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Disabled, but at What Cost? An Examination of Wheelchair Routing Algorithms


Abstract and Figures

Platforms like Google Maps or Bing Maps are used by a large number of users to find the shortest path to their destinations. While these services mainly focus on supporting drivers and pedestrians, first services exist that support wheelchair users. Routing algorithms for wheelchair users try to avoid obstacles like stairs or bollards and optimize on criteria like surface properties and slope of the route. In this study, we undertake the first controlled examination of wheelchair routing approaches. By analyzing three routing platforms, including two wheelchair routing algorithms and three pedestrian routing algorithms, across fifteen major cities in Germany, our results highlight that the routes for wheelchair users are significantly longer and partially also more complex than those for pedestrians. In addition, we show that today's pedestrian routing algorithms also output very diverse routes.
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Disabled, but at What Cost?
An Examination of Wheelchair Routing Algorithms
Benjamin Tannert
University of Bremen
Bremen, Germany
Johannes Schöning
University of Bremen
Bremen, Germany
Platforms like Google Maps or Bing Maps are used by a
large number of users to find the shortest path to their
destinations. While these services mainly focus on
supporting drivers and pedestrians, first services exist that
support wheelchair users. Routing algorithms for
wheelchair users try to avoid obstacles like stairs or
bollards and optimize on criteria like surface properties and
slope of the route. In this study, we undertake the first
controlled examination of wheelchair routing approaches.
By analyzing three routing platforms, including two
wheelchair routing algorithms and three pedestrian routing
algorithms, across fifteen major cities in Germany, our
results highlight that the routes for wheelchair users are
significantly longer and partially also more complex than
those for pedestrians. In addition, we show that today’s
pedestrian routing algorithms also output very diverse
Author Keywords
Accessibility; HCI; Disability; Wheelchair Users;
Pedestrian Navigation; Routing; City Planning.
ACM Classification Keywords
H.5.m. Information interfaces and presentation (e.g., HCI):
Besides a large number of unreported cases, more than 65
million people in the world need a wheelchair on a daily
basis [13]. In Germany, as per the Federal Statistical Office
[17,], 1.5 million people need a wheelchair every day. This
is 2.7 percent of the whole population of Germany.
Various mobile technologies, apps and web platforms can
support wheelchair users to master their daily lives. For
example, Wheelmap ( provides crowd-
sourced accessibility information for buildings, points of
interests (POIs) and restaurants based on OpenStreetMap
The Wheelmap service uses a traffic light metaphor to
indicate if a building is easily accessible or not. Google also
recently announced their decision to crowd-source
accessibility information for buildings [1] to be integrated
into Google Maps.
Besides accessibility information, obstacles along a route
are crucial for wheelchair users. While some obstacles are
obvious (e.g. steps or pillars), some others are often only
considered by disabled people (e.g. slope of sidewalks or
different surface properties like sand or cobblestone, which
are difficult to overcome in a wheelchair). Various
approaches analyzed the accessibility of sidewalks with
sensors and technical equipment such as depth cameras or
acceleration sensors [3,4,5,6,9,12,16,19,20].
MobileHCI '18, September 36, 2018, Barcelona, Spain
© 2018 Copyright is held by the owner/author(s). Publication rights
licensed to ACM.
ACM ISBN 978-1-4503-5898-9/18/09…$15.00
Figure 1: Routes for pedestrians (purple, green, orange) and
wheelchair users (blue, yellow) calculated by different routing
platforms for an origin-destination pair in Frankfurt,
Germany. Base map © OSM.
Besides these (semi-) automated approaches, Hara et al. [2]
explored the use of Amazon Mechanical Turk (AMT)
workers for classifying the accessibility of sidewalks for
wheelchair users. They compared the differences in the
annotation of the AMT workers and the experts (wheelchair
users) and found that even untrained personnel could easily
identify problems in the image of sidewalks.
Today, first routing services exist that help to guide
wheelchair users from an origin to a destination [10, 11,
17]. These services take numerous variables into account to
provide routes that are easy for wheelchair users to follow.
Most commonly, they avoid obstacles and barriers such as
stairs or bollards, utilize the surface properties, the slope,
and the height of the pavement edges to calculate routes
particularly suited for wheelchair users. Karimi et al. [8]
provide a good overview of requirements and components
needed for a routing service that can assist disabled people.
