基于核超限学习机群组算法的交通拥堵预测

doi:10.11896/jsjkx.191100507C

计算机科学 ›› 2019, Vol. 46 ›› Issue (11): 241-246.doi: 10.11896/jsjkx.191100507C

基于核超限学习机群组算法的交通拥堵预测

邢一鸣^1,2, 班晓娟¹, 刘旭¹, 尹航², 沈晴¹

(北京科技大学计算机与通信工程学院北京100086)¹
(沈阳航空航天大学工程训练中心沈阳110136)²

收稿日期:2018-11-07 出版日期:2019-11-15 发布日期:2019-11-14
通讯作者: 班晓娟(1970－),女,博士生,教授,CCF会员,主要研究方向为人工智能与人工生命理论研究、可视化技术、计算机动画与仿真技术研究、智能软件理论及应用,E-mail:banxj@ustb.edu.cn
作者简介:邢一鸣(1986－),女,硕士,讲师,主要研究方向为人工智能,E-mail:peachming@163.com;刘旭(1986－),男,博士生,主要研究方向为人工智能、流体仿真技术;尹航(1978－),男,博士生,副教授,主要研究方向为人工智能及大数据、重大设备健康管理;沈晴(1988－),男,博士生,主要研究方向为人工智能、智能软件理论及应用。
基金资助:
本文受国家重点研发计划(2016YFB0700500),国家自然科学基金项目(61702036,61572075),国家航空科学基金项目(2015ZB54007),辽宁省教育厅科学研究项目(L201627)资助。

Traffic Congestion Prediction Based on Kernel Extreme Learning Machine Group Algorithm

XING Yi-ming^1,2, BAN Xiao-juan¹, LIU Xu¹, YIN Hang², SHEN Qing¹

(School of Computer and Communication Engineering,University of Science and Technology,Beijing 100086,China)¹
(Engineering Training Center,Shenyang Aerospace University,Shenyang 110136,China)²

Received:2018-11-07 Online:2019-11-15 Published:2019-11-14

摘要/Abstract

摘要： 城市交通拥堵预测是智能交通系统研究的重要内容之一。交通运行状态具有高度不确定性和复杂性,目前已经有多种基于神经网络的预测技术被引入交通预测领域中。然而,传统的神经网络具有训练时间长、易陷入过拟合和局部最优等缺点,这严重阻碍了神经网络在交通预测领域的大规模应用。超限学习机是一种新型的单隐层前馈神经网络,具有泛化能力强、训练速度快、产生唯一最优解等诸多优点。基于超限学习机算法,文中提出了核超限学习机群组算法,此算法由多个超限学习机子模型组成,每个子模型只负责某一类样本的学习,该算法使每一类样本均能达到全局最优,整体可以获得比超限学习机更高的预测准确率。实验结果表明,单进程的核超限学习机群组算法比超限学习机的训练时间稍短,但前者的准确率较后者提高了8%;相比其他流行的机器学习算法,核超限学习机群组算法的训练速度快、预测准确度高;经过核超限学习机群组算法预测的结果与实际情况较为符合,可靠性高,具有很强的实用价值。

关键词: 核超限学习机, 交通拥堵, 群组, 神经网络, 预测

Abstract: Prediction of urban traffic congestion is one of the important research contents of intelligent transportation system (ITS).At present,a lot of neural networks are introduced into the field of traffic forecasting and are widely used.However,the traditional neural network training is time-consuming,easy to fall into local optimal and over fitting.It has seriously hindered the large-scale application of neural network in the field of traffic forecasting.ELM is a new kind of single hidden layer feed-forward neural network,which has the advantages of fast training spead,stronggenera-lization ability and unique optimal solution.In this paper,the new algorithm named KELM-Group was proposed,which is composed of multiple KELM sub-models.KELM-Group algorithm enables each class of samples to achieve the global optimum,and the overall prediction accuracy can be higher than that of ELM.The experimental results show that the KELM-Group algorithm is faster than other popular machine learning algorithms.The accuracy rate of KELM-Group algorithm is 8% higher than that of the ELM.The results predicted by the KELM-Group algorithm are more consistent with the actual situation,and have great practical value.

Key words: Group, Kernel extreme learning machine, Neural network, Prediction, Traffic congestion

中图分类号:

TP399

邢一鸣, 班晓娟, 刘旭, 尹航, 沈晴. 基于核超限学习机群组算法的交通拥堵预测[J]. 计算机科学, 2019, 46(11): 241-246. https://doi.org/10.11896/jsjkx.191100507C

XING Yi-ming, BAN Xiao-juan, LIU Xu, YIN Hang, SHEN Qing. Traffic Congestion Prediction Based on Kernel Extreme Learning Machine Group Algorithm[J]. Computer Science, 2019, 46(11): 241-246. https://doi.org/10.11896/jsjkx.191100507C

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基于核超限学习机群组算法的交通拥堵预测

Traffic Congestion Prediction Based on Kernel Extreme Learning Machine Group Algorithm

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