基于交通出行链的就医活动识别理论框架与方法体系

doi:10.12082/dqxxkx.2020.190566

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

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. 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 Fangye^1,^2,^*(), JIN Haitao³, LIU Yu⁴

^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/Abstract

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

王姣娥, 杜方叶, 靳海涛, 刘瑜. 基于交通出行链的就医活动识别理论框架与方法体系[J]. 地球信息科学学报, 2020, 22(4): 805-815.DOI:10.12082/dqxxkx.2020.190566

WANG Jiaoe, DU Fangye, JIN Haitao, LIU Yu. Identifying Hospital-seeking Behavior based on Trip Chain Data: Theoretical Framework and Methodological System[J]. Journal of Geo-information Science, 2020, 22(4): 805-815.DOI:10.12082/dqxxkx.2020.190566

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参考文献 47

[1]	Mcguckin N, Murakami E . Examining trip-chaining behavior: comparison of travel by men and women. Transportation Research Record Journal of the Transportation Research Board, 1999,1693(1):79-85.
[2]	Schwanen T . Geographies of transport II: Reconciling the general and the particular[J]. Progress in Human Geography, 2016,41(3):355-364.
[3]	Bagchi M, White P R . The potential of public transport smart card data[J]. Transport Policy, 2005,12(5):464-474.
[4]	Alsger A, Assemi B, Mesbah M , et al. Validating and improving public transport origin-destination estimation algorithm using smart card fare data[J]. Transportation Research Part C: Emerging Technologies, 2016,68:490-506.
[5]	Chen J, Yang D Y . Estimating smart card commuters origin-destination distribution based on APTS data[J]. Journal of Transportation Systems Engineering and Information Technology, 2013,13(4):47-53.
[6]	Zhou J, Murphy E, Long Y . Commuting efficiency in the Beijing metropolitan area: An exploration combining smartcard and travel survey data[J]. Journal of Transport Geography, 2014,41:175-183.
[7]	Devillaine F, Munizaga M, Trépanier M . Detection of activities of public transport users by analyzing smart card data[J]. Transport Research Record: Journal of the Transportation Research Board, 2012,2276:48-55.
[8]	Chu K, Chapleau R . Augmenting transit trip characterization and travel behavior comprehension: Multiday location-stamped smart card transactions[J]. Transport Research Record, 2010,2183:29-40.
[9]	Medina S, Erath A . Estimating dynamic workplace capacities by means of public transport smart card data and household travel survey in Singapore[J]. Transportation Research Record, 2013,2344:20-30.
[10]	Alsger A, Tavassoli A, Mesbah M , et al. Public transport trip purpose inference using smart card fare data[J]. Transportation Research Part C: Emerging Technologies, 2018,87:123-137.
[11]	刘丽敏, 虞虎, 靳海涛 . 基于公交刷卡数据的北京城市居民周末户外休闲行为特征研究[J]. 地域研究与开发, 2018,37(6):54-59.
	[ Liu L M, Yu H, Jin H T . Characteristics of outdoor recreation behaviors of beijing residents on weekends based on public transportation data[J]. Areal Research and Development, 2018,37(6):54-59. ]
[12]	Huang J, Levinson D, Wang J E , et al. Tracking job and housing dynamics with smartcard data[J]. Proceedings of the National Academy of Sciences, 2018,115(50):12710-12715.
[13]	龙瀛, 张宇, 崔承印 . 利用公交刷卡数据分析北京职住关系和通勤出行[J]. 地理学报, 2012,67(10):1339-1352.
	[ Long Y, Zhang Y, Cui C Y . Identifying commuting pattern of Beijing using bus smart card data[J]. Acta Geographica Sinica, 2012,67(10):1339-1352. ]
[14]	Ma X, Wu Y J, Wang Y , et al. Mining smart card data for transit riders' travel patterns[J]. Transportation Research Part C: Emerging Technologies, 2013,36:1-12.
