Cycle4Value: A Blockchain-based Reward System to Promote Cycling
and Reduce CO
2
Footprint
Alexander K. Seewald
1
, Mihai Ghete
4
, Thomas Wernbacher
2
, Mario Platzer
3
, Josefine Schneider
3
,
Dietmar Hofer
4
and Alexander Pfeiffer
2
1
Seewald Solutions, L
¨
archenstraße 1, A-4616 Weißkirchen a.d. Traun, Austria
2
Donau Universit
¨
at Krems, Dr.-Karl-Dorrek-Straße 30, A-3500 Krems, Austria
3
Yverkehrsplanung, Brockmanngasse 55, A-8010 Graz, Austria
4
Bike Citizens Mobile Solutions GmbH, Kinkgasse 7, A-8020 Graz, Austria
Keywords:
Cycling, Mobility of the Future, Blockchain, Reward Model, Cheating Detection, Fraud Detection.
Abstract:
In Cycle4Value (C4V), a transparent and low-threshold reward model to promote cycling based on the key
technology blockchain is being researched and tested in practice for the first time. The economic, health and
ecological benefits are presented in a simple and comprehensible way and, after a plausibility check using a
pretrained machine learning model, are converted into a real value, i.e. a cycle token. These units of value are
stored in a digital wallet and can be reimbursed in a marketplace set up for testing. The research project goes
beyond conventional incentive systems, since 1) the storage of the value units as well as the payment process
is decentralised, tamper-proof and transparent, and 2) the real economic and environmental benefit of active
cycling is monetized in a fair manner. Initially we describe the background of the project. The main part of
this paper concerns ongoing work on the plausibility check which also needs to be able to detect cheating.
1 INTRODUCTION
It is well known that global warming – mainly driven
by too high CO
2
concentrations in the atmosphere –
is a pressing issue and necessitates significant changes
in consumer behaviour over a relative short time pe-
riod. While legislation may helpful in forcing busi-
nesses to change their behaviour, consumer behaviour
is much harder to influence directly. One effect that
could be easily obtained would be to promote cycling
in cities instead of other means of transportation as it
is almost completely CO
2
free.
However, despite various measures to promote cy-
cling, the overall proportion of cycling in Austria has
improved only slightly in recent years (Tomschy and
Steinacher, 2017; Illek and Mayer, 2013). As part
of its mobility strategy, the Austrian Federal Govern-
ment has therefore set itself the target of doubling
the proportion of cycling within seven years. To this
end, not costly infrastructural measures but motiva-
tional or behavioural approaches should be pursued.
In this context mobile apps for tracking and incentivi-
sation of one’s own data – as a technological manifes-
tation of the quantified self – are developing rapidly.
Table 1: Monetary benefits by indicators and action sphere
for each bicycling kilometer. All values in e per km.
Action
sphere
Indicators
Monetarized savings
per bicycle kilometer
per
indicator
per
sphere
Ecology
Reduced CO
2
0.025
Reduced NO
x
0.0105
Reduced NH
3
0.0003
Red. NMVOC 0.00022 0.0377
Reduced SO
2
0.00002
Reduced PM
2.5
0.00006
Reduced PM
10
0.0009
Economy
Red. individual
mobility costs
0.26 0.26
Health
Health
promotion
0.94
1.07
Improvement
of traffic safety
0.13
Total
benefits
1.37
Sweepstakes or even performance-related rewards are
paid out depending on the underlying gratification
system in the form of cryptocurrencies. Blockchain
technology has a significant potential in the handling
1082
Seewald, A., Ghete, M., Wernbacher, T., Platzer, M., Schneider, J., Hofer, D. and Pfeiffer, A.
Cycle4Value: A Blockchain-based Reward System to Promote Cycling and Reduce CO2 Footprint.
DOI: 10.5220/0010318010821089
In Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021) - Volume 2, pages 1082-1089
ISBN: 978-989-758-484-8
Copyright
c
 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

of user data due to its decentralization, transparency
and security (Buhl et al., 2017). Applications on the
blockchain are treated as disruptive innovations for
a wide range of applications: from transaction pro-
cessing to land register entries to logistics chains, the
middleman is to be cut out in the future, and data is
to be stored in a forgery-proof and decentralised man-
ner (Hopf and Picot, 2018). The application of these
innovative measures could provide the impetus to pro-
mote cycling with all the associated benefits in terms
of reduced emissions, positive health effects and re-
duced infrastructural costs.
