Citation

Ustun I, Cetin M. Transp. Res. Rec. 2019; 2673(3): 65-73.

Copyright

(Copyright © 2019, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198119836977

PMID

unavailable

Abstract

This paper is focused on developing an algorithm to estimate vehicle speed from accelerometer data generated by an onboard smartphone. The kinetic theory tells that the integration of acceleration gives the speed of a vehicle. Thus, the integration of the acceleration values collected with the smartphone in the direction of motion would theoretically yield the speed. However, speed estimation by the integration of accelerometer data will not yield accurate results, as the accelerometer data in the direction of motion is not pure acceleration, but involves white noise, phone sensor bias, vibration, gravity component, and other effects. To account for these sources of noise and error, a calibration method that can adjust the speed at certain times or points is needed. The exact times when the vehicle stops and starts are identified and used to calibrate the estimated speed. Based on the collected sample data, the proposed method yields that the estimated speed is on average within 10 mph of the actual speed, with a lower margin at the street-level driving. This suggests that with more information to calibrate the speed, the model accuracy can be improved further.

Language: en