Volume 4 , Issue 2 , PP: 50-60, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud A. Zaher 1 * , Marwan Al-Akaidi 2
Doi: https://doi.org/10.54216/IJWAC.040201
This research proposes a novel procurement process for road traffic analysis by using the information error-based Pythagorean fuzzy cloud (PFC) method. First, a 20-factor assessment index method for road traffic was developed. The notion of PFCs was devised to represent the assessment information of an indication. Concurrently, the PFC-weighted Bonferroni mean (PFCWBM) operator was created to aggregate the evaluation data of multiple indications. Then, a method for evaluating and selecting road traffic based on the PFCWBM operator was developed. Furthermore, an application for demonstrating the efficacy of the suggested method was provided. Finally, the effectiveness of the proposed method was evaluated. Results demonstrate that our algorithm can define and assess complicated data with relatively high susceptibility and environmental adaptation.
Pythagorean fuzzy cloud , road traffic , information error , risk analysis
[1] P. Slovic, ―The Perception of Risk. London and Sterling,‖ VA: Earthscan Publications Ltd, 2000.
[2] T. Gayer, J. T. Hamilton, and W. K. Viscusi, ―Private values of risk tradeoffs at superfund sites: housing market evidence on learning about risk,‖ Review of Economics and Statistics, vol. 82, no. 3, pp. 439– 451, 2000.
[3] H. Bleichrodt and L. Eeckhoudt, ―Willingness to pay for reductions in health risks when probabilities are distorted,‖ Health Economics, vol. 15, no. 2, pp. 211–214, 2006.
[4] J. Hakes and W. K. Viscusi, ―Mortality risk perceptions: A Bayesian reassessment,‖ Journal of Risk and Uncertainty, vol. 15, no. 2, pp. 135–150, 1997.
[5] J. K. Hakes and W. K. Viscusi, ―Dead reckoning: Demographic determinants of the accuracy of mortality risk perceptions,‖ Risk Analysis: An International Journal, vol. 24, no. 3, pp. 651–664, 2004.
[6] S. Lichtenstein, P. Slovic, B. Fischhoff, M. Layman, and B. Combs, ―Judged frequency of lethal events.,‖ Journal of experimental psychology: Human learning and memory, vol. 4, no. 6, p. 551, 1978.
[7] M. G. Morgan et al., ―On judging the frequency of lethal events: A replication,‖ Risk Analysis, vol. 3, no. 1, pp. 11–16, 1983.
[8] W. Viscusi, J. Hakes, and A. Carlin, ―Measures of mortality risks,‖ Journal of Risk and Uncertainty, vol. 14, no. 3, pp. 213–233, 1997.
[9] O. Armantier, ―Estimates of own lethal risks and anchoring effects,‖ Journal of Risk and Uncertainty, vol. 32, no. 1, pp. 37–56, 2006.
[10] D. Benjamin and W. Dougan, ―Individuals’ estimates of the risks of death: Part I—A reassessment of the previous evidence,‖ Journal of Risk and Uncertainty, vol. 15, no. 2, pp. 115–133, 1997.
[11] D. K. Benjamin, W. R. Dougan, and D. Buschena, ―Individuals’ estimates of the risks of death: Part II— New evidence,‖ Journal of Risk and Uncertainty, vol. 22, no. 1, pp. 35–57, 2001.
[12] X. Peng and Y. Yang, ―Some results for Pythagorean fuzzy sets,‖ International Journal of Intelligent Systems, vol. 30, no. 11, pp. 1133–1160, 2015.
[13] R. R. Yager, ―Pythagorean membership grades in multicriteria decision making,‖ IEEE Transactions on Fuzzy Systems, vol. 22, no. 4, pp. 958–965, 2013.
[14] R. Verma and J. M. Merigó, ―On generalized similarity measures for Pythagorean fuzzy sets and their applications to multiple attribute decision‐ making,‖ International Journal of Intelligent Systems, vol. 34, no. 10, pp. 2556–2583, 2019.
[15] T.-Y. Chen, ―New Chebyshev distance measures for Pythagorean fuzzy sets with applications to multiple criteria decision analysis using an extended ELECTRE approach,‖ Expert Systems with Applications, vol. 147, p. 113164, 2020.
[16] K. T. Atanassov, ―Intuitionistic fuzzy sets,‖ in Intuitionistic fuzzy sets, Springer, 1999, pp. 1–137.
[17] S. Singh and A. H. Ganie, ―On some correlation coefficients in Pythagorean fuzzy environment with applications,‖ International Journal of Intelligent Systems, vol. 35, no. 4, pp. 682–717, 2020.
[18] X. Peng and Y. Yang, ―Fundamental properties of interval‐ valued Pythagorean fuzzy aggregation operators,‖ International Journal of Intelligent Systems, vol. 31, no. 5, pp. 444–487, 2016.
[19] N. Liao, G. Wei, and X. Chen, ―TODIM method based on cumulative prospect theory for multiple attributes group decision making under probabilistic hesitant fuzzy setting,‖ International Journal of Fuzzy Systems, vol. 24, no. 1, pp. 322–339, 2022.
