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Improving Transportation Mode Identification with
Limited GPS Trajectories
Yuanshao Zhu1,2 , Christos Markos1,3, James J.Q. Yu1,2,*
1Department of Computer Science and Engineering, Southern University of Science and Technology
2Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation
3Faculty of Engineering and Information Technology, University of Technology Sydney
yasozhu@gmail.com, christos.k.markos@gmail.com, yujq3@sustech.edu.cn
Abstract—The deployment of Global Positioning System (GPS)
sensors in modern smartphones and wearable devices has enabled
the acquisition of high-coverage urban trajectories. Extracting
knowledge from such diverse spatiotemporal data is essential
for optimizing intelligent transportation system operations. Yet
a deeper understanding of users’ mobility patterns also requires
identifying their associated transportation modes. Combined
with growing privacy concerns, the considerable effort involved
in manual data annotation means that GPS trajectories are
in reality not labeled by transportation mode. This poses a
significant challenge for machine learning classifiers, which often
perform best when trained on large amounts of labeled data.
As such, this paper investigates a wide range of time series
augmentation methods aiming to improve the real-world appli-
cability of transportation mode identification. In our extensive
experiments on Microsoft’s Geolife dataset, both discrete wavelet
transform and flip augmentations pushed the transportation
mode identification accuracy of a convolutional neural network
from 85.1% to 87.3% and 87.2%, respectively.
I. INTRODUCTION
The ability to associate users’ mobility patterns with their
corresponding transportation modes is crucial for urban plan-
ning and transportation management [1]–[3]. Knowledge of
the travel mode distribution along urban transportation net-
works can help develop more effective strategies towards opti-
mizing infrastructure utilization, thereby alleviating significant
issues such as traffic congestion [4]–[6]. It can also provide
individuals with better route recommendations, conditioned on
their desired travel mode and destination [7]. With Global
Positioning System (GPS) sensors being installed in modern
smartphones and other wearable devices, acquiring rich GPS
trajectories for transportation mode identification has become
easier than ever.
Most GPS-based transportation mode identification ap-
proaches have been in supervised learning settings. Because
raw GPS trajectories are ill-suited for direct processing by
machine learning models, the seminal work of [8] first com-
puted pointwise motion features such as speed and acceleration
from consecutive pairs of GPS points, before feeding them
to a decision tree classifier. This motion feature extraction
step has since become standard practice in the transportation
This work is supported by the Stable Support Plan Program of Shen-
zhen Natural Science Fund No. 20200925155105002 and by the General
Program of Guangdong Basic and Applied Basic Research Foundation No.
2019A1515011032. James J.Q. Yu is the corresponding author.
mode identification literature. [2] combined the predictions of
a random forest classifier with a rule-based method. [9] first
trained a sparse autoencoder to extract latent representations
of handcrafted motion features such as speed and accelera-
tion, before feeding them to a Convolutional Neural Network
(CNN) for the final classification. Inspired by computer vision
applications, [10] treated GPS trajectories as image pixels
by mapping GPS points to grid cells and adjusting pixel
intensity according to location stay time. The authors then
trained a CNN to extract high-level representations which were
ultimately fed to a logistic regression classifier. [11] proposed
a deep ensemble of CNNs, while [12], [13] leveraged a single
CNN equipped with the attention mechanism. Others success-
fully used recurrent neural networks based on the Long Short-
Term Memory (LSTM) module, due to their demonstrated
effectiveness in modeling long-term temporal dependencies
[14]–[16].
Despite the aforementioned advances in supervised GPS-
based transportation mode identification methods, the relative
lack of trajectories labeled by travel mode remains a limiting
factor. In reality, GPS trajectories are typically unlabeled,
since GPS sensors do not automatically capture travel mode
information. Another reason is that trajectory annotation is
both time-consuming and labor-intensive [17], with users often
citing privacy concerns [12]. Consequently, how to improve
the performance of transportation mode classifiers when few
labeled trajectories are available is an open problem.
