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.

Broken rails are the leading cause of major accidents on U.S. railroads and frequently cause delays. A multivariate statistical model was developed to improve the prediction of broken-rail incidences (i.e., service failures). Improving the prediction of conditions that cause broken rails can assist railroads in allocating inspection, detection, and preventive resources more efficiently, to enhance safety, reduce the risk of hazardous materials transportation, improve service quality, and maximize rail assets. The service failure prediction model (SFPM) uses a combination of engineering and traffic data commonly recorded by major railroads. A Burlington Northern Santa Fe Railway database was developed in which the locations of approximately 1,800 service failures over 2 years were recorded. The data on each location were supplemented with information on other engineering and traffic volume parameters. A complementary database with the same parameters was developed for a randomly selected set of locations at which service failures had not occurred. The combined databases were analyzed using multivariate statistical methods to identify the variables and their combinations most strongly correlated with service failures. SFPM accuracy in predicting service failures at specific locations exceeded 85%. Although further validation is necessary, SFPM is promising in the quantitative prediction of broken rails, thereby improving a railroad’s ability to manage its assets and risks.

The demand for freight rail transportation in North America is anticipated to substantially increase in the foreseeable future. Additionally, government agencies seek to increase the speed and frequency of passenger trains operating on certain freight lines, further adding to demand for new railway capacity. The majority of the North American mainline railway network is single track with passing sidings for meets and passes. Expanding the infrastructure by constructing additional track is necessary to maintain network fluidity under increased rail traffic. The additional track can be constructed in phases over time, resulting in hybrid track configurations during the transition from purely single track to a double-track route. To plan this phased approach, there is a need to understand the incremental capacity benefit as a single-track route transitions to a two-main-track route in the context of shared passenger and freight train operations. Consequently, in this study, the Rail Traffic Controller software is used to simulate various hybrid track configurations. The simulations consider different operating conditions to capture the interaction between traffic volume, traffic composition and speed differences between train types. A nonlinear regression model is then developed to quantify the incremental capacity benefit of double-track construction through exponential delay–volume relationships. Adding sections of double track reduces train delay linearly under constant volume. This linear delay reduction yields a convex increase in capacity as double track is installed. These results allow railway practitioners to make more-informed decisions on the optimal strategy for incremental railway capacity upgrades.

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.

North American railroads are facing increasing demand for safe, efficient, and reliable freight and passenger transportation. The high cost of constructing additional track infrastructure to increase capacity and improve reliability provides railroads with a strong financial motivation to increase the productivity of their existing mainlines by reducing the headway between trains. The objective of this research is to assess potential for advanced Positive Train Control (PTC) systems with virtual and moving blocks to improve the capacity and performance of Class 1 railroad mainline corridors. Rail Traffic Controller software is used to simulate and compare the delay performance and capacity of train operations on a representative rail corridor under fixed wayside block signals and moving blocks. The experiment also investigates possible interactions between the capacity benefits of moving blocks and traffic volume, traffic composition, and amount of second main track. Moving blocks can increase the capacity of single-track corridors by several trains per day, serving as an effective substitute to construction of additional second main track infrastructure in the short term. Moving blocks are shown to have the greatest capacity benefit when the corridor has more second main track and traffic volumes are high. Compared with three-aspect signal systems, much of the benefits of moving blocks can be obtained from adding signals and implementing a four-aspect signal system. Knowledge of train delay performance and line capacity under moving blocks will aid railway practitioners in determining if the benefits of these systems justify the required incremental investment over current PTC overlay implementations.