[20] M. Riaz and M. R. Hashmi, ―Soft rough Pythagorean m-polar fuzzy sets and Pythagorean m-polar fuzzy soft rough sets with application to decision-making,‖ Computational and Applied Mathematics, vol. 39, no. 1, pp. 1–36, 2020.
[21] P. Mandal, S. Samanta, M. Pal, and A. S. Ranadive, ―Pythagorean linguistic preference relations and their applications to group decision making using group recommendations based on consistency matrices and feedback mechanism,‖ International Journal of Intelligent Systems, vol. 35, no. 5, pp. 826–849, 2020.
[22] S. Xian, Z. Liu, X. Gou, and W. Wan, ―Interval 2‐ tuple Pythagorean fuzzy linguistic MULTIMOORA method with CIA and their application to MCGDM,‖ International Journal of Intelligent Systems, vol. 35, no. 4, pp. 650–681, 2020.
[23] G. Lang, D. Miao, and H. Fujita, ―Three-way group conflict analysis based on Pythagorean fuzzy set theory,‖ IEEE Transactions on Fuzzy Systems, vol. 28, no. 3, pp. 447–461, 2019.
[24] X. Gou, Z. Xu, and P. Ren, ―The properties of continuous Pythagorean fuzzy information,‖ International Journal of Intelligent Systems, vol. 31, no. 5, pp. 401–424, 2016.
[25] E. K. Zavadskas and Z. Turskis, ―A new additive ratio assessment (ARAS) method in multicriteria decision‐ making,‖ Technological and economic development of economy, vol. 16, no. 2, pp. 159–172, 2010.
[26] M. Keshavarz Ghorabaee, E. K. Zavadskas, L. Olfat, and Z. Turskis, ―Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS),‖ Informatica, vol. 26, no. 3, pp. 435–451, 2015.
[27] J. B. Talevska, M. Ristov, and M. M. Todorova, ―Development of methodology for the selection of the optimal type of pedestrian crossing,‖ Decision Making: Applications in Management and Engineering, vol. 2, no. 1, pp. 105–114, 2019.
[28] Baležentis, T. Baležentis, and A. Misiunas, ―An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods,‖ Technological and Economic Development of Economy, vol. 18, no. 1, pp. 34–53, 2012.
[29] Karaşan, İ. Kaya, and M. Erdoğan, ―Location selection of electric vehicles charging stations by using a fuzzy MCDM method: a case study in Turkey,‖ Neural Computing and Applications, vol. 32, no. 9, pp. 4553–4574, 2020.
[30] P. Tripathy, A. K. Khambete, and K. A. Chauhan, ―An innovative approach to assess sustainability of urban mobility—using fuzzy MCDM method,‖ in Innovative Research in Transportation Infrastructure, Springer, 2019, pp. 55–63.
[31] G. Stojić, Ž. Stević, J. Antuchevičienė, D. Pamučar, and M. Vasiljević, ―A novel rough WASPAS approach for supplier selection in a company manufacturing PVC carpentry products,‖ Information, vol. 9, no. 5, p. 121, 2018.
[32] J. A. Morente-Molinera, G. Kou, I. J. Pérez, K. Samuylov, A. Selamat, and E. Herrera-Viedma, ―A group decision making support system for the Web: How to work in environments with a high number of participants and alternatives,‖ Applied Soft Computing, vol. 68, pp. 191–201, 2018.
[33] S. Hashemkhani Zolfani, M. Yazdani, and E. K. Zavadskas, ―An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process,‖ Soft Computing, vol. 22, no. 22, pp. 7399–7405, 2018.
[34] D. Pamučar and G. Ćirović, ―The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC),‖ Expert systems with applications, vol. 42, no. 6, pp. 3016–3028, 2015.
[35] P. Morency, L. Gauvin, C. Plante, M. Fournier, and C. Morency, ―Neighborhood social inequalities in road traffic injuries: the influence of traffic volume and road design,‖ American journal of public health, vol. 102, no. 6, pp. 1112–1119, 2012.
[36] M. G. Karlaftis and I. Golias, ―Effects of road geometry and traffic volumes on rural roadway accident rates,‖ Accident Analysis & Prevention, vol. 34, no. 3, pp. 357–365, 2002.
[37] D. Nenadić, ―Ranking dangerous sections of the road using MCDM model,‖ Decision Making: Applications in Management and Engineering, vol. 2, no. 1, pp. 115–131, 2019.
[38] Q. Bao, D. Ruan, Y. Shen, E. Hermans, and D. Janssens, ―Improved hierarchical fuzzy TOPSIS for road safety performance evaluation,‖ Knowledge-based systems, vol. 32, pp. 84–90, 2012.
[39] G. Khorasani, A. Yadollahi, M. Rahimi, and A. Tatari, ―Implementation of MCDM methods in road safety management,‖ in International Conference on Transport, Civil, Architecture and Environment engineering (ICTCAEE’2012) December, 2012, pp. 26–27.
[40] F. Haghighat, ―Application of a multi-criteria approach to road safety evaluation in the Bushehr Province, Iran,‖ Promet-Traffic&Transportation, vol. 23, no. 5, pp. 341–352, 2011.