In this direction, some researchers have combined labeled
and unlabeled data in semi-supervised learning [17]–[19],
while others have strictly used unlabeled data in unsupervised
learning [20]. Among the semi-supervised approaches, [17]
jointly trained a convolutional autoencoder and a CNN by
first balancing their losses and then gradually assigning more
weight to the latter’s supervised loss. [18] instead leveraged
a semi-supervised LSTM ensemble trained on multiple views
of the data, including frequency-domain and latent represen-
tations thereof that were learned end-to-end. [19] used the
mixup augmentation technique [21] to train a convolutional
autoencoder on mixed batches of labeled, unlabeled, and syn-
thetic samples by simultaneously minimizing their associated
objective functions. On the other hand, [20] proposed a fully
unsupervised approach whereby a convolutional autoencoder
was equipped with a custom clustering layer and trained
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2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)
DOI 10.1109/ICTAI52525.2021.00104
2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) | 978-1-6654-0898-1/21/$31.00 ©2021 IEEE | DOI: 10.1109/ICTAI52525.2021.00104
978-1-6654-0898-1/21/$31.00 ©2021 IEEE
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4
(×1×)
4
RD
S
A
J
Preprocess
Fig. 1. Overview of the preprocessing framework for identifying transportation modes. Raw GPS trajectories are first segmented by transportation mode using
the available labels. Then, pointwise motion features are computed for each segment and converted to a 4-channel tensor.
by jointly optimizing a weighted sum of reconstruction and
clustering losses, thus encouraging clustering-friendly repre-
sentations at the autoencoder’s low-dimensional embedding
layer.
To address the limitations caused by the scarce availability
of labeled trajectories, we instead follow a different approach.
Specifically, we explore a collection of time series augmen-
tation methods1to assess their impact on the performance
of supervised transportation mode classifiers. We provide an
analysis of the underlying principles, effects on classification
performance, as well as hyperparameter selection guidelines
for each method. We conduct a series of comprehensive
experiments on Microsoft’s Geolife [8], [22] dataset, a real-
world dataset of GPS trajectories, showing that both discrete
wavelet transform and flip augmentations are effective meth-
ods towards improving transportation mode identification with
limited data.
The remainder of this paper is organized as follows. Section
II presents our preprocessing steps and formulates the problem
of data-augmented supervised transportation mode identifi-
cation. Section III introduces the time series augmentation
techniques that are investigated towards enhancing GPS-based
transportation mode identification with limited data. Section
IV analyzes our experimental results and provides guidelines
into hyperparameter selection for the above augmentation
methods, while Section V concludes this paper.
II. PRELIMINARIES
This section first presents how we preprocess GPS tra-
jectories into multivariate time series of motion features,
including relative distance, speed, acceleration, and jerk. It
then formulates the problem of data-augmented, supervised
transportation mode identification.
1While image augmentation techniques such as random rotations and
horizontal/vertical shifts have been shown to boost classification accuracy in
computer vision applications, they are not directly applicable to either raw
GPS data or the multivariate time series of motion features that trajectories
are typically converted to.
A. GPS Trajectory Preprocessing
We represent GPS trajectory Tias a sequence
p1,p
2, ..., p
LTiof length LTi. Within Ti, GPS
points are denoted by pi=lati,lng
i,t
i, where lati,
lngiindicate the device’s latitude and longitude in decimal
degrees at time ti. The relative distance RDibetween pi
and its successor pi+1 can be estimated in meters using the
Vincenty formula [23], denoted as:
RDi= Vincenty (lati,lng
i,lat
i+1,lng
i+1).(1)
Based on RDiand its associated time interval Δti=ti+1−ti,
we follow established literature [16], [17], [22] in calculating
pointwise motion features of speed Si, acceleration Aiand
jerk Jiaccording to the following equations:
Si=RDi
Δti
,1≤i≤n, Sn=Sn−1,(2)
Ai=Si+1 −Si
Δti
,1≤i≤n, An=0,(3)
Ji=Ai+1 −Ai
Δti
,1≤i≤n, Jn=0.(4)
After the above feature extraction steps, we eliminate any
timesteps with velocity or acceleration outliers based on
upper thresholds defined for each transportation mode in
[17]. We finally apply min-max normalization to each of the
four features and stack them into a 4-channel tensor Xi=
x1,x
2, ..., x
LTi, where xi={RDi,S
i,A
i,J
i}.
