| 城市短期交通流量预测方法的探讨 |
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| 引用本文： | 刘长虹,陈志恒,黄虎.城市短期交通流量预测方法的探讨[J].现代交通技术,2006,3(1):57-58,63. |
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| 作者姓名： | 刘长虹 陈志恒 黄虎 |
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| 作者单位： | 上海工程技术大学汽车工程学院,上海,仙霞路,200336 |
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| 摘 要： | 根据实际观测得到的交通流量数据，运用灰色预测模型、神经网络以及最小二乘拟合等三种交通流量预测模型．对上海市延安东路隧道浦西段入口处短期车流量进行短期的预测。计算结果表明，神经网络模型的精度最高。最后提出一种根据短期交通流量预测结果的人工智能解决交通拥堵的方案。
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| 关 键 词： | 短期交通流量 神经网络 灰色 拟合 预测 |
| 文章编号： | 1672-9889（2006）01-0057-02 |
| 收稿时间： | 2005-04-25 |
| 修稿时间： | 2005-04-25 |
Discussion on the Method of Short-term Expecting Traffic |
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| LIU Chang-hong,CHEN Zhi-heng,HUANG Hu.Discussion on the Method of Short-term Expecting Traffic[J].Modern Transportation Technology,2006,3(1):57-58,63. |
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| Authors: | LIU Chang-hong CHEN Zhi-heng HUANG Hu |
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| Institution: | College of Automobile Engineering ,Shanghai University of Engineering Science, Shanghai 200336, China |
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| Abstract: | Firstly, with the traffic instruments the traffic data on the tunnel of the East Yanan Road in Shanghai are measured. Secondly, the gray model, neural network and the minimum square fitting are presented to predicting short-term traffic flow. According to the traffic data in real time, the results show these models work exactly and correctly. By comparing with those results, the neural network is the best in terms of exactness. Finally a method of intelligent to solve traffic jam is presented. |
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| Keywords: | short-term traffic flow neural network grey fitting prediction |
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