Rail (passenger and freight) industry has been under pressure to tackle climate change, local and noise emissions. The current available powertrain technologies to reach the reduced greenhouse (GHG) and zero local emissions demands are electric (fed from the power grid), battery and fuel cell. However, the associated infrastructure costs (electrical equipments and the required overhead catenary infrastructure) have limited the electric option to heavy loaded corridors. Battery electric powered rail vehicles could be another potential option, but their system recharging requirements might significantly limit the system’s availability, thus, impacting the rail vehicle’s on-the-job performance. The recent breakthrough of fuel cell technology in the heavy duty road industry (mainly transit buses), allied with its operational flexibility and environmental performance has opened the way for this groundbreaking technology in the rail industry. Fuel cells generate electricity onboard, using hydrogen or hydrogen rich hydrocarbon fuels. Electricity is, then, stored in batteries or fed directly into a rail vehicle’s high voltage propulsion system. From an operational perspective, fuel cell powered rail vehicles might replace diesel ones in a one-to-one relationship, with the same range and running times, and a more efficient and less noisy powertrain. Moreover, the on-site refilling station is the only additional infrastructure element required, compared to diesel rail vehicle baselines. In short, fuel cell technology might offer a long term local zero emission alternative, fast refuelling (like diesel), flexibility, with self-electrification, integration with a renewable energy source and a quiet operation. Given their outstanding operational and environmental features, several rail market niches might be addressed by the fuel cell technology: i) light rail and trams in urban environments; ii) commuter and regional trains operating on non electrified tracks; iii) shunt or switch locomotives in rail yards (generally located on central portions of large cities or at the crossroads of major rail distribution hubs); iv) underground mining locomotives, and v) line haul locomotives. Since 2002, there has been an intense activity in the global development of fuel cell technology for the rail industry for both passenger and freight markets. This work is supposed to present, based on the compilation of information from a multitude of acknowledged sources, a review of fuel cell rail technology, followed by an overview of fuel cell rail experiences and feasibility studies, highlighting their main outcomes, as well as fuel cell technology potential to offer lower operational costs (fuel and maintenance) and improved performance for the rail industry.

Condition monitoring of rail infrastructure is an important task to ensure the safety and ride quality. The increasing travel demands of the rail network due to higher miles traveled requires regular monitoring of the infrastructure and efficient processing of the data for timely decision-making. Despite the regular data collection on different parameters such as acceleration and track geometry, the data processing is commonly performed to document the track performance and maintenance without further knowledge discovery to realize all the potential from historical data. Motivated by the wealth of historical track data in practice, this paper investigates the feasibility of using onboard data that is repeatedly collected over a period of time on a segment of track to potentially identify changes to the track. The proposed approach has been envisioned to learn from repeated historical time-series data to identify both the location and timing of unexpected changes to the track system. To account for stochastic nature of the collected data, associated with the temporal mismatch between the time-series across different inspection runs, we propose a framework by adopting the concept of Matrix Profile without relying on time series synchronization. The approach divides the entire data into smaller track segments, performs extensive similarity search of time-series signatures, and associate locations with higher dissimilarity to changes of the track either due to maintenance or a potential defect. To demonstrate the efficacy and potential of the method, evaluation on both synthetic data and the field geometry data from a revenue-service Class I railroad has been conducted. The findings provide promising results in predicting the location of track changes with a reasonably high degree of certainty, with an automated offline analysis.