Since our experiments leverage both recurrent and non-
recurrent neural network architectures, the latter requiring
fixed-size input, we finally split each motion feature tensor Xi
calculated for Tiinto LTi/Nsegments of length N. The last
segment is padded with zeros if it has fewer than Ntimesteps;
in this work, we empirically set N= 240. Please note that
all data augmentation methods discussed and evaluated in this
paper are based on motion feature tensors rather than raw GPS
trajectories.
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B. Problem Formulation
Given labeled dataset D={(Xi,y
i)}n
i=1 preprocessed
as per Section II-A and classifier fω(·)parameterized by
trainable parameters ω, we formalize transportation mode
identification as a standard supervised classification problem,
i.e., the problem of obtaining the optimal set of parameters ω
such that the following loss is minimized:
arg min
ωL(ω)= 1
n
n
i=1
i(yi,f
ω(Xi)),(5)
i=−[yilog ˆyi+(1−yi)log(1−ˆyi)],(6)
where ˆyi=fω(Xi),yiare the i-th predicted and ground-truth
transportation modes, and (·)is the categorical cross-entropy
loss function.
Next, we define the general data augmentation function
Aug(·)that produces synthesized sample Xwhen applied to
Xi, denoted by X=Aug(Xi). Assuming that each sample
is augmented exactly once, the above loss function can then
be rewritten as:
arg min
ωL(ω)= 1
2n
n
i=1
i(yi,f
ω(Xi))+
i(yi,f
ω(Aug(Xi))).
(7)
In this paper, we study a wide range of data augmentation tech-
niques (see Section III) in place of Aug(·)with the purpose of
evaluating their contribution towards improving transportation
mode identification with limited GPS trajectories.
III. METHODOLOGY
This section details the time series augmentation techniques
that we adopt towards improving the accuracy of transportation
mode classifiers. These include data perturbation, flipping,
mixup [21], mixing, and discrete wavelet transform.
A. Data Perturbation
Data perturbation refers to injecting each input motion
feature tensor Xiwith random noise. In practice, this is
achieved via addition with a noise tensor Zof the same
dimensionality. For simplicity, Zis sampled from a Gaussian
distribution; specifically, each z∈Zis sampled according to:
p(z;μ, σ)= 1
√2πσ exp −(z−μ)2
2σ2,(8)
where μ,σdenote the mean and standard deviation of z,
respectively. We determine the values for μand σby the mean
and standard deviation of Xi, controlled by hyperparameter k
as follows: μ=k·mean(Xi),
σ=k·stddev(Xi).(9)
For original sample Xi, the synthesized sample can thus be
written as: X=Xi+Z,
y=yi.(10)
B. Data Flip
In computer vision applications, data augmentation is usu-
ally performed by randomly rotating, cropping, or flipping im-
ages. However, most of the above methods would destroy the
motion features’ temporal correlations and interdependencies.
Considering that each input channel represents a different mo-
tion feature, we simply flip Xialong the temporal dimension
for each channel. The flip operation can be expressed as:
X={xn,x
n−1, ..., x
1},
y=yi.(11)
C. Mixup
Originally proposed for computer vision applications,
Mixup [21] expands the training data by mixing pairs of
images and their corresponding labels. The mixup method is
a form of data augmentation which encourages the classifier
fω(·)to learn linear interpolations between pairs of training
samples, generated as follows:
X=λXi+(1−λ)Xj,
y=λyi+(1−λ)yj,(12)
where λis sampled from a beta distribution Beta(α, α)
parameterized by α∈(0,∞). In eq. (12), (Xi,y
i)and
(Xj,y
j)are two randomly-selected samples from the original
training data with one-hot encoded labels yiand yj. The
mixing hyperparameter αcontrols the mixing strength between
feature-target pairs; when α≈0,Xis identical to Xi, i.e.,
no mixing is performed.