The solution envisaged in our project Cycle4Value
(C4V) will reward cyclists for regular cycling by
means of so-called Cycle Tokens. As such it is related
to other approaches which focus on promoting physi-
cal activity through the use of new technologies such
as (No
¨
el Racine et al., 2020; Van Hoye et al., 2019;
Weatherson et al., 2017). In our case, the technology
used in the form of the Ardor Blockchain
1
represents
an energy-efficient solution for validating route data
and transaction processing. In the sense of a proof of
concept, it is to be tested whether and how a safe and
transparent process of value generation for regular cy-
cling can be created via utility token, which can trans-
late the macroeconomic effects of cycling into value
units.
In the course of a broad field test in Graz, Berlin
and Krems, the usability, acceptance and scalability of
the developed solution will be analysed. The cost sav-
ings at individual and collective level have been eval-
uated in close cooperation with a stakeholder board
and will ultimately be transferred into a market place,
which will enable the exchange of cycle tokens into
incentives such as discounts or spare parts.
Within this project we have already estimated the
cost savings, or benefits, per cycling kilometer for a
variety of spheres and indicators – see Table 1. It
can be seen that within the ecological sphere, CO
2
has the highest benefits at e 0.025. Health has even
larger benefits at e 1.07. These numbers are based
on a variety of public data sources and were refined
in interviews with domains experts. Since the focus
within our research project C4V is on the benefits of
cycling, the costs of cycling were neglected. This
normally leads to an overestimation of the benefits.
However, since all non-monetary indicators that have
an additional benefit are not considered, it can still
be assumed that the monetary end values are rather
conservative. All calculations are based on the sim-
plifying assumption that each bicycle ride represents
a saved car ride. While this simplification does not do
justice to the complexity of reality, it still represents
1
https://www.jelurida.com/ardor
a common scientific approach to calculating costs or
benefits.
The remainder of this paper focusses on the ongo-
ing work within this project concerning plausibility
and cheating detection to ensure that cycle tokens are
only obtained via live bike rides.
2 RELATED RESEARCH
As we found out from extensive literature research,
the task of identifying fraud or finding cheaters in
the bike sector (i.e. those tracks that do not origi-
nate from live bike rides) is completely new. Only
the well-examined area of Transportation Mode De-
tection (TMD) is related, as cheaters sometimes use
other means of transportation than bicycles.
2
In later
experiments we will show that pre-trained systems for
TMD show competitive results compared to specifi-
cally trained systems and can reconstruct plausibility
according to a legacy system about equally well, but
without having seen the GPS tracks to be classified
before.
2.1 Motion Detection with 2D/3D GPS
Tracking
This group of systems detects means of transport us-
ing 2D (without altitude) or 3D (with altitude) GPS
position data. Since up to now we only had 3D GPS
data available (see Table 2), these are the only systems
that could be tested directly.
(Dabiri et al., 2019) describes a state-of-the-art
deep learning system for motion detection based on
2D GPS data. Both classic and DL-trained features
are used, but the difference is relatively small (see also
(Etemad, 2018)). The GeoLife 1.3 dataset was used
for training. Only transportation modes for walking,
cycling, bus, car/taxi and subway/railway/expressway
were trained. The GPS tracks were divided into seg-
ments when breaks were taken or a maximum length
was reached. An overall accuracy of 76.8% and F
1
(i.e. the balanced F-measure) of 0.764 was achieved.
A thesis by the same author gives more precise details
as well as the complete training code including the
data used for the training, which enabled us to recon-
struct the pre-trained system exactly. However, the
2
In Section 3, some attacks are mentioned where this is
not the case, and which could not be detected by a TMD
system, notably 1, 4 and 6.
3
All plausibility values were recomputed with latest
legacy model.
4
See (Zheng et al., 2010; Zheng et al., 2009).
Cycle4Value: A Blockchain-based Reward System to Promote Cycling and Reduce CO2 Footprint
1083

Table 2: Datasets used within this paper. All contain GPS-3D data (longitude, latitude, altitude, timestamp).
