IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v29y2013icp178-185.html
   My bibliography  Save this article

Aggregate estimation of the price elasticity of demand for public transport in integrated fare systems: The case of Transantiago

Author

Listed:
  • de Grange, Louis
  • González, Felipe
  • Muñoz, Juan Carlos
  • Troncoso, Rodrigo

Abstract

Price elasticities of demand for public transport are a key determinant in evaluating the impact of changes in fares on user flows, yet in many integrated fare transit systems, estimating these indicators is often hampered by two realities: the fare changes for different modes are implemented simultaneously and their magnitudes are highly correlated. This strong collinearity is particularly problematic in linear or log-linear models, commonly used for elasticity estimation, and in a case study of Santiago, Chile, robust results with such specifications proved elusive. This paper presents a method based on discrete choice models to estimate the elasticities in an integrated fare system that overcomes these econometric problems, generating results that are both robust and consistent with those reported in the literature. The proposed models are also easy to update and evaluate.

Suggested Citation

  • de Grange, Louis & González, Felipe & Muñoz, Juan Carlos & Troncoso, Rodrigo, 2013. "Aggregate estimation of the price elasticity of demand for public transport in integrated fare systems: The case of Transantiago," Transport Policy, Elsevier, vol. 29(C), pages 178-185.
  • Handle: RePEc:eee:trapol:v:29:y:2013:i:c:p:178-185
    DOI: 10.1016/j.tranpol.2013.06.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X13000899
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tranpol.2013.06.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Bresson, Georges & Dargay, Joyce & Madre, Jean-Loup & Pirotte, Alain, 2003. "The main determinants of the demand for public transport: a comparative analysis of England and France using shrinkage estimators," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(7), pages 605-627, August.
    2. H C W L Williams, 1977. "On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit," Environment and Planning A, , vol. 9(3), pages 285-344, March.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    4. Peter Romilly, 2001. "Subsidy and Local Bus Service Deregulation in Britain: A Re-evaluation," Journal of Transport Economics and Policy, University of Bath, vol. 35(2), pages 161-193, May.
    5. Paulley, Neil & Balcombe, Richard & Mackett, Roger & Titheridge, Helena & Preston, John & Wardman, Mark & Shires, Jeremy & White, Peter, 2006. "The demand for public transport: The effects of fares, quality of service, income and car ownership," Transport Policy, Elsevier, vol. 13(4), pages 295-306, July.
    6. Taplin, John H.E. & Hensher, David A. & Smith, Brett, 1999. "Preserving the symmetry of estimated commuter travel elasticities," Transportation Research Part B: Methodological, Elsevier, vol. 33(3), pages 215-232, April.
    7. Joyce M. Dargay & Mark Hanly, 2002. "The Demand for Local Bus Services in England," Journal of Transport Economics and Policy, University of Bath, vol. 36(1), pages 73-91, January.
    8. Kain, John F. & Liu, Zvi, 1999. "Secrets of success: assessing the large increases in transit ridership achieved by Houston and San Diego transit providers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(7-8), pages 601-624.
    9. Joaquin De Cea & J. Enrique Fernandez & Louis De Grange, 2007. "Combined Models with Hierarchical Demand Choices: A Multi‐Objective Entropy Optimization Approach," Transport Reviews, Taylor & Francis Journals, vol. 28(4), pages 415-438, October.
    10. Bhat, Chandra R. & Guo, Jessica, 2004. "A mixed spatially correlated logit model: formulation and application to residential choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 38(2), pages 147-168, February.
    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. Guzman, Luis A. & Beltran, Carlos & Bonilla, Jorge & Gomez Cardona, Santiago, 2021. "BRT fare elasticities from smartcard data: Spatial and time-of-the-day differences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 335-348.
