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Abstract: 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. [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]