In this study, we undertake the first controlled examination
of these wheelchair routing services and report on the
following three contributions:
We found that significant differences exist
between routes for pedestrian and wheelchair users
within same areas. For example, in Frankfurt,
Germany, routes (between 1.5 and 2.0 km straight-
line distance between origin and destination) for
wheelchair users were on average nearly double
the length of those for pedestrians and were also
more complex (e.g. in terms of # of turns).
Furthermore, a notable difference exists between
cities. While we observed a large difference in
Frankfurt, the difference between routes for
pedestrian and wheelchair users differed by just 1
% in Hamburg (again for routes between 1.5 and
2.0 km straight-line distance between origin and
destination and routes calculated with
OpenRouteService). Therefore, our results provide
a novel way to benchmark cities with regard to the
wheelchair accessibility.
We also noticed that pedestrian routing algorithms
calculate very different routes in terms of their
complexity even though they provide routes of
similar length. For example, Google Maps tries to
minimize the number of turns to reduce the
complexity of the calculated routes even so this
involved detours.
In our study, we investigated three different routing
platforms, namely Google Maps
(, OpenRouteService
( and Routino
( While Google Maps uses
proprietary data, OpenRouteService (ORS) and Routino
both rely on data from OpenStreetMap (OSM)
( for their calculations and
both offer dedicated routing algorithms for wheelchair
users. Google Maps provide routes for different modes of
locomotion but not for wheelchair users.
The ORS algorithms use default settings to calculate the
routes for pedestrians (ORS_Ped) and wheelchair users
(ORS_Wheel). ORS_Ped assumes a speed of 6 km/h and
ORS_Wheel uses 8 km/h. And while in the ORS_Wheel the
whole route has to be paved, the surface is not taken into
account in ORS_Ped.
Similarly, the ORS_Wheel algorithm avoids steps, while
ORS_Ped does not take this feature into consideration.
Additionally, ORS_Wheel takes the incline of route
segments (max. 6 % incline) and the height of sloped curbs
(max. height of 6 cm) into account. A detailed description
of the ORS routing algorithms can be found here [14].
In contrast to the ORS algorithms, the Routino algorithm
for pedestrians (Rou_Ped) and Routino for wheelchair users
(Rou_Wheel) both assume the speed of 4 km/h for their
calculations. While in the Rou_Wheel 90 % of the route has
to be paved, only 50 % needs to be paved in Rou_Ped.
Similar to ORS_Wheel, Rou_Wheel avoids steps. The
detailed description of the Routino algorithms can be found
here [15].
Given the fact that Google uses proprietary algorithm and
data, we cannot provide detailed information on the
implementation of the Google_Ped algorithm. The only
available information is that the average speed is assumed
to be 5 km/h to calculate the time of travel.
Table 1 provides a comparison of the main criteria used by
the algorithms analyzed in this paper. Figure 1 shows an
example of different routes calculated by the five
algorithms used in our study for one origin-destination pair
in Frankfurt, Germany.
To evaluate the 5 routing algorithms (Google_Ped,
ORS_Ped, ORS_Wheel, Rou_Ped, Rou_Wheel), we
extended a framework developed by Johnson et al. [7] and
integrated all five routing algorithms. Johnson et al. used
their framework to investigate externalities that arise with
three common approaches to the fastest path option
(“beauty”, “safety” and “simplicity”) for car-based routing
algorithms across four cities around the globe, namely
London, Manila, San Francisco and New York. They used
Base Data
4 km/h
4 km/h
6 km/h
8 km/h
5 km/h
Table 1: Overview of the main criteria of the algorithms.
around 1000 origin-destination pairs for each city to
compare the effects of the different routing options. For the
origin and destination pairs, they used information about
the most common pathways of taxi companies and
generated random points in the city.
Origin-Destination Pairs
In order to compare the outcome provided by the different
routing platforms, we had to identify a set of representative
origin-destination pairs for all fifteen cities. As no public
data was available for wheelchair routes in those cities, we
calculated origin-destination pairs between POIs and public
restrooms for disabled people. As confirmed by wheelchair
users, these pairs describe a set of typical routes.
