Rail Line Research Articles

Prediction of railroad track geometry change using a hybrid CNN-LSTM spatial-temporal model

Track geometry is highly important for ensuring railroad safety. Predicting track geometry degradation can support preventive maintenance by identifying and prioritizing track segments that are more prone to potential track geometry defects. This paper develops a novel machine learning approach that can simultaneously geospatially align track geometry data from multiple inspections accounting for positional errors, and also predict foot-by-foot track geometry change over time. The proposed position correction method considers multiple geometry parameters to provide highly accurate positional information for geometry measurements, which is a prerequisite for foot-by-foot track geometry prediction. A hybrid CNN (convolutional neural network)-LSTM (long short-term memory neural network) machine learning model is developed to account for spatial–temporal dependence with respect to foot-by-foot track geometry change. Specifically, CNN is used to incorporate spatial dependence of track geometry on adjacent segments. LSTM is applied to learn dynamic changes in track geometry data over time to consider the temporal dependence. The hybrid CNN-LSTM model is validated using track inspection data provided by one major freight railroad in the United States. The results show that our positional error correction method can reduce the relative positional error to less than 1 foot with a 99% confidence interval. Our proposed CNN-LSTM model outperforms four other models, including a naïve model (using the last observation), multi-layer perceptron (MLP), plain CNN, and plain LSTM, for both short-term and long-term prediction periods. The proposed hybrid machine learning methodology can be adapted to various other freight and passenger rail lines. The predictive track geometry change information can be used by the industry to plan and prioritize resources for preventative maintenance, yielding benefits in safety and operational efficiency.

Analysis of the impact of high-speed rail on the spatio-temporal distribution of residential population and industrial structure

With the development of high-speed rail, the spatiotemporal distance among regions has been shortened. However, the research on the spatiotemporal distribution and correlation of resident population and industrial structure caused by the opening of high-speed railway is relatively few. This study is aimed to explore the impact of high-speed rail on resident population and industrial structure, as well as the spatiotemporal agglomeration and evolution trend. The spatial-temporal distribution of the resident population, industrial structure, and the influence of high-speed rail was studied using panel data from 31 Chinese provinces from 2006 to 2018 using spatial autocorrelation analysis, standard deviation ellipse method, and DID model. The experimental results indicate that: (1) The resident population shows a significant positive spatial autocorrelation, while the industrial structure is vice versa. (2) The resident population and industrial structure are primarily centralized in the eastern and central areas, showing a “northeast-southwest” spatial distribution pattern. (3) High-speed rail and the resident population are negatively correlated, and there is heterogeneity. Although there is an association between high-speed rail and industrial structure that is positive, there is also a clear regional variability. This study contributes to providing the corresponding theoretical support and basis for the high-speed rail line planning in different regions and the relevant departments to formulate effective economic policies.

Rail noise across three states in Australia—Operational noise assessment on Inland Rail

Inland Rail Project is a proposed 1700 km freight rail line connecting Melbourne and Brisbane via three States in Australia–Victoria, New South Wales, and Queensland. Operational rail noise assessment is undertaken differently across these States in Australia. The assessment generally considers the existing and future noise exposure across day and night, along with single pass-by maximum levels. However, the assessment durations and statistical means of assessing single events differ across the States. The key acoustic criteria relevant to operational noise assessments and how they differ across the project footprint are discussed in this paper. Specifically, the effect of application of various criteria in the project context are discussed, along with statistical analysis of train pass-by noise data sets. A Monte-Carlo based methodology is proposed for estimating Single Event Maximum, a criterion for assessing single rail-noise events in Queensland. The methodology is discussed in the context of field data and greenfield scenarios where the method may be beneficial for acoustic consultants. The effects of inclusion of noise sources such as train horns and crossing bells are also briefly discussed. Finally, a proposed methodology for a consistent assessment of rail noise on such nationally spread projects is discussed.

