Magnetic levitation (maglev) is a highly advanced technology which provides, through magnetic forces, contactless movement with no wear and friction and, hence, improved efficiency, followed by reduced operational costs. It can be used in many fields, from wind turbines to nuclear energy and elevators, among others. Maglev trains, which use magnetic levitation, guidance and propulsion systems, with no wheels, axles and transmission, are one of the most important application of the maglev concept, and represents the first fundamental innovation of rail technology since the launch of the railroad era. Due to its functional features, which replaces mechanical components by a wear free concept, maglev is able to overcome some of the technical restrictions of steel-wheel on rail (SWR) technology, running smoother and somewhat quieter than wheeled systems, with the potential for higher speeds, acceleration & braking rates and unaffected by weather, which ultimately makes it attractive for both high speed intercity and low speed urban transport applications. From a technical perspective, maglev transport might rely on basically 3 technological concepts: i) electromanetic suspension (EMS), based on the attraction effect of electromagnets on the vehicle body, that are attracted to the iron reactive rails (with small gaps and an unstable process that requires a refined control system); ii) Electrodynamic Levitation (EDL), which levitates the train with repulsive forces generated from the induced currents, resulted from the temporal variation of a magnetic field in the conductive guide ways and iii) Superconducting Levitation (SML), based on the so called Meissner Effect of superconductor materials. Each of these technologies present distinct maturity and specific technical features, in terms of complexity, performance and costs, and the one that best fits will depend on the required operational features of a maglev system (mainly speed). A short distance maglev shuttle first operated commercially for 11 years (1984 to 1995) connecting Birmingham (UK) airport to the the city train station. Then, high-speed full size prototype maglev systems have been demonstrated in Japan (EDL) (552 kph - 343 mph), and Germany (EMS) (450 kph - 280 mph). In 2004, China has launched a commercial high speed service (based on the German EMS technology), connecting the Pudong International Airport to the outskirts of the city of Shanghai. Japan has launched a low speed (up to 100 kph - 62.5 mph) commercial urban EMS maglev service (LIMINO, in 2005), followed by Korea (Incheon, in 2016) and China (Changsha, in 2016). Moreover, Japan is working on the high speed Maglev concept, with the so called Chuo Shinkansen Project, to connect Tokio to Nagoya, in 2027, with top speeds of 500 kph (310 mph). China is also working on a high speed maglev concept (600 kph - 375 mph), supported on EMS Maglev technology. Urban Maglev concept seeks to link large cities, with their satellite towns and suburbs, to downtown areas, as a substitute for subways, due to its low cost potential, compared to metros and light rail (basically due to their lower turning radius, grade ability and energy efficiency). High Speed Maglev is also seen as a promising technology, with the potential do provide high quality passenger transport service between cities in the 240–1,000 km (150–625 mi) distance range into a sustainable and reliable way. This work is supposed to present, based on a compilation of a multitude of accredited and acknowledged technical sources, a review of the maglev transport technology, emphasizing its potential and risks of the low and high speed (urban and intercity) market, followed by a brief summary of some case studies.

This paper provides an introduction to a Rail Break Detection and Train Location Detection using fiber optic technology called Smart Rail. The technology concept detailed in this paper not only detects the location of a broken rail, it also provides advanced warning of rail damage that may evolve into a dangerous condition. The same technology allows for the detection of trains and vehicles in the detection zone and provides location, speed and direction information. The Smart Rail continuously monitors rails for changes in their ability support the train in a safe manner. Smart Rail technology is based on direct observations of the structural quality of the rail by means of rail strain detection. Smart Rail technology detects the location, speed and direction of vehicles on the rails without the need for onboard equipment. It reports the location of vehicles whether moving or not. Smart Rail technology is also capable of detecting and providing a warning of: rail gauge separation, lose or missing track hardware, missing tie plates and flat wheels. Taken together this technology can assist in the prevention of derailments. The technology can be applied to existing rails with no need to modify any existing equipment or track circuits. The train detection range can be up to 50 miles per direction from one wayside location.

