交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (4): 239-245.

• 案例分析 • 上一篇    下一篇

面向城市交通规划的多源手机信令数据相关性研究

张晓春 1, 2,于壮 1, 2,段冰若 1, 2,高永*1, 2,安健 1, 2   

  1. 1. 深圳市城市交通规划设计研究中心有限公司,广东 深圳 518026; 2. 深圳市交通信息与交通工程重点实验室,广东 深圳 518026
  • 收稿日期:2019-01-11 修回日期:2019-02-24 出版日期:2019-08-25 发布日期:2019-08-26
  • 作者简介:张晓春(1973-),男,安徽阜阳人,教授级高级工程师.
  • 基金资助:

    国家自然科学基金青年科学基金/ Young Scientists Fund of the National Natural Science Foundation of China (71501014);广东省交通运输厅2016—2017年度政府引导性课题/ Government Guiding Project of Guangdong Transportation Department in 2016-2017(科技-2016-03-023).

City Transport Planning Oriented Research on Multi Cellular Signaling Data Relationship

ZHANG Xiao-chun1, 2, YU Zhuang1, 2, DUAN Bing-ruo1, 2, GAO Yong1, 2, AN Jian1, 2   

  1. 1. Shenzhen Urban Transport Planning Center CO., LTD, Shenzhen 518026, Guangdong, China; 2. Shenzhen's Key Laboratory of Traffic Information and Traffic Engineering, Shenzhen 518026, Guangdong, China
  • Received:2019-01-11 Revised:2019-02-24 Online:2019-08-25 Published:2019-08-26

摘要:

随着智能手机的普及,基于手机信令数据获取城市交通出行和人口活动信息成为了一种常用手段.但在实际应用中,绝大部分的信令数据分析都是基于单个运营商的数据.由于无法确定不同运营商数据之间是否存在显著差异,也就无法保证基于单个运营商数据的计算结果能够满足城市交通分析的精度要求.针对这一问题,本文使用某城市 2个运营商 5个工作日的信令数据,分别计算了交通规划中常用的人口分布和交通出行信息.计算结果发现,在使用算法一致的前提下,2个运营商的人口和交通出行结果十分相似,特别是居民移动人口分布和出行 OD矩阵基本没有差距,相关系数在 0.9以上,且当时间和空间尺度发生变化时仍然保持较高的相似性.但是,从城市停留人口分布的计算结果来看,由于 2个运营商设置的位置更新周期的差异,导致计算的停留人口分布相关系数较低,在0.7左右.

关键词: 城市交通, 手机信令, 数据挖掘, 相关性分析

Abstract:

With the popularity of the smart phone, the cellular signaling data become an effective data resource to obtain the city traffic pattern. However, most of analyzing results of cellular signaling data were obtained from one carrier instead of all carriers in the city. It is hard to answer whether the one-carrier-based result can satisfy the demand of city transport planning. Aiming at this problem, this paper from the perspective of data analysis, based on two carriers’cellular signaling data of one city, China, analyzed whether the result of city population distribution and city traffic pattern obtained from two carriers signaling data was significantly different. Based on the research result of this paper, it is found that the city traffic and resident movement patterns computed by two carriers’data are pretty similar. Especially for city OD matrix and moving resident distribution, their correlation coefficients were large even the time and space scale was changing. However, there are big difference between the results of stop people distribution before dawn, which derived from the difference of position update cycle between carriers.

Key words: urban traffic, cellular signaling data, data mining, correlation analysis

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