Rail System Research Articles

Using "Enacted Purposes" to Interpret a Regulatory Statute

How does a regulatory agency or a reviewing court deal with the multiple “enacted purposes” that may exist in a regulatory statute? In this Article it is argued that the problem of multiple purposes can be optimally dealt with by looking at these purposes as forming an optimization problem—minimization or maximization. This novel approach requires identifying, if possible, one purpose as the primary objective and then treating the other clearly stated purposes as constraints. Depending on the issue at hand, not all constraints will be relevant. However, the primary objective will always need to be considered. Once the primary objective and applicable constraints are identified, then the result can be used to define ambiguous statutory terms such as “in the public interest.” This optimization approach is utilized in evaluating the legal validity of the Surface Transportation Board’s new rule, Reciprocal Switching for Inadequate Service. The Board regulates the freight rail industry under the authority of the Staggers Rail Act of 1980. Reciprocal switching “is an arrangement that permits an industry served by a single rail carrier to access additional rail services through a competing line haul carrier.” The validity of the new rule is currently under review by the U.S. Court of Appeals for the Seventh Circuit. The Staggers Act provides that the Board may promulgate a rule implementing prescribed reciprocal switching if it is based on the authority derived from the term “in the public interest.” Unfortunately, when determining if it was acting “in the public interest” in promulgating its new rule, the Board did not adequately take into consideration all of the Act’s enacted purposes, including the primary objective of minimizing regulation. This is an unreasonable approach, an arbitrary and capricious act, and therefore the Seventh Circuit is duty bound to vacate it. Moreover, in a post-Chevron world, even if the Board were to revise its rule so it would be considered reasonable, a reviewing court could still find that its interpretation is not correct. This is where the applying of the optimization approach will be of most value, minimizing the risk that this will happen.

Research on the pressure fluctuations and hydraulic resonance phenomena in the high-pressure pipelines of marine common rail systems

Increasing injection pressures in marine high-pressure common rail systems can lead to reliability issues due to pressure fluctuations. This study investigates the mechanism of hydraulic resonance caused by these fluctuations. A test bench assessed pressure fluctuations under various conditions and fuel pipe parameters, where hydraulic resonance was observed. Time-domain and frequency-domain analyses were conducted on the test results. The system pipeline was then optimized to eliminate hydraulic resonance. Resultsindicate that pressure fluctuations exhibit both high- and low-frequency coupling. Abnormal fluctuations are caused by hydraulic resonance, triggered by the proximity between the excitation frequency of the high-pressure fuel pump and the first-order natural frequency of the system. Resonance speed is influenced by the fuel pipe's structural parameters and the fuel's hydraulic properties. The frequency-domain model shows high predictive accuracy, identifying the interface between the pump and the pipeline as the most probable failure location. Optimization reduced pressure fluctuations at the pump end and the common rail by 73 % and 35.7 %, respectively, and eliminated hydraulic resonance at the original resonance speed. The maximum pressure fluctuation amplitude at the pump end decreased from 410 bar to 167 bar, a 59.3 % reduction. The research methodology presented in this paper provides a theoretical foundation for the structural design of the high-pressure pipeline in high-pressure common rail systems.

Navigating uncertainty with cybernetics principles: A scoping review of interdisciplinary resilience strategies for rail systems

AbstractCommon difficulties across industries are discovered in data management, where handling the volume, variety, and quality of data is crucial for informed decisions in uncertain environments. In this context, rail management must navigate complex decision‐making to ensure safety, service continuity, and cost‐effectiveness. The 2020 Stonehaven derailment is an example of the increasing vulnerability of rail infrastructure to environmental factors and systemic failures. It emphasizes the need for resilient systems, proficient at preventative maintenance and adaptable to escalating challenges. These matters further accentuate the need for context‐dependent strategies that bridge theoretical insights and practical applications. This scoping review explores strategies for decision‐making under uncertainty across sectors such as civil infrastructure, agriculture, water management, and emergency response. It unfolds a selection of procedures addressing the impacts of extreme weather and other unexpected disruptions. It also sets a foundation for future research to support rail infrastructure adaptation to climate change by advocating the use of cybernetic principles and artificial intelligence (AI) to enhance decision‐making processes. Cybernetics enables collaborative human‐AI methods, improving adaptability and resilience. However, balancing and incorporating diverse stakeholder viewpoints into decision chains remains difficult. While promising, substantial research and system improvements are needed to fully harness the potential of AI.

