Comparing the Performance of Bus Routes after Adjusting for the Environment Using Data Envelopment Analysis
Publication: Journal of Transportation Engineering
Volume 134, Issue 2
Abstract
Public transit managers strive to attain multiple goals with tightly constrained resources. Ratio analysis has evolved into a powerful tool for dealing with these goals and constraints. Ratio analysis provides analytical methods for comparing the performance of multiple agencies, as well as the performance of subunits within a particular agency, in order to identify opportunities for improvement. One ratio analysis procedure that has become increasingly popular is data envelopment analysis (DEA). DEA yields a single, comprehensive measure of performance, the ratio of the aggregated, weighted outputs to aggregated, weighted inputs. This paper makes two contributions to the practice of transit performance evaluation using DEA. First, instead of using DEA to compare the performance of multiple transit systems, it uses DEA to compare the performance of multiple bus routes of one transit system. Second, it introduces a new procedure for adjusting the raw DEA scores that modifies these scores to account for the environmental influences that are beyond the control of the transit agency.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The writers thank the three anonymous reviewers, whose detailed comments and perceptive suggestions greatly improved their work. Although they made many changes based on reviewer suggestions, the ones that were particularly significant included: (1) controlling for each route’s home garage in the Eq. (5) regression; (2) adding the subsection entitled “Influence of Other Organizational Levels on Organizational Subunits”; (3) conducting tests for seemingly unrelated regressions and econometric endogeneity, leading to stronger confirmation of the validity of the statistical models; and (4) adding a description of the method by which DEA assigns weights, which is one of its most important strengths. They also gratefully acknowledge the support and cooperation of the transit agency who provided the data and worked with them to suggest appropriate inputs and outputs.
References
Barnum, D. T. (1987). “Evaluating transit: The performance tree method.” Proc., Managing Urban Transportation as a Business, E. Bers and C. Hendrickson, eds., ASCE, New York, 216–230.
Barnum, D. T., and Gleason, J. M. (2006). “Biases in technical efficiency scores caused by intra-input aggregation: Mathematical analysis and a DEA application using simulated data.” Appl. Econom., 38, 1593–1603.
Barnum, D. T., and Gleason, J. M. (2007). “Bias and precision in the DEA two-stage method.” Appl. Econom., in press.
Barnum, D. T., Gleason, J. M., and Hemily, B. (2007a). “Estimating confidence intervals for DEA scores with panel data analysis.” Appl. Econom., in press.
Barnum, D. T., McNeil, S., and Hart, J. (2007b). “Comparing the efficiency of public transportation subunits using data envelopment analysis.” J. Public Transportation, 10(2), 1–16.
Benn, H. P. (1995). Bus route evaluation standards, Transit Cooperative Research Program, Synthesis of Transit Practice 10, Transportation Research Board, Washington, D.C.
Bertini, R. L., and El-Geneidy, A. M. (2004). “Modeling transit trip time using archived bus dispatch system data.” J. Transp. Eng., 130(1), 56–67.
Boame, A. K. (2004). “The technical efficiency of Canadian urban transit systems.” Transp. Res. Part E, 40(5), 401–416.
Boilé, M. P. (2001). “Estimating technical and scale inefficiencies of public transit systems.” J. Transp. Eng., 127(3), 187–194.
Breen, R. (1996). Regression models: Censored, sample-selected, or truncated data, Sage, Thousand Oaks, Calif.
Brons, M., Nijkamp, P., Pels, E., and Rietveld, P. (2005). “Efficiency of urban public transit: A meta analysis.” Transp. J., 32(1), 1–21.
Chu, X., Fielding, G. J., and Lamar, B. W. (1992). “Measuring transit performance using data envelopment analysis.” Transp. Res., Part A: Policy Pract., 26A(3), 223–230.
Coelli, T. J., Rao, D. S. P., O’Donnell, C. J., and Battese, G. E. (2005). An introduction to efficiency and productivity analysis, Springer, New York.
Cooper, W. W., Seiford, L. M., and Tone, K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software, Kluwer, Boston.
Cooper, W. W., Seiford, L. M., and Zhu, J. (2004). Handbook on data envelopment analysis, Kluwer Academic, Boston.
De Borger, B., Kerstens, K., and Costa, A. (2002). “Public transit performance: What does one learn from frontier studies?” Transport Rev., 22(1), 1–38.
Fan, W., and Machemehl, R. B. (2006a). “Optimal transit route network design problem with variable transit demand: Genetic algorithm approach.” J. Transp. Eng., 132(1), 40–51.
Fan, W., and Machemehl, R. B. (2006b). “Using a simulated annealing algorithm to solve the transit route network design problem.” J. Transp. Eng., 132(2), 122–132.
Färe, R., and Grosskopf, S. (2004). New directions: Efficiency and productivity, Kluwer Academic, Boston.
Färe, R., Grosskopf, S., and Lovell, C. A. K. (1994). Production frontiers, Cambridge University Press, Cambridge, U.K.
Gleason, J. M., and Barnum, D. T. (1982). “Toward valid measures of public sector productivity: Performance indicators in urban transit.” Manage. Sci., 28(4), 379–386.
Graham, D. J. (2006). “Productivity and efficiency in urban railways: Parametric and non-parametric estimates.” Transp. Res. Part E, in press.
Greene, W. H. (2003). Econometric analysis, 5th Ed., Prentice-Hall, Upper Saddle River, N.J.
Grosskopf, S. (1996). “Statistical inference and nonparametric efficiency: A selective survey.” J. Prod. Anal., 7(2–3), 161–176.
