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Protocol for Comprehensive Efficiency Analysis of Multi-Service Metropolitan Transit Agency Operators

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Managing Service Productivity

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 215))

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Abstract

This chapter presents a Data Envelopment Analysis (DEA) protocol for analyzing the efficiency of metropolitan transit agencies that oversee multiple types of transportation services. The protocol is illustrated by applying it to United States transit agencies that can serve their cities with four types of subunits: self-operated motorbus, outsourced motorbus, self-operated demand-responsive, and outsourced demand-responsive. Using DEA models adapted for non-substitutable inputs and outputs, scores estimated for a focus agency include: (1) technical efficiency of the focus agency as a whole, (2) technical efficiency of each of the focus agency’s subunit types when each subunit is compared only to others of the same type, (3) allocation efficiency of the focus agency in apportioning resources among its subunits, and (4) the effect of each subunit’s technical efficiency on its parent agency’s technical efficiency. Finally, a mathematical programming algorithm is illustrated that allocates the focus agency’s resources to its subunits with the objective of decreasing the cost of transit in an urban area while holding total ridership constant. The protocol thereby is a comprehensive analysis and synthesis of a focus transit agency’s efficiency in providing services to its metropolitan area.

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References

  • Allen, R., Athanassopoulos, A. D., Dyson, R. G., & Thanassoulis, E. (1997). Weight restrictions and value judgements in DEA: Evolution, development and future directions. Annals of Operations Research, 73, 13–34.

    Article  Google Scholar 

  • Banker, R. D., & Maindiratta, A. (1986). Piecewise loglinear estimation of efficient production surfaces. Management Science, 32(1), 126–135.

    Article  Google Scholar 

  • Barnum, D. T., & Gleason, J. M. (2006a). Biases in technical efficiency scores caused by intra-input aggregation: Mathematical analysis and a DEA application using simulated data. Applied Economics, 38, 1593–1603.

    Article  Google Scholar 

  • Barnum, D. T., & Gleason, J. M. (2006b). Measuring efficiency in allocating inputs among outputs with DEA. Applied Economics Letters, 13(6), 333–336.

    Article  Google Scholar 

  • Barnum, D. T., & Gleason, J. M. (2010). DEA efficiency analysis involving multiple production processes. Applied Economics Letters, 17(7), 627–632.

    Article  Google Scholar 

  • Barnum, D., & Gleason, J. (2011). Measuring efficiency under fixed proportion technologies. Journal of Productivity Analysis, 35(3), 243–262.

    Article  Google Scholar 

  • Barnum, D. T., Karlaftis, M. G., & Tandon, S. (2011). Improving the efficiency of metropolitan area transit by joint analysis of its multiple providers. Transportation Research Part E – Logistics and. Transportation, 47, 1160–1176.

    Google Scholar 

  • Beattie, B. R., & Taylor, C. R. (1985). The economics of production. New York: Wiley.

    Google Scholar 

  • Bogetoft, P. (1996). DEA on relaxed convexity assumptions. Management Science, 42(3), 457.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

    Article  Google Scholar 

  • Cooper, W. W., Seiford, L. M., Tone, K., & Zhu, J. (2007). Some models and measures for evaluating performances with DEA: Past accomplishments and future prospects. Journal of Productivity Analysis, 28(3), 151–163.

    Article  Google Scholar 

  • De Witte, K., & Marques, R. C. (2008). Big and beautiful? On non-parametrically measuring scale economies in non-convex technologies. http://www.econ.kuleuven.be/eng/ew/discussionpapers/Dps08/Dps0822.pdf

  • Dekker, D., & Post, T. (2001). A quasi-concave DEA model with an application for bank branch performance evaluation. European Journal of Operational Research, 132(2), 296–311.

    Article  Google Scholar 

  • Färe, R., & Grosskopf, S. (2004). New directions: Efficiency and productivity. Boston: Kluwer Academic.

    Google Scholar 

  • Färe, R., Grosskopf, S., & Lovell, C. A. K. (1994). Production frontiers. Cambridge: Cambridge University Press.

