东南大学学报（英文版）2017，Vol.33Issue(1)：106-114,9.DOI:10.3969/j.issn.1003-7985.2017.01.018
支持向量机在出行链模式识别和影响因素分析中的应用
Application of support vector machine in trip chaining pattern recognition and analysis of explanatory variable effects
摘要
Abstract
In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension,a trip chaining pattern recognition model was established based on activity purposes by applying three methods:the support vector machine (SVM) model,the radial basis function neural network (RBFNN) model and the multinomial logit (MNL) model.The effect of explanatory factors on trip chaining behaviors and their contribution to model performance were investigated by sensitivity analysis.Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy,indicating its recognition advantage under a small sample size scenario.It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately.The different contribution of explanatory factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns and exploring factors that are specific to each pattern.It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.关键词
出行链模式/支持向量机/预测性能/敏感性分析Key words
trip chaining patterns/support vector machine/recognition performance/sensitivity analysis分类
交通工程引用本文复制引用
杨硕,邓卫,程龙..支持向量机在出行链模式识别和影响因素分析中的应用[J].东南大学学报（英文版）,2017,33(1):106-114,9.基金项目
The Fundamental Research Funds for the Central Universities,the Scientific Innovation Research of College Graduates in Jiangsu Province (No.KYLX_0177). （No.KYLX_0177）
