[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]
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基于改进SVM的城市道路短时交通状态预测()
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《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:

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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