1. Mining Motion Sensor Data from Smartphones for Estimating Vehicle Motion Tamer Nadeem, PhD Department of Computer Science NSF Workshop on Large-Scale Traffic and Driving Activity Data October 30-31-,2014 Mecit Cetin, PhD Department of Civil and Environmental Engineering

2. Traffic Data Collection 2

3. Probe Vehicles with GPS Limitations – energy expensive: battery life – signal quality/availability: tunnels, bad weather, urban “canyons” – Privacy concerns 3

4. Smartphones Smartphones as a computing and communications platform 4 Smartphones equipped with various sensors – GPS, accelerometer, gyroscope, compass, proximity sensor, and ambient light sensor, etc.

5. Accelerometer Light weight, small size, low energy consumption 5

6. Three-axis Accelerometer Data 6

7. Objectives Develop robust algorithms to detect when the vehicle stops or starts to move based on the smartphone data Investigate which sensor data provide the most useful information for detecting stops more accurately Develop algorithms to estimate vehicle speed Evaluate the performance of the algorithms based on field data collected under various conditions 7

8. Potential Applications Traffic monitoring Signal timing optimization Fuel consumption and emission predictions Estimating vehicle trajectories while in a tunnel 8

9. Literature Sensor data used in: – Activity classification (sitting, walking, running, biking) – Travel mode identification (car, bus, light rail, riding a bike, walking) – Traffic Monitoring 9

10. Data Collection System 10 OBD GPS OBD GPS Sensors Vehicle, Driver, Time info

11. Mining the Data Machine Learning – Neural networks, SVMs, HMM Model training and testing Investigate impacts of different factors (e.g., mobile device make/model) 11

12. GoGreen App (in Development) 12

13. Example from Toyota Prius 13 Fuel consumption and CO 2 emission estimation

14. Three-axis Accelerometer Data 14

15. Raw Acceleration Data in Y and Z 15

16. Std Deviation of Y and Z 16

17. Orientation Invariant Total Acceleration 17

18. Total Acceleration vs Mean 18

19. Total Acceleration vs Range 19

20. Total Acceleration Histogram

21. Mean of Total Acceleration

22. Std dev of Total Acceleration

23. Range of Total Acceleration

24. Hidden Markov Model The Z vector contains the hidden state for each measurement (vehicle being stationary or in motion). The X matrix contains the visible observations (acceleration data Acc x, Acc y, Acc z ). 24

25. Experiment 1: Linear Threshold + HMM 25

26. Experiment 1: Total Acceleration Range Threshold + HMM 26

27. Experiment 2: Linear Threshold + HMM 27

28. Experiment 2: Total Acceleration Range Threshold + HMM 28

29. Experiment 1 Standstill Durations 29

30. Speed Estimation – No Calibration 30

31. Accelerometer Bias 31

32. Bias Correction (Trip 1)

33. Bias Correction (Trip 2)

34. Bias Correction (Trip 3)

35. Collected Data 35

36. Conclusions Developed an Android App and DB to collect high resolution data from smartphones Developed algorithms to detect stops The number of stops and their times can be estimated with relatively good accuracy Future work underway to estimate speed 36

37. Acknowledgements Funded by TranLIVE UTC (University Transportation Center) – Develop an app to estimate fuel consumption and CO 2 emissions for a multi-modal trip – Provide feedback to the driver Graduate Students: Ilyas Ustun, Abdulla Alasaadi, Ilho Nam, Matt Orensky, Olcay Sahin. Ref: I. Ustun, A. Alasaadi, T. Nadeem, M. Cetin, “Detecting Vehicle Stops from Smartphone Accelerometer Data”, Presented at the 21st ITS World Congress, Detroit, MI, Sept 7-11, 2014. 37

38. Questions? 38 Mecit Cetin mcetin@odu.edu Transportation Research Institute (TRI) www.tri-odu.org