Data Envelopment Analysis for Determining the Efficiency of Variant Solutions for Traffic Flow Organisation

  • Damir Budimir Faculty of Transport and Traffic Sciences
  • Marko Šoštarić Faculty of Transport and Traffic Sciences, University of Zagreb
  • Krešimir Vidović Ericsson Nikola Tesla
Keywords: data envelopment analysis, modelling methodology, traffic flow organisation, traffic flow intersection, efficiency analysis, transport network, sustainable mobility

Abstract

There is a small number of empirical modelling study cases available that are related to the calculation of variant solutions efficiency from the aspect of sustainable mobility in the urban areas. In practice, it is often necessary - especially when it comes to the urban transport network - to evaluate the solutions for traffic flow organisation and routing, in order to implement the one(s) with the maximum potential to reduce the possibility of congestion during peak travelling periods i.e. during transport network peak load. The paper presents an approach to the aforementioned problem by the application of the transport system efficiency analysis. The aspect of traffic flow organisation and routing efficiency in variant solutions is clarified through the analysis model development, built on the premises of Data Envelopment Analysis (DEA) method and the principles of unnecessary traffic flow intersections (TFI) theory. The proposed model defines the efficiency limit for data attributed to variant solutions, based on the calculation of the optimal TFI model and the possibilities of DEA method that include comparison and definition of relative routing efficiency for every optional traffic flow against the efficiency limit (optimal model) in order to calculate relative efficiency in relation to other solutions.

References

Brčić D, et al. Analiza funkcije javnog gradskog prijevoza u održivoj urbanoj mobilnosti; Zagreb, 2014.

Brčić D, et al. Razvoj planova održive urbane mobilnosti - SUMP; Zagreb, 2014.

Dadić I, Kos G, Ševrović M, Budimir D. Teorija i organizacija prometnih tokova. Sveučilište u Zagrebu; 2014.

Makarova I, Shubenkova K, Gabsalikhova L. Analysis of the city transport system’s development strategy design principles with account of risks and specific features of spatial development. Transportation. 2017;12(1): 125-138.

Dadić I, Kos G, Brlek P. Application of changeable message signs in traffic. Promet - Traffic & Transportation. 2003;15(5): 307-314.

Pestana Barros C, Dieke PUC. Measuring the economic efficiency of airports: A Simar-Wilson methodology analysis.

JASPERS Appraisal Guidance (Transport): The Use of Transport Models in Transport Planning and Project Appraisal. JASPERS, 2014.

Premachandra IM, Zhu J, Watson J, Galagedera DUA. Mutual fund industry performance: A network data envelopment analysis approach. International Series in Operations Research and Management Science. 2016;238: 165-228.

Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research. 1978;2(6): 429-444.

Seiford LM, Thrall RM. Recent developments in DEA The Mathematical Programming Approach to Frontier Analysis; 1990.

Demerjian PR. Calculating Efficiency with Financial Accounting Data: Data Envelopment Analysis for Accounting Researchers; 2017.

Kao C. Network data envelopment analysis: A review. European Journal of Operational Research. 2014;239(1): 1-16 .

Cooper WW, Seiford ALM, Tone AK, Zhu AJ. Some models and measures for evaluating performances with DEA: past accomplishments and future prospects; 2007.

Cooper WW, Seiford LM, Zhu J. Data Envelopment Analysis: History, Models, and Interpretations. Boston, MA: Springer; 2011. p. 1-39.

Toloo M. On classifying inputs and outputs in DEA: A revised model. European Journal of Operational Research. 2009;198(1): 358-360.

Kashim R, Kasim MM, Rosshairy AR. Measuring efficiency of a university faculty using a hierarchical network data envelopment analysis model; 2018.

Budimir D, Brčić D, Jelušić N. Analiza operativnih karakteristika javnog gradskog prijevoza temeljem prikupljanja podataka pokretnim osjetilima. Docs.mipro-proceedings.com. 2015. p. 1351-1356.

Banker R, Emrouznejad A, Lopes ALM, Rodrigues De Almeida M. (eds.) Data envelopment analysis: Theory and applications; 2012.

Backhaus K, et al. Effizienzmessung industrieller Dienstleistungen mittels Data Envelopment Analysis-Projekt ServDEA. In: Backhaus K, et al. (eds.) Effizienzmessung industrieller Dienstleistungen mittels Data Envelopment Analysis (ServDEA); 2014.

Backhaus K, Nikula A, Becker J, Beverungen D, Wilken R. Produktivitätsbenchmarkingals Bestandteil eines integrier-ten Ansatzeszur Vermarktung hybriderLeistungsbündel.

Kos G. Increasing the capacity of nodes by reducing of traffic flows crossings. PhD thesis. Sveučilište u Zagrebu; 2006. 189 p.

Highway Capacity Manual 2010 (HCM2010) | Blurbs | Main; 2010. Available from: http://www.trb.org/Main/Blurbs/164718.aspx [Accessed January 2019].

Dimter S. Smirivanje prometa u gradovima. 2012. p. 85-93.

Immers LH, Logghe S. Traffic Flow Theory. 2002.

Dyckhoff H, Gilles R. Zeitschrift für Betriebswirtschaft. Betriebswirtschaftlicher Verlag Dr. Th. Gabler; 2004.

Dyckhoff H, Allen K. Measuring ecological efficiency with data envelopment analysis (DEA). European Journal of Operational Research. 2001;132(2): 312-325.

Frederic-Willem Höcker DV. Die Erstellung von Regionenrankings unter Verwendung der Data Envelopment Analyse; 2018.

Doyle J, Green R. Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses, Journal of the Operational Research Society. 1994;45(5): 567-578.

Adler N, Friedman L. Review of ranking methods in the data envelopment analysis context. European Journal of Operational Research. 2002;140(2): 249-265.

Jablonsky J. Multicriteria approaches for ranking of efficient units in DEA models. Central European Journal of Operations Research. 2012;20(3): 435-449.

Banker RD, Chang H. The super-efficiency procedure for outlier identification, not for ranking efficient units. European Journal of Operational Research. 2006;175(2): 1311-1320.

Simar L, Wilson PW. Estimation and inference in twostage, semi-parametric models of production processes. Journal of Econometrics. 2007;136(1): 31-64.

Staat M. Exploring the efficiency of the selling function; 2016.

Budimir D. Mobile Sensor-Based Estimation Method of Spatio-Temporal Indicators in Public Urban Transportation; 2016.

Published
2019-06-14
How to Cite
1.
Budimir D, Šoštarić M, Vidović K. Data Envelopment Analysis for Determining the Efficiency of Variant Solutions for Traffic Flow Organisation. Promet [Internet]. 2019Jun.14 [cited 2024Mar.29];31(3):341-53. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/3202
Section
Articles

Most read articles by the same author(s)