Forecasting Urban Traffic Flow Based on Support Vector Machine Optimized by Genetic Algorithm

LI Xiao-bin

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Forecasting Urban Traffic Flow Based on Support Vector Machine Optimized by Genetic Algorithm

更新时间：2026-09-04

- Forecasting Urban Traffic Flow Based on Support Vector Machine Optimized by Genetic Algorithm
- Vol. 27, Issue 10, Pages: 186-188(2010)
- 作者机构：
  
  华南理工大学自动化科学与工程学院,广东,广州,510641
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
移动端阅览
李晓斌. 基于遗传算法优化支持向量机的交通流量预测[J]. 微电子学与计算机, 2010, 27(10): 186-188,192.

LI Xiao-bin. Forecasting Urban Traffic Flow Based on Support Vector Machine Optimized by Genetic Algorithm[J]. 2010, 27(10): 186-188.
李晓斌. 基于遗传算法优化支持向量机的交通流量预测[J]. 微电子学与计算机, 2010, 27(10): 186-188,192. DOI：

LI Xiao-bin. Forecasting Urban Traffic Flow Based on Support Vector Machine Optimized by Genetic Algorithm[J]. 2010, 27(10): 186-188. DOI：

摘要

为了得到性能优越的SVM预测模型

实现城市交通流量的准确预测

文中提出基于遗传算法优化支持向量机 (GA-SVM) 的城市交通流量预测方法.其中通过遗传算法对SVM中的训练参数进行优化处理

以得到优化的SVM预测模型.实验结果表明:用GA-SVM对城市交通流量预测

预测精度远优于人工神经网络.

Abstract

In order to gain the excellent SVM forecasting model and realize the accurate forecasting of urban traffic flow

support vector machine optimized by genetic algorithm is presented to forecast urban traffic flow.Genetic algorithm (GA) is introduced to optimize the parameters of support vector machine in this model

which can gain optimized SVM forecasting model.The experimental results indicate that the proposed GA-SVM model has better forecasting accuracy than artificial neural networks.

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