Particle Filter for Estimating Freeway Traffic State in Beijing

Abstract

Freeway traffic state estimation is useful for intelligent traffic guidance, control, and management. The freeway traffic state is featured with rapid and dramatic fluctuations, which presents a strong nonlinear feature. In theory, a particle filter has good performance in solving nonlinear problems. This paper proposes a particle filter based approach to estimate freeway traffic state. The freeway link between the west of Peace Bridge and the west of San Yuan Bridge of the third ring in Beijing is used as the experimental object. According to the traffic characteristics and measurement mode of the link, the second-order validated macroscopic traffic flow model is adopted to set up the link model. The implementation steps of the particle filter for freeway traffic state estimation are described in detail. The estimation error analysis for the experiments proves that the proposed approach has an encouraging estimation performance.

SVM‐based hybrid approach for corridor‐level travel‐time estimation

“…The main advantage of a PF is that it does not require the process and sensor noise to be Gaussian distributed. The PFs are widely used for various application like real‐time travel‐time estimation [40–43], studies related to traffic parameter estimation [21, 44–49] and vehicular tracking [40, 48, 50, 51]. PFs are also used for estimating the probability density functions (PDFs) of the system states [52].…”

Traffic Flow Signal based Traffic Event Reconstruction using Sequential Monte Carlo Methods

“…They use particle filter method and the ACD model to deal with the complex traffic signals. Bi Chang and Fan in [9] propose a particle filter based approach to estimate freeway traffic state, through the experiment which used the freeway link between the west of Peace Bridge and the west of San Yuan Bridge of the third ring in Beijing, they proved PF have an encouraging estimation performance.…”