Early Birds, Night Owls, and Tireless/Recurring Itinerants: An Exploratory Analysis of Extreme Transit Behaviors in Beijing, China

This paper seeks to understand extreme public transit riders in Beijing using both traditional household survey and emerging new data sources such as Smart Card Data (SCD). We focus on four types of extreme transit behaviors: public transit riders who (1) travel significantly earlier than average riders (the 'early birds'); (2) ride in unusual late hours (the 'night owls'); and (3) commute in excessively long distance (the 'tireless itinerants'); (4) travel over frequently in a day (the 'recurring itinerants). SCD are used to identify the spatiotemporal patterns of these three extreme transit behaviors. In addition, household survey data are employed to supplement the socioeconomic background and provide a tentative profiling of extreme travelers. While the research findings are useful to guide urban governance and planning in Beijing, the methods developed in this paper can be applied to understand travel patterns elsewhere.

[1]  Phil Blythe,et al.  IMPROVING PUBLIC TRANSPORT TICKETING THROUGH SMART CARDS , 2004 .

[2]  Yves Zenou,et al.  The Mechanisms of Spatial Mismatch , 2005 .

[3]  Carson Qing,et al.  The Emergence of the “Super-Commuter” , 2012 .

[4]  Mark W. Horner,et al.  Comparison of Socioeconomic and Demographic Profiles of Extreme Commuters in Several U.S. Metropolitan Statistical Areas , 2007 .

[5]  J. Corcoran,et al.  Exploring Bus Rapid Transit passenger travel behaviour using big data , 2014 .

[6]  David Banister,et al.  Excess Commuting: A Critical Review , 2006 .

[7]  Jinhua Zhao,et al.  Analyzing Passenger Incidence Behavior in Heterogeneous Transit Services Using Smartcard Data and Schedule-Based Assignment , 2012 .

[8]  Jiří Slavík,et al.  Estimation of a route choice model for urban public transport using smart card data , 2014 .

[9]  R. Cervero JOBS HOUSING BALANCE AS PUBLIC POLICY , 1991 .

[10]  J. Urry,et al.  Travel time use in the information age , 2005 .

[11]  Karel Williams,et al.  Not Enough Money: The Resources and Choices of the Motoring Poor , 2002 .

[12]  Jean-Claude Thill,et al.  Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing , 2013, Comput. Environ. Urban Syst..

[13]  Feng Chen,et al.  Transit smart card data mining for passenger origin information extraction , 2012, Journal of Zhejiang University SCIENCE C.

[14]  Jonathan Corcoran,et al.  Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap , 2014 .

[15]  Stuart Barr,et al.  Excess travelling—what does it mean? New definition and a case study of excess commuters in Tyne and Wear, UK , 2010 .

[16]  E. Côme,et al.  Understanding Passenger Patterns in Public Transit Through Smart Card and Socioeconomic Data: A case study in Rennes, France , 2014 .

[17]  Michael Batty,et al.  Big data, smart cities and city planning , 2013, Dialogues in human geography.

[18]  Mark Hickman,et al.  Trip purpose inference using automated fare collection data , 2014, Public Transp..

[19]  M. Meyer,et al.  Urban transportation planning , 1984 .

[20]  Ka Kee Alfred Chu,et al.  Augmenting Transit Trip Characterization and Travel Behavior Comprehension , 2010 .

[21]  Yasuo Asakura,et al.  Behavioural data mining of transit smart card data: A data fusion approach , 2014 .

[22]  Chaogui Kang,et al.  Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .

[23]  Catherine Morency,et al.  Smart card data use in public transit: A literature review , 2011 .

[24]  Jiangping Zhou,et al.  Jobs-Housing Balance of Bus Commuters in Beijing , 2014 .

[25]  Peter White,et al.  The Potential of Public Transport Smart Card Data , 2005 .

[26]  Brian D. Taylor,et al.  Spatial Mismatch or Automobile Mismatch? An Examination of Race, Residence and Commuting in US Metropolitan Areas , 1994 .

[27]  B. Jiang Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution , 2012, 1209.2801.

[28]  Richard Sliuzas,et al.  Beijing : city profile , 2013 .

[29]  Xingjian Liu,et al.  Profiling underprivileged residents with mid-term public transit smartcard data of Beijing , 2014, ArXiv.

[30]  Ying Long,et al.  Bus Commuters’ Jobs-Housing Balance in Beijing: An Exploration Using Large-Scale Synthesized Smart Card Data , 2013 .

[31]  Michael Batty,et al.  Smart Cities, Big Data , 2012 .