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首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Applicable filtering framework for online multiclass freeway network estimation
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Applicable filtering framework for online multiclass freeway network estimation

机译：在线多类高速公路网络估计的适用过滤框架

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摘要

Real-time traffic flow estimation is important for online traffic control and management. The traffic state estimator optimally matches traffic measurements from detectors with traffic flow predictions from a dynamic traffic model under a certain control strategy. The current and widely used estimator is based on the Extended Kalman Filter algorithm (EKF). Basically, EKF is developed from the recursive Bayesian estimation technique for Gaussian random distribution of the state. This approximation may result in large errors in the estimation and even lead to divergence of the filter in highly non-linear dynamic system such as heterogeneous traffic flow operations. The aims of this paper are therefore twofold. On the one hand, we present a generalized stochastic macroscopic traffic model for multiclass freeway networks. The model is developed in the form that can be applied by filtering methods. On the other hand, we implement an accurate probabilistic framework to the real-time multiclass freeway network estimation. The framework uses a variation of Kalman Filter, namely Unscented Kalman Filter, and a different filter that is based on a sequential Monte Carlo method, namely Unscented Particle Filter. We investigate the performance of the proposed framework with respect to accuracy and computational effort using real-life data collected in a freeway network in England. We expect that the developed tool is useful for traffic operators and planners in controlling large-scale multiclass freeway networks. (c) 2007 Elsevier B.V. All rights reserved.
机译：实时交通流量估算对于在线交通控制和管理非常重要。交通状态估计器在特定控制策略下，将来自检测器的交通测量值与来自动态交通模型的交通流量预测进行最佳匹配。当前广泛使用的估计器基于扩展卡尔曼滤波算法（EKF）。基本上，EKF是从递归贝叶斯估计技术开发的，用于状态的高斯随机分布。这种近似可能会导致估计中的大误差，甚至导致高度非线性的动态系统（例如异构业务流操作）中的滤波器发散。因此，本文的目的是双重的。一方面，我们提出了用于多类高速公路网络的广义随机宏观交通模型。以可以通过过滤方法应用的形式开发模型。另一方面，我们为实时多类高速公路网络估计实现了一个准确的概率框架。该框架使用Kalman过滤器的一种变体，即Unscented Kalman过滤器，以及基于顺序Monte Carlo方法的另一种过滤器，即Unscented粒子过滤器。我们使用在英格兰的高速公路网络中收集的真实数据来研究所提出框架的准确性和计算量方面的性能。我们希望开发的工具对交通运营商和规划者在控制大型多类高速公路网络方面很有用。 （c）2007 Elsevier B.V.保留所有权利。

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