Estimation for heterogeneous traffic using enhanced particle filters

Abstract

This article explores the state estimation problem for heterogeneous traffic (vehicles with distinct driving behaviours) using particle filtering (PF) approaches. We consider three variations of PF to enhance estimation. The benchmark PF utilises a deterministic partial differential equation and a state-independent additive process noise. We first consider a parameter-adaptive PF variation that also allows model parameters to be adjusted. The second variation is a PF with spatially-correlated noise. The last variation combines parameter-adaptive and the spatially-correlated-noise approaches. We compare the four filters in numerical experiments that represent heterogeneous traffic scenarios and on real-world heterogeneous traffic data. The results show that the enhanced filters can achieve up to an 80% and 46% of accuracy improvement as compared to an open loop simulation without measurement correction, with the synthetic settings and with real traffic data, respectively. Moreover, the enhanced filters outperform the standard PF in all the traffic scenarios based on accuracy.