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SubwayPS: Towards Enabling Smartphone Positioning in Underground Public Transportation Systems

Authors:
  • University of St.Gallen
SubwayPS: Towards Smartphone Positioning in
Underground Public Transportation Systems
Thomas Stockx
Expertise Ctr. For Digital Media
Hasselt University - tUL - iMinds
thomas.stockx@student.uhasselt.be
Brent Hecht
GroupLens Research
University of Minnesota
bhecht@cs.umn.edu
Johannes Schöning
Expertise Ctr. For Digital Media
Hasselt University - tUL - iMinds
johannes.schoening@uhasselt.be
ABSTRACT
Thanks to rapid advances in technologies like GPS and Wi-
Fi positioning, smartphone users are able to determine their
location almost everywhere they go. This is not true,
however, of people who are traveling in underground public
transportation networks, one of the few types of high-traffic
areas where smartphones do not have access to accurate
position information. In this paper, we introduce the
problem of underground transport positioning on
smartphones and present SubwayPS, an accelerometer-
based positioning technique that allows smartphones to
determine their location substantially better than baseline
approaches, even deep beneath city streets. We highlight
several immediate applications of positioning in subway
networks in domains ranging from mobile advertising to
mobile maps and present MetroNavigator, a proof-of-
concept smartphone and smartwatch app that notifies users
of upcoming points-of-interest and alerts them when it is
time to get ready to exit the train.
Categories and Subject Descriptors
H.5.m. Information interfaces and presentation (e.g., HCI):
Miscellaneous.
Keywords
mobile navigation, positioning, mobile devices,
underground public transport, GPS, accelerometer
INTRODUCTION & MOTIVATION
A smartphone’s ability to detect its location is the
cornerstone of mobile applications in domains ranging from
location-based services to mobile crowdsourcing. While
technologies like GPS and Wi-Fi positioning have made
location detection possible in most places smartphone users
go, this is not true everywhere. Underground public
transportation systems, which are largely inaccessible to
GPS signals and often out of range of Wi-Fi networks,
represent a particularly important type of space in which
smartphones cannot reliably determine their location. As a
result, the millions of locals and tourists who ride subways
around the world each day [21] cannot take full advantage
of popular location-aware smartphone applications
including mobile map apps while traveling to and from
their destinations.
In this paper, we present SubwayPS, the first positioning
technique that allows modern smartphones to determine
their location while traveling in an underground public
transportation network. Our technique, which we
demonstrate can achieve reasonablethough not perfect
accuracy, does not depend on any instrumentation of the
environment. Instead, SubwayPS merely requires an
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http://dx.doi.org/10.1145/2666310.2666396
Figure 1: A screenshot of the Google Maps iPhone
application used on the Blue Line subway in Chicago. The
gray dot shows the smartphone’s estimated position, while
the green dot shows the actual position of the user’s train.
Red dots are underground stations. One could easily miss
their stop if they relied solely on current positioning
technology. SubwayPS works towards addressing this issue by
providing improved positioning while traveling in
underground public transportation networks (Base map ©
Google Maps 2014).
accelerometer and gyroscope and an input origin location
and end destination. Accelerometers and gyroscopes are
included in most recent smartphones. Origins and
destinations can be easily extracted from routing requests
(especially if this type of positioning technology is built
into a mobile OS) or inferred from user behavior (as in
context-aware technologies like Google Now [6]).
Smartphone positioning in underground public
transportation networks affords wide-ranging benefits. The
most basic such benefit is that SubwayPS simply puts
subway riders “back on the map”. Figure 1 shows the result
of using Google Maps for iOS while traveling through an
underground portion of the Chicago Blue Line. Because the
app cannot obtain position information, it is rendered
effectively useless for navigation and orientation purposes.
Our technique can make mobile maps as useful in
underground transportation networks as they are above
ground. For example, a SubwayPS-enabled navigation app
could much more effectively help tourists navigate foreign
subway systems, especially when they do not speak and
cannot read the native language. Such an app could, for
instance, inform tourists who cannot understand the
announcements of when to prepare to exit the train, an
especially important concern when the train is crowded.
