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.

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.

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.

This paper examines the feasibility and limitations of Forward Looking Infrared Radiometer (FLIR) Aerial Technology for detecting fouled ballast. The method is intended to provide an efficient and ready-to-use approach that can help the railroads detect fouled ballast in their early stages. Ballast fouling commonly occurs as a result of fine particles clogging off water passage through them. Subsequently, this results in trapped water that often results in poor foundation strength, rotting of the ties, and other ill effects. This study includes a novel approach to evaluate the railway ballast fouling by using thermal imaging techniques. In particular, the thermal characteristics of clean and fouled ballast are studied using FLIR cameras that can be used onboard rolling stock, Hyrail trucks, or drones. Laboratory tests are primarily performed to measure the surface temperature changing rate of clean and fouled ballast in response to ambient temperature changes. For the purpose of laboratory testing, the camera is set up in stationary and moving configurations. The test results indicate that clean and fouled ballast have different thermal characteristics. In particular, different thermal patterns are obtained during naturally-occurring daily temperature change. The test results also indicate that the FLIR cameras can be used on a moving platform for quick scanning of thermal images of the ballast that could be used for assessing the early stage of fouling.

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.

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.

In a typical railyard environment, a myriad of large and dynamic objects pose significant risks to railyard workers. Unintentional falls, trips and collisions with dynamic rolling stock due to distractions or lack of situational awareness are an unfortunate reality in modern railyards. The challenges of current technologies in detecting and tracking multiple differently-sized mobile objects in situations such as i) one-on-one, ii) many-to-one, iii) one-to-many, iv) blind spot, and v) interfering/non-interfering separation creates the possibility for reduction or loss of situational awareness in this fast-paced environment. The simultaneous tracking of assets with different size, velocity and material composition in different working and environmental conditions can only be accomplished through joint infrastructure-based asset discovery and localization sensors that cause no interference or impediment to the railyard workers, and which are capable of detecting near-misses as well. Our team is investigating the design and performance of such a solution, and is currently focusing on the innovative usage of lightweight low-cost RADAR under different conditions that are expected to be encountered in railyards across North America. We are employing Ancorteks 580-AD Software Defined RADAR (SDRadar) system, which operates at the license-free frequency of 5.8 GHz and with a variety of different configuration options that make it well-suited for generalized object tracking. The challenges, however, stem from the unique interplay between tracking large metallic objects such as railcars, locomotives, and trucks, as well as smaller objects such as railyard workers, in particular their robust discernment from each other. Our design’s higher-level system can interact with the lower-level SDRadar design to change the parameters in real-time to detect and track large objects over significant distances. The algorithm optimally adjusts waveform, sweep time and sample rate based on one or multiple detected object cross-sections and subsequently alters these parameters to be able to discern other objects from them that are in close proximity. We also use an ensemble method to determine the velocity and distance of target objects to accurately track the subject and larger objects at a distance. The methodology has been field-tested with several test cases in a multitude of weather and lighting conditions. We have also tested the proper height, azimuth and elevation angles for positioning our SDRadar to alleviate the risk of blind spots and enhancing the detection and tracking capabilities of our algorithm. The approach has outperformed our previous tests using visual and acoustic sensors for detection and tracking railroad workers in terms of accuracy and operating flexibility. In this paper, we discuss the details of our proposed approach and present our results from the field tests.