    Google Scholar 

  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, 120(3), 253–290.

    Article  Google Scholar 

  • Fausset, R. (2009). A mass transit dilemma: Ridership up, funds down. In Los Angeles Times.

    Google Scholar 

  • Fried, H. O., Lovell, C. A. K., & Schmidt, S. S. (2008). The measurement of productive efficiency and productivity growth. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Golub, G. H., & Van Loan, C. F. (1989). Matrix computations. Baltimore, MD: Johns Hopkins University Press.

    Google Scholar 

  • Karlaftis, M. G., Gleason, J. M., Barnum, D. T. (2013). Bibliography of urban transit DEA publications. Social Science Research Network. [01 April 2013]. http://ssrn.com/abstract=1350583, http://dx.doi.org/10.2139/ssrn.1350583

  • Perk, V., & Kamp, N. (2004). Benchmark rankings for transit systems in the United States, National Center for Transit Research, University of South Florida.

    Google Scholar 

  • Petersen, N. C. (1990). Data Envelopment Analysis on a relaxed set of assumptions. Management Science, 36(3), 305.

    Article  Google Scholar 

  • Post, T. (2001a). Estimating non-convex production sets - imposing convex input sets and output sets in data envelopment analysis. European Journal of Operational Research, 131(1), 132–142.

    Article  Google Scholar 

  • Post, T. (2001b). Transconcave data envelopment analysis. European Journal of Operational Research, 132(2), 374–389.

    Article  Google Scholar 

  • Roll, Y., & Golany, B. (1993). Alternate methods of treating factor weights in DEA. Omega, 21(1), 99–109.

    Article  Google Scholar 

  • Stanley, R. G., & Hendren, P. G. (2004). Performance-based measures in transit fund allocation: A synthesis of transit practice. Transit Cooperative Research Program Synthesis 56, Transportation Research Board

    Google Scholar 

  • Thanassoulis, E., Portela, M. C., & Allen, R. (2004). Incorporating Value Judgements in DEA. In W. W. S. Cooper, M. Lawrence, & J. Zhu (Eds.), Handbook on Data Envelopment Analysis. Boston, MA: Kluwer Academic.

    Google Scholar 

  • United_States_Federal_Transit_Administration. (1997–2012). National Transit Database, United States Federal Transit Administration.

    Google Scholar 

  • Washington, S., Karlaftis, M. G., & Mannering, F. L. (2010). Statistical and econometric methods for transportation data analysis (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Managerial Studies, University of Illinois at Chicago (MC 243), 601 South Morgan Street, Chicago, IL, 60607-7123, USA

    Darold T Barnum

  2. Department of Information & Decision Sciences, University of Illinois at Chicago (MC 243), 601 South Morgan Street, Chicago, IL, 60607-7123, USA

    Darold T Barnum

  3. Department of Pharmacy Administration, University of Illinois at Chicago (MC 243), 601 South Morgan Street, Chicago, IL, 60607-7123, USA

    Darold T Barnum

  4. Department of Business Intelligence and Analytics, College of Business Administration, Creighton University, Omaha, NE, USA

    John M Gleason

  5. Department of Transportation Planning Engineering, School of Civil Engineering, National Technical University of Athens, Athens, Greece

    Matthew G Karlaftis

Authors
  1. Darold T Barnum
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  2. John M Gleason
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  3. Matthew G Karlaftis
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Corresponding author

Correspondence to Darold T Barnum .

Editor information

Editors and Affiliations

  1. Operations and Information Management Group, Aston Business School, Aston University, Birmingham, United Kingdom

    Ali Emrouznejad

  2. School of Business and Leadership, Regent University, Virginia Beach, Virginia, USA

    Emilyn Cabanda

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Barnum, D.T., Gleason, J.M., Karlaftis, M.G. (2014). Protocol for Comprehensive Efficiency Analysis of Multi-Service Metropolitan Transit Agency Operators. In: Emrouznejad, A., Cabanda, E. (eds) Managing Service Productivity. International Series in Operations Research & Management Science, vol 215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43437-6_16

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