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.

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.

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.

The interaction of the carried cargo and the carrying vehicle involves many aspects that can affect the transport safety from different perspectives. On the one side, the lateral inertial forces developed by sloshing cargo can affect the lateral stability of the vehicle, while at long term, such dynamic forces can affect the integrity of the infrastructure’s components, which can lead to transport accidents. In this paper, a tilt-table based testing rig is presented to simulate the dynamic response of the vehicle-cargo system to lateral accelerations associated to turning maneuvers. The conceptual design and final construction/instrumentation of the equipment are described. As a first use of the testing rig, a five-parameter two-degree of freedom simplified mechanical model for the liquid cargo – vehicle interaction is calibrated, yielding a high correlation with the experimental data (0.98). The improvement of the theoretical model is further discussed, as the model underestimates the loading cycles to the infrastructure, in 20%.

Over the past several years, Carbuilders have been developing car designs that utilize Crash Energy Management (CEM) as an alternative means to demonstrate compliance with carbody structural strength requirements. With the onset of CEM designs, the CFR has been amended to include requirements for alternative compliance to carbody structure requirements (see Docket No. FRA-2013-0060, Notice No. 3, [the Final Rule]) [14]. CEM designs often utilize crush zones with the overall goal of protecting occupant volume. These designs also change the structural behavior of the car during an accident, resulting in different acceleration responses than conventional car designs. This paper seeks to compare and contrast the interior fitting strength requirements between conventional car designs and a generic CEM design. The methodology utilizes simple mathematical simulations, modeling a conventional car design and a CEM design, with outputs of the simulation consisting of car response and behavior. Results indicate that alternative compliance requirements for all tiers of CEM-designed passenger equipment should be considered for interior fixtures.

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.

Restricted speed is the speed that permits stopping within one-half the range of vision, normally not exceeding 20 miles per hour. The occurrences of some severe accidents at restricted speeds have highlighted the importance of safety improvements in restricted-speed operations. The Federal Railroad Administration (FRA) has identified restricted-speed violations as a common rule compliance problem. Nevertheless, little prior research has been conducted on the analysis of train operations and safety risk under restricted speeds. This paper used Fault Tree Analysis to explore scenarios for restricted-speed operations to identify failure paths that lead to train accidents. Understanding restricted-speed train accident causal chains and corresponding structural representations of relevant contributory precursors can contribute to more accurate estimation of restricted-speed risk. Four recent restricted-speed accidents were studied based on the information from the National Transportation Safety Board (NTSB) and FRA. This study may serve as a reference leading to the further development of quantitative risk assessment and the evaluation of risk mitigation strategies for restricted-speed operations.

Factual evidence from locomotive event data recorders (EDR), locomotive image data recorders, accident site surveys, witness marks, rail equipment, track structure, photographs, video cameras, AEI readers, hot wheel or hot bearing detectors, wayside signal bungalows, train consist documents, and radio communication is integrated, validated, and visualized in a three-dimensional model environment. The goal is to build a physics-based, data-driven model of train position as a function of time to enhance the documentation, investigation, understanding, and analysis of in-service train derailments. Methods to construct, validate, and interrogate time-accurate, interactive visualizations of train movements for partial and complete train consists are discussed and demonstrated. In-service freight train derailments that occurred in Hoxie, Arkansas (offset frontal collision between opposing freight trains), Casselton, North Dakota (unit grain train derailment with car fouling opposing mainline track and subsequent crude oil unit train head-on collision), and Graettinger, Iowa (unit ethanol train derailment) are used to illustrate the accident reconstruction method. Similar vehicle path reconstructions for recent highway, aviation, and marine investigations are also presented.

This paper focuses on the safety aspect of passenger trains approaching a terminal station with a bumper block/post. As evidenced by the recent collision of a commuter train at Hoboken terminal on September 29, 2016, the consequences of a collision with a bumper post could be catastrophic, however, railroads can take preventative measures to reduce the element of risk. Case studies obtained from the National Transportation Safety Board (NTSB) involving similar bumper block accidents are analyzed to identify any potential common denominator. The objective of this paper is to comprehensively present the various mitigation techniques that railroads can adopt to safeguard their systems against these types of accidents. Although some of the mitigation techniques presented in the paper may already be known in the industry generically, their application to specifically mitigating the hazard of bumper collisions is a novel attempt to focus systematically on this topic. Examples of mitigation techniques discussed here in include speed restricting devices, driver alerted features, bumper blocks with more impact tolerance, and organizational safety culture. The effect of newer technologies such as PTC (in USA only) and CBTC towards mitigating this hazard as well as the unique constraints presented at terminal stations is also assessed.