[15]	Zhou J, Murphy E, Long Y . Commuting efficiency in the Beijing metropolitan area: An exploration combining smartcard and travel survey data[J]. Journal of Transport Geography, 2014,41:175-183.
[16]	黄洁, 王姣娥, 靳海涛 , 等. 北京市地铁客流的时空分布格局及特征——基于智能交通卡数据[J]. 地理科学进展, 2018,37(3):397-406.
	[ Huang J, Wang J E, Jin H T , et al. Investigating spatiotemporal patterns of passenger flows in the Beijing metro system from smart card data[J]. Progress in Geography, 2018,37(3):397-406. ]
[17]	Kuhlman W . The construction of purpose-specific OD matrices using public transport smart card data[D]. Delft University of Technology, TU Delft, 2015.
[18]	Axhausen K W, Wolf J, Schönfelder S , et al. 80 weeks of GPS-traces: Approaches to enriching the trip information[J]. Transportation Research Record, 2004,1870:46-54.
[19]	Gong L, Liu X, Wu L , et al. Inferring trip purposes and uncovering travel patterns from taxi trajectory data[J]. Cartography and Geographic Information Science, 2016,43(2):1-12.
[20]	Huang L, Li Q, Yue Y . Activity identification from GPS trajectories using spatial temporal POIs' attractiveness[R]. 2nd ACM Sigspatial International Workshop on Location Based Social Networks, 2010.
[21]	Hu Y, Miller H J, Li X . Detecting and analyzing mobility hotspots using surface networks[J]. Transactions in GIS, 2014,18(6):911-935.
[22]	Zheng Z, Rasouli S, Timmermans H . Two-regime Pattern in Human Mobility: Evidence from GPS Taxi Trajectory Data[J]. Geographical Analysis, 2016,48(2):157-175.
[23]	Liu X, Gong L, Gong Y . Revealing travel patterns and city structure with taxi trip data[J]. Journal of Transport Geography, 2015,43:78-90.
[24]	Chen C, Bian B, Ma J . From traces to trajectories: How well can we guess activity locations from mobile phone traces?[J]. Transportation Research Part C: Emerging Technologies, 2014,46:326-337.
[25]	杨格格, 宋辞, 裴韬 , 等. 北京对外交通枢纽乘客OD时空分布特征[J]. 地球信息科学学报, 2016,18(10):1374-1383.
	[ Yang G G, Song C, Pei T , et al. Passengers' OD temporal-spatial distribution characteristics of the external traffic hubs in Beijing. Journal of Geo-information Science, 2016,18(10):1374-1383. ]
[26]	Wu L, Zhi Y, Sui Z , et al. Intra-urban human mobility and activity transition: Evidence from social media check-in data[J]. PLoS One, 2014,9(5):e97010.
[27]	Yuan Y, Raubal M, Liu Y . Correlating mobile phone usage and travel behavior: A case study of Harbin, China[J]. Computers, Environment and Urban Systems, 2012,36(2):118-130.
[28]	陈卓然, 黄翀, 刘高焕 , 等. 基于出租车GPS 数据的居民就医时空特征分析[J]. 地球信息科学学报, 2018,20(8):1111-1122.
	[ Chen Z R, Huang C, Liu G H , et al. Analysis of spatial-temporal characteristics of resident travel for hospitals based on taxi GPS data[J]. Journal of Geo-information Science, 2018,20(8):1111-1122. ]
[29]	Kong X Q, Liu Y, Wang Y X , et al. Investigating public facility characteristics from a spatial interaction perspective: a case study of Beijing hospitals using taxi data[J]. ISPRS International Journal of Geo-information, 2017,6(2):38-51.
[30]	Pan X, Kwan M P, Yang L , et al. Evaluating the accessibility of healthcare facilities using an integrated catchment area approach[J]. International Journal of Environmental Research and Public Health, 2018,15(9):1-21.
[31]	Jia P, Wang F, Xierali I M . Using a Huff-based model to delineate hospital service areas[J]. The Professional Geographer, 2017,69(4):522-530.
[32]	Yang G G, Song C, Shu H , et al. Assessing patient bypass behavior using taxi trip origin-destination (OD) data[J]. ISPRS International Journal of Geo-information, 2016,5(9):157-177.
[33]	陈建国, 周素红, 柳林 , 等. 交通拥堵对急救医疗服务时空可达性的影响——以广州市为例[J]. 地理科学进展, 2016,35(4):431-439.
