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

Kaige Wen, Shiru Qu, Yumei Zhang
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引用次数: 3

Abstract

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.
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面向控制的城市多路口宏观交通流模型
道路上的交通流是一种非线性的随机现象,车辆之间存在复杂的相互作用。本文讨论了城市交叉口的动态特性,提出了一种适合于在线估计、仿真和控制的时间尺度和细节层次的交通流演化模型。交叉口被认为是城市道路网络中相互连接的组成部分。本文提出的宏观模型通过重新定义流入和流出向量,并指定控制向量,扩展了排-分散模型。在模型中加入转弯比例和车道分配方案两个变量。转弯比例是一个明显的变化,但在某些特殊情况下,车道分配是时变的。简单的随机矩阵方程描述了每个运动的宏观交通行为。这将允许模拟相当大的道路网络,组成许多交叉口模型。在实际交通流量需求突变的情况下,对模型进行了验证。
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