D. Data Mixing
The intuition behind data mixing comes from the fact that
GPS trajectories with the same transportation mode would
have similar trends in terms of motion features. To this end, we
perform a weighted mix of kmotion feature tensors having the
same transportation mode and assign the resulting synthetic
sample with the same label as the original ones. In theory,
such a scheme allows for synthesizing an infinite number
motion feature tensors. In this paper, we adopt two data
mixing schemes, namely double-trajectory mixing and multi-
trajectory weight decay mixing. For double-trajectory mixing,
we randomly select two samples with identical transportation
modes and mix them as follows:
X=w1X1+w2X2,w
1+w2=1,
y=yi.(13)
For multi-trajectory weight decay mixing, we randomly select
ktrajectories with the same transportation mode and mix them
using gradually smaller weights:
X=w1X1+w2X2+...+wkXk,
y=yi.(14)
where k
i=1 wi=1 and w1≥w2≥... ≥wk. Please note
that double-trajectory mixing is simply a special case of multi-
trajectory weight decay mixing where k=2.
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E. Discrete Wavelet Transform
Given that motion feature variations can also be distin-
guished in the frequency domain [16], [18], we examine the
effect of augmentation by Discrete Wavelet Transform (DWT)
on the performance of transportation mode identification.
Given time series x(t), DWT results in a multi-resolution
decomposition of the input signal [24] as follows:
x(t)=
b
AM,b2−M/2ϕt
2M−b
+
M
a
b
da,b(x(t),ψ(t))2−a/2ψt
2a−b
=AM(t)+
M
a
Da(t),
(15)
where AM,b =x(t),ϕ
M,b(t)is the approximation coeffi-
cient at decomposition level Mand ϕ(t)is an auxiliary scaling
function. In other words, x(t)is decomposed into an approx-
imation signal AM(t)and Mdetailed signals Da(t). When
augmenting Xi, the synthesized sample Xis again associated
with the same transportation mode label, i.e., y=yi.
IV. EXPERIMENTS
This section first introduces the real-world dataset of GPS
trajectories that we used for our experiments and describes
our simulation setup. It finally presents our experimental
results and provides hyperparameter tuning guidelines for the
evaluated time series augmentation methods.
A. Dataset Description and Simulation Setup
1) Dataset:All data augmentation methods in Section III
are evaluated on Microsoft’s Geolife dataset [8], [22], which
has been widely used in the transportation mode identification
literature [10], [17], [20]. It contains GPS trajectories collected
by 182 users over five years. Out of these users, 69 users
have labeled parts of their trajectories by transportation mode.
preprocess them as per Section II-A. Following the dataset au-
thors’ recommendation, we select main transportation modes
for identification, namely walking,biking,bus,driving and
railway. After preprocessing all GPS trajectories as per Section
II-A, we obtain a total of 24,741 labeled samples of length
240 (walking:7315,biking:3848,bus:5964,driving:4338
,railway:3278). Following a stratified data split to maintain
the transportation mode distribution, 85% of the above samples
are used for training and validation, while the remaining 15%
are used for testing. Please note that all data augmentation
methods are only applied to the training set.
2) Simulation Setup:We first present our hyperparameter
settings for the time series augmentation methods described
in Section III. Perturbation is applied with k=0.02, while
Mixup [21] uses α=0.5. Data mixing expands each data
class by 2000 samples,2where mixing-1 and mixing-2 denote
2Even though we could generate as many samples per class as required to
eliminate the training set class imbalance, this would lead to a different class
distribution compared to the test set.
TABLE I
ACCURACY PERCENTAGE (MEAN ±STANDARD DEVIATION)FOR
DIFFERENT DATA AUGMENTATION METHODS AND CLASSIFIERS
Augmentation MLP CNN LSTM
Baseline 70.8 ±1.32 85.1 ±0.31 76.3 ±0.28
Perturbation 71.4 ±0.51 85.9 ±0.18 76.8 ±0.23
Flip 71.8 ±0.92 87.3 ±0.23 77.4 ±0.27
Mixup [21] 69.8 ±0.88 84.2 ±0.22 75.1 ±0.31
Mixing-1 72.6 ±0.72 86.0 ±0.21 76.9 ±0.20
Mixing-2 71.3 ±0.56 85.5 ±0.19 76.7 ±0.23
DWT 80.1 ±0.92 87.2 ±0.13 78.5 ±0.18
the double-trajectory mixing and multi-trajectory weight decay
mixing methods, respectively. For the former, we set w1,w
2∼
Beta(0.5,0.5), while the latter uses k=5(i.e., we mix five
trajectories of the same transportation mode) with w1=0.9,
w2=0.04,w3=0.02,w4=0.02,w5=0.02.
