[1]梅朵[],郑黎黎[],刘春晓[],等. 基于混合算法优化SVM的短时交通流预测[J].计算机技术与发展,2017,27(11):92-95.
　MEI Duo[],ZHENG Li-li[],LIU Chun-xiao[],et al. A Short-term Traffic Flow Prediction Model Based on Support Vector Machine Optimized by Hybrid Algorithm[J].,2017,27(11):92-95.
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 基于混合算法优化SVM的短时交通流预测()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年11期
页码:
92-95
栏目:
智能、算法、系统工程
出版日期:
2017-11-10

文章信息/Info

Title:
 A Short-term Traffic Flow Prediction Model Based on Support Vector Machine Optimized by Hybrid Algorithm
文章编号:
1673-629X（2017)11-0092-04
作者:
 梅朵[1]; 郑黎黎[2]; 刘春晓[1]; 王秀芹[1]
 1.渤海大学 信息科学与技术学院；2.吉林大学 交通学院
Author(s):
 MEI Duo[1]; ZHENG Li-li[2]; LIU Chun-xiao[1] ; WANG Xiu-qin[1]
关键词:
 ; 城市交通; 短时交通流预测; 遗传算法; 粒子群算法; 支持向量机
Keywords:
 ; urban traffic; short-term traffic flow prediction; genetic algorithm; particle swarm optimization; support vector machine
分类号:
U491.2
文献标志码:
A
摘要:
 为了提高城市道路短时交通流预测的精度,提出了一种基于混合算法优化支持向量机的短时交通流预测模型.在粒子群算法中引入遗传算法的交叉和变异因子,对粒子群算法进行改进,然后用改进后的粒子群算法优化支持向量机,得到最优的支持向量机模型,最后实现城市道路的短时交通流预测.以检测器采集到的长春市路网数据为基础进行了实例验证,结果表明,优化支持向量机参数时,遗传粒子群算法不会陷入局部最优,优化效果更好;与传统的支持向量机模型、粒子群优化支持向量机模型相比,所提出的混合算法优化支持向量机模型的相对误差波动较稳定,得到的短时交通流平均预测精度分别提高了3.63%和2.46%,说明所提出模型的短时交通流预测效果更好.
Abstract:
 In order to improve the accuracy of short term traffic flow prediction,a short-term traffic flow forecasting model based on sup-port vector machine optimized by hybrid algorithm is proposed. The crossover and mutation factor of genetic algorithm is introduced to improve particle swarm optimization. And then,the support vector machine is optimized based on the improved particle swarm optimiza-tion to obtian the optimal support vector machine model. Finally,the short term traffic flow prediction is realized. It is verified based on the data collected from Changchun City Road Network. The results show that when optimizing the parameters of support vector machine, the genetic particle swarm optimization does not fall into local optimum and gets better effect of optimization. Compared with the model of traditional support vector machine and particle swarm optimized support vector machine,it is more stable and its average prediction ac-curacy of short term traffic flow is improved by 4. 96% and 3. 41% respectively,showing the better effect of prediction.

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更新日期/Last Update: 2017-12-26