Name #GPS tracks #GPS points Class Created by
Publicly
available
BC-GPS-10k 10,000 10,861,318 Plausibility
Bike Citizens
Mobile Solutions
No
BC-GPS-4M 4,082,450 4,439,163,525 Plausibility
Bike Citizens
Mobile Solutions
No
BC-GPS-4M-RCP
3
4,082,450 4,439,163,525 Plausibility
Bike Citizens
Mobile Solutions
No
GeoLife 1.3
4
17,621 24,876,978
Transport Mode
(on subset of data)
Microsoft Yes
written code was not scalable on several levels – run-
time, main memory and hard disk space requirements
– and required extensive modifications to process our
BC-GPS-4M dataset.
(Etemad, 2018) describes a PhD thesis that uses
classical learning algorithms (Random Forest, Sup-
port Vector Machines, Decision Trees, Multi-Layer
Perceptron, AdaBoost, ...) and handcrafted features
as well as other preprocessing steps. In his section
4.4. it is shown – among other results – that the ob-
tained performance is significantly better than the one
described in (Dabiri et al., 2019), but only at a signifi-
cance level of p=0.0796 (92.04%) which is less strin-
gent than the standard 95% level normally used. This
shows that in this field Deep Learning algorithms are
not yet superior and that classical learning algorithms
can still obtain equivalent results.
(Nawaz et al., 2020) describes a Long-Short-
Term-Memory (LSTM) Deep Learning model for
motion detection. It was again trained on the GeoLife
dataset as in (Dabiri et al., 2019; Etemad, 2018), but
unlike there, the class subway/railway/train was not
considered here. The results (Accuracy=83.81% and
F
1
=0.8397) are therefore not directly comparable and
are in fact slightly better than the other papers – possi-
bly because of the smaller number of classes. Again,
classical features were calculated (i.e. speed, acceler-
ation, and other derivatives of position) and the GPS
tracks were divided into individual segments. To take
advantage of the good performance of Deep Learning
networks on image data, the tracks were then scaled
and mapped to a 2D image and provided as additional
input. For the high effort involved, however, the re-
sults were not better than much simpler methods. For
example, the LSTM model already achieves an accu-
racy of 79.15% without these 2D image features and
the results in (Dabiri et al., 2019; Etemad, 2018) are
not much worse.
In general, it was surprising that - contrary to the
use of deep learning networks in speech, image and
video processing - no competitive Deep Learning sys-
tem yet exists that can process GPS data directly with-
out preprocessing. Also, (Dabiri et al., 2019) uses
mostly classical preprocessing, and the most impor-
tant features identified are relative distance, speed and
acceleration, which are also often used in classical
systems. As expected, learning algorithms are uni-
versally used.
2.2 Motion Detection with Local Motion
Sensors
(Soares et al., 2019) compares numerous local An-
droid mobile phone sensors (including some derived
from multiple sensors such as Orientation), classical
and deep learning learning algorithms on the rela-
tively small TM dataset. As pre-processing, common
time-based features (max, min, entropy, average, vari-
ance, median, standard deviation, quartile, ...) and
FFT features (spectrum, highest amplitude, spectral
density and entropy, ...) were calculated on the raw
data of all sensors. The used locomotion classes were
standstill, car, walking, railway and bus.
5
The prop-
agated deep learning model TMD-LSTM is not the
best model (Acc=90%, F
1
=0.90), but with 314 KB
it is relatively small. However, the Decision Tree
model is half as large and similarly good (Acc=91%,
F
1
=0.87) and certainly has comparable computational
complexity.
(Vu et al., 2016) describes one of the few mod-
els that can process sensor data directly without pre-
processing. However, it is limited to accelerometer
sensors. Here, the somewhat older HTC dataset is
used, from which the classes standstill, walking, run-
ning, bicycle, and other means of transport (a com-
bination of motorbike, car, bus, underground, train)
are trained. The sensor data is divided into windows
of 12s length, but the system can also predict a class
every 165ms after the training with a few tricks.
6
A
5
Sadly, cycling is missing.
6
A runtime of approx. 28ms per prediction is given, al-
beit without information about the computing platform.
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
1084

new type of recurrent network, CGRNN, is presented
and trained. The result is an overall window-level ac-
curacy of 93.10% (90.93% for bicycle only), which
is a very good result for a single sensor without any
explict preprocessing.