SubwayPS can also enable location-based applications
beyond basic navigation and orientation. For instance, if our
technique were built into the location API of a mobile OS,
smartphone users would be able to use cached content from
apps like Yelp to easily see the restaurants that would be
available if they were to get off at the next stop. In addition,
mobile advertising becomes possible underground (e.g.
“Exit at the next stop to get to restaurant X and receive $5
off”).
The core contribution of this paper is our underground
public transportation positioning technique SubwayPS,
along with three evaluations that show that our technique
can achieve reasonable accuracy and substantially
outperforms baseline approaches on multiple subway
systems. Below, we first put this core contribution in the
context of related work. Next, we present SubwayPS in
detail and introduce MetroNavigator, a proof-of-concept
application based on SubwayPS. We then demonstrate the
accuracy of SubwayPS using log data and in-the-wild user
studies, showing that it performs substantially better than
baseline approaches. We also highlight qualitative feedback
from participants, which included I want to have it
integrated in Google MapsandI feel safer when I can see
that we have stopped close to the next station when we’re
stopped in the middle of a tunnel(P11). Finally, we close
by highlighting in more detail the many applications of
subway positioning and discussing several means by which
accuracy can be improved in future work.
RELATED WORK
The work presented here is informed by research from three
areas: inertial navigation, transportation mode detection,
and other approaches to positioning in public transportation
networks. Below, we discuss each of these areas in turn.
Inertial navigation
SubwayPS uses an approach to positioning motivated by
the literature on accelerometer- and gyroscope-based
inertial navigation. Although inertial navigation approaches
have not been adapted to the context of public
transportation positioning as they are here, they have been
shown to be successful for other purposes, especially
pedestrian indoor navigation (e.g. [1,5,13,14]). For
instance, Robertson et al. [13] presented a simultaneous
localization and mapping (SLAM) approach for pedestrians
using only foot-mounted inertial sensors. Their method,
FootSLAM, can be used to generate an approximate 2D map
of an indoor layout with just 10 minutes of walking.
Other accelerometer-based approaches have combined
inertial navigation with GPS and other positioning signals
for improved accuracy. The NavMote Experience [4], for
example, includes a dead reckoning module, self-organizing
wireless ad hoc networks and an information center with a
map database. Another example is the Embedded
GPS/RFID/Self-contained Sensor System by Kourogi et al.
[9]. As described in the name of their system, Kourogi and
colleagues integrated self-contained dead reckoning sensors
(accelerometers, gyrosensors and magnetometers) with GPS
and an RFID tag system to adjust for errors in position and
direction that occur in the dead reckoning process.
A number of inertial navigation-based positioning
techniques for indoor navigation and related purposes have
been developed for smartphones. These techniques
generally use the detection of walking steps (“step
detection”) together with a method for heading
determination. Such techniques have seen huge accuracy
improvements in recent research (e.g. [8,10,11,15,16,17])
but are unsuitable for localization in underground public
transport systems where step detection is not an option.
Transportation Mode Detection
Wang et al. [19] showed that accelerometers on mobile
devices can be used to detect a user’s mode of transport
(e.g. walking vs. biking vs. driving). In this research, they
used a formula for acceleration synthesization to learn the
patterns of different transportation modes. We implement
their formula in our SubwayPS technique by using it to
detect if a train is moving or not. Hemminki et al. [7] have
recently achieved improved accuracy on transport mode
detection by providing a new algorithm for estimating the
gravity component and key characteristic pattern detection,
which involves the detection of mode- specific patterns
such as step detection for walking and smooth accelerations
for vehicle movements.
Positioning in Public Transportation Systems
The final area of work related to SubwayPS comes from the
literature on positioning in public transportation systems.
