Abstract: This paper proposed a radio frequency identification (RFID) data-driven vehicle speed prediction method using adaptive Kalman filter.First of all,when the vehicle moves through one RFID tag,the reader needs to acquire the state information (i.e.,current speed and time stamp) of the last vehicle across this tag,meanwhile transmits its own state information to this tag.Then,the state space model can be formulated according to the acquired state information.Finally,the adaptive Kalman filtering algorithm is used to predict and adjust the vehicle speed.Adaptive Kalman filtering algorithm realizes the adaptive updating of variable forgetting factor by using the error between the expected output value and the actual output value,and thus realize the online updating of the prediction model.The numerical results further show that compared with the least square method and the conventional Kalman filtering algorithm,the proposed algorithm can improve the speed prediction accuracy by 87.5% and 50% respectively,implying that the proposed algorithm can provide better real-time effectiveness for the practical applications.

Key words: Adaptive, Data acquisition, Kalman filter, RFID, Speed prediction