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.

U.S. freight railroad accident and hazardous materials release rates have declined substantially since 1980. Ironically, this trend has made the identification and implementation of further safety improvement options more challenging because less empirical information exists on which accident causes present the greatest risks. Consequently, more sophisticated methods are needed to identify the best options for transportation risk reduction. Of particular interest is identifying the principal causes of accidents that can result in a tank car release of hazardous materials, which can harm people, property, and the environment. Because large hazardous materials release accidents are relatively rare, railroads cannot effectively manage safety improvement efforts solely in response to the causes of specific accidents. Instead, a risk-based approach is needed to better understand predictive factors for conditions that can cause a release. Railroad derailment data were analyzed to identify the conditions most likely to lead to a release accident. The objective was to identify proxy variables that can be used as performance measures. The speed of derailment and number of derailed cars highly correlated with hazardous materials releases. Some accident causes are much more likely to lead to release conditions than others. Accident prevention efforts to reduce these causes are more likely to reduce the risk of major railroad hazardous materials release accidents.

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Annual safety statistics published by FRA provide train accident counts for various groupings, such as railroad, accident type, cause, track type and class, train length, and speed. However, hazardous materials transportation risk analysis often requires more detailed accident rate statistics for specific combinations of these groupings. The statistics that are presented enable more precise determination of the probability that Class I and non-Class I railroad freight trains will be involved in an accident on various classes of main-line track. An increase in the overall accident rate from 1997 to 2001 can be largely attributed to the increase in yard accidents. During that time, the main-line derailment rate for Class I freight trains remained nearly constant. Track class-specific derailment rates for Class I main-line freight trains show two orders of magnitude difference between the lowest and highest FRA track classes. Depending on the risk analysis question, accounting for these differences in rates will often be important in developing an accurate estimate of risk over the length of a route or at particular locations along a route. A sensitivity analysis suggests that the distribution of freight train miles by FRA track class may have changed since a study conducted by the Association of American Railroads in the early 1990s. More up-to-date estimates of track class-specific accident rates would require new data on this distribution.

Although much attention has been focused on the growth of intermodal traffic over the past decade, manifest freight (or carload) traffic is a major revenue generator for railroads. The high potential profitability of carload traffic suggests that railroads should try to grow this segment of traffic further, especially in an era of limited railway capacity. To do this, they must meet the increasing logistical needs of their customers by providing more reliable service. The classification terminal is a key determinant in service reliability of manifest freight. Terminal performance also affects network efficiency. Regression analysis showed that, as average dwell time increased, average manifest train speed decreased. Inadequate terminal capacity is viewed by many as a barrier to improved service reliability and network efficiency. Because terminals can be considered production systems, insight is gained by adapting tools that have led to significant performance improvement in manufacturing. A new approach is introduced: lean railroading. The most important manufacturing process analog to improving terminal capacity is the bottleneck. The train assembly (pull-down) process has been identified as the bottleneck in a majority of classification yards. A sensitivity analysis conducted on three bottleneck management alternatives suggests that pull-down capacity can be increased by as much as 26%, compared with the baseline case without large labor or capital expenses, through better management of the process and its interactions with the system. To maximize efficient use of rail yard infrastructure and resources, more emphasis should be placed on the quality of the classification process, rather than on quantity.