Abstract
As the population grows and industries develop, it is essential for countries to improve the environmental and safety impacts by evaluating the performance of their transportation systems. In this paper, data envelopment analysis (DEA) has been applied for performance evaluation of decision-making units (DMUs). Then, the network and leader–follower models are developed based on the DEA model for considering different time periods and dependency of the DMUs on each other. A multi-criteria decision-making (MCDM) model is proposed for determining the leader in the leader–follower model in which the uncertainty of data is also considered in the form of normal and chance modes. A multi-objective programming model is proposed for performance evaluation of the models which leads to improvement in the discrimination power of the models. The mathematical programming model has been investigated under interval uncertainty conditions and random variation of the data. Finally, the rank of the countries is aggregated by the proposed model. The proposed models are run with the road transportation data of European countries. Based on the results, Latvia, Slovakia and Bulgaria ranked first to third, where Latvia has the highest degree of randomness, while Slovakia and Bulgaria have the lowest degree of randomness. Then, as a case study, Iran is compared to European countries in terms of road safety, where the models are extended to consider non-homogenous data. The results show that Iran has the lowest ranking and road safety in Iran needs to be addressed with special attention.
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Bastos JT, Shen Y, Hermans E, Brijs T, Wets G, Ferraz ACP (2015) Traffic fatality indicators in Brazil: state diagnosis based on data envelopment analysis research. Accident Anal Prevent 81(August 2015):61–73. https://doi.org/10.1016/j.aap.2015.01.024
Behnood HR (2018) Best practice analysis of action for road safety in Iran amongst the leading developing countries using an optimized success indicator. Transport Policy 66(August 2018):76–84. https://doi.org/10.1016/j.tranpol.2018.01.017
Beliakov G, James S, Mordelová J, Rückschlossová T, Yager RR (2010) Generalized Bonferroni mean operators in multi-criteria aggregation. Fuzzy Sets Syst 161(17):2227–2242. https://doi.org/10.1016/j.fss.2010.04.004
Bootaki B, Mahdavi I, Paydar MM (2015) New bi-objective robust design-based utilisation towards dynamic cell formation problem with fuzzy random demands. Int J Comput Integr Manuf 28(6):577–592. https://doi.org/10.1080/0951192X.2014.880949
Cao L, Zhou Z, Wu Y, Huang Y, Cao G (2019) Is metabolism in all regions of China performing well?—evidence from a new DEA-Malmquist productivity approach. Ecol Indic 106(November 2018):15. https://doi.org/10.1016/j.ecolind.2019.105487
Chambers RG, Chung Y, Färe R (1996) Benefit and distance functions. J Econ Theory 70(2):407–419. https://doi.org/10.1006/jeth.1996.0096
Chang YT, Zhang N, Danao D, Zhang N (2013) Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 58(2013):277–283. https://doi.org/10.1016/j.enpol.2013.03.011
Charnes A, Cooper WW (1962) Programming with linear fractionals. Nav Res Logist 9(3–4):181–186
Chen C, Yan H (2011) Network DEA model for supply chain performance evaluation. Eur J Oper Res 213(1):147–155. https://doi.org/10.1016/j.ejor.2011.03.010
Chen Z, Wang W, Li F, Zhao W (2020) Congestion assessment for the Belt and Road countries considering carbon emission reduction. J Cleaner Product 242:17. https://doi.org/10.1016/j.jclepro.2019.118405
Cooper WW, Deng H, Huang Z, Li SX (2004) Chance constrained programming approaches to congestion in stochastic data envelopment analysis. Eur J Oper Res 155(2):487–501. https://doi.org/10.1016/S0377-2217(02)00901-3
Dadashi A, Mirbaha B (2019) Prioritizing highway safety improvement projects: a Monte-Carlo based Data Envelopment Analysis approach. Accident Anal Prevent 123(September 2018):387–395. https://doi.org/10.1016/j.aap.2018.11.003
Dantzig GB, Thapa MN (1997) Linear Programming. 1, Introduction {Springer Series in Operations Research}. Springer-Verlag New York Incorporated.