For instance, the EasyTracker system [2] provides a
location tracker for public transport that uses GPS traces
from above-ground transit vehicles (busses), but requires all
vehicles to be instrumented with equipment such as GPS
receivers and inertial navigation sensors. Zhou et al. [20]
developed a positioning technique that combines a variety
of sensor measurements from mobile devices owned by the
people on an above-ground transit vehicle to calculate an
average location. Thiagarajan et al. [18] describe a crowd-
sourced transit tracking system intended to improve real-
time arrival and departure estimates. As part of this
research, Thiagarajan and colleagues looked at subway
systems, but their approach is not designed for positioning
on mobile devices and requires a connection to a tracking
server, which is often not available while on a subway.
To summarize, in this paper we present the first smartphone
positioning technique (SubwayPS) for underground public
transportation systems that relies solely on the user’s
mobile device. As a result, our technique does not require
the infrastructure [2], connectivity [18], or the critical mass
of users [20] needed by existing approaches in this area.
Instead, SubwayPS merely requires a single accelerometer
and gyroscope, both of which are built into most modern
smartphones. In addition, SubwayPS has important privacy
benefits: because all calculations are done locally on the
user’s mobile device, SubwayPS does not share a user’s
position with any central server or third parties.
THE SUBWAYPS TECHNIQUE
With the SubwayPS technique, we show that it is possible
to achieve reasonable positioning accuracy on underground
public transportation networks. Our results suggest that
SubwayPS is usable as-is on most subway systems in the
world. We also show how accuracy can be improved by
fine-tuning certain parameters to the characteristics of a
specific underground public transport system.
Collecting accelerometer data in a pretest on multiple
underground public transport systems showed that station
locations overlapped with periods in our data collection
when there was generally less acceleration on all axes of the
accelerometer. This is due to the fact that train movement
results in multiple small accelerations in all directions (i.e.
the small amount of shaking back and forth experienced by
passengers when a train is at high speed). As such, by
measuring the amount of variance on all axes, it is possible
to detect whether the train is moving or not.
Figure 2, which shows data collected on the London
Underground subway system, demonstrates this
phenomenon in more detail. Figure 2a depicts the
accelerations measured on each axis of the accelerometer
over a small period of time. Stations are clearly visible in
the data in that they have accelerations closer to zero and
less variation (around minute 1 and 2.7 in Figure 2a). By
calculating a general acceleration value based on the three-
axis input of the linear accelerometer data, it is possible to
derive a statistic a that has a large value if there is a large
amount of acceleration on any (or all) of the axes. To
calculate this value, we use the following formula for
acceleration synthesization [19]:
𝑎=!𝑥!+!𝑦!+𝑧!
The resulting a values are smoothed by a rolling average
with a window of n samples. We found n = 100 samples at
50 Hz to be an appropriate general window. Lower values
would confuse user movement of the smartphone as false
train movement (see Study 2 for more details), while higher
values would increase the delay between events (e.g.
subway stops) and their detection by SubwayPS. Plotting
out a results in a graph like that shown in Figure 2b.
By comparing the smoothed data with a certain threshold, it
is possible to conclude if a train is moving or not. The
SubwayPS technique uses multiple parameters to parse the
data and uses states to define if a train is moving or not,
Figure 2: In Figure 2a, a visualization of accelerometer
values measured on each axis is shown. The blue dotted lines
indicate time in minutes while the red intervals indicate
stations. The data was collected on the Central line in
London. Figure 2b shows the result of the acceleration
synthesization used in SubwayPS, smoothed by a moving
average. This result is compared to the threshold to detect
train movement.
SubwayPS makes use of a smartphone’s accelerometer and
gyroscope by combining the accelerometer measurements
on all three axes to detect underground train movement.
The gyroscope is used to filter the gravity factor out of the
accelerometer measurements. As such, the accelerometer
reads (x = 0 m/s², y = 0 m/s², z= 0 m/s²) when the mobile
device is at rest and the coordinate system is defined
relative to a default orientation of the accelerometer sensor.