    2. Ramos, Raúl & Silva, Hugo E., 2023. "Fare evasion in public transport: How does it affect the optimal design and pricing?," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    3. Kamel, Islam & Shalaby, Amer & Abdulhai, Baher, 2020. "A modelling platform for optimizing time-dependent transit fares in large-scale multimodal networks," Transport Policy, Elsevier, vol. 92(C), pages 38-54.
    4. Chitresh KUMAR & Anirban GANGULY, 2018. "Travelling Together But Differently: Comparing Variations In Public Transit User Mode Choice Attributes Across New Delhi And New York," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 13(3), pages 54-73, August.
    5. Martin Besfamille & Nicolás Figueroa & León Guzmán, 2023. "Ramsey pricing revisited: Natural monopoly regulation with evaders," Documentos de Trabajo 576, Instituto de Economia. Pontificia Universidad Católica de Chile..
    6. Chen, Ruoyu & Zhou, Jiangping, 2022. "Fare adjustment’s impacts on travel patterns and farebox revenue: An empirical study based on longitudinal smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 111-133.
    7. Thommen, Christoph & Hintermann, Beat, 2023. "Price versus Commitment: Managing the demand for off-peak train tickets in a field experiment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    8. Tirachini, Alejandro & Proost, Stef, 2021. "Transport taxes and subsidies in developing countries: The effect of income inequality aversion," Economics of Transportation, Elsevier, vol. 25(C).
    9. Avoine, Gildas & Calderoni, Luca & Delvaux, Jonathan & Maio, Dario & Palmieri, Paolo, 2014. "Passengers information in public transport and privacy: Can anonymous tickets prevent tracking?," International Journal of Information Management, Elsevier, vol. 34(5), pages 682-688.
    10. Youzhi Zeng & Bin Ran & Ning Zhang & Xiaobao Yang, 2021. "Estimating the Price Elasticity of Train Travel Demand and Its Variation Rules and Application in Energy Used and CO 2 Emissions," Sustainability, MDPI, vol. 13(2), pages 1-19, January.
    11. Chowdhury, Subeh & Ceder, Avishai (Avi), 2016. "Users’ willingness to ride an integrated public-transport service: A literature review," Transport Policy, Elsevier, vol. 48(C), pages 183-195.
    12. 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.
    13. Hao Li & Xiaohui Yang & Xiao Zhang & Yuyan Liu & Kebin Zhang, 2018. "Estimation of Rural Households’ Willingness to Accept Two PES Programs and Their Service Valuation in the Miyun Reservoir Catchment, China," Sustainability, MDPI, vol. 10(1), pages 1-19, January.
    14. Wenshuang, Yu & Lindsay M., Tedds & Gillian, Petit, 2022. "Assessing Trends and Patterns of the Effect of COVID-19 on Public Transit Revenues in the City of Calgary," MPRA Paper 115350, University Library of Munich, Germany.
    15. Heike Link & Dennis Gaus & Neil Murray & Maria Fernanda Guajardo Ortega & Flavien Gervois & Frederik von Waldow & Sofia Eigner, 2023. "Combining GPS Tracking and Surveys for a Mode Choice Model: Processing Data from a Quasi-Natural Experiment in Germany," Discussion Papers of DIW Berlin 2047, DIW Berlin, German Institute for Economic Research.
    16. Hintermann, Beat & Thommen, Christoph, 2022. "Price versus Commitment: Managing the Demand for Off-peak Train Tickets in a Field Experiment," Working papers 2022/05, Faculty of Business and Economics - University of Basel.
    17. Rahman, Syed & Balijepalli, Chandra, 2016. "Understanding the determinants of demand for public transport: Evidence from suburban rail operations in five divisions of Indian Railways," Transport Policy, Elsevier, vol. 48(C), pages 13-22.
    18. Zi-jia Wang & Feng Chen & Bo Wang & Jian-ling Huang, 2018. "Passengers’ response to transit fare change: an ex post appraisal using smart card data," Transportation, Springer, vol. 45(5), pages 1559-1578, September.
    19. Antonín Pavlíček & František Sudzina, 2020. "Intergroup Comparison of Personalities in the Preferred Pricing of Public Transport in Rush Hours: Data Revisited," Sustainability, MDPI, vol. 12(12), pages 1-9, June.
    20. Luis A. Guzman & Santiago Gomez & Carlos Alberto Moncada, 2020. "Short run fare elasticities for Bogotá’s BRT system: ridership responses to fare increases," Transportation, Springer, vol. 47(5), pages 2581-2599, October.

