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Railway network is an integral part of the economy of many countries. Identifying critical network elements can help network executives to take appropriate preventive actions before the occurrence of catastrophic disruptions or to add necessary redundancy to enhance the resilience of the rail network. The criticality of an element or a link is measured by calculating the increased cost or delay when that element is disrupted. In this paper, we proposed a framework for assessing the vulnerability of the freight rail networks by introducing two bi-level models. The first model determines those critical links which have the greatest impact on the routing cost when interdicted, and the next one takes the cost of rescheduling into account, in addition to that of routing, over all origins and destinations. Rerouting effects are already well-captured by existing alternative measures, but this study allows capturing the ramifications of rescheduling measures. The trains are scheduled by a time–space framework considering customer demand, track and station capacities, and time planning horizon. To overcome the difficulty of solving bi-level models, they are converted to single level models. To verify the models and the proposed approach, we considered a case study focused on three disruption scenarios for the railway network of Iran. The accuracy of the obtained results indicates the effectiveness of the proposed methodology. In addition, our method has a very short computational time in comparison with the network scan method (a full enumeration approach).

Based on the big data-assisted algorithm, the paper uses the dynamic relationship data of railway passenger flow as a research case, adopts the urban network chain model, and uses the mobility and agglomeration of railway passenger flow between cities as the research entry point to analyse the network connection strength of provincial cities and spatial organization structure characteristics. At the same time, it puts forward the idea of using Wuhan’s rich railway resources as the framework of urban railway network passenger flow intensity simulation calculation system, demonstrates the necessity and feasibility of its construction, and puts forward an implementation plan based on the characteristics of the railway network and urban development for the reference of relevant decision makers.

We principally focus on evaluating the local and entire network performance of railway stations for sustainable logistics management in South Korea. Specifically, we aim to address the issue of dealing with vulnerability in logistics dependent on the degree of connectivity. To resolve this issue, we investigate (i) the current level of local railway station sustainability performance from the perspectives of the value of the station (node) and the geographical location (place), and (ii) how railway station network management can prepare for imminent internal and external risks. Integrating node–place analysis and social network analysis approaches, we demonstrate a means of assessing (i) local railway station performance by comparing how one station’s value differs from that of other stations, and (ii) overall railway network performance by measuring the degree of connectivity based on the centrality characteristics. Consequently, we recommend improvement in planning orders considering the degree of local performance and network vulnerability for railway station network sustainability.

Evaluation of the railway network distribution and its impacts on social and economic development has great significance for building an efficient and comprehensive railway system. To address the lack of evaluation indicators to assess the railway network distribution pattern at the macro scale, this study selects eight indicators—railway network density, railway network proximity, the shortest travel time, train frequency, population, Gross Domestic Product (GDP), the gross industrial value above designated size, and fixed asset investment—as the basis of an integrated railway network distribution index which is used to characterize China’s railway network distribution using geographical information system (GIS) technology. The research shows that, in 2015, the railway network distribution was low in almost half of China’s counties and that there were obvious differences in distribution between counties in the east and west. In addition, multiple dense areas of railway network distribution were identified. The results suggest that it might be advisable to strengthen the connections between large and small cities in the eastern region and that the major urban agglomerations in the midwest could focus on strengthening the construction of railway facilities to increase the urban vitality of the western region. This study can be used to guide the optimization of railway network structures and provide a macro decision-making reference for the planning and evaluation of major railway projects in China.

Delving into the spatiotemporal evolution of the railway network in different periods can provide guidance and reference for the planning and layout of the railway network. However, most of the existing studies tended to model the railway data separately and compare the network indices of adjacent periods based on the railway data of different periods, thus failing to integrate the railway network in different periods into a unified framework for evolution analysis. Therefore, this paper used the railway data from 2008, 2010, 2015, and 2019, and analyzed the spatiotemporal integration of the railway network evolution based on the complex network theory and the self-organizing maps (SOM) method. Firstly, this study constructed the geographical railway network in the four years and probed into how the network feature indices changed. Then, it used the SOM method to capture the spatiotemporal integration of the railway network evolution in multi-time series. Finally, it clustered the change trajectory of each city node and unveiled the relationship between the evolution of city nodes and the hierarchy of urban systems. The results show that from 2008 to 2019, the railway network feature indices showed an upward trend and that the expansion pattern of the railway network could be divided into the core–peripheral pattern, belt expansion pattern, strings of beads pattern, and multi-center network pattern. The evolution of the change trajectory of the city nodes was highly related to the hierarchical structure of the urban system. This study helps to understand the evolution process of the railway network in China, and provides decision-making reference for improving and optimizing China’s railway network.

Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management.