High Speed Trains (HST) face some of the most stringent requirements for ensuring passenger safety and comfort while operating at very high velocities. HST wireless communication systems therefore similarly require special considerations for network design, technology selection and system implementation. For infrastructure-based wireless communications, a handover occurs whenever a radio transitions from the coverage of the current base station to the next base station. The faster the train moves the shorter the time that it spends under coverage area before a handover is required. For HST this can be as short as 10–20 seconds. Furthermore, a HST moving at 220 mph experiences significant fast and small-scale wireless signal fading due to this velocity, but similarly may incur frequent connectivity losses due to the rapid transit across coverage areas. A route consisting of viaducts, bridges, tunnels or hilly regions drastically increases the need for rapid handovers since a large number of base stations are required to provide coverage and achieve acceptable Quality-of-Service (QoS) in such environments. Due to the relatively fixed amount of time needed to complete a handover, and the possibility of failed handovers, this reduces the usable time under coverage, and thus ensuring an optimal handover strategy selection is vital. Most of the routes span rural and suburban areas, which reinforces the need for a comprehensive network planning strategy, as these areas tend to be underdeveloped for cellular coverage. Given the complexity in available radio spectrum resources, detailed studies are required to aid in this technology selection process. However, with the predicted increase in the demand of railroad network data traffic generated by onboard sensors, onboard control and operation devices, passenger Internet services, etc., it becomes apparent that more resources are needed than are provided by current technologies such as LTE. Thus, including 5G New Radio for Railways (5G-NR2) in the consideration does not only provide railways a more cost-effective licensing option for frequencies, but also an opportunity to select the best possible radio access method for a given HST corridor. In this paper, we focus on the metrics related to handover and how they correlate with different radio access technologies for HST. Our results integrate the uncertainty of environmental factors to provide answers on technology selection driven by specific route, inter-site distances, and available bandwidth. Our wireless communication simulation results are driven by well recognized mathematical models that consider a variety of key parameters. The analysis of the handover performance will offer insights vital to future railroad network planning for multivariate radio access technologies, and to answer crucial questions about the potential for using frequency bands above 6 GHz in HST.

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.

By 2030, the United States Federal Transit Administration (FTA) plans to have High Speed Train (HST) systems deployed that span over 12,000 miles across the US. Given the rapidly accelerating growth in consumers demand for fast on-board Internet services, there is a need for a robust and dedicated railroad wireless network architecture for their onboard and Train-to-Ground (T2G) communication systems. And while there are several potential candidates for radio access technologies (RAT), a full understanding of the benefits and drawbacks of each is still missing. We therefore have developed and studied a simulation framework that offers railroads the ability to perform an in-depth evaluation of capabilities for different RATs in terms of interoperability, throughput, handover and bit error rate for various user-driven scenarios. The framework is capable of studying and analyzing conditions such as network performance at different train velocities, base station spacing requirements, as well as analyzing US-specific geographical or track-related architectural scenarios. Our Past experiences in researching railroad wireless solutions have shown that wireless network performance varies widely in environments like tunnels, viaducts, bridges, stations, etc. The simulator offers the network designers significant flexibility in terms of defining parameters to create simulation scenarios and obtaining a detailed understanding of network performance. The work has created a novel, flexible and adaptable simulation framework for high-speed passenger train wireless network evaluation. The simulation tool supports 220MHz-100GHz systems for simulating LTE and 5G-New Radio (5G-NR), and it can support other technologies such as 220MHz PTC, in a time-variant channel. In this paper we present the architecture and the capabilities of the simulator with a sample scenario evaluation. The developed framework aims to support HST wireless communication designers to conduct more detailed analyses and to make more informed decisions in optimizing system deployments.

The recent increase of train speed and frequency determines a rise of the loads transmitted to the superstructure. Therefore, railway components might experience service loads that have not considered at the design stage. Moreover, wear and backlash modification between components of a mechanical system might be able to modify the internal boundary conditions of the assembly. According to damage tolerance philosophy, an initial flaw is assumed to exist in the fatigue critical location of a structural component, and the analysis of the crack propagation life for such component needs accurate Stress Intensity Factor (SIF) evaluations. In this study, the effects of the boundary conditions on the crack propagation life have been evaluated for a semi-elliptical surface crack having semi-axes a and c and growing from the root of a shoulder fillet notch in a round bar loaded in bending. Two cases have been analyzed: - the shoulder is free from external forces; - the shoulder is in contact with an adjacent generic body. At first, the SIF distribution has been calculated with the Virtual Crack Closure Technique, considering or not the nonlinear effect induced by the contact forces arising from the interaction between the shoulder and the neighboring component. Successively, in both the above cases a two-parameters propagation law has been utilized to predict the evolution of both crack shape and crack depth when a cyclic bending load is applied to the rod. For this purpose, different values of the Stress Concentration Factor at the root of the fillet, and of the initial aspect ratio of the crack front, were considered in the calculations. It is found that the aspect ratio evolves to a unique asymptote, taking or not into account the non-linearity introduced by the contact at the shoulder, and this value depends on the notch severity. The ratio between the dimensionless SIFs obtained with and without the unilateral constraint at the shoulder, β cs / β fs , does not depend on the relative crack depth and crack shape. Also, the effect of the notch severity on the dimensionless SIF appears to be evident only for the portion of the crack front in the vicinity of the free surface.

