Passenger Classification for Urban Rail Transit by Mining Smart Card Data

被引：0

作者：

Zou Q.-R. ^{[1
]}

Zhao P. ^{[1
]}

Yao X.-M. ^{[1
]}

机构：

[1] School of Traffic and Transportation, Beijing Jiaotong University, Beijing

来源：

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2018年 / 18卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Automatic fare collection data; Passenger classification; Two-step clustering algorithm; Urban rail transit; Urban traffic;

D O I：

10.16097/j.cnki.1009-6744.2018.01.033

中图分类号：

学科分类号：

摘要：

Traditional passenger classification methods based on traffic survey have drawbacks on limited sample and subjective standard, this paper constructs a new method and indexes in perspective of "consumer behavior" by using automatic fare collection (AFC) data. In order to meet the computation requirements of large data set, the SPSS Modeler is used to cluster the passengers. In case study, one month's AFC data of Beijing rail transit is applied and results shows that it is the best to cluster passengers in five classes, and the stability is verified by comparison with the clustering results in five consecutive days. The departure time transferring elasticities of different passenger types under pre-peak discount pricing strategy of Beijing transit are also analyzed. This study improves the objectivity of passenger classification and provides method support for traffic policy formulation and operation strategy evaluation. Copyright © 2018 by Science Press.

引用

页码：223 / 230

页数：7

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