This paper examines alternative, improved materials for truck castings. The first part looks at steels that would not require post-weld heat treatment after repair welding. The second part investigates specific applications for temperatures below −50 °F. The rapid, dramatic temperature changes that occur during welding can form brittle phases or cracking in some steels. The weld has three areas of differing structure: the weld, the heat affected zone (HAZ), and the parent metal. The maximum hardness occurs in the HAZ and is the limiting factor in determining weldability. An ultraweldable steel is a steel that does not require post-weld heat treatment. Many steels were evaluated for their chemical composition and susceptibility to cracking; those that were likely to form brittle phases were eliminated from consideration. Four low alloy steels and one carbon steel were selected as being potentially ultraweldable. To evaluate the ultraweldability of these steels, groove welds and two types of spot welds were made on the five candidate alloys. The welds were then sectioned and prepared for microstructural and microhardness evaluation. Microhardness readings were taken across the weld, spanning the weld, HAZ, and base material. Three of the steels formed hard, brittle phases during most of the tests. This indicates these materials are not ultraweldable. Two of the low alloy steels did meet the requirements for ultraweldability. Future work in this area would include producing truck castings from these materials. At low temperatures, plain carbon steels, such as the types used in truck castings, can fracture in a brittle manner, with no visible deformation. The material property of deforming without fracture is toughness or ductility. Using materials that retain their toughness in low temperatures could prevent brittle failures of truck castings. Six grades of steel currently used in low temperature applications were selected for this research. Specimens from each of the six materials were evaluated for tensile properties at multiple temperatures. Charpy impact specimens were tested at temperatures ranging from −20 °F to −120 °F. The measured room temperature tensile properties of each of the six steels met or exceeded the requirements for Grade B+, the steel currently used for truck castings. Four of the steels showed impact energies far above that of the current Grade B+, but two of them gave consistently higher impact energies than all others. These would be the best candidates for future work in this area. Future work would involve producing full size truck castings from one or more of these alloys, then testing them for fatigue performance, preferably at low temperatures.

The dynamic nature of a train in operation is caused by variables in rail condition, track smoothness, wheel defects, truck hunting and skewing, and coupler effects. These conditions can each cause perturbations that may affect the accuracy of the weight measured by a Weigh In Motion (WIM) system. China is the only country that has mandatory requirements to calibrate a WIM system dynamically after it is initially installed and every year after it’s in revenue service. This paper talks about the importance of both static and dynamic calibrations to achieve the highest accuracy of a WIM system. This paper also describes the process of conducting a dynamic calibration, and the lessons learned in the past several years in the effort to improve the weight accuracy of a WIM system.

Thermoplastic elastomers (TPE’s) are increasingly being used in rail service in load damping applications. They are superior to traditional elastomers primarily in their ease of fabrication. Like traditional elastomers they offer benefits including reduction in noise emissions and improved wear resistance in metal components that are in contact with such parts in the railcar suspension system. However, viscoelastic materials, such as the railroad bearing thermoplastic elastomer suspension element (or elastomeric pad), are known to develop self-heating (hysteresis) under cyclic loading, which can lead to undesirable consequences. Quantifying the hysteresis heating of the pad during operation is therefore essential to predict its dynamic response and structural integrity, as well as, to predict and understand the heat transfer paths from bearings into the truck assembly and other contacting components. This study investigates the internal heat generation in the suspension pad and its impact on the complete bearing assembly dynamics and thermal profile. Specifically, this paper presents an experimentally validated finite element thermal model of the elastomeric pad and its internal heat generation. The steady-state and transient-state temperature profiles produced by hysteresis heating of the elastomer pad are developed through a series of experiments and finite element analysis. The hysteresis heating is induced by the internal heat generation, which is a function of the loss modulus, strain, and frequency. Based on previous experimental studies, estimations of internally generated heat were obtained. The calculations show that the internal heat generation is impacted by temperature and frequency. At higher frequencies, the internally generated heat is significantly greater compared to lower frequencies, and at higher temperatures, the internally generated heat is significantly less compared to lower temperatures. However, during service operation, exposure of the suspension pad to higher loading frequencies above 10 Hz is less likely to occur. Therefore, internal heat generation values that have a significant impact on the suspension pad steady-state temperature are less likely to be reached. The commercial software package ALGOR 20.3TM is used to conduct the thermal finite element analysis. Different internal heating scenarios are simulated with the purpose of obtaining the bearing suspension element temperature distribution during normal and abnormal conditions. The results presented in this paper can be used in the future to acquire temperature distribution maps of complete bearing assemblies in service conditions and enable a refined model for the evolution of bearing temperature during operation.