	[ Chen J G, Zhou S H, Liu L , et al. Estimating the effect of traffic congestion on accessibility to emergency medical care services: Take Guangzhou as an example[J]. Progress in Geography, 2016,35(4):431-439. ]
[34]	Boarnet M, Crane R . The influence of land use on travel behavior: specification and estimation strategies[J]. Transportation Research Part A: Policy Practice, 2001,35:823-845.
[35]	Ophem H V . The frequency of visiting a doctor: Is the decision to go independent of the frequency?[J]. Journal of Applied Econometrics, 2011,26(5):872-879.
[36]	温慧敏, 顾涛, 王志丽 , 等. 北京交通发展年报[R]. 北京:北京交通北京, 2018.
	[ Wen H M, Gu T, Wang Z L , et al. Annual report on Beijing's traffic development[R]. Beijing: Beijing Transport Institute, 2018. ]
[37]	李佳贤, 陈艳艳, 韩志玲 , 等. 北京市居民初诊就医出行行为特征分析[J]. 交通科技与经济, 2019,21(5):34-38.
	[ Li J X, Chen Y Y, Han Z L , et al. Analysis of first medical travel behavior characteristics of residents in Beijing[J]. Technology & Economy in Areas of Communications, 2019,21(5):34-38. ]
[38]	许欣悦, 曾多, 田玮 , 等. 北京某三甲医院预约挂号对患者候诊时间的影响分析[J]. 中国医院管理, 2015,35(10):38-40.
	[ Xu X Y, Zeng D, Tian W . Analysis of reservation registration effect on outpatient waiting time in one grade 3 first-class hospital[J]. Chinese Hospital Management, 2015,35(10):38-40. ]
[39]	苑继承, 高湘伟, 石岳琴 . 门诊患者挂号与就诊时间调查分析[J]. 第四军医大学学报, 2005,26(1):83-85.
	[ Yuan J C, Gao X W, Shi Y Q . Survey and analysis of distribution of outpatients registration and visiting time[J]. Journal of the Fourth Military Medical University, 2005,26(1):83-85. ]
[40]	中华人民共和国卫生部. 医院分级管理办法[R]. 北京, 1989.
	[ Chinese Ministry of Health. Measures for hierarchical management of hospitals[R]. Beijing, 1989. ]
[41]	Huang J, Levinson D, Wang J E , et al. Job-worker spatial dynamics in Beijing: Insights from Smart Card Data[J]. Cities, 2019,86:83-93.
[42]	Zhao P J, Bin Lü, Roo G D . Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era[J]. Journal of Transport Geography, 2011,19(1):60-69.
[43]	靳海涛, 金凤君, 陈卓 , 等. 基于换乘链断裂点时空信息的公交换乘行为识别[J]. 交通运输工程学报, 2018,18(5):176-184.
	[ Jin H T, Jin F J, Chen Z , et al. Commute activity identification based on spatial and temporal information of transit chaining breaks[J]. Journal of Traffic and Transportation Engineering, 2018,18(5):176-184. ]
[44]	史华英, 李习平 . 患者就医时间影响就医决策的调查研究[J]. 社区医学杂志, 2016,14(7):41-43.
	[ Shi H Y, Li X P . Investigation of the influence of patient's medical treatment time on decision making[J]. Journal of Community Medicine, 2016,14(7):41-43. ]
[45]	熊俊浩, 夏曦, 程小琴 , 等. 基于结构方程模型的患者选择医院影响因素研究[J]. 中国卫生事业管理, 2012,29(2):84-87.
	[ Xiong J H, Xia X, Cheng X Q , et al. A SEM-based study on influence factors of hospital choice[J]. The Chinese Health Service Management, 2012,29(2):84-87. ]
[46]	王森 . 我国居民的就医行为及其影响因素研究——基于CHNS调查面板数据的分析[J]. 西北人口, 2015(3):32-36.
	[ Wang S . Analysis on the Adjustment of Birth Control Policies and Development of China Population. Northwest Population, 2015(3):32-36. ]
[47]	京医通大数据中心. 2017京医通就医数据报告[R]. 北京, 2018.
	[ Jingyitong big data center. 2017 Jingyitong medical report[R]. Beijing, 2018. ]

	个体编号	上车点位置	上车时间	下车点位置	下车时间
公交IC卡刷卡数据	√	√	√	√	√
出租车GPS数据		√	√	√	√

	就医者数量/人				交通出行时间/min				就医时长/min
	公交车		出租车		公交车		出租车		公交车
	均值	标准差	均值	标准差	均值	标准差	均值	标准差	均值	标准差
所有医院	220	231.1	441	959.6	21.1	16.7	18.7	13.4	138.2	56.7
三级医院	433	321.5	1520	1575.9	22.5	17.3	19.1	13.8	142.4	59.8
二级医院	271	217.7	287	245.8	20.9	17.0	18.0	12.8	139.3	58.2
一级医院	114	116.1	88	130.9	19.7	15.8	17.0	11.4	133.3	52.5

基于交通出行链的就医活动识别理论框架与方法体系

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

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