A Neural network traffic flow model for heavy traffic conditions

机译：繁忙交通条件下的神经网络交通流模型

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Over the past few years, artificial intelligence techniques have played important roles in the design of sophisticated traffic management systems. In this paper, we propose a co-operation based neural networks traffic flow model, which aims at being integrated into a real time adaptive urban traffic control system. So far, the modle has been restricted to one signalized junction. It consists of two models; one dedicated to traffic flow on the signalized links, the other to traffic flow within the inner space of the junction. The former is performed by neural networks and the latter by simple heuristcs. Such an architecture enables enables the model to describe traffic in fluid conditions as well as in beavy conditions.

机译：在过去的几年中，人工智能技术在复杂的流量管理系统的设计中发挥了重要作用。在本文中，我们提出了一种基于合作的神经网络交通流模型，旨在将其集成到实时自适应城市交通控制系统中。到目前为止，该模型仅限于一个信号连接。它由两个模型组成；一个专用于信号链路上的交通流，另一个专用于路口内部空间内的交通流。前者由神经网络执行，而后者则由简单的启发式算法完成。这样的体系结构使模型能够描述流体条件和空旷条件下的交通。

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《Applications of advanced technologies in transportation》|1998年|p.265-279|共15页
会议地点 Newport Beach CA(US)
作者
Corinne Ledoux;
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正文语种 eng
中图分类 U-;
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A Neural network traffic flow model for heavy traffic conditions

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