Burgeoning oil and gas production in Texas with the application of hydraulic fracturing (fracking) and horizontal drilling techniques has dramatically impacted the condition of rural roadway infrastructure. Many rural roadways are now inundated with trucks traveling to and from oil and gas well areas. Recent estimates indicate that each horizontal well requires over 2,000 truckloads for construction, drilling operations, maintenance, and crude oil transport over the life of the well. Rail has been an active partner in oil and gas exploration by shuttling fracking sand and drilling supplies to the oil and gas regions and transporting crude and petroleum products from the regions not served by pipelines. This partnership is restricted by limited rail service points and infrastructure close to the active wells; however, expanding existing rail services and network infrastructure could move rail operations closer to the active production areas. The cost and safety impacts of extreme reduction in pavement life may be offset substantially with increased use of rail transport closer to drilling locations. This paper examines the potential options regarding expanded use of rail transportation to address the growing costs of roadway rehabilitation in energy production areas in order to reduce roadway network impacts. Rail service expansion can be through better use of the existing freight rail network, increasing the number of and better placement of transloading facilities along the rail network, and/or through improved rail capacity from double tracking or extending new rail lines. Both the private and public sectors potentially could play a role. A number of privately developed transloading facilities have sprung up along existing rail lines, attempting to deliver fracking sand, pipe, and other supplies. Additionally, public economic development corporations have been actively pursuing development. Expansion of the railroad network could come through the use of special districts such as Rural Rail Transportation Districts (RRTDs), Regional Mobility Authorities (RMAs), or through reactivation of abandoned rail lines. These trends are also examined.

Rolling stock, particularly of freight railroads, is currently maintained using regular preventative and corrective maintenance schedules. This maintenance approach recommends sets of inspections and maintenance procedures based on the average expected wear and tear across their inventory. In practice, however, this approach to scheduling preventative maintenance is not always effective. When scheduled too soon, it results in a loss of operating revenue, whereas when it is scheduled too late, equipment failure could lead to costly and disastrous derailments. Instead, proactive maintenance scheduling based on Big Data Analytics (BDA) could be utilized to replace traditional scheduling, resulting in optimized maintenance cycles for higher train safety, availability, and reliability. BDA could also be used to discover patterns and relationships that lead to train failures, identify manufacturer reliability concerns, and help validate the effectiveness of operational improvements. In this work, we introduce a train inventory simulation platform that enables the modelling of different train components such as wheels, brakes, axles, and bearings. The simulator accounts for the wear and tear in each component and generates a comprehensive data set suitable for BDA that can be used to evaluate the effectiveness of different BDA approaches in discerning patterns and extracting knowledge from the data. It provides the basis for showing that BDA algorithms such as Random Forest [9] and Linear Regression can be utilized to create models for proactive train maintenance scheduling. We also show the capability of BDA to detect hidden patterns and to predict failure of train components with high accuracy.

Railway track vertical alignment is an important indicator of serviceability condition and thus plays a critical role for maintenance planning. Estimating the rail profile through the vertical acceleration readings provides an efficient alternative to the current practice of optical methods using special vehicles. This paper proposes an algorithm to estimate the rail vertical profile using the vertical acceleration of the vehicle resulting from the train-track dynamic interaction. The algorithm is designed to approximate the inverse of the transfer function from the rail vertical roughness to the train’s measured acceleration. The approximation problem is formulated into an H-infinity optimal control design problem, which can be further transferred into a problem of convex optimization. The proposed algorithm possesses several advantages including easy design, little tuning effort, and low computational cost. In addition, to take into account the model uncertainty, an optimization-based learning framework is proposed to further enhance the performance of the proposed algorithm. The numerical study has been conducted comprehensively to validate the observer’s properties and effectiveness in reconstructing of the rail vertical roughness.