The Arctic zone of the Russian Federation is one of the most intensively developing regions of the country. Amongst the major domains of economic and industrial growth and improvement is transport infrastructure and particularly the railway network. This area is being exposed to negative factors of rapid climate change that can significantly affect and compromise this activity. Thus, it is vital to take them into account during design, construction, and operation of the railway infrastructure facilities. This work details the production of a digital atlas comprising the 1950–2021 dynamics of the main hydrometeorological parameters: air and soil temperature, precipitation, wind speed, air and soil humidity, and snow cover thickness. The maps are based on climatic data derived from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) reanalysis. In total there are 459, which are arranged into 7 chapters. The atlas geographically covers the western part of the Russian Arctic encompassing the regions of quite intensive transport development, which includes the construction of the Northern Latitudinal Railway. Original algorithms of geospatial data processing and their further representation as well as the maps compiled in GIS environment are discussed. Comprehensive analysis of climatic changes in the region of the Russian Arctic including detailed quantitative evaluation over 40 years is given. In the Discussion, we focus on those changes of the regional climate which, from our point of view, are the most significant for consideration by railway operators. The obtained results contribute to framing the theoretical basis of design, development, and sustainable operation of the railway infrastructure in the Arctic and facilitate the decision-making process. This is the first experience of building a specialized climatic cartographic product for the needs of the Russian railways, and to our knowledge the first atlas such as that in the world. In the future, the amassed experience may be transferred to other regions of the Russian Federation as well as similar regions in Canada, Sweden and Highland China that are also subject to significant climate change.

Peak shaving is one of the key mechanisms implemented in technically advanced power grids, including rail networks, to reduce the demand for costly power generation during peak hours. Energy storage systems are commonly used for this purpose. This article presents an analysis of peak load reduction using energy storage considering the specifics of the energy operation of the railway network. Two methods of peak shaving are proposed: a variable threshold value and a constant threshold value. The choice of one of them depends on the relationship between the frequency of occurrence of peak loads on the railway line and the charging time of the energy storage system. An innovative predictive analysis of the temporal characteristics of the railway line load was carried out to determine the likelihood of a peak load occurring during the charging of the energy storage system. The Poisson distribution and Long Short-Term Memory method were used to accomplish this task. The first experiment in Poland on peak shaving using a large-scale energy storage system is presented. It was also one of the first high-power installations of this type in the world to directly cooperate with a 3 kV DC traction network.

Twitter has become one of the most popular social media platforms, evidently stirred by a very popular trend of event detection with many applications, including delay detection and traffic congestion on the public transport network. In this paper, we propose a Twitter-based railway delay detection method based on topic propagation analysis of geo-tagged tweets between railway stations. In particular, we aim to discover delay events and to predict train delays due to traffic accidents by analyzing topic propagation using railway network topology of real space. To realize this, first, we construct the topology of the railway network (the physical space) as a graph in which nodes are railway stations and edges are represented as routes between them. Then, we extract the topology of the social network that is mapped on the railway network, based on topic propagation analysis of accident delays between stations and by analyzing geo-tagged tweets of each station with a neural network. This allows us to observe the influence of delays on railway stations even if there are a few tweets on them and to predict stations affected by delays with the tweets which contain indirect topics about delays such as “crowded!” and “raining!”. Overall, this paper proposes the method which enables us to analyze the topic propagation of geo-tagged tweets in order to predict accident delays by considering the railway topology of real space. In addition, we also evaluate the performance of the proposed method on datasets derived from Twitter with the actual delay information from 488 stations of 62 routes in Tokyo area in Japan.

In the planning stage of the intercity railway network, the ecological sensitivity evaluation of the planning scheme is not only the key content to explore the ecological environmental rationality of the planning scheme but also a scientific means to promote the sustainable development of intercity railway networks. The purpose of this study is to establish an evaluation method that can quantitatively evaluate the ecological sensitivity of intercity railway network planning to put forwards targeted optimization and adjustment suggestions for the planning scheme. Taking the intercity railway network planning of Guizhou Province as an example, its ecological sensitivity is predicted and evaluated. Six types of ecologically sensitive areas were selected as ecological sensitivity evaluation factors, including protected areas, drinking water sources, geological disaster-prone areas, soil erosion areas, cultivated land resource distribution areas and coal resource distribution areas. Based on the GIS overlay method, the quantitative measurement methods of each evaluation factor are established in turn, and the single factor sensitivity evaluation index is obtained. In addition, the weighted superposition model is used to quantitatively calculate the ecological sensitivity of the planned lines of the intercity railway network in Guizhou Province. Finally, the short board factor of each planned line is obtained, and targeted optimization and adjustment suggestions are put forwards. The research content of this paper can provide a theoretical reference for the practical evaluation of the ecological sensitivity of intercity railway network planning.