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Trip purpose inference using automated fare collection data

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Abstract

In this paper, we exploit the extensive smart card transaction data for deriving useful information about transit passenger behavior, namely trip purpose or activity. We show how the automated fare collection data (e.g., smart card) can be used to infer trip purpose and to reveal travel patterns in an urban area. A case study demonstrates the process of trip purpose inference based on smart card data from Metro Transit in the Minneapolis/St. Paul metropolitan area.

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References

  • Agard B, Morency C, Trépanier M (2006) Mining public transport user behavior from smartcard data. In: 12th IFAC symposium on information control problems in manufacturing—INCOM 2006, Saint-Etienne, France

    Google Scholar 

  • Agard B, Morency C, Trépanier M (2008) Mining smart card data from an urban transit network, Chap. 201. In: Wang J (ed) Encyclopedia of data warehouse and mining, 2nd edn. Information science reference, pp 1292–1302

    Chapter  Google Scholar 

  • American Public Transportation Association (APTA) (2012) http://www.apta.com/resources/statistics/Documents/Ridership/2011-q4-ridership-APTA.pdf

  • Bagchi M, White PR (2005) The potential of public transport smart card data. Transp Policy 12:464–474

    Article  Google Scholar 

  • Barry JJ, Newhouser R, Rahbee A, Sayeda S (2002) Origin and destination estimation in New York City with automated fare system data. In: Transportation research record 1817, TRB. National Research Council, Washington, pp 183–187

    Google Scholar 

  • Barry JJ, Freimer R, Slavin H (2009) Use of entry-only automatic fare collection data to estimate linked transit trips in New York City. In: Transportation research record 2112, TRB. National Research Council, Washington, pp 53–61

    Google Scholar 

  • Bohte W, Maat K (2009) Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands. Transp Res, Part C, Emerg Technol 17:285–297

    Article  Google Scholar 

  • Chapleau R, Trépanier M, Chu KK (2008) The ultimate survey for transit planning: complete information with smart card data and GIS, France. Paper presented at 8th international conference on survey in transport, Lac d’Annecy, France

  • Chu KK, Chapleau R (2008) Enriching archived smart card transaction data for transit demand modeling. In: Transportation research record 2063, TRB. National Research Council, Washington, pp 63–72

    Google Scholar 

  • Dempsey PS (2008) Privacy issues with the use of smart cards, legal research digest 25. Transit Cooperative Research Program (TCRP)

  • Farzin J (2008) Constructing an automated bus origin-destination matrix using farecard and GPS data in São Paulo, Brazil. In: Transportation research record 2072, TRB. National Research Council, Washington, pp 30–37

    Google Scholar 

  • General Transit Feed Specification (GTFS) Data Exchange (2010) http://www.gtfs-data-exchange.com/. Accessed December 2010

  • Hannes E, Janssens D, Wets G (2008) Destination choice in daily activity travel: mental map’s repertoire. In: Transportation research record 2054, TRB. National Research Council, Washington, pp 20–27

    Google Scholar 

  • Heikkila E, Gordon P, Kim JI, Peiser RB, Richardson HW, Dale-Johnson D (1989) What happened to the CBD-distance gradient? Land values in a policentric city. Environ Plan A 21:221–232

    Article  Google Scholar 

  • Kitamura R, Chen C, Narayanan R (1998) Traveler destination choice behavior: effects of time of day, activity duration, and home location. In: Transportation research record 1645, TRB. National Research Council, Washington, pp 76–81

    Google Scholar 

  • Lee SG, Hickman MD (2011) Travel pattern analysis using smart card data of regular users. In: Proceedings of the 90th annual meeting of the transportation research board, Washington

    Google Scholar 

  • Lee SG, Khani A, Tong D, Hickman MD (2011) Aggregate level origin-destination estimation using smart card data and transit schedule. In: Proceedings of the 2011 TRF annual forum, proceedings of the 52nd annual forum, Long Beach, CA

    Google Scholar 

  • Lee SG, Hickman MD, Tong D (2012) Stop aggregation model (SAM): development and application. In: Transportation research record 2276, TRB. National Research Council, Washington, pp 38–47

    Google Scholar 

  • Lee SG, Hickman MD, Tong D (2013) Development of a temporal and spatial linkage between transit demand and land use patterns. J Transp Land Use 6(2):33–46

    Article  Google Scholar 

  • Li B (2009) Markov models for Bayesian analysis about transit route origin-destination matrices. Transp Res, Part B, Methodol 43:301–310