The railroad industry currently utilizes two wayside detection systems to monitor the health of freight railcar bearings in service: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the conductor of high-risk defects. Many defective bearings may never be detected by TADS™ since a high-risk defect is a spall which spans more than 90% of a bearing’s raceway, and there are less than 20 systems in operation throughout the United States and Canada. Much like the TADS™, the HBD is a device that sits on the side of the rail-tracks and uses a non-contact infrared sensor to determine the temperature of the train bearings as they roll over the detector. These wayside detectors are reactive in the detection of a defective bearing and require emergency stops in order to replace the wheelset containing the defective bearing. These costly and inefficient train stoppages can be prevented if a proper maintenance schedule can be developed at the onset of a defect initiating within the bearing. This proactive approach would allow for railcars with defective bearings to remain in service operation safely until reaching scheduled maintenance. Driven by the need for a proactive bearing condition monitoring system in the rail industry, the University Transportation Center for Railway Safety (UTCRS) research group at the University of Texas Rio Grande Valley (UTRGV) has been developing an advanced onboard condition monitoring system that can accurately and reliably detect the onset of bearing failure using temperature and vibration signatures of a bearing. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here builds on previously published work that demonstrates the use of the advanced onboard condition monitoring system to identify defective bearings as well as the correlations developed for spall growth rates of defective bearing outer rings (cups). Hence, the system uses the root-mean-square (RMS) value of the bearing’s acceleration to assess its health. Once the bearing is determined to have a defective outer ring, the RMS value is then used to estimate the defect size. This estimated size is then used to predict the remaining service life of the bearing. The methodology proposed in this paper can prove to be a useful tool in the development of a proactive and cost-efficient maintenance cycle for railcar owners.

As transit vehicle wheels accrue mileage, they experience flange and tread wear based on the contact between the railhead and wheel-running surface. When wheels wear excessively, the likelihood of accidents and derailments increases. Thus, regular maintenance is performed on the wheels, until they require replacement. One common maintenance practice is truing; using a specially designed cutting machine to bring a wheel back to an acceptable profile. This process removes metal from the wheel and is often based on wheel flange thickness standards (and sometimes wheel flange angle). Wheel replacement is usually driven by rim thickness, which is continually reduced by wear and metal removed by truing. This research study used wheel wear data provided by the New York City Transit Authority (NYCTA) to analyze wheel wear trends and forecast wheel maintenance (truing based on flange thickness) and wheel life (replacement based on rim thickness). Using automatic wheel-scanning technology, NYCTA was able to collect wheel profile measurements for nearly 4,000 wheels in its fleet over a six-month period, measured weekly. The resulting wheel measurement data was analyzed using advanced stochastic techniques to determine relationships for the changes in flange thickness over time for each wheel in the fleet. Flange thickness wear rate relationships for each wheel were then used to forecast the time it would take for a wheel to reach the flange thickness maintenance threshold as defined by NYCTA standards. Furthermore, a subpopulation of wheels that exhibited very high rates of wear were classified as “bad actors” and identified for further investigation to understand the cause of accelerated wear. This allows for identification and addressing of causal factors that relate to accelerated wear, such as angle of attack and L/V ratio. NYCTA has recently started capturing such data that relates truck performance, which can be related to rate of wear.

1 In this paper we present an optimization based technique to allocate time windows for railroad track maintenance in order to minimize the impact on train circulation. The hypothesis is that it is possible to reduce the impact on train circulation if track maintenance slots are scheduled in naturally occurring gaps on the circulation, instead of just using a decision making process that does not take the train circulation into consideration. The proposed solution uses simulated annealing as the basis for the algorithm and yields promising results when a random initial placement of track blocks is used. We show the results of our experiments that consider as restrictions the earliest initial time for the track block and the latest termination time for the track blocks (to account for restriction in crew work hours, for example). For this work we do not restrict the number of simultaneous maintenance teams that could be working, future investigations will account for that restriction.

The increasing of traffic and operation speed in urban transits has accelerated the degradation of track components. Rail wear, especially extended side wear of the high rail on sharp curves has caused rail premature failure. The factors that contributing to the rail side wear should be investigated in order to seek possible solutions to extend the rail service life and reduce maintenance cost. The rail wear is caused by the aggressive contact between the wheel and the rail. Rail cant directly affects the wheel-rail contact and the values varies depending on the choice of the transit agencies. There are several popular rail cants cross the world, namely 1:40, 1:30, 1:20, and so on. However, the optimal rail cant to minimize rail wear remains unknown. This study performs a numerical parametric analysis on the effect of rail cant on high rail side wear based on the vehicle-track multi-body dynamics. The wheel-rail contact state is calculated first with trains passing sharp curves with different rail cants. Then the high rail wear value is quantified by the Archard wear model. The numerical simulation in this study covers the whole process of the development of high rail side wear from the new rail status to the heavily worn status. The findings from this study could potential help to set the optimal cant for sharp curves in transit tracks.

