改进支持向量回归机的短时交通流预测

交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (4): 130-134.

改进支持向量回归机的短时交通流预测

傅成红，杨书敏，张阳^*

福建工程学院交通运输学院，福州 350118

收稿日期:2019-01-28 修回日期:2019-03-23 出版日期:2019-08-25 发布日期:2019-08-26
作者简介:傅成红 (1971-)，男，重庆丰都人，教授，博士.
基金资助:
福建省自然科学基金/ Natural Science Foundation of Fujian Province, China(2016J01725)；福建工程学院科研发展基金/ Fujian University of Technology Research Development Foundation (GY-Z160133, GY-Z160123).

Promoted Short-term Traffic Flow Prediction Model Based on Deep Learning and Support Vector Regression

FU Cheng-hong, YANG Shu-min, ZHANG Yang

Department of Transport, Fujian University of Technology, Fuzhou 350118, China

Received:2019-01-28 Revised:2019-03-23 Online:2019-08-25 Published:2019-08-26

摘要/Abstract

摘要：

短时交通流预测是实施智能交通控制的基础和保障.针对目前短时交通流预测方法拟合交通数据的能力偏弱，以及过分依赖历史数据的不足，提出一种基于深度学习回归机的短时交通流预测方法.首先构建深度学习回归机算法模型，包括受限玻尔兹曼机的显层节点输入端，受限玻尔兹曼机的若干中间层，以及径向基支持向量回归机输出端.通过实验将深度学习回归机预测方法与其他典型的短时交通流预测算法进行比较，结果表明，在相同的数据和计算平台下，本文提出的深度学习回归机预测方法精度更高，且预测实时性也能满足实际的需求.

关键词: 智能交通, 深度学习, 支持向量回归, 短时交通流, 粒子群

Abstract:

Short- term traffic flow prediction is the basis of an Intelligent Transport Systems (ITS) project. However, in current practice, the methods for short-term traffic flow prediction have encountered many challenges in fitting the traffic flow data, one is it depends too much on historical data. Therefore, a novel short-term traffic flow forecasting method based on Deep Learning and Support Vector Regression (DL-SVR) is proposed in this paper. Firstly, the DL-SVR model is composed by a Restricted Boltzmann Machine (RBM) visible inputting layer with some RBM intermediate layers and a radial SVR output layer. Furthermore, in order to enhance the generalization of the model, an improved Particle Swarm Optimization (PSO) algorithm is designed to optimize the number of nodes in the inputting layer. Finally, the DL-SVR method is compared with other typical short-term traffic flow prediction algorithms on the same computing platform. The experimental results show that the proposed DL-SVR method gets a higher accuracy in its real-time prediction.

Key words: intelligent transportation, deep learning, support vector regression, short-time traffic flow, particle swarm optimization

中图分类号:

U268.6

傅成红，杨书敏，张阳. 改进支持向量回归机的短时交通流预测[J]. 交通运输系统工程与信息, 2019, 19(4): 130-134.

FU Cheng-hong, YANG Shu-min, ZHANG Yang. Promoted Short-term Traffic Flow Prediction Model Based on Deep Learning and Support Vector Regression[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(4): 130-134.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19880.shtml

http://www.tseit.org.cn/CN/Y2019/V19/I4/130

参考文献

[1] SUN H Y, LIU H, XIAO H, et al. Use of local linear regression model for short-term traffic forecasting[J]. Transportation Research Record: Journal of the Transportation Research Board, 2003, 1836(1): 143- 150.

[2] 龚勃文, 林赐云, 李静, 等. 基于核自组织映射—前馈神经网络的交通流短时预测[J]. 吉林大学学报(工学版), 2011, 41(4): 938-943. [GONG B W, LIN C Y, LI J, et al. Short- time traffic flow prediction based on KSOM_BP neural network[J]. Journal of Jilin University (Engineering and Technology Edition), 2011, 41(4): 938-943]

[3] N GARG, SK MANGAL, PK SAINI, et al. Comparison of ANN and analytical models in traffic noise modeling and predictions[J]. Acoustics Australia, 2015, 43(2): 1-11.

[4] ZHU J Z, CAO J X, ZHU Y. Traffic volume forecasting based on radial basis function neuralnetwork with the consideration of traffic flows at the adjacent intersections [J]. Transportation Research Part C, 2014, 47(2): 139- 154.

[5] YU B, WANG Y T, YAO J B, et al. A Comparison of the performance of ANN and SVM for the prediction of traffic accident duration[J]. Neural Network World, 2016, 26(3): 271-287.

[6] SUN Z Q, GEOFFREY FOX. Traffic flow forecasting based on combination of multidimensional scaling and SVM[J]. International Journal of Intelligent Transportation Systems Research, 2014, 12(1): 20-25.

[7] WANG J, SHI Q X. Short-term traffic speed forecasting hybrid model based on chaos-wavelet analysis-support vector machine theory[J]. Transportation Research Part C, 2013, 27(2): 219-232.

[8] 傅成红, 张阳. 基于参数优化的 SVR城市群交通需求预测方法[J]. 系统工程, 2016(2): 114-120. [FU C H， ZHANG Y. An improved SVR algorithm based on the nuclear parameter optimization for traffic demand forecasting of urban agglomeration[J]. System Engineering, 2016(2): 114-120.]

[9] CHEN B, POLATKAN GUNGOR, SAPIRO GUILLERMO. Deep learning with hierarchical convolutional factor analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1887-1901.

[10] VAPNIK V , GOLOWICH S E , SMOLA A. Support vector method for function approximation, regression estimation, and signal processing[J]. Advances in Neural Information Processing Systems, 1970(9): 281-287.

[11] FISCHER A, IGEL C. Training restricted Boltzmann machines: An introduction[J]. Pattern Recongination, 2014, 47(1): 25-39.

[12] HINTON G E. Training products of experts by minimizing contrastive divergence[J]. Neural Computation, 2002, 14(8): 1771-1880.

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