论文标题

由维纳过程驱动的分析随机宏观基本图

Analytical stochastic macroscopic fundamental diagram driven by Wiener process

论文作者

Qi, HongSheng

论文摘要

宏观基本图(MFD)是一种强大而流行的工具,它描述了网络规模的交通运营状态,并用作周围控制的植物模型。由于供应和需求都受到随机干扰的影响,因此交通流动性不能说是确定性的。随机MFD模型仍然缺乏汇总变量所需分布的系统状态的随机演变。提出了一种考虑累积依赖性变化的MFD的随机公式,以填补这一空白。该模型基于随机微分方程(SDE)理论。首先,出口流量变化被配制为维也纳驱动的过程,该过程接受了依赖变化的积累。然后,通过组合出口流量变化模型来构建随机MFD模型。系统状态的解决方案是由正向fokker-Planck方程得出的。分析模型的稳定性,并提供了校准方法的参数。然后测试了模型的几种情况。结果表明该模型可以应用于不同的功能MFD形式,并且磁滞和僵局现象被复制。所提出的MFD模型可用于考虑系统随机演变的网络分析和控制。

The macroscopic fundamental diagram (MFD) is a powerful and popular tool that describes a network scale traffic operational state and serve as the plant model of perimeter control. As both the supply and the demand suffer from random disturbances, the traffic flow dynamics cannot be said to be deterministic. A stochastic MFD model can generate a stochastic evolution of the system state with desired distribution of aggregated variables is still lacking. A stochastic formulation of MFD, that considers the accumulation-dependent variations, is proposed to fill this gap. The model is based on the stochastic differential equation (SDE) theory. First, the exit flow variation is formulated as a Wiener-driven process, which admits the accumulation-of dependent variations. The stochastic MFD model is then constructed by combining the exit flow variations model. The solution of the system state is derived by the forward Fokker-Planck equation. The stability of the model is analyzed, and the parameters of a calibration method are provided. Several cases of the model are then tested. The results show that the model can be applied to different functional MFD forms, and the hysteresis and gridlock phenomenon is reproduced. The proposed MFD model can be used in the network analysis and control that considers the system's stochastic evolution.

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