Railway Research Articles

CAMERA-BASED DETECTORS AS AN ALTERNATIVE TO DETECTING TRAINS IN A LEVEL CROSSING IMPLEMENTATION

Based on data from Indonesia Directorate General of Railways in 2017, it is mentioned that the problems at the level crossing of railroad tracks are mostly caused by human error factors themselves. The current train headway and the crossing system that is still operated manually can increase the potential for accidents. Therefore, the development of alternative camera-based detectors to support the railroad crossing automation system is needed at this time. The development of this camera-based train detector uses the basic program You Only Look Once (YOLO), where YOLOv3 has proven to be accurate enough to detect moving objects. The development results show promising results for several types of alternative trains.

Modeling of long-term train loads impacts on subgrade soils: a review

Subgrade is an important construction element of the railroad track. In the process of long-term operation, the subgrade is loaded by trains and exposed to natural climatic factors, such as wind and seismic loads, moisture caused by atmospheric precipitations and groundwater, exposure to positive and negative temperatures. At the same time, the subgrade must provide reliability and stable properties of the railway track because its renovations are the most expensive ones among those for the railway track in general. Due to this fact, national regulatory documents for the construction of the subgrade impose strict requirements on its deformity for the entire duration of modern railways operation. One of the ways to solve this problem on the stage of construction designing is computer predictive modelling of structural behavior that considers the changes of its elements and properties of construction materials under the long-term train impact taking into consideration the varying natural climatic factors. In this review, we analyze accumulated scientific and technical experience in formulating optimal approaches to the determination of initial data for computer simulation of repeated impact of the train loads for predicting railway track subgrade deformation considering engineering-geological conditions and natural climatic factors. We describe the stages of creating the subgrade computer modelling taking into account the analysis of trains load impact, the properties of model’s structural layers, and the change of soil properties under long-term loads.

Effect of Polynorbornene on Physico-Mechanical, Dynamic, and Dielectric Properties of Vulcanizates Based on Isoprene, α-Methylstyrene-Butadiene, and Nitrile-Butadiene Rubbers for Rail Fasteners Pads

The article studies the effect of polynorbornene (PNB) in the composition of PNB with Norman 747 LV plasticizer (RC) on the curing characteristics of the rubber compound and the physico-mechanical, dynamic, dielectric properties and the thermal behavior of vulcanizates based on a combination of isoprene, α-methylstyrene-butadiene, and nitrile-butadiene rubbers. It is shown that vulcanizates containing PNB in the composition of the RC had lower conditional tensile strength, hardness, and tear resistance compared to the vulcanizate of the base version of the rubber compound. Studies of dynamic mechanical analysis indicate that an increase in the content of RC, and hence PNB, in the rubber compound contributes to an increase in the mechanical loss factor (tanδ) and a decrease in the storage modulus of vulcanizates. It was found that vulcanized rubber, containing 24.0 parts per hundred of rubber (phr) (8.98 wt. %) PNB as part of the RC, is characterized by stable physico-mechanical, improved vibration-absorbing properties, as well as increased dielectric parameters. This rubber compound can be used as a base for rail fasteners for railroad tracks.

Stress Distribution in a Railroad Track at the Crosstie–Ballast Interface

Excessive crosstie wear and abrasion and ballast wear and fouling are fundamental problems contributing to inadequate railroad track performance. This adversely affects the attainment and long-term maintenance of desired track geometric requirements. The magnitudes and distribution of the stresses at the crosstie–ballast (CT-B) interface must be known to determine the stress distribution on and within the ballast. However, the track design recommendations to determine these pressures, which are largely based on a methodology from the 1980s, are currently valid for modern-day railroad applications for multiple reasons discussed in this study. This study analyzed CT-B interfacial pressure data measured on an active freight mainline in Mascot, Tennessee. Dynamic contact pressures at the CT-B interface were measured using hydraulic earth pressure cells for various wheel loads and train speeds. The test train was a Federal Railroad Administration (FRA) test train consisting of a diesel electric locomotive, a test car that had different wheel loads based on the deployable axle load, and an inspection car. Although the maximum train speed was limited to 64 km/h, this research found that speed variation has a minimal effect on the CT-B interfacial pressures. From the measured data, a Gaussian stress distribution equation is proposed to determine longitudinal pressure distribution transmitted to the CT-B interface for static conditions. In addition, the stress distribution along the length of a crosstie was investigated via laboratory experimentation using a half-length crosstie. As a result of the experimentation, a dimensionless trilinear approximation was developed to estimate the stress distribution along the length of the crosstie. In general, this research recommends that the longitudinal and lateral stress distributions be considered together to design a better railroad track.

