Efficient preparation and smooth operation of rail freight trains are essential for improving rail freight services and customer satisfaction. This study examines how automation and digitalisation − specifically Digital Automatic Coupling (DAC) and Virtual Coupling (VC) − can enable seamless rail freight transport within marshalling yards and along railway lines. For the first time, a combined simulation- and optimisation-based modelling approach is proposed to assess the impact of these technologies.A multi-agent simulation model of the Hallsberg marshalling yard was developed to analyse train handling and yard capacity. A 10-hour shunting operation was simulated under manual coupling and DAC technology, comparing standard train lengths and longer trains. The results indicate a substantial increase in processed trains when DAC was applied. Standard-length trains increased from 7 (manual) to 9 and 12 with DAC types 4 and 5, respectively, with similar gains observed for longer trains.Trains from the simulation’s departure yard were subsequently integrated into an optimisation model to assess their scheduling on the main railway line. dispatchers face challenges in optimising freight train routing, VC was proposed as a capacity-enhancing measure. The optimisation results showed that, with conventional timetables, only 70 freight trains could be scheduled while prioritising passenger services, whereas VC enables up to 128 freight trains − − an 82.86% capacity increase.Overall, these results demonstrate that integrating DAC and VC technologies can significantly enhance the efficiency and capacity of rail freight operations and systems, offering substantial benefits to stakeholders across the sector.

With currently deployed technologies for train completeness detection and positioning, it is not possible to reliably determine a train’s exact position and integrity within a block section. To enable advanced signaling concepts, a highly reliable train integrity (TI) monitoring technology is required. Although numerous TI technologies are currently under investigation, existing studies typically assess them individually, using only one or two criteria. This study presents the first comprehensive multi-criteria evaluation of key TI technologies using both an improved linear OPA and Fuzzy Z-OPA framework. Based on a literature review, eighteen TI-related technologies were identified and consolidated into eight representative TI alternatives, which were evaluated against eight criteria. The improved linear OPA model was first applied to establish rankings under crisp assumptions, after which the evaluation was repeated using Fuzzy Z-OPA to capture uncertainty in expert judgments. Due to its robustness in handling imprecision, the fuzzy-based results were used for the final analyzes. The results indicate that scalability, delays, energy utilization, and communication reliability are the most influential criteria in selecting TI alternative. Although the reliability and failure rate are widely recognized as safety–critical attributes, the literature review revealed that no measurable metrics exists for their systematic evaluation. To address this gap, this study derives and proposes corresponding evaluation metrics for the first time. The final ranking of TI alternatives identifies track-based train detection and transponder as the most suitable alternatives, while distributed sensing and Wireless Sensor Network (WSN) approaches show limited suitability for advanced signaling applications.

The digital Twin (DT) is a technology that can be used to create a virtual representation or model of any physical entity. With the help of DT it is therefore possible to optimise the performance of the virtualised physical system and have a direct impact on increasing its competitiveness, efficiency and productivity, as the data flow can also be bidirectional. The DT of rail level crossings (RLCs) holds great potential for increasing rail safety. With the introduction of digitised components of the railway signalling system, the development of a suitable DT of RLCs is of great importance. Therefore, the aim of this paper is to define the basic requirements for the DT development of RLCs. The paper first analyses the past practises in the development of DTs in railway applications. Furthermore, essential input parameters of RLCs are defined, including the time of closing and opening of RLCs, the warning time, and the operating and blocking times of RLCs. With the aim of reducing the waiting time of road users in the era of train automation and digitalisation, the optimal location of balises and train speed should also be considered. The defined requirements and parameters form the essential elements of the framework for the development of the digital RLC twin.

This work summarizes the first results of the project European Shift Enabler Portal for Freight (ESEP4Freight). An analysis of 8 online tools providing information in map format about the European freight transport system has been carried out. The analyzed tools provide information on the infrastructure or on the transport services but rarely on both aspects. In addition, non-commercial tools offer a limited range of quantitative features, such as CO<inf>2</inf> emissions tools or transportation time estimations. Information about transport services connecting terminals or first and last miles services from the terminals is also frequently missing. These results will be considered for the development of the new tool to be developed as part of the project.

The introduction of Digital Automatic Coupling (DAC) is recognized as a means of improving the performance of rail freight transport and competitiveness in the freight transport market. In order to increase the awareness and willingness of stakeholders to invest in the introduction of DAC, the benefits of DAC introduction for railway performance should be quantified. The aim of the study is to demonstrate and confirm the positive impacts of DAC introduction, namely the reduction of train handling time in the marshalling yard, the increase in capacity and the improvement of the efficiency of rail sections with longer trains. Based on different methods, data sources and assumptions, three different case studies were carried out for this purpose. In the first case, the impacts of DAC on train handling in the marshalling yard were analyzed. In the second case, the impact of DAC on capacity improvement was analyzed through the scenarios. In the third case, the Data Envelopment Analysis (DEA) model was applied to estimate the impact of longer freight trains enabled by DAC on the rail efficiency. The results of the first case show that DAC can save time in train handling in the marshalling yard, especially when DAC type 5, which even enables an automatic and remote uncoupling process, is used. A look at the results of the scenarios in the second case shows that due to the DAC and the reduced time needed for train handling, more trains can be used on the line, which increases the capacity of the line. The results of the third case show an improvement in rail freight efficiency due to the increase in the length of trains made possible by the introduction of DAC. The overall results therefore show that the introduction of DAC brings benefits that improve rail efficiency and overall competitiveness in the transportation market.

