基于聚类的地铁通勤行为时空规律挖掘方法
CSTR:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家重点研发计划项目(2019YFB2102503)


Spatial and Temporal Law Mining Method of Subway Commuting Behavior Based on Clustering
Author:
Affiliation:

Fund Project:

National Key Research and Development Program Project (2019YFB2102503)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    针对目前通勤群体的划分方法较少考虑通勤行程的时间连续性特征的问题,该文基于上海市一周的地铁刷卡数据,构建了通勤人群职住识别模型,并定义了一种通勤行程时间相似度计算方法,然后提取特征对通勤群体进行层次聚类,并利用热点分析模型进行空间分析和可视化,探究通勤人群的时空规律性及上海市的职住空间分布特征。结果表明:(1)就业单中心模式明显,不同簇的就业热点均分布在市中心,居住点呈“西热东冷”的空间组织特征。(2)主流通勤时段为 7:00—8:30 和 17:00—19:00,近半数通勤人群在早主流时段通勤。(3)不同通勤类型的出行时间特征总体上与其职住热点分布一致。该文提出的研究方法揭示了通勤人群的出行时间规律与其职住热点空间分布具有较强的关联性,可为城市运行管理和城市规划提供参考信息。

    Abstract:

    The current method of dividing commuting groups takes less into account the time continuity characteristics of commuting trips. Based on the one-week subway card swipe data in Shanghai, this paper constructs a work-life recognition model for commuters, defines a commuting trip time similarity calculation method, and then extracts the features to classify commuter groups hierarchically, and uses the hot spot analysis model to perform spatial analysis and visual expression for spatial analysis and visualization, and explores the spatiotemporal regularity of commuters and the spatial distribution characteristics of work-housing organization characteristics in Shanghai. The results show that: (1) The employment single center model is obvious, and the employment hotspots of different clusters are distributed in the city center, and the settlements are characterized by the spatial organization of “hot in the west and cold in the east”. (2) The mainstream commute hours are 7:00—8:30 and 17:00—19:00, with nearly half of the commuters commuting during the morning rush hours and 90% leaving the work places before 19:30. (3) The travel time characteristics of the different commuting types are generally consistent with the distribution of their work and housing hotspots. The proposed research method reveals that the travel time law of commuters has a strong correlation with the spatial distribution of work-housing hotspots, which provides reference information for urban operation management and urban planning.

    参考文献
    相似文献
    引证文献
引用本文

引文格式
李明珠,赵习枝,陈才,等.基于聚类的地铁通勤行为时空规律挖掘方法 [J].集成技术,2023,12(1):79-90

Citing format
LI Mingzhu, ZHAO Xizhi, CHEN Cai, et al. Spatial and Temporal Law Mining Method of Subway Commuting Behavior Based on Clustering[J]. Journal of Integration Technology,2023,12(1):79-90

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2023-01-12
  • 出版日期:
文章二维码