Train accidents can be attributed to human factors, equipment factors, track factors, signaling factors, and Miscellaneous factors. Not only have these accidents caused damages to railroad infrastructure and train equipment leading to excessive maintenance and repair costs, but some of these have also resulted in injuries and loss of lives. Big Data Analytics techniques can be utilized to provide insights into possible accident causes, thus resulting in improving railroad safety and reducing overall maintenance expenses as well as spotting trends and areas of operational improvements. We propose a comprehensive Big Data approach that provides novel insights into the causes of train accidents and find patterns that led to their occurrence. The approach utilizes a combination of Big Data algorithms to analyze a wide variety of data sources available to the railroads, and is being demonstrated using the FRA train accidents/incidents database to identify factors that highly contribute to accidents occurring over the past years. The most important contributing factors are then analyzed by means of association mining analysis to find relationships between the cause of accidents and other input variables. Applying our analysis approach to FRA accident report datasets we found that railroad accidents are correlating strongly with the track type, train type, and train area of operation. We utilize the proposed approach to identify patterns that would lead to occurrence of train accidents. The results obtained using the proposed algorithm are compatible with the ones obtained from manual descriptive analysis techniques.

Operational efficiency is one of the key performance indicators for all railroad systems. Infrastructure inspection and maintenance engineers are tasked with the responsibility of ensuring the reliability, availability, maintainability and safety of the railroad network. However, as rolling stock traffic density increases throughout the network, inspection and maintenance opportunities become less readily available. Inspection and maintenance activities normally take place at night, when there is little or no train movement to avoid disruption of normal railroad network operation. In addition, conventional inspection methodologies fail to deliver the efficiency required for the optimization of maintenance decisions, particularly with respect to track renewals, due to their defect detection sensitivity and level of resolution limitations. The fact that critical structural components such as rails and crossings (frogs) are randomly loaded increases the degree of uncertainty when trying to estimate their remaining service lifetime. Maintenance decisions are predominantly based on the feedback received from inspection engineers, coupled with empirical knowledge that has been gained over the years. The use of structural degradation models is too risky due to the uncertainty arising from the variable dynamic loads sustained by the rail track. The use of structural health monitoring techniques offers significant advantages over conventional approaches. First of all, it is non-intrusive and does not interrupt normal rail traffic operations. Secondly, defects can be detected and evaluated in real-time whilst their evolution can be monitored continuously, enabling maintenance to be scheduled in advance and at times where the need for rail network availability at the section concerned is at its lowest. This paper analyzes the potential risks and benefits of a gradual shift from traditional inspection approaches to advanced structural health monitoring techniques.

The Federal Railroad Administration (FRA) has partnered with Metro-North Railroad (MNR), Long Island Rail Road (LIRR) and New York & Atlantic Railway (NYA) to enhance operational safety through the implementation of wayside detection systems. Currently, MNR has a four-track Wheel Impact Load Detector (WILD) system that has been operating since 2010 near the Grand Central Terminal. This paper discusses a Receiver Operating Characteristic (ROC) analysis of this existing WILD system in conjunction with the wheel maintenance practices since 2010. Currently MNR’s operating procedures require a car with wheel(s) exhibiting a vertical peak load/mean load ratio, called dynamic ratio (DR), ≥3.0 to be shopped for repair. The analysis, using a 30-day repair window after detection, shows that 84% of the cars shopped for wheel(s) with DR≥3.0 required valid maintenance repairs. The minimum number of total false records (false positive + false negative records, combined) were observed within the DR range of 2.7–2.8 when considering wheel flat defects only. An analysis of the false negative records inclusive of both flat and shell spots, showed that the minimum number of false records dropped slightly to a DR range of 2.6–2.7. The reported ROC analysis shows that MNR’s current DR≥3.0 to trigger inspection and maintenance actions is reasonable.

