This paper provides an analysis of a designed underground station infrastructure for vacuum tube high-speed trains for the planned mega transport hub in Poland. The potential of integrating the infrastructure of the station building with sealed low-pressure tubes system is analyzed. The Solidarity Transport Hub Poland is a planned mega hub to be located in Baranów Municipality, Poland, which is comprised of an airport, an airport city, a road, and railway infrastructure. It is to be integrated with the first route of vactrains in Poland. The aim of this paper is to design a hyperloop station building adequate for the advanced technology of low-pressure high-speed trains. Designing a hyperloop station is not trivial, due to technological aspects which have not been hitherto present in airport or railway planning and design, such as low-pressure zones or airlocks which determine possible passenger paths and evacuation roads. Both the mega airport and Polish hyperloop are in the planning stage, therefore, in this paper, available models and designs of the hyperloop station building and infrastructure are used in order to formulate recommendations for further development and identify critical issues related to the safety and reduction of passenger transit time. The main contribution of this paper is a model of the hyperloop station building which respects the principles of spatial planning and safety standards.

The increasing number of heavy-duty vehicles (HDVs) on roads has become a major contributor to harmful emissions, posing critical environmental challenges and exacerbating global warming. This study aims to establish correlations between road types and the emissions they generate, offering actionable insights for logistics planning and strategies to mitigate diesel vehicle emissions. The analysis is based on input data from a selected transport company, covering parameters such as vehicle type, average mileage, speed, and driving style, as well as environmental conditions like ambient temperature and humidity. Emissions and energy consumption levels are estimated using the COPERT model. A key research challenge involves accurately predicting and managing air pollution caused by HDVs under varying vehicular, technological, and fuel conditions, as well as fluctuating atmospheric and operational factors. The findings indicate that highway driving produces the highest emissions of pollutants such as Se and Zn, while urban peak hours record the highest levels of NO_x, NO, and NO₂. These results emphasise the critical role of strategic route selection in reducing total emissions and managing levels of individual harmful substances. This research highlights the importance of integrating sustainable practices into transport planning to reduce environmental impacts, align with global climate objectives, and advance sustainable development in the transport sector.

The increasing use of autonomous mobile robots (AMRs) in internal logistics systems improves operational efficiency. However, it also introduces challenges related to safety, reliability, sensor-based monitoring and human–robot interaction. This study proposes a sensor-driven Digital Twin and fuzzy decision-support framework for operational risk monitoring in AMR-based transportation systems. The proposed approach integrates Digital Twin technology with fuzzy logic methods to support continuous sensing, operational data acquisition, and data-driven risk evaluation in autonomous logistics environments. In the proposed framework, the Digital Twin acts as a continuous monitoring and early-warning environment. It enables continuous observation of system states, robot condition, navigation performance, traffic intensity and operational disturbances. To support decision-making under uncertainty, the fuzzy Analytic Hierarchy Process (fuzzy AHP) is applied to determine the relative importance of selected safety and reliability indicators. These indicators include condition monitoring parameters, mean time between failures, sensor-related disturbances and task completion performance. Subsequently, a hierarchical Mamdani fuzzy inference system is used to evaluate the operational risk level of the transportation system based on aggregated KPI values derived from Digital Twin data. The applicability of the proposed approach is illustrated through a case study involving multiple AMRs operating in a dynamic intralogistics environment. The results indicate that the integration of sensor-based Digital Twin monitoring with fuzzy decision-support mechanisms improves operational risk visibility and supports more effective risk identification and management in Industry 4.0 intralogistics systems.

This paper presents a universal Excel-based cost estimation model for internal machinery relocation within manufacturing facilities. The objective of the study is to develop and validate a practical tool that enables accurate cost estimation and supports planning decisions in various industrial contexts. Although previous research has primarily focused on full-site relocations, internal repositioning of equipment has received limited academic attention despite its practical and financial relevance. The proposed model divides total relocation costs into three main components: equipment costs, labor costs and transport rental costs. To assist in decision-making and prioritization, the model applies the ABC classification method, grouping activities based on their financial impact. Selected principles of the OLESTR methodology, which integrates spatial and technological planning, are incorporated to enhance layout optimization. Developed in Microsoft Excel, the model was tested using a case study of a printing facility. Two layout variants were evaluated, each involving specific equipment relocation activities. Input parameters such as labor rates and equipment rental prices were defined as ranges to simulate real-world variability. The resulting variation in cost estimates across different simulation runs reflects the uncertain nature of industrial environments. The model offers a transparent, flexible and structured framework that supports industrial managers in evaluating layout alternatives, managing relocation budgets and minimizing disruption. Its adaptable design makes it applicable to a broad range of manufacturing settings and supports data-driven decision-making aimed at optimizing internal relocation processes and reducing associated costs.

This study focuses on developing the appropriate infrastructure between Poland and Ukraine to enable the import and export of hydrogen, ensuring EU consumers have access to sufficient quantities. Such infrastructure is crucial in achieving climate neutrality goals, as it will serve as a key transit area for hydrogen, thereby contributing to sustainable energy development in Europe. To address this, the article reviews existing solutions for hydrogen transportation via pipelines and proposes mapping the hydrogen transportation system between Poland and Ukraine. Specifically, it examines the possibility of constructing a hydrogen pipeline from Ukraine to Poland, with potential connections to the designated hydrogen supply corridors of the European Hydrogen Backbone (EHB) infrastructure. Through the use of Python-created maps and algorithms, the study demonstrates how a hydrogen supply corridor can be established between Poland and Ukraine and effectively integrated with the broader EU hydrogen supply corridors. Furthermore, the article discusses the complex challenges involved in building hydrogen pipelines, particularly the unification of design standards between EU and non-EU countries. Equally critical is the management of hydrogen export and import, given that no platform currently exists between Poland and Ukraine for this purpose. The integration of hydrogen pipeline transport systems is thus highlighted as a matter of international importance, providing a platform for cooperation between the two countries and facilitating the creation of a hydrogen corridor linked to the EU infrastructure. An original contribution of this study is the presentation of a map illustrating the potential corridor connecting the hydrogen pipeline systems between countries, along with the development of algorithms using Python, which open up possibilities for future expansion of the map.

Fuel consumption efficiency is one of the key areas for evaluating the operational performance of vehicle fleets in transport enterprises. Rising operating costs and the need for rational resource management are making the analysis of data recorded during real-world vehicle use increasingly important. This article aims to comprehensively assess the operational efficiency of a vehicle fleet based on a case study of a selected transport enterprise. The analysis also includes the identification of vehicles characterised by unfavourable fuel consumption parameters. As part of the study, the consistency of input data on fuel consumption obtained from the CAN bus, invoices and fuel level sensor readings was assessed against reference data from the telematics system. On this basis, the data source demonstrating the highest level of consistency was identified and used in further analysis. Efficiency was assessed based on total and unit fuel consumption, CO ₂ emissions and the costs of excess fuel consumption. The applied criteria made it possible to identify vehicles with elevated fuel demand and suboptimal operational parameters. In addition, a scenario analysis was conducted to estimate potential savings and CO ₂ emission reductions achievable by improving the efficiency of the vehicle identified as requiring further inspection. This approach supports fleet management by facilitating the assessment of potential reductions in operating costs and CO ₂ emissions. The results indicate that targeted efficiency improvements may reduce fuel consumption and environmental impact without the need for immediate vehicle replacement.