A Hybrid Best-Worst Method (BWM)—Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) Approach for Prioritizing Road Safety Improvements
Year: 2024
Type: article
Abstract: The increase in road accidents underscores the urgent need for effective methodologies to evaluate and prioritize road safety improvements. Traditional decision-making processes in road safety management often confront challenges due to the lack of a comprehensive approach, particularly in handling multiple evaluation criteria. This study introduces a novel Hybrid Multi-Criteria Decision-Making approach that amalgamates the Best-Worst Method (BWM), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Simple Additive Weighting (SAW) method. This approach is designed to prioritize road safety improvements effectively by analyzing various criteria and alternatives in a structured manner. Focusing on a 500-meter road section, the study identifies eight distinct road improvement criteria and divides the road section into five sub-sections for detailed analysis. The BWM is utilized to determine the criteria weights, which are subsequently integrated into the TOPSIS and SAW methodologies for prioritizing improvements in each road subsection. This hybrid approach provides a comprehensive framework for decision makers, including road safety auditors and transportation professionals, facilitating a nuanced and systematic evaluation of safety improvements. The methodology’s efficacy is validated through field expert consultations and comparative analysis with standalone SAW results. The validation underscores the potential of the proposed approach as a robust tool for road safety stakeholders, enabling them to make informed decisions based on a detailed, Chainage-wise analysis of road sections. The Python code for TOPSIS is available athttps://github.com/kanak02/TOPSIS.
Source: IEEE Access
Institutions: Institute of Infrastructure Technology Research and Management, VSB - Technical University of Ostrava, Al-Ahliyya Amman University, Universiti Sains Malaysia, Applied Science Private University, Al al-Bayt University, Lebanese American University, University of Tabuk, Sunway University, Middle East University, Chandigarh University, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,995
OpenAlex ID: W4391991749