This document discusses using big data from sensors on buses to analyze bus operations and improve efficiency. It summarizes: 1. 500GB+ of sensor data was collected from 26 buses over 4 years, including GPS, speed, engine data, fuel usage, temperature, and more. 2. Random forest models were used to automatically classify 11 operation states like idling or moving, achieving 81-93% accuracy and reducing the need for manual input. 3. Further models using random forest regressors and a few sensor features estimated passenger counts with 94-96% accuracy, helping optimize routes and charter profits without expensive dedicated equipment. 4. Additional applications discussed include detecting driver fatigue, road damage identification, and

1. BUS BIG DATA
500GB+ sensor data from 26 buses of a bus company for 4 years
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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. BUS OPERATION ISSUE
Employee shortage Too many operations of drivers
2
An operation manager vs 20+ Drivers

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. 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. 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
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http://www.trastem.co.jp/eng/product/passenger_counter.html

6. MANDATORY DRIVE RECORDER
Priory use:
Accident preservation
Secondary use:
Possibility for sensing various things
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7. PASSENGER COUNTER BASED ON
RANDOM FOREST REGRESSOR
Deep Learning based (YOLOv3 +
Deep SORT) image processing only
Random Forest Regressor
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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. DETECTING BAD DRIVING
Six consecutive days working caused
hard braking and speeding violation
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1st day 6th day

9. ROAD DAMAGE DETECTION
IEEE Bigdata held a contest of deep learning.
We can also tackle with the real data from buses!
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10. VERSATILITY OF CAMERA AS SENSOR
Treepedia@Senseable City Lab,
MIT
Block fence detection
­ Unwanted fragility against earth quake
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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…
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Minato Kanko Bus
Kobe, Japan
Online digital signage
for bus stop