Railway level crossings (LCs), as the intersection of road and rail transport, are the weak points in terms of safety, as they are used by different modes of transport. The safety level at LCs can therefore be affected by the behaviour of the users. However, the level of safety can also be affected by failures and errors in the operation of LC equipment. Apart from safety, errors and failures of the LC devices can lead to longer waiting times for road users. As the volume of traffic on rail and road increases, so does the risk that the level of safety will decrease. The increase in traffic volume via LC leads to higher traffic volume on the road and more frequent trains on the rail, which leads to longer waiting times for road users on the LCs. The longer waiting times can disrupt the traffic flow, especially during peak hours when the growing volume of traffic on road and rail increases road user dissatisfaction. Moreover, in the era of Industry 4.0 and Digital Rail, new digital and automated technologies are being introduced to improve rail performance and competitiveness. These technologies are aligned with the LCs and are intended to ensure the efficient operation of LC and the efficient use of LCs by conventional trains as well. To achieve this, a concept is needed that simultaneously monitors and visualises the operation of LC in real time, identifies potential faults and failures of the LC equipment, and updates and monitors the proper operation of LC based on the historical data and information of the operation of LC according to the road traffic volume and the characteristics of the rail traffic and trains. Therefore, in this study, a digital twin system (DT) for rail LC was initiated and built as a concept that can meet the above requirements for proper LC operation in real time. DT of LC includes all components of LC and communication between them to synchronise the operation of LC according to the real-time requirements. The DT system is able to optimise the operation time of LC by monitoring the operation of LC and collecting data to ensure efficient use of LC and reduce unnecessary waiting time for road users. In this paper, the operation time of LCs on Swedish and Taiwanese railways was compared using the developed level crossing optimisation model (OLC). Since the introduction of new signalling concepts requires an improvement of LC operating characteristics and their design, the operating strategies were modelled using the OLC model. The results of the work show that the optimal values of LC operation time are different for the case studies investigated. The replacement of track circuits as detection devices and the introduction of balises can also positively influence the operation time, as well as increasing the speed of trains via LCs. However, due to the formulation of the OLC model, the impact of a longer train length on the operation of LC is not recognised. The OLC model can be used to estimate the real-time operation time of LC under different traffic conditions as well as the impact of different changes and extensions of LC.

Today, digitalization is a "must" for all industries and sectors. One of the most promising and popular concepts of digitalization in the last decade is certainly the technology of the digital twin (DT). As rail transport ranks very high among transport modes in terms of sustainability, resilience and reliability, its digitalization is of great importance. Since any disruption or interruption of normal rail operations with the aim of improving conditions could have many negative consequences, the technology of DT is perfect in this sense, as it allows testing changes on a virtual copy of the real system without directly affecting its operation. Combined with machine learning and other artificial intelligence algorithms, it can also be used to predict and forecast operational characteristics, events and failures that can trigger rapid management and action and prevent disruptions and cost increases. This article, therefore, aims firstly to provide a comprehensive and overarching overview of the application of DT technology in the rail sector using a newly proposed multi-layered classification, and secondly to highlight the research gaps that need to be addressed in the near future to enable the transition to Rail 4.0. The result of this research, which examined 58 research articles published in scientific journals, shows that the application of DT in the railway sector has increased in recent years, that investigations in infrastructure for maintenance purposes receive the most attention in the scientific community, and that there is still much room for research and development of DT in the railway sector.

Heavy -Duty Vehicles (HDVs) pose a significant challenge in meeting emissions reduction targets. However, studies on estimating emissions from Irish HDV fleet are rare. This paper estimates fuel consumption and CO2 emissions from tractor -trailers that occupy nearly half of Ireland's HDV fleet using the European Commission recommended VECTO tool. To improve the estimation accuracy, driving cycles specific to Irish road conditions were developed utilising real naturalistic driving data from trucks. The Irish driving cycles significantly differ from the VECTO inbuilt driving cycles in percentage time spent in different gears, fuel consumption, and CO2 emissions. CO2 emissions estimated with Irish driving cycles range from 733 to 1961 g/km, while with VECTO driving cycles, the values ranged from 728 to 1865 g/km. Further, the study evaluated the sensitivity towards payload, road gradient, and idling percentages. The study insights would provide an essential evidence base for policymakers in Ireland.

Ports are important and central hubs for logistical activities in transportation chains that contribute to economic growth. Nevertheless, port activities are associated with undesirable impacts such as energy consumption and air pollutant emissions. Various policy instruments and measures have been developed and adopted to reduce energy consumption and emissions from ports. In an effort to manage all undesirable impacts of port operations, the search for best practices has proven to be an effective approach. This study examines one- and two-stage Data Envelopment Analysis (DEA) models for measuring port environmental efficiency. The adequacy of the models was analyzed using two case studies, i.e., the port of Koper and port of Dublin. The results of the study provided the best practices and the main differences between one-stage and two-stage models. Distance metrics were used to identify the necessary improvements of inefficient decision-making units (DMUs) to achieve the best practices.