The “axes are not swapped when the device’s
screen orientation changes” [22]. In our implementation
of SubwayPS, we made use of the virtual linear
accelerometer provided by the Android operating system,
which handles the gravity removal internally.
based on a window of samples. The parameters considered
are:
n = window size of moving average
𝛾 = threshold
𝛿!"#$% = min. amount of samples below threshold
𝛿!"#$% = min. amount of samples above threshold
The values for each of the four parameters was determined
empirically using data from four different subway systems
as described below in the evaluation section. SubwayPS
requires at least 𝛿!"#$% samples below the threshold before
it marks the current accelerometer measurements as a stop,
while at least 𝛿!"#$% samples above the threshold are
required for SubwayPS to conclude that the train is moving.
These values are used to prevent flagging user movement as
train movement (Study 3).
The accuracy of SubwayPS on individual public
transportation networks can be improved by customizing
the values of 𝛿!"#$% and 𝛿!"#$% for each network. For
instance, increased accuracy for the London and Cologne
subways can be achieved by using the values in the second
and third columns of Table 1, respectively. Trains in
London accelerated more slowly, resulting in a longer
period required to reach 𝛿!"#$% , while in Cologne, trains
accelerated rather quickly. Because the threshold was
reached much faster in Cologne, 𝛿!"#$% could be increased
to decrease the chance of false positives.
While the “world-wide” parameters were empirically
determined to work reasonably well on all subway systems
tested (see below), the parameters specifically tuned to the
data we collected on each subway system can increase local
performance. For example, using the world-wide
parameters for SubwayPS on data collected in London
results in an accuracy of 74.2%, while using the London
parameters increases the accuracy percentage to 85%
(Study 1).
Our stop detection method is complemented by a linear
interpolation of the current position based on the time
intervals between stations as defined in official timetables.
By interpolating over time, we can estimate the absolute
location between the previous and the next station. If
SubwayPS detects a stop that is not scheduled, SubwayPS
marks it as an “in-between” stop and uses the interpolation
of location over estimated time to show a position to the
user. Using this method we can provide the users with a
position estimate of where exactly the train stopped
between two stations. We consider any stop that occurs
with less than 70% of the estimated time between stations
to be an “in-between” stop, a number that was determined
empirically from our Study 1 data (see below). This
percentage can be set to a higher value if a timetable is
available with seconds-level precision, which was not the
case with the four subway systems considered here, all of
which list timetables at the precision of minutes.
IMPLEMENTATION
We implemented a working version of SubwayPS on the
Android smartphone platform, as well as a proof-of-concept
SubwayPS-based Android application called
MetroNavigator, which has both smartphone and
smartwatch versions. We first describe our implementation
of core SubwayPS and then discuss MetroNavigator.
Implementation of SubwayPS
MetroNavigator Smartphone Application
MetroNavigator (Figure 3) is a proof-of-concept application
we built that uses SubwayPS as its positioning technology
(by implementing an intent listener). The application’s UI
consists of two main parts. The upper part contains a
miniature map with an indicator of the user’s current
position between the previous and next station, as well as
the origin and destination station, the estimated time of
arrival and the number of stops to the end station. The
lower part of MetroNavigator’s UI is used to display event
cards. Currently our MetroNavigator application supports
multiple different types of event cards:
World-wide
London
Cologne
𝛾
0.2 m/s²
0.2 m/s²
0.2 m/s²
𝛿
!"#$%
250 samples
250 samples
250 samples
𝛿
!"#$%
350 samples
250 samples
500 samples
n
100 samples
at 50 Hz
100 samples
at 50 Hz
100 samples
at 50 Hz
Table 1: General, “world wide” parameters for
SubwayPS, as well as those specifically tuned for London
and Cologne.
SubwayPS makes use of the virtual linear accelerometer
class provided by the Android OS. It “measures the
acceleration force in m/s2 that is applied to a device on all
three physical axes (x, y, and z)” [22] without the
influence of gravity. These accelerometer measurements are
interpreted by the SubwayPS technique, which is
implemented as a background service and sends intents
(messages) such as StationDetected or MovingDetected to
the Android OS, which then forwards them to all
applications that implement a listener for these intents. Any
application (e.g. MetroNavigator) could implement an
intent listener and make use of our detection algorithm.