Experimental study on the acoustic roughness spectrum of high-speed railway rails

PurposeThis study aims to investigate the acoustic roughness of rails on China’s high-speed railways, with a focus on short-wavelength irregularities (less than 80 cm), which are known to significantly contribute to noise. The goal is to develop a specific acoustic roughness spectrum tailored for China’s high-speed railway system, as no such spectrum currently exists.Design/methodology/approachA long-term tracking study was conducted on major railway lines in China, monitoring rail roughness throughout the initial operational period and the rails’ service life. Data preprocessing techniques such as peak removal and curvature correction were applied for acoustic adjustments. A spatial-wavelength domain transformation was performed, providing the distribution patterns and statistical characteristics of acoustic roughness on China’s high-speed rails. Based on these analyses, a model for constructing the acoustic roughness spectrum was developed.FindingsThe study found that the acoustic roughness of China’s high-speed railway rails follows a χ2 distribution with six degrees of freedom. For wavelengths greater than 8 cm, the acoustic roughness spectrum remains below the ISO specified limits. In the wavelength range of 3.2 cm to 6.3 cm, the roughness is comparable to or within the limits specified by ISO 3095:2005 and ISO 3095:2013. However, for wavelengths shorter than 2.5 cm, the roughness exceeds ISO limits.Originality/valueThis research fills the gap in the lack of a specific acoustic roughness spectrum for China’s high-speed railways. By establishing a tailored spectrum based on long-term data analysis, the findings provide valuable insights for noise control and rail maintenance in the context of China’s high-speed rail system.

A novel TCN-GRU based open set method for unknown damage diagnosis

Abstract In the aerospace and high-speed rail industries, CFRP has seen widespread application. CFRP plates and connectors in operation are often subjected to impacts that can cause damage. The unpredictable nature of the impacts introduces uncertainties in both the location and extent of the damage, posing significant challenges to traditional supervised learning models, which often struggle with missed detections or misclassifications when identifying unknown damages. To address the issue, a deep learning model based on TCN-GRU is proposed. TCN extracts features from the raw time domain signals, and GRU selectively retains the significant features and completes sequence modeling. A center loss function is incorporated into the fully connected layer to improve the effects of intra-class aggregation and inter-class separation. An unknown detection module is introduced to realize the identification and classification of unknown damages based on a predefined threshold. The experimental results indicate that the proposed method can achieve effective unknown damage diagnosis in the open set case. This study provides a feasible solution for open set unknown damage diagnosis in CFRP plates and connectors.

Implementation gaps in cross-border co-operation: evidence from the regional train network in Greater Geneva

ABSTRACT The Léman Express (LEx) is Europe’s largest cross-border rail network. As the backbone of public transportation that connects Geneva with its French suburbs, the LEx should contribute to increasing sustainable mobility and to achieving the 2050 carbon neutrality objective. Accompanying measures such as new bus routes and tramways connected to LEx stations as well as bicycle lines, park and ride, and pedestrian facilities should be implemented to ensure that LEx achieves its full impact on the modal shift and active mobility. However, the findings in this study demonstrate that more than half of the LEx’s accompanying measures are overdue; planning quality, national co-funding, and municipal support are key factors for reducing delays in their implementation. Notably, these empirical findings hold across the Greater Geneva area despite the institutional peculiarities of the Swiss and French political and administrative systems. This case study adds to the literature regarding European border studies by analysing the consequences of a cross-border environment on territorially bounded projects.

Potential Reduction in Carbon Emissions in the Transport of Aggregates by Switching from Road-Only Transport to an Intermodal Rail/Road System

Aggregates are the second-most consumed product in the world after water. This geological resource is used as building and construction material, and its production in quarries and delivery to customers generates several environmental problems. Their transport from quarries to consumption points, almost entirely done by truck, also generates impacts such as an increase in traffic and noise and the emission of greenhouse gases and other pollutants. Transportation and storage of goods account for 15% of greenhouse gas emissions in Europe and will increase significantly by 2050. To mitigate this, the European Union suggested shifting 30% of long-distance road freight to cleaner alternatives, such as rail or waterborne transport. This approach neglects the enormous volume of short-distance freight movement and its impact on achieving the goal of reducing greenhouse gas emissions. In this study, the hypothesis to test is whether the use of an intermodal rail/road transport mode, instead of just roads, for the transport of some products can help reduce global CO2 emissions even for short distances. To test this, this study investigates the carbon emissions (and transport cost reduction) generated by rail/road intermodal aggregate transport for short distances in the Madrid region (Spain), rather than the currently used direct truck transport. An analysis of variables, such as aggregate supply, demand locations and amounts, and road and rail networks, using a geographical information system provides the associated carbon emissions of the different transport alternatives. To obtain a reduction in CO2 emissions, this study proposes the establishment of intermodal transfer facilities near consumption centers, where materials are primarily transported by rail, with road transport limited to the final delivery to consumption areas. The results anticipate a notable decrease in carbon emissions in aggregate transport and allow the establishment of more efficient and environmentally friendly rail/road intermodal transport that would help to meet the goals of reducing climate change while making the use of aggregates more environmentally friendly.