We selected the 15 biggest German cities namely, Berlin
(BER), Bremen (HB), Cologne (COL), Dortmund (DOR),
Dresden (DRE), Düsseldorf (DUE), Erfurt (ERF), Essen
(ESS), Frankfurt (FRA), Hamburg (HH), Hanover (HAN),
Leipzig (LEI), Munich (MUN), Nuremberg (NUR) and
Stuttgart (STU). One of the cities is the hometown of one of
the co-authors, who also uses a wheelchair, which offers the
possibility to use his knowledge to compare the quality of
the calculated routes. To generate the origin-destination
pairs we first gathered data on the location of public
restrooms for disabled people from
Then, we used the Google Maps API to identify POIs in a
radius of 2 km around these public restrooms. The POIs,
e.g. parks or restaurants, are places from where wheelchair
users would usually need to drive to a restroom. The
Google Maps API provides up to 200 POIs around each
location. Using this process, we generated 267.421 origin-
destination pairs for all 15 cities. We extracted 2715 pairs
for every city with the same average straight-line distance
and same variance to be comparable across all 15 cities. For
further analysis, we grouped these pairs into four classes of
routes that have a straight-line distance of 0.0-0.5 km, 0.5-
1.0 km, 1.0-1.5 km and 1.5-2.0 km.
In order to derive evidence on the statistical differences
between the routing alternatives per city and the summary
of them (in terms of length and number of turns), we used a
one-way ANOVA with Bonferroni correction. The
significance threshold was set to p<0.05.
Figure 2a summarizes the average length of the routes of all
five routing algorithms across the fifteen cities (40.725
origin-destination pairs for all cities, 2715 routes per city).
There is a significant difference between all pairs of routing
algorithms with the exception of the pair ORS_Ped and
Rou_Ped, which is somehow expected as both pedestrian
routing algorithms are operating on OSM data. We further
calculated the effect size of each pair with a significant
difference to analyze the impact of it. The results of this
analysis showed that the effect sizes were negligible.
Figure 2b shows the average route length grouped into
classes by the straight-line distance between origin and
destination. The chart illustrates that the difference of the
length between the pedestrian and the wheelchair
algorithms grow with the length of the straight-line
Figure 2: Average route length a) across all cities b) grouped in distance bins and c) in Frankfurt and d) Hanover.
For the classes 0-0.5 km, 0.5-1.0 km and 1.0-1.5 km there
are no significant differences, but for the class 1.5-2.0 km
all differences between routing algorithms are significant
(again with the exception of the pair ORS_Ped and
Rou_Ped). To summarize, Figures 2a and 2b show that the
routes for wheelchair users are indeed longer than those for
pedestrians. Moreover, this difference increases as the
straight-line distance between origin and destination point
grows. Google_Ped generates the shorter routes compared
to all other 4 routing algorithms. Routino_Wheel generates
longer route lengths than the pedestrian algorithms in all
fifteen cities.
We observe a continuous increase in the gap between the
average wheelchair and pedestrian route lengths, with the
rise of the straight-line measure between origin and
destination. We also found that Google_Ped is a good
representative of the other pedestrian algorithms, and for
straight-line distances (average of all cities) between 0.0-
0.5 km, the difference in route length of Google_Ped and
Rou_Wheel is approximately 50m. For the next class, this
difference is 120 m and for 1.0-1.5 km it is 250m. For the
last class between 1.5 km and 2.0 km the difference
increases to 300m.
Wheelchair vs. Pedestrian Routing across Cities
We expected that all three pedestrian routing algorithms
(Google_Ped, ORS_Ped, and Rou_Ped) generate shorter
routes as both algorithms for wheelchair users (ORS_Wheel
and Rou_Wheel). Therefore, we decided not only to
compare the groups of pedestrian and wheelchair
algorithms in general, but also between cities. We further
analyzed the differences between the five algorithms across
the 15 cities.
As can be seen in figure 2c in FRA, for the straight-line
distance class of 1.5-2.0 km, there is a significant average
difference of 1,5 km between Google_Ped and Rou_Wheel
as well as 1,6 km between Google_Ped and ORS_Wheel.
