中文核心期刊
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重庆交通大学学报（自然科学版）

重庆交通大学学报（自然科学版） ›› 2016, Vol. 35 ›› Issue (6): 109-114.DOI: 10.3969/j.issn.16740696.2016.06.23

• 交通运输工程 • 上一篇    下一篇

基于公交IC卡数据的乘客出行分类研究

李军，邓红平   

  1. （中山大学 广东省智能交通系统重点实验室，广东 广州 510006）
  • 收稿日期:2015-04-02 修回日期:2016-08-11 出版日期:2016-12-25 发布日期:2016-12-29
  • 作者简介:李军(1968—)，男，湖北武汉人，副教授，博士，主要从事交通工程、交通经济与智能交通系统及优化方面的研究。Email：stslijun@mail.sysu.edu.cn。
  • 基金资助:
    国家自然科学基金项目(51178475)

Classification of Passenger’s Travel Behavior Based on IC Card Data

LI Jun, DENG Hongping   

  1. (Guangdong Key Laboratory of Intelligent Transportation Systems, Sun YatSen University, Guangzhou 510006, Guangdong, P. R. China)
  • Received:2015-04-02 Revised:2016-08-11 Online:2016-12-25 Published:2016-12-29
  • Contact: 邓红平(1992—)，男，四川南充人，硕士，主要从事交通工程与智能交通方面的研究。Email：denghp3@mail2.sysu.edu.cn。

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摘要： 为得到体现公交乘客出行时空规律的数据，采用基于出行链方法推导出公共汽车乘客的下车站点；建立了描述单个乘客多天出行的完整数据框架；根据乘客参加不同活动所产生的出行时空特征定义了3类出行：通勤类出行、普通类出行和随机类出行，将出行频次与出发时间的标准差作为分类标准对公交乘客出行进行分类。研究表明：39.1%的乘客具有普通类或通勤类出行，生成总客流的76.4%；60.9%的乘客只具有随机类出行，生成总客流的23.6%。通过对乘客出行的分类研究可以更好地掌握乘客公交出行的规律和需求。

关键词: 交通运输工程, 公共交通, IC卡数据, 公交出行行为, 时空分析

Abstract: To obtain the data of the spatial and temporal patterns of public transit passengers, the first step was to infer the alighting stop for each cardholder based on trip chain method, and then a full data framework was established for describing each passenger’s travel behavior in several days. Meanwhile, three types of travel were defined derived from passenger’s different types of activity in terms of temporalspatial characteristics, including the commuting travel type, the ordinary type and the random travel type. Finally, each passenger’s travel was classified into the above three types according to travel frequency and the standard deviation of departure time. The result of classification shows that about 39.1% of total passengers have the commuting type or ordinary type and these passengers generate about 76.4% of total passenger flow; about 60.9% of total passengers only have random travel type and these passengers only generate about 23.6% of total passenger flow. It is possible to obtain the public transit passenger’s travel pattern and demand at a much more detail level by classifying each passenger’s multiday travel behavior.

Key words: traffic and transportation engineering, public transit, IC card data, transit travel behavior, spatialtemporal analysis

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