Automatic Rail Surface Defect Inspection Using the Pixelwise Semantic Segmentation Model

Rail surface defect (RSD) is an important railroad track quality indicator and impacts the overall track safety and ride quality. Railroads need to inspect and evaluate RSD conditions regularly to make proper maintenance plan for both freight and passenger services. Unfortunately, current field practices for rail surface defect defection have not been fully automated yet and significant amount of manual inspections are still involved, which could be labor-intensive but low-efficient. Earlier efforts have proposed multiple automatic rail surface defect detection systems, but the accuracy and equipment cost issues have limited their applications in the field. To address the needs in track inspections, this study proposes an improved Deeplabv3-plus model using a lightweight backbone, attention module, and the Lovász-Softmax loss to automatically detect RSD. The improved model not only improves the detection performance but also keeps the computational cost manageable. The ResNet-18 backbone is adopted for the encoder design to reduce the computational cost and maximize the inference speed. The CBAM attention module is unified with the decoder structure for critical features’ representation. The optimum crop size is determined in the training process. The Lovász-Softmax loss is implemented to address the severe class imbalance issue. The improved DeepLabv3-plus model is compared with five other models to validate the performance. Experimental results on both original data and noisy data confirm the proposed model achieves the best performance on evaluation metrics and visualization. This work provides a feasible solution for the future implementation of automatic railroad track inspection.

Identifying emissions hotspots and strategies to reduce real-world fuel use and emissions for passenger rail: A spatially resolved approach

The objectives of this work are to model spatially resolved passenger locomotive fuel use and emission rates, locate emissions hotspots, and identify strategies to reduce trip train fuel use and emissions. Train fuel use and emission rates, speed, acceleration, track grade, and track curvature were quantified based on over-the-rail measurements, using portable emission measurement systems, for diesel and biodiesel passenger rail service on the Amtrak-operated Piedmont route. Measurements included 66 one-way trips and 12 combinations of locomotives, consists, and fuels. A locomotive power demand (LPD) based emissions model was developed based on the physics of resistive forces opposing train motion, taking into account factors such as speed, acceleration, track grade, and curvature. The model was applied to locate spatially-resolved locomotive emissions hotspots on a passenger rail route, and also identify train speed trajectories with low trip fuel use and emissions. Results show that acceleration, grade, and drag are the major resistive forces affecting LPD. Hotspot track segments have 3 to 10 times higher emission rates than non-hotspot segments. Real-world trajectories are identified that reduce trip fuel use and emissions by 13 % to 49 % compared to the average. Strategies for reducing trip fuel use and emissions include dispatching energy-efficient and low-emitting locomotives, using a 20 % blend of biodiesel, and operating on low-LPD trajectories. Implementing these strategies will not only decrease trip fuel use and emissions but reduce the number and intensity of hotspots and, thus, lowering the potential for exposure to train-generated pollution near railroad tracks. This work provides insights on reducing railroad energy use and emissions, which would lead to a more sustainable and environmental-friendly rail transportation system.

Fuzzy multi-objective truck scheduling in multi-modal rail–road Physical Internet hubs

The Physical Internet (PI) is an innovative concept that has the potential to significantly improve the efficiency, cost-effectiveness, and sustainability of the global supply chain industry, particularly in cross-docking operations. This paper addresses the truck-scheduling problem in rail–road PI-Hubs, taking into account simultaneously both uncertainty and multi-objective decision-making, which has not been fully explored in the literature, particularly for PI-structures. Our proposed approach defines a Multi-Objective Mixed-Integer Programming model (FMO-MIP) that incorporates fuzzy chance-constrained programming and ϵ-constraint to minimize both the total delay and the sum of PI-containers traveled distances, while considering the uncertainty on truck arrival times. This work takes into account the particularities of the Physical Internet and presents a novel decision-making solution to generate a robust Pareto front that aligns with decision-makers’ attitudes towards risk (optimistic/pessimistic) while balancing trade-offs between conflicting objectives.

