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首页> 外文期刊>Promet-traffic & transportation >UNDERSTANDING DAILY TRAVEL PATTERNS OF SUBWAY USERS - AN EXAMPLE FROM THE BEIJING SUBWAY
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UNDERSTANDING DAILY TRAVEL PATTERNS OF SUBWAY USERS - AN EXAMPLE FROM THE BEIJING SUBWAY

机译：了解地铁用户的日常旅行模式 - 来自北京地铁的示例

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摘要

The daily travel patterns (DTPs) present short-term and timely characteristics of the users' travel behaviour, and they are helpful for subway planners to better understand the travel choices and regularity of subway users (SUs) in details. While several well-known subway travel patterns have been detected, such as commuting modes and shopping modes, specific features of many patterns are still confused or omitted. Now, based on the automatic fare collection (AFC) system, a data-mining procedure to recognize DTPs of all SUs has become possible and effective. In this study, DTPs are identified by the station sequences (SSs), which are modelled from smart card transaction data of the AFC system. The data-mining procedure is applied to a large weekly sample from the Beijing Subway to understand DTPs. The results show that more than 93% SUs of the Beijing Subway travel in 7 DTPs, which are remarkably stable in share and distribution. Different DTPs have their own unique characteristics in terms of time distribution, activity duration and repeatability, which provide a wealth of information to calibrate different types of users and characterize their travel patterns.
机译：日常旅行模式（DTPS）目前的短期和及时的用户旅行行为特征，他们有助于地铁规划者更好地了解地铁用户（SUS）的旅行选择和规律性详细信息。虽然已经检测到几种众所周知的地铁旅行模式，例如通勤模式和购物模式，但许多图案的特定特征仍然困惑或省略。现在，基于自动票价收集（AFC）系统，数据采矿程序识别所有SU的DTP已经成为可能并且有效。在本研究中，DTP由站序列（SSS）识别，该序列（SSS）由AFC系统的智能卡交易数据建模。数据挖掘程序应用于来自北京地铁的大型每周样本，以了解DTP。结果表明，北京地铁60多次在7名DTPS旅行中的93％以上，其股票和分布非常稳定。不同的DTP在时间分布，活动持续时间和可重复性方面具有它们独特的特征，这提供了丰富的信息来校准不同类型的用户并表征其旅行模式。

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  • 来源
    《Promet-traffic & transportation》 |2020年第1期|13-23|共11页
  • 作者

    Huang Hainan; Rong Jian; Lin Pengfei; Weng Jiancheng;

  • 作者单位

    Fujian Agr & Forestry Univ Coll Transportat & Civil Engn 63 Xiyuangong Rd Fuzhou 350108 Peoples R China|Beijing Univ Technol Key Lab Traff Engn 100 Pingleyuan Beijing 100124 Peoples R China;

    Beijing Univ Technol Key Lab Traff Engn 100 Pingleyuan Beijing 100124 Peoples R China;

    Beijing Univ Technol Key Lab Traff Engn 100 Pingleyuan Beijing 100124 Peoples R China;

    Beijing Univ Technol Key Lab Traff Engn 100 Pingleyuan Beijing 100124 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    daily travel pattern; smart card data; station sequence; subway user; data mining;

    机译：每日旅行模式;智能卡数据;站序列;地铁用户;数据挖掘;

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