Urban Railway Research Articles

Design and development of bridge erecting machine with small curve radius and bottom feeding beam.

As a key equipment in highway and railway construction, bridge erecting machines directly affect the feasibility, progress, and quality of project implementation. In order to solve the problem that traditional bridge building machines are only suitable for application scenarios with large turning radii and large lifting sites, and have low lifting efficiency, which cannot meet the requirements of urban elevated bridge construction, a small radius bottom feeding beam bridge building machine suitable for urban highway and railway construction is proposed. The feasibility and reliability were verified through force analysis and finite element analysis; Design a secondary turning structure and bridge construction process for the front leg, adjusting the turning angle to around 7°, significantly reducing the turning radius of the bridge building machine; Propose a technology for feeding and lifting beams at the bottom of dual-use bridge decks for highways and railways. By vertically lifting beams and slabs, the lifting space is significantly reduced and the lifting efficiency is improved. Finally, a small radius bottom feeding beam bridge erecting machine was developed and tested and applied. The results show that the small radius bottom feeding beam bridge erecting machine designed in this article reduces the turning radius of traditional bridge erecting machines from over 300m to about 80m, making it possible to carry out projects that were originally impossible, and solving the problem of elevated bridge construction in limited urban spaces.

Comparative analysis of methods for predicting train-induced vibrations

Railway systems cause more severe vibration pollution than road systems, and the vibration pollution caused by railway systems has been increasing annually over the past two decades. Despite this, there is limited investigation into the accuracy of train-induced vibration prediction methods. Therefore, the present study investigated the accuracy of the detailed and general procedures proposed by the US Federal Transit Administration (FTA) for predicting train-induced vibrations. The accuracy of these methods was determined by evaluating their predictions against in-situ measurements of ground vibration levels induced by passing trains. The field experiments conducted in this study involved the measurement of train-induced vibration levels, the estimation of line-source transfer mobility (LSTM) by dropping a weight on rail sleepers, the evaluation of force density levels, and the determination of ground LSTM by dropping a weight on the ground. In addition, graphs were plotted to determine the relationships of train-induced vertical vibration levels with train speed, distance from the track centreline, and train length. The study compared measured and predicted vibration levels to assess the accuracy of the FTA’s detailed and general prediction methods. Results indicate that detailed assessments tend to overestimate vibration levels, while an optimized general vibration assessment curve offers a more precise estimate. Additionally, on the basis of the measurement data, the study explored the impact of train length on ground vibration and demonstrated that track ballast can absorb vibration energy along the wave propagation path. A frequency propagation contour plot was also created to analyse vibration energy across different frequencies. These findings enhance the reliability of train-induced vibration prediction methods and provide insights for improving vibration mitigation strategies in urban railway systems.

Reducing low-frequency noise radiation from a concrete box girder bridge using acoustic short circuits

Low-frequency noise (20–200 Hz) radiated from concrete box girder bridges on urban viaduct railway lines is a significant source of disturbance to nearby residents. Many researchers have investigated methods which can be used to effectively reduce this bridge noise. Currently, most of the methods are based on the principles of vibration isolation and/or absorption. This paper, after modelling and analysing the contributions of various parts of a concrete box girder bridge to the total noise radiated from the bridge, presents a study on the potential in bridge noise control of making holes to create acoustic short circuits on the bridge wings. It is found that by this means the bridge noise can be reduced by up to 6.8 dB at standard-specified measurement points. Additionally, a 3dB-effectiveness distance ratio design method for acoustic short-circuiting is proposed, which effectively reduces bridge noise and achieves stable noise reduction outcomes.

Superconducting Magnetic Energy Storage (SMES) for Urban Railway Transportation

Superconducting Magnetic Energy Storage (SMES) for Urban Railway Transportation

Convolutional Autoencoder-Based Damage Detection for Urban Railway Tracks Using an Ultra-Weak FBG Array Monitoring System

Convolutional Autoencoder-Based Damage Detection for Urban Railway Tracks Using an Ultra-Weak FBG Array Monitoring System

Selective neglect of viral risk: Tokyo’s commuter trains during the COVID-19 pandemic

This article asks why Tokyo’s crowded trains were largely absent from official discourses of viral risk in Japan during the COVID-19 pandemic. While public transport environments were often framed as spaces of contagion in other countries, Japanese public health discourses instead tended to emphasize the viral safety of urban railway transport. Drawing on an analysis of media and expert discourse, auto-ethnographic fieldwork, and repeat interviews with commuters, this article examines the processes through which Tokyo’s urban railway network was extracted from discourses of viral risk. It identifies four factors that contributed to this: (1) dominant definitions of risk spaces in public health messaging, (2) trains’ integral role in the operation of socio-economic life, (3) elusiveness of public transport in the epidemiological data, and (4) a focus on “unruly” passenger conduct. This analysis of the selective treatment of viral risks is tied to a critical engagement with theoretical distinctions between “essential” and “existential” mobilities developed in prior anthropological scholarship on the pandemic. Exploring the discursive framing of pandemic mobility practices on one of the world’s most extensive urban railway networks, the article thus contributes to the anthropological study of mobilities and transport in Japan and beyond.

