Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter-Reference-Cited by-同舟云学术

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

Published:2019-04-01 Issue:2 Volume:20 Page:114-122
ISSN:1407-6179
Container-title:Transport and Telecommunication Journal
language:en
Short-container-title:

Author:

Ouessai Asmâa¹,Keche Mokhtar¹

Affiliation:

1. Signals and Images Laboratory , Department of Electronics , University of Sciences and Technology USTO-MB , B.P. 1505, El M’Naouar - Bir el Djir- Oran , Algeria

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Computer Science Applications,General Engineering

Link

https://www.sciendo.com/pdf/10.2478/ttj-2019-0010

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