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《城市交通》杂志
2020年 第5期
基于互联网位置数据的通勤特征挖掘技术
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文章编号: 1672-5328(2020)05-0061-07

阚长城1,闫浩强1,项雯怡1,万涛2,付凌峰3
(1. 百度时代网络技术(北京)有限公司,北京100085;2. 天津市城市规划设计研究院,天津300201;3. 中国城市 规划设计研究院,北京100037)

摘要: 针对传统通勤特征测算中存在的不足,提出一种基于互联网时空大数据的通勤特征挖掘技术 框架。基于互联网定位、地图数据,利用机器学习算法挖掘常驻点、提取通勤OD,基于通勤OD 进一步挖掘通勤距离、通勤时间以及通勤方式,并将上述通勤特征数据应用于全国主要城市通勤监 测报告和国土空间规划等方面。使用多源时空大数据对通勤监测指标和结果进行校验,结果表明基 于互联网位置数据的通勤特征与抽样调查获得的通勤特征具有一致性,且能够以大样本、低成本、 高空间精度提供高频更新的通勤监测指标,是对传统方法的有效补充和强化。

关键词: 通勤监测;通勤OD;通勤时间;时空大数据

中图分类号: U491.1+2

文献标识码:A

Commuting Travel Characteristics Based on Location Data

Kan Changcheng1, Yan Haoqiang1, XiangWenyi1,Wan Tao2, Fu Lingfeng3
(1.Baidu.com Times Technology (Beijing) Co., Ltd., Beijing 100085, China; 2.Tianjin Urban Planning & Design Institute, Tianjin 300201, China; 3.China Academy of Urban Planning & Design, Beijing 100037, China)

Abstract: To overcome the shortcomings of the traditional commuting travel analyzing method, this paper develops a technical framework based on the big spatial-temporal travel data on Internet. Using Internet location and map data, the developed learning algorithms can estimate commuters' residence location and workplace as well as commuting distance, time, and travel mode selection. The commuting information has been used in commuting monitoring, national land use planning and other aspects in major cities across China. Comparing commuting travel data from various sources containing spatialtemporal information verifies that the characteristics of commuting travel retrieved and estimated from the Internet are consistent with the conventional sample surveys' results. Furthermore, the huge data size, low cost and high accuracy makes the real- time Internet commuting data the best estimate for monitoring the commuting travel and a great supplement to traditional data methods.

Keywords: commuting monitoring; commuting OD; commuting time; big spatial-temporal data