|Table of Contents|

Short-term Traffic Condition Prediction ofUrban Road Based on Improved SVM(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年03期
Page:
129-137
Research Field:
·全国机器学习会议论文专栏·
Publishing date:

Info

Title:
Short-term Traffic Condition Prediction ofUrban Road Based on Improved SVM
Author(s):
Yan He1; Zhu Li2; 3; Qi Yong1
(1.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)(2.School of Transportation,Southeast University,Nanjing 210096,China)(3.JSTI Group Co., Ltd,Planning Institute,Nanjing 210019,China)
Keywords:
short-term traffic condition prediction; machine learning; MLSTBSVML1 algorithm; TBSVM algorithm
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.03.017
Abstract:
To improve the accuracy of short-term traffic condition prediction and make planning and management more effective for traffic managers,the least squares Twin Bounded Support Vector Machine(TBSVM)based on L1-norm distance is extended to a new algorithm(MLSTBSVML1)which could solve multi-classification problems. The validity of the proposed MLSTBSVML1 is verified through experiments and the results demonstrate that the MLSTBSVML1 algorithm has significant improvement in prediction accuracy compared with other prediction algorithms.

References:

[1] 刘擎超. 基于集成学习的交通状态预报方法研究[D]. 南京:东南大学,2015.
[2]姚智胜. 基于实时数据的道路网短时交通流预测理论与方法研究[D]. 北京:北京交通大学,2007.
[3]赵亚萍,张和生,杨军,等. 基于最小二乘支持向量机的交通流量预测模型[J]. 北京交通大学学报(自然科学版),2011,35(2):114-117.
[4]欧阳俊. 基于多核混合支持向量机的城市短时交通预测[D]. 长沙:中南大学,2011.
[5]孙占全,潘景山,张赞军,等. 基于主成分分析与支持向量机结合的交通流预测[J]. 公路交通科技,2009,26(5):127-131.
[6]樊娜,赵祥模,戴明,等. 短时交通流预测模型[J]. 交通运输工程学报,2012,12(4):114-119.
[7]刘燕. 城市道路交通流状态辨识及决策方法研究[D]. 合肥:合肥工业大学,2011.
[8]杨兆升,王媛,管青. 基于支持向量机方法的短时交通流量预测方法[J]. 吉林大学学报(工学版),2006,36(6):881-884.
[9]韦凌翔,陈红,王永岗,等. 短时交通流量预测方法[J]. 山东交通学院学报,2017,25(3):22-29.
[10]YAN H,YE Q,ZHANG T,et al. Least squares twin bounded support vector machines based on L1-norm distance metric for classification[J]. Pattern recognition,2017,74:434-447.
[11]YAN H,YE Q,ZHANG T,et al. L1-norm GEPSVM classifier based on an effective iterative algorithm for classification[J]. Neural processing letters,2017,4:1-26.
[12]YE Q,YANG X,GAO S,et al. L1-norm distance minimization based fast robust twin support vector k-plane clustering[J]. IEEE transactions on neural networks and learning systems,2018,29(9):4494-4503.
[13]YAN R,YE Q,ZHANG L,et al. A feature selection method for projection twin support vector machine[J]. Neural processing letters,2018,47(1):21-38.
[14]MANGASARIAN O L,WILD E W. Multisurface proximal support vector machine classification via generalized eigenvalues[J]. IEEE transactions on pattern analysis and machine intelligence,2006,28(1):69-74.
[15]SHAO Y H,ZHANG C H,WANG X B,et al. Improvements on twin support vector machines[J]. IEEE transactions on neural networks,2011,22(6):962-968.
[16]CARRASCO M,L PEZ J,MALDONADO S. A multi-class SVM approach based on the L1-norm minimization of the distances between the reduced convex hulls[J]. Pattern recognition,2015,48(5):1598-1607.
[17]TOMAR D,AGARWAL S. Multiclass least squares twin support vector machine for pattern classification[J]. International journal of database theory and application,2015,8(6):285-302.
[18]XIAO C,NIE F,HUANG H,et al. Multi-class L2,1-norm support vector machine[C]//Proceedings of the IEEE International Conference on Data Mining. Vancouver,Canada:IEEE,2012:91-100.
[19]NIE F,WANG X,HUANG H. Multiclass capped Lp-norm SVM for robust classifications[C]//Proceedings of the AAAI Conference on Artificial Intelligence. San Francisco,USA,2017:1-7.
[20]DING S,ZHAO X,ZHANG J,et al. A review on multi-class TWSVM[J]. Artificial intelligence,2017,2:1-27.
[21]BRERETON R G,LLOYD G R. Support vector machines for classification and regression[J]. Analyst,2010,135(2):230-267.
[22]HUANG W,SHEN L. Weighted support vector regression algorithm based on data description[C]//Proceedings of the Isecs International Colloquium on Computing,Communication,Control,and Management. USA:IEEE Computer Society,Computer Engineering and Applications,2008:250-254.
[23]CHE J X. Support vector regression based on optimal training subset and adaptive particle swarm optimization algorithm[J]. Applied soft computing,2013,13(8):3473-3481.
[24]DIVYA,AGARWAL S. Weighted support vector regression approach for remote healthcare monitoring[C]//Proceedings of the International Conference on Recent Trends in Information Technology. Piscataway:IEEE Press,2011:969-974.

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Last Update: 2019-09-30