FishTwoMask R-CNN: Two-stage Mask R-CNN approach for detection of fishplates in high-altitude railroad track drone images

FishTwoMask R-CNN: Two-stage Mask R-CNN approach for detection of fishplates in high-altitude railroad track drone images

Hazard assessment framework for statistical analysis of cut slopes using track inspection videos and geospatial information

ABSTRACT Transportation corridors constructed using through- and side-cuts are susceptible to hazardous slope failures, potentially causing infrastructure damage, operational suspensions and loss of life. To monitor the stability of known geohazards at the local scale, geotechnical investigation of each slope is typically performed to calculate a factor of safety. In many corridors, however, this method is labour-intensive due to the quantity of geohazards and statistical methods are instead used to identify hazardous sections. This paper introduces a new slope failure hazard assessment technique, utilising susceptibility mapping of geospatial information and computer vision-based analysis of right-of-way videos recorded by railroad track inspection vehicles, applied to a section of railroad track near Harrisburg, Pennsylvania. Combining these results, an enhanced relative hazard assessment algorithm was formulated. Using the developed framework, geohazards of primary concern were determined which should be prioritised for future geotechnical investigation and remediation efforts.

Toward Automated Field Ballast Condition Evaluation: Development of a Ballast Scanning Vehicle

Ballast plays an essential role in the response of a railroad track to repeated loading. Ballast degradation may lead to poor drainage, lateral instability, and excessive settlement. Extreme levels of ballast degradation may cause service interruptions and safety concerns. Therefore, ballast condition evaluation is of great importance in ensuring safe and efficient operations. Current state-of-the-practice evaluation methods are heavily dependent on visual inspection, field sampling, and laboratory testing, which are subjective and labor-intensive. Meanwhile, existing inspection systems for railroads have not been customized for conducting in-depth evaluation of the ballast layer, including determination of the level of fouling and aggregate size and shape characteristics. For this reason, there is an urgent need for the development of a novel, vison-based ballast scanning platform. This paper introduces the ballast scanning vehicle (BSV), an automated platform that acquires high-quality images, videos, and 3-D height maps of ballast from plan and profile views of cut sections and trenches. The BSV is capable of performing data analysis and generating accurate and comprehensive evaluations of ballast conditions through geotechnical analyses. The essential design components, prototyping, and development stages are described. Further, preliminary data collected from testing on two in-service railroad tracks behind a shoulder ballast cleaner are presented to validate the functions of the BSV. The fully developed BSV serves as a data collection device for ballast evaluation and provides continuous and high-quality images for a deep learning-based computer vision algorithm for field ballast condition evaluation and geotechnical analyses.

Micromechanical Analysis of Internal Instability during Shearing

Internal instability means that finer particles pass through the constrictions of coarser particles at a hydraulic gradient well below that of heave or piping, rendering the soil ineffective for its intended purpose. The soil could make a transition from an internally stable state to an unstable state or vice versa due to shear-induced deformation. The discrete element method (DEM) is adopted in this study to examine and quantify soil behavior by simulating the quasi-static shear deformation of internally stable and unstable soils at the micro- and macroscales. The dense bimodal specimens were sheared under drained conditions following a constant mean stress path in order to investigate the influence of stress heterogeneity. At the macroscale, the peak deviatoric stress was found to be a function of the fines content and the initial void ratios of the specimens. The development of the average number of contacts per particle and the stress transfer to the finer fraction during shearing are discussed. The simulation results innovatively show that a dense specimen could undergo a transition from an internally stable to an unstable soil as it dilates during shear. These numerical results have significant implications on the importance of real-life situations, such as predicting mud pumping in railroad tracks.

MODELS FOR FORECASTING RAILROAD TRACK GEOMETRY DEGRADATION USING MACHINE LEARNING METHODS

Railroad track maintenance has always been complex, both because of its responsibility in terms of ensuring the safety of train traffic, and because of the high labor intensity of work processes and continuous work planning. Diagnostics and monitoring of all elements of the railroad track is carried out to ensure the safety of train traffic. One of the main parameters affecting the safety and uninterrupted movement of trains is the condition of the track. Deviations and malfunctions in rail track geometry lead to both speed limitation and complete closure of the track for train traffic. Failure to correct faults in a timely manner can often lead to more serious consequences. The main parameters of track geometry have a significant impact on the smoothness of train movement and the risks of derailment of rolling stock. Therefore, monitoring and control of these parameters is a priority task to ensure the stable operation of railroads. To automate this process, data-driven fault detection and diagnosis models can be used. To solve the problem, we used modern methods of solving classification problems for tabular data collected by special track-measuring tools. Automated machine learning model generation systems served as the basis for solving the problem. These systems make it much easier to train and configure machine learning models, as well as to implement them in a production environment. The practical significance of this work is that the solution of the problem of predicting track geometry degradation can be considered as part of the decision-making system for track repair and maintenance.