Freight railroad classification yards have been compared to large-scale manufacturing plants, with inbound trains as the inputs and outbound trains as the outputs. Railcars often take up to 24 hours to be processed through a railyard due to the need for manual inbound inspection, car classification, manual outbound inspection, and other intermediate processes. Much of the inspection and repair process has historically been completed manually with handwritten documents. Until recently, car inspections were rarely documented unless repairs were required. Currently, when a defect is detected in the yard, the railcar inspector must complete a “bad order” form that is adhered to each side of the car. This process may take up to ten minutes per bad order. To reduce labor costs and improve efficiency, asset management technology and Internet-of-Things (IoT) frameworks can now be developed to reduce labor time needed to record bad orders, increase inspection visibility, and provide the opportunity to implement analytics and cognitive insights to optimize worker productivity and facilitate condition-based maintenance. The goal of this project is to develop a low-cost prototype electronic freight car inspection tracking system for small-scale (short line and regional) railroad companies. This system allows car inspectors to record mechanical inspection data using a ruggedized mobile platform (e.g. tablet or smartphone). This data may then be used to improve inspection quality and efficiency as well as reduce inspection redundancy. Data collection will involve two approaches. The first approach is the development of an Android-based mobile application to electronically record and store inspection data using a smartphone or rugged tablet. This automates the entire bad order form process by connecting to IBM’s Bluemix Cloudant NoSQL database. It allows for the information to be accessed by railroad mechanical managers or car owners, anywhere and at any time. The second approach is a web-based Machine-to-Machine (M2M) system using Bluetooth low energy (BLE) and beacon technology to store car inspection data on a secure website and/or a cloudant database. This approach introduces the freight car inspection process to the “physical web,” and it will offer numerous additional capabilities that are not possible with the current radio frequency identification device (RFID) system used for freight car tracking. By connecting railcars to the physical web, railcar specifications and inspection data can be updated in real-time and be made universally available. At the end of this paper, an evaluation and assessment is made of both the benefits and drawbacks of each of these approaches. The evaluation suggests that although some railroads may immediately benefit from these technological solutions, others may be better off with the current manual method until IoT and M2M become more universally accepted within the railroad industry. The primary value of this analysis is to provide a decision framework for railroads seeking to implement IoT systems in their freight car inspection practices. As an additional result, the software and IoT source code for the mobile app developed for this project will be open source to promote future collaboration within the industry.

The Federal Railroad Administration (FRA) has partnered with Metro-North Railroad (MNR), Long Island Rail Road (LIRR) and New York & Atlantic Railway (NYA) to promote operations safety through implementation of wayside detection systems and technologies. Under this partnership, opportunities were identified to enhance safety operations through the analysis of existing and planned wheel impact load detector (WILD) installations and operational procedures, including recommendations for future wayside detection systems implementations on these networks. Currently, MNR has a four-detector system operating near the Grand Central Terminal, since 2010. This paper includes an analysis of this WILD system and its impact on rolling stock maintenance. The analysis shows that the WILD system has gradually reduced the annual average number of high impact load wheels from 0.32/car in 2010 to 0.27/car in 2015. Review of data from the detectors on four tracks at the WILD site shows that train operations below a certain speed has a significant effect on the detection rate and should be a major consideration in selecting a location for WILD installation. The data show that the highest number of high impact wheels is detected in the month of November, potentially due to leaves on the rails during fall season. Our analysis shows that the currently used trigger threshold of dynamic ratio (DR)≥3 at MNR provides well-reasoned balance between the available fleet, maintenance demand and the maintenance shop capacity. At this threshold setting, the number of wheels detected per million wheel passages is quite small indicating a well maintained fleet.

