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Random Forest Based Bus Operation States Classification Using Vehicle Sensor Data

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dc.contributor.author Yonezawa, Takuya
dc.contributor.author Arai, Ismail
dc.contributor.author Akiyama, Toyokazu
dc.contributor.author Fujikawa, Kazutoshi
dc.date.accessioned 2020-08-26T00:32:04Z
dc.date.available 2020-08-26T00:32:04Z
dc.date.issued 2018-03-23
dc.identifier.isbn 978-1-5386-3227-7
dc.identifier.uri http://hdl.handle.net/10061/14032
dc.description 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops),Athens, Greece ja_JP
dc.description.abstract In bus companies, it is important for an operation manager to grasp operation states of vehicles from a viewpoint of safety management and improving an operation efficiency. Currently, for allowing operation managers to grasp operation states of vehicles, drivers should record operation states by manually operating a recorder called ”Digital-tachograph.” However, operating the digital tachograph is a heavy burden to the driver. In addition, the records may have driver's human error. In order to solve these problems and to realize efficient operation, we propose a method for automatic classification of operation states using sensor data obtained from buses. We implemented a classifier using Random Forest with the sensor data. As a results of experiments, the correct answer rate was 0.92 or more in each condition unless it was irregular operation. ja_JP
dc.language.iso en ja_JP
dc.publisher IEEE ja_JP
dc.rights Copyright © 2018, IEEE ja_JP
dc.subject Companies ja_JP
dc.subject Hidden Markov models ja_JP
dc.subject Forestry ja_JP
dc.subject Engines ja_JP
dc.subject Automobiles ja_JP
dc.subject Data models ja_JP
dc.title Random Forest Based Bus Operation States Classification Using Vehicle Sensor Data ja_JP
dc.type.nii Conference Paper ja_JP
dc.contributor.transcription アライ, イスマイル
dc.contributor.transcription フジカワ, カズトシ
dc.contributor.alternative 新井, イスマイル
dc.contributor.alternative 藤川, 和利
dc.textversion author ja_JP
dc.identifier.jtitle ja_JP
dc.relation.doi 10.1109/PERCOMW.2018.8480291 ja_JP
dc.identifier.NAIST-ID 74652785 ja_JP
dc.identifier.NAIST-ID 73293045 ja_JP


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