Passenger Flow Management in Front of Ticket Booths in Urban Railway Stations

In railway stations, queues often form in front of the ticketing booths that provide ticketing services. Proper design of service systems is key to effectively managing these queues, as waiting time is a critical factor affecting customer satisfaction. This research focuses on optimising the location and configuration of queues in front of ticket booths to minimise waiting times and increase service efficiency. Passenger flow management at the station can be understood as the planning and implementation of the orderly movement of the crowd through the infrastructure. Using operational Markov chain analysis, we evaluate different queue configurations and the number of service providers in urban railway stations. The study specifically focuses on the case of the Poprad-Tatry railway station in Slovakia, where we propose the introduction of a common queue for all ticket booths. We propose the distribution of lines and their schedule, based on mathematical analyses, by creating designated service zones with a common queue in front of the ticket booths. The results show that this approach significantly reduces waiting times and improves overall system efficiency. This research focusses on solving the shortcomings in the design of queues in railway stations, specifically on the use of a common queue, thereby contributing to the improvement of passenger movement management.

Study of Impact of COVID-19 Pandemic on Urban Railway Productivity: Focusing on Seoul Urban Railway with Malmquist Productivity Index

Study of Impact of COVID-19 Pandemic on Urban Railway Productivity: Focusing on Seoul Urban Railway with Malmquist Productivity Index

Finance arrangements and governance modes toward sustainable infrastructure: the case of urban railway projects in Manila, the Philippines

In recent years, the concept of sustainable infrastructure has gained popularity and is widely utilized and promoted by the international development community. However, implementation of sustainable infrastructure remains a challenge due to various enabling and disabling factors particularly related to financial arrangements and governance modes. This paper aims to fill the research gap in this area and investigate the factors that enable implementation of sustainable infrastructure, with a focus on the financial and governance aspects. To do so, the paper uses real urban railway projects in Manila, the Philippines as case studies for comparative analysis. The results suggest that from the finance and governance perspectives, public–private partnerships (PPPs) and official development assistance (ODA) finance are effective tools for implementing sustainable infrastructure, with some conditions, namely a mixture of vertical project management and horizontal process management, and the project environments having a positive effect on the trust building and collaboration among the stakeholders.

The Concept of Urban Railway Surplus Power System Utilization for Supplying Power to Hydrogen Charging Stations

The Concept of Urban Railway Surplus Power System Utilization for Supplying Power to Hydrogen Charging Stations

Economic and environmental assessment of the Korea urban railway and its greenhouse gas mitigation potential

Railways can simultaneously transport large quantities of freight and passengers, making them energy-efficient and economical modes of transportation. Changing the materials that constitute a significant portion of rail track systems can help mitigate climate change. Gravel and concrete trackbed methods are commonly employed in railway construction. This study proposes the utilization of the asphalt concrete trackbed method, which is currently being researched and developed in South Korea, in addition to traditional gravel and cement concrete methods, and presents its economic feasibility and environmental benefits. The asphalt concrete trackbed method, which has already been implemented in regions such as China, Australia, and Europe, can also be applied in other areas. This study analyzed the carbon emissions and economic feasibility in the construction, usage, and disposal stages of cement concrete and asphalt concrete trackbeds. Previous research on carbon emissions analysis has faced challenges in applying geographical and climatic, as well as energy sources, to individual cases in the context of construction in Korea. Using the Korean Life Cycle Inventory database, this study indicated that asphalt concrete exhibits approximately 2.65-times lower carbon emissions than cement concrete. In Korea, railway construction involves 1,998 tons of asphalt concrete mixture, 1,820 cubic meters of cement concrete, and 59 tons of rebar per kilometer. Furthermore, the asphalt concrete trackbed method shows potential cost savings of approximately 29,000 EUR when converted to the 2021 EAU value. Thus, asphalt concrete trackbeds not only provide environmental benefits, they are also economically viable. However, further research is required to establish precise standards for on-site construction. This study is expected to provide foundational data for promoting the widespread adoption of asphalt concrete trackbeds in areas that produce asphalt concrete.

