Freight Train Derailment Research Articles

Freight train derailment severity prediction: a physics-informed one-dimensional model

Purpose Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment. Design/methodology/approach In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity. Findings The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types Originality/value This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

Investigation on the mechanism and measures of derailment of empty freight train passing a turnout in the diverging route

The derailment of railway vehicles at a turnout is a worldwide concern. However, the derailment of empty freight trains in the switch section of a turnout in the diverging route is rarely concerned. Therefore, in order to analyse the derailment mechanism of empty freight trains in the turnout switch area and to propose appropriate preventive measures, field investigation and dynamic simulation of the derailment are carried out on the basis of an empty freight train derailment in the turnout switch area of the diverging route. Firstly, the background of the derailment is presented and the potential influencing factors of the derailment are analysed according to the field investigation. Secondly, a detailed train-turnout dynamic model is established, taking into account the variable cross-sections of the turnout switch rail and the effect of the longitudinal coupler force. Thirdly, based on the model, the derailment mechanism of the empty freight train is revealed through theoretical analysis and simulation calculation. Finally, the effect of the potential derailment influencing factors is analysed and preventive measures are proposed to reduce the derailment risk of the empty freight train at the turnout switch area in the diverging route. The results indicate that this derailment is caused by the combined effect of the inherent structural characteristics of the switch, the large longitudinal coupler force, and the high wheel rail friction coefficient. The possibility of derailment can be reduced by reducing the longitudinal coupler force and the wheel rail friction coefficient. In addition, the installation of a guard rail in front of the turnout can effectively prevent empty freight train derailment in the turnout area.

Statistical Analysis of Train Derailment Severity for Unit Trains Versus Manifest Trains

Using freight trains is a cost-effective and safe method for transporting numerous products over long distances. However, freight train derailments have significant consequences. Derailment severity, measured by the number of cars derailed per derailment, is an important risk factor. Based on prior literature and Federal Railroad Administration data, this research analyzes and estimates derailment severity of freight railroad transportation using a statistical method: the Truncated Geometric (TG) model. The methodology accounts for three train types: manifest train, loaded unit train, and empty unit train. Train length, speed, and gross tonnage per car are the input features, and the output variable is the number of cars derailed in a derailment incident. The TG model quantifies the influence of these factors contributing to derailment severity and estimates train derailment severity with a performance of the mean square error (MSE) of 68.91 and mean absolute error (MAE) of 6.05. When data outliers such as abnormally high or low severity are excluded, the MSE drops to 36.82 and the MAE drops to 4.22. Overall, the results indicate that the train derailment severity estimation performance based on the given factors is satisfactory. With all other factors being equal, a loaded unit train is likely to derail more cars than a manifest train and an empty unit train. When data outliers are excluded, there is no significant difference between manifest trains and empty unit trains as regards derailment severity, and both are less likely to derail than a loaded unit train.

Using Machine Learning Methods for Modeling Freight Train Derailment Severity

This paper focuses on identifying factors affecting the severity of freight train derailment. To examine the train derailment, it is necessary to study the point of derailment and the number of cars derailing. Previous studies have used truncated geometric distributions with two key assumptions: (1) cars in a train get involved in a derailment independently of one another, and (2) probabilities of cars involved in derailments are all the same along the train length. The underlying assumptions are clearly violated in the real world. Therefore, in this study, different classification approaches, including decision tree, random forest, support vector machine, and AdaBoost techniques, have been used to avoid fixed assumptions. The results show that the decision tree is the best classifier to predict the severity of train derailment for the US accident database, and the two-level severity scenario (one car derailed or more) presents better results to classify derailment severity. The research also shows that freight train derailment severity has been affected mainly by (1) train speed, (2) cause of the accident, and (3) train weight-to-train length ratio. Among these features, cause of accident is the most important feature in classifying accident severity; also, the causes of one-car derailments are mostly related to mechanical and electrical failures. In mechanical and electrical failure, train speed plays a significant role in determining the severity of accidents. The factor of train weight to length comes into account when an accident’s cause is related to human factors.

Quantitative analysis of freight train derailment severity with structured and unstructured data

Train safety has been a top priority in the railroad industry. Understanding accident risks is of paramount importance for prioritizing effective prevention strategies. Previous work has focused on estimating the severity of derailments and various statistical models based on structured data were used. However, unstructured data records which provide considerable information about train derailments have received minimal consideration due to a lack of procedures of processing and interpreting them. To narrow this knowledge gap, this study aims to quantitatively estimate derailment severity by considering unstructured data utilizing topic modeling. A statistical model that integrates both structured and unstructured data was established to analyze U.S. freight train derailments from 1996 to 2019. The comparative results of predictions revealed that the model with combined text information outperformed the one without the unstructured data.Quantile regression was also developed to assess various statistical distributions of derailment severity. Both models with unstructured data provide a deeper understanding of derailment severity and ultimately improve railroad safety performance.

