大数据与城市规划 (38).pdf
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1、Combining smart card data and household travel survey to analyzejobshousing relationships in BeijingYing Longa,Jean-Claude ThillbaBeijing Institute of City Planning,Beijing 100045,ChinabDepartment of Geography and Earth Sciences,The University of North Carolina at Charlotte,Charlotte,NC 28223,USAa r
2、 t i c l ei n f oArticle history:Available online 4 April 2015Keywords:Bus smart card dataJobshousing spatial mismatchCommuting tripRule-basedBeijinga b s t r a c tLocation Based Services(LBS)provide a new perspective for spatiotemporally analyzing dynamic urbansystems.Research has investigated urba
3、n dynamics using LBS.However,less attention has been paidto the analysis of urban structure(especially commuting pattern)using smart card data(SCD),whichare widely available in most large cities in China,and even in the world.This paper combines bus SCDfor a one-week period with a oneday household t
4、ravel survey,as well as a parcel-level land use mapto identify jobhousing locations and commuting trip routes in Beijing.Two data forms are proposed,one for jobshousing identification and the other for commuting trip route identification.The resultsof the identification are aggregated in the bus sto
5、p and traffic analysis zone(TAZ)scales,respectively.Particularly,commuting trips from three typical residential communities to six main business zonesare mapped and compared to analyze commuting patterns in Beijing.The identified commuting tripsare validated by comparison with those from the survey
6、in terms of commuting time and distance,and the positive validation results prove the applicability of our approach.Our experiment,as a first steptoward enriching LBS data using conventional survey and urban GIS data,can obtain solid identificationresults based on rules extracted from existing surve
7、ys or censuses.?2015 Elsevier Ltd.All rights reserved.1.IntroductionThis paper identifies jobhousing location dyads and commut-ing patterns in Beijing using smart card data(SCD)that store thedaily trip information of bus passengers.It proposes and imple-ments a method for deriving commuting patterns
8、 from increas-ingly common SCD for informing city planners and transitsystem managers about patterns of transit usage across spaceand through time as well as about mobility patterns in a largeand fast growing city region.Related research on jobshousingrelationships has conventionally used data acqui
9、red through sur-veys or censuses.The increasing pervasiveness of location-basedservices(LBS)associated with the prevalence of positioning tech-nologies has led to the creation of large-scale and high-qualityspace-time datasets(Jiang&Yao,2006).This development has alsocreated opportunities to better
10、describe and understand urbanstructures1in multiple dimensions.These datasets have been shownto be important for analyzing urban and environmental systemssuch as relationships between housing and jobs(Batty,1990).Meanwhile,a geo-tagged smart card system is an effective alterna-tive tool for individu
11、al data acquisition necessary to analyze urbanspatial structures.Various types of fine-granularity individual data generated byLBS technologies have been extensively leveraged to analyze urbanstructures(Ahas&Mark,2005;Lu&Liu,2012).With respect tohandheld Global Positioning System(GPS)devices,Newhaus
12、(2009)used location data to record and visualize urban diaries,while Gong,Chen,Bialostozky,and Lawson(2012a)elicited travelmodes of travelers in New York City.Liu,Andris,and Ratti(2010)identified taxi drivers behavior patterns from their daily digitaltrajectories,and Yue et al.(2012)used these traje
13、ctories to cali-brate a spatial interaction model.With respect to mobile phonesystems(see Steenbruggen,Borzacchiello,Nijkamp,&Scholten,2013 for a review),Ratti,Pulselli,Williams,and Frenchman(2006)evaluated the density and spatiotemporal characteristicsof urban activities using mobile phone data in
14、Milan,Italy,whereasWan and Lin(2013)studied fine-scale individual activities,Yuan,Raubal,and Liu(2012)correlated mobile phone usage and city-wide travel behavior in Harbin,China and Chi,Thill,Tong,Shi,and Liu(In press)exploited network properties of mobile phonehttp:/dx.doi.org/10.1016/penvurbsys.20
15、15.02.0050198-9715/?2015 Elsevier Ltd.All rights reserved.Corresponding author.Tel.:+86 10 88073660;fax:+86 10 68031173.E-mail address:(Y.Long).1The concept of urban structure concerns the spatial arrangement of public andprivate spaces in cities and the degree of connectivity and accessibility.In t
16、his paper,the concept is focused particularly on the spatial concentration of resident populationand employment(Anas,Arnott,&Small,1998).Computers,Environment and Urban Systems 53(2015)1935Contents lists available at ScienceDirectComputers,Environment and Urban Systemsjournal homepage: to reveal urb
17、an hierarchical structures at the regional scale.Asfor Wi-Fi,Rekimoto,Miyaki,and Ishizawa(2007)used Wi-Fi-basedlocation detection technology to log the locations of device holdersfrom received Wi-Fi beacon signals,a technology that works bothindoors and outdoors.Torrens(2008)developed a system to de
18、tectWi-Fi infrastructure and transmission and analyze their geographicproperties,and tested this system in Salt Lake City,Utah.Meanwhile,the discipline of time geography established byHagerstrand(1970)also benefited from the development of LBSby retrieving more objective data.In sum,various LBS tech
19、nologieshave been successfully applied in urban studies.However,thesetechnologies remain immature and most research on urban struc-ture continues to employ data from the urban physical space orquestionnaire surveys(with a few studies as exceptions,e.g.Kwan(2004).Access to large-scale micro datasets
20、remains a bar-rier to their widespread use for research,planning and manage-ment(Long&Shen,2013).A smart card that records full cardholders bus trip informationis an alternative form of location-acquisition technology.Smartcard automated fare collection systems are increasingly deployedin public tra
21、nsit systems.Along with collecting revenue,such sys-tems can capture a meaningful portion of travel patterns of card-holders,and the data are useful for monitoring and analyzingurban dynamics.Since the 1990s,the use of smart cards hasbecome significant owing to the development of the Internet andthe
22、 increased complexity of mobile communication technologies(Blythe,2004).As of 2007,Intelligent Transportation Systems(ITS)that incorporate smart card automated fare systems eitherexisted or were being established in over 100 Chinese cities,as wellas in many other cities around the world(Zhou,Zhai,&G
23、ao,2007).The data generated by smart card systems track the detailedonboard transactions of each cardholder.We argue that smart cardtechnology can deliver valuable information because it is a con-tinuous data collection technique that provides a complete andreal-time bus travel diary for all bus tra
24、velers.SCD can be usedto validate traditional travel models applied to public transit.Incontrast to SCD collection,conventional travel behavior surveyshave the drawbacks of being expensive and infrequent.Notably,transit SCD collects data in fundamentally the same way as anAVI(automatic vehicle ident
25、ification)system,which has beenwidely used in the United States to automatically identify vehicles.AVI is used in some states in the US for planning purposes.Onesuch example is New York,where the E-ZPass tag is used as partof the TRANSMIT system.Previous studies have advocated using SCD to make deci
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