Content uploaded by Amilcar Soares
Author content
All content in this area was uploaded by Amilcar Soares on Mar 02, 2018
Content may be subject to copyright.
Predicting Transportation Modes of GPS
Trajectories using Feature Engineering and
Noise Removal
Mohammad Etemad1, Am´ılcar Soares J´unior1, and Stan Matwin12
1Institute for Big Data Analytics, Dalhousie University, Halifax
2Institute for Computer Science, Polish Academy of Sciences, Warsaw
Abstract. Understanding transportation mode from GPS (Global Posi-
tioning System) traces is an essential topic in the data mobility domain.
In this paper, a framework is proposed to predict transportation modes.
This framework follows a sequence of five steps: (i) data preparation,
where GPS points are grouped in trajectory samples; (ii) point features
generation; (iii) trajectory features extraction; (iv) noise removal; (v)
normalization. We show that the extraction of the new point features:
bearing rate, the rate of rate of change of the bearing rate and the global
and local trajectory features, like medians and percentiles enables many
classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores. We
also show that the noise removal task affects the performance of all the
models tested. Finally, the empirical tests where we compare this work
against state-of-art transportation mode prediction strategies show that
our framework is competitive and outperforms most of them.
Keywords: Feature engineering, Noise removal, Trajectory classifica-
tion
1 Introduction
Research on trajectory analysis is a mature area since positioning devices are now
used to track people, vehicles, vessels, and animals. In the case of trajectory data,
the object’s movement is represented as a discrete collection of spatiotemporal
points.
A domain where trajectories are frequently analyzed is the prediction of
transportation modes from users, which is essential for cities and people to reduce
travel time and traffic congestion. Transportation mode estimation involves two
steps [11]: (i) extraction of segments of the same transportation modes; and
(ii) classification of transportation modes for each segment. For the first step,
several segmentation algorithms have been proposed in the past years and include
temporal-based [8], cost function-based [5] and semantic-based methods [7]. For
the second step, which is the focus of this work, the classification (or prediction)
of the transportation modes is performed by creating domain expert features for
supervised classification (e.g., the distance between consecutive points, velocities,
acceleration, and bearing).
arXiv:1802.10164v1 [cs.OH] 27 Feb 2018
2 Mohammad Etemad, Am´ılcar Soares J´unior, and Stan Matwin
We classify the research in transportation modes prediction regarding the
type of features in two branches: (i) domain expert features; and (ii) learned
features. From raw GPS data points (e.g., latitude, longitude and time) it is
possible to calculate many attributes regarding the moving object’s movement.
Examples include distance traveled between points, estimated speed, bearing,
acceleration, etc. For segments of trajectories, it is possible to extract mean,
median, minimum, maximum, standard deviations, etc., of point-wise features.
These are examples of domain expert features employed to predict transportation
modes. Examples of works that apply domain expert features include [6,11].
In this work, we also explore the effects of noise removal in the prediction
of transportation modes. Dealing with noise in trajectories is essential because
GPS recorder devices are not accurate in the moving object’s positioning due to
many reasons like satellite geometry, signal blockage, atmospheric conditions,
and receiver design features/quality. By removing GPS noise, it is expected
that the derived features from the trajectories are more likely to represent the
standard pattern of a transportation mode.
Noise-perturbed GPS data influences the quality of the domain expert fea-
tures, e.g. distance traveled, speed or acceleration are susceptible to errors. It is
important to point out that these errors may impact the distributions of values,
where statistics like the mean, in trajectory segments of transportation modes.
This uncertainty of data can lead a classifier to create models that are not able
to accurately predict a transportation mode from a trajectory. Thus, the works
in transportation mode prediction are classified regarding the (i) presence or
(ii) absence of noise removal strategies. An example of work in the transporta-
tion mode prediction that does not deal with noise removal is [11]. In others,
like [10,4,1,2,9], noise is removed. This paper applies domain expert features
and noise removal to predict transportation are as follows: (i) we introduce new
point and trajectory features; (ii) we propose a framework composed of 5 steps
for transportation mode prediction; (iii) we compare the proposed approach with
state-of-art strategies and show that our results are competitive.
2 A framework for transportation mode prediction
In this section, we present the sequence of steps used in this work to predict
transportation modes (Figure 1). This framework has five steps and is described
in detail below.
In this work, we define a trajectory as a sequence of GPS points that belongs
to the same transportation mode. In the first (step 1), we group the raw GPS
points by userid,day and transportation mode to create trajectory samples. We
discard trajectory samples with less than 10 GPS points because these examples
may affect our model since trajectories with low quality may be created.
In this work, we calculate some point features (step 2) that were used previ-
ously in literature [11]: distance, speed, acceleration, jerk[1], and bearing.
Two new features are introduced in this work, named bearing rate, and the
rate of bearing rate. They are detailed as follows. The bearing rate was computed
Predicting Trans. Mode using Feature Engineering&Noise Removal 3
Fig. 1. The steps of the proposed framework to predict transportation modes
using Eq. 1, where Biand Bi+1 are the bearing values in points iand i+ 1, and
∆t is the time difference.