Railways have a substantial contribution to the economy of the United States. However, a train accident can result in casualties and extensive damages to infrastructure and the environment. Most of the prior research focused on derailments or grade-crossing accidents rather than the study of train collisions. The Federal Railroad Administration (FRA) identifies over 300 causes for all types of accidents, among which we aim to recognize the major factors that cause train collisions. Evaluating how collision frequency and severity vary with the accident cause is the key part of this research, in order to identify, evaluate and mitigate transportation risk. This paper presents a statistical analysis of passenger and freight train collisions in the United States from 2001 to 2015 to statistically analyze train collision frequency, severity, accident cause, and safety risk. The analysis finds that human errors and signal failures are among the most common causes of train collisions in U.S. in the 15-year study period. There is a significant decline in the overall train collision frequency by year. By observing these trends with respect to train collisions, possible accident prevention strategies could be developed and implemented accordingly.

The Federal Railroad Administration (FRA) routinely conducts investigations of railroad accidents to determine causation and any contributing factors to help the railroad industry implement corrective measures that may prevent similar incidents in the future. Over the past decade, FRA has investigated multiple broken rail accidents in which fractures in the rail web were identified. The common features observed in the recovered rail fragments from these accidents included welds and spots or burn marks on the web, indicating that the rails were joined together by pressure electric welding. Pressure electric welding uses a welding head that clamps around two opposing rail ends, pressing an electrode on each rail, then hydraulically pulling the rail ends together while arcing current through the electrodes into the rails, causing them to essentially melt together to form a continuous rail. Based on the similarities observed in the web fractures, FRA rail integrity specialists hypothesized that stray (i.e. inadvertent and unwanted) arcing during pressure electric welding can result in the formation of burns or pits on the rail where it makes contact with the electrodes. Moreover, these electrode-induced pits behave as stress raisers (also referred to as stress concentrations). Fatigue cracks often develop at locations of stress concentration. Once a fatigue crack initiates, the localized stress encourages the growth of the crack, which may potentially lead to rail failure. This paper describes the forensic evaluations of three railroad rails containing electrode-induced pitting. These evaluations include: magnetic particle inspection to nondestructively detect cracks emanating from the pitting; fractography to study the fracture surfaces of the cracks; metallography to study the microstructure; analysis of chemical composition; and measurements of tensile mechanical properties and fracture toughness of rail steel. Moreover, the results of these evaluations confirm the hypothesis postulated by FRA that stray arcing during pressure electric welding can cause electrode-induced pitting.

This work intends to lay the groundwork for Computer Aided Engineering (CAE)-based occupant safety of a typical tier-III Indian Railway (IR) passenger coach in a collision accident. Our previous work presented in International Crashworthiness Conference 2010 under the title “Simulation of Crash Behaviour of a Common Indian Railway Passenger Coach” provided crashworthiness assessment of a typical tier-III passenger coach structure for representative head-on collision scenarios namely, against an identical passenger coach and against a stationary locomotive. These scenarios were envisioned to be part of a bigger accident scenario e.g - head-on collision between two trains moving towards each other. Analysis of involved chain of events for entire rolling stock and resulting internal collisions between individual passenger cars was out of scope of this work and necessary inputs were obtained from available literature on the same. This work used a full scale Finite Element (FE) simulation model and commercial explicit solver LS-Dyna. FE model was validated using International Railway Union (UIC) code OR566 specified proof loads for design. Simulation methodology used for dynamic impact was validated by component level crushing experiments using a drop tower facility. Material modelling incorporated strain rate effect on yield strength which is essential for obtaining accurate structural deformations under dynamic impact loading. Contacts were modelled using the penalty method option provided by the solver. This model was simulated for collisions at 30, 40 and 56 km/h against a stationary rigid barrier. Collision speeds were chosen to simulate impact energies involved in collision scenarios as mentioned above. The structure was found to exhibit global bending deformation and jackknifing with pivot position at the door section. In this paper, we present an extension of this work — coupled occupant safety simulation and injury assessment. It was accomplished by recording head, neck, chest and knee responses of a Hybrid-III 50 th percentile male Anthropomorphic Test Device (ATD) FE model, seated in passenger position on lower berth of the first cabin of a passenger car. Interiors were modelled to represent the actual structure. Dummy model was adapted to passenger cabin’s excessive mobility conditions and responses were revalidated against Federal Motor Vehicle Safety Standards (FMVSS) limits. Injury interpretation was based on Abbreviated Injury Scale (AIS), automotive injury criteria and injury risk curves for Head Injury Criterion (HIC), thoracic spine acceleration, neck bending moment in flexion and extension and knee force. This study provides with estimates of injury and fatality based on computer simulation of accident scenarios. However, attempts of correlating to any available injury and fatality statistics were out of scope of this study.

