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。

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

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.

Key words: Support Vector Machine(SVM), improved particle swarm, characteristic variable, travel mode, smartphone

中图分类号: 

• TP391