International Journal of Wireless and Ad Hoc Communication

Journal DOI

https://doi.org/10.54216/IJWAC

Submit Your Paper

2692-4056ISSN (Online)
Full Length Article

International Journal of Wireless and Ad Hoc Communication

Volume 4, Issue 2, PP: 50-60, 2022 | Cite this article as | XML | | Html PDF

Information error-based Pythagorean fuzzy cloud technique for managing road traffic risk

Mahmoud A. Zaher   1 * , Marwan Al-Akaidi   2

  • 1 Faculty of Artificial Intelligence, Egyptian Russian University (ERU), Cairo, Egypt - (mahmoud.zaher@eru.edu.eg)
  • 2 Al-Farahidi University - Baghdad, Iraq - (marwan1@ieee.org)
  • Doi: https://doi.org/10.54216/IJWAC.040201

    Received: January 11, 2022 Accepted: May 10, 2022
    Abstract

    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.

    Keywords :

    Pythagorean fuzzy cloud , road traffic , information error , risk analysis  ,

    References

    [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] A. 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] A. 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.

    Cite This Article As :
    Mahmoud A. Zaher, Marwan Al-Akaidi. "Information error-based Pythagorean fuzzy cloud technique for managing road traffic risk." Full Length Article, Vol. 4, No. 2, 2022 ,PP. 50-60 (Doi   :  https://doi.org/10.54216/IJWAC.040201)
    Mahmoud A. Zaher, Marwan Al-Akaidi. (2022). Information error-based Pythagorean fuzzy cloud technique for managing road traffic risk. Journal of , 4 ( 2 ), 50-60 (Doi   :  https://doi.org/10.54216/IJWAC.040201)
    Mahmoud A. Zaher, Marwan Al-Akaidi. "Information error-based Pythagorean fuzzy cloud technique for managing road traffic risk." Journal of , 4 no. 2 (2022): 50-60 (Doi   :  https://doi.org/10.54216/IJWAC.040201)
    Mahmoud A. Zaher, Marwan Al-Akaidi. (2022). Information error-based Pythagorean fuzzy cloud technique for managing road traffic risk. Journal of , 4 ( 2 ), 50-60 (Doi   :  https://doi.org/10.54216/IJWAC.040201)
    Mahmoud A. Zaher, Marwan Al-Akaidi. Information error-based Pythagorean fuzzy cloud technique for managing road traffic risk. Journal of , (2022); 4 ( 2 ): 50-60 (Doi   :  https://doi.org/10.54216/IJWAC.040201)
    Mahmoud A. Zaher, Marwan Al-Akaidi, Information error-based Pythagorean fuzzy cloud technique for managing road traffic risk, Journal of , Vol. 4 , No. 2 , (2022) : 50-60 (Doi   :  https://doi.org/10.54216/IJWAC.040201)