[1]凤少伟,凤 超,申 浩.基于 K-means 与 GRU 的短时交通流预测研究[J].计算机技术与发展,2020,30(07):125-129.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 027]
　FENG Shao-wei,FENG Chao,SHEN Hao.Research on Short-term Traffic Flow Prediction Based on K-means and GRU[J].,2020,30(07):125-129.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 027]
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基于 K-means 与 GRU 的短时交通流预测研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年07期
页码:
125-129
栏目:
应用开发研究
出版日期:
2020-07-10

文章信息/Info

Title:
Research on Short-term Traffic Flow Prediction Based on K-means and GRU
文章编号:
1673-629X(2020)07-0125-05
作者:
凤少伟1 ; 凤 超2 ; 申 浩1
1. 长安大学 信息工程学院,陕西 西安 710064; 2. 新疆理工学院 机电工程系,新疆 阿克苏 843100
Author(s):
FENG Shao-wei1 ; FENG Chao2 ; SHEN Hao1
1. School of Information Engineering,Chang’an University,Xi’an 710064,China; 2. Department of Mechanical and Electrical Engineering,Xinjiang Institute of Technology,Aksu 843100,China
关键词:
短期交通流; 神经网络; K-means 聚类; GRU; 预测
Keywords:
short-term traffic flow; neural network; K-means clustering; GRU; prediction
分类号:
U491. 2
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 07. 027
摘要:
随着神经网络的蓬勃发展,如今已在短时交通流预测领域得到了广泛的应用,并且有较高的预测准确度。 针对训练集的选取对短时交通流预测结果影响显著的问题,从时间序列的角度出发,提出了一种基于 K-means 与门限循环单元(gated recurrent unit,GRU)神经网络相结合的短时交通流预测方法。利用K-means 聚类算法建立交通流模式库,根据状态向量以及数据相似性确定训练集,并利用GRU 神经网络预测短时交通流,通过美国交通研究数据实验室的真实数据验证了该方法的有效性。实验结果显示,与经典 GRU 神经网络相比,该方法预测结果的均方根误差(root mean square  error,RMSE) 降低了 2.28,平均绝对百分比误差(mean absolute percent error,MAPE) 降低了 2.54% ,表明该方法与传统 GRU 神经网络预测模型相比,预测结果误差明显下降。 因此,基于 K-means 与 GRU 神经网络结合的交通流预测方法能够更好地挖掘交通流时间序列的关联性,可以为交通控制提供可靠的依据。
Abstract:
With the rapid development of neural network,it has been widely used in the field of short-term traffic flow prediction with high prediction accuracy. Aiming at the problem that the selection of training sets has a significant impact on short-term traffic flow prediction results,from the perspective of time series,we propose a short-term traffic flow prediction method based on the combination of K-means and gated recurrent unit (GRU) neural network. The K-means clustering algorithm is used to establish the traffic flow pattern database,and the training set is determined by the state vector and data similarity. The GRU neural network is used to predict the short-term traffic flow,and the proposed method is verified by real data from American Traffic Research Data Laboratory. The test results show that compared with the classical GRU neural network,the root mean square error (RMSE) and the mean absolute percent error (MAPE) of the proposed method decrease by 2.28 and 2.54% respectively,indicating that the proposed method has a significant decrease in the prediction result compared with the traditional GRU neural network prediction model. Therefore,the traffic flow prediction method based on K-means and GRU neural network can better mine the correlation of traffic flow time series,which can provide a reliable basis for traffic control.

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更新日期/Last Update: 2020-07-10