Performance evaluation of rail trackbed stiffness: pre and post stabilisation

Excessive deflection of a rail in response to axle loading can lead to discomfort for passengers and increased wear of both railway structures and trains. These oscillations are often caused by poor trackbed stiffness which may be due to either soft subgrade and/or contaminated ballast. A variety of trackbed stabilisation (TBS) techniques are available to remediate soft subgrades and increase the safety of tracks within the railway network. Traditional TBS methods require track removal, which is expensive, disruptive and often inefficient maintenance works. Micro-piling, using screw piles installed between sleepers, is an innovative low disruption TBS technique. This paper investigates the performance of a soft subgrade and contaminated ballast section of rail line in the UK, before and after screw pile TBS. Pre and post remediation, a computer vision-based system was used to measure rail vertical deflections during train passages and then analysed to quantify the trackbed stiffness. Additionally, 3D finite element models are created and validated by the site measurements. The finite element models are used to simulate a range of different scenarios exploring how changes to the TBS piling layout and/or further works, such as ballast improvement could add further improvements or design efficiencies. Site measurements show TBS reduced rail deflection by 20–30%, indicating that micro-piling is an effective technique for soft subgrades. The finite element analysis revealed the efficiency of micro-piling is highly dependent on the conditions of ballast, strength of the ground at the pile toe, and the pile arrangement. When the aforementioned are optimised the rail deflection could be reduced to approximately 50% of the pre TBS condition.

Green infrastructure and air pollution: Evidence from highways connecting two megacities in China

Following market liberalization, the vehicle population in China has increased dramatically over the past few decades. This paper examines the causal impact of the opening of a heavily used high speed rail line connecting two megacities in China in 2015, Chengdu and Chongqing, on air pollution. We use high-frequency and high spatial resolution data to track pollution along major highways linking the two cities. Our approach involves the use of a novel augmented regression discontinuity in time approach that incorporates machine learning to inform our specification choice in the first stage. Our estimates show that CO is reduced by 6.4% and PM2.5 by 7.1% along the main affected highway. These findings are supported using a difference-in-differences approach.

Review on the Prediction and Control of Structural Vibration and Noise in Buildings Caused by Rail Transit

As rail transportation continues to advance, it provides significant convenience to the public. However, the environmental vibration and noise generated during its operation have become major concerns for residents living near rail lines. In response to these concerns, the “Law on the Prevention and Control of Noise Pollution” was promulgated in China, bringing attention to this issue within the rail transportation sector. This review summarizes the regular features observed in environmental vibration and secondary structural noise tests on different sections, including embankment sections, bridge sections, underground railroads and vehicle sections. Furthermore, it introduces several physical models utilized in the study of environmental vibration and secondary structural noise, focusing on three key aspects: excitation sources, propagation paths and the modelling of building structures. This paper also explores the introduction of data-driven models related to big data and artificial intelligence to enhance the accuracy and efficiency of research in this field and provides an overview of commonly used measures to control train-induced environmental vibrations and secondary noise in buildings. These measures are discussed in terms of excitation sources, propagation paths, and receivers, offering insights into effective strategies for mitigating the impact of rail transportation on nearby residents. Finally, this study highlights the primary findings and offers pertinent recommendations. These recommendations include considerations regarding both laboratory and on-site testing procedures, challenges associated with the deployment of data-driven models and key parameters for designing and utilizing low-stiffness fasteners.

Способы контроля выполнения шунтового эффекта рельсовых цепей

The value of the transient resistance "wheel — rail" is the determining factor for ensuring the shunt ef-fect of track circuits. Taking into account the severity of consequences of failure to ensure the shunt ef-fect — accidents or train derailments due to a switch being thrown under a train or opening of a traffic light fencing an occupied track section, continuous monitoring of the condition of track circuits (shunt sen-sitivity) is necessary for timely detection of critical values of destabilising factors or operating conditions. The article discusses ways to automate measurements of train shunt resistance and ways to prevent shunt loss (non-fulfillment of the shunt effect) by an occupied track circuit. The work also examines the possibility of implementing continuous monitoring of the shunt effect (monitoring of shunt sensitivity) based on a model of changing the values of their parameters during the movement of rolling units along a rail line. The obtained results can be used to solve such analytical and practical problems as: calculation and investigation of the dynamics of change in the sensitivity coefficient of the track circuit to the actual train shunt; construction of a classifier of the track circuit states invariant to external perturbations during the train movement from the moment of entry to the moment of release of the rail line; organisation of continuous monitoring of the shunt sensitivity of the track circuit.