There is still a lot of research in the related field of
activity detection using similar sensors, preprocessing
steps and models, but cycling is not normally included
as a separate activity. For this reason, this kind of
work is not considered here.
Finally, it can be seen that of the local sensors, the
accelerometer seems to be the most important. Since
the gyroscope is already universally used and can be
read out easily with little energy consumption, we will
also consider it. With the derived sensors it should
be noted that although they save some computational
effort (e.g. for Orientation, which calculates the di-
rect orientation of the smartphone to Earth – thanks
to Gimbal Lock and other special cases a relatively
complex algorithm), they do not necessarily exist uni-
versally in the same way on all platforms. So a review
of the existing APIs (interfaces) would certainly be a
first useful step.
2.3 Combined Models for Motion
Detection
(Nitsche et al., 2014) describes a motion detection
system using GPS and accelerometers as a comple-
mentary source of information for travel surveys. The
classes used were walking, bicycle, motorbike, car,
bus, tram, metro, rapid transit and standstill. A sepa-
rate test set of 2,089 trips (of which about 50% were
walking and 10% were cycling) was recorded and
used for training and evaluation. As learning algo-
rithm an ensemble learning algorithm and a DHMM
were used to reconstruct the class and additional tran-
sition probabilities. The pre-processing of the sen-
sor data is similar to (Soares et al., 2019) (i.e. time-
and FFT-based features in a fixed window). With
leave-one-out cross-validation, precision for bicycle
was 0.88, recall 0.95 (F
1
=0.91) and accuracy 95.95%,
which is slightly better than in (Vu et al., 2016). The
overall accuracy was 80% across all classes, but for
more and more similar classes than in other systems.
(Feng and Timmermans, 2013) compare self-
trained systems with GPS, accelerometers and com-
bined data. The combined data gave the best re-
sults. A number of time-based features were used
as pre-processing, including derivations from altitude
according to GPS (i.e. 3D GPS), which is welcome as
so many papers ignore GPS altitude. A Bayesian Be-
lief Network was used as the learning algorithm. The
classes used were Activity, Walking, Running, Bicy-
cle, Bus, Motorcycle, Car, Train, Underground, Tram
and Express Train. A dataset with about 81,000 en-
tries (recorded on identical proprietary devices) was
used. The combined model had detection rates be-
tween 83% (activity, train) and 100% (walking, bi-
cycle, motorbike). For bicycle detection, however,
both the GPS-only and the combined model were al-
ready perfect at 100% and the accelerometer model
was significantly worse at 88%. However, since un-
realistic data was manually removed and the class
Activity is somewhat unclearly defined, the values
mentioned may be a bit too high. With the original
data, the bicycle detection with 95% accuracy in the
combined model and 94% in the accelerometer-only
model shows much less difference, which seems more
plausible to us.
2.4 Alternative Models
(Charv
´
atov
´
a et al., 2017) describes a system for
analysing the pulse rate when cycling using GPS. A
specific cycle route was followed 41 times and the
recorded 3D GPS data was related to the pulse rate.
Different classical ML models were trained to differ-
entiate between up and down cycling classes. This
could be seen both from the altitude gradients of the
GPS data and from the pulse rate (with a time delay)
with accuracies between 93% and 97%. Although
the objective was different and had a medical back-
ground, the clearly recognisable patterns of pulse rate
in cycling would make cheating much more difficult.
In addition, information about the health status of
the user would be directly obtainable. However, the
use of additional sensors is not feasible within our
project.
7
2.5 Blockchain
(Thomas et al., 2019) investigate various blockchain
systems with regard to their applicability for storing
personal metadata for grades and certificates. The re-
quirements for the blockchain system are similar to
those within our project C4V. A public blockchain
system is used, where utility-tokens are generated and
sent under certain conditions. These preconditions are
for example that tokens cannot be forwarded with-
out the operator’s consent, the separation of private
7
It would be possible to use the front camera of the mo-
bile phone – if the holder is positioned appropriately – to
determine the pulse rate from the video data (pulse-by-face)
and thus measure it for such a model. However, this idea
is out-of-scope for this project regarding the complexity of
such an implementation, and it would also increase power
consumption of the smartphone app quite dramatically.
