@inproceedings{2805be49d038457e83f96b060e8cb28f,
title = "Apply Scikit-learn in python to analyze driver behavior based on OBD data",
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.",
author = "Chipan Hwang and Chen, {Mu Song} and Shih, {Chih Min} and Chen, {Hsing Yu} and Liu, {Wen Kai}",
year = "2018",
month = "7",
day = "20",
doi = "10.1109/WAINA.2018.00159",
language = "English",
series = "Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "636--639",
editor = "Lidia Ogiela and Tomoya Enokido and Ogiela, {Marek R.} and Nadeem Javaid and Leonard Barolli and Makoto Takizawa",
booktitle = "Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018",
address = "United States",
note = "32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018 ; Conference date: 16-05-2018 Through 18-05-2018",
}