大数据与城市规划 (39).pdf
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1、Early birds,night owls,and tireless/recurring itinerants:Anexploratory analysis of extreme transit behaviors in Beijing,ChinaYing Longa,b,Xingjian Liuc,Jiangping Zhoud,*,Yanwei ChaieaHang Lung Center for Real Estate,Tsinghua University,ChinabSchool of Architecture,Tsinghua University,ChinacThe Unive
2、rsity of Hong Kong,ChinadUniversity of Queensland,AustraliaeCollege of Urban and Environmental Sciences,Peking University,Chinaa r t i c l e i n f oArticle history:Received 23 February 2016Received in revised form21 April 2016Accepted 20 August 2016Available online 29 August 2016Keywords:Extreme tra
3、nsit behaviorPublic transitSmart card data(SCD)Travel surveyBig dataa b s t r a c tThis paper seeks to understand extreme public transit riders in Beijing using both traditional householdsurveys and emerging new data sources such as Smart Card Data(SCD).We focus on four types ofextreme transit behav
4、iors:public transit riders who(1)travel significantly earlier than average riders(early birds);(2)ride in unusual late hours(night owls);(3)commute in excessively long distance(tireless itinerants);and(4)make significantly more trips per day(recurring itinerants).SCD are usedto identify the spatiote
5、mporal patterns of these four extreme transit behaviors.In addition,householdsurveys are employed to supplement the socioeconomic background and tentatively profile extremetravelers.While the research findings are useful to guide urban governance and planning in Beijing,ourmethodology and procedures
6、 can be extended to understand travel patterns elsewhere.2016 Elsevier Ltd.All rights reserved.1.IntroductionExtreme conditions often capture our attention and point toimportant underlying mechanism;we have learned a great dealabout our cities by examining the extremes,such as the emergenceand dynam
7、ics of the most dominant city of a nation,the mostdepressed city of a region,as well as the most popular gateway cityamong immigrants.In the past decade or so,against the backdropsof the global financial crisis,increased numbers of the unemployed,self-employed and part-time workers,the rise of telec
8、ommuters aswell as the relocation of low-paying jobs,extreme commuters havereceived increasing academic and public attention in recent years.As extreme commuting accounts for an increased portion of dailyresidential trips,recent analysis starts to look into travelers makingunusually long,early,late,
9、and/or frequent trips,which havediscretely explored or described by Barr,Fraszczyk,and Mulley(2010);Gregor(2013);Jones(2012);Landsman(2013);Marionand Horner(2007);Moss and Qing(2012);Rapino and Fields(2013);U.S.Census(2005).As scholarly work on extreme travelers has been largely devel-oped based on
10、North American and European cities,we are inter-ested in extending the framework to understand extreme trips inChina.We will examine extreme travel behaviors in public transit,as Chinese cities have historically relied on public transit,and theChinese government has sought public transit as a major
11、remedyfor congestions,pollution,and other issues caused by the rising carownership.To date,extreme traveler analyses have mostly used traditionaldata such as travel diaries and household surveys.More recently,emergingbig data sources suchas transit smart carddata havebeenutilized to investigate the
12、phenomenon.Transit smart card datarecord rich information about individual trips(e.g.,origin,desti-nation,and time length)and thus could be a useful supplementarydata source for understanding travel behaviors.For example,sincethe 1990s,the use of smart cards has become prevalent in a largenumber of
13、Chinese cities,partly owing to the development of theInternet and the advancement of mobile communication technol-ogies(Blythe,2004).Furthermore,Intelligent Transportation Sys-tems(ITS)that incorporate smartcard-automated fare systems hadbeen in place in over 100 Chinese cities as of 2007(Zhou&Long,
14、2014).The combination of these conventional and emerging data*Corresponding author.E-mail addresses:(Y.Long),xliu6hku.hk(X.Liu),jp.zhouuq.edu.au(J.Zhou),(Y.Chai).Contents lists available at ScienceDirectHabitat Internationaljournal homepage: Elsevier Ltd.All rights reserved.Habitat International 57(
15、2016)223e232sourcescouldoffernewopportunitiestodeepenourun-derstandings of cities and related routine activities such as trav-eling and commuting(Batty,2012;Liu et al.,2015).While most literature on extreme travelers focuses on exces-sively long trips,we extend the definition of extreme travelers in
16、tofour types with the context of Chinese society.The other threetypes of extreme travelers are public transit riders(1)who makesignificantly more trips(recurring itinerants),(2)travel signifi-cantly earlier than average riders(the early birds)during week-days,and(3)ride in unusual late hours(the nig
17、ht owls)duringweekdays.More specifically,we seek to identify these extremetravelers in Beijing,characterize their spatiotemporal trajectories,profile their socioeconomic backgrounds,and propose necessarypolicy implications on the phenomenon.Extreme transit behavior is becoming more prevalent in Chin
18、aand moving to the center of government agendas(Long&Thill,2015).Without a solid understanding of extreme transit patterns,government programs could easily result in misinformed in-terventions and policy failures.Therefore,this study seeks toexplore the spatiotemporal trajectories of extreme travele
19、rs basedon refined definitions of extreme transit behavior,using Beijing asa case study.Our analysis would leverage the power of emergingpublic transit data and seek to answer:(1)Are there large numberof extreme travelers?(2)Where do these extreme travelers live andwork?(3)What are their socioeconom
20、ic characters?In addition,our analysis will also tentatively address concerns about the causesof extreme transit behavior.The remainder of this article is organized in five main sections.First,werelate our analysistothe ongoing debate on the causes andconsequences of extreme transit behavior.Second,
21、we detail ourdata sources,which include both smart card data and conventionalhousehold travel surveys.Third,we provide working definitions ofextreme transit behavior and describe our empirical framework,which is a modification of the methodology detailed in Long andThill(2015).Fourth,we summarize ke
22、y findings based on ourexploratory analyses.We conclude with a discussion of empiricalcontributions and avenues for future research.2.Relevant literaturePassenger trips have long been of interest to transportationplanners and modelers.In existing literature,they have been pri-marilyclassified accord
23、ing totrip purpose,time of day,day of week,mode,person type,frequency,activity duration,and route choice(Meyer&Miller,2001).Emerging big data such as smart card datahave enabled scholars to examine more types or aspects of pas-senger trips,over more time horizons and in large sample sizes,ascompared
24、 to traditional data(Bagchi and White,2005).Multi-daydata of transit riders,for instance,were once difficult to collect ifwe rely on traditional methods such as surveys or interviews tocollect data.But smart card data make it possible.In addition,smartcard data can greatly facilitate our studies of
25、activity space,loca-tions and departure time of about 80%of all transit riders(Chu&Chapleau,2010).Extra information provided by smart card data enables us tobetter plan and manage our transit services(Frumin&Zhao,2012).Utilizing those data,for instance,we can now identify and visualizeover 80%of the
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