This paper deals with Physical Safety and Security at rail crossings. There are about 150,000 public railroad grade crossings in the U.S. Unfortunately, approximately 2,000 accidents occur every year in the U.S., resulting not only in many injuries, but also in over 200 deaths annually. The predicament is that for various reasons, people, cars, and trucks find themselves on the rail tracks of an oncoming train at a railroad crossing. The system discussed in this paper provides a relatively inexpensive Internet of Things (IoT)-based capability that can be used to alert a rail operator that there is an obstruction on the tracks, and/or possibly to interwork with (but not replace) a Positive Train Control (PTC) system thus attempting to automatically stop an incoming train. In fact, IoT is now being deployed in railroads for a variety of applications. A brief description of cybersecurity issues related to IoT deployment is also included.

The objective of this paper is to demonstrate how GIS and data visualization systems can be used to identify spatial relationships to add to our understanding of railroad accident factors. Examples are given of the spatial analysis of broken rail accidents and grade crossing accidents on GIS maps. Additionally, using the Weave data visualization system a data dashboard was constructed that shows the complex interaction between variables like track type, FRA track classification, train speed and track density with broken rail accident causes. The findings indicate that broken rail accidents occur most frequently in the Midwest. Possibly this trend is related to climate change and increased temperatures and precipitation in the United States. GIS visualizations also showed that many truck-trailer accidents at grade crossings occur in low population areas. This work indicates that GIS and data visualizations are a useful method of identifying trends in railroad accidents.

Transportation industry at large is a major consumer of fossil fuels and contributes heavily to the global greenhouse gas emissions. A significant portion of these emissions come from freight transportation and decisions on mode/route may affect the overall scale of emissions from a specific movement. It is common to consider several alternatives for a new freight activity and compare the alternatives from economic perspective. However, there is a growing emphasis for adding emissions to this evaluation process. One of the approaches to do this is through Life Cycle Assessment (LCA); a method for estimating the emissions, energy consumption and environmental impacts of the project throughout its life cycle. Since modal/route selections are often investigated early in the planning stage of the project, availability of data and resources for analysis may become a challenge for completing a detailed LCA on alternatives. This research builds on such detailed LCA comparison performed on a previous case study by Kalluri et al. (2016), but it also investigates whether a simplified LCA process that only includes emissions from operations phase could be used as a less resource intensive option for the analysis while still providing relevant outcomes. The detailed LCA is performed using SimaPro software and simplified LCA is performed using GREET 2016 model. The results are obtained in terms of Kg CO 2 equivalents of GHG emissions. This paper introduces both detailed and simplified methodologies and applies them to a case study of a nickel and copper mine in the Upper Peninsula of Michigan. The analysis’ are done for three modal alternatives (two truck routes and one rail route) and for multiple mine lives.