This background service is just a proof of concept.
Preferably, the Subway P S techni q u e w o u l d b e
implemented on an OS level such as within the
location services of Android.
1. The first, and most-often-used type is the
notification event card shown in Figure 3b. When
the train approaches the next station, it shows the
name of the next station and can indicate possible
connections. When the train approaches the final
station, this is prominently indicated.
2. The application also triggers event cards when the
train undergoes unplanned stops in between
stations. This is shown in Figure 3c.
3. Another event card type notifies the user of “rude”
driving by the train conductor based on measured
accelerations. This is shown in figure 4 and one
can share this event on Twitter (once connectivity
is obtained) with a direct mention of the public
transportation company.
4. MetroNavigator also contains point-of-interest
(POI) event cards that contain information about
POIs that are located above the user or at the next
station (see Figure 3e) and provide a link to more
information about the POI on Wikipedia, which is
locally stored on the device.
The MetroNavigator Smartwatch Extension
We also implemented a smartwatch version of
MetroNavigator that can be linked to an Android device
running SubwayPS via Bluetooth. Due to screen size
constraints, the smartwatch version has a much simpler UI
than the smartphone application. It displays to users only
the current or next station, as well as the estimated time of
arrival. A screenshot of the smartwatch application is
shown in Figure 5.
The smartwatch version also has a feature not yet available
in the smartphone version: when users reach the final
subway station of their trip, the smartwatch version tells
users in which direction to go to reach their destination
(Figure 6). In addition, when selecting a POI in the
smartphone app, the smartwatch will show the direction to
go to reach the POI once the user has reach the next station.
Figure 3: MetroNavigator application showing different “event cards”.
Figure 4: Sharing an event card of MetroNavigator with a
few clicks.
EVALUATION
To evaluate SubwayPS, we conducted three separate
studies with the help of the MetroNavigator application.
The first study was focused on assessing the technical
soundness of the positioning technique in a variety of
public transportation systems around the globe using log
data. The second study was designed to compare SubwayPS
against a timetable-only baseline and to evaluate the effect
of arbitrary smartphone movements by users. The third
evaluation was focused on understanding the accuracy and
user experience of the MetroNavigator application in the
context of actual subway journeys made by locals and
tourists.
Study 1
The goal of our first study was to collect accelerometer data
from a variety of diverse subway systems and use this data
to inform and evaluate SubwayPS.
Participants & Apparatus
For the data collection process, we developed an Android
application that captures and stores the data measured by
smartphone accelerometer sensors. The data collection
application supports the public underground transportation
network of four major cities, namely Brussels, London,
Cologne, and Minneapolis. Each of these subway systems
has unique characteristics: they use different trains, some
go above ground, and others merge with vehicular traffic.
We wanted to study a diverse set of subway systems in
order to support broader generalizability.
To record data, participants first selected a city, a line and
an origin and destination station. Participants were advised
to select the start and end station of their journey before
boarding the train and were told to mainly place their phone
in their pockets.
We recruited twelve participants that downloaded the data
collection application to their mobile device and asked them
to record data “whenever they use an underground public
transport system”. We also asked them to record very long
tracking segments to get tracks that are longer than normal
underground rides so our algorithm could be tested
exhaustively (21.51 minutes trip length vs. about 9 minutes
in the follow up studies). Most of the participants were
Android developers and computer science students and
collected the data on a voluntary basis on their daily
commutes or on business trips. These participants collected
a total of about 70 tracks in a period of around two months
and we received tracks from every underground transport
system supported by the application.
Results
The average number of stations per track was 9.23 and the
average track length was 21.51 minutes. We analyzed this
data to determine the values for each of the SubwayPS
parameters described above (e.g. 𝛿!"#$%, 𝛿!"#$%). Using the
subway system-specific values shown in Table 1, we found
that for London, 103 out of 120 stops (105 stations and 15
“in-between” stops) were classified correctly (85.8%
excluding the start stations). 10 stations and 7 “in-between”
stops were missed. No false positives (detection of a stop
where there was none) were recorded.
