchers to
combine both SVI and non-SVI indicators to assess bikeability, complementing the best of the two worlds.
5. Challenges and future directions
5.1. Data quality
This study faced several challenges. One of such challenges was the quality of data. This issue surfaced in data
sources, such as SVI, OSM, and AQI. GSV was used to collect SVI in this study, and the sample points of collected
GSV panorama images were randomly selected to cover the entire study areas with a limited number of sample points
of GSV that can be obtained for free. A study by Kim et al. (2021) reported that larger sampling intervals lead to larger
variances of elements that can be extracted from SVI. Therefore, one of the limitations of this study is possible biases
and larger variances of data introduced by random sampling intervals when conducting random sampling. Future
studies can deﬁne shorter sampling intervals to reduce the possible bias. Another possible bias is the perspective of
SVI. Because SVI is collected from vehicles, it might not always represent the typical view of bicyclists, especially for
wide roads. This limitation is also faced by other previous studies on walkability that as well use 360-degree panoramas
for assessment, as they aim to gauge what pedestrians perceive (Wakamiya et al.,2019;Yencha,2019;Nagata et al.,
First Author et al.: Preprint submitted to Elsevier Page 18 of 27
Summary statistics of the bikeability measures calculated for the study area.
Category City Mean Standard deviation
Bikeability Singapore 54.26 3.66
Tokyo 53.98 3.73
Connectivity Singapore 16.63 1.59
Tokyo 18.72 1.70
Environment Singapore 5.83 0.94
Tokyo 6.58 0.94
Infrastructure Singapore 9.88 1.90
Tokyo 7.86 2.36
Perception Singapore 11.42 1.57
Tokyo 10.40 1.74
Vehicle–cyclist interaction Singapore 10.50 2.12
Tokyo 10.42 1.61
Figure 9: A scatter plot matrix of bikeability score
with only SVI and non-SVI indicators.
Figure 10: A correlation matrix of bikeability score
with only SVI and non-SVI indicators.
2020;Villeneuve et al.,2018;Li et al.,2018;Zhang et al.,2018;Weld et al.,2019). In this study, the result showed
that cycling paths are not highly prevalent in both Singapore and Tokyo; thus, the panorama views possibly present
most of the actual cycling perspectives. Although SVI on cycling paths is not widely available currently, future studies
might be able to work on SVI on cycling paths with the rise of crowdsourced SVI services, such as Mapillary.
As for OSM data, while in general of high quality for both locations, its data completeness became a bottleneck
when evaluating some particular indicators (e.g. traﬃc speed and the number of traﬃc lanes) and resulted in their
eliminations, so future studies should explore diﬀerent data sources to collect them.
AQI data are available in both cities, but the number of AQI stations in Singapore, ﬁve, was far fewer than Tokyo,
First Author et al.: Preprint submitted to Elsevier Page 19 of 27
126, and such low spatial granularity of data might have aﬀected the result. Therefore, future studies can incorporate
predictive modeling to estimate AQI for each sample point by using traﬃc data, meteorological data, and land use data
(Chen et al.,2010).
Low variances among sample points for some indicators were found as well. This is potentially problematic because
indicators with extremely low variances cannot diﬀerentiate sample points; thus, this needs to be examined more by
investigating more diverse sets of cities.
Perception modeling was another challenge faced in this study due to the nature of the approach. The compre-
hensive survey was conducted on a service on which the majority of participants are not residents of the study areas,
which may be advantageous but also detrimental. On the one hand, survey results might have been diﬀerent if they
were conducted with residents in the study areas (Difallah et al.,2018). To mitigate the potential bias, future studies
can utilize diﬀerent survey services that can specify the residence of the participants. On the other hand, having par-
ticipants of a cross-city study residing in neither study area may mitigate the bias of residents living in one city but not
being familiar with the other one.
5.2. Required skills
Because the data collection and processing was conducted using Python, this method requires users to have a
moderately advanced understanding of programming. Computational power is also a challenge because this study’s
method uses CV techniques that require graphics processing capabilities, which are not available widely. To mitigate
these limitations, future studies may consider developing a GUI software and an API to allow users to input data and
5.3. Development of the index
This study selected the indicators based on the systematic review. However, purposes of cycling and socio-
economic characteristics might aﬀect the indicator selection and weight assignment, as examined by Arellana et al.
(2020). Moreover, a bikeability assessment based on route simulations between origin and destinations called capability-
wise walkability score has been proposed by a previous study (Blečić et al.,2021). Thus, the incorporation of such
new designs of bikeability index might be able to enhance our method as well.