Karlaftis, M. G. (2003). “Investigating transit production and performance: A programming approach.” Transp. Res., Part A: Policy Pract., 37A(3), 225–240.
Karlaftis, M. G. (2004). “A DEA approach for evaluating the efficiency and effectiveness of urban transit systems.” Eur. J. Oper. Res., 152(2), 354–364.
Kerstens, K. (1996). “Technical efficiency measurement and explanation of French urban transit companies.” Transp. Res., Part A: Policy Pract., 30A(6), 431–452.
Khasnabis, S., Alsaidi, E., Liu, L., and Ellis, R. D. (2002). “Comparative study of two techniques of transit performance assessment: AHP and GAT.” J. Transp. Eng., 128(6), 499–508.
Muniz, M., Paradi, J., Ruggiero, J., and Yang, Z. (2006). “Evaluating alternative DEA models used to control for nondiscretionary inputs.” Comput. Oper. Res., 33(5), 1173–1183.
Ngamchai, S., and Lovell, D. J. (2003). “Optimal time transfer in bus transit route network design using a genetic algorithm.” J. Transp. Eng., 129(5), 510–521.
Nolan, J. F. (1996). “Determinants of productive efficiency in urban transit.” Transp. Res. Part E, 32(3), 319–342.
Nolan, J. F., Ritchie, P. C., and Rowcroft, J. R. (2001). “Measuring efficiency in the public sector using nonparametric frontier estimators: A study of transit agencies in the USA.” Appl. Econom., 33(7), 913–922.
Nolan, J. F., Ritchie, P. C., and Rowcroft, J. E. (2002). “Identifying and measuring public policy goals: ISTEA and the U.S. bus transit industry.” J. Econ. Behav. Organ., 48(3), 291–304.
Novaes, A. G. N. (2001). “Rapid transit efficiency analysis with the assurance-region DEA method.” Pesquisa Operacional, 21(2), 179–197.
Nunamaker, T. R. (1985). “Using data envelopment analysis to measure the efficiency of nonprofit organisations: A critical evaluation.” Manage. Dec. Econ., 6(1), 50–58.
Odeck, J. (2006). “Congestion, ownership, region of operation, and scale: Their impact on bus operator performance in Norway.” Socio-Econ. Plan. Sci., 40(1), 52–69.
Odeck, J., and Alkadi, A. (2001). “Evaluating efficiency in the Norwegian bus industry using data envelopment analysis.” Transp. J., 28(3), 211–232.
Petersen, N. C. (1990). “Data envelopment analysis on a relaxed set of assumptions.” Manage. Sci., 36(3), 305–314.
Pina, V., and Torres, L. (2001). “Analysis of the efficiency of local government services delivery: An application to urban public transport.” Transp. Res., Part A: Policy Pract., 35A(10), 929–944.
Raudenbush, S. W., and Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods, Sage, Thousand Oaks, Calif.
Ruggiero, J. (2004a). “Performance evaluation in education: Modeling educational production.” Handbook on data envelopment analysis, W. W. Cooper, L. M. Seiford, and J. Zhu, eds., Kluwer, Boston, 323–348.
Ruggiero, J. (2004b). “Performance evaluation when nondiscretionary factors correlate with technical efficiency.” Eur. J. Oper. Res., 159(1), 250–257.
Scheel, H. (2000). “EMS: Efficiency measurement system.” ⟨http://www.wiso.uni-dortmund.de/lsfg/or/scheel/ems/⟩ (Dec. 17, 2000).
Shalaby, A. S. (1999). “Simulating performance impacts of bus lanes and supporting measures.” J. Transp. Eng., 125(5), 390–397.
Shalaby, A. S., and Farhan, A. (2004). “Prediction model of bus arrival and departure times using AVL and APC data.” J. Public Transportation, 7(1), 41–61.
Sheth, C., Triantis, K., and Teodorovic, D. (2006). “Performance evaluation of bus routes: A provider and passenger perspective.” Transp. Res. Part E, in press.
Simar, L., and Wilson, P. W. (2000). “Statistical inference in nonparametric frontier models: The state of the art.” J. Prod. Anal., 13(1), 49–78.
Simar, L., and Wilson, P. W. (2007). “Estimation and inference in two-stage, semi-parametric models of production processes.” J. Econometr., 136(1), 31–64.
Skrondal, A., and Rabe-Hesketh, S. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models, Chapman & Hall/CRC, Boca Raton, Fla.
Tandon, S. (2006). “Performance measurement of bus routes using data envelopment analysis.” Thesis, Univ. of Illinois at Chicago, Chicago.
Tom, V. M., and Mohan, S. (2003). “Transit route network design using frequency coded genetic algorithm.” J. Transp. Eng., 129(2), 186–195.
Viton, P. A. (1997). “Technical efficiency in multi-mode bus transit: A production frontier analysis.” Transp. Res., Part B: Methodol., 31(1), 23–39.
Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data, MIT Press, Cambridge, Mass.
Zelenyuk, V. (2005). “Power of significance test of dummies in Simar–Wilson two-stage efficiency analysis model.” ⟨http://www.stat.ucl.ac.be/ISpub/ISdp.html#2005⟩ (Apr. 12, 2006).
Zellner, A. (1962). “An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias.” J. Am. Stat. Assoc., 57(298), 348–368.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 134 • Issue 2 • February 2008
Pages: 77 - 85
Copyright
© 2008 ASCE.
History
Received: Sep 19, 2006
Accepted: Aug 2, 2007
Published online: Feb 1, 2008
Published in print: Feb 2008
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.