| 基于混沌理论和PSO神经网络的短时交通流预测 |
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| 引用本文： | 魏文,余立建,龚炯.基于混沌理论和PSO神经网络的短时交通流预测[J].商品储运与养护,2010(2). |
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| 作者姓名： | 魏文 余立建 龚炯 |
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| 作者单位： | 西南交通大学交通信息工程及控制实验室; |
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| 摘 要： | 交通流预测已成为智能交通的重要组成部分,针对短时交通流的非线性和不确定性,文中根据实际交通流中存在的混沌,利用C-C方法和小数据量法对交通流混沌进行了分析,在交通流混沌时间序列相空间重构的基础上构建了基于粒子群优化神经网络的单点单步预测模型,运用该模型对实际采集的美国加州城市快速路交通流数据进行了仿真研究,结果表明,该预测模型具有较高的预测精度,能够满足智能交通控制和诱导的需求。
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| 关 键 词： | 短时交通流 预测 混沌时间序列 粒子群优化 神经网络 |
Short-time Traffic Flow Prediction based on Chaos and Particle Swarm Optimized Neural Network |
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| WEI Wen,YU Li-jian,GONG Jiong.Short-time Traffic Flow Prediction based on Chaos and Particle Swarm Optimized Neural Network[J].Storage Transportation & Preservation of Commodities,2010(2). |
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| Authors: | WEI Wen YU Li-jian GONG Jiong |
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| Institution: | WEI Wen,YU Li-jian,GONG Jiong (Traffic Information Engineering & Control Lab,Southwest Jiaotong University,Chengdu 610031,China) |
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| Abstract: | Traffic flow prediction has become an important part of intelligent transportation system. Aiming at the nonlinear and uncertainty of the short-term traffic flow. In this paper,chaos of the traffic flow is analyzed with C-C method and small data sets. After the phase space reconstruction using the traffic flow data,a prediction model is developed based on particle swarm optimized neural network. The model proposed in this paper is applied to predict the real traffic flow in California,USA,It is proved that ... |
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| Keywords: | Short-time Traffic Flow Prediction Chaotic Time Series Particle Swarm Optimization Neural Network |
| 本文献已被 CNKI 等数据库收录！ |
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