计算机应用研究2016，Vol.33Issue(12)：3527-3529,3558,4.DOI:10.3969/j.issn.1001-3695.2016.12.002
基于智能手机大数据的交通出行方式识别研究
Travel mode recognition based on smart phone big data
摘要
Abstract
The big data generated by smart phones can bring a lot of information for traffic investigators,this paper proposed a model based on particle swarm optimization and support vector machine to recognize different travel modes based on the smart-phone data.After analyzing the characteristics of data collected by smartphones,it proposed several feature variables for mode-ling.Further on,it used particle swarm optimization for optimizing the support vector machine model,which was trained and tested for travel mode recognition based on the empirical data in Chengdu,Sichuan province.The results indicate that, the recognition accuracy of the proposed model attains 95 .1%,is higher than that of the decision trees,back propagation neu-ral network model and the support vector machine based on grid search optimization.The time efficiency of the proposed model has good performance in urban travel mode recognition.关键词
粒子群/支持向量机/出行方式识别/智能手机大数据/模式识别Key words
particle swarm/support vector machine/travel mode recognition/smart phone big data/pattern recognition分类
交通工程引用本文复制引用
李喆,孙健,倪训友..基于智能手机大数据的交通出行方式识别研究[J].计算机应用研究,2016,33(12):3527-3529,3558,4.基金项目
国家自然科学基金资助项目（71101109）；上海市“科技创新行动计划”软科学研究重点资助项目 （）
