Xiang Liu

Analysis of Causes of Major Train Derailment and Their Effect on Accident Rates

Analysis of the causes of train accidents is critical for rational allocation of resources to reduce accident occurrence in the most cost-effective manner possible. Train derailment data from the FRA rail equipment accident database for the interval 2001 to 2010 were analyzed for each track type, with accounting for frequency of occurrence by cause and number of cars derailed. Statistical analyses were conducted to examine the effects of accident cause, type of track, and derailment speed. The analysis showed that broken rails or welds were the leading derailment cause on main, yard, and siding tracks. By contrast to accident causes on main tracks, bearing failures and broken wheels were not among the top accident causes on yard or siding tracks. Instead, human factor–related causes such as improper use of switches and violation of switching rules were more prevalent. In all speed ranges, broken rails or welds were the leading cause of derailments; however, the relative frequency of the next most common accident types differed substantially for lower-versus higher-speed derailments. In general, at derailment speeds below 10 mph, certain track and human factor causes—such as improper train handling, braking operations, and improper use of switches—dominated. At derailment speeds above 25 mph, those causes were nearly absent and were replaced by equipment causes, such as bearing failure, broken wheel, and axle and journal defects. These results represent the first step in a systematic process of quantitative risk analysis of railroad freight train safety, with an ultimate objective of optimizing safety improvement and more cost-effective risk management.

Analysis of derailments by accident cause: evaluating railroad track upgrades to reduce transportation risk

The risk of train derailment associated with rail transportation is an ongoing concern for the rail industry, government, and the public. Various approaches have been considered or adopted to analyze, manage, and reduce risk. Upgrading track quality has been identified as one possible strategy for preventing derailment. The quality of freight railroad track is commonly divided into five principal classes by FRA on the basis of track structure, track geometry, and inspection frequency and method. The higher the track class, the more stringent are the track safety standards and thus a higher maximum train speed is allowed. Upgrading track class is likely to prevent certain track-related derailments; however, this upgrade may also increase the risk of certain types of equipment failure that are more likely to occur at higher speeds. Consequently, more sophisticated approaches need to be developed to examine the interactions among accident causes that may be differently affected by upgrades to track infrastructure. This paper analyzes several critical parameters for predicting train derailment risk by using derailment statistics from the FRA accident database and related literature. A general method was developed to assess derailment risk by accident cause and FRA track class. The safety benefits of track class upgrade in reducing the risks from certain accident causes were quantitatively evaluated. The model can be extended by incorporating additional risk factors to more accurately assess the effectiveness of various derailment prevention efforts for reducing transportation risk.

Integrated Risk Reduction Framework to Improve Railway Hazardous Materials Transportation Safety

Rail transportation plays a critical role to safely and efficiently transport hazardous materials. A number of strategies have been implemented or are being developed to reduce the risk of hazardous materials release from train accidents. Each of these risk reduction strategies has its safety benefit and corresponding implementation cost. However, the cost effectiveness of the integration of different risk reduction strategies is not well understood. Meanwhile, there has been growing interest in the U.S. rail industry and government to best allocate resources for improving hazardous materials transportation safety. This paper presents an optimization model that considers the combination of two types of risk reduction strategies, broken rail prevention and tank car safety design enhancement. A Pareto-optimality technique is used to maximize risk reduction at a given level of investment. The framework presented in this paper can be adapted to address a broader set of risk reduction strategies and is intended to assist decision makers for local, regional and system-wide risk management of rail hazardous materials transportation.

Analysis of U.S. freight-train derailment severity using zero-truncated negative binomial regression and quantile regression.

Derailments are the most common type of freight-train accidents in the United States. Derailments cause damage to infrastructure and rolling stock, disrupt services, and may cause casualties and harm the environment. Accordingly, derailment analysis and prevention has long been a high priority in the rail industry and government. Despite the low probability of a train derailment, the potential for severe consequences justify the need to better understand the factors influencing train derailment severity. In this paper, a zero-truncated negative binomial (ZTNB) regression model is developed to estimate the conditional mean of train derailment severity. Recognizing that the mean is not the only statistic describing data distribution, a quantile regression (QR) model is also developed to estimate derailment severity at different quantiles. The two regression models together provide a better understanding of train derailment severity distribution. Results of this work can be used to estimate train derailment severity under various operational conditions and by different accident causes. This research is intended to provide insights regarding development of cost-efficient train safety policies.