Dostępność przestrzenna wybranych usług wokół łódzkich stadionów piłkarskich w oparciu o ideę miasta 15-minutowego

One of the main problems and challenges of today’s cities is to reduce individual motorised transport and ensure good pedestrian accessibility. New urban concepts, such as the 15-minute city concept related to the spatial accessibility of services around people’s places of residence, are an attempt to respond to those challenges. The purpose of the article is to assess the spatial accessibility of selected services around two football stadiums in Łódź. In the article, the main assumptions of the 15-minute city concept were transferred from the point of view of a city resident to that of a tourist and a football fan. Indeed, modern stadiums are meeting venues for sports, cultural and business events, and sports tourism is an increasingly popular type of tourist activity. The accessibility areas of the stadiums were determined using the analysis tool in ArcGIS Online, while the service data were obtained from the Database of Topographic Objects (BDOT10k). Final accessibility of services was determined using a cumulative method. The accessibility areas of the two stadiums differ in shape, size and service facilities. The main spatial determinants of these areas are railroad tracks and major transport arteries. The area designated around the LKS stadium is larger and more accessible in terms of selected services, whereas the Widzew stadium has poorer access to all services.

Influence of Bogie Maintenance and Retrofitting on Wheel Wear: Analysis Using Integer Programming and Multibody Simulation

Brazilian railroads are continuously looking to increase their asset performance. In this sense, wheel/rail interface studies have been conducted, which allowed benefits that were not previously considered. Improved wheel/rail contact can significantly reduce maintenance costs, increase energy efficiency, and increase operational safety. For rolling stock, wheel consumption usually has the highest maintenance cost and is among the highest total costs of a railroad. Therefore, the understanding of wheel wear behavior, which is carried out in an integrated manner with permanent track and operating conditions, must be investigated in depth. This study aims to analyze the influence of bogies on wheel wear. Two aspects were considered: maintenance conditions and bogie design. The maintenance conditions were evaluated through wayside and field measurements. The collected data were later statistically evaluated and modeled in integer programming to optimize and review the maintenance strategy, which could avoid premature wheel scrapping after 5 years, allowing for a regular wheel life of 9 years. This analysis has allowed for the development of a new bogie maintenance parameter, which is not defined in manual and rule standards. The bogie design modernization was analyzed using the VAMPIRE multibody simulation software. The retrofitted bogie was modeled to improve the dynamic behavior and analyzed using wheel/rail contact theory. The polymer suspension elements’ modelings were thoroughly detailed for each property to illustrate the retrofit’s design features. Therefore, to calculate the gains of the retrofitted bogie, a methodology was built to compare the results of wheel wear in a real railroad track section. This methodology can be used to show that the retrofit can provide a reduction in wheel wear of 40% for ride control for a broad gauge (1600 mm).

Frequency and Amplification for Assessing site Effects and PVS in the Padang City Railway

The train uses a special line in the form of railroad tracks that produce vibrations during movement. This study aims to determine the type of soil layer and the peak particle velocity due to the train around the Padang City train line. The acquisition was carried out at 8 points with a recording duration of 65 minutes using three geophone components. Microtremor data was analyzed using horizontal to vertical spectral ratio (HVSR) method. The results show that the research area has a type of surface layer of soil is sediment with a thickness of 30 meters or more. This is indicated by the dominant frequency value ranging from 0.64 Hz – 1.67 Hz. The research area has an amplification value ranging from 0.85 to 1.29. The train vibration has a dominant frequency ranging from 2.20 Hz – 13.54 Hz and an amplification of 1.11 – 1.82. The particle velocity values ​​obtained from the PVS values ​​ranged from 0.1605 mm/s – 0.7592 mm/s. The research area can be categorized as safe from train vibrations because of the low amplification value and the PVS value which is below the safe limit (<3 mm/s) according to SNI 7571:2010.

Sustainable Wireless Sensor Networks for Railway Systems Powered by Energy Harvesting from Vibration

The railroad is a particularly efficient means of transporting passengers and goods. In this field, safety, reliability, and punctuality are of great importance, despite harsh environmental conditions and variable loads. Predictive maintenance of train wagons, rails, and stations is becoming increasingly essential [1] and requires massive use of sensors for measuring relevant quantities, such as acceleration, temperature, pressure, strain, and changes in railroad tracks and train wheels.

