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.

Starting from the emergence of 1st Generation network (1G), wireless mobile communications have been undergoing an evolution - from 2nd Generation (2G), 3rd Generation (3G), 4th Generation (4G) networks to 5th Generation network (5G) at present. The fifth era is only a continuation of the ongoing evolution as it is still in the research phase and is also the basis for further development of industries and the society in general. The paper presents and compares the fourth and fifth generation of wireless mobile communications, focusing on the differences and progress in terms of data transmission rate, capacity, architecture, technology and applied multiple-technique approaches and services provided.

Development of high-speed railways set up challenges for new communication technologies. With the increase in speed, new requirements for communication systems have emerged that HSR requires greater reliability, capacity and shorter response time for efficient and safe operations. Mobile communication systems are crucial for the competitiveness of the railway industry and therefore have become one of the priorities addressed by the participants in the railway system to take advantage of technological opportunities to improve operational processes and the quality of provided transport services. The European Rail Traffic Management System (ERTMS) uses the Global System for Mobile Communications for Railways (GSM-R) for voice and data communication to communicate between trains and control centers. The International Railway Union is exploring new ways of communicating for high-speed railways because as speed increases this system becomes unreliable in information transmission. This paperwork presents an analysis of the evolution of communications on European railways since the usage of GSM-R. In addition, an overview of the various alternative solutions proposed during the time (LTE-R, Future Railway Mobile Communication System) as possible successors to GSM-R technology is given.

Machine learning (ML) is a crucial component of artificial intelligence that has recently attracted attention for its application in logistics. ML algorithms are used on large datasets. They create logic correlations among given data and provide predictions of specific values. This research paper aims to conduct a systematic literature review to showcase the potential applications of machine learning in urban logistics systems, specifically focusing on enhancing satisfaction for postal logistics operators and their customers. The authors used various research publication databases in this context (Web of Science, Scopus, Google Scholar etc). The analysis of different models provides insights into diverse aspects, such as predicting product prices and types of cargo, evaluating user satisfaction, forecasting user departures, assessing optimal geographical locations for implementing postal centres, predicting purchase times before online orders, estimating delivery times in the last phase of the logistics chain and more. The significance of this research is highlighted through the identification of shortcomings in existing literature, offering guidelines for future research in developing new machine learning model for optimal operator selection. This model aims to achieve improvements in both customer and operator satisfaction simultaneously.