Du J, Zhu J, Cook WD, Huo J (2015) DEA models for parallel systems: game-theoretic approaches. Asia-Pacific J Operational Res 32(2):22. https://doi.org/10.1142/S0217595915500086
Egilmez G, McAvoy D (2013) Benchmarking road safety of U.S. states: a DEA-based Malmquist productivity index approach. Accident Anal Prevent 53(1):55–64. https://doi.org/10.1016/j.aap.2012.12.038
Ehrgott M, Holder A, Nohadani O (2018) Uncertain Data Envelopment Analysis. Eur J Oper Res 268(1):231–242. https://doi.org/10.1016/j.ejor.2018.01.005
García-Palomares JC, Gutiérrez J, Martín JC, Moya-Gómez B (2018) An analysis of the Spanish high capacity road network criticality. Transportation 45(2018):1139–1159. https://doi.org/10.1007/s11116-018-9877-4
Gordis L (2013) Epidemiology (5th edition). W B Saunders
Gupta P, Mehlawat MK, Aggarwal U, Charles V (2018) An integrated AHP-DEA multi-objective optimization model for sustainable transportation in mining industry. Resources Policy, in Press. https://doi.org/10.1016/j.resourpol.2018.04.007
Gupta M, Bandyopadhyay KR, Singh SK (2019) Measuring effectiveness of carbon tax on Indian road passenger transport: a system dynamics approach. Energy Econ 81(2019):341–354. https://doi.org/10.1016/j.eneco.2019.03.013
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier
Hahn JS, Kho SY, Choi K, Kim DK (2017) Sustainability evaluation of rapid routes for buses with a network DEA model. Int J Sustain Transp 11(9):659–669. https://doi.org/10.1080/15568318.2017.1302022
Ignatius J, Ghasemi MR, Zhang F, Emrouznejad A, Hatami-Marbini A (2016) Carbon efficiency evaluation: an analytical framework using fuzzy DEA. Eur J Oper Res 253(2):428–440. https://doi.org/10.1016/j.ejor.2016.02.014
Islam R, Saaty TL (2010) The analytic hierarchy process in the transportation sector. In: Ehrgott M., Naujoks B., Stewart T., Wallenius J. (eds) Multiple criteria decision making for sustainable energy and transportation systems. Lecture Notes in Economics and Mathematical Systems, volume 634, 13 pages . Springer: Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04045-0_7
Ji X, Wu J, Zhu Q (2016) Eco-design of transportation in sustainable supply chain management: a DEA-like method. Transp Res Part d: Transp Environ 48:451–459. https://doi.org/10.1016/j.trd.2015.08.007
Joro T, Korhonen P (2015) Extension of data envelopment analysis with preference information. Springer
Kao C (2009) Efficiency decomposition in network data envelopment analysis: a relational model. Eur J Oper Res 192(3):949–962. https://doi.org/10.1016/j.ejor.2007.10.008
Kao C (2014) Network data envelopment analysis: a review. Eur J Oper Res 239(1):1–16. https://doi.org/10.1016/j.ejor.2014.02.039
Lan LW, Chiou YC, Yen BTH (2014) Integrated fuzzy data envelopment analysis to assess transport performance. Transportmetrica A: Transport Sci 10(5):401–419. https://doi.org/10.1080/23249935.2013.775611
Li W, Guo G, Zhou X (2015) Generalizing and integrating TOPSIS and Cook-Seiford method for multicriteria group decision-making with both cardinal and ordinal data. Math Problems Eng 2015:7. https://doi.org/10.1155/2015/154848
Li Z, Crook J, Andreeva G (2017) Dynamic prediction of financial distress using Malmquist DEA. Expert Syst Appl 80:94–106. https://doi.org/10.1016/j.eswa.2017.03.017
Li H, Chen C, Cook WD, Zhang J, Zhu J (2018) Two-stage network DEA: who is the leader? Omega (United Kingdom) 74:15–19. https://doi.org/10.1016/j.omega.2016.12.009
Liu JWS (2012) Efficiency measures in DEA with grey interval data under the hypotheses of data consistency. Grey Syst: Theory Appl 2(1):63–69
Lotfi FH, Fallahnejad R (2010) Imprecise Shannon’s entropy and multi attribute decision making. Entropy 12(1):53–62. https://doi.org/10.3390/e12010053
Mahmoudi R, Shetab-Boushehri SN, Hejazi SR, Emrouznejad A, Rajabi P (2019) A hybrid egalitarian bargaining game-DEA and sustainable network design approach for evaluating, selecting and scheduling urban road construction projects. Transport Res E: Logist Transport Rev 130(August):161–183. https://doi.org/10.1016/j.tre.2019.08.008
Melo IC, Nocera P, Junior A, Perico AE, Gabriela M, Guzman S, Aparecida D (2016) Benchmarking freight transportation corridors and routes with data envelopment analysis (DEA). Benchmarking4: Int J 25(2):713–742.
