<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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

University of Sciences and Technology USTO-MB

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Reliable road traffic state identification systems should be designed to provide accurate traffic state information anywhere and anytime. In this paper we...

Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter

Online veröffentlicht: 02. Mai 2019

Seitenbereich: 114 - 122

DOI: https://doi.org/10.2478/ttj-2019-0010

Schlüsselwörter
Divided Difference Kalman filter, Extended Kalman filter, road traffic estimation, road traffic classification, support vector machine

© 2019 Transport and Telecommunication Institute, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter

Online veröffentlicht: 02. Mai 2019

Seitenbereich: 114 - 122

DOI: https://doi.org/10.2478/ttj-2019-0010

SchlüsselwörterDivided Difference Kalman filter, Extended Kalman filter, road traffic estimation, road traffic classification, support vector machine

© 2019 Transport and Telecommunication Institute, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Divided Difference Kalman filter, Extended Kalman filter, road traffic estimation, road traffic classification, support vector machine