Risk Analysis of Transporting Crude Oil by Rail: Methodology and Decision Support System

Liu, Xiang

doi:10.3141/2547-09

Cited by 10 publications

(7 citation statements)

References 30 publications

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“…Assuming that this trend continues, these percentages can be used as temporal adjustment factors to estimate the derailment rate for a future year. A similar adjustment methodology has been used in the literature ( 36 ). In this study, let the reference year be 2008, as its derailment rate is closest to the 17-year average.…”

Section: Parameter Estimations In Risk Modelingmentioning

confidence: 99%

“…Table 3 shows derailment and release probabilities per train-mile for both accident causes, by FRA track class. It is assumed that all tank cars conform to DOT 117 standards, with the estimated conditional probability of release being 0.029 ( 36 ). It is also conservatively assumed that the accident speed is the maximum track speed for each track class.…”

Section: Numerical Examplementioning

confidence: 99%

“…On one hand, a higher track class has a lower train derailment rate caused by broken rails or track geometry failures. On the other hand, a higher track class has a higher maximum operating speed, thus perhaps increasing the probability of tank car derailment and release ( 36 ). Because the decline in train derailment rate more than offsets the increase of tank car derailment and release probabilities, the net result is that higher track classes are associated with lower hazmat release probabilities per train-mile.…”

Section: Numerical Examplementioning

confidence: 99%

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Accident-Cause-Specific Risk Analysis of Rail Transport of Hazardous Materials

Liu

Turla

Zhang

2018

Transportation Research Record: Journal of the Transportation R

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Rail plays a key role in the transportation of hazardous materials (hazmat). Improving railroad hazmat transportation safety is a high priority for both industry and government. Many severe railroad hazmat release incidents occur because of train accidents. The Federal Railroad Administration identifies over 300 accident causes, including infrastructure defects, rolling stock failures, human factors, and other causes. Understanding how hazmat transportation risk varies with accident cause is a key step in identifying, developing, evaluating, and prioritizing cost-justified accident prevention strategies, thereby mitigating hazmat transportation risk. The objective of this paper is to develop an integrated, generalized risk analysis methodology that can estimate accident-cause-specific hazmat transportation risk, accounting for various train and track characteristics, such as train length, speed, point of derailment, the number and placement of tank cars in a train, tank car safety design, and population density along rail lines. Using the two major causes of accidents on freight railroads—broken rails and track geometry defects—as an example, this paper demonstrates a step-by-step analytical procedure and decision support tool to assess how accident frequency, severity, and hazmat transportation risk vary by accident cause. The research method can be adapted for risk analysis at corridor- or network-level accounting for other accident causes.

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Section: Parameter Estimations In Risk Modelingmentioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

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Accident-Cause-Specific Risk Analysis of Rail Transport of Hazardous Materials

Liu

Turla

Zhang

2018

Transportation Research Record: Journal of the Transportation R

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“…It can be measured using different proxy variables. Some previous studies used the number of railcars derailed as the severity proxy [3][4][5]26 and some used the number of casualties as the severity proxy. 14,27 For hazardous material transportation, the number of tank cars releasing was used to measure the severity.…”

Section: Severity Analysismentioning

confidence: 99%

Analysis of freight train collision risk in the United States

Turla

Liu

Zhang

2018

Proceedings of the Institution of Mechanical Engineers, Part F:

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Rail transportation is pivotal for the national economy. Despite being rare, a train accident can potentially result in severe consequences, such as infrastructure damage costs, casualties, and environmental impacts. An understanding of accident frequency, severity, and risk is important for rail safety management. In the United States, extensive prior research has focused on risk analyses of train derailments and highway–rail grade crossing accidents. Relatively less work has been conducted regarding train collision risk. The US Federal Railroad Administration identifies various accident causes, among which the authors of this study have analyzed the major collision causes. For each major accident cause, the authors have analyzed its resultant collision frequency, severity (in terms of damage cost or casualties), and correspondingly the risk, which is the combination of the frequency and severity. The analysis was based on train collision data in the United States from 2001 to 2015. This analysis focuses on freight trains in the United States, due to their immense traffic exposure. On the temporal scale, collision rate (the number of collisions normalized by traffic exposure) has an approximately 5% annual reduction. In terms of collision cause, failures to obey signals, overspeeds, and violations of mainline operating rules accounted for more collisions than other causes. Two alternative risk measures, namely the expected consequence and conditional value at risk, were used to evaluate the freight train collision risk on main tracks, accounting for both the average and worst-case scenarios. This collision risk analysis methodology may provide the US Department of Transportation and railroad industry with information and decision support for identifying, evaluating, and implementing cost-effective risk mitigation strategies.

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“…Unfortunately, the correlation between crash risk factors as independent variables hurt the statistical analysis has been reported by some literature that argues that, once the assumptions of the generalized linear model (GLM) are violated, it could introduce biased inferences about the influence of the factors of interest [17]. Machine learning approaches are adaptable to processing outliers, missing data, and noisy data and are versatile, requiring no or few previous assumptions about input variables [18][19][20][21][22][23][24][25][26]. These methods can effectively solve the problems associated with the above statistical methods and achieve more accurate predictions of accident severity [17].…”

Section: Introductionmentioning

confidence: 99%

Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

et al. 2021

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With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predict the severity of accidents; and develop targeted, extensive and refined preventive measures to guarantee the safety of Hazmat road transportation. Based on the philosophy of graded risk management, this study used a priori algorithms in association rule mining (ARM) technology to analyze Hazmat transport accidents, using road types as classification criteria to find rules that had strong associations with property-damage-only (PDO) accidents and casualty (CAS) accidents under different road types. The results indicated that accidents involving PDO had a strong association with weather (WEA), traffic signals (TS), surface conditions (SC), fatigue (FAT) and vehicle safety status (VSS), and that accidents involving CAS had a strong association with VSS, equipment safety status (ESS), time of day (TOD) and WEA when urban roads were used for Hazmat transportation. Among Hazmat transport incidents on rural roads, the incidence of PDO accidents was associated with intersections (IN), SC, WEA, vehicle type (VT), and segment type (ST), while the occurrence of CAS accidents was associated with qualification (QUA), ESS, TS, VSS, SC, WEA, TOD, and month (MON). Strong associations between the occurrence of PDO accidents and related items, such as IN, SC, WEA and FAT, and the occurrence of CAS accidents and related items, such as ESS, TOD, VSS, WEA and SC, were identified for Hazmat road transport accidents on highways. The accident characteristics exemplified by strongly correlated rules were used as the input to the prediction model. Considering the scarcity of these events, four prediction models were selected to predict the severity of Hazmat accidents on each road type employing four analyses, and the most suitable prediction model was determined based on the evaluation criteria. The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of Hazmat accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads.

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Risk Analysis of Transporting Crude Oil by Rail: Methodology and Decision Support System

Cited by 10 publications

References 30 publications

Accident-Cause-Specific Risk Analysis of Rail Transport of Hazardous Materials

Accident-Cause-Specific Risk Analysis of Rail Transport of Hazardous Materials

Analysis of freight train collision risk in the United States

Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

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