Educational Application of Artificial Intelligence for Diagnosing the State of Railway Tracks

The aim of the work is to present an innovative solution based on artificial intelligence for examining the condition of railway tracks in real time. The system, based on fuzzy logic and metaheuristics such as Fuzzy Logic, Neural Networks and Bee Behavior Optimization, combines hardware and software to provide reliable data on the technical characteristics of the railway. Installed in rail vehicles, hardware collects this data, while software uses artificial intelligence to improve operational reliability and safety. The aforementioned technology is not only useful for infrastructure diagnostics, but also for urban railways such as trams and metros, ensuring a high level of passenger safety. The introduction of artificial intelligence in the railway sector is a key step towards modernisation, improving efficiency, resource optimization and safety. Although still in its infancy, artificial intelligence already shows great potential in transforming the railway sector towards a more efficient, reliable and sustainable future.

GHG emission quantification and reduction pathway of subway shield tunnel engineering: a case study on Guangzhou Metro, China.

The shield method is a commonly used construction technique in subway tunnel engineering. However, studies on greenhouse gas (GHG) emissions specifically in subway shield tunnel engineering are lacking. This study aims to investigate the GHG emission characteristics and GHG reduction pathways during the construction period of subway shield tunnels. Firstly, based on the life cycle assessment (LCA) method, a greenhouse gas (GHG) emission quantification model for the shield tunnel construction period was developed using a multi-level decomposition of construction. Then, the GHG emission level and intensity during the construction period of a case project are quantified, and its emission characteristics and GHG reduction potential points are assessed. Finally, a comprehensive path for GHG reduction in subway shield tunnel engineering is proposed. The research results indicate that constructing 1 km of subway shield tunnel can generate 19,294.28 t CO2eq. Among these, material production element dominates the emissions with a percentage of 89.05%, while transportation and mechanical construction elements contribute 1.81% and 9.14%, respectively. From the structure perspective, the main structure contributes 88.73% of total emissions, while the ancillary structure contributes 11.27%. Among them, the working shaft and tunnel segments are the main sources of emissions for the main structure, accounting for 23.65% and 65.08%, respectively. Connecting channel and end reinforcement are the main emission sources of the ancillary structures, accounting for 43.63% and 31.30%, respectively. These findings provide a scientific foundation for the environmentally friendly transformation of urban railway development regarding pursuing "carbon peaking and carbon neutrality" strategic goals.

Simulation Optimization of Station-Level Control of Large-Scale Passenger Flow Based on Queueing Network and Surrogate Model

Urban rail transit encounters supply–demand contradictions during peak hours, seriously affecting passenger experience. Therefore, it is necessary to explore and optimize passenger-flow control strategies for urban rail transit stations during peak hours. However, current research mostly focuses on passenger-flow control at the network level, and there is insufficient exploration of specific operational strategies at the station level. At the same time, the microscopic simulation model for passenger-flow control at the station level faces the challenge of balancing efficiency and accuracy. This paper presents a simulation optimization approach to optimize the station-level passenger-flow controlling measures, based on a queueing network and surrogate model, aiming to improve throughput, minimize congestion, and enhance passenger experience. The first stage of the method modeled the urban railway station using queueing network theory and multi-agent theory, and then built a mesoscale simulation model that was based on an urban railway station. In the second stage, a passenger flow management and control model for ingress flow was established by combining the Kriging model with a queuing network model, and the particle swarm optimization algorithm was used to solve the model. On this basis, a simulation optimization method for station passenger-flow control was established. Finally, we conducted an example analysis of Zhongguancun Station on the Beijing subway. By comparing the simulation results before and after control, as well as comparing the optimal control scheme obtained by this method with the results of other control schemes, the results showed that the simulation optimization method proposed in this paper can propose an optimal passenger-flow control scheme. By using this method, stations can significantly enhance sustainability. For example, the method not only saves human resources but also effectively avoids or reduces congestion, boosting passenger travel efficiency and safety. By minimizing wait times, these methods lower energy consumption and support the sustainable development of public transportation systems, contributing to more sustainable urban environments.

Experimental and numerical investigation of the track structure elasticity induced by resilient fastener on vehicle-track interaction and train running stability

The resilient fastener slab track (RFS track) has found extensive application in urban railways, effectively mitigating vibration and noise resulting from train operations. However, the structure elasticity of the RFS track is higher than the normal slab track (NS track), which can affect train running stability and cause abnormal vibration phenomenon. This paper explores the track structure elasticity on vehicle-track interaction and train running stability through experimental tests and numerical simulations. The vibrational properties of the RFS track and the NS track are obtained through field tests. A 3D metro vehicle-track coupled dynamics model is developed and utilized to simulate the dynamic performance of metro vehicles moving on different track structures. The impact of track structure parameters upon the vehicle-track interaction and train running stability are discussed. The findings indicate that the track structure elasticity can reduce train hunting stability and cause abnormal vibration phenomena in metro vehicles under certain conditions. The reasonable matching of the fastener lateral and vertical stiffness can not only accomplish vibration attenuation but also minimize the impact on train running stability. In addition, the rail surface wear and track gauge variation also exert influence on the running stability and dynamic performances of metro vehicles.

