[1]陈月霞,陈龙,查奇芬,等.基于潜变量SVM的出行方式预测模型[J].东南大学学报(自然科学版),2016,46(6):1313-1317.[doi:10.3969/j.issn.1001-0505.2016.06.034]
 Chen Yuexia,Chen Long,Zha Qifen,et al.Forecasting model of travel mode based on latent variable SVM[J].Journal of Southeast University (Natural Science Edition),2016,46(6):1313-1317.[doi:10.3969/j.issn.1001-0505.2016.06.034]
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基于潜变量SVM的出行方式预测模型()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
46
期数:
2016年第6期
页码:
1313-1317
栏目:
交通运输工程
出版日期:
2016-11-20

文章信息/Info

Title:
Forecasting model of travel mode based on latent variable SVM
作者:
陈月霞1陈龙1查奇芬2景鹏1谢君平1熊晓夏1
1江苏大学汽车与交通工程学院, 镇江 212013; 2江苏大学财经学院, 镇江 212013
Author(s):
Chen Yuexia1 Chen Long1 Zha Qifen2 Jing Peng1 Xie Junping1 Xiong Xiaoxia1
1School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
2School of Finance & Economics, Jiangsu University, Zhenjiang 212013, China
关键词:
混合选择模型 支持向量机 多原因多指标 计划行为理论 交叉验证算法
Keywords:
mixed selection model support vector machine(SVM) multiple indicators and multiple causes theory of planned behavior cross validation algorithm
分类号:
U491.1
DOI:
10.3969/j.issn.1001-0505.2016.06.034
摘要:
为提高小样本下的出行方式选择模型的预测精度,提出了考虑低碳出行心理变量的支持向量机(SVM)算法.首先基于计划行为理论,考虑低碳出行心理因素,建立多原因多指标潜变量模型.然后将预测后的潜变量带入SVM分类器,构建了带潜变量的SVM选择模型.最后,利用交叉验证优化所建模型参数,并以长三角地区城市居民为研究对象实证检验了模型性能.实证结果表明,所建带潜变量的SVM选择模型具有较好的预测效果,比不带潜变量的SVM选择模型的精度提高了4.54%,比传统的带潜变量的混合选择模型提高了2.56%,同时验证了小样本下模型仍然具有很高的精度.本研究为出行方式选择模型和低碳出行方式选择研究提供了一定的理论参考.
Abstract:
In order to improve the prediction accuracy of the travel mode choice model under small samples, a support vector machine(SVM)algorithm considering the low carbon travel psychological variables is proposed. Based on the theory of planned behavior(TPB), considering low carbon travel psychological factors, latent variable models with multiple causes and indicators are established. Substituting the forecasted latent variables into the SVM classifier, a SVM selection model with latent variables is then proposed. The mixed selection parameters are obtained using cross validation optimization, and the model performance is validated based on urban residents’ data in Yangtze River Delta region. Empirical results show that the established SVM selection model with latent variables has a better prediction accuracy, improved by 4.54% compared with the SVM without latent variables, and 2.56% by the traditional model with latent variables. Results prove that the model still has a high precision with small samples. This study provides a theoretical reference for the travel choice model and low carbon travel choice research.

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备注/Memo

备注/Memo:
收稿日期: 2016-05-08.
作者简介: 陈月霞(1982—),女,博士生;陈龙(联系人),男,博士,教授,博士生导师,chenlong@ujs.edu.cn.
基金项目: 国家自然科学基金资助项目(71373105, 61573171, 51208232)、江苏省“六大人才高峰”资助项目(2015-JY-025)、江苏省高校科研创新计划资助项目(CXZZ12_0663).
引用本文: 陈月霞,陈龙,查奇芬,等.基于潜变量SVM的出行方式预测模型[J].东南大学学报(自然科学版),2016,46(6):1313-1317. DOI:10.3969/j.issn.1001-0505.2016.06.034.
更新日期/Last Update: 2016-11-20