地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (6): 1254-1267.doi: 10.12082/dqxxkx.2020.190576

• 大数据与智慧交通 • 上一篇    下一篇

基于刷卡数据的公共汽车客流网络复杂性日内变化研究

赵韶雅1,2, 杨星斗2, 戴特奇1,2,*(), 张超3   

  1. 1. 北京师范大学 地理科学学部环境遥感与数字城市北京市重点实验室,北京 100875
    2. 北京师范大学地理科学学部地理学院,北京 100875
    3. 北京市第二十四中学,北京 100005
  • 收稿日期:2019-10-05 修回日期:2019-11-28 出版日期:2020-06-25 发布日期:2020-08-25
  • 通讯作者: 戴特奇 E-mail:daiteqi@bnu.edu.cn
  • 作者简介:赵韶雅(1994— ),女,山西长治人,硕士生,主要研究方向为大数据与城市交通。E-mail: zhaoshaoya0820@mail.bnu.edu.cn
  • 基金资助:
    国家自然科学基金面上项目(41871128)

Within-Day Variation of the Complexity of Bus Passenger Flow Network based on Smart Card Data

ZHAO Shaoya1,2, YANG Xingdou2, DAI Teqi1,2,*(), ZHANG Chao3   

  1. 1. Beijing Key Laboratory for Remote Sensing of Environment and Digital City, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    2. School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    3. Beijing No.24 middle school, Beijing 100005, China
  • Received:2019-10-05 Revised:2019-11-28 Online:2020-06-25 Published:2020-08-25
  • Contact: DAI Teqi E-mail:daiteqi@bnu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(41871128)

摘要:

大城市公共交通是一个典型的复杂巨系统,采用复杂网络方法分析大城市公共交通网络系统对于城市交通发展具有重要意义。已有大量的研究采用复杂网络理论进行了公共交通线路网络分析,也有研究基于刷卡数据分析了公共交通客流网络的复杂特征,但少有研究探讨客流网络复杂性日内变化特征。鉴于此,本文基于北京市公共汽车刷卡数据识别的不同时间段客流双向邻接矩阵,通过复杂网络指标对比分析公共汽车客流网络的日内变化特征。结果表明:① 各个时间段公共汽车客流分布遵循距离衰减规律,5 km以下的短距离出行约占总出行量的一半左右;② 度中心性和加权度中心性的空间格局在不同时间段整体呈现出明显的核心-边缘特征,但随时间有一定程度的变化,加权度中心性排名前10的节点存在较大变化;③ 累积度分布和累积加权度分布服从指数分布,属于小世界网络。本文还进一步讨论了基于大数据的动态复杂网络研究对城市交通规划建设的启示意义。

关键词: 公交汽车, 大数据, 客流分布, 日内变化, 空间格局, 复杂网络, 政策启示, 北京市

Abstract:

Public transportation in large cities is a typical complex giant system. The use of complex network methods to analyze large city public transport network systems is of great significance for urban transport development. Most of current studies take public transport stations as nodes and routes as connecting edges to construct an abstract network adjacency matrix, so to analyze the complexity characteristics of public transport networks according to indicators such as average (shortest) path length, clustering coefficient, degree distribution, node proximity or median centrality. However, the complexity of urban public transport network is not a static topology network composed of bus stops, routes, and their interconnections, but also the dynamic traffic information, which is seldom considered in existing studies. Distinguishing the characteristics of the passenger flow network in different time periods is of great value for formulating time-sharing public transport management policies. Meanwhile, the widely used big data recently provide high-precision traffic information for the study of dynamic traffic network structures. In this paper, we constructed the bidirectional adjacency matrix of passenger flows in different time periods based on swipe card data of Beijing buses, then compared and analyzed the within-day variation of the bus passenger flow network through complex network indexes. In terms of the structural characteristics, the bus passenger flow network in each time period had a small average shortest path and a large clustering coefficient, meaning a small-world network; the distribution of accumulation degree was fitted as an exponential distribution, which indicates that the bus passenger flow network did not have scale-free characteristics. The bus passenger flow distribution in each time period had an obvious distance attenuation rule, and it was mainly for short-distance travels below 10km, which suggests the bus line and operation management should focus on the distance range below 10 km. Degrees centricity and weighted degree centrality of the spatial pattern in different times presented an obvious core-edge character but changing over time; the weighted degree centrality in the top 10 nodes changed a lot, according to which dynamic public transport hubs should be considered in a precious and accurate traffic planning and management. Our findings provide a reference for public transport planning and management policies. In the future, more comprehensive passenger flow data should be used to explore the structural characteristics from a multi-scale perspective.

Key words: bus, big data, passenger flow distribution, within-day variation, spatial pattern, complex network, policy implications, Beijing