We evaluate the above time series augmentation methods on
a MultiLayer Perceptron (MLP), a CNN, and an LSTM. (1)
The MLP has three fully connected layers with {512,128,5}
neurons. (2) The CNN consists of three one-dimensional (1D)
convolution layers with a kernel size of 3and {32,64,128}
channels, respectively. Each convolution layer is followed by a
max pooling operation with a pool size of 2. The convolution
layers are followed by a flattening operation resulting in 3840
features, followed by a fully connected layer with 960 neurons.
(3) The LSTM has three LSTM layers with {64,64,64}units,
respectively. The output of the last LSTM layer is flattened and
fed to two fully connected layers with {256,5}neurons. For
all three neural networks, all hidden layers are activated using
the Rectified Linear Unit (ReLU) function, while the softmax
activation function is used to predict the transportation mode at
the output layer. Please note that we do not use regularization
methods such as dropout or batch normalization; instead, we
prevent our networks from overfitting by reducing their size
(i.e., number of layers and hidden units) and therefore the
number of trainable parameters. All models are trained for
200 epochs using the Adam optimizer with a learning rate of
0.001. We report the mean classification accuracy calculated
over the last 20 training epochs.
Our experiments were developed using Python 3.7. All
neural networks were built using PyTorch 1.7 and trained on
a server with an Intel Xeon Silver 4210 CPU and an NVIDIA
GeForce RTX 2080 Ti GPU with 11GB of GDDR6 memory.
B. Results
Our experimental results are shown in Table I. With the ex-
ception of Mixup [21], which performed worse than just using
the original samples, all evaluated augmentation methods con-
tributed to improving classification performance. Among them,
discrete wavelet transform and flip augmentations achieved the
best results for our CNN and LSTM models, pushing the for-
mer’s accuracy from 85.1% to 87.2% and 87.3%, respectively.
DWT was also by far the most effective augmentation method
for our MLP, increasing its baseline accuracy of 70.8% to
80.1% . Both mixing-1 and mixing-2 resulted in modest im-
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k = 0.02
k = 0.1
k = 0.2
k = 1
Original
Accuracy
(mean)
SpeedNoise
85.1%
85.9%
84.5%
66.5%
80.2%
Fig. 2. Changes in the speed signal of a randomly-selected sample after adding
noise to all training samples with k∈{0.02,0.1,0.2,1}. The CNN’s
mean accuracy declines beyond a certain noise magnitude, indicating that
the classifier fails to identify meaningful information within the augmented
samples.
provement, with the former outperforming the latter. Moreover,
perturbation attained nearly identical results to mixing-1. The
above experimental results confirm the potential of time series
augmentation in improving GPS-based transportation mode
identification with limited data.
1) Data Perturbation:As described in Section III-A, the
intuition behind data perturbation is that learning from noisy
counterparts of the original data may help the classifier learn
more general features. However, adding too much noise may
result in unrealistic samples that are hard to learn meaningful
representations from. Fig. 2 shows that perturbation indeed
boosted classification accuracy from 85.1% to 85.9% for
k=0.02, which is the hyperparameter value used throughout
our experiments. Values of k>0.1, however, resulted in
significant accuracy degradation.
2) Flip:By simply flipping Xialong the temporal di-
mension for each motion feature, our expectation is that the
generated sample would still realistically correspond to the
same transportation mode. Each feature would demonstrate
the same minimum and maximum values, despite having
different temporal dynamics. According to our results in Table
I, flipping resulted in the highest classification accuracy for
our CNN and LSTM but only modestly benefited our MLP.
This is likely due to the latter not accounting for temporal
dependencies.