Railway ballast under repeated traffic loading deforms and deteriorates. Increases in the rate of settlement in ballast decreases its useful life and contributes to geometry roughness and poor ride quality. Based on laboratory and field studies, as well as mechanic-based models, the fouling condition of the ballast has been shown to have a profound effect on settlement and overall ballast life. Quantifying ballast settlement and the effect of maintenance at different stages of ballast life can define the State of Good Repair (SGR) for ballast. This paper presents an approach for predicting ballast life based on geotechnical principles and maintenance management philosophy. Over 200 revenue-service locations have been studied for developing this model for different tracks including mixed passenger and freight as well as only freight traffic. The approach presented in this paper can be used for optimizing ballast’s life-cycle costs through a maintenance management approach.

The increasing movement of people and products caused by modern economic dynamics has burdened transportation systems. Both industrialized and developing countries have faced transportation problems in urbanized regions and in their major intercity corridors. Regional and highway congestion have become a chronic problem, causing longer travel times, economic inefficiencies, deterioration of the environment and quality of life. Congestion problems are also occurring at airports and air corridors, with similar negative effects. In the medium distance travel market (from 160 up to 800 km), too far to drive and too short to fly, High Speed Rail (HSR) technology has emerged as a modern transportation system, as it is the most efficient means for transporting large passenger volumes with high speed, reliability, safety, passenger comfort and environmental performance. HSR system’s feasibility will depend on its capacity to generate social benefits (i.e. increased mobility rates, reduced congestion, capacity increase and reduced environmental costs), to be balanced with the high construction, maintenance and operational costs. So, it is essential to select HSR corridors with strong passenger demands to maximize these benefits. The first HSR line was Japan’s Shinkansen service, a dedicated HSR system, between Tokyo and Osaka, launched in 1964, which is currently the most heavily loaded HSR corridor in the world. France took the next step, launching the Train à Grande Vitesse (TGV), in 1981, with a dedicated line with shared-use segments in urban areas, running between Paris and Lyon. Germany joined the venture in the early 1990 with the Inter City Express – ICE, with a coordinated program of improvements in existent rail infrastructure and Spain, in 1992, with the Alta Velocidad Espanola – AVE, with dedicated greenfield lines. Since then, these systems have continuously expanded their network. Currently, many countries are evaluating the construction of new HSR lines, with European Commission deeming the expansion of the Trans European Network as a priority. United Kingdom, for example, has just awarded construction contracts for building the so called HS2, an HSRexpanded line linking London to the northern territory. China, with its dynamic economic development, has launched its HSR network in 2007 and has sped up working on its expansion, and currently holds the highest HSR network. United States, which currently operates high speed trainsets into an operationally restricted corridor (the so called Northeast Corridor (NEC), linking Washington, New York and Boston), has also embarked into the high speed rail world with the launch of Californian HSR Project, currently under construction, aimed to link Los Angeles to San Francisco mega regions, the ongoing studies for Texas HSR project, to connect Dallas to Houston, into a wholly privately funding model, as well as studies for a medium to long term NEC upgrade for HSR. Australia and Brazil are also seeking to design and launch their first HSR service, into a time consuming process, in which a deep discussion about social feasibility and affordability is under way. This work is supposed to present an overview of HSR technology worldwide, with an assessment of the main technical, operational and economical features of Asian and European HSR systems, followed by a snapshot of the general guidelines applied to some planned HSR projects, highlighting their demand attraction potential, estimated costs, as well as their projected economic and environmental benefits.

