An advanced multi-attribute decision-making model for Urban transportation planning based on complex intuitionistic...

Area of Science:

• Operations Research
• Fuzzy Set Theory
• Transportation Engineering

Background:

• Global traffic congestion significantly impacts urban life, causing delays, health issues, and economic losses.
• Developing sustainable transportation systems is critical for densely populated areas.
• Data imprecision and incomplete information pose challenges in analyzing sustainable transport factors.

Purpose of the Study:

• Introduce and investigate set-theoretical operations for Complex Intuitionistic Fuzzy Hypersoft Sets (CIFHSS).
• Develop a novel Multi-Attribute Decision Making (MADM) approach using CIFHSS to address urban traffic congestion.
• Evaluate and rank sustainable transportation alternatives.

Main Methods:

• Defined and explored fundamental set-theoretical operations within the CIFHSS framework.
• Developed a MADM approach incorporating decision-valued matrices, a CIFHSS scoring framework, and matrix-based aggregations.
• Applied the algorithm to a real-world urban traffic congestion scenario.

Main Results:

• The CIFHSS-based MADM model evaluated several sustainable transport options.
• Scores were assigned: Electric Bus Rapid Transit (0.6161), Expanded Bicycle Lane Network (0.6089), Light Rail Transit (0.6571), and Hybrid Car-Sharing Program (0.7627).
• The Hybrid Car-Sharing Program was identified as the most suitable solution.

Conclusions:

• The proposed CIFHSS framework and MADM approach effectively address complex decision-making problems in sustainable transportation.
• The Hybrid Car-Sharing Program emerged as the optimal choice for mitigating urban traffic congestion.
• Sensitivity analysis confirmed the robustness of the model, with future research directions outlined.