    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. Louis Grange & Felipe González & Ignacio Vargas & Rodrigo Troncoso, 2015. "A Logit Model With Endogenous Explanatory Variables and Network Externalities," Networks and Spatial Economics, Springer, vol. 15(1), pages 89-116, March.
    2. Philippe Gagnepain & Marc Ivaldi & Catherine Muller-Vibes, 2011. "The Industrial Organization of Competition in Local Bus Services," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 32, Edward Elgar Publishing.
    3. Wang, Jiangbo & Yamamoto, Toshiyuki & Liu, Kai, 2021. "Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models," Transport Policy, Elsevier, vol. 105(C), pages 166-180.
    4. Crôtte, Amado & Noland, Robert B. & Graham, Daniel J., 2009. "Is the Mexico City metro an inferior good?," Transport Policy, Elsevier, vol. 16(1), pages 40-45, January.
    5. Fullerton, Thomas M. Jr & Walke, Adam G., 2012. "Border Zone Mass Transit Demand in Brownsville and Laredo," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 51(2).
    6. Zi-jia Wang & Feng Chen & Bo Wang & Jian-ling Huang, 2018. "Passengers’ response to transit fare change: an ex post appraisal using smart card data," Transportation, Springer, vol. 45(5), pages 1559-1578, September.
    7. Michaelides, Panayotis G. & Konstantakis, Konstantinos N. & Milioti, Christina & Karlaftis, Matthew G., 2015. "Modelling spillover effects of public transportation means: An intra-modal GVAR approach for Athens," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 1-18.
    8. Rahman, Syed & Balijepalli, Chandra, 2016. "Understanding the determinants of demand for public transport: Evidence from suburban rail operations in five divisions of Indian Railways," Transport Policy, Elsevier, vol. 48(C), pages 13-22.
    9. Kaushik Deb & Massimo Filippini, 2013. "Public Bus Transport Demand Elasticities in India," Journal of Transport Economics and Policy, University of Bath, vol. 47(3), pages 419-436, September.
    10. Wang, Zi-jia & Li, Xiao-hong & Chen, Feng, 2015. "Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 213-224.
    11. Souche, Stéphanie, 2010. "Measuring the structural determinants of urban travel demand," Transport Policy, Elsevier, vol. 17(3), pages 127-134, May.
    12. Stephane Hess & Denis Bolduc & John Polak, 2010. "Random covariance heterogeneity in discrete choice models," Transportation, Springer, vol. 37(3), pages 391-411, May.
    13. Manel Daldoul & Sami Jarboui & Ahlem Dakhlaoui, 2016. "Public transport demand: dynamic panel model analysis," Transportation, Springer, vol. 43(3), pages 491-505, May.
    14. Guzman, Luis A. & Beltran, Carlos & Bonilla, Jorge & Gomez Cardona, Santiago, 2021. "BRT fare elasticities from smartcard data: Spatial and time-of-the-day differences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 335-348.
    15. Gkritza, Konstantina & Karlaftis, Matthew G. & Mannering, Fred L., 2011. "Estimating multimodal transit ridership with a varying fare structure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 148-160, February.
    16. Milioti, Christina P. & Karlaftis, Matthew G., 2014. "Estimating multimodal public transport mode shares in Athens, Greece," Journal of Transport Geography, Elsevier, vol. 34(C), pages 88-95.
    17. Yaman, Firat & Offiaeli, Kingsley, 2022. "Is the price elasticity of demand asymmetric? Evidence from public transport demand," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 318-335.
    18. Weiss, Adam & Habib, Khandker Nurul, 2017. "Examining the difference between park and ride and kiss and ride station choices using a spatially weighted error correlation (SWEC) discrete choice model," Journal of Transport Geography, Elsevier, vol. 59(C), pages 111-119.
    19. Gopalakrishnan, Raja & Guevara, C. Angelo & Ben-Akiva, Moshe, 2020. "Combining multiple imputation and control function methods to deal with missing data and endogeneity in discrete-choice models," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 45-57.
    20. de Jong, Gerard & Daly, Andrew & Pieters, Marits & van der Hoorn, Toon, 2007. "The logsum as an evaluation measure: Review of the literature and new results," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(9), pages 874-889, November.

    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:trapol:v:29:y:2013:i:c:p:178-185. 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/30473/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.