Railroads contribute to the national economy by carrying over 40% of intercity freight ton-miles in the United States. Train accidents cause damage to infrastructure and rolling stock, disrupt operations, and have the potential to result in casualties and damage the environment. A clear understanding and analysis of accident risk based on historical accidents can support the development and prioritization of effective accident prevention strategies. While extensive previous studies have focused on the safety risks associated with a variety of train operation conditions, much less work has been undertaken to evaluate train risk and safety under restricted speeds. As defined in 49 CFR 236 Subpart G, restricted speed is a speed that permits stopping within one-half the range of vision, but not exceeding 20 miles per hour. Nevertheless, some severe accidents at restricted speeds occurred in the last few years and are also highlighted in both NTSB and FRA reports. In this paper, we develop a quantitative analysis of restricted-speed accidents occurring between 2000 and 2016, based on the data from the U.S. Federal Railroad Administration. While overall accident rates have been proven to decline in prior studies, the preliminary results show that the rate of train accidents under restricted speeds fluctuates in the study period, without a significant increasing or decreasing trend. Furthermore, the distribution of restricted-speed accident severity, accident risk, and other pertinent characteristics are covered in this study.

Although accidents at Highway-Rail Grade Crossings (HRGCs) have been greatly reduced over the past decades, they continue to be a major problem for the rail industry, causing injuries, loss of life, and loss of revenue. Recently, the Strategic Highway Research Program sponsored a Naturalistic Driving Study, the SHRP2 NDS, which produced a unique opportunity to look at how drivers behave while traversing HRGCs. This research deviates from previous studies by concentrating on day-to-day actions of drivers who traverse the HRGCs without an incident, instead of focusing on the accident events that have formed the foundation most earlier studies. This paper will focus on the effects of the external environment, weather and day/night conditions, on driver behavior at HRGCs. We will present the methodology and data used for the study and provide some early results from the analysis, such as differences in compliance during poor versus clear weather. We will use both a compliance score based on scanning and speed reduction and an analysis of brake and gas pedal usage during the approach to a HRGC. The paper will conclude with a brief discussion of future research concepts.

Travel by rail in the United States is one of the safest modes of transportation available. On the rare occasion that major accidents do occur, they represent an opportunity for railroads to learn what has happened and what needs to be done to prevent reoccurrence. This paper provides several, detailed case studies of noteworthy passenger and rail transit accidents that have occurred in the United States, from the 1960s to the present. It discusses the outcome of these accidents, including changes that were implemented due to lessons learned. It also discusses unique and/or noteworthy aspects of each accident.

A review of past accident data shows that several fatalities have been attributed to passenger ejection through window openings during passenger train accidents. To study and address this issue, literature review and accident analyses were performed to investigate the safety aspects of passenger rail window glazing. A common failure mode is when the external gaskets that hold the glazing pane in place shear off and the windows are pushed inside the carbody during rollover derailments. This leads to passengers being ejected, often fatally, out of the train. Passenger containment was identified as the main improvement to be made to glazing systems. New or updated retention methods are thought to be necessary in the pursuit of safety. Considering feasibility, implementation time, likelihood of success, and the potential for retrofit, a few concepts including various methods of zip-strip protection, a revised zip-strip location, and recessed window glazing have been ideated and the top rated concepts are being developed further. In the next phase of work, field tests and additional analyses will help determine the efficacy of the proposed solutions and the necessity for additional engineering design requirements.

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.

Public agencies involved with highway-railroad grade crossing safety must allocate available funding to projects which are considered the most in need for improvements. Mathematical models provide a ranking of hazard risk at crossings and support the project selection process. This paper reports the results of a research study sponsored by the Ohio Rail Development Commission (ORDC) and the Ohio Department of Transportation (ODOT) examining hazard ranking models for grade crossing project selection. The goal of the research was to provide ORDC, ODOT, and other stakeholders with a better understanding of the grade crossing hazard ranking formulas and other methods used by States to evaluate grade crossing hazards and select locations for hazard elimination projects. A comprehensive literature review along with personal interviews of state DOT personnel from eight states yielded best practices for hazard ranking and project selection. The literature review found that more than three-quarters of states utilize some type of hazard ranking formula or other systematic method for project prioritization. The most commonly-used hazard ranking model in use is the U.S. DOT Accident Prediction Model; however, at least eleven states utilize state-specific hazard ranking models. Detailed evaluation of several different hazard ranking models determined that the existing hazard ranking model used in Ohio, the U.S. DOT Accident Prediction Model, should continue to be used. The research also recommends greater use of sight distance information at crossings and expanding the preliminary list of crossings to be considered in the annual program as enhancements to the existing project selection process used by the ORDC and ODOT.