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首页> 外文期刊>Transportation Research Part B: Methodological >A stochastic model of traffic flow: Gaussian approximation and estimation
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A stochastic model of traffic flow: Gaussian approximation and estimation

机译：交通流量的随机模型：高斯近似和估计

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摘要

A Gaussian approximation of the stochastic traffic flow model of Jabari and Liu (2012) is proposed. The Gaussian approximation is characterized by deterministic mean and covari-ance dynamics; the mean dynamics are those of the Godunov scheme. By deriving the Gaussian model, as opposed to assuming Gaussian noise arbitrarily, covariance matrices of traffic variables follow from the physics of traffic flow and can be computed using only few parameters, regardless of system size or how finely the system is discretized. Stationary behavior of the covariance dynamics is analyzed and it is shown that the covariance matrices are bounded. Consequently, Kalman filters that use the proposed model are stochastically observable, which is a critical issue in real time estimation of traffic dynamics. Model validation was carried out in a real-world signalized arterial setting, where cycle-by-cycle maximum queue sizes were estimated using the Gaussian model as a description of state dynamics. The estimated queue sizes were compared to observed maximum queue sizes and the results indicate very good agreement between estimated and observed queue sizes.
机译：提出了Jabari和Liu（2012）的随机交通流模型的高斯近似。高斯近似的特征在于确定性均值和协方差动力学。平均动力学是戈杜诺夫方案的动力学。通过推导高斯模型，与任意假定高斯噪声相反，交通变量的协方差矩阵来自交通流的物理性质，并且可以使用很少的参数来计算，而与系统大小或系统的离散程度无关。分析了协方差动力学的平稳行为，结果表明协方差矩阵是有界的。因此，使用所提出模型的卡尔曼滤波器是随机可观察到的，这是实时估计交通动态的关键问题。模型验证是在现实世界中的信号化动脉环境中进行的，其中使用高斯模型作为状态动态描述来估算逐周期最大队列大小。将估计的队列大小与观察到的最大队列大小进行比较，结果表明估计和观察到的队列大小之间的一致性很好。

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