Human mobility behavior: Transport mode detection by GPS data

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Sadeghian, PariaHögskolan Dalarna,Mikrodataanalys (författare)

Human mobility behavior : Transport mode detection by GPS data

BokEngelska2021

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Borlänge :Dalarna University,2021
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:du-36346
ISBN:9789188679123
https://urn.kb.se/resolve?urn=urn:nbn:se:du-36346URI

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Språk:engelska
Sammanfattning på:engelska

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Serie

Dalarna Licentiate Theses ;16

Anmärkningar

GPS tracking data are widely used to understand human travel behavior and to evaluate the impact of travel. A major advantage with the usage of GPS tracking devices for collecting data is that it enables the researcher to collect large amounts of highly accurate and detailed human mobility data. However, unlabeled GPS tracking data does not easily lend itself to detecting transportation mode and this has given rise to a range of methods and algorithms for this purpose. The algorithms used vary in design and functionality, from defining specific rules to advanced machine learning algorithms. There is however no previous comprehensive review of these algorithms and this thesis aims to identify their essential features and methods and to develop and demonstrate a method for the detection of transport mode in GPS tracking data. To do this, it is necessary to have a detailed description of the particular journey undertaken by an individual. Therefore, as part of the investigation, a microdata analytic approach is applied to the problem areas, including the stages of data collection, data processing, analyzing the data, and decision making.In order to fill the research gap, Paper I consists of a systematic literature review of the methods and essential features used for detecting the transport mode in unlabeled GPS tracking data. Selected empirical studies were categorized into rule-based methods, statistical methods, and machine learning methods. The evaluation shows that machine learning algorithms are the most common. In the evaluation, I compared the methods previously used, extracted features, types of dataset, and model accuracy of transport mode detection. The results show that there is no standard method used in transport mode detection. In the light of these results, I propose in Paper II a stepwise methodology to detect five transport modes taking advantage of the unlabeled GPS data by first using an unsupervised algorithm to detect the five transport modes. A GIS multi-criteria process was applied to label part of the dataset. The performance of the five supervised algorithms was evaluated by applying them to different portions of the labeled dataset. The results show that stepwise methodology can achieve high accuracy in detecting the transport mode by labeling only 10% of the data from the entire dataset. For the future, one interesting area to explore would be the application of the stepwise methodology to a balanced and larger dataset. A semi-supervised deep-learning approach is suggested for development in transport mode detection, since this method can detect transport modes with only small amounts of labeled data. Thus, the stepwise methodology can be improved upon for further studies.

Ämnesord och genrebeteckningar

TEKNIK OCH TEKNOLOGIER Samhällsbyggnadsteknik Transportteknik och logistik hsv//swe
ENGINEERING AND TECHNOLOGY Civil Engineering Transport Systems and Logistics hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
Transport mode detection
Machine learning
Statistical learning
Rule-based method
Data labeling

Biuppslag (personer, institutioner, konferenser, titlar ...)

Håkansson, JohanHögskolan Dalarna,Institutionen för information och teknik(Swepub:du)jhk (preses)
Zhao, Xiaoyun,Ph.D.Högskolan Dalarna,Institutionen för information och teknik(Swepub:du)xzh (preses)
Carling, Kenneth,1967-Högskolan Dalarna,Institutionen för information och teknik(Swepub:du)kca (preses)
Gidofalvi, Gyözö,Associate ProfessorRoyal Institute of Technology (opponent)
Högskolan DalarnaMikrodataanalys (creator_code:org_t)

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Av författaren/redakt...: Sadeghian, Paria; Håkansson, Johan; Zhao, Xiaoyun, P ...; Carling, Kenneth ...; Gidofalvi, Gyözö ...

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Samhällsbyggnads ...; och Transportteknik ...

NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Datavetenskap

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Av lärosätet: Högskolan Dalarna

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