As can be seen in figure 2d in HAN, the differences are
around 100 m on average between Google_Ped and
ORS_Wheel and 120 m between Google_Ped and
Rou_Wheel, but still significant. Frankfurt was a
representative city for a rather high difference between the
algorithms, whereas Hanover was an example for a city
with rather small differences between the five routing
When comparing other pairs of algorithms, we found
similar trends. For example, while the average difference of
ORS_Ped and ORS_Wheel for 1.5-2.0 km origin-
destination pairs is around 100 m in HAN, it is around 2.0
km in FRA. We observe similar patterns between Rou_Ped
and Rou_Wheel. In the group of the fifteen cities, FRA is
special because, the route length of the wheelchair
algorithms is much longer than in the other cities.
Route Complexity
Finally, we examined the complexity of the routes that were
calculated by the five routing algorithms. We analyzed if
there is a significant difference between the pedestrian and
wheelchair algorithms in terms of the overall number of
turns for each route.
Figure 3 shows the averages number of turns for the cities
HAM, LEI, DOR and FRA. Here it can be seen that the
complexity is not always higher for wheelchair users than
for pedestrians. For example, in DOR the ORS_Ped
algorithm is more complex than ORS_Wheel for routes
between 1.0-1.5 km, but for routes between 1.5-2.0 km the
ORS_Wheel algorithm indicates a higher number of turns.
The Rou_Ped and the Rou_Wheel perform in a similar way
in FRA for the equivalent route lengths. Instead in HAM
the higher complexity does not switch between the
pedestrian and wheelchair routes, but it is different between
the providers. Here the ORS algorithms show always that
the routes for wheelchair users are more complex, whereas
the Rou algorithms indicate that the pedestrians have more
turns on their routes.
We inferred that the difference in the number of turns
depends strongly on the provider (Google, ORS, Rou). In
comparison to Google_Ped, the ORS_Ped algorithm require
more turns for the 1.5-2.0 km class in all cities; HH (1.9),
LEI (14.2), DOR (14.1) and FRA (13.5). The Rou_Ped
seems to generate the routes not optimizing for a low
number of turns and complexity. It includes twice as many
turns as the ORS_Ped and many times more turns than
Google_Ped. For the straight-line distance section of 1.5 km
and 2.0 km the difference in the number of turns between
Google_Ped and Rou_Ped is in HH 46.5, in LEI 40.9, in
DOR 39.3 and in FRA 37.8.
Wheelchair Routing Differences
As can be seen in figure 4, the fifteen analyzed cities are
slightly different in terms of wheelchair accessibility. For
every of the 15 cities the figure shows the averaged route
length of all routes generated for wheelchair users
(ORS_Wheel and Rou_Wheel). Thus, it can be seen in
which cities the route lengths are similar and in which one
of the algorithms perform different. On the other hand, the
average route lengths of ORS_Wheel are sometimes similar
to those of Rou_Wheel but are often shorter even though
both use the same data e.g about the surface.
If we look at the pedestrian algorithms (Google_Ped,
ORS_Ped, and Rou_Ped), it is obvious that the average
length of the routes is very similar. Only for higher straight-
line distances in FRA, Rou_Ped generates longer routes
than the other pedestrian algorithm. For the 1.5-2.0 km
class, the average route length is approximately 0.15 km
longer. Although the average route lengths of the pedestrian
algorithms are very similar, the complexity and therefor the
number of turns of the routes are different (Figure 3). It can
thus be concluded that the route length is similar, but the
way is different.
The results of our analysis show that the accessibility of
cities can differ significantly.
Societal Impact of Wheelchair Routing
The results for FRA illustrate a weak implementation of
accessibility standards, which has a direct impact on lives
of many wheelchair users. This is accentuated by the long
routes generated by ORS_Wheel and Rou_Wheel. The
difference between Rou_Ped and Rou_Wheel includes
obstacles that wheelchair users have to avoid. A detailed
survey of such obstacles could be useful not only to
wheelchair users but also other stakeholders.
Complexity of Routing Algorithms
Trying to reach a destination from a given origin point can
be very difficult if a turn is missed. Figure 3 shows that
Google_Ped is trying to reduce the complexity of the route
by minimizing the number of turns to prevent wrong turns.
Compared to Google_Ped the algorithms of Routino
(Rou_Ped and Rou_Wheel) produce more complex routes
and therefore have more decision points, where users can
make mistakes. In addition to that, the ORS algorithms
(ORS_Ped and ORS_Wheel) generate the double number of
turns as the Google algorithm. Overall, the complexity of a
route is more depending on the provider than the modality
(pedestrians and wheelchair users).
