Dynamic car following data collection and noise cancellation based on the Kalman smoothing

Abstract

This paper introduces a data collection method that we used in a project on modeling driver behavior in microscopic traffic simulation. A modern instrumented vehicle was employed to study a crucial element of driver behavior, that of car following, on Swedish roads. The collected car following data shows noisy patterns. To eliminate the measurement noise, Kalman smoothing algorithm is applied to the state-space formulation of the physical states (acceleration, speed and position) of tracked vehicles. The smoothed data shows clear car following patterns and has been further applied in our car following model calibration and validation study.