BIM capability assessment for improving the efficiency of design in railway projects

The construction industry is increasingly adopting Building Information Modelling (BIM) due to its benefits, including early collaboration, error reduction, and improved quality. This study systematically assesses BIM capabilities in the design processes of railway projects managed by Network Rail (NR), a national authority in the UK, and Gaziantep Metropolitan Municipality (GMM), a local authority in Turkey. Using a tailored BIM capability assessment tool, the study evaluates the performance of BIM attributes across architectural, structural, and building services design. The findings indicate that NR achieved Integrated BIM (Level 2) across all design disciplines driven by enhanced BIM uses such as code and compliance checking and phase and 4D planning. In contrast, GMM achieved Performed BIM (Level 1), particularly in structural design where BIM is applied selectively for tasks like design authoring and 3D coordination. The difference in scale leads to varying BIM implementations, with NR benefiting from a standardised approach supported by national-level mandates and resources. The study highlights that smaller authorities like GMM can improve BIM practices by adopting national-level strategies. Furthermore, organisations in countries with mandatory BIM requirements show more advanced BIM applications, such as integrating BIM with GIS for clash detection and improved coordination.

Indoor and ambient influences on PM2.5 exposure and well-being for a rail impacted community and implications for personal protections

Abstract Background
Higher air pollution concentrations can be observed near rail networks, local and highway automobile corridors, and shipyards. Communities adjacent to such sources are disproportionately exposed to air pollution from these stationary and mobile sources. One such community is West San Bernardino in California, where households are feet away from the Burlington Northern Santa Fe intermodal facility and are impacted by activities that are estimated to continuously emit air pollutants due to 24/7 operation.
Objective
This study aimed to (1) quantify the impact of personal mobility and housing characteristics on daily PM2.5 exposures and well-being for West San Bernardino community members, and (2) develop individualized resilience plans for community collaborators to support future PM2.5 exposure reduction. 
Methods
Personal PM2.5 exposures were measured for community collaborators for seven consecutive days during three deployment periods: October 2021, January 2022, and March 2022. Indoor and ambient PM2.5 levels were also continuously measured for five households over six months using PurpleAir Classic monitors. Demographic and well-being data were collected upon recruitment and after each week of engagement, respectively. 
Results
Personal exposures in home microenvironments were highest near the railyard and lower farthest away from the railyard. Home exposures were 40% higher on average compared to non-home microenvironments. Household PM2.5 had a higher-than-expected average infiltration factor of 0.55, and indoor 98th percentiles across the households far exceeded a healthy level at an average of 165 μg/m3. Resilience plans featured summaries of personal data and recommendations for mitigating exposures. 
Significance
Results suggest that surrounding land use and residential building characteristics compound to worsen air pollution exposures beyond what is expected for exposures in non-industrialized areas. Findings prompt a call for stronger regulation, not only for emissions, but also for indoor air quality and zoning standards that specifically protect disproportionately impacted communities.

Effects of Wheel Flats on Vibration and Stress Response of the Wheel-Rail System in High-Speed Trains

With the advancement of high-speed and lightweight vehicles, the occurrence of wheel tread wear, including wheel flats, has escalated. The resulting impact loads on axle-end components and the dynamic response of the axle cannot be overlooked. On-track tests were conducted on wheel flats to obtain wheel-rail forces, axle box vibration accelerations, and axle stresses during service. Time–frequency analysis was employed to uncover patterns of variation with speed. Additionally, a dynamic model of the high-speed train–track system was developed to replicate test conditions, investigating the characteristics of wheel–rail loads and structural elastic vibrations under high-frequency excitation for various flat sizes and higher operating speeds. The findings reveal that under wheel flats, the time-domain response of the wheel–rail system exhibits periodic impacts, while the frequency-domain shows high-frequency energy comprising multiple harmonics of the wheel rotation frequency. Wheel–rail forces, axle box vibration accelerations, and axle stresses exhibit inflection points with increasing speed, particularly within the 120–160 km/h range. In the high-frequency domain, axle stresses display characteristic frequencies that remain unchanged with speed, attributed to the coupled vertical bending vibration modes of the wheel–rail system. These research results offer foundational support for predicting fatigue damage to axle-end components, assessing axle stress, and establishing tolerances for tread defect limits.