Miao W, Chiou P (2008) Confidence intervals for the difference between two means. Comput Stat Data Anal 52(4):2238–2248. https://doi.org/10.1016/j.csda.2007.07.017
Muñuzuri J, Muñoz-Díaz ML (2019) Use of DEA to identify URBAN geographical zones with special difficulty for freight deliveries. J Transport Geogr 79(June):9. https://doi.org/10.1016/j.jtrangeo.2019.102490
Nikolaou, P., & Dimitriou, L. (2018). Evaluation of road safety policies performance across Europe: results from benchmark analysis for a decade. Transportation Research Part A: Policy and Practice, volume 116(March 2018), pages 232–246. https://doi.org/10.1016/j.tra.2018.06.026
Omrani H, Amini M, Alizadeh A (2019) An integrated group best-worst method—data envelopment analysis approach for evaluating road safety : a case of Iran. Measurement 152:48. https://doi.org/10.1016/j.measurement.2019.107330
Paladugula AL, Kholod N, Chaturvedi V, Ghosh PP, Pal S, Clarke L, Evans M, Kyle P, Koti PN, Parikh K, Qamar S, Wilson SA (2018) A multi-model assessment of energy and emissions for India’s transportation sector through 2050. Energy Policy 116(July 2017):10–18. https://doi.org/10.1016/j.enpol.2018.01.037
Park YS, Lim SH, Egilmez G, Szmerekovsky J (2018) Environmental efficiency assessment of U.S. transport sector: a slack-based data envelopment analysis approach. Transp Res Part d: Transp Environ 61:152–164. https://doi.org/10.1016/j.trd.2016.09.009
Romero C (2004) A general structure of achievement function for a goal programming model. Eur J Oper Res 153(3):675–686. https://doi.org/10.1016/S0377-2217(02)00793-2
Rosić M, Pešić D, Kukić D, Antić B, Božović M (2017) Method for selection of optimal road safety composite index with examples from DEA and TOPSIS method. Accid Anal Prev 98:277–286. https://doi.org/10.1016/j.aap.2016.10.007
Rubem AP dos S, Soares de Mello JCCB, Angulo Meza L (2017) A goal programming approach to solve the multiple criteria DEA model. Eur J Operational Res 260(1):134–139. https://doi.org/10.1016/j.ejor.2016.11.049
Saaty TL (2004) Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP). J Syst Sci Syst Eng 13:1–35. https://doi.org/10.1007/s11518-006-0151-5
Saaty TL, Vargas LG (1982) The logic of priorities: applications in business, energy, health, and transportation. Springer, New York (ISBN 978-94-017-3383-0)
Saaty TL, Vargas LG (2006) Decision making with the analytic network process: economic, political, social and technological applications with benefits, opportunities, costs and risks. Springer, New York (ISBN 978-0387-33987-0 (e-book))
Sadeghi A, MohammadzadehMoghaddam A (2016) Uncertainty-based prioritization of road safety projects: an application of data envelopment analysis. Transp Policy 52:28–36. https://doi.org/10.1016/j.tranpol.2016.07.003
Sayadi MK, Heydari M, Shahanaghi K (2009) Extension of VIKOR method for decision making problem with interval numbers. Appl Math Model 33(5):2257–2262. https://doi.org/10.1016/j.apm.2008.06.002
Sevastjanov PV, Róg P (2003) Fuzzy optimization using direct crisp and fuzzy interval comparison. Neural Networks Soft Comput, pages 316–321. https://doi.org/10.1007/978-3-7908-1902-1_46
Shah SAR, Ahmad N, Shen Y, Kamal MA, Basheer MA, Brijs T (2019) Relationship between road traffic features and accidents: an application of two-stage decision-making approach for transportation engineers. J Safety Res 69:201–215. https://doi.org/10.1016/j.jsr.2019.01.001
