Serverless data processing applied to big mobility data

Thumbnail Image

Files

master_Zugliani_Marco_2018.pdf (2.79 MB)

URL

Journal Title

Journal ISSN

Volume Title

Perustieteiden korkeakoulu | Master's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2018-10-08

Department

Major/Subject

Digital Media Technology

Mcode

SCI3023

Degree programme

Master's Programme in ICT Innovation

Language

en

Pages

61 + 5

Series

Abstract

Personal mobility has become a relevant aspect of daily life in the modern society. The knowledge of how people move in the territory plays a central role in how cities develop as an ecosystem. Citizens gain insight into their everyday movements and can act in order to improve their lifestyle. This thesis describes an automated system capable of discovering daily trips and personal carbon footprint with the most common transport means, including public transports. Several studies have been performed on the topic of transport mode recognition. While agreeing on the use of smartphones to collect data, these studies vary mainly in the number of modalities recognized and in the sensors used. This work presents a novel approach that exploits an existing activity recognition technique and the serverless technology as a means to process and enrich the data with more information. This approach greatly affects the accuracy of the off-line recognition system by adding GPS and public transport information.

Description

Supervisor

Vuorimaa, Petri

Thesis advisor

Mineraud, Julien

Keywords

mobility recognition, serverless, carbon footprint, smartphone sensors

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201810175463

Collections

[dipl] Perustieteiden korkeakoulu / SCI