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.

Designed for detecting train presence on tracks, track circuits must maintain a level of high availability for railway signaling systems. Due to the fail-safe nature of these critical devices, any failures will result in a declaration of occupancy in a section of track which restricts train movements. It is possible to automatically diagnose and, in some cases, predict the failures of track circuits by performing analytics on the track signals. In order to perform these analytics, we need to study the coded signals transmitted to and received from the track. However, these signals consist of heterogeneous pulses that are noisy for data analysis. Thus, we need techniques which will automatically group homogeneous pulses into similar groups. In this paper, we present data cleansing techniques which will cluster pulses based on digital analysis and machine learning. We report the results of our evaluation of clustering algorithms that improve the quality of analytic data. The data were captured under revenue service conditions operated by Alstom. For clustering algorithm, we used the k -means algorithm to cluster heterogeneous pulses. By tailoring the parameters for this algorithm, we can control the pulses of the cluster, allowing for further analysis of the track circuit signals in order to gain insight regarding its performance.

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.

Transport Canada, the regulatory arm of the Canadian Government commissioned a report from the Canadian Rail Research Laboratory (CaRRL) to investigate the risk mitigation of train to train collisions and other train control preventable accidents. This paper looks at the risks reviewed in the report and proposes a formula to provide a level of mitigation for individual railway lines to use in determining the minimum level of train control as discussed in the CaRRL report. Risks to be included are traffic levels, traffic type, track speed, track configuration, among others. Data used will be taken from the ROD database results used in the CaRRL report.

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.

The science and technology used in highway crossings in the United States and around the world have come a long way from a single flagman sitting in a booth, equipped with a red flag or lantern in his hand, to clear tracks and stop pedestrians, horses, and or a motor coaches for an approaching train to a fully automatic warning system requiring limited monthly testing. Today’s highway crossings are monitored by railways and municipalities such that, any changes in railway or roadway traffic conditions can be scrutinized. These changes, an increase in train or vehicle traffic, may trigger the need for additional protection devices to be implemented to make highway crossings safer for all; passing trains, motorists and pedestrians. But, are these requirements enough to eliminate accidents. Historically speaking, these accidents range from; failures with the activation of the warning system; distracted motorists or motorists not willing to comply to the warning; pedestrians rushing to beat the train while underestimating the trains speed or; fully knowing and willing to not stop at the flashing lights and gates and willing to take the risk and go around flashing gates. This paper will investigate the current and future technologies that are being tested and implemented on highway crossings as well as look into the predictive behavior of motorists and pedestrians as they approach crossings and how changes can be implemented to maximize the effectiveness of a highway crossing. Key elements from various studies will be included that have been suggested through analyzing driver’s behavior at highway crossings, as well as the additional technologies that have and can be implemented to provide additional warnings to alert motorist of trains approaching.

According to the Federal Railroad Administration (FRA) Highway-Rail Grade Crossing Accident/Incident database, more than 12,000 accidents occurred between 2012 and 2017 in the United States with casualties of around 3900. Despite repeated efforts to fully understand the risk factors that contribute to highway-rail grade crossing collisions, there still remain many uncertainties. A machine learning approach is proposed in this paper to find out significant factors, along with their individual impacts of crash severities at grade crossings. One of the most efficient and accurate machine learning algorithms, extreme gradient boosting (XGB or XGBoost), is applied to analyze 21 different accident and crossing -related characteristics per driver severities. The XGB model has been proven in previous studies across many research areas in transportation to outperform other machine learning-based methods and statistical classification methods, such as multinomial logit model, multiple additive regression trees, decision tree, and random forest, especially in prediction accuracy. Thereby, applying the algorithm is expected to provide highly reliable results to identify important factors that have impacts on injury severities at grade crossings. Such application will further aid the discovery of potential crossings with significant factors. The FRA’s Highway-Rail Grade Crossing Accident/Incident database from 2012 to 2017 is fused with the FRA Highway-Rail Crossing Inventory database for the analysis. Observations with missing information were removed from the original database. Crossing position under or over the railroad and pedestrian or other types of highway users were also not considered since they were not specifically of interest in this study. After the database cleaning process, it condensed to the total of 1,250 accidents out of the retrieved 12,630 from the combined database. The results show that adjacent highway traffic volume and train speed are the most significant factors causing accidents and injury severity. They are followed by the driver’s age and the estimated vehicle speed. It also indicated that truck-involved accidents and crossings with gates, flashing lights, and other types of warning devices combined, and highway user’s gender as a male also pertain to the higher injury rate. Through this study, it is possible to provide guidance to decision-makers in recognizing possible risks at-grade crossings that may cause driver casualties.

