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A Control-oriented Macroscopic Traffic Flow Model for Urban Diverse Intersections

机译:面向控制的城市交叉口宏观交通流模型

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Traffic flow on roads is a non-linear, stochastic phenomenon, with complex interactions between vehicles. This paper discussed the dynamic nature of urban intersections, and presents a novel traffic flow evolution model at a time scale and of a level of detail suitable for on-line estimation, simulation and control. The intersection is considered as interconnected components of urban road network. The macroscopic model proposed here extends the platoon-dispersion model by redefining inflow and outflow vectors, and by also specifying the control vector. Two variables were added to the model, i.e. the turning proportion and the lane assignment scheme. The turning proportion is an obvious variant, and yet the lane assignment is time-varying in some especial condition. Simple stochastic matrix equations described the macroscopic traffic behavior of each movement. This will allow the simulation of quite large road networks by composing many intersection models. The model is validated over real traffic data with abrupt changes in the demand of flow.
机译:道路上的交通流量是一种非线性的,随机的现象,车辆之间的相互作用十分复杂。本文讨论了城市交叉口的动态性质,并提出了一种适用于在线估计,模拟和控制的时标和详细程度的新型交通流演化模型。十字路口被认为是城市道路网络的相互联系的组成部分。这里提出的宏观模型通过重新定义流入和流出向量,并通过指定控制向量来扩展排扩散模型。向模型添加了两个变量,即转弯比例和车道分配方案。转弯比例是一个明显的变体,但是在某些特殊条件下,车道分配是随时间变化的。简单的随机矩阵方程式描述了每个运动的宏观交通行为。通过组合许多交叉路口模型,这将允许对相当大的道路网络进行仿真。该模型通过流量需求突然变化的实际交通数据进行了验证。

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