2026-09-18T22:55:58Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1206062026-06-06T00:56:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomas Palacios, Hari Balakrishnan and Bill Bradley.
Nguyen, Linh Vuong
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-03-01T19:33:33Z
2019-03-01T19:33:33Z
2018
2018
http://hdl.handle.net/1721.1/120606
1088412052
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 45-46).
In the thesis, I develop an algorithm to identify the vehicle model from telematics data. By extracting the features from the accelerometer and GPS data, we obtain the classification features, which then goes through a multiclass random forest classifier. We apply this results into problems of driver and vehicle identification. The result shows that, while the algorithm could identify the vehicle models to some extent, the dominating signal comes from driving style, and an approach running purely unsupervised learning is harder to achieve good classification results compared to supervised methods.
by Linh Vuong Nguyen.
M.Eng.
56 ages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A vehicle classification algorithm based on telematics data
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="dd795658-6cda-43dd-969c-e097f5a47e5e">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>A vehicle classification algorithm based on telematics data</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2018</PublicationDate>
<Authors>
<Author>
<DisplayName>Nguyen, Linh Vuong</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>In the thesis, I develop an algorithm to identify the vehicle model from telematics data. By extracting the features from the accelerometer and GPS data, we obtain the classification features, which then goes through a multiclass random forest classifier. We apply this results into problems of driver and vehicle identification. The result shows that, while the algorithm could identify the vehicle models to some extent, the dominating signal comes from driving style, and an approach running purely unsupervised learning is harder to achieve good classification results compared to supervised methods.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>