A Hybrid Similarity-Based Method for Wind Monitoring System Deployment Optimization Along Urban Railways

Urban railways in coastal areas are exposed to the risk of extreme weather conditions. A cost-effective and robust wind monitoring system, as a vital part of the railway infrastructure, is essential for ensuring safety and efficiency. However, insufficient sensors along urban rail lines may result in failure to detect local strong winds, thus impacting urban rail safety and operational efficiency. This paper proposes a hybrid method based on historical wind speed data analysis to optimize wind monitoring system deployment. The proposed methodology integrates warning similarity and trend similarity with a linear combination and develops a constrained quadratic programming model to determine the combined weights. The methodology is demonstrated and verified based on a real-world case of an urban rail line. The results show that the proposed method outperforms the single similarity-based method and spatial interpolation approach in terms of both evaluation accuracy and robustness. This study provides a practical data-driven tool for urban rail operators to optimize their wind sensor networks with limited data and resources. It can contribute significantly to enhancing railway system operational efficiency and reducing the hazards on rail infrastructures and facilities under strong wind conditions. Additionally, the novel methodology and evaluation framework can be efficiently applied to the monitoring of other extreme weather conditions, further enhancing urban rail safety.

Challenges in the (Re-)Connection of Peripheral Areas to the Rail Network from a Rolling Stock Perspective: The Case of Germany

Currently, in the course of the German mobility transition, an increasing number of disused rail lines are already being or intended to be reactivated in order to increase capacities, decrease transport-related emissions and reconnect rural areas to passenger rail services, thus creating a more comprehensive rail service. However, the use of state-of-the-art regional railcars on old rural infrastructure often leads to problems since they are often worn out and do not meet today’s technical standards. This applies, for example, to the axle loads and dimensions of the vehicles but also to operational aspects, such as the vehicle’s passenger capacity and accessibility. First, this work gives an overview of the available rolling stock and the given infrastructure, as well as an analysis of the (system) interfaces. Subsequently, various challenges for the re-connection of peripheral areas to the rail network were identified through data research and comparison of the vehicle and infrastructure parameters. In addition, the requirements related to possible autonomous operation and the related absence of the driver and crew personnel in the vehicle, which require new solutions in terms of safety, were taken into consideration. Orientation of future rolling stock generations towards the existing infrastructure and the required transport needs, including lower axle loads, accessibility and smaller capacities, can contribute to the economic operation of low-capacity lines and bring more passengers to public transport.

Evaluating spotted lanternfly (Hemiptera: Fulgoridae) infestation in the Northern Ohio Valley.

Lycorma delicatula White (spotted lanternfly; SLF) is an invasive pest insect threatening increased agricultural costs as it spreads rapidly westward across the United States. As such, surveying was conducted adjacent to the insect's westernmost quarantine area in 2021-2022 to support multi-state monitoring. Specifically, 2,077 visual and sticky-trap surveys were performed in 13 repeatedly surveyed plots strategically located near high-traffic roadways and rail-lines along the Ohio-West Virginia border. Sites were located in Jefferson (Ohio), Brooke (West Virginia), and Hancock (West Virginia) counties. Only one SLF was detected in 2021 (the third documented Ohio site containing SLF) in close proximity to a railway, consistent with rail-mediated dispersal trends recorded throughout the United States. Thirty-one SLF were captured in 2 Ohio sites in 2022, 30 of which were captured at the same railway site as in 2021. However, 1 of the 31 SLF was found in a plot on a university campus 1.25 km from the nearest railway, along with 10 additional specimens found in a follow-up visual survey of a neighboring woodlot. Failure to detect SLF at nearby survey plots nearer to the closest rail line and commuter parking lots suggests local unaided dispersal in a state with primarily train-mediated dispersal-mirroring trends in affected states with more established SLF populations. Data from this survey are valuable for establishing baselines and early-invasion patterns of SLF dispersal into Ohio, anticipating SLF expansion patterns in Ohio, and eventually contributing to improved SLF dispersal modeling in Ohio, the Midwest, and the United States.

