基于峰值密度聚类的公交出行目的分类模型

Classification model for public-transport trip destinations based on density-peak clustering

针对如何利用公交卡刷卡数据自动对公交出行目的进行分类问题,基于峰值密度聚类的理论,建立了一个能够在指定区域下对公交乘客出行目的进行准确分类的方法模型。本文根据出行目的不同提取相应特征,将特征相似的乘客进行聚类。得到结果后将每个类簇的特征均值作为该类簇群体的出行特征,根据出行特征可以得出每位乘客的出行目的和群体出行目的的统计结果。利用北京市西单地区乘客的公交卡刷卡数据,通过将实际数据与调查问卷结果进行比对验证,证明了该模型方法与传统调查问卷方法相比节省了大量的人力物力,具有良好的效果和实用价值。

In this study,to classify the purpose of public-transport trips in specific zones,we developed a model that uses smart card data to automatically classify the purpose of public-transport trips,based on a density-peak cluster algorithm.This model extracts features corresponding to different trip purpose and clusters groups having similar features.Based on the results,we can determine the average feature of each trip feature cluster and,based on the trip features,obtain statistical analyses for the trip purpose of every passenger and group.Using data obtained from the smart public-transportation cards of Xidan regional travelers in Beijing,we compared and verified actual data with questionnaire results.The results show that,compared to the questionnaire approach,the model saves considerable manpower and material resources,obtains good results,and thus has practical value.