Exploring trip fuel consumption by machine learning from GPS and CAN bus data

@inproceedings{Zeng2015ExploringTF,
  title={Exploring trip fuel consumption by machine learning from GPS and CAN bus data},
  author={Weiliang Zeng and Tomio Miwa and Takayuki Morikawa},
  year={2015}
}
This study aims to explore the trip fuel consumption from a large-scale dataset. To better understand how the multiple variables (e.g., average travel speed, trip distance) influence the trip fuel consumption, we propose the support vector machine (SVM) to learn the relationship between the trip fuel consumption and the corresponding factors. A large-scale GPS and CAN (Controller Area Network) bus data provided by 153 probe vehicles during one month are used. Elasticity analysis indicates that… CONTINUE READING
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