[1]闫 贺,朱 丽,戚 湧.基于改进SVM的城市道路短时交通状态预测[J].南京师范大学学报(自然科学版),2019,42(03):129-137.[doi:10.3969/j.issn.1001-4616.2019.03.017]
 Yan He,Zhu Li,Qi Yong.Short-term Traffic Condition Prediction ofUrban Road Based on Improved SVM[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):129-137.[doi:10.3969/j.issn.1001-4616.2019.03.017]
点击复制

基于改进SVM的城市道路短时交通状态预测()
分享到:

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

卷:
第42卷
期数:
2019年03期
页码:
129-137
栏目:
·全国机器学习会议论文专栏·
出版日期:
2019-09-30

文章信息/Info

Title:
Short-term Traffic Condition Prediction ofUrban Road Based on Improved SVM
文章编号:
1001-4616(2019)03-0129-09
作者:
闫 贺1朱 丽23戚 湧1
(1.南京理工大学计算机科学与工程学院,江苏 南京 210094)(2.东南大学交通学院,江苏 南京 210096)(3.苏交科集团股份有限公司规划研究所,江苏 南京,210019)
Author(s):
Yan He1Zhu Li23Qi 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)
关键词:
短时交通状态预测机器学习MLSTBSVML1算法TBSVM算法
Keywords:
short-term traffic condition predictionmachine learningMLSTBSVML1 algorithmTBSVM algorithm
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.03.017
文献标志码:
A
摘要:
为提高短时交通状态预测的精度,使交通管理者更有效地进行交通规划和管理,本文把基于L1范数距离度量的最小二乘孪生有界支持向量机(twin bounded support vector machine,TBSVM)扩展成多分类算法用于短时交通状态预测,简称MLSTBSVML1. 在实验数据上对MLSTBSVML1算法的有效性进行验证,实验结果表明,相比于其他预测算法,提出的MLSTBSVML1算法在预测精度上有较大提升.
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.

参考文献/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.

相似文献/References:

[1]赵文芳,林润生,唐 伟,等.基于深度学习的PM2.5短期预测模型[J].南京师范大学学报(自然科学版),2019,42(03):32.[doi:10.3969/j.issn.1001-4616.2019.03.005]
 Zhao Wenfang,Lin Runsheng,Tang Wei,et al.Forecasting Model of Short-Term PM2.5 ConcentrationBased on Deep Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):32.[doi:10.3969/j.issn.1001-4616.2019.03.005]
[2]梁星星,黄魁华,马 扬,等.周界防护的最优替换调度[J].南京师范大学学报(自然科学版),2019,42(03):52.[doi:10.3969/j.issn.1001-4616.2019.03.007]
 Liang Xingxing,Huang Kuihua,Ma Yang,et al.Optimal Replacement Scheduling for Perimeter Guarding[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):52.[doi:10.3969/j.issn.1001-4616.2019.03.007]
[3]张旭辉,张 郴,李雅南,等.城市旅游餐饮体验的注意力机制模型建构——基于机器学习的网络文本深度挖掘[J].南京师范大学学报(自然科学版),2022,45(01):32.[doi:10.3969/j.issn.1001-4616.2022.01.006]
 Zhang Xuhui,Zhang Chen,Li Yanan,et al.Construction of Attention Mechanism Model of Urban Tourism Catering Experience:Deep Mining of Online Text Based on Machine Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2022,45(03):32.[doi:10.3969/j.issn.1001-4616.2022.01.006]
[4]项晓宇,朱敏捷,周灵刚,等.基于机器学习的短期规上行业工业增加值预测[J].南京师范大学学报(自然科学版),2023,46(02):99.[doi:10.3969/j.issn.1001-4616.2023.02.013]
 Xiang Xiaoyu,Zhu Minjie,Zhou Linggang,et al.Short-Term Industrial Added Value Prediction of the Above-Scale Industry Based on Machine Learning[J].Journal of Nanjing Normal University(Natural Science Edition),2023,46(03):99.[doi:10.3969/j.issn.1001-4616.2023.02.013]
[5]赵宇奔,王鑫宁,李 崇.基于K-XGBoost融合模型的高校学生学情预测研究[J].南京师范大学学报(自然科学版),2023,46(03):89.[doi:10.3969/j.issn.1001-4616.2023.03.012]
 Zhao Yuben,Wang Xingning,Li Chong.Research on Undergraduate Academic Prediction Based on K-XGBoost Fusion Model[J].Journal of Nanjing Normal University(Natural Science Edition),2023,46(03):89.[doi:10.3969/j.issn.1001-4616.2023.03.012]

备注/Memo

备注/Memo:
收稿日期:2018-04-14.基金项目:国家重点研究计划政府间国际科技创新合作重点专项(2016YFE0108000)、国家重点自然科学基金项目(51238008)、国家自然科学基金(61272419、61772273)、江苏省自然科学基金(BK20141403)、2018江苏省普通高校学术学位研究生科研创新计划项目(KYCX18_0424). 通讯联系人:戚湧,教授,博士生导师,研究方向:机器学习,数据挖掘,网络信息安全,社会计算,智能交通系统. E-mail:qyong@njust.edu.cn
更新日期/Last Update: 2019-09-30