A Decision‑Support Framework for Virtual Reality Platform Selection to Enhance Railway Training and Emergency Preparedness

Rail transport plays a strategically significant role in global transportation systems due to its high capacity, energy efficiency, and operational reliability. The safe and effective functioning of railway operations depends not only on advanced infrastructure and technologies but also on the competence of human personnel performing safety-critical tasks across diverse operational roles. Therefore, a comprehensive and adaptive training programs is essential for maintaining operational safety and system resilience. However, conventional railway training approaches, typically based on classroom instruction, limited hands-on exercises, and restricted access to physical simulators, are increasingly inadequate for addressing the complexity of modern railway operations, particularly in preparing personnel for rare but high-consequence scenarios. To this end, Virtual Reality (VR) has emerged as a promising technology for delivering immersive, realistic, and risk-free training environments capable of supporting both routine operational training and emergency response preparedness. Despite its growing adoption, the literature lacks systematic and human-centered methodologies for evaluating and selecting VR platforms tailored to the specific requirements of railway training. Existing studies largely focus on general training effectiveness and provide limited guidance for comparing VR platforms in terms of usability, simulation fidelity, pedagogical value, and operational suitability. To address this gap, this study proposes a structured, evidence-based decision-support framework for selecting VR platforms for railway personnel training. The proposed framework adopts a hybrid multi-attribute decision-making approach, integrating the Best Worst Method (BWM) for consistent criteria weighting with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate and rank alternative platforms. The evaluation considers eight candidate VR platforms assessed against twenty-two sub-criteria organized into five evaluation categories. The computational results identify Bentley Synchro XR as the most suitable platform, demonstrating superior performance across key operational, technical, and training-related dimensions. The proposed framework provides actionable insights for railway stakeholders, supporting transparent and informed investment decisions in high-fidelity VR-based training systems and contributing to the advancement of safety-critical railway training practices.

کلیدواژه ها:

Railway Personnel Training ، Train Driving Simulation ، Emergency Responses and Safety Procedures ، Virtual Reality (VR) Platform Selection ، Multiple-Criteria Decision-Making (MCDM)