An Empirical analysis of freight train derailment rates for unit trains and manifest trains

Rail is a safe and efficient mode of transporting hazardous materials (hazmat). In the past decade, the hazmat traffic transported by unit trains has significantly increased in the United States. As a result, a comprehensive understanding of the safety and risk of hazmat unit trains is important and can contribute to the identification, evaluation, and implementation of risk mitigation strategies. Limited prior research has focused on unit train derailment risk analysis. This paper develops a quantitative analysis of freight unit train derailment characteristics and compares those statistics to non-unit, manifest trains (mixed trains). Mainline freight train derailment data on Class I railroads between 1996 and 2018 were analyzed for hazmat unit trains, non-hazmat unit trains, and manifest trains. Derailment rates, measured by three traffic exposure metrics (train-miles, ton-miles, and car-miles) were estimated and compared. The analyses showed that a unit train has a 30% lower derailment rate in terms of ton-miles and car-miles than manifest trains, while the derailment rate per million train-miles of unit trains is slightly greater than that of manifest trains. Loaded unit trains have roughly four-fold higher derailment rate in terms of train-miles and car-miles than that of empty unit trains. Within loaded unit trains, hazmat unit trains have lower derailment rates than non-hazmat unit trains. Overall, heavier and shorter loaded unit trains tend to have greater derailment rates in terms of all three traffic exposure metrics. A causal analysis was also conducted for the three types of train. Infrastructure causes were the most frequent in all train types and length followed by equipment-related causes. These statistics provided important information for rational allocation of risk mitigation resources to improve rail hazmat transportation safety.

Statistical Analysis of Seasonal Effect on Freight Train Derailments

AbstractFreight train accidents can damage infrastructure and rolling stock, disrupt operations, and possibly cause casualties and harm the environment. Understanding accident risks associated with...

Quantitative Analysis of Changes in Freight Train Derailment Causes and Rates

AbstractThe mainline freight train derailment rate of major railroads in the United States declined 49% from 2006 to 2015. Nevertheless, derailments remain the leading cause of major railroad accid...

Conditional value-at-risk (CVaR) methodology to optimal train configuration and routing of rail hazmat shipments

Hazardous materials (hazmat) incidents are rare though the consequences could be catastrophic. The low probability–high consequence nature of such events mandate that a risk-averse plan be implemented for routing hazmat shipments. We propose a conditional value-at-risk (CVaR) methodology for routing rail hazmat shipments, using the best train configuration, over a given railroad network using the pre-defined train services such that the transport risk as measured by CVaR is minimized. Freight train derailment records were analyzed to model the behavior of railroad accidents, and to estimate conditional probabilities. The proposed methodology was used to study several problem instances generated using the realistic network of a railroad operator, and to demonstrate that the proposed methodology is superior to other measures for risk-averse routing of hazmat shipments and versatile enough to yield routes based on risk preferences of the decision makers.

Functional dependency between the number of wagons derailed due to wagon or track defects and the traffic factors

Aim. Rolling stock derailment is one of the most hazardous transportation incidents. Depending on the gravity of the consequences they may also be classified as accidents or train wrecks. The consequences of a derailment may vary from routine maintenance of the track and one or two wagons to an overhaul of the track and depot repairs of three or more wagons, as well as loss of cargo and long interruption of service. It must be noted that beside the damage to infrastructure and rolling stock caused by derailments there is a risk of environmental disaster. The Russian Federation along with the US, China and India has some of the world’s longest rail networks that in places border with environmentally sensitive areas, e.g. national reserves and parks. Therefore, if a train carries hazardous cargo, e.g. gasoline or toxic gases and some of its wagons derailed, the harm related to the repair or decommissioning of rolling stock, track and possible loss of cargo may be aggravated by the damage caused by an environmental disaster that would cause great material losses to JSC RZD. In this context it appears to be of relevance to evaluate the functional dependency between the potential number of cars derailed and various factors, e.g. speed or amount of cargo carried by the train, for subsequent preparation of recommendations for the reduction of the potential number of derailed cars and, subsequently, reduction of possible harm. Methods. Probability theory and mathematical statistics methods were used, i.e. maximum likelihood method, negative binomial regression. Results. For various groups of incidents, i.e. derailment as the result of wagon or locomotive unit malfunction out of switch, derailment as the result of rail malfunction out of switch, derailment at a switch not caused by previous derailment, specific functions of the average number of derailed wagons are identified. The paper shows a formula that allows – under a defined set of various factors, e.g. train speed, plan and profile of track, length and mass of the train – identifying the distribution series of the number of derailed wagons. Conclusions. The preliminary analysis of available Russian freight train derailment records it was shown that for various groups of transportation incidents the descriptive statistics of respective samples significantly differ, which is also the case for the US records. The construction of a functional dependence between the average number of derailed wagons and various traffic factors due to malfunction of wagons or locomotive units out of switches, it was identified that the available records do not suffice to forecast the number of derailed wagons in tangents. Mathematical models with a low superdispersion parameter were constructed for derailments due to track malfunction out of switches and derailments at switches.

