IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v45y2011i7p640-652.html
   My bibliography  Save this article

Geodemographic analysis and the identification of potential business partnerships enabled by transit smart cards

Author

Listed:
  • Páez, Antonio
  • Trépanier, Martin
  • Morency, Catherine

Abstract

Smart card automated fare payment systems are being adopted by transit agencies around the world. The data-storage characteristics of smart cards present novel opportunities to enhance transit services. On the one hand, there are fare policies, where smart card holders are given specific rebates on the use of the service based on usage patterns or levels. On the other, there are non-fare policies, for instance if holders receive advantages, such as rebates and offers, from commercial partners. The purpose of this paper is to present a geodemographic framework to identify potential commercial partnerships that could exploit the characteristics of smart cards. The framework is demonstrated using data from Montreal, Canada. Household survey data, specifically trip ends, and business data points are jointly used to determine the exposure of various types of establishments to users of the Montreal Metro network. Spatial analysis of business establishments in the neighborhood of metro stations helps to identify potential commercial partners. The results illustrate the potential of geodemographic analysis to generate intelligence of commercial interest.

Suggested Citation

  • Páez, Antonio & Trépanier, Martin & Morency, Catherine, 2011. "Geodemographic analysis and the identification of potential business partnerships enabled by transit smart cards," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(7), pages 640-652, August.
  • Handle: RePEc:eee:transa:v:45:y:2011:i:7:p:640-652
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856411000607
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ron Buliung & Tarmo Remmel, 2008. "Open source, spatial analysis, and activity-travel behaviour research: capabilities of the aspace package," Journal of Geographical Systems, Springer, vol. 10(2), pages 191-216, June.
    2. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    3. Deleersnyder, B. & Dekimpe, M.G. & Steenkamp, J.E.B.M. & Koll, O., 2007. "Win-win strategies at discount stores," Other publications TiSEM 34fbe624-0ee7-4c52-b640-7, Tilburg University, School of Economics and Management.
    4. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    5. Xingjian Liu & James LeSage, 2010. "Arc_Mat: a Matlab-based spatial data analysis toolbox," Journal of Geographical Systems, Springer, vol. 12(1), pages 69-87, March.
    6. Cohen, Jeffrey P. & Paul, Catherine J. Morrison, 2005. "Agglomeration economies and industry location decisions: the impacts of spatial and industrial spillovers," Regional Science and Urban Economics, Elsevier, vol. 35(3), pages 215-237, May.
    7. Sergio Rey, 2009. "Show me the code: spatial analysis and open source," Journal of Geographical Systems, Springer, vol. 11(2), pages 191-207, June.
    8. Morency, Catherine & Paez, Antonio & Roorda, Matthew J. & Mercado, Ruben & Farber, Steven, 2011. "Distance traveled in three Canadian cities: Spatial analysis from the perspective of vulnerable population segments," Journal of Transport Geography, Elsevier, vol. 19(1), pages 39-50.
    9. Hannah Badland & Grant Schofield, 2008. "Understanding the relationships between private automobile availability, overall physical activity, and travel behavior in adults," Transportation, Springer, vol. 35(3), pages 363-374, May.
    10. Anthony May & Simon Shepherd & Paul Timms, 2000. "Optimal transport strategies for European cities," Transportation, Springer, vol. 27(3), pages 285-315, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Páez, Antonio & Trépanier, Martin & Morency, Catherine, 2012. "Modeling isoexposure to transit users for market potential analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1517-1527.
    2. Roger Beecham & Jo Wood, 2014. "Exploring gendered cycling behaviours within a large-scale behavioural data-set," Transportation Planning and Technology, Taylor & Francis Journals, vol. 37(1), pages 83-97, February.
    3. Donghwa Shon & Seungbum Kim & Nahyang Byun, 2022. "Derivation Method of Architectural Asset Value Enhancement Zones in South Korea," Land, MDPI, vol. 11(4), pages 1-22, April.
    4. Ann Shawing Yang, 2015. "Lottery Payment Cards: A Study of Mental Accounting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(3), pages 201-226, July.