Analysis the Principle and Applications for Locomotive Engine

Abstract. The evolution of locomotives is the cornerstone of modern transport and has had a profound impact on economic growth and social mobility. From the pioneering steam locomotives of the 19th century, which marked the beginning of a transformative journey for rail transport, to today's complex diesel and electric locomotives, each technological advance has shaped the efficiency, speed and environmental impact of rail systems. With this in mind, this study will offer a systematically analysis for the principle as well as applications for such engine. Steam locomotives revolutionized the transportation of goods and passengers, laying the foundation for the future of rail travel. Diesel locomotives further enhance operational flexibility and reliability, while electric locomotives are becoming mainstream due to their energy efficiency and low emissions. This study explores the historical development of locomotives, highlighting key technological milestones and their impact on rail transport. Meanwhile, the current advances in locomotive technology, including the integration of hybrid power systems and alternative energy sources, are studied. As the rail industry faces increasing pressure for sustainability, it is important to understand the evolution and future prospects of locomotives. By analyzing these developments, this paper aims to provide insights into different locomotive principles and how locomotive technology is evolving in an era of increased environmental awareness and technological innovation, shaping the future of rail transportation.

Multi-modal and multi-layer robustness analysis of the European rail and air networks

The robustness of long-distance transport services is paramount for ensuring network connectivity under disruptions. We conduct a comparative analysis of the European rail and air networks of 124 main metropolitan areas, assessing their ability to withstand successive network degradation. We undertake a multi-modal and multi-layer approach in our analysis of the robustness of long-distance transport networks. In particular, we are interested in the role of individual nodes for both air and rail networks as well as for the integrated multi-modal network. Given the hierarchical nature of long-distance transport services, we adopt a multi-layer perspective by means of clustering nodes based on their criticality in order to identify common performance profiles. Original metrics are formulated to measure the impact of nodes on network fragmentation, both at the individual level and as cluster members. Additionally, a new metric is introduced to assess cities’ reliance on air transportation, when considering the integrated multi-layer air-rail network during disruptions in air connections. Our findings indicate that the air network exhibits significantly greater robustness compared to rail, i.e. 10% versus 71% performance loss in the worst case scenario, respectively. Furthermore, primary sub-graph of nodes whose protection from attacks can greatly enhance network’s overall robustness is identified. We discuss our findings in terms of the relationship between network structure, robustness, and the role of critical nodes as well as propose potential mitigation measures.

Mitigating an Epidemic on a Geographic Network Using Vaccination

We consider a mathematical model describing the propagation of an epidemic on a geographical network. The size of the outbreak is governed by the initial growth rate of the disease given by the maximal eigenvalue of the epidemic matrix formed by the susceptibles and the graph Laplacian representing the mobility. We use matrix perturbation theory to analyze the epidemic matrix and define a vaccination strategy, assuming vaccination reduces the susceptibles. When mobility and the local disease dynamics have similar time scales, it is most efficient to vaccinate the whole network because the disease grows uniformly. However, if only a few vertices can be vaccinated, then we show that it is most efficient to vaccinate along an eigenvector corresponding to the largest eigenvalue of the Laplacian. We illustrate these results by calculations on a seven-vertex graph and a realistic example of the French rail network. When mobility is slower than the local disease dynamics, the epidemic grows on the vertex with largest number of susceptibles. The epidemic growth rate is more reduced when vaccinating a larger degree vertex; it also depends on the neighboring vertices. This study and its conclusions provide guidelines for the planning of a vaccination campaign on a network at the onset of an epidemic.