Metrolinx, Toronto’s rail authority currently has 200 engineering projects underway with a value of $16 billion. One of the largest projects is a $4 billion Electrification Project for the Toronto commuter rail lines. In support of the engineering design of the project, in November of 2015 Tulloch Engineering was contracted to provide a complete engineering survey of six Metrolinx railway commuter corridors originating from Union Station in Toronto, Canada. Tulloch used a unique combination of mobile LiDAR, static LiDAR, and conventional infill ground survey to complete the project. LiDAR, which stands for Light Detection and Ranging, is a surveying method that measures distance to a target by illuminating that target with a laser light. Using LiDAR technology provided significant advantages to the Electrification Project over using convention ground survey techniques. Metrolinx is a Canadian crown corporation responsible for the Greater Toronto and Hamilton Area’s GO Transit rail and bus commuter system. GO Transit trains currently carry 190,000 commuters per day. Electrification of Metrolinx GO Transit rail commuter rail corridors requires the upgrading of infrastructure and providing a means of getting the electricity to the trains which includes new electrical substations, overhead power lines and new equipment. The electrification is part of the GO Regional Express Rail program, which will expand the capacity of the GO rail network to provide customers with faster, more frequent and more convenient service to and from dozens of stations in core sections of the GO rail network throughout the day, evenings and weekends. Electrification is planned for most of Metrolinx commuter rail corridors by 2022–2024. The engineering technical and program management consultant for the Electrification Project is Gannett Fleming. An initial requirement for Metrolinx Electrification project is an up to date engineering survey to enable the preliminary engineering design. Our survey project involves surveying approximately 170 miles of railway corridor for 6 GO Transit tracks originating from Union Station in downtown Toronto. Our mobile LiDAR survey system was mounted on a GO Transit hi-rail truck; with most of the surveying occurring at night due to the heavy train traffic and since LiDAR is an active sensor. Tulloch provided a unique hybrid surveying approach, using mobile LiDAR surveying to collect all the visible features in the corridor, followed by conventional ground surveys to fill in missing features obscured from the LiDAR system’s field of view and static LiDAR surveys for some of the bridges inaccessible with mobile LiDAR. This is the first time Metrolinx has contracted an engineering survey using these multiple survey technologies. This survey approach reduces delivery timelines, limits track disruptions, and greatly improves safety. A major advantage of mobile LiDAR surveying for the GO-Transit rail corridors is that collection can occur at night when train activity is low and in a fraction of the time it takes to survey using conventional ground crews. This enabled project schedules to be advanced, as base mapping was completed in about 60% of the normal time required for the engineering survey. Using mobile scanning on the tracks reduced safety risks associated with on track field surveys. In addition, the resultant LiDAR point cloud can be revisited in the office, and additional features and critical information picked up without having to send field crews back to do so. The homogeneous nature of the point cloud, combined with the conventional in-fill survey provides a rich, full feature data set that can be used at various stages in the engineering design process.

This paper illustrates the impact of progressive settlement on a railway bridge transition using a three-dimensional dynamic numerical model that includes the train truck, rails, ties, ballast, subgrade, and bridge abutment and structure. A settlement law that relates tie load to ballast settlement is presented and demonstrated using an iterative fashion to evaluate bridge transition response to 28 MGT. The results illustrate: (1) development of the commonly observed dip about 2.5 to 3.7 m (8 to 12 feet) from the entrance bridge abutment, (2) tie-ballast gaps progressively increase in height and expand to ties outwards from the bridge abutment, (3) a redistribution of load to ties outwards from the bridge abutment as tie-ballast gaps develop and increase, and (4) a ballast surface profile that attempts to minimize tie loads by evenly distributing the wheel load amongst adjacent ties.

Train carbody and truck structures are designed to exhibit primary natural frequency modes great enough to avoid unwanted resonant oscillations with normal track interactions. Critical bounce modes can be excited by typical track in the 2–4 Hz range. Trains are designed with first modes above this threshold. Historically, simplified approaches are employed to predict natural frequencies of the main truck and carbody train structures independently. Since the advent of high powered computing, more detailed finite element analysis (FEA) eigenvalue approaches have been used to more accurately predict natural frequency of structures. Still, the typical FEA approach uses simplified boundary conditions and partial models to determine natural frequencies of individual components, neglecting the interaction with other connected structures. In this paper, a detailed, holistic approach is presented for an entire Light Rail Vehicle (LRV). The analysis is performed on a fully detailed FEA model of the LRV, including trucks and suspension, carbody structures, non-structural mass, articulation, as well as intercar and truck-carbody connections. The model was developed for detailed crashworthiness investigations, which requires a high level of fidelity compared to what is typically required for static and modal analysis. Using the same model for multiple purposes speeds up development while also improving the accuracy of the analyses. In this paper, the modal analysis methodology developed is described. A case study is presented investigating the often neglected contribution of windows, cladding, and flooring on the overall carbody natural frequency.

