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.
Railroads have a strong economic incentive to maximize the length and weight of freight trains. Since the mid-1990s, various technological innovations have facilitated operation of longer and heavier trains, and railroads have made infrastructure investments to accommodate them. Recent shifts in railway operating and management strategies have placed added emphasis on long trains but have also drawn public and agency scrutiny. The advantages and disadvantages of increased train size are difficult to analyze because public data on train length and weight over time are limited. Articulated intermodal railcars, artificially short local trains, and light empty unit trains skew industry averages across all train types and mask trends over time. To provide greater insight on the average and distribution of train length and weight for different train types over time, the research team conducted a detailed analysis of Class 1 railroad annual report financial data and Surface Transportation Board waybill sample data collected for the years 1996 through 2018. Dividing traffic statistics by train type allows for a specific focus on loaded unit train length and weight distributions that isolates many factors skewing overall averages. Over the past 23 years, the average length and weight of loaded non-hazmat unit trains have steadily increased. Train size distributions indicate that unit trains exceeding 140 railcars in length have become more frequent over the past 10 years, whereas hazmat unit trains are typically smaller in size. This information can aid researchers and industry practitioners in assessing the benefits and disadvantages of operating longer trains.
The rapid expansion of demand for efficiently and safely transporting crude oil and other flammable liquids by rail in North America has highlighted the need to understand the relative derailment risk of two main freight train types operating in the United States (U.S.): unit trains and manifest trains. Previous studies have quantified the line-haul accident rates for these train types. However, the relative derailment likelihood of these two train types associated with train arrival/departure processes and yard switching operations has yet to be quantified. This study analyzes U.S. freight train traffic and yard/terminal derailment data between 1996 and 2018 by train type. For manifest trains, derailment rates are calculated per train arrival and departure in yards, and yard switching accidents per railcar handled in classification yards. For unit trains, the number of accidents per arrival and departure event in loading and unloading terminals is quantified. These rates are further refined to reflect particular unit train loading conditions and yard type for manifest trains. The analyses suggest that manifest trains have a four-times larger rate per yard arrival/departure than unit trains per terminal arrival/departure. Regardless of yard type, railcar shunting movements in classification yards are significant to the overall manifest train shipment derailment likelihood. An example case study demonstrates how a manifest train may be ten times more likely to derail than a unit train, while varying the number and type of intermediate yards for manifest train and the loading factor for unit train has distinct impacts on the overall derailment rate.
As freight transportation demand increases worldwide, railway practitioners must carefully manage the capacity of existing facilities to ensure efficient and reliable operations. Railroad gravity hump classification (marshalling) yards, where individual railcars (wagons) are sorted into new trains to reach their destination, are an integral part of the freight rail network. Efficient operation of yard processes is critical to overall freight railway performance as individual carload shipments moving in manifest trains spend most of their transit time waiting for connections at intermediate yards, with more than half of this waiting time spent dwelling on classification bowl tracks. Previous research has developed optimal strategies to allocate bowl tracks to blocks for a given set of yard track lengths. However, these strategies make simple assumptions about the performance impact of over-length blocks due to a lack of basic analytical models to describe this relationship. To meet this need, this paper develops an original hump classification yard model using AnyLogic simulation software. A representative yard with accurate geometry and operating parameters reflecting real-world practice is constructed using AutoCAD and exported to AnyLogic. The AnyLogic discrete-event simulation model uses custom Java code to determine traffic flows and railcar movements in the yard, and output performance metrics. With complete flexibility to change track layout patterns, a series of simulation experiments quantify fundamental classification yard capacity relationships between performance metrics and the distribution of track lengths, as a function of the railcar throughput volume and size of outbound blocks created in the yard. The resulting relationships are expected to better inform railway yard operating strategies as traffic, train length, and block size increase but yard track lengths remain static.
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