This can be directly compared to the results of the same
tracks tested with the “world-wide” parameters, which
caused SubwayPS to correctly detect and classify about
74.2% of these stops.
Similar results (around 85% in comparison to around 75%
for world-wide” parameters) were measured in the other
three subway systems.
These results are somewhat comparable to those of a study
done by Thiagarajan et al. [18]. Using four tracks in the
subway system of the Chicago public transportation
network (as opposed to our 70 tracks across 4 subway
systems), Thiagarajan et al. were able to achieve around
55% accuracy (compared to our 85.8%), although it is
important to note that their focus was on station detection
Figure 5: Screenshot of the SubwayPS smartwatch
application that provides basic feedback to the user without
needing to take the smartphone out of their pocket.
Figure 6: Screenshot of the SubwayPS smartwatch
application that provides guidance on in which direction to
exit the final station to reach a certain POI.
rather than location prediction (which involves detecting in-
between stops). In Study 3, we show that SubwayPS also
strongly outperforms a “timetable” baseline.
Examining where SubwayPS failed, we noticed that trains
can run smoother” on certain lines (such as parts of the
Hammersmith & City line in London) than others in a given
subway network. Because SubwayPS’s parameters were
tuned on a subway system-wide basis, this variation
resulted in errors. We are working to implement track-level
parameters, which should increase accuracy by a substantial
margin.
Another issue arose out of huge variations in travel time for
trips between the same two stations. We recorded multiple
readings over multiple days for some lines and noticed a
standard deviation of about 7.12 minutes for a trip that
normally takes 29 minutes. We looked into this particular
case, and found out it was due to extensive waiting times at
stations when trains were overcrowded. These delays could
cause misclassification due to the integrated time-tables, as
“in-between” stops could be classified as a station if the trip
takes too long. An example of these variations in trip length
is visible in Figure 7. The graphs depict two trips between
the same stations and differ greatly in the in-between
measurements.
Finally, another source of error was the quality of
accelerometer data. For example, badly calibrated
gyroscopes in all data captured by Samsung devices
resulted in incorrect measurements by the virtual linear
accelerometer (e.g. accelerations of above 1 m/s² on some
axes while at rest). It would be straightforward to correct
for these errors on a device-by-device basis if these errors
are known a-priori.
Study 2
A second user study was conducted with the
MetroNavigator application to directly compare the use of
SubwayPS against an approach that merely interpolated
from an official timetable.
Procedure
The study was conducted across two consecutive days
between 8 and 6 pm in May 2014 on the Cologne, Germany
subway. One experimenter engaged in 50 trips in which he
randomly boarded a train, travelled with it for exactly seven
stations and then left the train and boarded the next train
arriving at that station. In order to compare against a
timetable baseline, the experimenter recorded the exact time
at which the train he was riding arrived at each station. No
restrictions were placed on the placement of the smartphone
in this study. The Study 2 experimenter performed common
activities normally executed on a smartphone as reported by
Böhmer et al. [3], which involved periodically having the
phone in his hands and storing the phone in his pocket
during the trips.
Results
Across all 50 trips, 282 of 335 stops (300 stations and 35
“in-between” stops) were classified correctly (82.7%,
excluding the start stations; 85.0% including the start
stations). 35 stations and 18 “in-between” stops were
missed. Again, no false positive stops were recorded. These
results are in line with our previous results from Study 1
and Study 3 presented later in this paper.
To evaluate SubwayPS against the timetable baseline, we
compared the actual arrival time at each stop on all 50 trips
to (1) the arrival time indicated by SubwayPS and (2) the
arrival time indicated by the official subway schedule.
Using a 30-second “tolerance” window, we found that
SubwayPS tracked 39 entire trips with perfect accuracy
(78% of trips), while using the official timetable, only 21
entire trips (42%) were accurate.
