基于多源出行数据的居民行为模式分析方法

doi:10.11772/j.issn.1001-9081.2017.08.2362

计算机应用 ›› 2017, Vol. 37 ›› Issue (8): 2362-2367.DOI: 10.11772/j.issn.1001-9081.2017.08.2362

基于多源出行数据的居民行为模式分析方法

徐晓伟^1,2, 杜一¹, 周园春¹

1. 中国科学院计算机网络信息中心大数据技术与应用发展部, 北京 100190;
2. 中国科学院大学, 北京 100049

收稿日期:2017-02-13 修回日期:2017-04-27 出版日期:2017-08-10 发布日期:2017-08-12
作者简介:徐晓伟(1993-),男,河北邯郸人,硕士研究生,主要研究方向:数据挖掘、机器学习;杜一(1988-),男,山东聊城人,副研究员,博士,CCF会员,主要研究方向:数据挖掘、数据可视化;周园春(1975-),男,江西鹰潭人,研究员,博士,CCF会员,主要研究方向:大数据管理与处理技术、数据挖掘。
基金资助:
国家重点研发计划项目（2016YFB0501900，2016YFB1000600）；国家自然科学基金资助项目（61402435）。

Resident behavior model analysis method based on multi-source travel data

XU Xiaowei^1,2, DU Yi¹, ZHOU Yuanchun¹

1. Department of Big Data Technology and Application Development, Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2017-02-13 Revised:2017-04-27 Online:2017-08-10 Published:2017-08-12
Supported by:
This work is supported by the National Key Research and Development Program (2016YFB0501900,2016YFB1000600),the National Natural Science Foundation of China (61402435).

摘要/Abstract

摘要： 基于对智能交通卡数据的挖掘与分析能够为城市交通建设和城市管理提供有力支持，但现有研究数据大都仅包含公交或地铁这两方面数据，且主要关注群体性宏观出行规律。针对这一问题，以某城市交通卡数据为例，该数据包含着城市居民日常出行公交、地铁、出租车等多源数据，首先提出行程链的概念对居民出行行为建模，在此基础上给出不同维度的周期性出行特征；然后提出一种基于最长公共子序列的空间周期性特征提取方法，并对城市居民出行规律进行聚类分析；最后通过规则定义5个评价指标对该方法的有效性进行初步验证。结果表明引入该方法的聚类算法对聚类结果有6.8%的效果提升，有利于发现居民的行为模式。

关键词: 智能交通卡, 多源数据, 序列匹配, 聚类分析, 时空数据挖掘

Abstract: The mining and analysis of smart traffic card data can provide strong support for urban traffic construction and urban management. However, most of the existing research data only include data about bus or subway, and mainly focus on macro-travel patterns. In view of this problem, taking a city traffic card data as the example, which contains the multi-source daily travel data of urban residents including bus, subway and taxi, the concept of tour chain was put forward to model the behavior of residents. On this basis, the periodic travel characteristics of different dimensions were given. Then a spatial periodic feature extraction method based on the longest common subsequence was proposed, and the travel rules of urban residents were analyzed by clustering analysis. Finally, the effectiveness of this method was verified by five evaluation indexes defined by the rules, and the clustering result was improved by 6.8% by applying the spatial periodic feature extraction method, which is helpful to discover the behavior pattern of residents.

Key words: smart traffic card, multi-source data, sequence matching, clustering analysis, spatio-temporal data mining

中图分类号:

徐晓伟, 杜一, 周园春. 基于多源出行数据的居民行为模式分析方法[J]. 计算机应用, 2017, 37(8): 2362-2367.

XU Xiaowei, DU Yi, ZHOU Yuanchun. Resident behavior model analysis method based on multi-source travel data[J]. Journal of Computer Applications, 2017, 37(8): 2362-2367.

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基于多源出行数据的居民行为模式分析方法

Resident behavior model analysis method based on multi-source travel data

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