Passenger Flow Forecast for Low Carbon Urban Transport Based on Bi-Level Programming Model

<p>In the context of low-carbon city development, this paper further implements a rail transit passenger flow forecasting method to optimize energy consumption by combining the MMA allocation model with a two-tier planning model for carbon emission control. Through this approach, this paper not only fills the gap of rail transportation planning theories and methods compatible with low-carbon city development, but also emphasizes the importance of energy consumption in transportation planning. Based on a two-tier planning model, this paper considers the Starkberg game between multi-modal and multi-type passenger flow forecasting of rail transit and CO2 emissions of integrated transportation systems. By optimizing the allocation of users in the transportation network from the perspective of both users and planners, while optimizing the CO2 emissions of the integrated transportation system, the dual optimization of energy consumption and environmental benefits is achieved. The method will also be tested in Shanghai, and this paper will comparatively study three different carbon emission control schemes. By assigning passenger flows to the entire transportation system network in Shanghai based on information from the Fourth Integrated Transport Survey, including passenger flows on each road in the road network, passenger flows on each rail line, and characteristic indicators, this paper provides a reliable data base. This study provides a solid foundation for planning the layout of rail transit in a low-carbon mode and makes a positive contribution to sustainable urban development by optimizing energy consumption.</p> <p> </p>

An exact optimization method for coordinating the arrival times of urban rail lines at a common corridor

The trips of a high-frequency urban rail line are typically planned with the aim of achieving even time headways. This results in reliable services for each urban rail line, where successive trips have the same time headway. Maintaining even time headways for each service line has significant advantages for the passengers of the line, but it might result in safety issues, vehicle bunching, and increased transfer times at a common corridor served by multiple urban rail lines. This study investigates the problem of urban rail corridor coordination and develops an exact optimization method for coordinating the vehicle trips of different lines that serve stations along a joint corridor. The proposed formulation is a non-convex mathematical program which is reformulated as a mixed-integer quadratic program with a convex objective function. A branch-and-bound algorithm coupled with the Active-set method is proposed for solving the model to global optimality. Results from a toy network and a case study of the light rail service in The Hague, The Netherlands, demonstrate the improvement potential of time headways at a common corridor, while accounting for the effect on the variation of time headways at isolated segments of the individual service lines.

Life cycle greenhouse gases emissions from high-speed rail in Spain: The case of the Madrid – Toledo line

The Madrid – Toledo high-speed rail (HSR) line is 74.5 km long, has an average speed of 140 km/h and a maximum speed of 270 km/h. It averages almost 104 million passenger-kilometres travelled and has a utilisation rate (passengers per available seats) of 0.75 as of 2019. This study analyses the life cycle greenhouse gases (GHG) emissions of the Madrid – Toledo HSR line using the life cycle assessment methodology, taking into consideration the stages of construction, maintenance and operation for the infrastructure, as well as the manufacturing, maintenance and operation stages for trains. The most significant stage is infrastructure construction, which accounts for an average 75 % emissions, followed by train operation, with an average 18.2 %.The results highlight several environmental benefits of the deployment of the line, as after 40 years of operation there will be an average 21 % reduction in GHG emissions for passenger mobility between Madrid and Toledo compared to the case where no HSR line was built. Moreover, the line's GHG emissions are offset between 19 and 23 years after starting operation, mainly due to the emissions avoided by passengers switching to a carbon-friendlier mode of transportation in terms of operation, and the adoption of a 100 % renewable energy mix for the operation of trains since 2019.These findings point to opportunities for improving the life cycle GHG emissions of high-speed rail lines, such as increasing the occupancy of trains by applying new pricing policies and investing in intermodal infrastructures to achieve higher modal shift rates, promoting proximity criteria for the supply of raw materials during the infrastructure construction stage and improving the maintenance stage to prolong the lifetime of the infrastructure, thus deferring life cycle emissions for longer.

