非线性随机交通模型中的动态容量下降
Dynamical capacity drop in a nonlinear stochastic traffic model
摘要：
在这项工作中，我们表明，逆$λ$形状的基本图的交通流可以动态地产生一个简单的非线性介观模型与随机噪声。该模型是基于交通系统的气体动力学理论。在我们的方法中，非线性导致不同的交通状态的共存。因此，分散的数据归因于随机微分方程中引入的噪声项和各种交通状态之间的过渡。最重要的是，所观察到的逆$λ$形状和相关的突然跳跃的物理量的出现是由于随机噪声对系统的稳定性的影响。模型参数进行了校准，并获得了定性的协议之间的数据和数值模拟。
英文摘要：
In this work, we show that the inverse-$λ$ shape in the fundamental diagram of traffic flow can be produced dynamically by a simple nonlinear mesoscopic model with stochastic noises. The proposed model is based on the gas-kinetic theory of the traffic system. In our approach, the nonlinearity leads to the coexistence of different traffic states. The scattering of the data is thus attributed to the noise terms introduced in the stochastic differential equations and the transition among the various traffic states. Most importantly, the observed inverse-$λ$ shape and the associated sudden jump of physical quantities arise due to the effect of stochastic noises on the stability of the system. The model parameters are calibrated, and a qualitative agreement is obtained between the data and the numerical simulations.
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