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.

本文使用粒子滤波 (PF) 方法探讨了异构交通（具有不同驾驶行为的车辆）的状态估计问题。我们考虑 PF 的三种变体来增强估计。基准 PF 利用确定性偏微分方程和与状态无关的加性过程噪声。我们首先考虑一个参数自适应 PF 变化，它也允许调整模型参数。第二种变体是具有空间相关噪声的 PF。最后一个变体结合了参数自适应和空间相关噪声方法。我们在代表异构交通场景和现实世界异构交通数据的数值实验中比较了四个过滤器。结果表明，与没有测量校正的开环模拟、合成设置和真实交通数据相比，增强型滤波器的精度分别提高了 80% 和 46%。此外，增强型过滤器在所有交通场景中的准确性均优于标准 PF。