IDEAS home Printed from https://ideas.repec.org/p/col/000089/006884.html

Evaluación de impacto de las fases I y II del sistema de transporte masivo TransMilenio sobre el tiempo total de desplazamiento de los usuarios del tr

Author

Listed:
  • Jorge Andr√©s Perdomo Calvo
  • Hasbleidy CastaÔøΩeda
  • Juan Carlo Mendieta

Abstract

El transporte público de Bogotá, durante décadas, ha representado uno de los grandes problemas por resolver en la ciudad. Con el fin de mejorar su eficiencia, fue implementado el sistema de transporte masivo TransMilenio (TM); cuyo objetivo principal, consiste en disminuir el tiempo total de viaje (acceso, espera y desplazamiento dentro del vehículo) para los usuarios. Por consiguiente, el presente trabajo busca evidenciar esta premisa empleando la metodología no paramétrica Propensity Score Matching (PSM, nombre y sigla en inglés); a partir de los datos compilados en la encuesta de movilidad bogotana, realizada durante 2005. Esta técnica, permitió comparar el promedio del tiempo total de viaje en dos grupos de individuos con características socioeconómicas homogéneas, diferenciados por el modo de transporte utilizado. El soporte común de ambas muestras, manejado en PSM, se estableció bajo el criterio que tiene cada individuo para acceder al sistema TransMilenio o transporte público colectivo (TPC), dado que la decisión de realizar el viaje es mutuamente excluyente y gozan con el privilegio de los dos servicios simultáneamente o misma probabilidad de consumirlos. De esta forma, el grupo detratamiento es representado por las personas encuestadas que se transportan en TM y a su vez tienen la misma oportunidad de tomar TPC para desplazarse; y el grupo de control, se constituyó de aquellos entrevistados trasladados en TPC y cuentan con la alternativa de realizar su viaje en TM. Entre los principales resultados, se destaca que el sistema TransMilenio ha reducido el promedio del tiempo total de viaje entre 11,92 y 13,89 minutos, para un usuario de TM; estos valores, equivalen a una disminución del 19% aproximadamente. El mayor impacto, fue observado sobre los individuos de estratos uno y dos, quienes obtuvieron una reducción de 24,5%; dado que realizan susdesplazamientos en TM.

Suggested Citation

  • Jorge Andr√©s Perdomo Calvo & Hasbleidy CastaÔøΩeda & Juan Carlo Mendieta, 2010. "Evaluaci√≥n de impacto de las fases I y II del sistema de transporte masivo TransMilenio sobre el tiempo total de desplazamiento de los usuarios del tr," Documentos CEDE 6884, Universidad de los Andes, Facultad de Economía, CEDE.
    • Handle: RePEc:col:000089:006884
    as

    Download full text from publisher

    File URL: https://repositorio.uniandes.edu.co/bitstream/handle/1992/8157/dcede2010-11.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    2. Daniel P. McMillen & John F. McDonald, 2002. "Land Values In A Newly Zoned City," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 62-72, February.
    3. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    4. Juan Carlos Mendieta & Jorge AndrÔøΩs Perdomo, 2007. "Especificaci√≥n y estimaci√≥n de un modelo de precios hed√≥nico espacial para evaluar el impacto de Transmilenio sobre el valor de la propiedad en Bogot√°," Documentos CEDE 4280, Universidad de los Andes, Facultad de Economía, CEDE.
    5. Irma Chaparro, 2002. "Evaluación del impacto socio económico del transporte urbano en la ciudad de Bogotá. El caso del sistema de transporte masivo, Transmilenio," Documentos de Investigación 2640, Cepal Naciones Unidas.
    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. Somik Vinay Lall & J. Vernon Henderson & Anthony J. Venables & Paolo Avner, 2017. "Africa's Cities," World Bank Publications - Books, The World Bank Group, number 25896, April.
    2. Jorge Perdomo & Maria Arzuza, 2014. "Beneficios económicos de Transmetro sobre la reducción de la accidentalidad vial en el área metropolitana de Barranquilla, Colombia," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 82, pages 219-245.
    3. Jorge Andres, Perdomo Calvo & Jorge Andres, Perdomo Calvo, 2010. "Una propuesta metodológica para estimar los cambios sobre el valor de la propiedad: estudio de caso para Bogotá aplicando Propensity Score Matching y Precios Hedónicos Espaciales [A Propensity Score Matching and Spatial Hedonic Prices Approach for," MPRA Paper 37178, University Library of Munich, Germany.
    4. Juan Carlos Rodríguez Marín & Jhon Alexis D�az Contreras, 2014. "Evaluación de impacto del sistema de transporte Metrolínea: revisión de metodologías," Revista Equidad y Desarrollo, Universidad de la Salle.

