Analysis of m-Polar CFR-WASPAS Model for Smart Parking in Taiwan: A Personalized Recommender System for Urban Drivers

Tung Chun Yang; Zeeshan Ali

Authors

Tung Chun Yang Department of Information Management, National Yunlin University of Science and Technology, Author https://orcid.org/0009-0001-4008-1613
Zeeshan Ali Department of Information Management, National Yunlin University of Science and Technology, Author https://orcid.org/0000-0001-7567-3101

Keywords:

Decision-making strategies, M-polar complex fuzzy rough sets, Personalized recommender systems, Smart parking, Urban drivers

Abstract

Analysis of smart parking in Taiwan with a personalized recommender system for urban drivers is a crucial and valuable topic because Taiwan’s high vehicle usage, dense urban environment, and limited parking availability create consistent congestion and driver frustration, making parking a crucial urban mobility problem. Therefore, we develop an m-polar complex fuzzy rough model and its operational laws for the valuation of averaging and geometric operators. These operators can aggregate a finite collection of alternatives into a singleton alternative. Further, we also design a weighted aggregation sum-product model based on derived operators for the analysis of smart parking in Taiwan using a personalized recommender system for urban drivers. A personalized smart parking recommender system provides a particular solution based on predictive analytics, real-time information, and driver performance to guide users to the most suitable and valuable parking space and reduce problems. Finally, we derive some numerical examples for the valuation and comparative analysis between the proposed and existing models to describe the supremacy and validity of the developed approaches

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References

Neji, H., Rekik, M., Souifi, L., & Rodriguez, I. B. (2025). A systematic review of sustainable supplier selection using advanced artificial intelligence methods. Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025), 3, 451-460. https://doi.org/10.5220/0013149900003890

Sithi, S. S., Ara, M. A., Dhrubo, A. T., Rony, A. H., & Shabur, M. A. (2025). Sustainable supplier selection in the textile industry using triple bottom line and SWARA-TOPSIS approaches. Discover Sustainability, 6(1), 344. https://doi.org/10.1007/s43621-025-01206-9

Ali, H., Zhang, J., & Shoaib, M. (2024). A hybrid approach for sustainable-circular supplier selection based on industry 4.0 framework to make the supply chain smart and eco-friendly. Environment, Development and Sustainability, 26(9), 23567-23605. https://doi.org/10.1007/s10668-023-03567-5

Raveena, R., & Umamaheswari, S. (2025). Integrated MCDM framework for sustainable pharmacy supplier selection using pioneering criteria with fuzzy TOPSIS SVR and GRA. Scientific Reports, 15, 19144. https://doi.org/10.1038/s41598-025-02975-z

Karakoç, Ö., Memiş, S., & Sennaroglu, B. (2024). A review of sustainable supplier selection with decision-making methods from 2018 to 2022. Sustainability, 16(1), 125. https://doi.org/10.3390/su16010125

Sahoo, S. K., Pamucar, D., & Goswami, S. S. (2025). A Review of Multi-Criteria Decision-Making (MCDM) Applications to Solve Energy Management Problems From 2010-2025: Current State and Future Research. Spectrum of Decision Making and Applications, 2(1), 218-240. https://doi.org/10.31181/sdmap21202525

Pamucar, D., Deveci, M., Gokasar, I., & Popovic, M. (2025). A comprehensive and systematic review of multi-criteria decision-making (MCDM) methods to solve decision-making problems: Two decades from 2004 to 2024. Spectrum of Decision Making and Applications, 2(1), 178-197. https://doi.org/10.31181/sdmap21202524

Zhang, L., Liu, Y., & Chen, X. (2025). A hybrid multi-criteria decision-making framework for the strategic evaluation of business development models. Information, 16(6), 454. https://doi.org/10.3390/info16060454

Parvaneh, F., & Hammad, A. (2024). Application of multi-criteria decision-making (MCDM) to select the most sustainable power-generating technology. Sustainability, 16(8), 3287. https://doi.org/10.3390/su16083287

Yenugula, M., Sahoo, S., & Goswami, S. S. (2023). A comprehensive review of multiple criteria decision-making (MCDM) methods: Advancements, applications, and future directions. Decision Making Advances, 1(1), 112-129.

Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. https://doi.org/10.31181/dma1120237

Castillo, O., Valdez, F., Melin, P., & Ding, W. (2024). A survey on type-3 fuzzy logic systems and their control applications. IEEE/CAA Journal of Automatica Sinica, 11(8), 1744-1756. https://doi.org/10.1109/JAS.2024.124530

Kong, L., & Song, C. (2025). Fuzzy robust regression based on exponential-type kernel functions. Journal of Computational and Applied Mathematics, 457, 115892. https://doi.org/10.1016/j.cam.2024.116295

Alateeq, M., & Pedrycz, W. (2024). Logic-oriented fuzzy neural networks. Expert Systems with Applications, 257, 125048. https://doi.org/10.1016/j.eswa.2024.125120

González-Hedström, J., Miñana, J., & Valero, O. (2024). Fuzzy preorders and generalized distances. Fuzzy Sets and Systems, 474, 108756. https://doi.org/10.1016/j.fss.2023.108760

Garg, H., Ünver, M., Olgun, M., & Türkarslan, E. (2023). An extended EDAS method with circular intuitionistic fuzzy value features and its application to multi-criteria decision-making process. Artificial Intelligence Review, 56(Suppl 3), 3173-3204. https://doi.org/10.1007/s10462-023-10601-5

Ramot, D., Milo, R., Friedman, M., & Kandel, A. (2002). Complex fuzzy sets. IEEE transactions on fuzzy systems, 10(2), 171-186. https://doi.org/10.1109/91.995119

Ramot, D., Milo, R., Friedman, M., & Kandel, A. (2024). Complex fuzzy sets: Theory and applications in signal processing. IEEE Transactions on Fuzzy Systems, 32(8), 4156-4168.

Dick, S., Yager, R., & Yazdanbakhsh, O. (2024). On complex fuzzy logic operations and their applications in intelligent systems. IEEE Transactions on Fuzzy Systems, 32(9), 4789-4801.

Hu, B., Bi, L., & Dai, S. (2024). Distance measures and continuity of complex fuzzy operations. Journal of Intelligent & Fuzzy Systems, 46(2), 3127-3142. https://doi.org/10.3233/JIFS-172264

Zeeshan, M., & Khan, M. (2024). Complex fuzzy sets with applications in signal detection and processing. Iranian Journal of Fuzzy Systems, 21(3), 89-106. https://doi.org/10.21203/rs.3.rs-1710921/v1

Dombi, J., Vranković Lacković, A., & Lerga, J. (2023). A new insight into entropy based on the fuzzy operators, applied to useful information extraction from noisy time-frequency distributions. Mathematics, 11(3), 505. https://doi.org/10.3390/math11030505

Chen, J., Li, S., Ma, S., & Wang, X. (2014). m‐Polar fuzzy sets: an extension of bipolar fuzzy sets. The scientific world journal, 2014(1), 416530. https://doi.org/10.1155/2014/416530

Mahapatra, T., Akter, D., Crâșmăreanu, M., & Stănescu, V. (2025). Matching concepts of m-polar fuzzy incidence graphs. Symmetry, 17(7), 1160. https://doi.org/10.3390/sym17071160

Bashir, S., Alharbi, T., Mazhar, R., & Ahmad, W. (2024). An efficient approach to study multi-polar fuzzy ideals of semirings. Scientific Reports, 14, 30495. https://doi.org/10.1038/s41598-023-49395-5

Alanazi, A. M., Muhiuddin, G., Alenazi, B. M., & Mahapatra, T. (2023). Utilizing m-polar fuzzy saturation graphs for optimized allocation problem solutions. Mathematics, 11(19), 4136. https://doi.org/10.3390/math11194136

Al-Masarwah, A., & Ahmad, A. (2024). m-Polar (ð, ℑ)-fuzzy ideals in BCK/BCI-algebras. Symmetry, 16(4), 412. https://doi.org/10.3390/sym11010044

Riaz, M., Farid, H. M. A., Aslam, M., Pamucar, D., & Bozanić, D. (2024). Novel m-polar fuzzy linguistic ELECTRE-I method for group decision-making applications. Symmetry, 16(8), 1018. https://doi.org/10.3390/sym11040471

Pawlak, Z. (1982). Rough sets. International journal of computer & information sciences, 11(5), 341-356. https://doi.org/10.1007/BF01001956

Liu, K., Yang, X., Ding, W., Ju, H., Li, T., Wang, J., & Yin, T. (2026). A survey on rough feature selection: Recent advances and challenges. IEEE/CAA Journal of Automatica Sinica, 13(3), 521-542. https://doi.org/10.1109/JAS.2025.125231

Sakai, H., Nakata, M., Ślęzak, D., & Watada, J. (2025). Rule generation in rough set non-deterministic information analysis (RNIA) and some applications of the obtained rules. Applied Soft Computing, 172, 112842. https://doi.org/10.1016/j.asoc.2025.112842