Brate(i+1) = (Bi+1 −Bi)/∆t (1)
Some moving objects tend to change the bearing more often because they
commute in a straightforward route. This behavior can be captured by using the
rate of the bearing rate. This feature is calculated using Eq.2.
Brrate(i+1) = (Br ate(i+1) −Brate(i))/∆t (2)
After calculating all the point features for each trajectory, we extract some
statistical attributes referred to as trajectory features (step 3). Trajectory fea-
tures are divided into two different types: (i) global trajectory features, which
summarize information regarding the whole trajectory in a single value; and (ii)
local trajectory features, which describe a local part of the trajectory. In this
work, we extracted global features like the Minimum, Maximum, Mean, Median,
and Standard Deviation values of each trajectory point feature to feed our clas-
sifier. The local trajectory features extracted in this work was the percentiles of
every point feature. Five different percentiles were extracted (10, 25, 50, 75, and
90) and were used in the models tested in this work. In summary, we compute
70 trajectory features (10 statistical measures including five global and five local
features calculated for 7 point features) for each transportation mode example.
In step 4, the framework deals with noise in the data. In this work, we used
a simple method called median filter to create a mask. The method is described
in Algorithm 1 (threshold = 3) and it removes the noise based on speedmean
(i.e. the average speed of a trajectory) attribute since a human can classify the
transportation mode mostly by knowing the mean speed of a trajectory.
Finally, we normalized the features (step 5) using the Min-Max normaliza-
tion method, since this method preserves the relationship between the values to
transform features to the same range and improve the quality of classification
process [3].
4 Mohammad Etemad, Am´ılcar Soares J´unior, and Stan Matwin
Data: Speed mean of trajectories
Result: mask vector to remove the noisy trajectories
difference ←− |speedmeanT raj ectory −median(speedmean)|;
median dif f erence ←− median(dif f erence);
if median difference == 0 then
indicator ←− 0;
else
indicator ←− difference/median dif f erence;
end
return indicator >threshold ;
Algorithm 1: mask the noisy samples to remove from dataset using median
3 Experiments
In this section, we detail the experiments performed in this work to validate
our framework. The data used in this work is the GeoLife GPS dataset, that
was collected by Microsoft Research Asia from April 2007 to October 2011 [11].
The dataset has a 5,504,363 number of records labeled by eleven transporta-
tion modes: taxi (4.41%); car (9.40%); train (10.19%); subway (5.68%); walk
(29.35%); airplane (0.16%); boat (0.06%); bike (17.34%); run (0.03%); motorcy-
cle (0.006%); and bus (23.33%).
In the literature, we observed different sub-selections of these classes for
evaluating transportation mode prediction strategies; therefore, we decided to
select different target subsets for comparing our result with other papers.
To evaluate the performance of classifiers in this work we used the Accuracy
and the F1 measure. In all our experiments, we used a 10-fold cross-validation
strategy and computed a paired t-test to verify if the difference in the means were
statistically different. We executed our framework with different classifiers such
as Decision Tree (DT) (with maxdepth equals five), Random Forest (RF) (with
50 trees estimators), Neural Network (NN), Naive Bayes (NB), and Quadratic
Discriminant Analysis (QDA). In all cases, the random forest surpasses all the
other classifiers in both accuracy and f1.
Subsequently, we compared the RF using all the steps of our framework
against the results of five papers. It is important to point out that all these papers
reported their accuracy values on the Geolife dataset. Table 1 shows a side-by-
side comparison between some related works and the results of our framework.
Our work does not surpass Jiang’s et al. accuracy [4] but outperforms all the
others. It is important to highlight that the complexity and high training time
of the RNN model used in his work may not be worth the 1.42% difference in
accuracy.
Finally, we evaluated the effects of noise removal performed by our frame-
work. We established as a baseline the performance of our framework using the
data to train classifiers with noise and without noise (clean). Table 2 shows
the mean of the f1 values obtained by 10-fold cross-validation for the different
group of classes. We can observe in Table 2 that for all classifiers and different
Predicting Trans. Mode using Feature Engineering&Noise Removal 5
Table 1. Comparison of accuracy and f1 measure of proposed model against related
works
Related work Proposed Model
Reference: classes used in the experiments acc acc f1
Dabiri et al. [1] : walk, bike, bus, driving, and train 84.8% 93.35% 93.22%
Jiang et al.[4]: bike, car, walk, and bus 97.9% 96.45% 96.31%
Xiao et al. [9] : walk, bus&taxi, bike, car, subway, and train 90.77% 93.19% 92.81%
Zheng et al.[11] : walk, driving, bus, and bike 76.2% 93.61% 93.51%
Endo et al.[2] : walk, car, taxi, bike, subway, bus, and train 83.2% 90.20% 89.95%
subgroups of classes, performance gains ranging from 2.56 (Decision Tree, using
classes of [2]) to 28.15 (QDA, using classes of [11]) in f1.
Table 2. F1 measures to classifiers for different class groups.