Probability Analysis of Multiple-tank-car Release Incidents in Railway Hazardous Materials Transportation

Railroads play a key role in the transportation of hazardous materials in North America. Rail transport differs from highway transport in several aspects, an important one being that rail transport involves trains in which many railcars carrying hazardous materials travel together. By contrast to truck accidents, it is possible that a train accident may involve multiple hazardous materials cars derailing and releasing contents with consequently greater potential impact on human health, property and the environment. In this paper, a probabilistic model is developed to estimate the probability distribution of the number of tank cars releasing contents in a train derailment. Principal operational characteristics considered include train length, derailment speed, accident cause, position of the first car derailed, number and placement of tank cars in a train and tank car safety design. The effect of train speed, tank car safety design and tank car positions in a train were evaluated regarding the number of cars that release their contents in a derailment. This research provides insights regarding the circumstances affecting multiple-tank-car release incidents and potential strategies to reduce their occurrences. The model can be incorporated into a larger risk management framework to enable better local, regional and national safety management of hazardous materials transportation by rail.

Safety Effectiveness of Integrated Risk Reduction Strategies for Rail Transport of Hazardous Materials

Railroad transportation plays a critical role in safely and economically moving hazardous materials throughout North America. Effective management of the risk of hazardous materials transportation is a high priority of both the American rail industry and government. A number of strategies and technologies have been implemented or are being developed to reduce this risk. Each risk reduction strategy has an effect on rail safety as well as a corresponding implementation cost. In addition, risk reduction strategies may have interactive effects. However, little prior research has addressed the interactive effects between different risk reduction strategies or how elements of them should be compared or combined, or both, to achieve the maximum risk reduction in the most cost-effective manner. A preliminary methodology was developed to estimate the reduction in the risk of the release of hazardous materials by implementing integrated risk reduction strategies, including accident prevention, tank car safety design enhancement, and changes in train operating practices such as train speed reduction. An analysis showed that risk reduction was affected in differing degrees by operating conditions, accident cause, effectiveness of accident prevention technologies, tank car safety design, percentage of tank car fleet requiring upgrade, and train speed. This study represents the first step in a systematic process of quantitative risk analysis of railroad freight transportation for local, regional, and systemwide safety improvement and is intended to assist decision makers in the development of an integrated cost-efficient risk reduction framework.

Benefit-Cost Analysis of Infrastructure Improvement for Derailment Prevention

U.S. railroad accident rates have declined substantially since the 1980s; however, further improvement in train safety remains an important objective of the railroad industry. In this paper, we describe a framework developed to assess the cost-effectiveness of railroad infrastructure improvement to reduce railroad train accidents. Higher FRA track classes have been shown to be statistically correlated with lower accident rates, thereby indicating potential safety benefits. However, such infrastructure improvement also increases both capital and operating costs for track maintenance. We use accident data from the U.S. DOT Federal Railroad Administration (FRA) accident database and cost data from several recent U.S. railroad infrastructure maintenance projects presented in an FRA report to quantitatively evaluate the safety benefits and costs associated with infrastructure improvement decisions. Our model is intended to consider the trade-off between reduced accident rates and increased costs in evaluating railroad risk reduction strategies and operational decisions. The benefit-cost analysis framework is illustrated by considering the upgrade of track class 3 to class 4 in a hypothetical case study.Copyright

Statistical temporal analysis of freight train derailment rates in the United States: 2000 to 2012

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.

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.

Analysis of railroad tank car releases using a generalized binomial model

The United States is experiencing an unprecedented boom in shale oil production, leading to a dramatic growth in petroleum crude oil traffic by rail. In 2014, U.S. railroads carried over 500,000 tank carloads of petroleum crude oil, up from 9500 in 2008 (a 5300% increase). In light of continual growth in crude oil by rail, there is an urgent national need to manage this emerging risk. This need has been underscored in the wake of several recent crude oil release incidents. In contrast to highway transport, which usually involves a tank trailer, a crude oil train can carry a large number of tank cars, having the potential for a large, multiple-tank-car release incident. Previous studies exclusively assumed that railroad tank car releases in the same train accident are mutually independent, thereby estimating the number of tank cars releasing given the total number of tank cars derailed based on a binomial model. This paper specifically accounts for dependent tank car releases within a train accident. We estimate the number of tank cars releasing given the number of tank cars derailed based on a generalized binomial model. The generalized binomial model provides a significantly better description for the empirical tank car accident data through our numerical case study. This research aims to provide a new methodology and new insights regarding the further development of risk management strategies for improving railroad crude oil transportation safety.