To generate origin destination pairs, we used only randomly
generated pairs (between public restrooms for disabled
people and POIs). It would be interesting to include more
realistic data into our framework, but no such data exists
that captures typical routes of wheelchair users across
multiple cities (similar to the taxi data used by Johnson et
al. [7]) according to the Federal Statistical Office of
Germany as well as the building authorities and
departments of town planning of the 10 largest cities in
Germany. This data would not only be useful to be plugged
in our framework, but also for more general planning
The fifteen cities examined in our study are scattered over
Germany, hence their geographical topology can differ due
to various causes. However, we could still inspect
differences in routing algorithms and analyze the results in
respect to the wheelchair accessibility issue.
In general, the more spatial data and attributes are available
to calculate the route, the better the algorithms of the
routing platforms can perform. The data to be used by the
pedestrian routing algorithms is mostly complete and
requires less attributes information. However, this is not the
case with the algorithms for wheelchair users that have to
use incomplete data for different parts of the city collected
mostly by volunteers. Therefore, missing or incomplete
information can also lead to failures in wheelchair
navigation, producing longer and/or inaccessible routes.
Figure 3: Average number of turns across the five routing algorithms for four cities grouped by straight-line distance.
By analyzing three pedestrian routing platforms, including
two wheelchair routing algorithms, across fifteen major
cities in Germany we show that the routes for wheelchair
users are significantly longer and partially also more
complex than those for pedestrians. As this could be due to
missing attribute information the wheelchair routing
algorithms rely now, we manually investigated those cases
in the city of Bremen. We found out that, even so attribute
information is missing, many barriers still exist that could
be removed by decision makers to minimize route lengths
for wheelchair users. In addition, we as technologist can
help to collect missing attribute information to improve the
route generation of wheelchair routing algorithms.
Automatic and semi-automatic approaches, similar to the
ones proposed by [2,9,16,20], could be used to fill this gap.
We would like to thank Ankit Kariryaa and Reuben
Kirkham for their valuable comments and input on this
paper. Furthermore, we thank Isaac Johnson for the help
adapting his framework and Brent Hecht for his support in
the beginning of this study. This work is also partially
supported by the project “InWi Inklusion in der
Wissenschaft” and by the Volkswagen Stiftung through a
Lichtenberg Professorship.
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... For each of these platforms, we generated 2715 routes. We adopt the approach towards generating origin-destination pairs as per Tannert et al. [38] This approach involves determining all possible routes 'A' and 'B' within a city, where in 'A' is a publicly available bathroom, and 'B' is a point of interest. Bathrooms are used because their locations are critical for wheelchair users in two respects: (i) the nature of many mobility impairments means an increased need to visit the bathroom (e.g. ...
... obtaining ground truth) is required when evaluating any 'A to B' tool, as well as taking into account the real data that is likely to be available when navigating, rather than some idealized expectation of it. There is a further implication, namely that these tools cannot be used as proxies for the general accessibility of a city as proposed in [38], because (unlike what someone would reasonably expect) they simply do not sufficiently reflect the real world. ...
Conference Paper
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This paper explores 'A to B' routing tools designed to chart accessible routes for wheelchair users. We develop and present a novel measurement framework based upon cost-benefit analysis in order to evaluate the real-world utility of routing systems for wheelchair users. Using this framework, we compare proposed routes generated by accessibility tools with the pedestrian routes generated by Google Maps by means of conducting expert assessments of the situation on the ground. Relative to tools aimed at pedestrians, we find that these tools are not significantly more likely to produce an accessible route, and more often than not, they present longer routes that arise from imaginary barriers that do not exist in the real world. This analysis indicates how future routing tools for wheelchair users should be designed to ensure that they genuinely ameliorate the effects of accessibility barriers in the built environment.
... Moreover, people with visual impairments (VI) make half of their decisions because of their disability during their journey [4]. For wheelchair users, reaching certain destinations can be difficult [49] and their trips often include significantly longer and partially more complex routes than for pedestrians [68]. In some cases, people with MI choose their travel destinations depending on its accessibility [29,64]. ...
