Smart City Traffic Management Using Dynamic Fuzzy Hypersoft Set Algorithms

Zaffar Ahmed Shaikh; Muhammad Naveed Jafar; Ali Elrashidi; Hamiden Abd El-Wahed Khalifa

doi:10.48084/etasr.18996

Authors

Zaffar Ahmed Shaikh Department of Computer Science, Benazir Bhutto Shaheed University Lyari, Karachi, Pakistan | School of Engineering, Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
Muhammad Naveed Jafar Department of Mathematics, School of Science, University of Management and Technology, Lahore, Pakistan
Ali Elrashidi Electrical Engineering Department, University of Business and Technology, Jeddah, Saudi Arabia
Hamiden Abd El-Wahed Khalifa Department of Mathematics, College of Science, Qassim University, Buraydah, Saudi Arabia | Department of Operations and Management Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt

Volume: 16 | Issue: 4 | Pages: 37935-37941 | August 2026 | https://doi.org/10.48084/etasr.18996

Received: 29 March 2026 | Revised: 13 April 2026, 22 April 2026, and 2 May 2026 | Accepted: 4 May 2026 | Online: 23 June 2026

Corresponding author: Zaffar Ahmed Shaikh

Abstract

Smart city traffic management is a multi-attribute decision-making problem where city authorities continuously evaluate alternative intersections under dynamic conditions. Uncertain, interdependent criteria, such as traffic density (e.g., 1200–2800 vehicles/hour), accident risk (5–15% variance), pedestrian flow, weather, pollution, and emergency access, render traditional models inadequate. This study presents a dynamic hypersoft set-based framework for selecting the best traffic management zone using two algorithms. A dynamic choice matrix is constructed, and alternatives are ranked using scalar DHSM values and dynamic choice vectors in Algorithm 1, whereas in Algorithm 2, dynamic value, utility, and score matrices are developed to obtain a final ranking of alternatives. The study demonstrates how the Dynamic Hypersoft Set (DHSS) theory can support adaptive and transparent decision-making in smart city traffic systems.

Keywords:

Dynamic Fuzzy Hypersoft Sets (DFHSS), Utility Matrix (UM), Score Value (SV), Smart City Traffic Management (SCTM), optimization

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