Implementation of an Advanced Macroelement Beam Model under Moving Load (Case Study: Iranian Railroad Projects)

This study aims to present a new three-dimensional moving macroelement for the numerical analysis of beam on the elastic foundation under moving loads as a railroad track vibration. Our research is based on a case study conducted in Iranian Railroad Projects. Due to involving a large number of elements, vibration analysis of beams as railroad tracks with standard three-dimensional elements is time-consuming. Two approaches are used and combined in this research to improve analysis of realistic models. The former is to use a macroelement with a number of degrees of freedom that can be used in lieu of standard three-dimensional elements. The latter is to use moving elements to ensure that the loads do not approach the boundaries of the model, thus leading to boundary errors. Accordingly, the moving element was formulated at different velocities to allow correct evaluation of the trains’ acceleration and deceleration effect. The analysis is based on a linear model. Examples with different number of elements and boundary conditions were included to evaluate the effects of the track parameters. One important aspect of the formulation is the asymmetric nature of stiffness and damping matrices due to the effects of velocity and the moving load. In the moving element method, with a sufficient number of elements, no end condition effect exists. However, because acceleration may be a more critical parameter than displacement, the number of elements must be determined for acceleration, as well. Our important achievements include formulating the interaction of lateral and torsional degrees of freedom, the possibility of calculating probable warping in the beam due to cross-sectional slenderness both directly and based on introducing a dependent degree of freedom, and also determining the model length based on vibrational acceleration at the end of the model. Besides, reduction of analysis time is a prominent feature of the present model.

Level of service analysis of rail road grade crossing from the perspective of walking and bicycling: a perception based study

ABSTRACT This article suggests reliable pedestrian level of service (PLOS) and bicycle level of service (BLOS) models by analyzing the effects of various factors to define perceived PLOS and BLOS criteria at rail road grade crossing (RRGC). Questionnaire survey was carried out near 21 RRGCs across nine Indian states, and 2272 responses were obtained from the survey. Ordered probit models were developed to investigate the impact of variables over PLOS and BLOS. The models result illustrate that inflating certain variable (age, trip type, gate blocking phase, distance between gates and presence of heavy vehicles) degrade perceived PLOS and BLOS. However, enhancing the road surface quality at grade crossing, lateral visibility, road marking & warning sign and aesthetics have a favorable impact on perceived PLOS and BLOS. Thus, the developed models and policy regulations suggested in this research would be useful to improve the service quality level for pedestrians and bicyclists.

Toward Automated Field Ballast Condition Evaluation: Algorithm Development Using a Vision Transformer Framework

Ballast degradation in the track substructure may cause poor drainage, settlement, and reduced lateral stability that may affect safety, daily operations, and long-term maintenance of a railroad system. Extreme levels of degradation in the ballast may result in service interruptions because of safety concerns. Therefore, field ballast condition evaluation is deemed crucial. Current state-of-the-practice methods for evaluating ballast condition primarily rely on subjective visual inspection, labor-intensive sampling, and laboratory sieve analyses of collected field samples. A network-level condition assessment of ballast and track substructure is commonly performed using ground-penetrating radar. For site-specific and detailed geotechnical analyses, development of a reliable, accurate, and cost-effective technique for ballast condition evaluation is urgently needed. This paper presents an innovative approach to accomplish image-based ballast condition evaluation based on deep learning techniques. A ballast image dataset (library) is established by collecting images from various railroad sites and laboratory setups of ballast piles. A vision transformer-based segmentation framework is implemented and trained with the established dataset and employed to serve as the image segmentation kernel to relate the image-based Percent Degraded Segments (PDS) with the ground-truth Fouling Index (FI). Based on the presented research findings, the proposed approach for field ballast condition evaluation will serve as the core data analysis component of an automated ballast scanning vehicle to conduct field ballast inspection, which is being developed to serve as an efficient and reliable system for the evaluation of ballast condition in ballasted railroad tracks.

PERENCANAAN PERKUATAN PERLINTASAN KERETA API SEBIDANG DENGAN MENGGUNAKAN PLAT BETON BERTULANG

lroad track and a motorized highway. Based on data and field reports at railroad crossings with highways, it is often found that damage to the asphalt layer (Surface Coarse). The cause of asphalt damage occurs due to a shift between the ballast gravel under the asphalt surface. Where the asphalt layer should be on a solid pavement or foundation, so that the life of the asphalt surface will be more durable. So to overcome these problems, a comparative study was conducted on the planning of retrofitting of the crossing using reinforced concrete slabs and using used railroad tracks. In the design of the reinforced concrete slab, a plate measuring 3x0.6 meters thick 7 cm with a concrete quality of K-225 (18.68 Mpa) is used which can withstand a train axle load of 18 tons. The load on the used railroad tracks is used by large-loaded vehicles, namely truck loads of 10 tons