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Train derailments are important safety concerns, and they become increasingly so when dangerous goods (DG) are involved. One way to reduce the risk of DG derailments is through effective DG railway car placement along the train consist. This paper investigates the relationship between DG railway car placement and derailment for different route attributes and DG shipments. A model is presented for estimating the probability of derailment by position, based on the estimated point of derailment (POD) and the number of cars derailing. A DG placement model that considers in-transit derailment risk is shown to provide a sound scientific basis for effective DG marshalling in conventional rail hump yard operations., (Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.)

Train derailments are important safety issues, and they become even more critical when dangerous goods (DG) are involved. This paper is concerned with mitigating derailment risk through improved operational strategies, with a specific focus on DG marshalling practices in the train-assembly process. A new modelling framework is proposed to investigate how the position of DG railway cars affects their chances of being involved in a derailment as the train travels over a given track segment. The underlying research problem can be formulated as a linear integer programming technique. However, since solving this formulation is computationally intractable, a heuristic method has been developed based on a genetic algorithm that gives a near-optimum solution. The proposed model is applied to a hypothetical rail corridor to demonstrate how effective marshalling of DG along a train can reduce overall derailment risks. Les déraillements de train représentent une importante question de sécurité, plus particulièrement lorsque des matières dangereuses sont impliquées. Le présent article traite de l’atténuation du risque de déraillement par l’amélioration des stratégies opérationnelles, plus spécifiquement sur le positionnement des wagons de matières dangereuses durant le classement des wagons. Un nouveau cadre de modélisation est proposé pour examiner la manière dont la position des wagons de matières dangereuses affecte leur chance d’être impliqués dans un déraillement lorsque le train roule sur un segment de voie ferrée donné. Le problème sous-jacent peut être formulé comme une technique de programmation linéaire par nombres entiers. Toutefois, étant donné que la résolution de cette formulation est difficilement calculable, une méthode heuristique a été développée en se basant sur un algorithme génétique qui offre une solution près de l’optimal. Le modèle proposé est appliqué à un corridor hypothétique de voie ferrée afin de démontrer comment un classement efficace des matières dangereuses le long d’un train peut réduire les risques lors d’un déraillement. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with the problem of how to place hazardous material cars in the train assembly process so that the overall derailment risk can be minimized. The approach considers both the probability of railway cars derailing en route by position as well as the risk associated with additional operations in the rail yard using recent US FRA data. The merits of this car placement model are illustrated through a case study of a railway corridor that connects Los Angeles (CA) to Chicago (IL). The case study demonstrates that the proposed risk minimization strategy could be implemented with minimal rail yard operation cost. [Copyright &y& Elsevier]

• This research developed a segment‐level derailment prediction model using Canadian derailment inventory data. • The research used a negative binomial modelling technique to predict the number of derailment for each railway segment. • The maximum daily train traffic, maximum train speed, segment length, and number of stations are used to estimate the derailment frequency. Train related accidents, particularly derailments, can lead to severe consequences especially when they involve injuries or fatalities or when they involve hazardous materials that might result in environmental impacts. Whereas numerous road safety studies have suggested appropriate approaches to predicting vehicle-to-vehicle collisions, very few railway safety studies have considered predicting the number of derailments on rail tracks in North America. In addition, the existing few rail safety assessment and derailment prediction models have often been constrained by aggregated data limiting the safety assessments by, for example, failing to consider segment-level characteristics. This paper focused on the development of an integrated database for the development of a segment-level derailment prediction model for Canada's rail network. The primary objective of this paper is to report how challenges in the data integration process were overcome and also to develop a network screening tool to identify segments with high derailment risk in Canada's rail network. Negative binomial regression and the Empirical Bayes technique were used to estimate the predicted number of derailments on Canada's rail network at the segment level. A network screening process was then successfully applied to identify key segments of safety concern: the top ten segments of concern accounted for approximately 1% of the rail network allowing decision makers to focus their derailment mitigation efforts on a manageable part of Canada's vast rail network. The data processing approach and analysis in this study have strong implications for advancing research on rail safety in North America. [ABSTRACT FROM AUTHOR]