DSpace Repository

Random Forest Based Bus Operation States Classification Using Vehicle Sensor Data

Show simple item record

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

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search


My Account