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Artificial Neural Network Model of Traffic Operations at Signalized Junction in Johor Bahru, Malaysia

机译:马来西亚柔佛州柔佛州的信号交叉路口交通运营人工神经网络模型

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摘要

Driving behavior models are an important component of microscopic traffic simulation tools. Artificial Neural Networks (ANN) are systems that try to make use of some of the known or expected organizing principles of the human brain. Today neural networks can be trained to solve problems that are difficult for conventional computers or human beings. In this research four signalized junction in Johor Bahru have been considered and simulation of driver's behavior in terms of delay and queue length have been implemented. The neural network approach seems to be more natural and reasonable than the conventional method. The neural network is also more effective and efficient in determining appropriate traffic terms of study.
机译:驾驶行为模型是微观交通仿真工具的重要组成部分。人工神经网络(ANN)是试图利用人脑的一些已知或预期组织原则的系统。今天可以训练神经网络,以解决传统计算机或人类难的问题。在这项研究中,已经考虑了柔佛州柔佛州的四个信号交界处,并在延迟和队列长度方面进行了驾驶员行为的模拟。神经网络方法似乎比传统方法更自然和合理。在确定适当的交通学期时,神经网络也更有效和有效。

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