3) Mixup:As per Section III-C, mixup [21] generates
synthetic data via a linear combination of paired samples and
their corresponding ground-truth labels. This practice aims
to encourage the classifier to interpolate smoothly between
samples and reduce the effect of adversarial ones. Our hyper-
parameter sensitivity tests, shown in Table II, demonstrate that
mixup did not outperform the non-augmented transportation
TABLE II
HYPERPARAMETER SENSITIVITY OF CNN ACCURACY (MEAN ±
STANDARD DEVIATION)TO MIXUP
Augmentation Accuracy
Baseline 85.1 ±0.31
Mixup (α=0.2) 83.9 ±0.21
Mixup (α=0.5) 84.2 ±0.22
Mixup (α=1) 83.6 ±0.27
Mixup (α=10) 82.8 ±0.46
TABLE III
HYPERPARAMETER SENSITIVITY OF CNN ACCURACY TO DATA MIXING
Augmentation Parameter Settings Mean (%)
mixing-1
w1=0.5,w
2=0.584.3
w1=0.8,w
2=0.285.6
w1=0.95,w
2=0.05 85.8
w1,w
2∼Beta(0.5,0.5) 86.0
mixing-2
{0.5,0.3,0.1,0.05,0.05}82.6
{0.7,0.1,0.1,0.05,0.05}83.1
{0.8,0.05,0.05,0.05,0.05}85.3
{0.9,0.04,0.02,0.02,0.02}85.5
mode identification baseline. However, note that mixup assigns
labels to the synthesized samples by simply blending their
original ones. As such, we expect that it could perform better
in semi-supervised training, where the effect of the generated
labels on the learned representations would be attenuated. This
is out of the scope of this paper and is left for future work.
4) Data Mixing:Although data mixing did not dramati-
cally boost classification accuracy, we found that it resulted in
higher training stability during our experiments. This may be
due to how data mixing is performed, which is via timestep-
wise addition of two or more samples of the same transporta-
tion mode. We hypothesize that this may help the classifier
learn the main motion feature trends of each transportation
mode while simultaneously becoming more robust to trajectory
variations not observed in the original data.
We also analyzed the impact of different data mixing hyper-
parameter settings on classification accuracy; our experimental
results are summarized in Table III. Although mixing the
motion features of either two or five trajectories did increase
model accuracy compared to the baseline, mixing-2 did not
result in significant improvement. This is not surprising, as
mixing more sets of motion features will also incur an increase
in uncertainty.
5) DWT:Here, we explore the effect of extracting features
via different wavelet decomposition functions on classification
accuracy. As shown in Table IV, using different wavelet de-
composition functions did not significantly affect classification
accuracy, with Daubechies wavelets achieving the best results.
This suggests that DWT has the desirable property of not being
particularly sensitive to the choice of wavelet function.
Recall that, according to eq. (15), x(t)can be decomposed
into approximate signal AM(t)and detailed signal Da(t).
Having compared the influence of DWT on classification
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TABLE IV
SENSITIVITY OF CNN ACCURACY TO DIFFERENT WAVELET
DECOMPOSITION FUNCTIONS IN DWT
Wavelet Mean (%) w/ AM(t)Mean (%) w/ Da(t)
Daubechies 87.2 87.0
Symlets 87.0 87.1
Coiflets 86.8 86.9
Haar 87.1 87.0
accuracy when using either AM(t)or Da(t), our experimental
results showed no significant prevalence of one over the other.
This is consistent with recent work indicating that capturing
motion feature trends rather than details may be more impor-
tant when distinguishing among transportation modes [18].
V. C ONCLUSION
In this paper, we investigated a range of data augmentation
techniques to improve GPS-based transportation mode identi-
fication performance when limited labeled data are available.
Since the literature typically performs transportation mode
identification on time series of motion features extracted from
GPS trajectories rather than the raw trajectories themselves, we
followed the same procedure and investigated the impact of
several time series augmentation techniques on classification
accuracy. We also provided guidelines into tuning their hy-
perparameters to encourage their use in future transportation
mode identification research. Through a set of comprehensive
experiments on Microsoft’s Geolife, an openly available real-
world dataset of GPS trajectories, we demonstrated that the
simple operation of flipping resulted in the highest accuracy
of 87.3% for a convolutional neural network. In addition,
extracting features in the frequency domain via DWT pushed
classification accuracy from the baseline of 85.1% to 87.2%.
In future work, we will investigate the influence of time
series augmentation methods on the transportation mode
identification accuracy of more sophisticated neural network
architectures, such as generative adversarial networks and
Transformers.
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