Cycle4Value: A Blockchain-based Reward System to Promote Cycling and Reduce CO2 Footprint
1085

3
4
5
6
7
8
9
-1 -0.5 0 0.5 1
Speed in m/s
Gradient in m
Lowest 10% plausibility
Highest 10% plausibility
Figure 1: Scatterplot of gradient vs. speed, colored by 10%
lowest and highest plausibility.
and public information, the assumption of transac-
tion costs by the operator, the possibility to operate
an own node on the own laptop or smartphone and
even the possibility that private data may be automat-
ically deleted from the blockchain after a fixed period
of time with only the evidence of the transaction itself
remaining.
3 POSSIBLE ATTACKS
The semi-monetary compensation in form of tokens
which are potentially convertable
8
to cash makes
cheating the system (respectively fraud) much more
likely. While catching cheaters is essential to create
and maintain trust in the reward system, and there-
fore false negatives (Type II errors, i.e. not detected
cheaters) should be small, the number of false posi-
tives (Type I errors, i.e. people wrongly classified as
cheaters) should also be small to prevent disillusion-
ment and reduced trust in the reward system. To some
extent this can be optimized by using learning algo-
rithmus that output confidence values and choosing
appropriate thresholds.
The number of tokens generated corresponds to
the square root of cycling kilometers per track, and is
cut-off at two levels: at most 4 tokens per track, and
at most 8 tokens per day, yielding some buffer against
attacks by limiting payoff for a single user. However
as this is probably not sufficient, we have collected
six types of possible attacks on the system. This list
may be of course be incomplete and is only intended
as a first overview.
1. Uploading tracks from sports events (e.g. cycle
racing)
2. Uploading tracks from delivery services
8
E.g. by reselling obtained discounts and spare parts.
3. Uploading tracks made with other vehicles (may
include e-Bikes)
4. Using more than one phone on a bicycle
5. Uploading automatically generated fake tracks
6. Uploading modified real-life tracks
Attacks 1,2 and 3 are not a large problem. In 1, the re-
striction of the maximum number of tokens per track
and day – mentioned above – severely restrict the ob-
tainable gain per user. 2. should not yield any payoff
as these bike rides would happen anyway even with-
out incentives. However as long as only a single per-
son profits, it is again not a large problem. We could
address it by either filtering out common bike routes
for delivery services, or severely reducing their pay-
off. The same approach could be applied to attack 1.
should too many similar tracks be uploaded. Attack
3 should be easily detectable by the fraud detection
system although the differentiation of e-bikes is still
an open problem.
9
The more interesting cases are attacks 4, 5 and 6.
4. should be detectable – especially once we have gy-
roscope and accelerometer sensors – by aligning mul-
tiple tracks and determining their similarity. The local
sensor movements on these tracks will be extremely
similar, which however can only be detected on the
level of multiple tracks and not from a single track.
We will address this in future work.
For attack 5., it is important that not all details
of the system are known, otherwise engineering a re-
verse system is made correspondingly easier. How-
ever, since we focus on machine learning techniques
throughout, it is likely that creating realistic fake
tracks will be very hard, and people will mainly re-
sort to replaying modified existing tracks (6.), which
should again be detectable by their similarity. Also,
only 4-6 (and 3 when combined with 4) ensure po-
tentially unlimited payoff, which is surely the most
tempting fraud scenario. It should be noted that only
attacks 3 and 5 can be addressed at single track level
with models described here. The other four attacks
must be addressed at the level of multiple tracks and
will need completely different learning systems.
As this list may be incomplete, we plan to start a
competition for cheating with appropriate incentives
before deploying the system widely. This should give
us sufficient data on other scenarios not considered
here.
9
We speculate that gyroscope and acceleration data
could be used to determine approximate bicycle mass on
sharp turns, however it would still not allow us to differ-
entiate e-Bikes from Styrian ”Waffenr
¨
ader” (an old type of
bike which weights about 20 kilograms, similar to a modern
e-Bike).
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
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Table 3: Experimental results concerning replication of the legacy model. Precision and recall of minority class no
(plaus.<50).
