Reliable road traffic state identification systems should be designed to provide accurate traffic state information anywhere and anytime. In this paper we propose a road traffic classification system, based on traffic variables estimated using the second order Divided Difference Kalman Filter (DDKF2). This filter is compared with the Extended Kalman Filter (EKF) using both simulated and real-world dataset of highway traffic. Monte-Carlo simulations indicate that the DDKF2 outperforms the EKF filter in terms of parameters estimation error. The real-word evaluation of the DDKF2 filter in terms of classification rate confirms that this filter is promising for real-world traffic state identification systems.
Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter
2019
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Real-Time Freeway Traffic State Estimation Based on Extended Kalman Filter: A Case Study
Online Contents | 2007
Real-time freeway traffic state estimation based on extended Kalman filter: a general approach
Online Contents | 2005
|Real-Time Freeway Traffic State Estimation Based on Extended Kalman Filter: A Case Study
British Library Online Contents | 2007
|