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Random Forest Based Bus Operation States Classification Using Vehicle Sensor Data

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dc.contributor.author Yonezawa, Takuya en
dc.contributor.author Arai, Ismail en
dc.contributor.author Akiyama, Toyokazu en
dc.contributor.author Fujikawa, Kazutoshi en
dc.date.accessioned 2020-08-26T00:32:04Z en
dc.date.available 2020-08-26T00:32:04Z en
dc.date.issued 2018-03-23 en
dc.identifier.isbn 978-1-5386-3227-7 en
dc.identifier.uri http://hdl.handle.net/10061/14032 en
dc.description 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops),Athens, Greece en
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
dc.language.iso en en
dc.publisher IEEE en
dc.rights Copyright © 2018, IEEE ja
dc.subject Companies en
dc.subject Hidden Markov models en
dc.subject Forestry en
dc.subject Engines en
dc.subject Automobiles en
dc.subject Data models en
dc.title Random Forest Based Bus Operation States Classification Using Vehicle Sensor Data en
dc.type.nii Conference Paper en
dc.contributor.transcription アライ, イスマイル ja
dc.contributor.transcription フジカワ, カズトシ ja
dc.contributor.alternative 新井, イスマイル ja
dc.contributor.alternative 藤川, 和利 ja
dc.textversion author en
dc.identifier.jtitle en
dc.relation.doi 10.1109/PERCOMW.2018.8480291 en
dc.identifier.NAIST-ID 74652785 en
dc.identifier.NAIST-ID 73293045 en

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