地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (4): 805-815.doi: 10.12082/dqxxkx.2020.190566

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基于交通出行链的就医活动识别理论框架与方法体系

王姣娥1, 杜方叶1,2,*(), 靳海涛3, 刘瑜4   

  1. 1. 中国科学院地理科学与资源研究所 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
    2. 中国科学院大学资源与环境学院,北京 100049
    3. 北京信息科技大学计算机学院,北京 100085
    4. 北京大学遥感与地理信息系统研究所,北京 100871
  • 收稿日期:2019-09-30 修回日期:2019-11-06 出版日期:2020-04-25 发布日期:2020-06-10
  • 通讯作者: 杜方叶 E-mail:dufy.18b@igsnrr.ac.cn
  • 作者简介:王姣娥(1981— ),女,湖南涟源人,博士,研究员,主要从事交通地理与区域发展、城市交通大数据等研究。E-mail:wangjiaoe@163.com
  • 基金资助:
    中国科学院战略性先导科技专项(A类XDA19040402);国家自然科学基金优秀青年基金项目(41722103)

Identifying Hospital-seeking Behavior based on Trip Chain Data: Theoretical Framework and Methodological System

WANG Jiaoe1, DU Fangye1,2,*(), JIN Haitao3, LIU Yu4   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Computer, Beijing Information Science and Technology University, Beijing 100085, China
    4. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
  • Received:2019-09-30 Revised:2019-11-06 Online:2020-04-25 Published:2020-06-10
  • Contact: DU Fangye E-mail:dufy.18b@igsnrr.ac.cn
  • Supported by:
    Strategic Priority Research Program of the Chinese Academy of Sciences(A类XDA19040402);National Natural Science Foundation of China(41722103)

摘要:

交通是人们实现出行目的的重要工具和载体,也是研究城市居民出行目的的重要手段。本文试图采用交通出行数据来识别就医活动目的的行程,以深化交通大数据研究的应用领域。在合并交通出行链的基础上,构建了就医活动识别的理论框架和方法体系,提出6大准则:邻近性准则、出行链闭合准则、单一出行目的准则、时间耦合性准则、路径偶发准则。以北京市为例,基于公交车刷卡和出租车GPS数据,明确就医出行的关键参数与阈值,最终甄别出以就医为目的的交通出行链,并对识别结果进行分析与验证。基于交通出行链的就医活动识别研究可以弥补传统研究中病例数据和问卷数据样本量小和难获取的不足,为就医活动研究提供了新的方法体系,也为基于其他交通出行目的识别研究提供理论和方法借鉴。

关键词: 大数据, 公交车, 出租车, 医院, 出行目的, 出行链, 北京市

Abstract:

Transportation is an important tool and carrier for people to realize their trip purposes. Thus, it's a vital measurement for studying spatio-temporal pattern of individuals. Trip chain refers that a series of displacements completed by an individual in order to do one or more activities using a transportation. The time period of trip chain is one day. There are lots of information on individuals' trip purpose contained in trip chains. Extract this information from trip chains help to explore individuals travel behavior, which help understand the urban space. In previous studies, researchers have been focused on inferring and exploring the dynamic characteristics of commuting behavior, go to school activities, go home activities, entertainment, and leisure activities in urban space with the help of smart card data and taxi trajectory data. But limited studies have been carried on detecting hospital-seeking behavior with the assistance of trip record. With this in mind, this paper attempted to extend the application of trip records on hospital-seeking behavior. Specifically, we proposed a theoretical framework used to detecting hospital-seeking behavior from trip record. It consists of six principle, such as proximity principle, ring-closure principle, single-purpose principle, infrequency principle, time-coherence principle, and accompany principle. Also, a methodology for detecting hospital-seeking behavior was put forward based on the theoretical framework. Taking Beijing as an example, we found the key parameters of detecting hospital-seeking behavior and calibrated their thresholds. Finally, spatial and temporal patterns of hospital-seeking behavior were explored to reveal the accuracy of our results. On the one hand, the spatial patterns of hospital-seeking behavior showed that patients were mainly concentrated in tertiary hospitals. Tertiary hospitals have better professional skills and a larger service area than secondary and primary hospitals. Thus, they attracted and served more patients. On the other hand, patients' arrival time shows a high peak during 8:00 am and 10:00 am and a low peak during 13:00 pm and 15:00 pm, which closed to start time of registration and treatment. Two aspects above both supported the accuracy of results and rationality of the theoretical framework. The application of trip chains on detecting hospital-seeking behavior could make up for the shortages of traditional data, which is a small sample and difficult to access. This paper provided a new perspective, methodology, and data source for researching hospital-seeking behavior. Moreover, it could provide methodology references for other activities based on trip records.

Key words: big data, bus, taxi, hospital, trip purpose, trip chain, Beijing