Benchmarking Analysis of Railway Infrastructure Managers: A Hybrid Principal Component Analysis (PCA), Grey Best–Worst Method (G-BWM), and Assurance Region Data Envelopment Analysis (AR-DEA) Model

Snežana Tadić, Aida Kalem, Mladen Krstić (), Nermin Čabrić, Adisa Medić and Miloš Veljović
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Snežana Tadić: Logistics Department, Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia
Aida Kalem: Faculty of Traffic and Communications, University of Sarajevo, Zmaja od Bosne 8, 71000 Sarajevo, Bosnia and Herzegovina
Mladen Krstić: Logistics Department, Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia
Nermin Čabrić: Faculty of Traffic and Communications, University of Sarajevo, Zmaja od Bosne 8, 71000 Sarajevo, Bosnia and Herzegovina
Adisa Medić: Faculty of Traffic and Communications, University of Sarajevo, Zmaja od Bosne 8, 71000 Sarajevo, Bosnia and Herzegovina
Miloš Veljović: Logistics Department, Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia

Mathematics, 2025, vol. 13, issue 5, 1-27

Abstract: Benchmarking railway infrastructure managers (RIMs) has become a crucial tool in the context of European transport market liberalization, facilitating efficiency improvements and strategic decision-making. RIMs face challenges in increasing capacity, optimizing operations, and ensuring competitive, safe, and economically sustainable services. To address these challenges, this study proposes a hybrid benchmarking model that integrates Principal Component Analysis (PCA) to identify key performance indicators (KPIs) and reduce data dimensionality, the Grey Best–Worst Method (G-BWM) to determine KPI weight coefficients based on expert evaluations, and Assurance Region Data Envelopment Analysis (AR-DEA) to assess the relative efficiency of RIMs while incorporating real-world constraints. The research findings confirm that RIM8 is the most efficient unit, driven by high electrification levels, strong accident prevention measures, and optimal use of infrastructure. In contrast, RIM2 and RIM4 record the lowest efficiency scores, primarily due to poor safety performance, high infrastructure-related delays, and suboptimal resource utilization. By introducing weight constraints through AR-DEA, the model ensures that efficiency assessments reflect actual operational conditions, rather than relying on unrestricted weight allocations. The main contribution of this study lies in developing a systematic and objective framework for evaluating RIM efficiency, ensuring consistency and reliability in performance measurement. The practical implications extend to policy development and operational decision-making, providing insights for infrastructure managers, regulatory bodies, and policymakers to optimize resource allocation, enhance infrastructure resilience, and improve railway sector sustainability. The results highlight key efficiency factors and offer guidance for targeted improvements, reinforcing benchmarking as a valuable tool for long-term railway infrastructure management and investment planning. By offering a quantitatively grounded efficiency assessment, this model contributes to the competitiveness and sustainability of railway networks across Europe.

Keywords: benchmarking; railway infrastructure managers; KPIs; performance; PCA; G-BWM; AR-DEA (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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