Choosing the right sensor is extremely important in mode detection. Thus, several variables should be considered before selecting the sensors. The table 2.2 summarises all the investigated sensors and the classifiers used to perform mode detection.
Looking at table2.2,it is possible to notice that the accelerometer and the GPS are the most used sensors. The accelerometer sensor calculates the device’s acceleration along the three axes. The GPS sensor is able to retrieve the user’s location in terms of longi-tude and latilongi-tude.
Analysing the literature, it is possible to observe that the accelerometer has no common sampling rate; each research work defined its own sampling rate. On the contrary, the GPS has a tendency for one sample every second. The accelerometer’s rate needs to be sufficiently high to recognize the mode. For example, the steps while walking or the legs movement while biking. Regarding the GPS, the rate should be suitable to obtain reliable data to calculate the features (in most cases the speed). Low sampling rate, in both sensors, will results in high misclassification due to lack of data.
Occasionally, the above sensors are supported by others sensors, Wi-Fi, Cellular Net-work, Bluetooth and GTFS applications, that are never used alone to perform mode detection. Those supporting sensors are used to improve the user position when the GPS is not properly working (no sufficient satellites in sight). GTFS information play an important role in classification activities (Bucher et al. (2016) and Stenneth et al.
(2011)) but they are related to specific cities; thus, GTFS, to correctly work everywhere, it is mandatory to have information from each city. This constraint is the major dis-advantage of GTFS information. A similar problem is encountered by the Bluetooth
Authors Classifier Accelerometer GPS Other sensors Nitsche et al.(2012) Random
Sub-space Method
100 Hz 1 Hz
Geurs et al.(2015) Bayesian X 2 Hz Wi-Fi and Cell id
info
Bucher et al.(2016) Bayesian Moves app Moves app. Open street map
Montoya et al.(2015) Bayesian 20 Hz 1 Hz Wi-Fi, Bluetooth
and Cell ID info
Manzoni et al.(2011) Decision Tree 25 Hz 1 Hz Occasionally
WIFI
Montini et al.(2015) Decision Tree 5 Hz 1 Hz For location also
WIFI
Xiao et al.(2012) Decision Tree N.A N.A GSM
Wang et al.(2017) Decision Tree/Rnd. forest
X 1 Hz X
Sauerlnder-Biebl et al.(2017)
Fuzzy X 0.25 Hz X
Sch¨ussler and Ax-hausen(2009)
Fuzzy X X
Shin et al.(2015) Fuzzy 1 Hz X Network based
lo-cation Widhalm et al.
(2012)
Decision tree fol-lowed by HMM
50 Hz 1 Hz Cell. Net for
loca-tion Sonderen(2016) Decision tree,
random forest and Nearest Neighbour
140 Hz. X X
Reddy et al.(2008) Decision tree fol-lowed by HMM
35 Hz 1 Hz X
Martin et al.(2017) Movelet ap-proach, k-NN and random forest
5 Hz 1 Hz X
Cardoso et al.(2016) Naive Bayes, SVM and deci-sion tree
33 Hz X X
Stenneth et al.(2011) Bayesian net, De-cision Tree, Ran-dom forest, Naive Bayes and MLP
X 1 every 15
Sec-onds
GIS info regard-ing bus stops
Fang et al.(2016) Decision tree, k-NN and SVM
30 Hz X Gyroscope and
magnetometer (30 Hz) Shafique and Hato
(2015)
SVM, Adaboost, Random forest and decision tree
16 Hz collected but not used
X
Yu et al.(2014) Adaboost, de-cision tree and SVM
30 Hz X Gyroscope and
magnetometer (30 Hz) Gonz´alez et al.
(2008)
Neural network X 0.25 Hz Cell ID (when
there is no GPS)
Vu et al.(2016) RNN 30 Hz X X
Table 2.2: list of sensors and classifiers used by the authors
sensor; to properly work, the Bluetooth needs to communicate with beacons that should be spread all over the city. Others sensors like the gyroscope and the magnetometer have been tested by Yu et al.(2014),Fang et al.(2016) andVu et al. (2016) and allow improving the accelerometer classification. The disadvantage is that such sensors are not always available in smartphones, while the accelerometer is almost always available.
Another important aspect is the device’s battery life. Montini et al.(2015) compare the
battery drain issue of a smartphone using the GPS or the accelerometer. The GPS sen-sors needs lots of energy to calculate the user’s position, while the accelerometer sensen-sors need much less energy to collect the data. In conclusion, if the battery life is important, the GPS is not recommended, while the accelerometer is battery friendly.
Objectives and Methodology
The thesis has been carried out within the research work of the Chair MIDT (Mobilit´e In-telligente et Dynamiques Territoriales) in which a smartphone app, Mobilit´e Dynamique, has been developed. The app is an Android application (the iOS version will be soon released) that allows the users to keep track of their mobility patterns. Several statistics let the users understand their travel behaviours such as the number of daily trips and the means used for travelling. Moreover, a heat map shows the most visited or favourite places. The application is user friendly as it only requires the GPS sensor to work in the background (the smartphone’s screen is off or another application is running in fore-ground). Furthermore, a section where the users can chat and share news about the traffic, car accidents or buses delay is also available. Users are allowed to register the trips by themselves, by selecting the transport mode they are going to use. The trip is stored in memory, by simply clicking on the check in button to start the trip and on the check out button once arrived at destination. This action confirms to the application the used mode. The figures 3.1,3.2and 3.3show some of the application’s screens.
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Figure 3.1: applica-tion’s home page
Figure 3.2: statis-tics of movements
Figure 3.3: applica-tion’s trips map
The application is able to recognize the following modes: Still (no movement), Walk, Bike and Vehicle (car, bus, train etc.). In order to perform the mode detection, the Google APIs (Google APIs 2017) are used.
Only low power sensors (not specified by Google) are used to retrieve the mode in order to save battery. Cardoso et al.(2016) performed a test on the APIs and showed that the on vehicle method does not have a good accuracy. The same problem is related to the Mobilit´e Dynamique application. In the figure3.4, a train trip (from Paris to Bruxelles) has not been recognized and, thus, classified as unknown. This is a problem that needs to be solved.
Figure 3.4: The application’s fail on mode detection
The objective of the thesis is to design a smartphone API able to perform transport mode detection. The API which will be integrated in Mobilit´e Dynamique, will have to rely on smartphone sensors to recognize in real-time the user’s mode. The most impor-tant requirements are: high accuracy in mode detection and low energy consumption.
Both requests are essential if we want the application is actually used. Moreover, since we want all users to take advantage of the application features, the API should run on all smartphones models or as much as possible.