A vehicle classification algorithm based on telematics data
Name
1088412052-MIT.pdf
Description
Full printable version
Size
2.31 MB
Format
Adobe PDF
Checksum (MD5)
ac8ec2668b119e6100cef4b1ac65a395
Author(s)
Nguyen, Linh Vuong
Advisor(s)
Tomas Palacios, Hari Balakrishnan and Bill Bradley.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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