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This study traced the architectural, urban and social characteristics of the Bugok Railway Official Residences (BRORs) in South Korea. It also explored the modern elements of Western (or Japanized Western) or traditional Korean characteristics embodied in the BRORs in the modernization process of Korea in the early 20th century.

Through literature reviews, field trips and archive investigation, this study uncovered new critical facts concerning the origin of the BRORs’ construction plan and architectural characteristics.

The BRORs’ value can be described as follows. First, the BRORs are the first modern housing complex in the Uiwang region. Second, they are meaningful as a housing area built during the Japanese colonial period, and many houses were concentrated in the center of a large city. Third, each official residence shows that various phenomena (mass production, standardization, efficiency and so on) are concentrated in buildings from premodern to modern period. Finally, the image of a group residential complex about to be demolished due to redevelopment is recorded in detail.

In the 1940s, the Railway Bureau of the Japanese Government-General of Korea planned a new small-scale town where mainly railway workers would live. The BRORs in Sam-dong, Uiwang were the first-phase plan. Specifically, 200 households in 100 buildings (two households per building) were built in 1943 during the end of the Japanese colonial period. After the liberation in 1945, these residences were made available to the general public and only 27 households remained through modification and renovation. The remaining residences will be demolished in 2023.

This research examined the meaning of the BRORs, which had not previously been researched in-depth, from diverse perspectives; accordingly, the basic research required for sustainable archiving can be performed after demolition using the study data.

The study principally aims to investigate the relationship among work–life balance (WLB), job satisfaction (JS) and employee commitment (EC) among the railway staff by integrating the social exchange theory (SET). The study also explores JS as a mediator in the context of the new normal.

A mixed-method sequential explanatory design was utilized for this study. The research instrument was administered to 533 railway employees, using purposive sampling to ensure reliability and validity. Structural equation modeling (SEM) was employed to analyze the quantitative data and measure mediation effects. Additionally, 15 interviews were conducted with employees from three job positioning groups (A, B and C) to gain further insights into commitment-related concerns.

The study found that WLB and JS positively influenced EC, with JS acting as a mediator between WLB and EC. Furthermore, factors such as fostering friendships among colleagues, effectively managing work–life integration and recognizing the importance of job roles were identified as crucial in enhancing the relationship between WLB and EC.

The study includes SET to examine the social exchange process while considering WLB benefits as a reward from the employer and EC as the outcome of this reward. This study contributes by examining the effects of COVID-19 on the railway industry and EC. The mixed-methods sequential explanatory design gave a comprehensive understanding of the relationships between WLB, JS and EC. The study’s implications highlight the importance of implementing supportive policies, such as flexible work schedules and a supportive organizational culture, to enhance employee commitment and reduce attrition rates. The study emphasizes the significance of prioritizing employee well-being to achieve organizational goals and enhance organizational commitment.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

The design goal for the tracking interval of high-speed railway trains in China is 3 min, but it is difficult to achieve, and it is widely believed that it is mainly limited by the tracking interval of train arrivals. If the train arrival tracking interval can be compressed, it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval. The goal of this article is to study how to compress the train arrival tracking interval.

By simulating the process of dense train groups arriving at the station and stopping, the headway between train arrivals at the station was calculated, and the pattern of train arrival headway was obtained, changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains.

When the running speed of trains is high, the headway between trains is short, the length of the station approach throat area is considerable and frequent train arrivals at the station, the arrival headway for the first group or several groups of trains will exceed the headway, but the subsequent sets of trains will have a headway equal to the arrival headway. This convergence characteristic is obtained by appropriately increasing the running time.

According to this pattern, there is no need to overly emphasize the impact of train arrival headway on the headway. This plays an important role in compressing train headway and improving high-speed railway capacity.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

With the rise in demand and adoption of smart city initiatives, it is imperative to plan the railway infrastructure, as it will have a huge positive impact if adequately integrated into the planning process. Given the complexities involved, a whole systems thinking framework provides a useful platform for rail transport planners.

This paper proposes a simple, adoptable framework utilising systems thinking concepts and techniques taking into cognisance the key stakeholders. Milton Keynes in the United Kingdom is the adopted case study.

Selected systems thinking tools and techniques are adopted to develop a framework for mapping stakeholders and attributes when developing sustainable rail transport systems, taking note of their core functionalities and the complex systems wherein they exist.

The desire to build future (smart) cities is to effectively match infrastructural resources with a rapidly growing population, and the railway sector can play a strategic role in building a much more competitive low-carbon-emission transport system, which is a driving force for sustainable development.

The urban rail service has become vital to urban development as railway stations serve as hubs for sustainable mobility to meet local requirements. Moreover, it takes extra effort to input railway development into smart city plans, as it is a herculean task to get governments to focus on it with clarity of purpose in passing legislation.

The developed framework reduces complexities when planning and designing rail transport systems compared to many of the existing reductionist planning approaches. The simplicity of the framework would also make it easily adoptable by a wide range of users.

This techno-centric and too much busy day-to-day living style of citizens pressurizes the implementation of E-ticketing service to adapt with change. Thus, this study aims to examine the factors influencing railway passengers’ E-ticketing service acceptance and usage intention in Bangladesh and to extend the widely used Technology Acceptance Model through inserting two new constructs.