Optimizing Railway Tribology: A Systematic Review and Predictive Modeling of Twin-Disc Testing Parameters

Twin-disc testing is crucial for understanding wheel–rail interactions in railway systems, but the vast array of testing parameters and conditions makes data interpretation challenging. This review presents a comprehensive analysis of the twin-disc literature experimental data, focusing on how various parameters influence friction and wear characteristics under stationary contaminant conditions. We systematically collected and analyzed data from numerous studies, considering factors such as contact pressure, speed, material hardness, sliding speeds, adhesion, and a range of contaminants. This research showed inconsistent data reporting across different studies and statistical analyses revealed significant correlations between testing parameters and wear rates. For sand-contaminated tests, a correlation between particle size and flow rate was also highlighted. Based on these findings, we developed a simple predictive model for forecasting wear rates under varying conditions. This model achieved an adjusted R2 of 0.650, demonstrating its potential for optimizing railway component design and maintenance strategies. Our study provides a valuable resource for researchers and practitioners in railway engineering, offering insights into the complex tribological interactions in wheel–rail systems and a tool for predicting wear behavior.

Capturing the Value of Walkability

Capturing the value infrastructure investments add to the residential market is a longstanding policy to defray their expense. Unfortunately, estimates of the added value of infrastructure, generally, and estimates of the added value of walkability, specifically, are scarce. Novel, multiscale models free independent variables to manifest simultaneously at different scales of analysis to greatly improve specifications to precisely estimate walkability valuation. Results from analysis of years of transactions within walking distance of heavy rail stations suggest that walkability adds value available for capture locally, not systemically. Stakeholders confront myriad problems to replicate the accessibility characteristics shown to add value given the distinct cluster where walkability adds value available for capture in a heavy rail system.

An Online Train-Grid Integration Energy Optimization Method for Urban Rail System

An Online Train-Grid Integration Energy Optimization Method for Urban Rail System

Analysis of the case of China’s high-speed Railway Bidding negotiation in 2004

This paper analyzes the bidding negotiation of China’s high-speed railway in 2004 from the following three elements: alternative plan, target and initial offer.In 2004, China sought high-speed rail technology, negotiating with international companies such as Siemens, Shinkansen, Alstom and Bombardier. Negotiations with Japan and Germany have faced challenges due to reluctance and high costs of technology transfer.However, China shifted strategically to negotiating with Alstom and Bombardier, and in the end, China obtained a variety of high-speed rail technologies, successfully established its own high-speed rail industry, and demonstrated an effective negotiation strategy.

Advanced Methods for Monitoring and Fault Diagnosis of Control Loops in Common Rail Systems

Common rail systems are a key component of modern diesel engines and highly increase their performance. During their working lifetime, there could be critical damages or failures related to aging, like backlash or friction, or out-of-spec operating conditions, like low-quality fuel with, e.g., the presence of water or particles or a high percentage of biodiesel. In this work, suitable data-driven methods are adopted to develop an automatic procedure to monitor, diagnose, and estimate some types of faults in common rail systems. In particular, the pressure control loop operating within the engine control unit is investigated; the system is described using a Hammerstein model composed of a nonlinear model for the control valve behavior and an extended linear model for the process dynamics, which also accounts for the presence of external disturbances. Three different sources of oscillations can be successfully detected and quantified: valve stiction, aggressive controller tuning, and external disturbance. Selected case studies are used to demonstrate the effectiveness of the developed methodology.

Railway embankment failure trends and monitoring – lessons learned from the Anglia route

UK railways were constructed with little consideration of engineering practice and with climate change and the associated greater frequency of extreme weather events, comes the impact on earthworks stability and risk to public safety. Many embankments on Network Rail’s Anglia route have shown a gradual degradation through their steep upper slopes and progressive failure within predominantly clay embankment material and underlying soft deposits. A regional wholistic view allows identification of common geotechnical trends and risk categorisation to better enable prioritisation of asset management. Understanding when to intervene is crucial and monitoring the behaviour of earthworks is a vital asset management tool to control risk and extend lifespan. To be a wise investment, slope instrumentation must be properly thought out in line with a detailed interrogation of the site and alongside a well-developed conceptual model. Automated monitoring can bring greater control and allow more accurate evaluation of intervention but must be assessed in line with prioritising large areas with limited budgets. Correct targeting of ground investigation and ensuring sufficient and reliable data can optimise mitigation and provide confidence the solution has provided resilience to railway infrastructure.

AI-Driven Neural Networks for Real-Time Passenger Flow Optimization in High-Speed Rail Networks

AI-Driven Neural Networks for Real-Time Passenger Flow Optimization in High-Speed Rail Networks