It is much more difficult for people with visual or mobility impairments to prepare for a trip or visit unfamiliar places than it is for people without disabilities. In addition to the usual travel arrangements, one needs to know if the various parts of the travel chain are accessible. To the best of our knowledge, there is no previous work that examines in depth travel behaviour for indoor environments for both trip planning and execution, highlighting the special needs of people with low vision, blindness or mobility impairments. In this paper, we present a survey with 125 participants with blindness, low vision and mobility impairments. We investigate how mobile they are, what strategies they use to prepare a journey to an unknown building, how they orient themselves there and what materials they use. For all three groups, our results provide insights into the problem space of the specific information needs when planning and executing a trip. We found that most of our participants have specific mobility problems depending on their disability. Feedback from the participants reveals there is a large information gap, especially for orientation in buildings, regarding availability of high-quality digital, tactile and printable indoor maps, accessibility of buildings and mobility supporting systems. In particular, there is a lack of available and high-quality indoor maps. Our analysis also points out that the specific needs differ for the three groups. Besides the expected between-group differences, also large in-group differences can be found. The current paper is an expanded version of [18] augmented by data of people with mobility impairments.
... The service is now available in three di↵erent USA cities. Di↵erent types of routing services and tagging schema exist into the OpenStreetMap database and are evolving during the years: the main interesting and improvable that promise a wheelchair routing are OpenRoute-Service (ORS) 15 [33] and Routino 16 . The two routing services were tested in 15 di↵erent German cities [34], in the pedestrian and the wheelchair version against the Google Maps API. The test was done according to paths of four di↵erent class of length: 0-0.5 km, 0.5-1 km, 1-1.5km, 1.5-2 km. ...
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Architectural barriers are physical elements that limit the freedom of movement and use of services of a person. They are identified differently according to the needs of the individual. The legislation mainly considers barriers the built elements in public spaces. Such elements, as steps or narrow doors, limit the freedom of a person with disabilities to move. In particular, accessibility is defined as the ability to move in public spaces for people with reduced or limited mobility. The lack of accessibility is one of the physical barriers that most limit people with motor disabilities, as recognized by the World Health Organization. The thesis aims to identify the optimal methodology for mapping accessible routes and critical points; to create maps that can be used by people with wheelchairs. It should also be a tool that allows citizens to report barriers to public authority. Several projects have been analyzed, which in the past attempted to map accessibility and architectural barriers. These projects were based on the use of authoritative data collected by government agencies. More recently, the concepts of Voluntary Geographic Information (VGI) and crowdsourcing data collection have spread. Newer approaches have started to use OpenStreetMap as database. OpenStreetMap is a 2004 project by the University of London, with the aim of creating a free and editable map of the world. The thesis work is part of the ViaLibera?! project, which aims to apply this methodology in the Municipality 9 of the city of Milan. The project is founded by Fondazione di Comunità Milano; Politecnico di Milano is the technical partner, and the other partners are associations of people with disabilities: Spazio Vita Niguarda Onlus, Ledha Milano and AUS Niguarda Onlus. The elements of interest for the project were identified in collaboration with the other partners and based on state of the art. The use of OpenStreetMap was chosen as a collaborative database to insert the data, and its structure was analyzed to identify the data model for the elements of interest. In the thesis the comparison between the different existing mapping techniques is done to select the most rigorous and simple, in order to be attractive to the volunteers. In addition, the different techniques must be suitable for the chosen tagging scheme to map accessibility elements and also to be used by wheelchair users. The techniques analyzed involve the use of paper maps, Field Papers, and street-level images or applications for smartphones. They are compared to identify the best one.
News headlines about privacy invasions, discrimination, and biases discovered in the platforms of big technology companies are commonplace today, and big tech's reluctance to disclose how they operate counteracts ideals of transparency, openness, and accountability. This book is for computer science students and researchers who want to study big tech's corporate surveillance from an experimental, empirical, or quantitative point of view and thereby contribute to holding big tech accountable. As a comprehensive technical resource, it guides readers through the corporate surveillance landscape and describes in detail how corporate surveillance works, how it can be studied experimentally, and what existing studies have found. It provides a thorough foundation in the necessary research methods and tools, and introduces the current research landscape along with a wide range of open issues and challenges. The book also explains how to consider ethical issues and how to turn research results into real-world change.