The challenges presented to timetables from the “ripple
effects of a single delay have led some public transport
systems to abandon timetables for an “interval”-based
approach. In this approach, which was recently examined
by Pritchard et al. [12], service is guaranteed every n
minutes rather than at specific times. To understand
SubwayPS’s performance in this type of subway network,
Figure 7: Example of day-to-day variability measured on the Piccadilly line in London. Both graphs picture the data
calculated by SubwayPS. Stations A, B, and C are clearly visible. These graphs provide an example of how much variability
there is in the measurements. Times between the same two stations can vary greatly (see Track A-B) with a difference of
almost one minute. The period during which a train stands still at a station can also vary greatly (see B).
we also evaluated SubwayPS against a relative time
baseline, which uses just the reported travel time between
stations. This baseline was able to track 25 out of 50 trips
correctly (50%), as opposed to SubwayPS’s 39 of 50.
The trip-level accuracy of SubwayPS is directly influenced
by trip length and accumulation errors (if one station is
missed, the trip is not tracked correctly), which are known
issues for inertial navigation techniques (i.e. “drift” errors).
Fortunately, there has been extensive research on solutions
for recalibration in inertial navigation. We are currently
working to adapt one of these solutions to a public
transportation network context.
Study 3
To test the “in the wild” feasibility and appeal of SubwayPS
and its proof-of-concept implementation in
MetroNavigator, a user study was conducted.
MetroNavigator was pre-installed on a Google Nexus 4
device and the device was handed out to randomly selected
passengers waiting at different stations in the Cologne,
Germany subway system (lner U-Bahn). Due to novelty
effect concerns, we focused on the smartphone version of
MetroNavigator for the evaluation (instead of the
smartwatch version) and restricted our participant
population to subway riders who were already using an
Android device.
Sixteen participants (8 male, 8 female) with an average age
of 39 years took part in the study. Eight participants were
employees at different local companies in Cologne, seven
participants were tourists, and one participant was a student
at a local university. The study was conducted across two
days between 8 and 10am (peak travel time) within the
period of a week in February 2014.
Procedure
The experimenter approached passengers that were using
Android devices at one of the selected subway stations to
invite them to participate in the study. If they agreed, the
experimenter read a quick script that explained the purpose
and functionality of MetroNavigator and informed the
participant that the experimenter would be riding with
her/him to their final destination. Participants were then
asked to enter their start and end subway stops and were
also requested to “think aloud when interacting with the
application. The script was kept short as we did not want to
cause our participants to miss their train. The experimenter
boarded the train with the participants and recorded their
reaction during the ride.
Due to the relatively short nature of subway trips and our
desire to have participants engage with the application,
participants were asked to hold the device in their hands
during the trip and not store it in their pockets. The
robustness of SubwayPS to normal user movement was
established in Study 2. After leaving the train, participants
filled out a background questionnaire about their age,
gender and occupation, and the experimenter conducted a
semi-structured interview. The experimenter also explained
how the system derives position information by using the
accelerometer of the smartphone and the smartwatch
extension was explained to the participants to get feedback
on its possible uses.
ResultsAccuracy
The participants took the train for an average of 6 stations
(including the start and end station) and the average time on
the train was about 8.75 minutes. 78 out of 91 stops (80
stations and 11 “in-between” stops) were classified
correctly (85.7 % excluding the start stations; 87.9 % also
including the start stations). Eight stations and 5 “in-
between” stops were missed. No false positives were
recorded. Out of the 16 trips, 12 were tracked completely
correctly (75%).