Optimizing passengers’ experience: A goal-oriented reinforcement learning speed control approach for urban railway trains

Prolonged vibration can be uncomfortable for passengers utilizing urban rail transit systems. This study proposes an automatic speed control framework for urban railway trains to reduce vertical vibrations experienced by passengers. We suggest the concept of the “segmented comfort speed limit” to represent the vertical passing comfort of oncoming sections. This speed limit is calculated from 1/3 octave band acceleration and smoothed through lag-type speed control mode. The deep deterministic policy gradient algorithm with hindsight experience replay mechanism (HER-DDPG) is designed, to balance safety, comfort, and energy efficiency driving. Verify the speed control framework based on HER-DDPG through the rail data collected from Dalian Metro Line 12. Compared with the DDPG-based model, the vertical comfort is improved by 2.34%, and the longitudinal acceleration and total energy consumption are reduced by 45% and 8.1%. Compared with the real-world train control trajectory, HER-DDPG improves vertical comfort by 9.76% and reduces energy consumption by 12.4%. The results show that the proposed framework can effectively improve the ride experience of passengers.

DeepAGS: Deep learning with activity, geography and sequential information in predicting an individual's next trip destination

AbstractIndividual mobility is driven by activities and thus restricted geographically, especially for trip destination prediction in public transport. Existing statistical learning based models focus on extracting mobility regularity in predicting an individual's mobility. However, they are limited in modeling varied spatial mobility patterns driven by the same activity (e.g. an individual may travel to different locations for shopping). The paper proposes a deep learning model with activity, geographic and sequential (DeepAGS) information in predicting an individual's next trip destination in public transport. DeepAGS models the semantic features of activity and geography by using word embedding and graph convolutional network. An adaptive neural fusion gate mechanism is proposed to dynamically fuse the mobility activity and geographical information given the current trip information. Besides, DeepAGS uses the gated recurrent unit to capture the temporal mobility regularity. The approach is validated by using a real‐world smartcard dataset in urban railway systems and comparing with state‐of‐the‐art models. The results show that the proposed model outperforms its peers in terms of accuracy and robustness by effectively integrating the activity and geographical information relevant to a trip context. Also, we illustrate and verify the working mechanism of the DeepAGS model using the synthetic data constructed using real‐world data. The DeepAGS model captures both the activity and geographic information of hidden mobility activities and thus could be potentially applicable to other mobility prediction tasks, such as bus trip destinations and individual GPS locations.

Diversity and Influencing Factors of Public Service Facilities in Urban (Suburban) Railway Life Circle—Evidence from Beijing Subway Line S1, China

Diversity and Influencing Factors of Public Service Facilities in Urban (Suburban) Railway Life Circle—Evidence from Beijing Subway Line S1, China

Quantifying the Co-benefits of Air Pollution and Greenhouse Gas emission mitigation by operating Metro Line 1 in Ho Chi Minh City, Vietnam

Abstract Motorcycles, cars, and buses in urban areas contribute significantly to greenhouse gas (GHG) emissions and air pollutants. Particularly, Ho Chi Minh City (HCMC) has the highest vehicle traffic intensity in Vietnam. To improve the traffic system, HCMC has implemented urban railway projects, and this research aims to quantify the co-benefits of these public transport systems. The study used the EMISENS model and the TAMP-EARMOD simulation model system to assess the current situation and to predict the GHG and air pollution emissions when Metro Line 1 (Ben Thanh – Suoi Tien) operates stably. The reduction of air pollution and GHG is calculated to identify the co-benefits when a part of personal transport means is substituted by Metro Line 1. The results show that, if Metro Line 1 is put into operation and reaches 80% of its transport capacity, the air emissions on the route parallel to Metro Line 1 decrease 429.94 tons/year, 13,639.32 tons/year, 11.36 tons/year, 1,137.84 tons/year, 24.27 tons/year for NOx, CO, SO2, NMVOC, and PM2.5, respectively. Importantly, the emission of CO2eq reduces above 49.6 million tons per year, equivalent to 25.4%. Based on these results, the study proposes measures to optimize the deployment of Metro Line 1 to maximize the attainment of co-benefits.

Assessing climate-induced risks to urban railway infrastructure

Assessing climate-induced risks to urban railway infrastructure