Volume 16, Issue 3 (6-2024)
jorar 2024, 16(3): 181-188 |
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Amini M, Dabbagh R, Omrani H. Efficiency Estimation of Road Transport Safety in Iranian Provinces under Uncertainty Conditions. jorar 2024; 16 (3) :181-188
URL: http://jorar.ir/article-1-865-en.html
URL: http://jorar.ir/article-1-865-en.html
Associate Professor, Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran
Abstract: (70 Views)
INTRODUCTION: Road safety is a recognized global issue and according to the WHO, road traffic injuries are the eighth leading cause of death in all age groups, especially 5 to 29 years. Therefore, in this article, the road safety performance of Iran's provinces is examined.
METHODS: This research was done using Data Envelopment Analysis (DEA) method which is used in two deterministic and non-deterministic situations in order to evaluate road safety efficiency scores. This method gives scores (inefficiency) that allow road sections to be ranked appropriately in terms of being accident-prone. Uncertainty is one of the inevitable features of real-world problems, for which fuzzy theory and extend the DEA-RS model is used by considering its limitations as probability, necessity, and credibility constraints, and propose three fuzzy models such as possibility of DEA-RS (PosDEA-RS); necessary DEA-RS (NecDEA-RS); and the credibility of the DEA-RS (CreDEA-RS).
FINDINGS: Three models which are extensions of the Data Envelopment Analysis based on the Road Safety (DEA-RS) model are proposed for evaluating road safety performance and the CreDEA-RS model is suitable for assessing the safety of roads in the provinces of Iran.
CONCLUSION: The results show that the provinces located in mountain and forest areas like Gilan have a lower performance in terms of road safety, and provinces located in desert areas like Yazd have a higher road safety performance.
METHODS: This research was done using Data Envelopment Analysis (DEA) method which is used in two deterministic and non-deterministic situations in order to evaluate road safety efficiency scores. This method gives scores (inefficiency) that allow road sections to be ranked appropriately in terms of being accident-prone. Uncertainty is one of the inevitable features of real-world problems, for which fuzzy theory and extend the DEA-RS model is used by considering its limitations as probability, necessity, and credibility constraints, and propose three fuzzy models such as possibility of DEA-RS (PosDEA-RS); necessary DEA-RS (NecDEA-RS); and the credibility of the DEA-RS (CreDEA-RS).
FINDINGS: Three models which are extensions of the Data Envelopment Analysis based on the Road Safety (DEA-RS) model are proposed for evaluating road safety performance and the CreDEA-RS model is suitable for assessing the safety of roads in the provinces of Iran.
CONCLUSION: The results show that the provinces located in mountain and forest areas like Gilan have a lower performance in terms of road safety, and provinces located in desert areas like Yazd have a higher road safety performance.
Keywords: Road safety performance, Data Envelopment Analysis (DEA), Possibility theory, Necessity theory, Credibility theory
Letter to the editor: Research Article |
Subject:
New educational methods in the management of crisis and disasters
References
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2. Tabatabaei S.M, Tabatabaei F A. Integrating inverse data envelopment analysis and machine learning for enhanced road transport safety in Iran. Journal of Soft Computing in Civil Engineering. 2024; 8(1): 141-160. (In Persian)
