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为提高高速公路交通参数的估计准确度,在宏观交通流模型和状态空间模型的基础上,基于贝叶斯理论,提出了一种基于混合粒子滤波的交通参数估计方法。考虑到估计结果对模型参数变化的敏感性,避免采用预设固定模型参数对估计准确度的影响,通过建立自由流速度与饱和度之间的变化关系,提出了交通状态影响下的模型参数自适应调整策略。仿真结果表明:基于混合粒子滤波的交通参数估计准确度要明显高于卡尔曼滤波估计,在正常和事故场景下,能够快速识别交通量和速度较明显的波动,表现出了更强的稳定性;交通状态影响下的模型参数自适应调整策略会明显提高交通参数估计准确度,在发生事故情况下,也可达到较好的估计效果。
In order to improve the accuracy of highway traffic parameters estimation, a hybrid particle filter based traffic parameter estimation method is proposed based on macro traffic flow model and state space model based on Bayesian theory. Considering the sensitivity of the estimation results to the change of model parameters and avoiding the influence of the preset fixed model parameters on the estimation accuracy, by establishing the relationship between the free-flow velocity and the saturation, the model parameters Adapt to the adjustment strategy. The simulation results show that the accuracy of traffic parameter estimation based on hybrid particle filter is significantly higher than that of Kalman filter estimation. Under normal and accident scenarios, the traffic volume and speed fluctuations can be quickly identified, showing a stronger stability ; Traffic model adaptive adjustment strategy under the influence of traffic conditions will significantly improve the accuracy of traffic parameter estimation, and in the event of an accident, it can achieve better estimation results.