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Bus bigdata


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Lab introduction material

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Bus bigdata

  1. 1. BUS BIG DATA 500GB+ sensor data from 26 buses of a bus company for 4 years 1 Tachograph Digital tachograph with sensors • GPS (Lat., Lng., Alt., Azimuth) • Vehicle speed • Engine speed • Travel distance • Engine pulse • Direction • Atmospheric pressure • Temperature • Humidity • Amount of fuel • Near future • Accelerometer • Gyroscope • CAN data
  2. 2. BUS OPERATION ISSUE Employee shortage Too many operations of drivers 2 An operation manager vs 20+ Drivers
  3. 3. CLASSIFICATION OF BUS OPERATION STATE IoT devices in a bus still require drivers to manually input 11 operation state! It should be automatic! 3 !
  4. 4. RANDOM FOREST BASED CLASSIFICATION OF BUS OPERATION STATES Achieved both labor cutting and improving accuracy! 4 ! 81% 93% manually input 12 features from sensors 11 operation states Random Forest Takuya Yonezawa, Ismail Arai, Toyokazu Akiyama, Kazutoshi Fujikawa, “Random Forest Based Bus Operation States Classification Using Vehicle Sensor Data,” 2018 International Workshop on Pervasive Flow of Things (PerFot 2018), IEEE PerCom 2018, pp.819--824, Greece, March, 2018.
  5. 5. PASSENGER COUNTER Accurate counter optimizes… ­ profit rate (refining bus routes) ­ profit of charter buses (avoiding underestimate of the number of passengers) Dedicated equipment is still expensive ­ $2,000+/bus 5
  6. 6. MANDATORY DRIVE RECORDER Priory use: Accident preservation Secondary use: Possibility for sensing various things 6
  7. 7. PASSENGER COUNTER BASED ON RANDOM FOREST REGRESSOR Deep Learning based (YOLOv3 + Deep SORT) image processing only Random Forest Regressor 7 94% 96% 3 features from sensors Random Forest Regressor Number of passengers Hayato Nakashima, Ismail AraiKazutoshi Fujikawa, “Passenger Counter Based on Random Forest Regressor Using Drive Recorder and Sensors in Buses,” 2019 International Workshop on Pervasive Flow of Things (PerFot 2019), IEEE PerCom 2019, pp. 561—566, Kyoto, March, 2019.
  8. 8. DETECTING BAD DRIVING Six consecutive days working caused hard braking and speeding violation 8 1st day 6th day
  9. 9. ROAD DAMAGE DETECTION IEEE Bigdata held a contest of deep learning. We can also tackle with the real data from buses! 9
  10. 10. VERSATILITY OF CAMERA AS SENSOR Treepedia@Senseable City Lab, MIT Block fence detection ­ Unwanted fragility against earth quake 10
  11. 11. NEW SENSORS ARE WELCOME Initially, collaborative company has sensors (-2014) ­ GPS ­ Vehicle speed, Engine speed, Amount of fuel We proposed to add sensors (2014-2018) ­ Temperature, Humidity, Air pressure ­ Door (open/close), Wiper ­ Multi purpose 6ch drive recorders (70GB/day from a bus!) Still propose to add sensors (2018-) ­ Accelerometer, Gyroscope, RTK GNSS ­ CO2 ­ Many in vehicle sensors connected with CAN ­ Cameras and sensors at bus stops We can tell them your idea and realize them! (2019-) ­ Thermal camera, Air pollution, Pedestrians… 11 Minato Kanko Bus Kobe, Japan Online digital signage for bus stop