A Value-at-Risk (VAR) approach to routing rail hazmat shipments

Hazardous materials (hazmat) accidents are rare though the consequences could be disastrous. Given the possibility of low probability – high consequence event, a risk-averse routing hazmat shipment is necessary. We propose a value-at-risk (VaR) approach to route rail hazmat shipments, using the best train configuration, over a given railroad network with limited number of train services such that the transport risk as measured by VaR is minimized. Freight train derailment reports of the Federal Railroad Administration were analyzed to develop expressions that would incorporate characteristics of railroad accidents, and then to estimate the different inputs. The proposed methodology was used to study several problem instances generated using the realistic network of a railroad operator, and to demonstrate that it is possible to develop different routes for shipments depending on the risk preference of the decision maker.

Optimizing rail defect inspection frequency to reduce the risk of hazardous materials transportation by rail

Rail defects are the most frequent cause of freight train derailments and a major hazard to the transportation of hazardous materials in the United States. Railroads periodically inspect their rails using ultrasonic technologies to prevent train derailments, thereby mitigating transportation risk. This research quantifies the relationship between ultrasonic rail defect inspection frequency and railroad hazardous materials transportation risk. A Pareto optimization model is developed to determine optimal annual inspection frequencies on different track segments with different risk levels. The model provides an evaluation of segment-specific hazardous materials transportation risk due to rail failures, as well as an assessment of risk-based prioritization of rail defect inspection. The model can be adapted to other types of hazardous materials or account for other accident causes in the future.

Statistical Causal Analysis of Freight-Train Derailments in the United States

AbstractFreight railroads contribute to the national economy by moving over 40% of intercity ton-miles of freight. Meanwhile, train accidents can damage infrastructure and rolling stock, disrupt op...

Probabilistic analysis of the release of liquefied natural gas (LNG) tenders due to freight-train derailments

Liquefied natural gas (LNG) has emerged as a possible alternative fuel for freight railroads in the United States, due to the availability of cheap domestic natural gas and continued pursuit of environmental and energy sustainability. A safety concern regarding the deployment of LNG-powered trains is the risk of breaching the LNG tender car (a special type of hazardous materials car that stores fuel for adjacent locomotives) in a train accident. When a train is derailed, an LNG tender car might be derailed or damaged, causing a release and possible fire. This paper describes the first study that focuses on modeling the probability of an LNG tender car release incident due to a freight train derailment on a mainline. The model accounts for a number of factors such as FRA track class, method of operation, annual traffic density level, train length, the point of derailment, accident speed, the position(s) of the LNG tender(s) in a train, and LNG tender car design. The model can be applied to any specified route or network with LNG-fueled trains. The implementation of the model can be undertaken by the railroad industry to develop proactive risk management solutions when using LNG as an alternative railroad fuel.

Freight-train derailment rates for railroad safety and risk analysis

Derailments are the most common type of train accident in the United States. They cause damage to infrastructure, rolling stock and lading, disrupt service, and have the potential to cause casualties, and harm the environment. Train safety and risk analysis relies on accurate assessment of derailment likelihood. Derailment rate – the number of derailments normalized by traffic exposure – is a useful statistic to estimate the likelihood of a derailment. Despite its importance, derailment rate analysis using multiple factors has not been previously developed. In this paper, we present an analysis of derailment rates on Class I railroad mainlines based on data from the U.S. Federal Railroad Administration and the major freight railroads. The point estimator and confidence interval of train and car derailment rates are developed by FRA track class, method of operation and annual traffic density. The analysis shows that signaled track with higher FRA track class and higher traffic density is associated with a lower derailment rate. The new accident rates have important implications for safety and risk management decisions, such as the routing of hazardous materials.