This paper provides a unique perspective on successful brownfield railroad applications. It presents realistic challenges and solutions when applying a turnkey solution with a replacement or an overlay system. Brownfield commissioning takes place when an existing infrastructure is to upgrade to a new system with a different technology than the incumbent one. As signaling systems are getting more and more complex, it is extremely important to maintain robustness in the system design as well as project execution, such as logistics, documentation, and issue reporting. Many transportation authorities are moving from their current train control signaling system to a new system to combat obsolescence issues, to gain better system capacity, and to lower operation and maintenance costs. This paper discusses brownfield commissioning in general, and also presents specific cases in migration from a track circuit interlocking system to a Communications Based Train Control (CBTC) system. These two systems have distinct characteristics that provide opportunities of coexistence, but also introduce difficulties in mixed-mode operations.

An important consideration when operating a fleet of passenger rail consists is the remaining service life of both the car structure and the trucks. Agencies may choose to undertake studies like this when considering a fleet wide capital improvement program, ranging from minor aesthetic upgrades to major system replacements and interior reconfigurations. With this in mind, the owner needs to determine the remaining fatigue life of the individual cars and trucks within the fleet. This paper describes the fitness-for-service (FFS) assessment performed on railcars and trucks and an example of the method applied in practice. To establish the current condition of the fleet, an initial structural and service history review was undertaken. In addition, nondestructive examinations (NDE) of a sample of cars and trucks were performed to investigate any regions that have experienced damage due to the years of service. After the baseline condition of the cars and trucks was determined, finite element analyses (FEA) were performed on both the cars and the trucks to assist in locating the potential fatigue critical regions. Strain gages and accelerometers were then installed on both the cars and trucks for field testing. Multiple runs of in-service testing were performed and a typical revenue service fatigue life of both the cars and trucks was calculated based on an S-N approach. Based on the calculated fatigue life and the current accumulated consist mileage, the remaining car and truck service lives were determined. For regions with known flaws more detailed fracture mechanics based crack growth analyses were developed to determine critical flaw sizes and their propagation times to critical from the known initial flaw sizes. Results of the FFS assessment provide information on the susceptible areas within the car structure and trucks. Depending on those results, decisions can be made on the required inspections, repairs, or decommissioning necessary to operate the fleet in the short term, while also providing valuable insight into long term fleet planning.

Configuration Management (CM) has been traced back to the early 1950s where the principles were first applied as change management for computer hardware. CM was later expanded to early software development which has continued to the modern age. While the mantra has not changed in 60+ years — to define and manage change — updated techniques have streamlined the process making it easier to implement and more responsive in utilization in order to keep pace with changes for both micro (software) and macro (large-scale infrastructure project) applications. In order to advance the state of good repair (SGR) for rail and transit assets in the United States, CM has become an essential tool and must coordinate with Project Integration and Asset Management so that an organized process for change control with accurate record keeping can be established and maintained long term. The importance of CM has also been mandated at the Federal level for rail assets under 49 CFR 236. To bring wider understanding of CM to the industry, this paper will present Configuration Management and Project Integration techniques as applied to rail transportation infrastructure including assets that must meet Federal regulations, and it will discuss the coordination between CM, Integration and Asset Management under the SGR initiative. This is the final paper in a series that began in 2012, presented at the Joint Rail Conferences, and featured Denver Transit Partners (DTP) and the Denver Eagle P3.

Rail defects are a significant safety concern for the world’s railway systems. Broken rails may lead to the catastrophic derailment of vehicles and have severe consequences including death, injury and economic losses. Precise estimation of the impact of descriptive factors on the occurrence of rail fatigue defects is of great significance for the development of rail defect statistical prediction models. Meanwhile, improvement of prediction models will assist railroads in allocating inspection maintenance resources efficiently. Thus, this paper focuses on reviewing principal risk factors affecting the occurrence of rail defects. A better understanding of the influence of rail defect explanatory factors could aid in model improvement. Previous data collection and analysis in rail defects are highlighted in this review in order to improve the understanding of the impact of potential risk factors. The overview of rail defects aims to aid researchers in improving their understanding of what factors affect the occurrence of rail fatigue defects and how to analyze these factors during the processing of statistical models.