This paper employs structural equation modeling to test model’s paths developed through theoretical research framework. Moreover, a structured questionnaire was administered at different railway stations in northern and western parts of Bangladesh to collect data. Total of 302 responses were considered for statistical analysis to test hypotheses after considering anomalies and outliers in raw data.

The study results show that technology trust (TT) has the strongest impact on passengers’ E-ticketing usage intention rather than perceived ease of use and perceived usefulness (PU). Meanwhile, the easiness of using technology to reserve tickets does matter to female passengers rather than male passengers wherein PU and TT do not do that.

The findings of this study might be helpful for the railway authorities to improve the ticket reservation service quality online by developing the advanced booking application and minimizing the pressure on other transportation. Therefore, this empirical study will contribute to this domain for further study that ensures full satisfaction of passengers and uplift the railway passengers’ usage intention for E-ticketing which then helps the government to implement the digitization slogan with efficiency and effectiveness.

Project complexity (PC) governs project success, but the project management literature primarily focuses on performance measures and rarely examines the complexity factors, especially for megaprojects. This paper aims to determine the most significant complexity factors for the railway megaprojects in India.

A mixed approach using the Delphi and best–worst method (BWM) helped to identify, validate and determine the most critical factors that require intervention to diminish variance from project performance.

The BWM resulted in stakeholder management, followed by organizational and technological complexity as significant complexity factors, and the varied interests of the stakeholder as the most important among the 40 subfactors.

The finding indicates the necessity for strategic, tactical and operational-level interventions to effectively manage the complexity affecting project efficiency because of the varied stakeholders. This paper will guide the project and general managers to prioritize their resources to handle complexity for effective project performance measured in terms of time, cost and quality and help them make strategic decisions. The research findings of this study are expected to help researchers and practitioners in better planning and smoother execution of projects. In addition, this study would help the researchers formulate policies and strategies for better handling of the projects.

This study adds significant value to the body of knowledge related to PC in megaprojects in developing countries. The result of the investigation underlined that nine complexity factors and seven unique subfactors, namely, the sustainable environment, timely availability of information, communication in both directions, interdepartmental dependency and coordination, design, statutory norms, site challenges, socioeconomic conditions, the tendency of staff to accept new technology and the frequent changes in the requirements of stakeholders are significant in railway megaprojects. The BWM is applied to rank the complexity factors and subfactors in the case area.

This study establishes a method for predicting ground vibrations caused by railway tunnels in unsaturated soils with spatial variability.

First, an improved 2.5D finite-element-method-perfect-matching-layer (FEM-PML) model is proposed. The Galerkin method is used to derive the finite element expression in the ub-pl-pg format for unsaturated soil. Unlike the ub-v-w format, which has nine degrees of freedom per node, the ub-pl-pg format has only five degrees of freedom per node; this significantly enhances the calculation efficiency. The stretching function of the PML is adopted to handle the unlimited boundary domain. Additionally, the 2.5D FEM-PML model couples the tunnel, vehicle and track structures. Next, the spatial variability of the soil parameters is simulated by random fields using the Monte Carlo method. By incorporating random fields of soil parameters into the 2.5D FEM-PML model, the effect of soil spatial variability on ground vibrations is demonstrated using a case study.

The spatial variability of the soil parameters primarily affected the vibration acceleration amplitude but had a minor effect on its spatial distribution and attenuation over time. In addition, ground vibration acceleration was more affected by the spatial variability of the soil bulk modulus of compressibility than by that of saturation.

Using the 2.5D FEM-PML model in the ub-pl-pg format of unsaturated soil enhances the computational efficiency. On this basis, with the random fields established by Monte Carlo simulation, the model can calculate the reliability of soil dynamics, which was rarely considered by previous models.

As an emerging socio-technical paradigm, high-speed railways profoundly change individuals' lifestyle and allow for the shift toward a green transportation. Digital technologies open an opportunity window for the development of enterprises. This study aims to clarify the impact of firm digitalization on the innovation efficiency of the Chinese high-speed rail industry. In addition, human capital is the important non-physical capital of enterprises. The authors also elucidate the moderating role of human capital on the above relationship.

Based on the data of Chinese high-speed railway listed companies from 2015 to 2021, this study explores the impact of digital transformation on the innovation efficiency, and further clarifies the boundary role of human capital with two-way fixed effect regression models.

The empirical results indicate that digital transformation has a positive impact on the innovation efficiency of the Chinese high-speed railway enterprises. Furthermore, human capital significantly enhances the above relationship. In addition, digital transformation fosters the innovation efficiency of small- and medium-sized enterprises and private-owned enterprises, but the correlation coefficients between digital transformation and the innovation efficiency of large enterprises and state-owned enterprises are not significant.

This is one of the earliest studies to explore how digital technologies shape R&D activities. From the perspective of relative efficiency, this study evaluates the effectiveness of digital transformation and provides empirical evidence for the formulation and implementation of corporate digital strategies. Moreover, this study links human capital with digital transformation and identifies condition factors that affect the effectiveness of digital transformation, thereby supplementing existing knowledge.