    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. James R. Meldrum, 2016. "Floodplain Price Impacts by Property Type in Boulder County, Colorado: Condominiums Versus Standalone Properties," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 64(4), pages 725-750, August.
    2. Jorge Perdomo & Maria Arzuza, 2014. "Beneficios económicos de Transmetro sobre la reducción de la accidentalidad vial en el área metropolitana de Barranquilla, Colombia," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 82, pages 219-245.
    3. Paolo Naticchioni & Silvia Loriga, 2011. "Short and Long Term Evaluations of Public Employment Services in Italy," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 57(3), pages 201-229.
    4. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    5. Balima, Wenéyam Hippolyte & Combes, Jean-Louis & Minea, Alexandru, 2017. "Sovereign debt risk in emerging market economies: Does inflation targeting adoption make any difference?," Journal of International Money and Finance, Elsevier, vol. 70(C), pages 360-377.
    6. Federico Biagi & Daniele Bondonio & Alberto Martini, 2015. "Counterfactual Impact Evaluation of Enterprise Support Programmes. Evidence from a Decade of Subsidies to Italian Firm," ERSA conference papers ersa15p1619, European Regional Science Association.
    7. Marco Vega & Diego Winkelried, 2005. "Inflation Targeting and Inflation Behavior: A Successful Story?," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
    8. Apps, Patricia & Mendolia, Silvia & Walker, Ian, 2013. "The impact of pre-school on adolescents’ outcomes: Evidence from a recent English cohort," Economics of Education Review, Elsevier, vol. 37(C), pages 183-199.
    9. Slottje, Daniel J. & Millimet, Daniel L. & Buchanan, Michael J., 2007. "Econometric analysis of copyrights," Journal of Econometrics, Elsevier, vol. 139(2), pages 303-317, August.
    10. Begum, Ismat & Alam, Mohammad & Haque, M., 2015. "Productive Impacts of Cash Transfer and Conditional Cash Transfer Programs in Bangladesh: Propensity Score Matching Analysisi," 2015 Conference, August 9-14, 2015, Milan, Italy 211215, International Association of Agricultural Economists.
    11. Gregory Berg, 2011. "An application of kernel-based versus one-to-one propensity score matching for a nonexperimental causal study: example from a disease management program evaluation," Applied Economics Letters, Taylor & Francis Journals, vol. 18(5), pages 439-447.
    12. Hämäläinen, Kari & Ollikainen, Virve, 2004. "Differential Effects of Active Labour Market Programmes in the Early Stages of Young People's Unemployment," Research Reports 115, VATT Institute for Economic Research.
    13. Tapsoba, René, 2012. "Do National Numerical Fiscal Rules really shape fiscal behaviours in developing countries? A treatment effect evaluation," Economic Modelling, Elsevier, vol. 29(4), pages 1356-1369.
    14. Aregash Getachew Hailu & Zerihun Yohannes Amare, 2022. "Impact of productive safety net program on food security of beneficiary households in western Ethiopia: A matching estimator approach," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-17, January.
    15. Yechan Park & Yuya Sasaki, 2024. "Matching $\leq$ Hybrid $\leq$ Difference in Differences," Papers 2411.07952, arXiv.org, revised Feb 2025.
    16. Fatema, Naureen, 2019. "Can land title reduce low-intensity interhousehold conflict incidences and associated damages in eastern DRC?," World Development, Elsevier, vol. 123(C), pages 1-1.
    17. Mathieu Bunel & Gilles Raveaud, 2011. "Union Membership does not pay," Working Papers halshs-00868217, HAL.
    18. Guerguil, Martine & Mandon, Pierre & Tapsoba, René, 2017. "Flexible fiscal rules and countercyclical fiscal policy," Journal of Macroeconomics, Elsevier, vol. 52(C), pages 189-220.
    19. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
    20. von Greiff, Jenny, 2009. "Displacement and self-employment entry," Labour Economics, Elsevier, vol. 16(5), pages 556-565, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • L92 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Railroads and Other Surface Transportation
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy

    Statistics

    Access and download statistics

    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:col:000089:006884. 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: Universidad De Los Andes-Cede (email available below). General contact details of provider: https://edirc.repec.org/data/ceandco.html .

    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.