Analysis of concrete tub at-grade railroad crossing by field instrumentation

Concrete tub crossings are at-grade railroad crossings used primarily on low-speed and low-tonnage rail lines. Concerns have been raised about the stiffness differential present between the track structure and crossing that could cause a localized shear strain increase. Due to this concern, much of the industry has limited speed of trains through these crossings to 40 kph (25 mph). This investigation evaluates a concrete tub crossing that experiences higher speeds and higher tonnage than typical. Visual observations and field performance indicate satisfactory performance of the crossing with little maintenance. Strain gauges mounted on the rail web were used to measure shear strains of trains at differing speeds. Results show little difference in maximum shear strain for train speeds up to 97 kph (60 mph). Concrete tub crossing shear demands are below shear capacity and independent of train speed, suggesting crossings can safely handle heavy railway traffic at greater speeds.

Train schedule optimization for commuter-metro networks

The interconnection and synchronization among different transport modes have been more and more attractive as the modern transportation system is moving towards Mobility-as-a-Service. In this study, we address the train scheduling problem for “commuter rail-metro” systems, where the trains from commuter rail lines can go directly into metro systems to provide seamless services for passengers. To optimize the schedule of trains for both commuter rail lines and metro lines, we propose a job shop scheduling model where precedence constraints from commuter-metro networks are taken into account and develop a mixed-integer programming (MIP) model with quadratic constraints. Our model considers the orders of different types of trains and the safety constraints, due to different types of signalling equipment in commuter and metro systems. Since these constraints involve a set of IF-THEN rules, we prove that these constraints can be equivalently reformulated as linear inequalities, without adding new variables. To solve the proposed model efficiently, we design an iterative solution framework, which generates a feasible solution using dynamic programming, next solves a MIP model, then calculates the train speed profiles, and if train speed profiles violate the safety constraints, re-optimizes the MIP model with modified alternative constraints. To verify the effectiveness of the proposed approaches, numerical experiments are performed on small and real-world instances based on the Beijing metro Line 1 and the Batong Line operational data.

Incentives for local government expenditures on people’s livelihood: the role of high-speed rail

Can high-speed rail (HSR) lines create effective incentives for local governments to supply public goods for people's livelihood? This paper builds a time-varying difference-in-differences (TVDID) model and uses 2002–2018 Chinese data. The empirical results show that HSR enhances the fiscal responsiveness of local governments and creates effective incentives for local governments' expenditures on education and medical/health care; however, this “HSR effect” does not significantly influence expenditures on social security or employment. The mechanism analysis shows that HSR has a significant amplification effect on interregional competition, specifically in terms of market access and human capital agglomeration. Notably, the opening of HSR lines forms a new “incentive-constraint” mechanism for local governments to supply public services for people's livelihood from both the supply and demand sides, and the fiscal and administrative capacities of local governments can strengthen the influence of HSR lines on the supply of public goods for people's livelihood.

An approach toward correct policy based on mass transit lines' demand analysis

When a BRT line overlaps with the metro lines, it is sometimes believed that the Bus Rapid Transit (BRT) line should be removed, and passengers can use the rail lines instead. The tendency to remove BRT lines is because of the BRT system's negative factors, such as street occupation, political pressures, and local businesses' protests (due to the removal of curbside parking) against the BRT. In this study, the coverage capability of BRT by metro was evaluated. In addition, an analysis of the origin–destination potential attraction was also performed. The station overlap analysis results of this study contradict the common belief that the rail system is preferable for passengers. Station overlap analysis checks if the BRT station accessibility radius is potentially compensated by metro accessibility radius overlap. In addition, an analysis of the origin–destination potential attraction is also performed. This analysis aims to assess the potential of a metro line in attracting existing demand for BRT line segments that are candidates for removal. The methodology proposed here is applied to BRT Line 800 in Mashhad, Iran. The data was obtained from Mashhad entry-only smart card transactions. While initially it was supposed that the BRT line segment parallels to Metro Line 2 is useless, the results of the station overlap analysis showed that metro stations could cover only 31% of the BRT stations. Also, the origin–destination potential attraction analysis showed that only 20% of BRT trips could be shifted to the metro line. The BRT is more consistent with sustainable transportation principles than the metro. Our research also shows that people prefer the BRT more because of its station's proximity and coverage, which underground modes cannot replace it. This contradicts the common belief that the rail system is preferable by users.

