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Recognizing driving styles based on topic models

Recognizing driving styles based on topic models
Recognizing driving styles based on topic models

With the explosion of information in our current era, senders of information increasingly need to target their messages to recipients. However, messages within transportation systems, including traffic information and commercial advertisements, tend to be transmitted to all drivers indiscriminately. This is because the information providers (such as other vehicles, roads, facilities, buildings etc.), can hardly recognize the variations within drivers, who should be treated differently as information recipients. As a result of the rapid development of data collection technologies and machine learning techniques in recent years, extraction and recognition of drivers’ unique driving style from actual driving behaviour data become possible. In this paper, two kinds of topic models are investigated: mLDA and mHLDA, to discover distinguishable driving style information with hidden structure from the real-world driving behaviour data. The results show that the proposed models can successfully recognize the differences between driving styles. The study is of great value for providing deep insight into the underlying structure of driving styles and can effectively support the recognition of drivers with different driving styles.

Driving behaviour, Driving environment, Driving style, LDA, Topic model
1361-9209
Qi, Geqi
c8931109-0e34-4057-aa98-584cef36e779
Wu, Jianping
db314ad9-d011-4c77-9ae1-b190f82fd013
Zhou, Yang
ba6f473e-0abb-4761-9aa1-654f5d4af099
Du, Yiman
43db0cdc-2833-48d4-b4b0-9c16655225c1
Jia, Yuhan
dd5c23b2-d726-4d4d-8731-6b98ea96ccdc
Hounsell, Nick
54781702-9b09-4fb7-8d9e-f0b7833731e5
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd
Qi, Geqi
c8931109-0e34-4057-aa98-584cef36e779
Wu, Jianping
db314ad9-d011-4c77-9ae1-b190f82fd013
Zhou, Yang
ba6f473e-0abb-4761-9aa1-654f5d4af099
Du, Yiman
43db0cdc-2833-48d4-b4b0-9c16655225c1
Jia, Yuhan
dd5c23b2-d726-4d4d-8731-6b98ea96ccdc
Hounsell, Nick
54781702-9b09-4fb7-8d9e-f0b7833731e5
Stanton, Neville A.
351a44ab-09a0-422a-a738-01df1fe0fadd

Qi, Geqi, Wu, Jianping, Zhou, Yang, Du, Yiman, Jia, Yuhan, Hounsell, Nick and Stanton, Neville A. (2018) Recognizing driving styles based on topic models. Transportation Research Part D: Transport and Environment. (doi:10.1016/j.trd.2018.05.002).

Record type: Article

Abstract

With the explosion of information in our current era, senders of information increasingly need to target their messages to recipients. However, messages within transportation systems, including traffic information and commercial advertisements, tend to be transmitted to all drivers indiscriminately. This is because the information providers (such as other vehicles, roads, facilities, buildings etc.), can hardly recognize the variations within drivers, who should be treated differently as information recipients. As a result of the rapid development of data collection technologies and machine learning techniques in recent years, extraction and recognition of drivers’ unique driving style from actual driving behaviour data become possible. In this paper, two kinds of topic models are investigated: mLDA and mHLDA, to discover distinguishable driving style information with hidden structure from the real-world driving behaviour data. The results show that the proposed models can successfully recognize the differences between driving styles. The study is of great value for providing deep insight into the underlying structure of driving styles and can effectively support the recognition of drivers with different driving styles.

Full text not available from this repository.

More information

Accepted/In Press date: 3 May 2018
e-pub ahead of print date: 17 July 2018
Keywords: Driving behaviour, Driving environment, Driving style, LDA, Topic model

Identifiers

Local EPrints ID: 425605
URI: http://eprints.soton.ac.uk/id/eprint/425605
ISSN: 1361-9209
PURE UUID: 2b40dc5d-ed8d-438c-97ac-4c83991bf1f1
ORCID for Neville A. Stanton: ORCID iD orcid.org/0000-0002-8562-3279

Catalogue record

Date deposited: 25 Oct 2018 16:30
Last modified: 17 Dec 2019 01:42

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