交通运输系统工程与信息

交通运输系统工程与信息 ›› 2010, Vol. 10 ›› Issue (6): 70-75 .

• 系统工程理论与方法 • 上一篇    下一篇

基于支持向量机的出行链活动类型识别研究

杨扬1;姚恩建*1;岳昊1,2;刘宇环1   

  1. 1. 北京交通大学 城市交通复杂系统理论与技术教育部重点实验室，北京 100044； 2. 北京工业大学 交通研究中心，北京 100022
  • 收稿日期:2010-05-14 修回日期:2010-08-24 出版日期:2010-12-25 发布日期:2010-12-25
  • 通讯作者: 姚恩建
  • 作者简介:杨扬（1989-），男，江西人，硕士生.
  • 基金资助:

    北京交通大学基本科研业务费(2009JBM056).

Trip Chain’s Activity Type Recognition Based on Support Vector Machine

YANG Yang1;YAO En-jian1;YUE Hao 1,2; LIU Yu-huan1   

  1. 1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology,Beijing Jiaotong University, Beijing 100044, China; 2. Transportation Research Center, Beijing University of Technology, Beijing 100022, China
  • Received:2010-05-14 Revised:2010-08-24 Online:2010-12-25 Published:2010-12-25
  • Contact: YAO En-jian

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被引次数

23

摘要： 基于支持向量机理论对出行链活动类型的识别方法进行了研究. 首先对居民出行的时间序列位置信息做数据预处理，提取出行链的出行过程和活动地点信息，并结合地理信息系统（GIS）提取活动的备选类型；然后从出行链和活动的时间和空间因素提取活动类型识别的特征，形成特征向量作为分类器的输入，并建立基于支持向量机的两两分类器，采用分类器投票的方法从备选集中选择活动的类型；最后利用模拟数据和交叉验证的方法对两两分类器进行训练检验，分别从高斯径向机核函数和多层感知器核函数的角度分析活动类型识别率. 结果表明：在两两分类中，高斯径向机核函数的最高识别率为99%，最低识别率为62%；多层感知器核函数的最高识别率为97%，最低识别率为54%.

关键词: 城市交通, 出行链, 活动类型, 模式识别, 支持向量机

Abstract: This paper focuses on support vector machine (SVM) based trip chain’s activity type recognition. First, the time-series location information of person trip is processed to obtain the trip chain elements including moving processes and activities, and the activity options are extracted from the geographic information system (GIS) around the activity sites. Second, the activity features are drawn from spatio-temporal factors of trip chain to serve as the input feature vector of classifier. A SVM based one-to-one classifier is established and the method of one-to-one classifier voting is adopted to decide the most likely activity type from the activity options. Finally, the classifiers are trained with simulation data based on the gaussian radial basis (RBF) kernel function and the multilayer perception (MLP) kernel function respectively, and then examined by cross validation. The result shows that in the one-to-one classifying scheme, the highest and lowest right recognition rate with RBF are 99% and 62%, and the corresponding results with MLP are 97% and 54%, respectively.

Key words: urban traffic, trip chain, activity type, pattern recognition, support vector machine

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