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Navigation systems have become increasingly available and more complex over the past few decades as maps have changed from largely static visual and paper-based representations to interactive and multimodal computerized systems. In this introductory article to the Special Issue on Human-computer Interaction, Geographic Information, and Navigation, we review literature across a variety of fields to generate nine design principles to guide future research and development of navigation systems. Specifically, we suggest making mobile navigation systems more accessible and multimodal, which will make the systems more inclusive and usable for all types of users. We also introduce the research articles contributed to the present special issue and suggest future research directions to empirically evaluate emerging and untested features of user-adapted and context-aware mobile navigation systems.
There has been a considerable amount of research aimed at automating the documentation of accessibility in the built environment. Yet so far, there has been no fully automatic system that has been shown to reliably document surface quality barriers in the built environment in real-time. This is a mixed problem of HCI and applied machine learning, requiring the careful use of applied machine learning to address the real-world concern of practical documentation. To address this challenge, we offer a framework for designing applied machine learning approaches aimed at documenting the (in)accessibility of the built environment. This framework is designed to take into account the real-world picture, recognizing that the design of any accessibility documentation system has to take into account a range of factors that are not usually considered in machine learning research. We then apply this framework in a case study, illustrating an approach which can obtain a f-ratio of 0.952 in the best-case scenario.
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Purpose of Review This review describes recent research regarding challenges to mobility experienced by older adult mobility device users. Recent Findings Different elements that affect mobility in this population were highlighted using the Human Activity Assistive Technology model. Key constructs in this model include the human (personal factors); the desired activity (mobility); assistive technology (mobility devices); and context (physical and social environmental factors). Poor mobility skills and lack of mobility confidence are personal factors that can limit the places that older adults go. However, new mobility devices are being developed to facilitate independent mobility. The design of the built environment and poor signage are aspects of the physical environment that can constrain mobility and impair wayfinding. Likewise, social factors including funding and prescription policies can reduce access to devices and needed services, and stigma can cause older adults to self-limit their mobility. Summary Addressing these challenges could reduce the difficulties that users encounter while navigating the environment and facilitate the mobility and social participation of older adults.
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The built environment remains a persistent accessibility challenge for people with mobility impairments. Whilst platforms to report these inaccessible locations exist, the underlying documentation processes are verbose, time-consuming and fail to effectively communicate the barrier at hand. We propose WheelieMap, a platform which uses the motion of manual wheelchair users to support the identification and documentation of potentially problematic locations. WheelieMap captures and segments device video footage and GPS as evidence of the problematic space, which can then be shared with both other people with disabilities and the relevant authorities. We document the use of the WheelieMap prototype by both manual wheelchair users and planning experts through semi-structured interviews. The qualitative findings revealed this approach to be the most viable route for documenting inaccessibility, compared to the existing alternatives. We also offer guidance on how to design and develop similar community driven reporting and annotation systems in the accessibility setting.
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Millions of people use platforms such as Google Maps to search for routes to their desired destinations. Recently, researchers and mapping platforms have shown growing interest in optimizing routes for criteria other than travel time, e.g. simplicity, safety, and beauty. However, despite the ubiquity of algorithmic routing and its potential to define how millions of people move around the world, very li"le is known about the externalities that arise when adopting these new optimization criteria, e.g. potential redistribution of traffic to certain neighborhoods and increased route complexity (with its associated risks). In this paper, we undertake the first controlled examination of these externalities, doing so across multiple mapping platforms, alternative optimizations, and cities. We find, for example, that scenic routing (i.e. " beauty "-optimized routing) would remove vehicles from highways, greatly increase traffic around parks, and, in certain cases, do the same for high-income areas. Our results also highlight that the interaction between routing criteria and urban structure is complex and effects vary from city to city, an important consideration for the growing literature on alternative routing strategies. Finally, to address the lack of open implementations of alternative routing algorithms and controlled routing evaluation frameworks, we are releasing our alternative routing and evaluation platform with this paper.