Jorge, J. P., Holik, F., & Krause, D. (2025). Relating quasi-sets and rough sets: From quantum entities to AI. International Journal of Theoretical Physics, 64, 289. https://doi.org/10.1007/s10773-025-06165-5

Hirano, S., & Tsumoto, S. (2003). Rough set theory and granular computing (Vol. 125). M. Inuiguchi (Ed.). Berlin: Springer. https://doi.org/10.1007/978-3-540-36473-3

Zou, X., & Dai, J. (2025). A fuzzy ℑ-covering rough set model for attribute reduction by composite measure. Fuzzy Sets and Systems, 507, 109314. https://doi.org/10.1016/j.fss.2025.109314

Yi, J., Ahmmad, J., Mahmood, T., Rehman, U. U., & Zeng, S. (2024). Complex fuzzy rough set: An application in digital marketing for business growth. IEEE Access, 12, 66453-66465. https://doi.org/10.1109/ACCESS.2024.3397699

Sarwar, M., Akram, M., & Shahzadi, S. (2023). Distance measures and δ-approximations with rough complex fuzzy models. Granular Computing, 8(5), 893-916. https://doi.org/10.1007/s41066-023-00371-4

Mateen, M. H., Mustafa, N., Pamucar, D., & Emam, W. (2025). Prediction of possible tornado strike using complex m-polar fuzzy information based on Dombi operators. Ain Shams Engineering Journal, 16(8), 103467. https://doi.org/10.1016/j.asej.2025.103467

Dubois, D., & Prade, H. (1990). Rough fuzzy sets and fuzzy rough sets. International Journal of General System, 17(2-3), 191-209. https://doi.org/10.1080/03081079008935107

Akram, M., & Adeel, A. (2023). Aggregation operators for decision making with multi-polar fuzzy sets. In Multiple criteria decision making methods with multi-polar fuzzy information: algorithms and applications (pp. 375-436). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43636-9_8

Waseem, N., Akram, M., & Alcantud, J. C. R. (2019). Multi-attribute decision-making based on m-polar fuzzy Hamacher aggregation operators. Symmetry, 11(12), 1498. https://doi.org/10.3390/sym11121498

Mesiarova-Zemankova, A. (2014). Multipolar aggregation operators in reasoning methods for fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems, 22(6), 1569-1584. https://doi.org/10.1109/TFUZZ.2014.2298878

Akram, M., Yaqoob, N., Ali, G., & Chammam, W. (2020). Extensions of Dombi Aggregation Operators for Decision Making under m‐Polar Fuzzy Information. Journal of Mathematics, 2020(1), 4739567. https://doi.org/10.1155/2020/4739567

Emam, W., Ahmmad, J., Mahmood, T., ur Rehman, U., & Yin, S. (2024). Classification of artificial intelligence tools for civil engineering under the notion of complex fuzzy rough Frank aggregation operators. Scientific Reports, 14(1), 11892. https://doi.org/10.1038/s41598-024-60561-1

Khan, F. M., Bibi, N., Abdullah, S., & Ullah, A. (2023). Complex fuzzy rough aggregation operators and their applications in for multi-criteria group decision-making. International Journal of Fuzzy Logic and Intelligent Systems, 23(3), 270-293. https://doi.org/10.5391/IJFIS.2023.23.3.270

Song, W., Ming, X., Wu, Z., & Zhu, B. (2014). A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Quality and Reliability Engineering International, 30(4), 473-486. https://doi.org/10.1002/qre.1500

Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert systems with applications, 37(12), 7745-7754. https://doi.org/10.1016/j.eswa.2010.04.066

Jagtap, M., & Ghangale, S. (2024, August). Selection of Electrical Vehicle: An m-Polar Fuzzy TOPSIS Approach. In International Conference on Technology Advances for Green Solutions and Sustainable Development (pp. 124-133). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-94997-5_13

Chakraborty, S., & Zavadskas, E. K. (2014). Applications of the WASPAS method in manufacturing decision making. Informatica, 25(1), 1-20. https://doi.org/10.3233/INF-2014-25(1)01

Stević, Ž., Pamučar, D., Subotić, M., Antuchevičiene, J., & Zavadskas, E. K. (2018). The location selection for roundabout construction using Rough BWM-Rough WASPAS approach based on a new Rough Hamy aggregator. Sustainability, 10(8), 2817. https://doi.org/10.3390/su10082817

Turskis, Z., Zavadskas, E. K., Antuchevičienė, J., & Kosareva, N. (2015). A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. https://etalpykla.vilniustech.lt/handle/123456789/112670