3. Mansouri K et al. A novel data envelopment analysis framework for performance evaluation of European road transport systems. Promet-Traffic & Transportation. 2024; 36(1): 24-41. (In Persian) [DOI:10.7307/ptt.v36i1.321]
4. Andjelković D. et al. A novel data-envelopment analysis interval-valued fuzzy-rough-number multi-criteria decision-making (DEA-IFRN MCDM) model for determining the efficiency of road sections based on headway analysis. Mathematics. 2024, 12(7): 976. [DOI:10.3390/math12070976]
5. Bonera M. et al. Measuring safety performance in the extra-urban road network of Lombardy Region (Italy). Transportation research Procedia. 2023; 69: 155-162. [DOI:10.1016/j.trpro.2023.02.157]
6. Kang L Chao. Evaluating the performance of Chinese provincial road safety based on the output-input ratio. Transportation Letters. 2022; 14.2: 114-123. [DOI:10.1080/19427867.2020.1819077]
7. Fancello G, Carta M, Fadda P. Road intersections ranking for road safety improvement: comparative analysis of multi-criteria decision-making methods. Transport policy. 2019; 80:188-96. [DOI:10.1016/j.tranpol.2018.04.007]
8. Dabbagh R, Ahmadi Chokalaei H. Optimal location of rescue centers using GIS and multi-criteria decision-making methods (case study: Urmia City). JORAR. 2020; 12 (1):12-1. (In Persian). [DOI:10.32592/jorar.2020.12.1.1]
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10. Wang J, Huang H. Road network safety evaluation using Bayesian hierarchical joint model. Accident Analysis & Prevention. 2016; 90:152-8. [DOI:10.1016/j.aap.2016.02.018]
11. Zamani P, Mehrizi H. Ranking of Iranian provinces to improve road safety by using of AHP. Journal of New Researches in Mathematics. 2021;7(32):59-71. (In Persian)
12. Zhu J.H, Chen J. Li G. F., Shuai, B. Using cross efficiency method integrating regret theory and WASPAS to evaluate road safety performance of Chinese provinces. Accident Analysis & Prevention. 2021; 162:106395 [DOI:10.1016/j.aap.2021.106395]
13. Nikolaou P, Dimitriou L. Evaluation of road safety policies performance across Europe: results from benchmark analysis for a decade. Transportation research part A: policy and practice. 2018; 116:232-46. [DOI:10.1016/j.tra.2018.06.026]
14. Shen, Y., Hermans, E., Brijs, T., Wets, G., & Vanhoof, K. Road safety risk evaluation and target setting using data envelopment analysis & its extensions. Accident Analysis & Prevention. 2012; 48: 430-441. [DOI:10.1016/j.aap.2012.02.020]
15. Shen Y, Hermans E, Bao Q, Brijs T, Wets G. Serious injuries: an additional indicator to fatalities for road safety benchmarking. Traffic injury prevention. 2015; 16(3):246-53. [DOI:10.1080/15389588.2014.930831]
16. Egilmez G, McAvoy D. Benchmarking road safety of US states: a DEA-based Malmquist productivity index approach. Accident Analysis & Prevention. 2013; 53:55-64. [DOI:10.1016/j.aap.2012.12.038]
17. Ganji SS, Rassafi A, Xu DL. A double frontier DEA cross efficiency method aggregated by evidential reasoning approach for measuring road safety performance. Measurement. 2019; 136:668-88. [DOI:10.1016/j.measurement.2018.12.098]
18. Dabbagh R, Nasirifard B. safe points in critical situations with passive defense approach (case study: Tabriz City). JORAR. 2019; 11 (3):214-223. [In Persian]. [DOI:10.52547/jorar.11.3.214]
19. Shah SA, Ahmad N, Shen Y, Pirdavani A, Basheer MA, Brijs T. Road safety risk assessment: an analysis of transport policy and management for low, middle and high-income Asian countries. Sustainability. 2018; 10(2):389. [DOI:10.3390/su10020389]
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22. World Health Organization. Global status report on road safety: time for action. 2024.