Shen Y, Hermans E, Ruan D, Wets G, Brijs T, Vanhoof K (2011) A generalized multiple layer data envelopment analysis model for hierarchical structure assessment: a case study in road safety performance evaluation. Expert Syst Appl 38(12):15262–15272. https://doi.org/10.1016/j.eswa.2011.05.073
Shen Y, Hermans E, Brijs T, Wets G, Vanhoof K (2012) Road safety risk evaluation and target setting using data envelopment analysis and its extensions. Accid Anal Prev 48:430–441. https://doi.org/10.1016/j.aap.2012.02.020
Shen Y, Hermans E, Bao Q, Brijs T, Wets G (2013) Road safety development in Europe: a decade of changes (2001–2010). Accid Anal Prev 60:85–94. https://doi.org/10.1016/j.aap.2013.08.013
Shen Y, Hermans E, Bao Q, Brijs T, Wets G (2015) Serious injuries: an additional indicator to fatalities for road safety benchmarking. Traffic Inj Prev 16(3):246–253. https://doi.org/10.1080/15389588.2014.930831
Shen H, Hu L, Lai KK (2018) A mathematical programming model to determine objective weights for the interval extension of TOPSIS. Math Problems Eng 2018:6. https://doi.org/10.1155/2018/3783101
Shi Y, Arthanari T, Liu X, Yang B (2019) Sustainable transportation management: integrated modeling and support. J Clean Prod 212:1381–1395. https://doi.org/10.1016/j.jclepro.2018.11.209
Shirazi F, Mohammadi E (2019) Research in transportation business & management evaluating efficiency of airlines : a new robust DEA approach with undesirable output. Res Transport Business Manag 3:16. https://doi.org/10.1016/j.rtbm.2020.100467
Tang M, Liao H (2021) From conventional group decision making to large-scale group decision making: what are the challenges and how to meet them in Bog data era? A state-of-the-art survey. 100:44. https://doi.org/10.1016/j.omega.2019.102141
Tang M, Zhou X, Liao H, Xu J, Fujita H, Herrera F (2019) Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making. Knowl-Based Syst 180:62–74. https://doi.org/10.1016/j.knosys.2019.05.019
Tasic I, Elvik R, Brewer S (2017) Exploring the safety in numbers effect for vulnerable road users on a macroscopic scale. Accid Anal Prev 109(July):36–46. https://doi.org/10.1016/j.aap.2017.07.029
Tavana M, Keramatpour M, Santos-Arteaga FJ, Ghorbaniane E (2015) A fuzzy hybrid project portfolio selection method using Data Envelopment Analysis, TOPSIS and Integer Programming. Expert Syst Appl 42(22):8432–8444. https://doi.org/10.1016/j.eswa.2015.06.057
Tešić M, Hermans E, Lipovac K, Pešić D (2018) Identifying the most significant indicators of the total road safety performance index. Accident Analysis and Prevention 113(July 2017):263–278. https://doi.org/10.1016/j.aap.2018.02.003
Tian N, Tang S, Che A, Wu P (2020) Measuring regional transport sustainability using super-efficiency SBM-DEA with weighting preference. Journal of Cleaner Production 242:11. https://doi.org/10.1016/j.jclepro.2019.118474
Todd MJ (2002) The many facets of linear programming. Math Program 91(3):417–436
Tone K (2002) A slacks-based measure of super-efficiency in data envelopment analysis. Eur J Oper Res 143(1):32–41. https://doi.org/10.1016/S0377-2217(01)00324-1
Wan S, Dong J (2014) A possibility degree method for interval-valued intuitionistic fuzzy multi-attribute group decision making. J Comput Syst Sci 80(1):237–256. https://doi.org/10.1016/j.jcss.2013.07.007
