具有前瞻势的交通流元胞自动机模型研究
On Cellular Automata Models of Traffic Flow with Look-Ahead Potential
摘要：
研究了具有前瞻势的交通流元胞自动机模型的统计特性。该模型定义了随机规则的汽车在一个格子上的运动。我们分析了基础的粗粒度模型的推导所需的统计假设，并证明，它是可能的放松其中一些，以获得一个改进的粗粒度ODE模型。我们还证明了空间相关性在前瞻电位的存在下起着至关重要的作用，并提出了一个简单的经验校正来解释相邻细胞之间的空间依赖性。
英文摘要：
We study the statistical properties of a cellular automata model of traffic flow with the look-ahead potential. The model defines stochastic rules for the movement of cars on a lattice. We analyze the underlying statistical assumptions needed for the derivation of the coarse-grained model and demonstrate that it is possible to relax some of them to obtain an improved coarse-grained ODE model. We also demonstrate that spatial correlations play a crucial role in the presence of the look-ahead potential and propose a simple empirical correction to account for the spatial dependence between neighboring cells.
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