The Paper describes the past, present and future of high speed trains following the passing of the High Speed Ground Transportation Act fifty years ago. In the distant past, electrically-powered Metroliners ran between Washington and New York and Turbo Trains powered by gas turbines ran between Boston and New York on the Northeast Corridor. In the recent past, AEM-7 and HHP-8 electric locomotives supplied by EMD and Alstom respectively for operation with Amfleet railcars also ran on the Northeast Corridor. At the present time, electrically-powered Acela trainsets and ACS-64 electric locomotives supplied by Siemens are in service on the Northeast Corridor. Trains of Horizon and Amfleet railcars hauled by General Electric P-42-DC diesel locomotives operate between Chicago and St. Louis and Chicago and Detroit on the Mid West Regional Rail System. The future consists of two on-going projects that are being implemented, three projects that are in the planning stage and six projects that are in the concept phase. Features to be considered in the design of a high speed train system include: Design Standards and Regulations, Motive Power, Train Configuration, Maximum Axle Load, Dual-Mode Propulsion, Emergency Power, Double Deck Configuration, Jumbo Train Arrangement, Radial Trucks and Hi-Lo Bi-Track System.

Freight transportation of goods and commodities is a necessity and is often a significant portion of the overall investment in industrial development, especially in the natural resource industry. The economic costs of developing infrastructure have long been factored into the project costs, but environmental or social impacts have received less attention. In addition, alternative transportation modes are rarely compared from both economic and environmental perspectives. This paper performs a Life Cycle Assessment (LCA) for truck-only, multimodal and rail transportation options to transport ore and concentrate. In this paper, LCA is performed in SimaPro for construction/manufacturing, operations, maintenance, and end of life phases to obtain the overall Global Warming Potential (GWP) in terms of kilogram equivalents of CO2 (kg CO2eq). After emissions from alternative options have been defined, the cost of each option can be investigated through Life Cycle Cost Analysis (LCCA) This paper also discusses the past work on LCCA and its application to transportation projects. The final part provides a methodology to convert the emission results from LCA for integration with the costs from LCCA.

The potential opportunities for fuel cost and emissions reduction as a result of converting from diesel to natural gas (NG) are well known in the rail industry. Recent developments in domestic sources for petroleum have both lowered the cost and stabilized the natural gas market making the adoption of NG more strategically viable than ever before. NG Engine conversion systems for medium speed diesel engines have been available for over 20 years and environmental incentives are helping to further support the adoption of NG not only as an alternate fuel, but possibly as the new primary fuel. The major challenge for railroad transition to NG is now to develop a manageable and cost effective on board storage architecture with capacity to provide sufficient range to meet industry acceptance for general use. For long haul applications, the solution has been to add a large locomotive NG tender. For short line, switcher and commuter applications, the fuel cost benefits do not warrant the cost and complexity of such a system. For these short to medium range applications, an “on locomotive” storage system with a capacity equivalent to a minimum of 500 diesel gallons is a better solution. This paper summarizes the development of a crashworthy, high capacity, underframe NG storage solution packaged in the form factor normally occupied by the conventional locomotive diesel fuel tank (Fig. 1). Primary challenges for the CNG storage system solution are to achieve high storage density and crashworthiness while within the confines of existing AAR M-1001Plate C profile. It is expected that the system is to meet both the CFR/FRA structural requirements for diesel tanks and relevant DOT requirements for CNG cylinders used in mobile applications. The NG storage system presented consists of an outer enclosure which mounts to the locomotive similarly to the existing diesel fuel tank. The enclosure contains and interfaces with NG cylinders specifically designed for the application. The system design withstands the various load requirements defined in 49 CFR Part 238 Appendix D, that simulate both jackknife derailment and class 8 truck side impact scenarios. NG cylinders within the enclosure must be approved for use in this application and have sufficient capacity and maintainability to create new opportunities for NG conversion in the rail industry. The primary design goal for the storage system is to integrate the cylinder size and configuration within the enclosure such that storage volume is maximized and crash impact loads are minimized. Impact loads are distributed through the system ensuring that point loading sufficient to breach one or more of the NG cylinders or fuel lines does not occur. An overview of the development and results of physical derailment and side impact testing performed in support of this effort are presented.