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AI-powered decision making for road safety optimization under probabilistic linguistic Sugeno-Weber aggregation
Shahzaib Ashraf1, Tooba Shahid1, Jungeun Kim2
1Institute of Mathematics Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
Heliyon
|December 17, 2024
Summary
Artificial intelligence (AI) enhances road safety by integrating with Multi Criteria Decision Making (MCDM) for accident prevention. New methods aggregate uncertain data using probabilistic linguistic term sets and the Sugeno-Weber framework.
Area of Science:
- Road safety
- Artificial Intelligence
- Decision Making
Background:
- Traffic accidents pose significant global risks, necessitating advanced solutions.
- Current decision-making processes require enhancement for effective accident prevention.
- Probabilistic linguistic expression sets are emerging for aggregating uncertain data.
Purpose of the Study:
- To explore AI integration within Multi Criteria Decision Making (MCDM) for global road safety improvement.
- To develop novel methodologies for information aggregation using probabilistic linguistic environments.
- To introduce procedural laws based on the Sugeno-Weber (SW) framework for handling probabilistic linguistic term elements (PLTEs).
Main Methods:
- Utilizing AI for data analysis, predictive modeling, and intelligent traffic management.
- Developing aggregation techniques like probabilistic linguistic SW Average (PLSWA) and Geometric (PLSWG).
- Implementing weighted and ordered aggregation operators (PLSWWA, PLSWWG, PLSWOWA, PLSWOWG) based on SW τ-norm and τ-conorm.
Main Results:
- Introduction of versatile aggregation tools for information reinforcement.
- Development of strategies for integrating probabilistic linguistic term sets (PLTs) in MCDM.
- Comparison of proposed procedures with the TOPSIS approach, highlighting operator features.
Conclusions:
- AI-driven MCDM offers a revolutionary approach to accident prevention and road safety.
- The proposed probabilistic linguistic aggregation methods effectively handle uncertain data within MCDM.
- These novel operators provide enhanced capabilities for decision-making in road safety applications.
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