Evaluation of Railway Intelligent Transportation Systems to Construct Safer Railway Transport Systems with a Novel Decision-Making Model

Abstract

While end users typically perceive rail transport as safer than other forms of transportation, it still confronts substantial threats and risks that demand meticulous management. One of the most crucial challenges in rail transport is the management of dense railway traffic on limited infrastructure. The effectiveness of this management is critical to ensuring safety and reliability. To address these challenges, integrating and adapting Railway Intelligent Transportation Systems (RITS) into railway transport systems has become essential for creating a safer and more reliable railway system. A railway system that is poorly structured and does not use advanced technology appropriately struggles to manage these risks effectively. Therefore, the integration of RITS is crucial. Decision-makers must carefully evaluate and select the most suitable RITS to ensure safety and reliability. However, since many conflicting criteria and decision factors affect the evaluation process, selecting the most appropriate RITS is a complex decision problem. This study proposes a new decision-making model by considering these requirements. In this context, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, enhanced with Intuitionistic Fuzzy Sets and reinforced by integrating Schweizer-Sklar Hamy Mean Operators, was developed as a practical solution to address the decision-making problem. According to the research results, reliability and the use of the most advanced technology are the effective criteria that influence the selection of appropriate RITSs. In addition, A3 Aselsan, one of the key players in the intelligent transport system manufacturing industry, has been determined to be the most suitable alternative for railway transportation systems. Ultimately, extensive reality tests involving sensitivity and comparative analysis were conducted to check the robustness of the model. The analysis proves the model's soundness and practicality.