基于IPOS-SVM的大学生出行方式识别研究  

Research on Travel Mode Identification of University Students Based on IPOS-SVM

作　　者：吴麟麟[1] 杨彪 景鹏[1] 

机构地区：[1]江苏大学汽车与交通工程学院,江苏镇江212013

出　　处：《计算机工程》2018年第1期193-198,共6页Computer Engineering

基　　金：教育部人文社会科学研究项目(11YJA630152);江苏省"六大人才高峰"项目(2015-JY-025)

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.

关 键 词：支持向量机 改进粒子群 特征变量 出行方式 智能手机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]