When a signaling system reaches its end of life, an overall replacement is necessary. The replacement may be in-kind but for many mass transit agencies, it is common that capacity needs will have grown significantly, and the transit agency is required to increase capacity while tackling obsolescence. Capacity and obsolescence are the primary reasons for deploying Communications Based Train Control (CBTC) technology to replace conventional trip stop and track circuit-based systems operated by New York City Transit (NYCT) or cab signaling systems operated by Baltimore Maryland Transit Administration (MTA) and the San Francisco Bay Area Rapid Transit District (BART). Resignaling projects are often refereed as brownfield train control projects. Due to the technical and commercial complexity, the quantity of stakeholders, the number of interfaces, the regulatory involvement via safety certification, the temporal and geographic scale of the project and the continual evolution of the existing system, a robust Systems Engineering (SE) process is necessary to achieve success in large signaling replacement projects. This paper describes the software tools and processes that were deployed to meet the technical, legal and commercial needs of a large signaling replacement project during the specification development phase. The processes put in place were tailored to the size and location of the team members. The paper describes the software tools, such as IBM Rational DOORS, Microsoft SharePoint Online and Office 365 which were integrated and configured to allow for collaboration while accounting for the security, commercial and legal requirements of the agency. The selection, deployment and management of these processes and tools enabled the large, complex project to succeed.

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.

Thermal track buckling is probably the major problem due to the advent of continuous welded rail track. In fact, when the rails temperature rises over a critical value, the track can buckle, suddenly or progressively, in the lateral plane. Both poor ballast conditions and large lateral alignment defects are the principal causes of such phenomenon. In a previous paper, a parametric finite element model for thermal track buckling simulation was presented and validated by comparison with analytical results of the literature. In this study, the finite element model has been further validated by comparison with analytical and numerical results obtained by three other authors. Moreover, to take into account the effect on the buckling temperatures of the vertical loads due to train passes, the tie-ballast lateral resistance has been modified along the track, taking into account the vertical reaction forces distribution induced by axle loads. A sensitivity analysis has been carried out both for tangent and curved track, considering two values of the alignment defect amplitude, and different values of the parameters that characterize actual railway vehicles. It is found that the conditions to trigger progressive buckling (△ T max ≈ △ T min ) are attained with small values of the truck center distance, and in a more accentuated manner in the presence of high values of the lateral alignment defect. △ T max and △ T min increase with axle spacing, and this increase is more pronounced for low values of the truck center spacing. △ T max and △ T min also increase with curve radius, but decrease for increasing values of the misalignment defect amplitude. In explosive buckling conditions (△ T max ≠ △ T min ), there is a limit value of the truck center distance above which the vertical load has no more effects, and the results of the static thermal buckling are found.

