计算机工程2018，Vol.44Issue(1)：193-198,6.DOI:10.3969/j.issn.1000-3428.2018.01.033
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
摘要
Abstract
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Key words
Support Vector Machine (SVM)/improved particle swarm/characteristic variable/travel mode/smartphone分类
信息技术与安全科学引用本文复制引用
吴麟麟,杨彪,景鹏..基于IPOS-SVM的大学生出行方式识别研究[J].计算机工程,2018,44(1):193-198,6.基金项目
教育部人文社会科学研究项目(11YJA630152) （11YJA630152）
江苏省“六大人才高峰”项目(2015-JY-025). （2015-JY-025）
