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

  • Páez, Antonio
  • Trépanier, Martin
  • Morency, Catherine
Registered author(s):

    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.

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    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 45 (2011)
    Issue (Month): 7 (August)
    Pages: 640-652

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    Handle: RePEc:eee:transa:v:45:y:2011:i:7:p:640-652
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    1. 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.
    2. 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.
    3. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    4. 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.
    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. Anthony May & Simon Shepherd & Paul Timms, 2000. "Optimal transport strategies for European cities," Transportation, Springer, vol. 27(3), pages 285-315, June.
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    10. Xiaobai Yao & Bin Jiang, 2009. "Geospatial modeling of urban environments," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 36(5), pages 769-771, September.
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