基于IPOS-SVM的大学生出行方式识别研究
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摘要
依据在校大学生的出行特征,确定7种出行特征变量,选择大学生的常用6种出行方式(步行、自行车、电动车、校园公交、公交车和出租车)。利用改进粒子群优化支持向量机(IPSO-SVM)对选择的出行方式进行识别,使用IPSO来优化SVM的参数,给出大学生出行识别方法。实验结果表明,该方法平均识别精度为94.22%,在大学生出行方式识别精度方面优于BP神经网络、决策树、支持向量机和粒子群优化支持向量机。
The specific practices of this model are that: firstly,seven feature variables are selected for travel mode detection based on the travel characteristics of university students; afterwards, six travel modes(walk,bicycle,electric bicycle, campus bus,bus and taxi) university students selected commonly are selected; finally, IPSO-SVM is used to identify six selected travel modes. This model is using IPSO to optimize SVM parameters, and a travel mode identification method of university students is proposed. Experimental result shows that the average detection accuracy of the proposed method is 94. 22%,higher than that of BP neural networks,the decision trees, support vector machine and particle swarm optimization-support vector machine.
The specific practices of this model are that: firstly,seven feature variables are selected for travel mode detection based on the travel characteristics of university students; afterwards, six travel modes(walk,bicycle,electric bicycle, campus bus,bus and taxi) university students selected commonly are selected; finally, IPSO-SVM is used to identify six selected travel modes. This model is using IPSO to optimize SVM parameters, and a travel mode identification method of university students is proposed. Experimental result shows that the average detection accuracy of the proposed method is 94. 22%,higher than that of BP neural networks,the decision trees, support vector machine and particle swarm optimization-support vector machine.
引文
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[9]XIAO Guangnian,JUAN Zhicai,GAO Jingxian.Travel Mode Detection Based on Neural Networks and Particle Swarm Optimization[J].Information,2015,6(3):522-535.
[10]XIAO Guangnian,JUAN Zhicai,ZHANG Chunqin.Travel Mode Detection Based on GPS Track Data and Bayesian Networks[J].Computers,Environment and Urban Systems,2015,54:14-22.
[11]李喆,柏丛,孙健,等.基于PSO-SVM的出行方式识别研究[J].计算机应用研究,2016(12):3527-3529.
[12]王晓霞,王涛,谷根代.基于改进粒子群优化的神经网络及应用[J].华北电力大学学报,2009,36(5):99-102.
[13]王建国,张文兴.支持向量机建模及其智能优化[M].北京:清华大学出版社,2015.
[14]安旭,张树东.基于支持向量机的模糊特征分类算法研究[J].计算机工程,2017,43(1):237-240,246.
[15]王园.基于SVM_AdaBoost模型的上市公司退市预警研究[D].广州:华南理工大学,2013.
[16]胡程磊.数据驱动的建筑电能耗预测方法研究[D].镇江:江苏大学,2016.
[17]BROACH J,MCNEIL N W,DILL J.Travel Mode Imputation Using GPS and Accelerometer Data from a Multi-day Travel Survey[C]//Proceedings of Transportation Research Board the 93rd Annual Meeting.Washington,D.C.,USA:[s.n.]2014:256-268.
[18]BOLBOL A,CHENG T,TSAPAKIS I,et al.Inferring Hybrid Transportation Modes from Sparse GPS Data Using a Moving Window SVM Classification[J].Computers Environment&Urban Systems,2012,36(6):526-537.
[2]CHRISTIAN A K,THOMAS F.A Multi-level Approach to Travel Mode Choice-How Person Characteristics and Situation Specific Aspects Determine Car Use in a Student Sample[J].Transportation Research Part F Traffic Psychology&Behaviour,2011,14(4):261-277.
[3]张治华.基于GPS轨迹的出行信息提取研究[D].上海:华东师范大学,2010.
[4]ZHOU J,GOLLEDGE R.Real-time Tracking of Activity Scheduling/schedule Execution Within a Unified Data Collection Framework[J].University of California Transportation Center Working Papers,2004,41(5):444-463.
[5]ZHENG Yu,LIU Like,WANG Longhao,et al.Learning Transportation Mode From Raw GPS Data for Geographic Applications on the Web[C]//Proceedings of International Conference on World Wide Web.Washington D.C.,USA:IEEE Press,2008:247-256.
[6]REDDY S,MUN M,BURKE J,et al.Using Mobile Phones to Determine Transportation Modes[J].ACMTransactions on Sensor Networks,2010,6(2):662-701.
[7]ZHANG L,DALYOT S,EGGERT D,et al.Multi-stage Approach to Travel-mode Segmentation and Classification of GPS Traces[J].ISPRS——International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2012,25(4):87-93.
[8]NITSCHE P,WIDHALM P,BREUSS S,et al.Supporting Large-scale Travel Surveys with Smartphones——APractical Approach[J].Transportation Research Part C:Emerging Technologies,2014,43:212-221.
[9]XIAO Guangnian,JUAN Zhicai,GAO Jingxian.Travel Mode Detection Based on Neural Networks and Particle Swarm Optimization[J].Information,2015,6(3):522-535.
[10]XIAO Guangnian,JUAN Zhicai,ZHANG Chunqin.Travel Mode Detection Based on GPS Track Data and Bayesian Networks[J].Computers,Environment and Urban Systems,2015,54:14-22.
[11]李喆,柏丛,孙健,等.基于PSO-SVM的出行方式识别研究[J].计算机应用研究,2016(12):3527-3529.
[12]王晓霞,王涛,谷根代.基于改进粒子群优化的神经网络及应用[J].华北电力大学学报,2009,36(5):99-102.
[13]王建国,张文兴.支持向量机建模及其智能优化[M].北京:清华大学出版社,2015.
[14]安旭,张树东.基于支持向量机的模糊特征分类算法研究[J].计算机工程,2017,43(1):237-240,246.
[15]王园.基于SVM_AdaBoost模型的上市公司退市预警研究[D].广州:华南理工大学,2013.
[16]胡程磊.数据驱动的建筑电能耗预测方法研究[D].镇江:江苏大学,2016.
[17]BROACH J,MCNEIL N W,DILL J.Travel Mode Imputation Using GPS and Accelerometer Data from a Multi-day Travel Survey[C]//Proceedings of Transportation Research Board the 93rd Annual Meeting.Washington,D.C.,USA:[s.n.]2014:256-268.
[18]BOLBOL A,CHENG T,TSAPAKIS I,et al.Inferring Hybrid Transportation Modes from Sparse GPS Data Using a Moving Window SVM Classification[J].Computers Environment&Urban Systems,2012,36(6):526-537.