Investment timing and length choice for a rail transit line under demand uncertainty

This paper is motivated by the inadequate treatment of uncertainty in public transit infrastructure investments. As observed in the past, rare but dramatic events can heavily disrupt public transit system operations and negatively affect the transit riders. For example, the COVID-19 pandemic caused an 80%-90% transit demand decline in March 2020 in the U.S. However, the existing transit infrastructure planning studies have not modeled such sudden demand shocks. We thus improve the modeling realism of uncertain transit demand by formulating demand evolution as a jump-diffusion process, which is a combination of continuous-time Brownian motion and a discrete counting process, namely Poisson process, and present analytical optimization models for the development of a rail transit line under such uncertainty. We jointly optimize two related decisions, namely the timing for introducing rail transit to a commuter corridor and length choice for the rail line. We refute a misconception that investment in a project should always start immediately if a positive cost saving over the planning horizon is expected. We also find that investment timing and sizing decisions are closely related and behave quite differently for the same change in some parameters, such as the infrastructure construction period. The developed modeling and analysis framework should be transferable to other civil infrastructure development and investment problems under uncertainty.

Progress toward SDG‐2: Assessing food security in 93 countries with a multidimensional indicator system

AbstractThe second goal of Sustainable Development Goals (SDGs) (SDG‐2: zero hunger), which integrates food security, nutrition, and sustainable agriculture, is the common aim of human beings. Assessment of food security performance is the premise of understanding the global food security to tailor strategies toward SDG‐2. This study quantitatively assesses the food security of 93 countries using a multiple‐dimensional indicator system (MDIS) with five dimensions (i.e., availability, access, utilization, stability, and other) consisting of 34 indicators using the data for the years 2015, 2016, 2017, and 2018. The results show imbalance in global food security, which is generally high in Europe, Oceania, and North America, fair in East Asia and South America, and low in Africa and South Asia. In the dimensions, countries with low availability are mainly owing to the bad performances of indicators related to energy, protein, and meat supply; the low utilization is expressed in the number of obese adults, stunted children, prevalence of anemia of women, obese adults and overweight children; the low food access showed in the low GDP per capita and rail line density; and the low‐stability countries mainly have the high food import dependency and low political stability. Policy implications include promoting the adoption of agricultural technologies to boost food production, implementing inclusive finance among vulnerable groups to enhance health equality, developing national economy and transport systems to increase access to food, and encouraging local food reserves and calling for world peace to stable food supply. This study provides national governments and international organizations with a new MDIS, status quo, and possible solutions to eliminating hunger.

In the Time of Megaprojects: Classed Temporal Scales along a Moroccan Highspeed-Rail Corridor

ABSTRACT Capitalizing on its comparative political and social stability in the region, the Moroccan regime has been attracting global and regional investors with the promise of new ‘megaprojects’ that aim to radically transform local natural, economic and social landscapes. Inaugurated in 2018, Morocco’s (and Africa’s) first high-speed rail line (LGV) is a flagship megaproject. Part of a wider ‘development corridor’, this 2-billion-euro infrastructure has become invested with political, ideological, and affective meanings related to ideas of mobility and future prosperity, even as it violently displaced informal housing communities and created disruptions along the existing network. This paper focuses on the LGV's temporal dimensions and organizes the discussion around three overlapping time-scales, which I identify as co-productive of the politics, promises and experiences of contemporary megaprojects. Scaling emerges as a political process of spatiotemporal re-ordering that contributes to the consolidation of unequal social orders while also providing a conduit for their critique.