23. Tabatabaei S.M, Tabatabaei F A. Integrating inverse data envelopment analysis and machine learning for enhanced road transport safety in Iran. Journal of Soft Computing in Civil Engineering. 2024; 8(1): 141-160. (In Persian)
24. Mansouri K et al. A novel data envelopment analysis framework for performance evaluation of European road transport systems. Promet-Traffic & Transportation. 2024; 36(1): 24-41. (In Persian) [DOI:10.7307/ptt.v36i1.321]
25. Andjelković D. et al. A novel data-envelopment analysis interval-valued fuzzy-rough-number multi-criteria decision-making (DEA-IFRN MCDM) model for determining the efficiency of road sections based on headway analysis. Mathematics. 2024, 12(7): 976. [DOI:10.3390/math12070976]
26. Bonera M. et al. Measuring safety performance in the extra-urban road network of Lombardy Region (Italy). Transportation research Procedia. 2023; 69: 155-162. [DOI:10.1016/j.trpro.2023.02.157]
27. Kang L Chao. Evaluating the performance of Chinese provincial road safety based on the output-input ratio. Transportation Letters. 2022; 14.2: 114-123. [DOI:10.1080/19427867.2020.1819077]
28. Fancello G, Carta M, Fadda P. Road intersections ranking for road safety improvement: comparative analysis of multi-criteria decision-making methods. Transport policy. 2019; 80:188-96. [DOI:10.1016/j.tranpol.2018.04.007]
29. Dabbagh R, Ahmadi Chokalaei H. Optimal location of rescue centers using GIS and multi-criteria decision-making methods (case study: Urmia City). JORAR. 2020; 12 (1):12-1. (In Persian). [DOI:10.32592/jorar.2020.12.1.1]
30. Chen F, Wu J, Chen X, Wang J, Wang D. Benchmarking road safety performance: identifying a meaningful reference (best-in-class). Accident Analysis & Prevention. 2016; 86:76-89. [DOI:10.1016/j.aap.2015.10.018]
31. Wang J, Huang H. Road network safety evaluation using Bayesian hierarchical joint model. Accident Analysis & Prevention. 2016; 90:152-8. [DOI:10.1016/j.aap.2016.02.018]
32. Zamani P, Mehrizi H. Ranking of Iranian provinces to improve road safety by using of AHP. Journal of New Researches in Mathematics. 2021;7(32):59-71. (In Persian)
33. Zhu J.H, Chen J. Li G. F., Shuai, B. Using cross efficiency method integrating regret theory and WASPAS to evaluate road safety performance of Chinese provinces. Accident Analysis & Prevention. 2021; 162:106395 [DOI:10.1016/j.aap.2021.106395]
34. Nikolaou P, Dimitriou L. Evaluation of road safety policies performance across Europe: results from benchmark analysis for a decade. Transportation research part A: policy and practice. 2018; 116:232-46. [DOI:10.1016/j.tra.2018.06.026]
35. Shen, Y., Hermans, E., Brijs, T., Wets, G., & Vanhoof, K. Road safety risk evaluation and target setting using data envelopment analysis & its extensions. Accident Analysis & Prevention. 2012; 48: 430-441. [DOI:10.1016/j.aap.2012.02.020]
36. Shen Y, Hermans E, Bao Q, Brijs T, Wets G. Serious injuries: an additional indicator to fatalities for road safety benchmarking. Traffic injury prevention. 2015; 16(3):246-53. [DOI:10.1080/15389588.2014.930831]
37. Egilmez G, McAvoy D. Benchmarking road safety of US states: a DEA-based Malmquist productivity index approach. Accident Analysis & Prevention. 2013; 53:55-64. [DOI:10.1016/j.aap.2012.12.038]
38. Ganji SS, Rassafi A, Xu DL. A double frontier DEA cross efficiency method aggregated by evidential reasoning approach for measuring road safety performance. Measurement. 2019; 136:668-88. [DOI:10.1016/j.measurement.2018.12.098]
39. Dabbagh R, Nasirifard B. safe points in critical situations with passive defense approach (case study: Tabriz City). JORAR. 2019; 11 (3):214-223. [In Persian]. [DOI:10.52547/jorar.11.3.214]
40. Shah SA, Ahmad N, Shen Y, Pirdavani A, Basheer MA, Brijs T. Road safety risk assessment: an analysis of transport policy and management for low, middle and high-income Asian countries. Sustainability. 2018; 10(2):389. [DOI:10.3390/su10020389]
41. Shah SA, Ahmad N, Shen Y, Kamal MA, Basheer MA, Brijs T. Relationship between road traffic features and accidents: An application of two-stage decision-making approach for transportation engineers. Journal of Safety Research. 2019; 69:201-15. [DOI:10.1016/j.jsr.2019.01.001]
42. Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science. 1984; 30(9):1078-92. [DOI:10.1287/mnsc.30.9.1078]
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