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Transportation mode detection based on mobile sensor data
Urbančič, Jasna (Author), Pejović, Veljko (Mentor) More about this mentor... This link opens in a new window, Mladenić, Dunja (Co-mentor)

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Abstract
This thesis addresses transportation mode detection based primarily on mobile phone data using machine learning methods. Our approach uses short samples of accelerometer readings taken while traveling in a vehicle to distinguish between three modalities --- car, bus, and train. We use gravity estimation to pre-process the samples. We extract features from statistical, frequency-based, and peak-based domain. With statistical analysis of the features we gain an introspective into the data. To additionally analyze the features we construct several feature sets for classification. As a classifier we use random forest, support vector machine, and neural network. Our approach correctly classifies 65% cars, 63% buses, and 18% trains using neural network.

Language:English
Keywords:machine learning, mobile sensing, data mining, pattern recognition, intelligent transportation systems
Work type:Master's thesis/paper (mb22)
Organization:FRI - Faculty of computer and information science
Year:2018
Views:528
Downloads:254
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Secondary language

Language:Slovenian
Title:Detekcija prevoznega sredstva z mobilnimi senzorji
Abstract:
V delu obravnavamo detekcijo prevoznega sredstva z mobilnimi senzorji in metodami strojnega učenja. Pri tem uporabljamo kratke vzorce podatkov iz pospeškometra, ki jih zajamemo med uporabnikovim potovanjem v vozilu. Razločujemo med tremi prevoznimi sredstvi --- avtom, avtobusom in vlakom. Vzorce predobdelamo tako, da iz pospeškov izločimo gravitacijsko komponento. Iz vzorcev izločimo statistične in frekvenčne značilke ter značilke vrhov. S statistično analizo značilk dobimo vpogled v podatke. Dodatno analiziramo značilke preko različnih množic značilk, ki jih uporabljamo za klasifikacijo. Kot klasifikatorje uporabljamo naključne gozdove, metodo podpornih vektorjev in nevronske mreže. Z uporabo nevronskih mrež smo pravilno razpoznali 65% avtomobilov, 63% avtobusov in 18% vlakov.

Keywords:strojno učenje, mobilno zaznavanje, podatkovno rudarjenje, razpoznava vzorcev, inteligentni transportni sistemi

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