In previous studies, it has been a challenge to make bikeability assessment scalable while keeping a good balance of
objectivity and subjectivity, and minimizing the work required. The few studies that have used SVI and CV techniques
to automate the process and increase the geographic coverage rather assessed limited aspects of bikeability and have
not done so critically, nor have they investigated the inclusion of multiple cities.
We advanced the comprehensive assessment of bikeability using street view imagery and computer vision. The
contributions of our study are the creation of an exhaustive bikeability index inspired by previous studies with SVI indi-
cators extracted with CV techniques as the major data source, novel exploration of automatable subjective assessment
of bikeability, comparison of SVI and non-SVI indicators for the ﬁrst time, and a novel investigation of the potential
of using SVI indicators independently for bikeability assessment.
Non-perception SVI indicators were extracted using semantic segmentation and object detection, and perception
SVI indicators were predicted by training models with survey results as target variables and visual features extracted
from SVI as independent variables. Bikeability indexes that range from 0 to 100 were developed in Singapore and
Tokyo and compared with each other, which resulted in slightly higher bikeability scores in Singapore, 54.26 on
average, than in Tokyo, 53.98 on average. A thorough comparison between SVI and non-SVI indicators was made
to examine which has more inﬂuence when predicting conditions and the appeal of cycling. SVI indicators turned
out to have a much stronger correlation with the estimated bikeability index, an 𝑅2of 0.85, outperforming non-SVI
indicators, which had an 𝑅2of 0.4. However, the usefulness of the latter should not be discounted.
In summary, the takeaways of this research are:
•This study has demonstrated that we can use CV techniques and SVI to comprehensively assess bikeability
within and among cities. The paper details the design and implementation of a bikeability index that relies on
SVI and CV, which is calculated at both a ﬁne spatial scale and aggregated at the level of a city. The study asserts
that this index at least supplements traditional instruments used in this research domain.
First Author et al.: Preprint submitted to Elsevier Page 20 of 27
•Indicators that are derived from SVI dwarf those that are computed from non-SVI counterparts. A large portion
of the variance in the overall index was explained by SVI indicators, overshadowing those prominent in orthodox
mechanisms used hitherto.
•SVI may potentially be used on its own to assess bikeability in the built environment. However, as elaborated
in the previous sections, this study faced several challenges that, despite the convincing usability of SVI, may
not make taking this independent route always viable. Thus, future studies need to ameliorate several practical
issues. Further, the comparative advantage comes at a price — it is more diﬃcult to obtain SVI indicators in
comparison to the non-SVI counterparts. Therefore, despite the relative usability and independence of either, it
might be beneﬁcial to use both to assess bikeability, complementing their pros and cons.
Based on the ﬁndings from this study, future research should focus not only on overcoming the challenges discussed
in the previous section but also on further enhancing the index, which advances the state of the art but may nevertheless
beneﬁt from further work. The index can be improved by expanding the scope of SVI indicators to be included and
also improving the indicator weights according to cyclists’ preferences. The enlargement of the indicators can be done
by building CV models to extract more indicators, such as road type, land use, and type of pavement. Some studies
have explored creating weights based on cyclists’ preferences through surveys, creating diﬀerent weights for diﬀerent
types of cyclists (Arellana et al.,2020). Such improvement of the weights can beneﬁt better decision-making in urban
planning according to the demographics in target areas, thus, future studies should also incorporate such methods.
Another direction for future work would be coupling the developed index with instances introduced to assess other
urban aspects such as walkability and livability (Zhao et al.,2020;Benita et al.,2020), to investigate relationships or
List of acronyms
AQI Air Quality Index
CV Computer Vision
DEM Digital Elevation Model
IC Image Classiﬁcation
LU Land Use
MAE Mean Absolute Error
MAPE Mean Absolute Percentage Error
GSV Google Street View
OD Object Detection
POI Point of Interest
RMSE Root Mean Squared Error
SS Semantic Segmentation
SVI Street View Imagery
V–C Interaction Vehicle–Cyclist Interaction
We appreciate the comments by Jeﬀrey Ho and the design contribution by April Zhu (National University of
Singapore). We thank the members of the NUS Urban Analytics Lab for the discussions and the reviewers for their
suggestions. The data sources used in this study are gratefully acknowledged. This research is part of the project Large-
scale 3D Geospatial Data for Urban Analytics, which is supported by the National University of Singapore under the
Start Up Grant R-295-000-171-133. This study has received an exemption from the Institutional Review Board (IRB)
of the National University of Singapore under the reference code NUS-IRB-2021-29.
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