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The accessibility of street as a social arena that fulfils the need for people with disabilities (PwDs) is an important consideration in the urban design of an area. With the rising number of PwDs in Malaysia, this aspect of street design is even more critical. This paper evaluates the accessibility level of sidewalk along Jalan Hang Jebat, Melaka to PwDs. On-site access audit simulation was carried out. Actual PwDs were engaged for the simulation. It was found that the sidewalk in inaccessible to PwDs due to presence of barriers and the design of the sidewalk itself. This paper suggest that the minimum requirement of MS1184:2014 must be implemented in the sidewalk design and the concept of ‘shared space’ can be adopted in the study area.
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Navigation systems allow drivers to find the shortest or fastest path between two or multiple locations mostly using time or distance as input parameters. Various researchers extended traditional route planning approaches by taking into account the user's preferences, such as enjoying a coastal view or alpine landscapes during a drive. Current approaches mainly rely on volunteered geographic information (VGI), such as point of interest (POI) data from OpenStreetMap, or social media data, such as geotagged photos from Flickr, to generate scenic routes. While these approaches use proximity, distribution or other spatial relationships of the data sets, they do not take into account the actual view on specific route segments. In this paper, we propose Autobahn: a system for generating scenic routes using Google Street View images to classify route segments based on their visual characteristics enhancing the driving experience. We show that this vision-based approach can complement other approaches for scenic route planning and introduce a personalized scenic route by aligning the characteristics of the route to the preferences of the user.
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Tobler's First Law of Geography (TFL) is one of the key reasons why "spatial is special". The law, which states that "everything is related to everything else, but near things are more related than distant things", is central to the management, presentation, and analysis of geographic information. However, despite the importance of TFL, we have a limited general understanding of its domain-neutral properties. In this paper, we leverage recent advances in the natural language processing domain of semantic relatedness estimation to, for the first time, robustly evaluate the extent to which relatedness between spatial entities decreases over distance in a domain-neutral fashion. Our results reveal that, in general, TFL can indeed be considered a globally recognized domain-neutral property of geographic information but that there is a distance beyond which being nearer, on average, no longer means being more related.
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In this paper we describe a bottom-up approach to integrate GIS maps (endorsed by discrete features, such as points, lines, polygons), in order to develop a route planner for wheelchair users. We integrate public available data with a novel model for route planning, based on sidewalks, crosswalks and curb ramps, as opposed to traditional street-based approaches. We show that our sidewalk-based model is more suitable than available planning routes under mobility constraints, using a case study in Curitiba, Brazil.
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This research proposes a methodology for digitizing street level accessibility with human sensing of wheelchair users. The dig- itization of street level accessibility is essential to develop accessibility maps or to personalize a route considering accessibility. However, current digitization methodologies are not sufficient because it requires a lot of manpower and therefore money and time cost. The proposed method makes it possible to digitize the accessibility semi-automatically. In this research, a three-axis accelerometer embedded on iPod touch sensed actions of nine wheelchair users across the range of disabilities and aged groups, in Tokyo, approximately 9hours. This paper reports out attempts to estimate both environmental factors: the status of street and subjective factors: driver's fatigue from human sensing data using machine learning.
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This paper describes approach and methods used for the development of navigation services for people with disabilities. Our previous papers contained the description of informational environment development for disabled people. The environment includes several applications utilizing unified database and providing the information support to disabled people. The service "Accessibility Passports" was developed to collect information about accessibility of the objects. "Accessibility map" service visualizes the information on the geographical map. This paper is focused on the development of the key service of environment - "Social navigator". Recently we have presented the service only conceptually without detailed description of navigation approach and methods with adaptation to personal mobility restriction. The paper describes developed methods of navigation for disabled people and elaborated algorithms. Also the practical opportunities of the service to support disabled people are presented. The results of approach study and development work are also presented in the paper.
Conference Paper
This paper proposes a methodology for developing large scale accessibility map with personal sensing by using smart phone and machine learning technologies. The strength of the proposed method is its low cost data collection, which is a key to break through stagnations of accessibility map that currently applied to limited areas. This paper developed and evaluated a prototype system that estimates types of ground surfaces by applying supervised learning techniques to activity sensing data of wheelchair users recorded by a three-axis accelerometer, focusing on knowledge extraction and visualization. As a result of evaluation using nine wheelchair users' data with Support Vector Machine, three ground surface types, curb, tactile indicator, and slope, were detected with f-score (and accuracy) of 0.63 (0.92), 0.65 (0.85), and 0.54 (0.97) respectively.