This work explores the state estimation problem for heterogeneous traffic (a multiclass flow composed of vehicles with distinct sizes and driving behaviours) using particle filtering (PF) approaches. We consider three enhanced variations of the bootstrap PF to improve estimation. The benchmark PF utilises a deterministic partial differential equation and an additive process noise that is state-independent. For the enhanced variations we first consider a parameter-adaptive PF that also allows model parameters to be adjusted. The second variation is a standard 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 complex heterogeneous traffic scenarios, as well as 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.
Estimation for heterogeneous traffic states
Nov 18, 2021
Invited talk at VSA Expert Night