Dataset Learning Algorithm Evaluiation type Acc. Prec. Rec. F
1
AUC
BC-GPS-10k JRip/RIPPER 10-fold CV 79.67% 0.772 0.776 0.774 0.814
BC-GPS-4M JRip/RIPPER
test set (trained
on BC-GPS-10k)
83.04% 0.432 0.779 0.556 0.824
BC-GPS-4M TMD
test set (trained
on GeoLife1.3)
81.20% 0.950 0.843 0.893 n/a
BC-GPS-4M-RCP TMD
test set (trained
on GeoLife 1.3)
72.02% 0.764 0.877 0.817 n/a
4 EXPERIMENTS
4.1 Legacy Model
At the start of our project, BikeCitizens had al-
ready deployed a plausibility checking system since
2013, primarily to prevent the upload of obvious non-
cycling tracks. It outputs a plausibility of 0 to 100
(in integer values) with 100 being the highest possi-
ble confidence in the given data being a real cycling
track, and 0 the lowest. The algorithm was mainly
tracking speed values and giving positive points for
plausible and negative points for implausible speeds.
These points were summed up separately, thresholded
to manually determined maximum values, weighted
by track length and normalized to obtain the plausi-
bility value. Since 2013, the algorithm was changed
several times in minor ways,
While it is a legacy system and does not corre-
spond exactly to our stated purpose to build a cheating
and fraud detection system, it still remains a resonable
starting point for a first analysis.
Initially – for the rule learning model – we worked
with BC-GPS-10K (see Table 2), but later we received
a much larger dataset, BC-GPS-4M, which was used
for subsequent experiments.
4.2 Rule Learning Model
One somewhat trivial observation is that when cy-
cling, going downhill is usually much faster than go-
ing uphill. If we therefore compute speed and gra-
dient and plot them against each other, coloring by
the lowest and highest 10% plausibility values – see
Fig. 1 – we can distinguish this pattern quite clearly.
The fake tracks mostly show a pattern without any de-
pendence on gradient except for very few outliers, and
in some cases the gradient is even opposite to what we
would expect (i.e. higher speed for higher gradient).
Emboldened by these results, we aimed to repli-
cate the ad-hoc model using the rule learning algo-
rithm JRip, a java-based reimplementation of RIP-
PER by (Cohen, 1995). We first discretized the plau-
sibility value into two approximately equally sized
bins: from 0-49 (no) and the value 100 (yes) on its
own. As input we discretized the gradient (com-
puted between each sample, i.e. once a second
10
) into
four
11
bins: (−∞, −1), [−1, 0), [0, 1) and [1, +∞) For
each bin, we then computed mean (x), standard devi-
ation (σ) and relative standard deviations (
σ
x
) of 3D
speed according to consecutive GPS coordinates (in-
cluding altitude). Additionally, we added normalized
mean values over all bins (∀
i
{
x
i
∑
4
j=1
x
j
}).
By ten-fold crossvalidation on all selected tracks
from BC-GPS-10k we obtained a precision of 0.772,
recall of 0.776, F
1
of 0.774 and area-under-ROC
0.814 for class no (i.e. low plausibility). All re-
sults can also be found in Table 3. An analysis of
the model from the whole dataset shows that two out
of the four rules obtains use a lower bound for down-
hill cycling (corresponding to mean0/1) and an upper
bound for uphill cycling (corresponding to mean2/3).
This clearly indicates that the model found and uti-
lized the somewhat trivial pattern mentioned earlier.
After obtaining the larger BC-GPS-4M dataset,
we applied the previously trained model to it and ob-
tained somewhat worse results: a precision of 0.432,
recall of 0.779, F
1
of 0.556 and area-under-ROC of
0.824. This may have been caused by the incon-
sistently computed plausibility measure in this larger
dataset, as noted later.
4.3 TMD Model
As already mentioned, only (Dabiri et al., 2019;
Etemad, 2018; Nawaz et al., 2020) could be evalu-
ated because we only received 3D-GPS data for eval-
uation. Since (Etemad, 2018) is more of a classical
10
We also tested 5s and 15s – results were slightly worse.
11
We also tried two and three bins centered on zero. Here,
the results were very similar.
Cycle4Value: A Blockchain-based Reward System to Promote Cycling and Reduce CO2 Footprint
1087

model and (Nawaz et al., 2020) presents a very com-
plex model that would be very time-consuming to im-
plement from scratch, we chose (Dabiri et al., 2019)
where the underlying code is also available in a usable
form in the corresponding master thesis. This how-
ever does not include the pre-trained Transport Mode
Detection (TMD) model itself, which we had to train
ourselves on the GeoLife 1.3 dataset.
