2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (2018)
March 19, 2018 to March 23, 2018
Takuya Yonezawa , Graduate school of Information Science, Nara Institute of Science and Technology, Japan
Ismail Arai , Graduate school of Information Science, Nara Institute of Science and Technology, Japan
Toyokazu Akiyama , Faculty of Computer Science and Engineering, Kyoto Sangyo University, Japan
Kazutoshi Fujikawa , Graduate school of Information Science, Nara Institute of Science and Technology, Japan
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 man- ually 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.
Companies, Hidden Markov models, Forestry, Engines, Automobiles, Data models
T. Yonezawa, I. Arai, T. Akiyama and K. Fujikawa, "Random Forest Based Bus Operation States Classification Using Vehicle Sensor Data," 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)(PERCOM WORKSHOPS), Athens, Greece, 2018, pp. 747-752.