Wang DD (2019a) Assessing road transport sustainability by combining environmental impacts and safety concerns. Transp Res Part d: Transp Environ 77:212–223. https://doi.org/10.1016/j.trd.2019.10.022
Wang DD (2019b) Performance assessment of major global cities by DEA and Malmquist index analysis. Comput Environ Urban Syst 77(July):11. https://doi.org/10.1016/j.compenvurbsys.2019b.101365
Wang B, Zhang H, Yuan M, Wang Y, Menezes BC, Li Z, Liang Y (2019) Sustainable crude oil transportation: design optimization for pipelines considering thermal and hydraulic energy consumption. Chem Eng Res Des 151:23–39. https://doi.org/10.1016/j.cherd.2019.07.034
Wang QZ, Chen J-M, Tseng M-L, Luan H-M, Ali MH (2020) Modelling green multimodal transport route performance with witness simulation software. J Cleaner Product 248:12. https://doi.org/10.1016/j.jclepro.2019.119245
Wiegmans B, Champagne-gelinas A, Duchesne S, Slack B, Witte P (2018) Research in transportation business & management rail and road freight transport network efficiency of Canada, member states of the EU, and the USA. Res Transp Bus Manag 28:54–65. https://doi.org/10.1016/j.rtbm.2018.10.004
Wu J, Zhu Q, Chu J, Liu H, Liang L (2016) Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach. Transp Res D: Transp Environ 48:460–472. https://doi.org/10.1016/j.trd.2015.08.001
Wu J, Chu J, An Q, Sun J, Yin P (2018) Resource reallocation and target setting for improving environmental performance of DMUs: an application to regional highway transportation systems in China. Transp Res D: Transp Environ 61:204–216. https://doi.org/10.1016/j.trd.2016.09.002
Zhao Y, Triantis K, Murray-Tuite P, Edara P (2011) Performance measurement of a transportation network with a downtown space reservation system: a network-DEA approach. Transport Res E: Logistics Transport Rev 47(6):1140–1159. https://doi.org/10.1016/j.tre.2011.02.008
Zhou P, Ang BW, Poh KL (2008) A survey of data envelopment analysis in energy and environmental studies. Eur J Oper Res 189(1):1–18. https://doi.org/10.1016/j.ejor.2007.04.042
Zhou H, Yang Y, Chen Y, Zhu J (2018) Data envelopment analysis application in sustainability: the origins, development and future directions. Eur J Oper Res 264(1):1–16. https://doi.org/10.1016/j.ejor.2017.06.023
Zhou H, Huang H, Xu P, Chang F, Abdel-Aty M (2019) Incorporating spatial effects into temporal dynamic of road traffic fatality risks: a case study on 48 lower states of the United States, 1975–2015. Accident Analysis and Prevention 132(September):12. https://doi.org/10.1016/j.aap.2019.105283
Zhu J (2009) Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets, vol 2. Springer, New York
Zhu W, Yu Y, Sun P (2018) Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: the case of United States Companies’s low carbon investment to attain corporate sustainability. Eur J Oper Res 269(1):99–110. https://doi.org/10.1016/j.ejor.2017.08.007
Ziari S, Raissi S (2016) Ranking efficient DMUs using minimizing distance in DEA. Journal of Industrial Engineering International 12:237–242
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Babaei, A., Khedmati, M. & Jokar, M.R.A. A New Model for Evaluation of the Passenger and Freight Transportation Planning Based on the Sustainability and Safety Dimensions: A Case Study. Process Integr Optim Sustain 6, 1201–1229 (2022). https://doi.org/10.1007/s41660-022-00272-0
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DOI: https://doi.org/10.1007/s41660-022-00272-0