Apply Scikit-learn in python to analyze driver behavior based on OBD data

Chipan Hwang, Mu Song Chen, Chih Min Shih, Hsing Yu Chen, Wen Kai Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The long term accumulated driving information can effectively summarize the specific driver behavior by statistical analysis. In order to widely and chronically collect driving information of drivers, the cloud computing platform is the most suitable mechanism to log the dynamic vehicle information stream from OBD port to build up Big Data for data mining about driver behavior, currently. The research of this paper has focused on the application layer in the cloud computing platform, Python has been adopted to as the main development tool accompanying with the packages of numpy, pandas, and scipy to calculate the kurtosis and skewness in statistics of each driving route, then decision tree classification technique was applied to generate the analyzing knowledge for driver behavior analysis. Finally the driver behavior are summarized from the completed decision tree classifier to defensive, weak defensive, weak aggressive, and aggressive to complete the overall operations.

Original languageEnglish
Title of host publicationProceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018
EditorsLidia Ogiela, Tomoya Enokido, Marek R. Ogiela, Nadeem Javaid, Leonard Barolli, Makoto Takizawa
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages636-639
Number of pages4
ISBN (Electronic)9781538653944
DOIs
Publication statusPublished - 2018 Jul 20
Event32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018 - Krakow, Poland
Duration: 2018 May 162018 May 18

Publication series

NameProceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018
Volume2018-January

Other

Other32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018
CountryPoland
CityKrakow
Period18-05-1618-05-18

Fingerprint

Cloud computing
Decision trees
Data mining
Statistical methods
Classifiers
Statistics
Big data
Decision tree

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management

Cite this

Hwang, C., Chen, M. S., Shih, C. M., Chen, H. Y., & Liu, W. K. (2018). Apply Scikit-learn in python to analyze driver behavior based on OBD data. In L. Ogiela, T. Enokido, M. R. Ogiela, N. Javaid, L. Barolli, & M. Takizawa (Eds.), Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018 (pp. 636-639). [8418144] (Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WAINA.2018.00159
Hwang, Chipan ; Chen, Mu Song ; Shih, Chih Min ; Chen, Hsing Yu ; Liu, Wen Kai. / Apply Scikit-learn in python to analyze driver behavior based on OBD data. Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018. editor / Lidia Ogiela ; Tomoya Enokido ; Marek R. Ogiela ; Nadeem Javaid ; Leonard Barolli ; Makoto Takizawa. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 636-639 (Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018).
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abstract = "The long term accumulated driving information can effectively summarize the specific driver behavior by statistical analysis. In order to widely and chronically collect driving information of drivers, the cloud computing platform is the most suitable mechanism to log the dynamic vehicle information stream from OBD port to build up Big Data for data mining about driver behavior, currently. The research of this paper has focused on the application layer in the cloud computing platform, Python has been adopted to as the main development tool accompanying with the packages of numpy, pandas, and scipy to calculate the kurtosis and skewness in statistics of each driving route, then decision tree classification technique was applied to generate the analyzing knowledge for driver behavior analysis. Finally the driver behavior are summarized from the completed decision tree classifier to defensive, weak defensive, weak aggressive, and aggressive to complete the overall operations.",
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Hwang, C, Chen, MS, Shih, CM, Chen, HY & Liu, WK 2018, Apply Scikit-learn in python to analyze driver behavior based on OBD data. in L Ogiela, T Enokido, MR Ogiela, N Javaid, L Barolli & M Takizawa (eds), Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018., 8418144, Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 636-639, 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018, Krakow, Poland, 18-05-16. https://doi.org/10.1109/WAINA.2018.00159

Apply Scikit-learn in python to analyze driver behavior based on OBD data. / Hwang, Chipan; Chen, Mu Song; Shih, Chih Min; Chen, Hsing Yu; Liu, Wen Kai.

Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018. ed. / Lidia Ogiela; Tomoya Enokido; Marek R. Ogiela; Nadeem Javaid; Leonard Barolli; Makoto Takizawa. Institute of Electrical and Electronics Engineers Inc., 2018. p. 636-639 8418144 (Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - The long term accumulated driving information can effectively summarize the specific driver behavior by statistical analysis. In order to widely and chronically collect driving information of drivers, the cloud computing platform is the most suitable mechanism to log the dynamic vehicle information stream from OBD port to build up Big Data for data mining about driver behavior, currently. The research of this paper has focused on the application layer in the cloud computing platform, Python has been adopted to as the main development tool accompanying with the packages of numpy, pandas, and scipy to calculate the kurtosis and skewness in statistics of each driving route, then decision tree classification technique was applied to generate the analyzing knowledge for driver behavior analysis. Finally the driver behavior are summarized from the completed decision tree classifier to defensive, weak defensive, weak aggressive, and aggressive to complete the overall operations.

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M3 - Conference contribution

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Hwang C, Chen MS, Shih CM, Chen HY, Liu WK. Apply Scikit-learn in python to analyze driver behavior based on OBD data. In Ogiela L, Enokido T, Ogiela MR, Javaid N, Barolli L, Takizawa M, editors, Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 636-639. 8418144. (Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018). https://doi.org/10.1109/WAINA.2018.00159