    Article  Google Scholar 

  • Liu L, Hou A, Biderman A, Ratti C, Chen J (2009) Understanding individual and collective mobility patterns from smart card records: a case study in Shenzhen. In: Proceedings of the 12th international IEEE conference on intelligent transportation systems, St. Louis, MO

    Google Scholar 

  • McNamara L, Mascolo C, Carpa L (2008) Media sharing based on colocation prediction in urban transport. In: Proceedings of the 14th ACM international conference on mobile computing and networking, San Francisco, CA

    Google Scholar 

  • MetroGIS (2010) http://www.metrogis.org/. Accessed December 2010

  • Morency C, Trépanier M, Agard B (2006) Analysing the variability of transit users behaviour with smart card data. In: The 9th international IEEE conference on intelligent transportation systems—ITSC, Toronto, Canada

    Google Scholar 

  • Morency C, Trépanier M, Agard B (2007) Measuring transit use variability with smart-card data. Transp Policy 14(3):193–203

    Article  Google Scholar 

  • Nassir N, Khani A, Lee SG, Noh H, Hickman MD (2011) Transit stop-level origin-destination estimation through transit schedule and automated data collection system. In: Transportation research record 2263, TRB. National Research Council, Washington, pp 140–150.

    Google Scholar 

  • Pelletier M-P, Trépanier M, Morency C (2011) Smart card data use in public transit: a literature review. Transp Res, Part C, Emerg Technol 19:557–568

    Article  Google Scholar 

  • Reddy A, Lu A, Kumar S, Bashmakov V, Rudenko S (2009) Application of entry-only automated fare collection (AFC) system data to infer ridership, rider destinations, unlinked trips, and passenger miles. In: Transportation research record 2110, TRB. National Research Council, Washington, pp 128–136

    Google Scholar 

  • Seaborn C, Wilson NHM, Attanucci J (2009) Analyzing multimodal public transport journeys in London with smart card fare payment data. In: Transportation research record 2121, TRB. National Research Council, Washington, pp 55–62

    Google Scholar 

  • SPSS Inc. (2008) SPSS statistics base 17.0 user’s guide

  • SPSS Inc. (2009) PASW decision trees 18

  • Stopher P, FitzGerald C, Zhang J (2008) Search for a global positioning system device to measure person travel. Transp Res, Part C, Emerg Technol 16:350–369

    Article  Google Scholar 

  • Trépanier M, Tranchant N, Chapleau R (2007) Individual trip destination estimation in a transit smart card automated fare collection system. J Intell Transp Syst 11(1):1–14

    Article  Google Scholar 

  • Trépanier M, Morency C, Blanchette C (2009) Enhancing household travel surveys using smart card data. In: 88th annual meeting of the transportation research board, Washington

    Google Scholar 

  • Utsunomiya M, Attanucci J, Wilson NHM (2006) Potential uses of transit smart card registration and transaction data to improve transit planning. In: Transportation research record 1971, TRB. National Research Council, Washington, pp 119–126

    Google Scholar 

  • Waddell P, Berry BJL, Hoch I (1993) Residential property values in a multinodal urban area: new evidence on the implicit pricing of location. J Real Estate Finance Econ 7(2):117–141

    Article  Google Scholar 

  • Wang W, Attanucci J, Wilson NHM (2011) Bus passenger origin-destination and related analyses using automated data collection systems. J Public Transp 14(4):131–150

    Google Scholar 

  • Wolf J (2000) Using GPS data loggers to replace travel diaries in the collection of travel data. Dissertation, Georgia Institute of Technology, Atlanta, GA

    Google Scholar 

  • Zhao J, Rahbee A, Wilson NHM (2007) Estimating a rail passenger trip origin-destination matrix using automatic data collection systems. Comput-Aided Civ Infrastruct Eng 22(5):376–387

    Article  Google Scholar 

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Authors and Affiliations

  1. East-West Gateway Council of Governments, One Memorial Dr. Suite 1600, St. Louis, MO, 63102, USA

    Sang Gu Lee

  2. School of Civil Engineering, The University of Queensland, St. Lucia, QLD, 4072, Australia

    Mark Hickman

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  1. Sang Gu Lee
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  2. Mark Hickman
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Correspondence to Mark Hickman.

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Lee, S.G., Hickman, M. Trip purpose inference using automated fare collection data. Public Transp 6, 1–20 (2014). https://doi.org/10.1007/s12469-013-0077-5

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