| 基于LS-SVM的交通流组合预测模型 |
| |
| 引用本文： | 张朝元,陈丽. 基于LS-SVM的交通流组合预测模型[J]. 湖南工程学院学报(自然科学版), 2010, 20(4) |
| |
| 作者姓名： | 张朝元 陈丽 |
| |
| 作者单位： | 大理学院,数学与计算机学院,大理,671003;大理学院,物理与电子信息学院,大理,671003 |
| |
| 基金项目： | 大理学院科研基金资助项目(2005X23) |
| |
| 摘 要： | 智能交通系统是目前世界上公认的解决城市交通拥堵问题的最佳措施,而实时准确地交通流量预测则是实现智能交通系统和智能交通诱导控制的重要依据.针对城市交通智能运输系统和交通流的特性,在多元线性回归、支持向量机和改进的BP神经网络等三种预测模型的基础上,提出了基于最小二乘支持向量机方法的交通流组合预测模型.实验预测结果表明该组合预测模型具有较高的预测精度,为交通流量提供了一个更好的预测模型.
|
| 关 键 词： | 多元线性回归 支持向量机 BP神经网络 LS-SVM 交通流量 组合预测 |
Traffic Flow Combining Forecast Model Based on Least Squares Support Vector Machine |
| |
| ZHANG Chao-yuan,CHEN Li. Traffic Flow Combining Forecast Model Based on Least Squares Support Vector Machine[J]. Journal of Hunan Institute of Engineering(Natural Science Edition), 2010, 20(4) |
| |
| Authors: | ZHANG Chao-yuan CHEN Li |
| |
| Affiliation: | ZHANG Chao-yuan1,CHEN-Li 2(1.Department of Mathematics and Computer Science,Dali University,Dali 671003,China,2.Department of Physics and Electronic Information,China) |
| |
| Abstract: | At present,intelligent transportation system is considered as the best practice to solve the problem of urban traffic congestion.Timely and accurate forecast of traffic flow is an important basis to achieve intelligent transportation system and intelligent transportation guidance and control.According to the city intelligent transportation system and the characteristics of traffic flow,the combining forecast model is set up based on least squares support vector machine after support vector machine,back prop... |
| |
| Keywords: | multiple linear regression support vector machines back propagation neural network least squares support vector machine traffic flow combining forecast |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
|