ResultsQualitative Feedback
During the interviews, 15 participants expressed a positive
opinion of SubwayPS and were interested in using
MetroNavigator (and other applications using its SubwayPS
engine) in the future. We received comments like “As my
GPS does not work here, this is like a GPS for
undergrounds trains I want to have it integrated in
Google Maps” (P3) or It is just cool to see the train
moving and stopping on the map I feel safer when I can
see that we have stopped close to the next station when
we’re stopped in the middle of a tunnel (P11). The main
advantage of MetroNavigator for most participants was the
ability to be guided to their destination stop (14 out of 16
participants) and general location awareness (12 out of 16
participants). “Wow - This is like magic” (P3) was the
comment of one participant when the visualization of her
current position stopped between two stations as the train
did. She commented at first, I though this app is a bit
boring as it just moves a metro along with the schedule
from station to station, but now I see, that it has GPS”.
Participants also enjoyed specific features of the
MetroNavigator app. In particular, several of the tourists
found value in MetroNavigator’s POI information cardsas
[they] can easily build local knowledge of a city(P2). The
smartwatch was generally thought to be a useful extension
for such an application. One participant remarked as I do
not want look at my smartphone all the time, this
(SubwayPS) could be the killer app for smartwatches in the
future” (P13, an IT consultant).
During the ride of one participant the MetroNavigator
application missed three stops in a row. She commented on
that by saying Oops, it seems that the system missed a stop
that is not good. I would also be happy to help the system
to detect the stops, if the system could help me to get out at
the right station. That would be totally fine with me(P3, a
tourist). Other critical comments were targeted at the
limited set of POIs in the app (e.g. Can it also show shops
and café places?”, P3) . In our prototype, we stored just a
few POIs and the dataset could be easily extended.
DISCUSSION AND CONCLUSION
In this paper, we presented SubwayPS, a smartphone
positioning system that puts users back on the map” when
they are traveling in underground public transportation
networks. SubwayPS does not require any instrumentation
of the environment, meaning it can be implemented without
any expense to the often-cash-strapped operators of public
transportation systems. SubwayPS merely requires an
accelerometer and a gyroscope both of which are standard
on many modern smartphones and a start and an end
destination as input, which can be inferred from user
behavior (as in Google Now) or extracted from routing
requests.
Our evaluation showed that SubwayPS works “out of the
box” with four subway systems from two different
continents. However, we also showed that SubwayPS’s
accuracy can be increased if its four parameters are tuned
specifically for an individual subway network. This tuning
is simple, and merely requires a single user to collect
accelerometer data as they travel on the subway.
In the short term, SubwayPS is straightforward enough to
be implemented directly into a mobile app as we have done
with MetroNavigator, our proof-of-concept SubwayPS
application. We have taken care to ensure that any
developer who wishes to implement SubwayPS can do so
by following the instructions (and using the parameters)
laid out in the “SubwayPS Technique section above.
However, if SubwayPS (or a technology like it) were built
into a mobile OS, it would have the greatest impact,
allowing all location-aware mobile apps including mobile
map apps to function while their users traveled in subway
networks.
That said, before OS integration can occur, several
limitations of SubwayPS must be addressed. In its current
implementation, accuracy at a trip level is highly dependent
on trip length due to accumulation errors. Fortunately, there
are various solutions to this issue. For instance, in many
subway networks, subway stations have localized Wi-Fi
coverage. This means that traditional Wi-Fi positioning
techniques can be used to correct any errors before they
accumulate. In addition, per-stop accuracy can likely be
improved by using relatively straightforward machine
learning approaches rather than the simple, simple subway
system-wide approaches considered here.
Future work is proceeding along three directions. First, we
are working to implement Wi-Fi-based positioning
correction directly into the SubwayPS technique to correct
for drift errors. This should greatly increase per-trip
accuracy rates. Second, we are working on developing more
sophisticated stop detection approaches using trained
models as well as making small changes to support per-
track parameters. Finally, we are considered the means by
which crowdsourcing may be used to collect training data,
as suggested by P3 in Study 2.
ACKNOWLEDGMENTS
This research was supported in part by a Google Faculty
Research Award, a 3M Non-Tenured Faculty Award, a
Yahoo! ACE Award, and BOF project R-5209. The authors
would like to thank our anonymous reviewers for their
feedback.
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reference issue that appeared in the original version.
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We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.
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Conference Paper
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