Reference DT RF NN NB QDA
with
noise clean with
noise clean with
noise clean with
noise clean with
noise clean
Dabiri et al. [1] 85.56 92.31 88.07 93.22 85.18 89.87 63.30 82.91 54.76 79.83
Jiang et al.[4] 88.26 95.47 91.56 96.31 88.63 94.11 65.68 85.19 54.70 82.55
Xiao et al. [9] 84.38 89.79 88.75 92.81 82.93 89.01 51.40 70.03 47.81 71.45
Zheng et al.[11] 85.62 91.92 88.72 93.51 85.76 91.33 64.61 84.22 51.33 79.48
Endo et al.[2] 79.53 82.09 85.57 89.95 79.33 85.70 57.31 72.68 49.13 72.30
Finally, Table 3 shows the mean of the accuracy values obtained by 10-
fold cross-validation. For all classifiers and different subgroups of classes and
classifiers, performance gains ranging from 3.36 (Decision Tree, using classes of
[2]) to 29.04 (QDA, using classes of [4]) in accuracy were observed. The results
presented in this section indicate that dealing with noise in transportation mode
prediction is an important topic, and the lack of this step in the classification
task decreases the performance of the classifiers.
Table 3. Accuracy to classifiers for different class groups.
Class
group
DT RF NN NB QDA
with
noise clean with
noise clean with
noise clean with
noise clean with
noise clean
Dabiri et al. [1] 85.54 92.36 88.47 93.35 85.54 90.13 63.56 83.28 53.65 79.76
Jiang et al.[4] 88.41 95.54 91.91 96.45 88.80 94.21 63.70 84.31 53.03 82.07
Xiao et al. [9] 85.01 89.96 89.33 93.19 83.61 89.43 51.96 69.90 46.59 70.99
Zheng et al.[11] 85.77 92.13 89.09 93.61 86.10 91.45 64.36 84.53 50.85 79.50
Endo et al.[2] 80.25 83.61 86.36 90.20 80.27 86.28 56.66 73.27 47.92 71.60
6 Mohammad Etemad, Am´ılcar Soares J´unior, and Stan Matwin
4 Conclusions and Future Works
In this work, we propose a framework for transportation mode prediction using
feature engineering and noise removal. The results showed that the newly engi-
neered features (e.g., bearing rate, and rate of bearing rate) and the application
of a noise removal technique improve the performance of all tested classifiers.
We intend to extend this work in two directions: (i) test and evaluate different
noise removal techniques like wavelet-based, MCMC and fast Fourier based de-
noising methods, and (ii) investigate the performance of trajectory segmentation
algorithms and include this step in our framework.
Acknowledgments The authors would like to thank NSERC (Natural Sciences
and Engineering Research Council of Canada) for financial support.
References
1. Sina Dabiri and Kevin Heaslip. Inferring transportation modes from gps tra-
jectories using a convolutional neural network. Transportation Research Part C:
Emerging Technologies, 86:360–371, 2018.
2. Yuki Endo, Hiroyuki Toda, Kyosuke Nishida, and Akihisa Kawanobe. Deep feature
extraction from trajectories for transportation mode estimation. In Pacific-Asia
Conference on Knowledge Discovery and Data Mining, pages 54–66. Springer, 2016.
3. Jiawei Han, Jian Pei, and Micheline Kamber. Data mining: concepts and tech-
niques. Elsevier, 2011.
4. X. Jiang, E. N. Souza, A. Pesaranghader, B. Hu, D. L. Silver, and S. Matwin.
Trajectorynet: An embedded gps trajectory representation for point-based classi-
fication using recurrent neural networks. arXiv preprint arXiv:1705.02636, 2017.
5. A. Soares J´unior, B. N. Moreno, V. C. Times, S. Matwin, and L. A. F. C. GRASP-
UTS: an algorithm for unsupervised trajectory segmentation. Int. J. of Geograph-
ical Information Science, 29(1):46–68, 2015.
6. Miao Lin and Wen-Jing Hsu. Mining gps data for mobility patterns: A survey.
Pervasive and Mobile Computing, 12:1–16, 2014.
7. S. Spaccapietra, C. Parent, M. L. Damiani, J. A. Macedo, F. Porto, and
C. Vangenot. A conceptual view on trajectories. Data and Knowledge Engineering,
65(1):126–146, 2008.
8. Leon Stenneth, Ouri Wolfson, Philip S. Yu, and Bo Xu. Transportation mode
detection using mobile phones and gis information. In Proceedings of the 19th ACM
SIGSPATIAL International Conference on Advances in Geographic Information
Systems, GIS ’11, pages 54–63, New York, NY, USA, 2011. ACM.
9. Z. Xiao, Y. Wang, K. Fu, and Fan Wu. Identifying different transportation modes
from trajectory data using tree-based ensemble classifiers. ISPRS, 6(2):57, 2017.
10. G. Yanyun, Z. Fang, C. Shaomeng, and L. Haiyong. A convolutional neural net-
works based transportation mode identification algorithm. In 2017 International
Conf. on Indoor Positioning and Indoor Navigation (IPIN), pages 1–7, Sept 2017.
11. Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, and Wei-Ying Ma. Understanding
mobility based on gps data. In Proceedings of the 10th international conference on
Ubiquitous computing, pages 312–321. ACM, 2008.