    5. Hiroaki Nishiuchi & Yasuyuki Kobayashi & Tomoyuki Todoroki & Tomoya Kawasaki, 2018. "Impact analysis of reductions in tram services in rural areas in Japan using smart card data," Public Transport, Springer, vol. 10(2), pages 291-309, August.
    6. Bernal, Margarita & Welch, Eric W. & Sriraj, P.S., 2016. "The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line," Transportation Research Part A: Policy and Practice, Elsevier, vol. 87(C), pages 11-21.
    7. Renee Zahnow & Jonathan Corcoran, 2021. "Crime and bus stops: An examination using transit smart card and crime data," Environment and Planning B, , vol. 48(4), pages 706-723, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Antonio Páez, 2021. "Open spatial sciences: an introduction," Journal of Geographical Systems, Springer, vol. 23(4), pages 467-476, October.
    2. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    3. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    4. Benito Zaragozí & Sergio Trilles & Aaron Gutiérrez & Daniel Miravet, 2021. "Development of a Common Framework for Analysing Public Transport Smart Card Data," Energies, MDPI, vol. 14(19), pages 1-22, September.
    5. Qingru Zou & Xiangming Yao & Peng Zhao & Heng Wei & Hui Ren, 2018. "Detecting home location and trip purposes for cardholders by mining smart card transaction data in Beijing subway," Transportation, Springer, vol. 45(3), pages 919-944, May.
    6. De Zhao & Wei Wang & Amber Woodburn & Megan S. Ryerson, 2017. "Isolating high-priority metro and feeder bus transfers using smart card data," Transportation, Springer, vol. 44(6), pages 1535-1554, November.
    7. Amarin Siripanich & Taha Hossein Rashidi & Emily Moylan, 2019. "Interaction of Public Transport Accessibility and Residential Property Values Using Smart Card Data," Sustainability, MDPI, vol. 11(9), pages 1-24, May.
    8. Bernal, Margarita & Welch, Eric W. & Sriraj, P.S., 2016. "The effect of slow zones on ridership: An analysis of the Chicago Transit Authority “El” Blue Line," Transportation Research Part A: Policy and Practice, Elsevier, vol. 87(C), pages 11-21.
    9. Sung-Pil Hong & Yun-Hong Min & Myoung-Ju Park & Kyung Min Kim & Suk Mun Oh, 2016. "Precise estimation of connections of metro passengers from Smart Card data," Transportation, Springer, vol. 43(5), pages 749-769, September.
    10. Amaya, Margarita & Cruzat, Ramón & Munizaga, Marcela A., 2018. "Estimating the residence zone of frequent public transport users to make travel pattern and time use analysis," Journal of Transport Geography, Elsevier, vol. 66(C), pages 330-339.
    11. Zijia Wang & Hao Tang & Wenjuan Wang & Yang Xi, 2020. "The Pattern of Non-Roundtrip Travel on Urban Rail and Its Application in Transit Improvement," Sustainability, MDPI, vol. 12(9), pages 1-16, April.
    12. Md. Kamruzzaman & Tan Yigitcanlar & Jay Yang & Mohd Afzan Mohamed, 2016. "Measures of Transport-Related Social Exclusion: A Critical Review of the Literature," Sustainability, MDPI, vol. 8(7), pages 1-30, July.
    13. Zhang, Shanqi & Yang, Yu & Zhen, Feng & Lobsang, Tashi & Li, Zhixuan, 2021. "Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach," Journal of Transport Geography, Elsevier, vol. 90(C).
    14. Luc Anselin, 2012. "From SpaceStat to CyberGIS," International Regional Science Review, , vol. 35(2), pages 131-157, April.
    15. Ying Song & Yingling Fan & Xin Li & Yanjie Ji, 2018. "Multidimensional visualization of transit smartcard data using space–time plots and data cubes," Transportation, Springer, vol. 45(2), pages 311-333, March.
    16. Xingjian Liu & James LeSage, 2010. "Arc_Mat: a Matlab-based spatial data analysis toolbox," Journal of Geographical Systems, Springer, vol. 12(1), pages 69-87, March.
    17. repec:asg:wpaper:1047 is not listed on IDEAS
    18. Agarwal, Sumit & Diao, Mi & Keppo, Jussi & Sing, Tien Foo, 2020. "Preferences of public transit commuters: Evidence from smart card data in Singapore," Journal of Urban Economics, Elsevier, vol. 120(C).
    19. Cecilia Viggiano & Haris N. Koutsopoulos & Nigel H. M. Wilson & John Attanucci, 2017. "Journey-based characterization of multi-modal public transportation networks," Public Transport, Springer, vol. 9(1), pages 437-461, July.
    20. Takahiko Kusakabe & Takamasa Iryo & Yasuo Asakura, 2010. "Estimation method for railway passengers’ train choice behavior with smart card transaction data," Transportation, Springer, vol. 37(5), pages 731-749, September.
    21. Huanfa Chen & Alan T. Murray & Rui Jiang, 2021. "Open-source approaches for location cover models: capabilities and efficiency," Journal of Geographical Systems, Springer, vol. 23(3), pages 361-380, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:45:y:2011:i:7:p:640-652. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.