We were interested to find how well the pre-
trained model would be able to recontruct plausibility
without any retraining at all. We therefore computed
the proportion of segments from each track in BC-
GPS-4M with transport mode classification cycling,
weighted the results by model confidence and normal-
ized it to [0, 100], thus creating an alternative plau-
sibility measure. On the original BC-GPS-4M data
set, the results of this pre-trained model with accuracy
81.20% and F
1
=0.893 are even slightly better than the
classical motion detection – and this without any ac-
tual training.
Since there was some concern that the plausibility
measure may not have been consistent over the whole
dataset as it was stored at the time of recording and
the code was sometimes changed, we recomputed the
plausibility measure using the most recent code and
obtained BC-GPS-4M-RCP, which was used to all
remaining experiments. When recomputing perfor-
mance of the TMD model with the recomputed plau-
sibility values, accuracy is reduced to 72.02% and F
1
to 0.817. However as we will see this may have been
caused by a shift in the plausibility values that may be
easily addressable by recalibrating the TMD model.
To better compare both measures,
we split the original plausibility values
into five (almost) equally large intervals:
[0, 20),[20, 40), [40, 60), [60, 80),[80, 100] and
computed arithmetic mean and standard deviation
of the new plausibility measure over each interval.
Fig 2 shows the results and also the same values
averaged for each possible legacy model plausibility
as Raw Mean.
12
The latter roughly corresponds to a
ROC curve. As you can see, the mean TMD-derived
plausibility measure increases strictly monotonically
from the lowest to the highest interval. Although
standard deviation is initially high, it shrinks for
higher intervals. A recalibration of the TMD model
or an adaptation of the threshold for plausibility
could improve the match considerably. The Pearson’s
correlation coefficient between legacy and TMD
measure is 0.31, indicating a weak to moderate
correlation. Note again that the TMD model has not
been trained on any part of the BC-GPS datasets,
12
This corresponds to using 101 bins, one for each plau-
sibility values in the interval [0,100].
0
20
40
60
80
100
0 20 40 60 80 100
Pretrained TMD model
Legacy model
Mean +/- StD of TMD model plausibility
Raw Mean TMD model plausibility (ROC)
Figure 2: Comparison of the ad-hoc plausibility (legacy)
model with transport-mode-detection (TMD) model.
so these results are quite surprising and indicate that
TMD models are a good starting point for a trainable
plausibility – and possibly also cheating – detection
system. Note also that the legacy model was not
explicitly designed to detect cheating and fraud, but
rather to increase the quality of uploaded data by
ensuring that only bicycle tracks were accepted.
5 CONCLUSION
We have shown that the results of a legacy model for
plausibility detection of cycling tracks can to a mod-
erate extent be reconstructed by a classically trained
rule learning model with handcrafted features, as well
as to a somewhat lesser extent by a pretrained trans-
port mode detection (TMD) model, even though the
latter was trained on a completely different dataset.
Improvements to the TMD model may be made by
adding GPS altitude to its input data and model struc-
ture; retraining at least part of the existing plausibility
data; and calibrating the model to give more similarily
distributed plausibility outputs.
However, as our primary goal is not to replicate
the existing legacy model but rather to build a more
general model both for plausibility and cheating/fraud
detection, more data is needed. To address this and
other open issues, we will 1) Start a competition for
cheating with appropriate incentives for a small test
groups; 2) Research fast algorithms for track align-
ment, thus taking care of Attack 4: Multiple phones
on one bike; 3) After deployment, regularily analyze
outliers with high and/or similar payoff and update
the system accordingly, keeping in mind the trade-off
between Type I and II errors.
We will also consider the use of synthetically gen-
erated data for various attack types to expand our
datasets. As the most tempting attacks call for a large
ICAART 2021 - 13th International Conference on Agents and Artificial Intelligence
1088

number of users to be automatically created, another
option may be to simply make user registration non-
scriptable e.g. by using captchas or requiring a short
cycling sequence with pulse rate (for example, by us-
ing the front smartphone camera and pulse-by-face).
ACKNOWLEDGEMENTS
This project is currently funded by the Austrian Re-
search Promotion Agency (FFG) and by the Austrian
Federal Ministry for Climate action, Environment,
Energy, Mobility, Innovation and Technology (BMK)
as project Cycle4Value (873384).
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