U.S. freight railroads produce about 40 percent of freight gross ton-miles while consuming only about 1/20th of the total U.S. diesel fuel 1 . Compared to heavy-duty trucks, freight railroads have significant energy (and emissions) advantages including the low coefficient of friction of steel wheel-on-rail (compared to rubber tires-on-pavement) and multiple-vehicle trains. However, improved heavy-duty truck technologies are being federally-funded and developed which may create some challenges to freight rail’s long-standing environmental (and economic) advantage in certain transportation markets and corridors. This paper reviews U.S. freight rail fuel efficiency (measured in gallons of fuel per thousand gross ton-miles) from 1920 to 2015, using published records from the former Interstate Commerce Commission (ICC) archived and made available by the Association of American Railroads (AAR). All freight locomotive energy consumption (all types of coal, crude oil, electricity kilowatt-hours and diesel fuel) are converted into approximations of diesel gallons equivalent based on the nominal energy content of each locomotive energy type, in order to show the effect of transitioning from steam propulsion to diesel-electric prior to 1960 and the application of other new technologies after World War II. Gross ton-miles (rail transportation work performed) will similarly be tracked from historic ICC and AAR records. Annual U.S. freight rail fuel efficiency is calculated and plotted by dividing total calculated diesel gallons equivalent (DGe) consumed by gross (and by lading-only net) ton-miles produced. New technologies introduced since 1950 which have likely contributed to improvements in freight rail fuel efficiency (such as introduction of unit coal trains, distributed power, alternating current locomotives, etc) will also be discussed and assessed as to relative contribution to fuel efficiency improvements. The paper includes a discussion about U.S. freight rail fuel efficiency compared to heavy-duty truck fuel efficiency, with comments on projected improvements in heavy-duty truck technologies and fuel efficiency. A conclusion is that U.S. freight railroads and equipment suppliers need to be more aware of projected heavy-duty truck fuel efficiency improvements and their potential for erosion of some aspects of traditional railroad competitiveness. Numerous suggested action plans are discussed, with particular focus on reducing the aerodynamic drag (a delta velocity-squared factor in train resistance and power requirement) of double-stack container trains. Last, this paper discusses possible courses of action for U.S. freight railroads to achieve fuel efficiency improvements greater than the historic ∼1 percent improvement achieved over the past 50 years. If freight rail is to remain economically competitive vis a vis heavy duty trucking, railroads will have to identify, evaluate and implement new technologies and/or new operating practices which can help them achieve fuel efficiency improvements matching (or exceeding) those projected for heavy trucks over the next 7-to-12 years. A specific example for improving fuel efficiency of double-stack container trains is discussed. Failure to address the future of freight rail fuel efficiency is likely not an option for U.S. railroads.

Cab signaling enforces the separation between trains as well as enforcing trains to reduce speed as the train approaches signals displaying STOP. Cab signaling allow for and provides a safe way to eliminate the number of wayside automatic signals while the number of controlled speeds can be increased. Light Rail Transit (LRT) systems today are built completely with cab signaling and only fixed wayside signals are placed at interlockings for routing information. Experimental cab signaling systems began in the United States in the 1920s, kicked off by the Interstate Commerce Commission (ICC) ruling that required some form of Automatic Train Control (ATC) be installed on one passenger division by 1925. This paper will begin with examining the initial ATC designs (intermediate and continuous), the first experimental installations, the testing challenges and the overall enhancements that pioneered cab signaling systems in the US. The focus will include the teaming of the Pennsylvania Railroad with Union Switch and Signal (US&S) to develop, build and successfully test the continuous cab signaling system which later became the de facto standard. The early systems implemented used two (2) speeds, methods on adding a third speed and how the system became integrated with the existing automatic block signaling. How Pennsylvania Railroad (currently Amtrak) is still using the technology that started 100 years ago on the North East Corridor. It will also introduce how Light Rail systems operate on speed commands using cab codes.

With any new mode of transportation comes new fears for both the public and those involved in the industry. The advent of the transcontinental railroad was no different. When the transcontinental railroad was complete and trains became more commonplace for travel, the biggest fear became the worst case scenario: a head on collision between two trains. The idea of the head on collision remained the biggest fear of the public because it happened and was based on reality, but was rarely witnessed, which made the idea even more lofty. But with the standardization of time in the 1880’s, there were fewer crashes and collisions of railroads, but people were still afraid. Railroad companies began to brainstorm the best way to change public perception, and began to stage head on collisions open to public viewing for a small fee. Naturally, the idea took off, and head on collisions between trains became the next source of entertainment. For $2, spectators could watch two locomotives crash into each other at speeds of 58 miles an hour in Crush Texas, or even cheaper in Ohio. But this was more than just entertainment. William Crush, the most famous locomotive smasher had actually worked on the railroad known as the Katy. When asked by the executives of the railroad to boost sales, head on collision was his solution. Despite multiple injuries suffered in the crowd from shrapnel and an exploded boiler, this showcase worked, and ridership of the Katy increased dramatically. Crush’s display was not the first, or last time this took place around the country, but it was the most deadly, which makes it the most memorable and begs the question “what role do these staged collisions play in railroad history?” Ridership in the decades leading up to these staged collisions was steadily declining, and safety measures were not taken into consideration. But with these staged collisions that turned around. People, not just the public were able to see and study the different collisions and put minds at ease. But it also tells about the United States population at the time. These staged collisions could not have happened in any other era because of the industrial revolution which allowed railroad companies to begin to replace old locomotives and iron tracks with steel.

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.