A vehicle classification algorithm based on telematics data

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Nguyen, Linh Vuong

Advisor dc:contributor.advisor

• Tomas Palacios, Hari Balakrishnan and Bill Bradley.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/120606

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/120606