Home > Research > Publications & Outputs > Probability hypothesis density filtering for re...

Electronic data

  • NHM final

    Submitted manuscript, 1.03 MB, PDF document


Text available via DOI:

View graph of relations

Probability hypothesis density filtering for real-time traffic state estimation and prediction

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>09/2013
<mark>Journal</mark>Network and Heterogeneous Media
Issue number3
Number of pages18
Pages (from-to)825-842
Publication StatusPublished
<mark>Original language</mark>English


The probability hypothesis density (PHD) methodology is widely used by the research community for the purposes of multiple object tracking. This problem consists in the recursive state estimation of several targets by using the information coming from an observation process. The purpose of this paper is to investigate the potential of the PHD filters for real-time traffic state estimation. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the state estimation problem and allows to estimate the densities in traffic networks in the presence of measurement origin uncertainty, detection uncertainty and noises. In this work, we compare the PHD filter performance with a particle filter (PF), both taking into account the measurement origin uncertainty and show that they can provide high accuracy in a traffic setting and real-time computational costs. The PHD filtering framework opens new research avenues and has the abilities to solve challenging problems of vehicular networks.

Bibliographic note

Special Issue on: Mathematics of Traffic Modelling, Estimation and Control