Statistical Temporal Analysis of Freight Train Derailment Rates in the United States

Safety is the top priority for every rail system in the world. A widely used measure for rail safety is the accident rate, which is the number of train accidents normalized by traffic exposure. Of interest in rail safety research is understanding the temporal trend of accident rates, the significant factors affecting the trend, and how to predict accident rates. This paper uses a negative binomial regression model to present a statistical analysis of U.S. Class I railroad freight train derailment rates on main tracks by year and accident cause for 2000 to 2012. The accident and traffic data used in the analysis come from FRA. The analysis led to several observations. There is a significant temporal decline in freight train derailment rate (-5.9% per year). The rate of change in accident rate varied by accident cause. Rates of freight train derailment caused by broken rails or welds and track geometry defects declined by 6% and 5% annually, respectively; the rate of derailment caused by bearing failure decreased by 11% annually; and rate of derailment caused by train handling errors fell by 7% annually. The regression model is used to project train derailment rates by accident causes and can be used to evaluate the safety benefit of potential accident prevention strategies. This research provides policy makers and practitioners with a statistical method for analyzing the temporal trend of train accident rate for development of rail safety policy and practice.

Optimization of Ultrasonic Rail-Defect Inspection for Improving Railway Transportation Safety and Efficiency

Broken rails are the most frequent cause of freight-train derailments in the United States. Consequently, reducing their occurrence is a high priority for the rail industry and the U.S. Federal Railroad Administration. Current practice is to periodically inspect rails to detect defects using nondestructive technology such as ultrasonic inspection. Determining the optimal rail inspection frequency is critical to efficient use of infrastructure management resources and maximizing the beneficial impact on safety. Minimization of derailment risk, costs of inspection vehicle operation, rail defect repair, and corresponding train delay are all affected by rail inspection frequency. However, no prior research has incorporated all of these factors into a single integrated framework. The objective of this paper is to develop an analytical model to address the trade-offs among various factors related to rail defect inspection frequency, so as to maximize railroad safety and efficiency. The analysis shows that the optimal inspection frequency will vary with traffic density, rail age, inspection technology reliability, and other factors. The optimization model provides a tool that can be used to aid development of better-informed, more effective infrastructure management and accident prevention policies and practices.

Study on Critical Speed of Freight Train Derailment on Bridges

The mechanism of train derailment was demonstrated to be the loss of lateral vibration stability of train-bridge (track) system. Based on the theory of energy increment analysis for system movement stability, the energy increment criterion for derailment evaluation and the method of analyzing critical speed of train derailment Vcr were presented. The critical speed of empty freight train on Laoluanhe bridge, Huanghe bridge and Youshui bridge were calculated as 65.2 km/h，64.8 km/h and 66.5 km/h respectively, which were close to actual derailment speed. It is manifested that the method of analyzing critical speed of train derailment is reasonable. According to the calculated results of the critical speed on bridges with different lateral rigidity, it is testified that the critical speed of train derailment rises with the bridge lateral rigidity strengthened. The measure to prevent derailment on bridge is to ensure enough bridge lateral rigidity to meet the running safety of train.

Study on Stability of Lateral Vibration for Train-Bridge (Track) System

The mechanism of train derailment is demonstrated to be the loss of the lateral vibration stability of train-bridge (track) system. Based on the theory of energy increment analysis for system movement stability, the method of stability analysis of lateral vibration for train-bridge (track) system and the energy increment criterion for derailment evaluation are put forward. The method of analyzing critical speed of train derailment is proposed on the basis of the energy increment criterion for derailment evaluation. The critical speed of freight train derailment on Youshui bridge and Laoluanhe bridge are calculated as 66.5 km/h and 65.2 km/h respectively, which are close to actual derailment speed 70 km/h. It is manifested that the method of stability analysis of lateral vibration for train-bridge (track) system is reasonable. The critical speed of train derailment on the Yanconggou bridge of Jingtong line are calculated as 73.0 km/h, and the results has provided important reference for operation department to take appropriate measures.

Railroad transportation of dangerous goods: A conditional exposure approach to minimize transport risk

Hazardous materials (hazmat) are potentially harmful to people and environment due to their toxic ingredients. Although a significant portion of hazmat is transported via railroads, prevailing studies on dangerous goods focus on highway shipments. In this work, we develop a risk assessment methodology that takes into consideration the differentiating features of trains and the characteristics of train accident. The proposed approach incorporates train-length, train-decile position of hazmat railcar, the sequence of events leading to hazmat release, and the associated consequence from ruptured railcars. Freight-train derailment reports of the Federal Railroad Administration were analyzed to both get a better understanding of the problem, and also to propose a more precise assessment methodology. The assessment methodology, which includes Bayes Theorem and Logical Diagrams, were used to study a US based case example, which was further analyzed to gain relevant managerial insights.